Spectral variations of canopy reflectance in support of precision agriculture

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi; Borisova, Denitsa; Nikolov, Hristo

2014-05-01

Agricultural monitoring is an important and continuously spreading activity in remote sensing and applied Earth observations. It supplies precise, reliable and valuable information on current crop condition and growth processes. In agriculture, the timing of seasonal cycles of crop activity is important for species classification and evaluation of crop development, growing conditions and potential yield. The correct interpretation of remotely sensed data, however, and the increasing demand for data reliability require ground-truth knowledge of the seasonal spectral behavior of different species and their relation to crop vigor. For this reason, we performed ground-based study of the seasonal response of winter wheat reflectance patterns to crop growth patterns. The goal was to quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties in main ontogenetic periods. Phenology and agro-specific relationships allow assessing crop condition during different portions of the growth cycle and thus effectively tracking plant development, and finally make yield predictions. The applicability of a number of vegetation indices (VIs) for monitoring crop seasonal dynamics, its health condition, and yield potential was examined. Special emphasis we put on narrow-band indices as the availability of data from hyperspectral imagers is unavoidable future. The temporal behavior of vegetation indices revealed increased sensitivity to crop growth. The derived spectral-biophysical relationships allowed extraction of quantitative information about crop variables and yield at different stages of the phenological development. Relating plant spectral and biophysical variables in a phenology-based manner allows crop monitoring, that is crop diagnosis and predictions to be performed multiple times during plant ontogenesis. During active vegetative periods spectral data was highly indicative of plant growth trends and yield potential. The VIs values contributed to reliable yield prediction and showed very good correspondence with the estimates from biophysical models. For dates before full maturity most of the examined VIs proved to be meaningful statistical predictors of crop state-indicative biophysical variables. High correlations were obtained for canopy cover fraction, LAI, and biomass. Sensitivity to red, near-infrared and green reflectance showed both vigorous and stressed plants. As crops attained advanced growth stages, decreased sensitivity of VIs and weaker correlations with bioparameters were observed, yet still significant in a statistical sense. The results highlight the capability of the presented approach to track the dynamics of crop growth from multitemporal spectral data, and illustrate the prediction accuracy of the spectral models. The results are useful in assessing the efficiency of various spectral band ratios and other vegetation indices often used in remote sensing studies of natural and agricultural vegetation. They suggest that the used algorithm for data processing is particularly suitable for airborne cropland monitoring and could be expanded to sites at farm or municipality scale. The results reported are from pilot study carried out on a plot located in one of the established polygons for experimental crop monitoring. In the mentioned research GIS database is established for supporting the experiments and modelling process. Recommendations on good farming practices for medium sized farms for monitoring stress conditions such as drought and overfertilizing are developed.

A National Crop Progress Monitoring and Decision Support System Based on NASA Earth Science Results

NASA Astrophysics Data System (ADS)

di, L.; Yang, Z.

2009-12-01

Timely and accurate information on weekly crop progress and development is essential to a dynamic agricultural industry in the U. S. and the world. By law, the National Agricultural Statistics Service (NASS) of the U. S. Department of Agriculture’s (USDA) is responsible for monitoring and assessing U.S. agricultural production. Currently NASS compiles and issues weekly state and national crop progress and development reports based on reports from knowledgeable state and county agricultural officials and farmers. Such survey-based reports are subjectively estimated for an entire county, lack spatial coverage, and are labor intensive. There has been limited use of remote sensing data to assess crop conditions. NASS produces weekly 1-km resolution un-calibrated AVHRR-based NDVI static images to represent national vegetation conditions but there is no quantitative crop progress information. This presentation discusses the early result for developing a National Crop Progress Monitoring and Decision Support System. The system will overcome the shortcomings of the existing systems by integrating NASA satellite and model-based land surface and weather products, NASS’ wealth of internal crop progress and condition data and Cropland Data Layers (CDL), and the Farm Service Agency’s (FSA) Common Land Units (CLU). The system, using service-oriented architecture and web service technologies, will automatically produce and disseminate quantitative national crop progress maps and associated decision support data at 250-m resolution, as well as summary reports to support NASS and worldwide users in their decision-making. It will provide overall and specific crop progress for individual crops from the state level down to CLU field level to meet different users’ needs on all known croplands. This will greatly enhance the effectiveness and accuracy of the NASS aggregated crop condition data and charts of and provides objective and scientific evidence and guidance for the adjustment of NASS survey data. This presentation will discuss the architecture, Earth observation data, and the crop progress model used in the decision support system.

The Joint Experiment for Crop Assessment and Monitoring (JECAM): Synthetic Aperture Radar (SAR) Inter-Comparison Experiment

NASA Astrophysics Data System (ADS)

Dingle Robertson, L.; Hosseini, M.; Davidson, A. M.; McNairn, H.

2017-12-01

The Joint Experiment for Crop Assessment and Monitoring (JECAM) is the research and development branch of GEOGLAM (Group on Earth Observations Global Agricultural Monitoring), a G20 initiative to improve the global monitoring of agriculture through the use of Earth Observation (EO) data and remote sensing. JECAM partners represent a diverse network of researchers collaborating towards a set of best practices and recommendations for global agricultural analysis using EO data, with well monitored test sites covering a wide range of agriculture types, cropping systems and climate regimes. Synthetic Aperture Radar (SAR) for crop inventory and condition monitoring offers many advantages particularly the ability to collect data under cloudy conditions. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for (1) operational SAR & optical; multi-frequency SAR; and compact polarimetry methods for crop monitoring and inventory, and (2) the retrieval of Leaf Area Index (LAI) and biomass estimations using models such as the Water Cloud Model (WCM) employing single frequency SAR; multi-frequency SAR; and compact polarimetry. The results from these activities will be discussed along with an examination of the requirements of a global experiment including best-date determination for SAR data acquisition, pre-processing techniques, in situ data sharing, model development and statistical inter-comparison of the results.

Radio/antenna mounting system for wireless networking under row-crop agriculture conditions

USDA-ARS?s Scientific Manuscript database

Interest in and deployment of wireless monitoring systems is increasing in many diverse environments, including row-crop agricultural fields. While many studies have been undertaken to evaluate various aspects of wireless monitoring and networking, such as electronic hardware components, data-colle...

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Nellis, M. Duane

1996-01-01

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

A National Crop Progress Monitoring System Based on NASA Earth Science Results

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Zhang, B.; Deng, M.; Yang, Z.

2011-12-01

Crop progress is an important piece of information for food security and agricultural commodities. Timely monitoring and reporting are mandated for the operation of agricultural statistical agencies. Traditionally, the weekly reporting issued by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is based on reports from the knowledgeable state and county agricultural officials and farmers. The results are spatially coarse and subjective. In this project, a remote-sensing-supported crop progress monitoring system is being developed intensively using the data and derived products from NASA Earth Observing satellites. Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 product - MOD09 (Surface Reflectance) is used for deriving daily normalized vegetation index (NDVI), vegetation condition index (VCI), and mean vegetation condition index (MVCI). Ratio change to previous year and multiple year mean can be also produced on demand. The time-series vegetation condition indices are further combined with the NASS' remote-sensing-derived Cropland Data Layer (CDL) to estimate crop condition and progress crop by crop. To facilitate the operational requirement and increase the accessibility of data and products by different users, each component of the system has being developed and implemented following open specifications under the Web Service reference model of Open Geospatial Consortium Inc. Sensor observations and data are accessed through Web Coverage Service (WCS), Web Feature Service (WFS), or Sensor Observation Service (SOS) if available. Products are also served through such open-specification-compliant services. For rendering and presentation, Web Map Service (WMS) is used. A Web-service based system is set up and deployed at dss.csiss.gmu.edu/NDVIDownload. Further development will adopt crop growth models, feed the models with remotely sensed precipitation and soil moisture information, and incorporate the model results with vegetation-index time series for crop progress stage estimation.

A dense camera network for cropland (CropInsight) - developing high spatiotemporal resolution crop Leaf Area Index (LAI) maps through network images and novel satellite data

NASA Astrophysics Data System (ADS)

Kimm, H.; Guan, K.; Luo, Y.; Peng, J.; Mascaro, J.; Peng, B.

2017-12-01

Monitoring crop growth conditions is of primary interest to crop yield forecasting, food production assessment, and risk management of individual farmers and agribusiness. Despite its importance, there are limited access to field level crop growth/condition information in the public domain. This scarcity of ground truth data also hampers the use of satellite remote sensing for crop monitoring due to the lack of validation. Here, we introduce a new camera network (CropInsight) to monitor crop phenology, growth, and conditions that are designed for the US Corn Belt landscape. Specifically, this network currently includes 40 sites (20 corn and 20 soybean fields) across southern half of the Champaign County, IL ( 800 km2). Its wide distribution and automatic operation enable the network to capture spatiotemporal variations of crop growth condition continuously at the regional scale. At each site, low-maintenance, and high-resolution RGB digital cameras are set up having a downward view from 4.5 m height to take continuous images. In this study, we will use these images and novel satellite data to construct daily LAI map of the Champaign County at 30 m spatial resolution. First, we will estimate LAI from the camera images and evaluate it using the LAI data collected from LAI-2200 (LI-COR, Lincoln, NE). Second, we will develop relationships between the camera-based LAI estimation and vegetation indices derived from a newly developed MODIS-Landsat fusion product (daily, 30 m resolution, RGB + NIR + SWIR bands) and the Planet Lab's high-resolution satellite data (daily, 5 meter, RGB). Finally, we will scale up the above relationships to generate high spatiotemporal resolution crop LAI map for the whole Champaign County. The proposed work has potentials to expand to other agro-ecosystems and to the broader US Corn Belt.

Towards a Solid Foundation of Using Remotely Sensed Solar-Induced Chlorophyll Fluorescence for Crop Monitoring and Yield Forecast

NASA Astrophysics Data System (ADS)

Chen, Y.; Sun, Y.; You, L.; Liu, Y.

2017-12-01

The growing demand for food production due to population increase coupled with high vulnerability to volatile environmental changes poses a paramount challenge for mankind in the coming century. Real-time crop monitoring and yield forecasting must be a key part of any solution to this challenge as these activities provide vital information needed for effective and efficient crop management and for decision making. However, traditional methods of crop growth monitoring (e.g., remotely sensed vegetation indices) do not directly relate to the most important function of plants - photosynthesis and therefore crop yield. The recent advance in the satellite remote sensing of Solar-Induced chlorophyll Fluorescence (SIF), an integrative photosynthetic signal from molecular origin and a direct measure of plant functions holds great promise for real-time monitoring of crop growth conditions and forecasting yields. In this study, we use satellite measurements of SIF from both the Global Ozone Monitoring Experiment-2 (GOME-2) onboard MetOp-A and the Orbiting Carbon Observatory-2 (OCO-2) satellites to estimate crop yield using both process-based and statistical models. We find that SIF-based crop yield well correlates with the global yield product Spatial Production Allocation Model (SPAM) derived from ground surveys for all major crops including maize, soybean, wheat, sorghum, and rice. The potential and challenges of using upcoming SIF satellite missions for crop monitoring and prediction will also be discussed.

Near Real-time Operational Use of eMODIS Expedited NDVI for Monitoring Applications and Famine Early Warning

NASA Astrophysics Data System (ADS)

Rowland, J.; Budde, M. E.

2010-12-01

The Famine Early Warning Systems Network (FEWS NET) has requirements for near real-time monitoring of vegetation conditions for food security applications. Accurate and timely assessments of crop conditions are an important element of food security decision making. FEWS NET scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center are utilizing a new Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset for operational monitoring of crop and pasture conditions in parts of the world where food availability is highly dependent on subsistence agriculture and animal husbandry. The expedited MODIS, or eMODIS, production system processes NDVI data using MODIS surface reflectance provided by the Land Atmosphere Near-real-time Capability for EOS (LANCE). Benefits of this production system include customized compositing schedules, near real-time data availability, and minimized re-sampling. FEWS NET has implemented a 10-day compositing scheme every five days to accommodate the need for timely information on vegetation conditions. The data are currently being processed at 250-meter spatial resolution for Central America, Hispaniola, and Africa. Data are further enhanced by the application of a temporal smoothing filter which helps remove contamination due to clouds and other atmospheric effects. The results of this near real-time monitoring capability have been the timely provision of NDVI and NDVI anomaly maps for each of the FEWS NET monitoring regions and the availability of a consistently processed dataset to aid crop assessment missions and to facilitate customized analyses of crop production, drought, and agro-pastoral conditions.

Monitoring crop and vegetation condition using the fused dense time-series landsat-like imagery

USDA-ARS?s Scientific Manuscript database

Since the launch of the first Landsat satellite in the early 1970s, Landsat has been widely used in many applications such as land cover and land use change monitoring, crop yield estimation, forest fire detection, and global ecosystem carbon cycle studies. Medium resolution sensors like Landsat hav...

Monitoring cover crops using radar remote sensing in southern Ontario, Canada

NASA Astrophysics Data System (ADS)

Shang, J.; Huang, X.; Liu, J.; Wang, J.

2016-12-01

Information on agricultural land surface conditions is important for developing best land management practices to maintain the overall health of the fields. The climate condition supports one harvest per year for the majority of the field crops in Canada, with a relative short growing season between May and September. During the non-growing-season months (October to the following April), many fields are traditionally left bare. In more recent year, there has been an increased interest in planting cover crops. Benefits of cover crops include boosting soil organic matters, preventing soil from erosion, retaining soil moisture, and reducing surface runoff hence protecting water quality. Optical remote sensing technology has been exploited for monitoring cover crops. However limitations inherent to optical sensors such as cloud interference and signal saturation (when leaf area index is above 2.5) impeded its operational application. Radar remote sensing on the other hand is not hindered by unfavorable weather conditions, and the signal continues to be sensitive to crop growth beyond the saturation point of optical sensors. It offers a viable means for capturing timely information on field surface conditions (with or without crop cover) or crop development status. This research investigated the potential of using multi-temporal RADARSAT-2 C-band synthetic aperture radar (SAR) data collected in 2015 over multiple fields of winter wheat, corn and soybean crops in southern Ontario, Canada, to retrieve information on the presence of cover crops and their growth status. Encouraging results have been obtained. This presentation will report the methodology developed and the results obtained.

VegScape: U.S. Crop Condition Monitoring Service

NASA Astrophysics Data System (ADS)

mueller, R.; Yang, Z.; Di, L.

2013-12-01

Since 1995, the US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has provided qualitative biweekly vegetation condition indices to USDA policymakers and the public on a weekly basis during the growing season. Vegetation indices have proven useful for assessing crop condition and identifying the areal extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. With growing emphasis on more extreme weather events and food security issues rising to the forefront of national interest, a new vegetation condition monitoring system was developed. The new vegetation condition portal named VegScape was initiated at the start of the 2013 growing season. VegScape delivers web mapping service based interactive vegetation indices. Users can use an interactive map to explore, query and disseminate current crop conditions. Vegetation indices like Normal Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and mean, median, and ratio comparisons to prior years can be constructed for analytical purposes and on-demand crop statistics. The NASA MODIS satellite with 250 meter (15 acres) resolution and thirteen years of data history provides improved spatial and temporal resolutions and delivers improved detailed timely (i.e., daily) crop specific condition and dynamics. VegScape thus provides supplemental information to support NASS' weekly crop reports. VegScape delivers an agricultural cultivated crop mask and the most recent Cropland Data Layer (CDL) product to exploit the agricultural domain and visualize prior years' planted crops. Additionally, the data can be directly exported to Google Earth for web mashups or delivered via web mapping services for uses in other applications. VegScape supports the ethos of data democracy by providing free and open access to digital geospatial data layers using open geospatial standards, thereby supporting transparent and collaborative government initiatives. NASS developed VegScape in cooperation with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. VegScape Ratio to Median NDVI

The Joint Experiment for Crop Assessment and Monitoring (JECAM) Initiative: Developing methods and best practices for global agricultural monitoring

NASA Astrophysics Data System (ADS)

Champagne, C.; Jarvis, I.; Defourny, P.; Davidson, A.

2014-12-01

Agricultural systems differ significantly throughout the world, making a 'one size fits all' approach to remote sensing and monitoring of agricultural landscapes problematic. The Joint Experiment for Crop Assessment and Monitoring (JECAM) was established in 2009 to bring together the global scientific community to work towards a set of best practices and recommendations for using earth observation data to map, monitor and report on agricultural productivity globally across an array of diverse agricultural systems. These methods form the research and development component of the Group on Earth Observation Global Agricultural Monitoring (GEOGLAM) initiative to harmonize global monitoring efforts and increase market transparency. The JECAM initiative brings together researchers from a large number of globally distributed, well monitored agricultural test sites that cover a range of crop types, cropping systems and climate regimes. Each test site works independently as well as together across multiple sites to test methods, sensors and field data collection techniques to derive key agricultural parameters, including crop type, crop condition, crop yield and soil moisture. The outcome of this project will be a set of best practices that cover the range of remote sensing monitoring and reporting needs, including satellite data acquisition, pre-processing techniques, information retrieval and ground data validation. These outcomes provide the research and development foundation for GEOGLAM and will help to inform the development of the GEOGLAM "system of systems" for global agricultural monitoring. The outcomes of the 2014 JECAM science meeting will be discussed as well as examples of methods being developed by JECAM scientists.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

PubMed

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-11-11

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

Early warning and crop condition assessment research

NASA Technical Reports Server (NTRS)

Boatwright, G. O.; Whitehead, V. S.

1986-01-01

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

A low-cost microcontroller-based system to monitor crop temperature and water status

USDA-ARS?s Scientific Manuscript database

A prototype microcontroller-based system was developed to automate the measurement and recording of soil-moisture status and canopy-, air-, and soil-temperature levels in cropped fields. Measurements of these conditions within the cropping system are often used to assess plant stress, and can assis...

The Joint Experiment for Crop Assessment and Monitoring (JECAM): Update on Multisite Inter-comparison Experiments

NASA Astrophysics Data System (ADS)

Jarvis, I.; Gilliams, S. J. B.; Defourny, P.

2016-12-01

Globally there is significant convergence on agricultural monitoring research questions. The focus of interest usually revolves around crop type, crop area estimation and near real time crop condition and yield forecasting. Notwithstanding this convergence, agricultural systems differ significantly throughout the world, reflecting the diversity of ecosystems they are located in. Consequently, a global system of systems for operational monitoring must be based on multiple approaches. Research is required to compare and assess these approaches to identify which are most appropriate for any given location. To this end the Joint Experiments for Crop Assessment and Monitoring (JECAM) was established in 2009 to as a research platform to allow the global agricultural monitoring community to work towards a set of best practices and recommendations for using earth observation data to map, monitor and report on agricultural productivity globally. The JECAM initiative brings together researchers from a large number of globally distributed, well monitored agricultural test sites that cover a range of crop types, cropping systems and climate regimes. The results of JECAM optical inter-comparison research taking place in the Stimulating Innovation for Global Monitoring of Agriculture (SIGMA) project and the Sentinel-2 for Agriculture project will be discussed. The presentation will also highlight upcoming work on a Synthetic Aperture Radar (SAR) inter-comparison study. The outcome of these projects will result in a set of best practices that cover the range of remote sensing monitoring and reporting needs, including satellite data acquisition, pre-processing techniques, information retrieval and ground data validation. These outcomes provide the R&D foundation for GEOGLAM and will help to inform the development of the GEOGLAM system of systems for global agricultural monitoring.

Space Data for Crop Management

NASA Technical Reports Server (NTRS)

1990-01-01

CROPIX, Inc., formed in 1984 by Frank Lamb, president of the Eastern Oregon Farming Company, monitors primarily potato crops in a 20,000 square mile area of northern Oregon and central Washington. Potatoes are a high value specialty crop that can be more profitable to the farmer if he has advance knowledge of market conditions, knows when to harvest, and when to take it to market. By processing and collecting data collected by the NASA-developed Landsat Earth Resources survey satellites, Lamb is able to provide accurate information on crop acreage and conditions on a more timely basis than the routine estimates by the USDA. CROPIX uses Landsat data to make acreage estimates of crops, and to calculate a field-by-field vegetative index number. CROPIX then distributes to its customers a booklet containing color-coded maps, an inventory of crops, plus data and graphs on crop conditions and other valuable information.

International Global Crop Condition Assessments in the framework of GEOGLAM

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.; Vermote, E.; Whitcraft, A. K.; Claverie, M.

2013-12-01

The Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative in response to the growing calls for improved agricultural information. The goal of GEOGLAM is to strengthen the international community's capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through the use of Earth observations. This initiative is designed to build on existing agricultural monitoring initiatives at national, regional and global levels and to enhance and strengthen them through international networking, operationally focused research, and data/method sharing. GEOGLAM was adopted by the G20 as part of the action plan on food price volatility and agriculture and is being implemented through building on the extensive GEO Agricultural Community of Practice (CoP) that was initiated in 2007 and includes key national and international agencies, organizations, and universities involved in agricultural monitoring. One of the early GEOGLAM activities is to provide harmonized global crop outlooks that offer timely qualitative consensus information on crop status and prospects. This activity is being developed in response to a request from the G-20 Agricultural Market Information System (AMIS) and is implemented within the global monitoring systems component of GEOGLAM. The goal is to develop a transparent, international, multi-source, consensus assessment of crop growing conditions, status, and agro-climatic conditions, likely to impact global production. These assessments are focused on the four primary crop types (corn, wheat, soy and rice) within the main agricultural producing regions of the world. The GEOGLAM approach is to bring together international experts from global, regional and national monitoring systems that can share and discuss information from a variety of independent complementary sources in order to reach a convergence of evidence based assessment. Information types include earth observations (EO) data and products, agro-meteorological data, crop models and field reports. To date, representatives from close to 20 different agencies have participated in these outlook assessments, which are submitted to AMIS on a monthly basis as well as shared with the international community. This talk will discuss the process, operational R&D, and progress towards developing these harmonized global crop assessments.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil

PubMed Central

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-01-01

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters. PMID:26569243

A Modernized System for Agricultural Monitoring for Food Security in Tanzania

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Nakalembe, C. L.; Becker-Reshef, I.; Justice, C. J.; Tumbo, S.; Mbilinyi, B.; Maurice, S.; Mtalo, M.

2016-12-01

Accurate and timely information on agriculture, particularly in many countries dominated by complex smallholder, subsistence agricultural systems is often difficult to obtain or not available. This includes up-to-date information during the growing season on crop type, crop area and crop condition such as developmental stage, damage from pests and diseases, drought or flooding. These data are critical for government decision making on production forecasts, planning for commodity market transactions, food aid delivery, responding to disease outbreaks and for implementing agricultural extension and development efforts. In Tanzania we have been working closely with the National Food Security Division (NFSD) at the Ministry of Agriculture, Livestock and Fisheries (MALF) on designing and implementing an advanced agricultural monitoring system, utilizing satellite remote sensing, smart phone and internet technologies. Together with our local implementing partner, the Sokoine University of Agriculture we trained a large number of agricultural extension agents in different regions of Tanzania to deliver field data in near-realtime. Using our collaborative internet portal (Crop Monitor) the team of analysts compiles pertinent information on current crop and weather conditions from throughout the country in a standardized, consistent manner. Using the portal traditionally collected data are combined with electronically collected field data and MODIS satellite image time series from GLAM East-Africa (Global Agricultural Monitoring System, customized for stakeholders in East Africa). The main outcome of this work has been the compilation of the National Food Security Bulletin for Tanzania with plans for a public release and the intention for it to become the main avenue to dispense current updates and analysis on agriculture in the country. The same information is also a potential contribution to the international Early Warning Crop Monitor, which currently covers Tanzania mainly through assessments provided by international agencies.

Assessing the remote sensing derived evaporative stress index with ground observations of crop conditions to advance drought early warning

USDA-ARS?s Scientific Manuscript database

Drought has significant impacts over broad spatial and temporal scales, and information about the timing and extent of such conditions is of critical importance to many end users in the agricultural and water resource management communities. The ability to accurately monitor effects on crops, and p...

Imputing historical statistics, soils information, and other land-use data to crop area

NASA Technical Reports Server (NTRS)

Perry, C. R., Jr.; Willis, R. W.; Lautenschlager, L.

1982-01-01

In foreign crop condition monitoring, satellite acquired imagery is routinely used. To facilitate interpretation of this imagery, it is advantageous to have estimates of the crop types and their extent for small area units, i.e., grid cells on a map represent, at 60 deg latitude, an area nominally 25 by 25 nautical miles in size. The feasibility of imputing historical crop statistics, soils information, and other ancillary data to crop area for a province in Argentina is studied.

Near-Real-Time Monitoring and Reporting of Crop Growth Condition and Harvest Status Using an Integrated Optical and Radar Approach at the National-Scale in Canada

NASA Astrophysics Data System (ADS)

Shang, J.

2015-12-01

There has been an increasing need to have accurate and spatially detailed information on crop growth condition and harvest status over Canada's agricultural land so that the impacts of environmental conditions, market supply and demand, and transportation network limitations on crop production can be understood fully and acted upon in a timely manner. Presently, Canada doesn't have a national dataset that can provide near-real-time geospatial information on crop growth stage and harvest systematically so that reporting on risk events can be linked directly to the grain supply chain and crop production fluctuations. The intent of this study is to develop an integrated approach using Earth observation (EO) technology to provide a consistent, comprehensive picture of crop growth cycles (growth conditions and stages) and agricultural management activities (field preparation for seeding, harvest, and residue management). Integration of the optical and microwave satellite remote sensing technologies is imperative for robust methodology development and eventually for operational implementation. Particularly, the current synthetic aperture radar (SAR) system Radarsat-2 and to be launched Radarsat Constellation Mission (RCM) are unique EO resources to Canada. Incorporating these Canadian SAR resources with international SAR missions such as the Cosmesky-Med and TerraSAR, could be of great potential for developing change detection technologies particularly useful for monitoring harvest as well as other types of agricultural management events. The study revealed that radar and multi-scale (30m and 250m) optical satellite data can directly detect or infer 1) seeding date, 2) crop growth stages and gross primary productivity (GPP), and 3) harvest progress. Operational prototypes for providing growing-season information at the crop-specific level will be developed across the Canadian agricultural land base.

Monitoring water use and crop condition in California vineyards at multiple scales using multi-sensor satellite data fusion

USDA-ARS?s Scientific Manuscript database

Recent weather patterns have left California’s agricultural areas in severe drought. Given the reduced water availability in much of California it is critical to be able to measure water use and crop condition over large areas, but also in fine detail at scales of individual fields to support water...

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

An Updated Decision Support Interface: A Tool for Remote Monitoring of Crop Growing Conditions

NASA Astrophysics Data System (ADS)

Husak, G. J.; Budde, M. E.; Rowland, J.; Verdin, J. P.; Funk, C. C.; Landsfeld, M. F.

2014-12-01

Remote sensing of agroclimatological variables to monitor food production conditions is a critical component of the Famine Early Warning Systems Network portfolio of tools for assessing food security in the developing world. The Decision Support Interface (DSI) seeks to integrate a number of remotely sensed and modeled variables to create a single, simplified portal for analysis of crop growing conditions. The DSI has been reformulated to incorporate more variables and give the user more freedom in exploring the available data. This refinement seeks to transition the DSI from a "first glance" agroclimatic indicator to one better suited for the differentiation of drought events. The DSI performs analysis of variables over primary agricultural zones at the first sub-national administrative level. It uses the spatially averaged rainfall, normalized difference vegetation index (NDVI), water requirement satisfaction index (WRSI), and actual evapotranspiration (ETa) to identify potential hazards to food security. Presenting this information in a web-based client gives food security analysts and decision makers a lightweight portal for information on crop growing conditions in the region. The crop zones used for the aggregation contain timing information which is critical to the DSI presentation. Rainfall and ETa are accumulated from different points in the crop phenology to identify season-long deficits in rainfall or transpiration that adversely affect the crop-growing conditions. Furthermore, the NDVI and WRSI serve as their own seasonal accumulated measures of growing conditions by capturing vegetation vigor or actual evapotranspiration deficits. The DSI is currently active for major growing regions of sub-Saharan Africa, with intention of expanding to other areas over the coming years.

Large Area Crop Inventory Experiment (LACIE). Detecting and monitoring agricultural vegetative water stress over large areas using LANDSAT digital data. [Great Plains

NASA Technical Reports Server (NTRS)

Thompson, D. R.; Wehmanen, O. A. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The Green Number Index technique which uses LANDSAT digital data from 5X6 nautical mile sampling frames was expanded to evaluate its usefulness in detecting and monitoring vegetative water stress over the Great Plains. At known growth stages for wheat, segments were classified as drought or non drought. Good agreement was found between the 18 day remotely sensed data and a weekly ground-based crop moisture index. Operational monitoring of the 1977 U.S.S.R. and Australian wheat crops indicated drought conditions. Drought isoline maps produced by the Green Number Index technique were in good agreement with conventional sources.

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation.

PubMed

Villarrubia, Gabriel; Paz, Juan F De; Iglesia, Daniel H De La; Bajo, Javier

2017-08-02

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

PubMed Central

Villarrubia, Gabriel; De Paz, Juan F.; De La Iglesia, Daniel H.; Bajo, Javier

2017-01-01

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device. PMID:28767089

Integrating NASA Earth Science Enterprise (ESE) Data Into Global Agricultural Decision Support Systems

NASA Astrophysics Data System (ADS)

Teng, W.; Kempler, S.; Chiu, L.; Doraiswamy, P.; Liu, Z.; Milich, L.; Tetrault, R.

2003-12-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of U.S. agricultural products and for global food security. Two major operational users of satellite remote sensing for global crop monitoring are the USDA Foreign Agricultural Service (FAS) and the U.N. World Food Programme (WFP). The primary goal of FAS is to improve foreign market access for U.S. agricultural products. The WFP uses food to meet emergency needs and to support economic and social development. Both use global agricultural decision support systems that can integrate and synthesize a variety of data sources to provide accurate and timely information on global crop conditions. The Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (GES DAAC) has begun a project to provide operational solutions to FAS and WFP, by fully leveraging results from previous work, as well as from existing capabilities of the users. The GES DAAC has effectively used its recently developed prototype TRMM Online Visualization and Analysis System (TOVAS) to provide ESE data and information to the WFP for its agricultural drought monitoring efforts. This prototype system will be evolved into an Agricultural Information System (AIS), which will operationally provide ESE and other data products (e.g., rainfall, land productivity) and services, to be integrated into and thus enhance the existing GIS-based, decision support systems of FAS and WFP. Agriculture-oriented, ESE data products (e.g., MODIS-based, crop condition assessment product; TRMM derived, drought index product) will be input to a crop growth model in collaboration with the USDA Agricultural Research Service, to generate crop condition and yield prediction maps. The AIS will have the capability for remotely accessing distributed data, by being compliant with community-based interoperability standards, enabling easy access to agriculture-related products from other data producers. The AIS? system approach will provide a generic mechanism for easily incorporating new products and making them accessible to users.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Growth and reflectance characteristics of winter wheat canopies

NASA Technical Reports Server (NTRS)

Hinzman, L. D.; Bauer, M. E.; Daughtry, C. S. T.

1984-01-01

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. The effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat (Triticum aestivum L.) were determined through field experiments. Spectral reflectance was measured during the 1979 and 1980 growing seasons with a spectroradiometer. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index (LAI), plant moisture, and fresh and dry phytomass. Nitrogen deficiency caused increased visible, reduced near infrared, and increased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the N-deficient plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. The potential of remote sensing in distinguishing stressed from healthy crops is demonstrated. Evidence suggests multispectral imagery may be useful for monitoring crop condition.

Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Tagestad, J. D.; Downs, J. L.; Murray, C. J.

2015-07-01

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986-2011) of Landsat data for 400 km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans - the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

[The new method monitoring crop water content based on NIR-Red spectrum feature space].

PubMed

Cheng, Xiao-juan; Xu, Xin-gang; Chen, Tian-en; Yang, Gui-jun; Li, Zhen-hai

2014-06-01

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

Multi-year strongest California drought from 500 m SNPP/VIIRS

NASA Astrophysics Data System (ADS)

Guo, W.; Kogan, F.

2016-12-01

Starting in 2006, the western United States was affected by a 10-year long mega-drought. Among 17 western states, California was the most severely drought-affected, especially in 2012-2015, when the area of stronger than moderate vegetation stress reached 70%. This drought had considerable impacts on California's environmental, economy and society. Currently, drought in the USA is monitored by the US Drought Monitor (USDM), which estimates drought area and intensity on an area with an effective resolution of around 30-by-30 km. California produces more than 90% of US fruits, vegetables, berries and nuts, which are grown on relatively small areas (200-500 acres, or 0.5 to 2 km²). Since most of these crops are irrigated, it is important to estimate crop conditions on the area comparable to the size of the planted crop. This paper demonstrates how the new 0.5-by-0.5 km Vegetation health (VH) technology (VH-500) developed from the data collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite launched in 2011, monitors the current mega-drought in California, distinguishing drought-affected area with and without irrigation and estimating drought start/end, intensity, duration and impacts. The VH-500 method and data showed that California's vegetation was under medium-to-exceptional stress, especially in 2013 and 2014. However, in the middle of such intensive stress, in some of the 500-m areas of the Central Valley where principal crops are growing, vegetation experienced favorable conditions because some of these crops were irrigated. The VH-500 drought estimates showed general similarities with the assessed economic drought impacts (crop fallowing, employment loss and crop revenue change) in California.

Combining novel monitoring tools and precision application technologies for integrated high-tech crop protection in the future (a discussion document).

PubMed

Zijlstra, Carolien; Lund, Ivar; Justesen, Annemarie F; Nicolaisen, Mogens; Jensen, Peter Kryger; Bianciotto, Valeria; Posta, Katalin; Balestrini, Raffaella; Przetakiewicz, Anna; Czembor, Elzbieta; van de Zande, Jan

2011-06-01

The possibility of combining novel monitoring techniques and precision spraying for crop protection in the future is discussed. A generic model for an innovative crop protection system has been used as a framework. This system will be able to monitor the entire cropping system and identify the presence of relevant pests, diseases and weeds online, and will be location specific. The system will offer prevention, monitoring, interpretation and action which will be performed in a continuous way. The monitoring is divided into several parts. Planting material, seeds and soil should be monitored for prevention purposes before the growing period to avoid, for example, the introduction of disease into the field and to ensure optimal growth conditions. Data from previous growing seasons, such as the location of weeds and previous diseases, should also be included. During the growing season, the crop will be monitored at a macroscale level until a location that needs special attention is identified. If relevant, this area will be monitored more intensively at a microscale level. A decision engine will analyse the data and offer advice on how to control the detected diseases, pests and weeds, using precision spray techniques or alternative measures. The goal is to provide tools that are able to produce high-quality products with the minimal use of conventional plant protection products. This review describes the technologies that can be used or that need further development in order to achieve this goal. Copyright © 2011 Society of Chemical Industry.

USDA Foreign Agricultural Service overview for operational monitoring of current crop conditions and production forecasts.

NASA Astrophysics Data System (ADS)

Crutchfield, J.

2016-12-01

The presentation will discuss the current status of the International Production Assessment Division of the USDA ForeignAgricultural Service for operational monitoring and forecasting of current crop conditions, and anticipated productionchanges to produce monthly, multi-source consensus reports on global crop conditions including the use of Earthobservations (EO) from satellite and in situ sources.United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) International Production AssessmentDivision (IPAD) deals exclusively with global crop production forecasting and agricultural analysis in support of the USDAWorld Agricultural Outlook Board (WAOB) lockup process and contributions to the World Agricultural Supply DemandEstimates (WASE) report. Analysts are responsible for discrete regions or countries and conduct in-depth long-termresearch into national agricultural statistics, farming systems, climatic, environmental, and economic factors affectingcrop production. IPAD analysts become highly valued cross-commodity specialists over time, and are routinely soughtout for specialized analyses to support governmental studies. IPAD is responsible for grain, oilseed, and cotton analysison a global basis. IPAD is unique in the tools it uses to analyze crop conditions around the world, including customweather analysis software and databases, satellite imagery and value-added image interpretation products. It alsoincorporates all traditional agricultural intelligence resources into its forecasting program, to make the fullest use ofavailable information in its operational commodity forecasts and analysis. International travel and training play animportant role in learning about foreign agricultural production systems and in developing analyst knowledge andcapabilities.

Toward daily monitoring of vegetation conditions at field scale through fusing data from multiple sensors

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires remote sensing data at fine spatial and temporal resolution. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for crop and rangeland monitoring. The Landsat satellite s...

a Weather Monitoring System for Application to Apple and Corn Production

NASA Astrophysics Data System (ADS)

Stirm, Walter Leroy

Many crop management decisions are based on weather -crop development relationships. Daily weather data is currently used in most crop development research and applied models. Present weather and computer technology now makes possible monitoring of crop development on a realtime basis. This research tests a method of computing crop sensitive temperatures for corn and apple using standard hourly meteorological data. The method also makes use of detailed plant physiological stage measurements to determine timing of vital cultural operations tied to the observed weather conditions. The sensitive temperature method incorporates very short term weather variability accounting for changes in the cloud cover, radiation rates, evaporative cooling and other factors involved in the plant's energy balance. The relationship of plant and weather measurements are also used to determine corn emergence, corn grain drydown rate and fruit harvest duration. The monitoring system also incorporates a crop growth unit forecast technique employing short and medium range temperature forecasts of the National Weather Service. The projections of growth units are made for five and ten days into the future. Predicted growth unit accumulations are compared to historical growth unit accumulations to determine the forecast stage. The sensitive temperature crop monitoring system removes some of the error involved in evaluation of growth units by average daily temperature. Carry over maximum and minimums, extended duration of warm or cool periods within the day and disruption of diurnal temperature curve by passage of fronts are eliminated.

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Teng, William; Chiu, Long; Doraiswamy, Paul; Kempler, Steven; Liu, Zhong; Pham, Long; Rui, Hualan

2005-01-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Modelling crop yield in Iberia under drought conditions

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

2017-04-01

The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining vegetation and hydro-meteorological drought indices for the assessment of cereal yield. Moreover, the present study will provide some guidance on user's decision making process in agricultural practices in the IP, assisting farmers in deciding whether to purchase crop insurance. Acknowledgements: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014). Ana Russo thanks FCT for granted support (SFRH/BPD/99757/2014). Andreia Ribeiro also thanks FCT for grant PD/BD/114481/2016.

Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada

PubMed Central

Zhang, Chunhua; Walters, Dan; Kovacs, John M.

2014-01-01

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate the LARS and to process the imagery, and the influence from local weather conditions (e.g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams. PMID:25386696

Applications of low altitude remote sensing in agriculture upon farmers' requests--a case study in northeastern Ontario, Canada.

PubMed

Zhang, Chunhua; Walters, Dan; Kovacs, John M

2014-01-01

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate the LARS and to process the imagery, and the influence from local weather conditions (e.g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams.

ASSESSING THE SUITABILITY OF WINDBREAKS AS WILDLIFE HABITAT - 1994 PILOT PLAN

EPA Science Inventory

The Environmental Monitoring and Assessment Program's (EMAP) Agroecosystem Resource Group is developing a program to monitor and evaluate the ecological condition of United States agricultural lands. indbreaks are an important non-crop element in the Great Plains, an extensive ag...

LACIE - A look to the future. [Large Area Crop Inventory Experiment

NASA Technical Reports Server (NTRS)

Macdonald, R. B.; Hall, F. G.

1977-01-01

The Large Area Crop Inventory Experiment (LACIE) is a 'proof of concept' project designed to demonstrate the applicability of remote sensing technology to the global monitoring of wheat. This paper discusses the need for more timely and reliable monitoring of food and fiber supplies, reviews the monitoring systems currently utilized by the USDA and United Nations Food and Agriculture Organization in the United States and in foreign countries, and elucidates the fundamentals involved in assessing the impact of variable weather and economic conditions on wheat acreage, yield, and production. The experiment's approach to production monitoring is described briefly, and its status is reviewed as of the conclusion of 2 years of successful operation. Examples of acreage and yield monitoring in the Soviet Union are used to illustrate the experiment's approach.

Operationalizing crop monitoring system for informed decision making related to food security in Nepal

NASA Astrophysics Data System (ADS)

Qamer, F. M.; Shah, S. N. Pd.; Murthy, M. S. R.; Baidar, T.; Dhonju, K.; Hari, B. G.

2014-11-01

In Nepal, two thirds of the total population depend on agriculture for their livelihoods and more than one third of Gross Domestic Product (GDP) comes from the agriculture sector. However, effective agriculture production across the country remains a serious challenge due to various factors, such as a high degree of spatial and temporal climate variability, irrigated and rain-fed agriculture systems, farmers' fragile social and economic fabric, and unique mountain practices. ICIMOD through SERVIR-Himalaya initiative with collaboration of Ministry of Agricultural Development (MoAD) is working on developing a comprehensive crop monitoring system which aims to provide timely information on crop growth and drought development conditions. This system analyzes historical climate and crop conditions patterns and compares this data with the current growing season to provide timely assessment of crop growth. Using remote sensing data for vegetation indices, temperature and rainfall, the system generated anomaly maps are inferred to predict the increase or shortfall in production. Comparisons can be made both spatially and in graphs and figures at district and Village Developmental Committee (VDC) levels. Timely information on possible anomaly in crop production is later used by the institutions like Ministry of Agricultural Development, Nepal and World Food Programme, Nepal to trigger appropriate management response. Future potential includes integrating data on agricultural inputs, socioeconomics, demographics, and transportation to holistically assess food security in the region served by SERVIR-Himalaya.

Multisensor Capacitance Probes for Simultaneously Monitoring Rice Field Soil-Water- Crop-Ambient Conditions.

PubMed

Brinkhoff, James; Hornbuckle, John; Dowling, Thomas

2017-12-26

Multisensor capacitance probes (MCPs) have traditionally been used for soil moisture monitoring and irrigation scheduling. This paper presents a new application of these probes, namely the simultaneous monitoring of ponded water level, soil moisture, and temperature profile, conditions which are particularly important for rice crops in temperate growing regions and for rice grown with prolonged periods of drying. WiFi-based loggers are used to concurrently collect the data from the MCPs and ultrasonic distance sensors (giving an independent reading of water depth). Models are fit to MCP water depth vs volumetric water content (VWC) characteristics from laboratory measurements, variability from probe-to-probe is assessed, and the methodology is verified using measurements from a rice field throughout a growing season. The root-mean-squared error of the water depth calculated from MCP VWC over the rice growing season was 6.6 mm. MCPs are used to simultaneously monitor ponded water depth, soil moisture content when ponded water is drained, and temperatures in root, water, crop and ambient zones. The insulation effect of ponded water against cold-temperature effects is demonstrated with low and high water levels. The developed approach offers advantages in gaining the full soil-plant-atmosphere continuum in a single robust sensor.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2014-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2015-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

USGS Publications Warehouse

Funk, Chris; Budde, Michael E.

2009-01-01

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-??-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.

Soil moisture monitoring for crop management

NASA Astrophysics Data System (ADS)

Boyd, Dale

2015-07-01

The 'Risk management through soil moisture monitoring' project has demonstrated the capability of current technology to remotely monitor and communicate real time soil moisture data. The project investigated whether capacitance probes would assist making informed pre- and in-crop decisions. Crop potential and cropping inputs are increasingly being subject to greater instability and uncertainty due to seasonal variability. In a targeted survey of those who received regular correspondence from the Department of Primary Industries it was found that i) 50% of the audience found the information generated relevant for them and less than 10% indicted with was not relevant; ii) 85% have improved their knowledge/ability to assess soil moisture compared to prior to the project, with the most used indicator of soil moisture still being rain fall records; and iii) 100% have indicated they will continue to use some form of the technology to monitor soil moisture levels in the future. It is hoped that continued access to this information will assist informed input decisions. This will minimise inputs in low decile years with a low soil moisture base and maximise yield potential in more favourable conditions based on soil moisture and positive seasonal forecasts

[Simplification of crop shortage water index and its application in drought remote sensing monitoring].

PubMed

Liu, Anlin; Li, Xingmin; He, Yanbo; Deng, Fengdong

2004-02-01

Based on the principle of energy balance, the method for calculating latent evaporation was simplified, and hence, the construction of the drought remote sensing monitoring model of crop water shortage index was also simplified. Since the modified model involved fewer parameters and reduced computing times, it was more suitable for the operation running in the routine services. After collecting the concerned meteorological elements and the NOAA/AVHRR image data, the new model was applied to monitor the spring drought in Guanzhong, Shanxi Province. The results showed that the monitoring results from the new model, which also took more considerations of the effects of the ground coverage conditions and meteorological elements such as wind speed and the water pressure, were much better than the results from the model of vegetation water supply index. From the view of the computing times, service effects and monitoring results, the simplified crop water shortage index model was more suitable for practical use. In addition, the reasons of the abnormal results of CWSI > 1 in some regions in the case studies were also discussed in this paper.

Remote sensing-based Information for crop monitoring: contribution of SAR and Moderate resolution optical data on Asian rice production

NASA Astrophysics Data System (ADS)

Boschetti, Mirco; Holectz, Francesco; Manfron, Giacinto; Collivignarelli, Francesco; Nelson, Andrew

2013-04-01

Updated information on crop typology and status are strongly required to support suitable action to better manage agriculture production and reduce food insecurity. In this field, remote sensing has been demonstrated to be a suitable tool to monitor crop condition however rarely the tested system became really operative. The ones today available, such as the European Commission MARS, are mainly based on the analysis of NDVI time series and required ancillary external information like crop mask to interpret the seasonal signal. This condition is not always guarantied worldwide reducing the potentiality of the remote sensing monitoring. Moreover in tropical countries cloud contamination strongly reduce the possibility of using optical remote sensing data for crop monitoring. In this framework we focused our analysis on the rice production monitoring in Asian tropical area. Rice is in fact the staple food for half of the world population (FAO 2004), in Asia almost 90% of the world's rice is produced and consumed and Rice and poverty often coincide. In this contest the production of reliable rice production information is of extreme interest. We tried to address two important issue in terms of required geospatial information for crop monitoring: rice crop detection (rice map) and seasonal dynamics analysis (phenology). We use both SAR and Optical data in order to exploit the potential complementarity of this system. Multi-temporal ASAR Wide Swath data are in fact the best option to deal with cloud contamination. SAR can easily penetrate the clouds providing information on the surface target. Temporal analysis of archive ASAR data allowed to derived accurate map, at 100m spatial resolution, of permanent rice cultivated areas. On the other and high frequency revisiting optical data, in this case MODIS, have been used to extract seasonal information for the year under analysis. MOD09A1 Surface Reflectance 8-Day L3 Global 500m have been exploited to derive time series of Vegetation Index. A temporal smoothing procedure based on Savitzky-Golay polynomial filter function was applied to the original 8-day composite VI data (EVI and NDVI) in order to eliminate spurious data which affect the time series and to produce an interpolated VI temporal profile. Finally within the area previously identify as rice by SAR analysis phenological estimation have been conducted. Crop growth minima and maxima, respectively indicator of rice transplanting and heading, have been identify from the derivative analysis time series. This procedure was tested in Bangladesh for the year 2011. Results showed that the combined use of both data typology represents the more suitable multisource framework to provide reliable information on rice crop growth. Preliminary maps analysis reveals how SAR rice detection was in agreement with local information and phenology extracted by MODIS data provided spatially distributed data comparable with local knowledge of crop calendar.

The design of composite monitoring scheme for multilevel information in crop early diseases

NASA Astrophysics Data System (ADS)

Zhang, Yan; Meng, Qinglong; Shang, Jing

2018-02-01

It is difficult to monitor and predict the crops early diseases in that the crop disease monitoring is usually monitored by visible light images and the availabilities in early warning are poor at present. The features of common nondestructive testing technology applied to the crop diseases were analyzed in this paper. Based on the changeable characteristics of the virus from the incubation period to the onset period of crop activities, the multilevel composite information monitoring scheme were designed by applying infrared thermal imaging, visible near infrared hyperspectral imaging, micro-imaging technology to the monitoring of multilevel information of crop disease infection comprehensively. The early warning process and key monitoring parameters of compound monitoring scheme are given by taking the temperature, color, structure and texture of crops as the key monitoring characteristics of disease. With overcoming the deficiency that the conventional monitoring scheme is only suitable for the observation of diseases with naked eyes, the monitoring and early warning of the incubation and early onset of the infection crops can be realized by the composite monitoring program as mentioned in this paper.

Monitoring and predicting shrink potential and future processing quality of potato tubers

USDA-ARS?s Scientific Manuscript database

Long-term storage of potato tubers increases risks, which are often attributed to shrink and quality loss. To minimize shrink and ensure high quality tubers, producers must closely monitor the condition of the crop during storage and make necessary adjustments to management plans. Evaluation procedu...

Monitoring and Modeling Crop Health and Water Use via in-situ, Airborne and Space-based Platforms

NASA Astrophysics Data System (ADS)

McCabe, M. F.

2014-12-01

The accurate retrieval of plant water use, health and function together with soil state and condition, represent key objectives in the management and monitoring of large-scale agricultural production. In regions of water shortage or stress, understanding the sustainable use of available water supplies is critical. Unfortunately, this need is all too often limited by a lack of reliable observations. Techniques that balance the demand for reliable ground-based data with the rapid retrieval of spatially distributed crop characteristics represent a needed line of research. Data from in-situ monitoring coupled with advances in satellite retrievals of key land surface variables, provide the information necessary to characterize many crop health and water use features, including evaporation, leaf-chlorophyll and other common vegetation indices. With developments in UAV and quadcopter solutions, the opportunity to bridge the spatio-temporal gap between satellite and ground based sensing now exists, along with the capacity for customized retrievals of crop information. While there remain challenges in the routine application of autonomous airborne systems, the state of current technology and sensor developments provide the capacity to explore the operational potential. While this presentation will focus on the multi-scale estimation of crop-water use and crop-health characteristics from satellite-based sensors, the retrieval of high resolution spatially distributed information from near-surface airborne and ground-based systems will also be examined.

Monitoring, analysis and classification of vegetation and soil data collected by a small and lightweight hyperspectral imaging system

NASA Astrophysics Data System (ADS)

Mönnig, Carsten

2014-05-01

The increasing precision of modern farming systems requires a near-real-time monitoring of agricultural crops in order to estimate soil condition, plant health and potential crop yield. For large sized agricultural plots, satellite imagery or aerial surveys can be used at considerable costs and possible time delays of days or even weeks. However, for small to medium sized plots, these monitoring approaches are cost-prohibitive and difficult to assess. Therefore, we propose within the INTERREG IV A-Project SMART INSPECTORS (Smart Aerial Test Rigs with Infrared Spectrometers and Radar), a cost effective, comparably simple approach to support farmers with a small and lightweight hyperspectral imaging system to collect remotely sensed data in spectral bands in between 400 to 1700nm. SMART INSPECTORS includes the whole remote sensing processing chain of small scale remote sensing from sensor construction, data processing and ground truthing for analysis of the results. The sensors are mounted on a remotely controlled (RC) Octocopter, a fixed wing RC airplane as well as on a two-seated Autogyro for larger plots. The high resolution images up to 5cm on the ground include spectra of visible light, near and thermal infrared as well as hyperspectral imagery. The data will be analyzed using remote sensing software and a Geographic Information System (GIS). The soil condition analysis includes soil humidity, temperature and roughness. Furthermore, a radar sensor is envisaged for the detection of geomorphologic, drainage and soil-plant roughness investigation. Plant health control includes drought stress, vegetation health, pest control, growth condition and canopy temperature. Different vegetation and soil indices will help to determine and understand soil conditions and plant traits. Additional investigation might include crop yield estimation of certain crops like apples, strawberries, pasture land, etc. The quality of remotely sensed vegetation data will be tested with ground truthing tools like a spectrometer, visual inspection and ground control panel. The soil condition will also be monitored with a wireless sensor network installed on the examined plots of interest. Provided with this data, a farmer can respond immediately to potential threats with high local precision. In this presentation, preliminary results of hyperspectral images of distinctive vegetation cover and soil on different pasture test plots are shown. After an evaluation period, the whole processing chain will offer farmers a unique, near real- time, low cost solution for small to mid-sized agricultural plots in order to easily assess crop and soil quality and the estimation of harvest. SMART INSPECTORS remotely sensed data will form the basis for an input in a decision support system which aims to detect crop related issues in order to react quickly and efficiently, saving fertilizer, water or pesticides.

Measurement of physiological traits of paddy rice in temperature gradient chamber using Normalized Difference Vegetation Index and Photochemical Reflectance Index

NASA Astrophysics Data System (ADS)

Ryu, J. H.; Oh, D.; Cho, J.

2017-12-01

Global warming has been affecting the phenological and physiological conditions of crop plants due to heat stress. Thus, the scientific understanding of not only crop-yield change, but also growth progress during high temperature condition is necessary. In this study, growth response and yield of paddy rice depending on air temperature (Ta) has been studied in a Temperature Gradient Chamber (TGC) that is composed of higher Ta than actual Ta (ambient temperature). The results on imitating experiment of global warming provided the reduced production of crop by heat stress. Therefore, it is important to quickly detect the condition of a plant in order to minimize damage to heat stress on global warming. Phenological and physiological changes depending on Ta was detected using optical spectroscopy sensors because remote sensing is useful and efficient technology to monitor quickly and continually. Two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI), were applied to monitor paddy rice growth using hyperspectral and multispectral radiometer. Ta in TGC was gradually set from actual Ta + 0 ° to actual Ta + 3 °. The variations of NDVI and PRI were different during rice growth period, and also these patterns were changed depending on Ta condition. NDVI and PRI under +3 ° condition increase faster than ambient temperature. After heading stage, the values of NDVI and PRI were dropped. However, the NDVI and PRI of rice under heat stress were relatively slowly decreased. In addition, we found that the yield of rice decreased in the case of delayed drop patterns of NDVI and PRI after heading stage. Our results will be useful to understand crop plant conditions using vegetation index under global warming situations.

Identifying grasslands suitable for cellulosic feedstock crops in the Greater Platte River Basin: dynamic modeling of ecosystem performance with 250 m eMODIS

USGS Publications Warehouse

Gu, Yingxin; Boyte, Stephen P.; Wylie, Bruce K.; Tieszen, Larry L.

2012-01-01

This study dynamically monitors ecosystem performance (EP) to identify grasslands potentially suitable for cellulosic feedstock crops (e.g., switchgrass) within the Greater Platte River Basin (GPRB). We computed grassland site potential and EP anomalies using 9-year (2000–2008) time series of 250 m expedited moderate resolution imaging spectroradiometer Normalized Difference Vegetation Index data, geophysical and biophysical data, weather and climate data, and EP models. We hypothesize that areas with fairly consistent high grassland productivity (i.e., high grassland site potential) in fair to good range condition (i.e., persistent ecosystem overperformance or normal performance, indicating a lack of severe ecological disturbance) are potentially suitable for cellulosic feedstock crop development. Unproductive (i.e., low grassland site potential) or degraded grasslands (i.e., persistent ecosystem underperformance with poor range condition) are not appropriate for cellulosic feedstock development. Grassland pixels with high or moderate ecosystem site potential and with more than 7 years ecosystem normal performance or overperformance during 2000–2008 are identified as possible regions for future cellulosic feedstock crop development (ca. 68 000 km2 within the GPRB, mostly in the eastern areas). Long-term climate conditions, elevation, soil organic carbon, and yearly seasonal precipitation and temperature are important performance variables to determine the suitable areas in this study. The final map delineating the suitable areas within the GPRB provides a new monitoring and modeling approach that can contribute to decision support tools to help land managers and decision makers make optimal land use decisions regarding cellulosic feedstock crop development and sustainability.

Effect of intercropping period management on runoff and erosion in a maize cropping system.

PubMed

Laloy, Eric; Bielders, C L

2010-01-01

The management of winter cover crops is likely to influence their performance in reducing runoff and erosion during the intercropping period that precedes spring crops but also during the subsequent spring crop. This study investigated the impact of two dates of destruction and burial of a rye (Secale cereale L.) and ryegrass (Lolium multiflorum Lam.) cover crop on runoff and erosion, focusing on a continuous silage maize (Zea mays L.) cropping system. Thirty erosion plots with various intercrop management options were monitored for 3 yr at two sites. During the intercropping period, cover crops reduced runoff and erosion by more than 94% compared with untilled, post-maize harvest plots. Rough tillage after maize harvest proved equally effective as a late sown cover crop. There was no effect of cover crop destruction and burial dates on runoff and erosion during the intercropping period, probably because rough tillage for cover crop burial compensates for the lack of soil cover. During two of the monitored maize seasons, it was observed that plots that had been covered during the previous intercropping period lost 40 to 90% less soil compared with maize plots that had been left bare during the intercropping period. The burial of an aboveground cover crop biomass in excess of 1.5 t ha(-1) was a necessary, yet not always sufficient, condition to induce a residual effect. Because of the possible beneficial residual effect of cover crop burial on erosion reduction, the sowing of a cover crop should be preferred over rough tillage after maize harvest.

Relation between Ocean SST Dipoles and Downwind Continental Croplands Assessed for Early Management Using Satellite-based Photosynthesis Models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2015-04-01

Crop-monitoring systems with the unit of carbon-dioxide sequestration for environmental issues related to climate adaptation to global warming have been improved using satellite-based photosynthesis and meteorological conditions. Early management of crop status is desirable for grain production, stockbreeding, and bio-energy providing that the seasonal climate forecasting is sufficiently accurate. Incorrect seasonal forecasting of crop production can damage global social activities if the recognized conditions are unsatisfied. One cause of poor forecasting related to the atmospheric dynamics at the Earth surface, which reflect the energy budget through land surface, especially the oceans and atmosphere. Recognition of the relation between SST anomalies (e.g. ENSO, Atlantic Niño, Indian dipoles, and Ningaloo Niño) and crop production, as expressed precisely by photosynthesis or the sequestrated-carbon rate, is necessary to elucidate the mechanisms related to poor production. Solar radiation, surface air temperature, and water stress all directly affect grain vegetation photosynthesis. All affect stomata opening, which is related to the water balance or definition by the ratio of the Penman potential evaporation and actual transpiration. Regarding stomata, present data and reanalysis data give overestimated values of stomata opening because they are extended from wet models in forests rather than semi-arid regions commonly associated with wheat, maize, and soybean. This study applies a complementary model based on energy conservation for semi-arid zones instead of the conventional Penman-Monteith method. Partitioning of the integrated Net PSN enables precise estimation of crop yields by modifying the semi-closed stomata opening. Partitioning predicts production more accurately using the cropland distribution already classified using satellite data. Seasonal crop forecasting should include near-real-time monitoring using satellite-based process crop models to avoid social difficulties that can derive from uncertain seasonal predictions produced from long-term forecasting. Acknowledgement The author appreciates scientific discussions held with the application team of seasonal prediction at the Japan Agency for Marine-Earth Science and Technology. Key words: crop production, monitoring, forecasting, SST anomaly, remote sensing

Changing Pattern of Crop Fraction in Late Blight Induced Potato Crops in Potato Bowl of West Bengal by using Multi-temporal Time Series AWiFs Data

NASA Astrophysics Data System (ADS)

Chakrabarty, Abhisek

2016-07-01

Crop fraction is the ratio of crop occupying a unit area in ground pixel, is very important for monitoring crop growth. One of the most important variables in crop growth monitoring is the fraction of available solar radiation intercepted by foliage. Late blight of potato (Solanum tuberosum), caused by the oomycete pathogen Phytophthora infestans, is considered to be the most destructive crop diseases of potato worldwide. Under favourable climatic conditions, and without intervention (i.e. fungicide sprays), the disease can destroy potato crop within few weeks. Therefore it is important to evaluate the crop fraction for monitoring the healthy and late blight affected potato crops. This study was conducted in potato bowl of West Bengal, which consists of districts of Hooghly, Howrah, Burdwan, Bankuara, and Paschim Medinipur. In this study different crop fraction estimation method like linear spectral un-mixing, Normalized difference vegetation index (NDVI) based DPM model (Zhang et al. 2013), Ratio vegetation index based DPM model, improved Pixel Dichotomy Model (Li et al. 2014) ware evaluated using multi-temporal IRS AWiFs data in two successive potato growing season of 2012-13 and 2013-14 over the study area and compared with measured crop fraction. The comparative study based on measured healthy and late blight affected potato crop fraction showed that improved Pixel Dichotomy Model maintain the high coefficient of determination (R2= 0.835) with low root mean square error (RMSE=0.21) whereas the correlation values of NDVI based DPM model and RVI based DPM model is 0.763 and 0.694 respectively. The changing pattern of crop fraction profile of late blight affected potato crop was studied in respect of healthy potato crop fraction which was extracted from the 269 GPS points of potato field. It showed that the healthy potato crop fraction profile maintained the normal phenological trend whereas the late blight affected potato crop fraction profile suddenly fallen after late blight disease affected in potato crops. Therefore, it can be concluded that based on the result of this study the improved Pixel Dichotomy Model is the most convenient method for crop fraction estimation for this region with satisfactory accuracy.

Overview of the AgRISTARS research program. I. [AGgriculture and Resources Inventory Surveys Through Aerospace Remote Sensing

NASA Technical Reports Server (NTRS)

Caudill, C. E.; Hatch, R. E.

1985-01-01

An account is given of the activities and accomplishments to date of the U.S. Department of Agriculture's Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS) program, which is a cooperative venture with NASA and the Departments of the Interior and of Commerce. AgRISTARS research activities encompass early warning and crop condition assessment, inventory technology development for production forecasting, crop yield model development, soil moisture monitoring, domestic crops and land cover sensing, renewable resources inventory, and conservation and pollution assessment.

Crop monitoring & yield forecasting system based on Synthetic Aperture Radar (SAR) and process-based crop growth model: Development and validation in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.

2014-12-01

Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies and enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe. Such delivery of rice growth and yield information is made possible by regular earth observation using space-born Synthetic Aperture Radar (SAR) technology combined with crop modeling approach to estimate yield. Radar-based remote sensing is capable of observing rice vegetation growth irrespective of cloud coverage, an important feature given that in incidences of flooding the sky is often cloud-covered. The system allows rapid damage assessment over the area of interest. Rice yield monitoring is based on a crop growth simulation and SAR-derived key information, particularly start of season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of SAR data into crop model improves yield estimation for actual yields. Remote-sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. Such improvement of actual yield estimates offers practical application such as in a crop insurance program. Process-based crop simulation model is used in the system to ensure climate information is adequately captured and to enable mid-season yield forecast.

Spectrally-Based Assessment of Crop Seasonal Performance and Yield

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Borisova, Denitsa; Georgiev, Georgy

The rapid advances of space technologies concern almost all scientific areas from aeronautics to medicine, and a wide range of application fields from communications to crop yield predictions. Agricultural monitoring is among the priorities of remote sensing observations for getting timely information on crop development. Monitoring agricultural fields during the growing season plays an important role in crop health assessment and stress detection provided that reliable data is obtained. Successfully spreading is the implementation of hyperspectral data to precision farming associated with plant growth and phenology monitoring, physiological state assessment, and yield prediction. In this paper, we investigated various spectral-biophysical relationships derived from in-situ reflectance measurements. The performance of spectral data for the assessment of agricultural crops condition and yield prediction was examined. The approach comprisesd development of regression models between plant spectral and state-indicative variables such as biomass, vegetation cover fraction, leaf area index, etc., and development of yield forecasting models from single-date (growth stage) and multitemporal (seasonal) reflectance data. Verification of spectral predictions was performed through comparison with estimations from biophysical relationships between crop growth variables. The study was carried out for spring barley and winter wheat. Visible and near-infrared reflectance data was acquired through the whole growing season accompanied by detailed datasets on plant phenology and canopy structural and biochemical attributes. Empirical relationships were derived relating crop agronomic variables and yield to various spectral predictors. The study findings were tested using airborne remote sensing inputs. A good correspondence was found between predicted and actual (ground-truth) estimates

Organic vs. organic - soil arthropods as bioindicators of ecological sustainability in greenhouse system experiment under Mediterranean conditions.

PubMed

Madzaric, Suzana; Ceglie, F G; Depalo, L; Al Bitar, L; Mimiola, G; Tittarelli, F; Burgio, G

2017-11-23

Organic greenhouse (OGH) production is characterized by different systems and agricultural practices with diverse environmental impact. Soil arthropods are widely used as bioindicators of ecological sustainability in open field studies, while there is a lack of research on organic production for protected systems. This study assessed the soil arthropod abundance and diversity over a 2-year crop rotation in three systems of OGH production in the Mediterranean. The systems under assessment differed in soil fertility management: SUBST - a simplified system of organic production, based on an input substitution approach (use of guano and organic liquid fertilizers), AGROCOM - soil fertility mainly based on compost application and agroecological services crops (ASC) cultivation (tailored use of cover crops) as part of crop rotation, and AGROMAN - animal manure and ASC cultivation as part of crop rotation. Monitoring of soil fauna was performed by using pitfall traps and seven taxa were considered: Carabidae, Staphylinidae, Araneae, Opiliones, Isopoda, Myriapoda, and Collembola. Results demonstrated high potential of ASC cultivation as a technique for beneficial soil arthropod conservation in OGH conditions. SUBST system was dominated by Collembola in all crops, while AGROMAN and AGROCOM had more balanced relative abundance of Isopoda, Staphylinidae, and Aranea. Opiliones and Myriapoda were more affected by season, while Carabidae were poorly represented in the whole monitoring period. Despite the fact that all three production systems are in accordance with the European Union regulation on organic farming, findings of this study displayed significant differences among them and confirmed the suitability of soil arthropods as bioindicators in protected systems of organic farming.

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.

2012-12-01

Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production and driving crop water balance models. We present a series of derived rainfall products and provide an update on efforts to improve satellite-based estimates. We also present advancements in monitoring tools, namely, the Early Warning eXplorer (EWX) and interactive rainfall and NDVI time series viewers. The EWX is a data analysis and visualization tool that allows users to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The interactive time series viewers allow users to analyze rainfall and NDVI time series over multiple spatial domains. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

General description and operation of the agro-environmental system: Crop management modeling. [Virginia

NASA Technical Reports Server (NTRS)

Gross, E.; Scott, J. H., Jr.

1981-01-01

Input for a data management system to provide farmers with information to improve crop management practices in Virginia requires monitoring of control crops at field stations, crop surveys derived from remotely sensed aircraft data, meteorological data from synchronous satellites, and details of local agricultural conditions. Presently models are under development for determining pest problems, water balance in the soil, stages of plant maturity, and optimum planting date. The status of the Cerospora leafspot model for peanut crop management is considered. Other models under development planned relate to Cylindtocladium Blackrot and Sclerotinia blight of peanuts, cyst nematode (Globerdena solanacearum) of tobacco, and red crown rot of soybeans. A software for program for estimating precipitation and solar radiation on a statewise basis is also being developed.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

USGS Publications Warehouse

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-01-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

NASA Astrophysics Data System (ADS)

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-08-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia's cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

Evaluation of Precipitation Indices for Global Crop Modeling and Definition of Drought Response Function to Yields

NASA Astrophysics Data System (ADS)

Kaneko, D.

2017-12-01

Climate change initiates abnormal meteorological disasters. Drought causes climate instability, thus producing poor harvests because of low rates of photosynthesis and sterile pollination. This research evaluates drought indices regarding precipitation and includes this data in global geophysical crop models that concern with evaporation, stomata opening, advection-effects from sea surface temperature anomalies, photosynthesis, carbon partitioning, crop yields, and crop production. Standard precipitation index (SPI) is a useful tool because of related variable not used in the stomata model. However, SPI is not an adequate tool for drought in irrigated fields. Contrary to expectations, the global comparisons of spatial characteristics between stomata opening/evapotranspiration and SPI for monitoring continental crop extremes produced serious defects and obvious differences between evapotranspiration and the small stomata-opening phenomena. The reason for this is that SPI does not include surface air temperature in its analysis. The Penman equation (Epen) describes potential evaporation better than SPI for recent hot droughts caused by climate change. However, the distribution of precipitation is a necessary condition for crop monitoring because it affirms the trend of the dry results computed by crop models. Consequently, the author uses global precipitation data observed by microwave passive sensors on TRMM and GCOM-W satellites. This remote sensing data conveniently supplies spatial distributions of global and seasonal precipitation. The author has designed a model to measure the effects of drought on crop yield and the degree of stomata closure related to the photosynthesis rate. To determine yield effects, the drought injury function is defined by integrating stomata closure during the two seasons from flowering to pollination. The stomata, defined by ratio between Epen and Eac, reflect the effects of drought and irrigation. Stomata-closure model includes the factors of soil moisture or irrigation effects inside the actual evapotranspiration computed using a complimentary model. The evaluation of precipitation indices provides necessary but not sufficient conditions for drought. They supply reference information for the trend/accuracy of an injury response function.

Compositing MODIS Terra and Aqua 250m daily surface reflectance data sets for vegetation monitoring

USDA-ARS?s Scientific Manuscript database

Remote sensing based vegetation Indices have been proven valuable in providing a spatially complete view of crop’s vegetation condition, which also manifests the impact of the disastrous events such as massive flood and drought. VegScape, a web GIS application for crop vegetation condition monitorin...

Remote sensing in precision farming: real-time monitoring of water and fertilizer requirements of agricultural crops

NASA Astrophysics Data System (ADS)

Zilberman, Arkadi; Ben Asher, Jiftah; Kopeika, Norman S.

2016-10-01

The advancements in remote sensing in combination with sensor technology (both passive and active) enable growers to analyze an entire crop field as well as its local features. In particular, changes of actual evapo-transpiration (ET) as a function of water availability can be measured remotely with infrared radiometers. Detection of crop water stress and ET and combining it with the soil water flow model enable rational irrigation timing and application amounts. Nutrient deficiency, and in particular nitrogen deficiency, causes substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield. In the present work, to retrieve ET a novel deterministic approach was used which is based on the remote sensing data. The algorithm can automatically provide timely valuable information on plant and soil water status, which can improve the management of irrigated crops. The solution is capable of bridging between Penman-Monteith ET model and Richards soil water flow model. This bridging can serve as a preliminary tool for expert irrigation system. To support decisions regarding fertilizers the greenness of plant canopies is assessed and quantified by using the spectral reflectance sensors and digital color imaging. Fertilization management can be provided on the basis of sampling and monitoring of crop nitrogen conditions using RS technique and translating measured N concentration in crop to kg/ha N application in the field.

Different techniques of multispectral data analysis for vegetation fraction retrieval

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi

2012-07-01

Vegetation monitoring is one of the most important applications of remote sensing technologies. In respect to farmlands, the assessment of crop condition constitutes the basis of growth, development, and yield processes monitoring. Plant condition is defined by a set of biometric variables, such as density, height, biomass amount, leaf area index, and etc. The canopy cover fraction is closely related to these variables, and is state-indicative of the growth process. At the same time it is a defining factor of the soil-vegetation system spectral signatures. That is why spectral mixtures decomposition is a primary objective in remotely sensed data processing and interpretation, specifically in agricultural applications. The actual usefulness of the applied methods depends on their prediction reliability. The goal of this paper is to present and compare different techniques for quantitative endmember extraction from soil-crop patterns reflectance. These techniques include: linear spectral unmixing, two-dimensional spectra analysis, spectral ratio analysis (vegetation indices), spectral derivative analysis (red edge position), colorimetric analysis (tristimulus values sum, chromaticity coordinates and dominant wavelength). The objective is to reveal their potential, accuracy and robustness for plant fraction estimation from multispectral data. Regression relationships have been established between crop canopy cover and various spectral estimators.

On the decomposition of foliar hyperspectral signatures for the high-fidelity discrimination and monitoring of crops

NASA Astrophysics Data System (ADS)

Baranoski, Gladimir V. G.; Van Leeuwen, Spencer; Chen, Tenn F.

2016-04-01

Hyperspectral technologies are being increasingly employed in precision agriculture. By separating the surface and subsurface components of foliar hyperspectral signatures using polarization optics, it is possible to enhance the remote discrimination of different plant species and optimize the assessment of different factors associated with the crops' health status such as chlorophyll levels and water content. These initiatives, in turn, can lead to higher crop yield and lower environmental impact through a more effective use of freshwater supplies and fertilizers (reducing the risk of nitrogen leaching). It is important to consider, however, that the main varieties of crops, represented by C3 (e.g., soy) and C4 (e.g., maize) plants, have markedly distinct morphological characteristics. Accordingly, the influence of these characteristics on their interactions with impinging light may affect the selection of optimal probe wavelengths for specific applications making use of combined hyperspectral and polarization measurements. In this work, we compare the sensitivity of the surface and subsurface reflectance responses of C3 and C4 plants to different spectral and geometrical light incidence conditions. In our comparisons, we also consider intra- species variability with respect to specimen characterization data. This investigation is supported by measured biophysical data and predictive light transport simulations. The results of our comparisons indicate that the surface and subsurface reflectance responses of C3 and C4 plants depict well-defined patterns of sensitivity to varying illumination conditions. We believe that these patterns should be considered in the design of new high-fidelity crop discrimination and monitoring procedures.

Value of Available Global Soil Moisture Products for Agricultural Monitoring

NASA Astrophysics Data System (ADS)

Mladenova, Iliana; Bolten, John; Crow, Wade; de Jeu, Richard

2016-04-01

The first operationally derived and publicly distributed global soil moil moisture product was initiated with the launch of the Advanced Scanning Microwave Mission on the NASA's Earth Observing System Aqua satellite (AMSR-E). AMSR-E failed in late 2011, but its legacy is continued by AMSR2, launched in 2012 on the JAXA Global Change Observation Mission-Water (GCOM-W) mission. AMSR is a multi-frequency dual-polarization instrument, where the lowest two frequencies (C- and X-band) were used for soil moisture retrieval. Theoretical research and small-/field-scale airborne campaigns, however, have demonstrated that soil moisture would be best monitored using L-band-based observations. This consequently led to the development and launch of the first L-band-based mission-the ESA's Soil Moisture Ocean Salinity (SMOS) mission (2009). In early 2015 NASA launched the second L-band-based mission, the Soil Moisture Active Passive (SMAP). These satellite-based soil moisture products have been demonstrated to be invaluable sources of information for mapping water stress areas, crop monitoring and yield forecasting. Thus, a number of agricultural agencies routinely utilize and rely on global soil moisture products for improving their decision making activities, determining global crop production and crop prices, identifying food restricted areas, etc. The basic premise of applying soil moisture observations for vegetation monitoring is that the change in soil moisture conditions will precede the change in vegetation status, suggesting that soil moisture can be used as an early indicator of expected crop condition change. Here this relationship was evaluated across multiple microwave frequencies by examining the lag rank cross-correlation coefficient between the soil moisture observations and the Normalized Difference Vegetation Index (NDVI). A main goal of our analysis is to evaluate and inter-compare the value of the different soil moisture products derived using L-band (SMOS) versus C-/X-band (AMSR2) observations. The soil moisture products analyzed here were derived using the Land Parameter Retrieval Model.

Asia Rice Crop Estimation and Monitoring (Asia-RiCE) for GEOGLAM

NASA Astrophysics Data System (ADS)

Oyoshi, K.; Tomiyama, N.; Okumura, T.; Sobue, S.

2013-12-01

Food security is a critical issue for the international community because of rapid population and economic growth, and climate change. In June 2011, the meeting of G20 agriculture ministers was held to discuss food security and food price volatility, and they agreed on an 'Action Plan on Food Price Volatility and Agriculture'. This plan includes a GEO Global Agricultural Monitoring (GEOGLAM) initiative. The aim of GEOGLAM is to reinforce the international community's ability to produce and disseminate relevant, timely, and accurate forecasts of agricultural production on regional, national, and global scales by utilizing remote sensing technology. GEOGLAM focused on four major grain crops, wheat, maize, soybeans and rice. In particular, Asian countries are responsible for approximately 90% of the world rice production and consumption, rice is the most significant cereal crop in Asian region. Hence, Asian space and agricultural agencies with an interest in the development of rice crop monitoring technology launched an Asia-Rice Crop Estimation & Monitoring (Asia-RiCE) component for the GEOGLAM initiative. In Asian region, rice is mainly cultivated in rainy season, and a large amount of cloud limits rice crop monitoring with optical sensors. But, Synthetic Aperture RADAR (SAR) is all-weather sensor and can observe land surface even if the area is covered by cloud. Therefore, SAR technology would be powerful tool to monitor rice crop in Asian region. Asia-RiCE team required mainly SAR observation data including ALOS-2, RISAT-1, Sentinel-1 and RADARSAT, TerraSAR-X, COSMO-SkyMed for Asia-RiCE GEOGLAM Phase 1 implementation (2013-2015) to the Committee on Earth Observations (CEOS) in the GEOGLAM-CEOS Global Agricultural Monitoring Co-community Meeting held in June 2013. And also, rice crop has complicated cropping systems such as rein-fed or irrigated cultivation, single, double or sometimes triple cropping. In addition, each agricultural field is smaller than that of other regions. The methodology for rice crop monitoring is different from that for other crops, and these characteristics make rice crop monitoring by Earth observation data more difficult and complicated. Now, Asian-RiCE team has selected four technical demonstration sites, Indonesia, Thailand, Vietnam (North and South) for Phase1A implementation (June 2013 to November 2014) to verify methodologies that estimate multi-season crop calendar, rice planted area, yield and production by the blending of Earth observation data including satellite data from SAR or optical sensor and in-situ data. We already developed some prototype systems for rice planed area mapping by SAR and agro-weather monitoring including soil moisture or drought index by microwave and optical data. These technologies would be contribute to the development of rice crop monitoring framework for Asia-RiCE. In this presentation, we introduce the framework and ongoing activities of Asia-RiCE component for GEOGLAM and developed systems for rice crop and agro-weather monitoring.

Research in satellite-aided crop inventory and monitoring

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Dragg, J. L.; Bizzell, R. M.; Trichel, M. C. (Principal Investigator)

1982-01-01

Automated information extraction procedures for analysis of multitemporal LANDSAT data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develope and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed.

Geophysical Global Modeling for Extreme Crop Production Using Photosynthesis Models Coupled to Ocean SST Dipoles

NASA Astrophysics Data System (ADS)

Kaneko, D.

2016-12-01

Climate change appears to have manifested itself along with abnormal meteorological disasters. Instability caused by drought and flood disasters is producing poor harvests because of poor photosynthesis and pollination. Fluctuations of extreme phenomena are increasing rapidly because amplitudes of change are much greater than average trends. A fundamental cause of these phenomena derives from increased stored energy inside ocean waters. Geophysical and biochemical modeling of crop production can elucidate complex mechanisms under seasonal climate anomalies. The models have progressed through their combination with global climate reanalysis, environmental satellite data, and harvest data on the ground. This study examined adaptation of crop production to advancing abnormal phenomena related to global climate change. Global environmental surface conditions, i.e., vegetation, surface air temperature, and sea surface temperature observed by satellites, enable global modeling of crop production and monitoring. Basic streams of the concepts of modeling rely upon continental energy flow and carbon circulation among crop vegetation, land surface atmosphere combining energy advection from ocean surface anomalies. Global environmental surface conditions, e.g., vegetation, surface air temperature, and sea surface temperature observed by satellites, enable global modeling of crop production and monitoring. The method of validating the modeling relies upon carbon partitioning in biomass and grains through carbon flow by photosynthesis using carbon dioxide unit in photosynthesis. Results of computations done for this study show global distributions of actual evaporation, stomata opening, and photosynthesis, presenting mechanisms related to advection effects from SST anomalies in the Pacific, Atlantic, and Indian oceans on global and continental croplands. For North America, climate effects appear clearly in severe atmospheric phenomena, which have caused drought and forest fires through seasonal advection thermal effects on potential evaporation by winds blowing eastward over California, the Grand Canyon, Monument Valley, and into the Great Plains. These coupled SST photosynthesis models constitute an advanced approach for crop modeling in the era of recent new climate.

A blended approach to analyze staple and high-value crops using remote sensing with radiative transfer and crop models.

NASA Astrophysics Data System (ADS)

Davitt, A. W. D.; Winter, J.; McDonald, K. C.; Escobar, V. M.; Steiner, N.

2017-12-01

The monitoring of staple and high-value crops is important for maintaining food security. The recent launch of numerous remote sensing satellites has created the ability to monitor vast amounts of crop lands, continuously and in a timely manner. This monitoring provides users with a wealth of information on various crop types over different regions of the world. However, a challenge still remains on how to best quantify and interpret the crop and surface characteristics that are measured by visible, near-infrared, and active and passive microwave radar. Currently, two NASA funded projects are examining the ability to monitor different types of crops in California with different remote sensing platforms. The goal of both projects is to develop a cost-effective monitoring tool for use by vineyard and crop managers. The first project is designed to examine the capability to monitor vineyard water management and soil moisture in Sonoma County using Soil Moisture Active Passive (SMAP), Sentinel-1A and -2, and Landsat-8. The combined mission products create thorough and robust measurements of surface and vineyard characteristics that can potentially improve the ability to monitor vineyard health. Incorporating the Michigan Microwave Canopy Scattering (MIMICS), a radiative transfer model, enables us to better understand surface and vineyard features that influence radar measurements from Sentinel-1A. The second project is a blended approach to analyze corn, rice, and wheat growth using Sentinel-1A products with Decision Support System for Agrotechnology Transfer (DSSAT) and MIMICS models. This project aims to characterize the crop structures that influence Sentinel-1A radar measurements. Preliminary results have revealed the corn, rice, and wheat structures that influence radar measurements during a growing season. The potential of this monitoring tool can be used for maintaining food security. This includes supporting sustainable irrigation practices, identifying crop health and yield across and within fields, and improving the identification of crop areas ready for harvest.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Monitoring corn and soybean crop development by remote sensing techniques

NASA Technical Reports Server (NTRS)

Tucker, C. J.; Elgin, J. H., Jr.; Mcmurtrey, J. E., III

1978-01-01

A system for spectrally monitoring the stages of crop development for corn and soybeans based upon red and photographic infrared spectral radiances is proposed. The red and photographic infrared spectral radiance, highly correlated with the green leaf area index or green leaf biomass, enable nondestructive monitoring of the crop canopy throughout the growing season. Five distinct periods are apparent which are related to crop development for corn and soybeans.

A new relative referencing method for crop monitoring using chlorophyll fluorescence

NASA Technical Reports Server (NTRS)

Norikane, J.; Goto, E.; Kurata, K.; Takakura, T.

2003-01-01

The measurement of plant chlorophyll fluorescence has been used for many years as a method to monitor a plant's health status. These types of methods have been mostly relegated to the laboratory. The newly developed Relative Referencing Method allows for the measurement of chlorophyll fluorescence under artificial lighting conditions. The fluorescence signal can be determined by first taking a reference signal measurement, then a second measurement with an additional fluorescence excitation source. The first signal can then be subtracted from the second and the plant's chlorophyll fluorescence due to the second lighting source can be determined. With this simple approach, a photosynthesizing plant can be monitored to detect signs of water stress. Using this approach experiments on tomato plants have shown that it was possible to detect water stress, while the plants were continuously illuminated by fluorescent lamps. This method is a promising tool for the remote monitoring of crops grown in a CELSS-type application. Published by Elsevier Science Ltd on behalf of COSPAR.

The commercial use of satellite data to monitor the potato crop in the Columbia Basin

NASA Technical Reports Server (NTRS)

Waddington, George R., Jr.; Lamb, Frank G.

1990-01-01

The imaging of potato crops with satellites is described and evaluated in terms of the commercial application of the remotely sensed data. The identification and analysis of the crops is accomplished with multiple images acquired from the Landsat MSS and TM systems. The data are processed on a PC with image-procesing software which produces images of the seven 1024 x 1024 pixel windows which are subdivided into 21 512 x 512 pixel windows. Maximization of imaged data throughout the year aids in the identification of crop types by IR reflectance. The classification techniques involve the use of six or seven spectral classes for particular image dates. Comparisons with ground-truth data show good agreement; for example, potato fields are identified correctly 90 percent of the time. Acreage estimates and crop-condition assessments can be made from satellite data and used for corrective agricultural action.

Regulation of Bt crops in Canada.

PubMed

Macdonald, Phil; Yarrow, Stephen

2003-06-01

The Canadian Food Inspection Agency (CFIA) regulates environmental releases of plants with novel traits, which include transgenic plants such as Bt crops. Bt crops are regulated in Canada because they express insect resistance novel to their species. Commercialization of crops with novel traits such as the production of insecticidal Bt proteins requires an approval for environmental release, as well as approvals for use as feed and food. Environmental factors such as potential impacts on non-target species are considered. Insect resistance management (IRM) may be imposed as a condition for environmental release of Bt crops to delay the development of resistance in the target insect. Bt potato and European corn borer-resistant Bt corn have been released with mandatory IRM. The CFIA imposes an IRM plan consisting of appropriate refugia, education of farmers and seed dealers, and monitoring and mitigation. Industry, regulators, government extension staff and public researchers provide expert advice on IRM.

Water Stress & Biomass Monitoring and SWAP Modeling of Irrigated Crops in Saratov Region of Russia

NASA Astrophysics Data System (ADS)

Zeyliger, Anatoly; Ermolaeva, Olga

2016-04-01

Development of modern irrigation technologies are balanced between the need to maximize production and the need to minimize water use which provides harmonious interaction of irrigated systems with closely-spaced environment. Thus requires an understanding of complex interrelationships between landscape and underground of irrigated and adjacent areas in present and future conditions aiming to minimize development of negative scenarios. In this way in each irrigated areas a combination of specific factors and drivers must be recognized and evaluated. Much can be obtained by improving the efficiency use of water applied for irrigation. Modern RS monitoring technologies offers the opportunity to develop and implement an effective irrigation control program permitting today to increase efficiency of irrigation water use. These technologies provide parameters with both high temporal and adequate spatial needed to monitor agrohydrological parameters of irrigated agricultural crops. Combination of these parameters with meteorological and biophysical parameters can be used to estimate crop water stress defined as ratio between actual (ETa) and potential (ETc) evapotranspiration. Aggregation of actual values of crop water stress with biomass (yield) data predicted by agrohydrological model based on weather forecasting and scenarios of irrigation water application may be used for indication of both rational timing and amount of irrigation water allocation. This type of analysis facilitating an efficient water management can be easily extended to irrigated areas by developing maps of water efficiency application serving as an irrigation advice system for farmers at his fields and as a decision support tool for the authorities on the large perimeter irrigation management. This contribution aims to communicate an illustrative explanation about the practical application of a data combination of agrohydrological modeling and ground & space based monitoring. For this aim some results of analyzing water stress during growing season of 2012 and yielded biomass of crops three types of crops alfalfa, corn and soya irrigated by sprinkling machines at left bank of Volga River at Saratov Region of Russia are presented and analyzed. For that a combination of data received from satellite, local meteorological station and farmers as well as SWAP model was used. Analyze of data sets of monitored water deficit of each crop averaged for irrigation period was done by linear regression with yielded biomass values. Following analyze of effectiveness of irrigation water application was done by SWAP agrohydrological model.

Integrating remote sensing, geographic information system and modeling for estimating crop yield

NASA Astrophysics Data System (ADS)

Salazar, Luis Alonso

This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

Global, Frequent Landsat-class Mosaics for Real Time Crop Monitoring and Analysis

NASA Astrophysics Data System (ADS)

Varlyguin, D.; Crutchfield, J.; Hulina, S.; Reynolds, C. A.; Frantz, R.; Tetrault, R. L.

2016-12-01

The presentation will discuss the current status of GDA technology for operational, automated generation of near global mosaics of Landsat-class data for visualization, monitoring, and analysis. Current version of the mosaic combines Landsat 8 and Landsat 7. Sentinel-2A and ASTER imagery are to be added shortly. The mosaics are surface reflectance calibrated and are analysis ready. They offer full spatial resolution and all multi-spectral bands of the source imagery. Each mosaic covers all major agricultural regions of the world for the last 18 months with a 16 day frequency. The mosaics are updated in real-time, as soon as GDA downloads the imagery, calibrates it to the surface reflectances, and generates data gap masks (all typically under 10 minutes for a Landsat scene). Best pixel value from available opportunities is selected during the mosaic update. The technology eliminates the complex, multi-step, hands-on process of data preparation and provides imagery ready for repetitive, field-to-country analysis of crop conditions, progress, acreages, yield, and production. The mosaics are used for real-time, on-line interactive mapping and time series drilling via GeoSynergy webGIS platform and for off line in-season crop mapping. USDA FAS uses this product for persistent monitoring of selected countries and their croplands and for in-season crop analysis. The presentation will overview Landsat-class mosaics and their use in support of USDA FAS efforts.

Research on agricultural ecology and environment analysis and modeling based on RS and GIS

NASA Astrophysics Data System (ADS)

Zhang, Wensheng; Chen, Hongfu; Wang, Mingsheng

2009-07-01

Analysis of agricultural ecology and environment is based on the data of agricultural resources, which are obtained by RS monitoring. The over-exploitation of farmlands will cause structural changes of the soil composition, and damage the planting environment and the agro-ecosystem. Through the research on the dynamic monitoring methods of multitemporal RS images and GIS technology, the crop growth status, crop acreage and other relevant information in agricultural production are extracted based on the monitor and analysis of the conditions of the fields and crop growth. The agro-ecological GIS platform is developed with the establishment of the agricultural resources management database, which manages spatial data, RS data and attribute data of agricultural resources. Using the RS, GIS analysis results, the reasons of agro-ecological destruction are analyzed and the evaluation methods are established. This paper puts forward the concept of utilization capacity of farmland, which describes farmland space for development and utilization that is influenced by the conditions of the land, water resources, climate, pesticides and chemical fertilizers and many other agricultural production factors. Assessment model of agricultural land use capacity is constructed with the help of Fuzzy. Assessing the utilization capacity of farmland can be helpful to agricultural production and ecological protection of farmland. This paper describes the application of the capacity evaluation model with simulated data in two aspects, namely, in evaluating the status of farmland development and utilization and in optimal planting.

New and Improved Remotely Sensed Products and Tools for Agricultural Monitoring Applications in Support of Famine Early Warning

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Pedreros, D.; Husak, G. J.; Bohms, S.

2011-12-01

The high global food prices in 2008 led to the acknowledgement that there is a need to monitor the inter-connectivity of global and regional markets and their potential impacts on food security in many more regions than previously considered. The crisis prompted an expansion of monitoring by the Famine Early Warning Systems Network (FEWS NET) to include additional countries, beyond those where food security has long been of concern. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of this increased mandate for remote monitoring. We present a new product for measuring actual evapotranspiration (ETa) based on the implementation of a surface energy balance model and site improvements of two standard FEWS NET monitoring products: normalized difference vegetation index (NDVI) and satellite-based rainfall estimates. USGS FEWS NET has implemented a simplified surface energy balance model to produce operational ETa anomalies for Africa. During the growing season, ETa anomalies express surplus or deficit crop water use which is directly related to crop condition and biomass. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with a much improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a vastly improved spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production. By combining high resolution (0.05 deg) rainfall mean fields with Tropical Rainfall Measuring Mission rainfall estimates and infrared temperature data, we provide pentadal (5-day) rainfall fields suitable for crop monitoring and modeling. We also present two new monitoring tools, the Early Warning eXplorer (EWX) and the Decision Support Interface (DSI). The EWX is a data analysis tool which provides the ability to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The DSI uses remote sensing data in an automated fashion to map areas of drought concern and ranks their severity at both crop zone and administrative levels. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

Using Landsat digital data to detect moisture stress in corn-soybean growing regions

NASA Technical Reports Server (NTRS)

Thompson, D. R.; Wehmanen, O. A.

1980-01-01

As a part of a follow-on study to the moisture stress detection effort conducted in the Large Area Crop Inventory Experiment (LACIE), a technique utilizing transformed Landsat digital data was evaluated for detecting moisture stress in humid growing regions using sample segments from Iowa, Illinois, and Indiana. At known growth stages of corn and soybeans, segments were classified as undergoing moisture stress or not undergoing stress. The remote-sensing-based information was compared to a weekly ground-based index (Crop Moisture Index). This comparison demonstrated that the remote sensing technique could be used to monitor the growing conditions within a region where corn and soybeans are the major crop.

Tradeoffs between vigor and yield for crops grown under different management systems

NASA Astrophysics Data System (ADS)

Simic Milas, Anita; Keller Vincent, Robert; Romanko, Matthew; Feitl, Melina; Rupasinghe, Prabha

2016-04-01

Remote sensing can provide an effective means for rapid and non-destructive monitoring of crop status and biochemistry. Monitoring pattern of traditional vigor algorithms generated from Landsat 8 OLI satellite data represents a robust method that can be widely used to differentiate the status of crops, as well as to monitor nutrient uptake functionality of differently treated seeds grown under different managements. This study considers 24 factorial parcels of winter wheat in 2013, corn in 2014, and soybeans in 2015, grown under four different types of agricultural management. The parcels are located at the Kellogg Biological Station, Long-Term Ecological Research site in the State of Michigan USA. At maturity, the organic crops exhibit significantly higher vigor and significantly lower yield than conventionally managed crops under different treatments. While organic crops invest in their metabolism at the expense of their yield, the conventional crops manage to increase their yield at the expense of their vigor. Landsat 8 OLI is capable of 1) differentiating the biochemical status of crops under different treatments at maturity, and 2) monitoring the tradeoff between crop yield and vigor that can be controlled by the seed treatments and proper conventional applications, with the ultimate goal of increasing food yield and food availability, and 3) distinguishing between organic and conventionally treated crops. Timing, quantity and types of herbicide applications have a great impact on early and pre-harvest vigor, maturity and yield of conventionally treated crops. Satellite monitoring using Landsat 8 is an optimal tool for coordinating agricultural applications, soil practices and genetic coding of the crop to produce higher yield as well as have early crop maturity, desirable in northern climates.

NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

NASA Astrophysics Data System (ADS)

Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

2007-12-01

National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.

Evaluation of remote sensing in control of pink bollworm in cotton. [Southern California deserts

NASA Technical Reports Server (NTRS)

Lewis, L. N. (Principal Investigator); Coleman, V. B.

1973-01-01

The author has identified the following significant results. The main objective is to evaluate the use of a satellite in monitoring the cotton production regulation program of the State of California as an aid in controlling pink bollworm infestation in the southern deserts of California. Color combined images of ERTS-1 multispectral images simulating color infrared are being used for crop identification. The status of each field (i.e., crop, bare, harvested, wet, plowed) is mapped from the imagery and is then compared to ground survey information taken at the time of ERTS-1 overflights. A computer analysis has been performed to compare field and satellite data to a crop calendar. Correlation to data has been 97% for field condition. Actual crop identification varies; cotton identification is only 63% due to lack of full season coverage.

Miscanthus productivity and nutrient export on 22 producer fields

USDA-ARS?s Scientific Manuscript database

On-farm assessments of Miscanthus × giganteus growth and nutrition across a wide range of management and environmental conditions are needed to determine and model how this crop performs and where it should be placed on the landscape. Therefore, Miscanthus growth and nutrition were monitored during ...

UAV-Based Hyperspectral Remote Sensing for Precision Agriculture: Challenges and Opportunities

NASA Astrophysics Data System (ADS)

Angel, Y.; Parkes, S. D.; Turner, D.; Houborg, R.; Lucieer, A.; McCabe, M.

2017-12-01

Modern agricultural production relies on monitoring crop status by observing and measuring variables such as soil condition, plant health, fertilizer and pesticide effect, irrigation and crop yield. Managing all of these factors is a considerable challenge for crop producers. As such, providing integrated technological solutions that enable improved diagnostics of field condition to maximize profits, while minimizing environmental impacts, would be of much interest. Such challenges can be addressed by implementing remote sensing systems such as hyperspectral imaging to produce precise biophysical indicator maps across the various cycles of crop development. Recent progress in unmanned aerial vehicles (UAVs) have advanced traditional satellite-based capabilities, providing a capacity for high-spatial, spectral and temporal response. However, while some hyperspectral sensors have been developed for use onboard UAVs, significant investment is required to develop a system and data processing workflow that retrieves accurately georeferenced mosaics. Here we explore the use of a pushbroom hyperspectral camera that is integrated on-board a multi-rotor UAV system to measure the surface reflectance in 272 distinct spectral bands across a wavelengths range spanning 400-1000 nm, and outline the requirement for sensor calibration, integration onto a stable UAV platform enabling accurate positional data, flight planning, and development of data post-processing workflows for georeferenced mosaics. The provision of high-quality and geo-corrected imagery facilitates the development of metrics of vegetation health that can be used to identify potential problems such as production inefficiencies, diseases and nutrient deficiencies and other data-streams to enable improved crop management. Immense opportunities remain to be exploited in the implementation of UAV-based hyperspectral sensing (and its combination with other imaging systems) to provide a transferable and scalable integrated framework for crop growth monitoring and yield prediction. Here we explore some of the challenges and issues in translating the available technological capacity into a useful and useable image collection and processing flow-path that enables these potential applications to be better realized.

Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought

USGS Publications Warehouse

Otkin, Jason A.; Anderson, Martha C.; Hain, Christopher; Svoboda, Mark; Johnson, David; Mueller, Richard; Tadesse, Tsegaye; Wardlow, Brian D.; Brown, Jesslyn

2016-01-01

This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS).Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6 wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4 wk), whereas corn conditions were more closely related to longer-range (8–12 wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.

Analysis of remote reflectin spectroscopy to monitor plant health

NASA Technical Reports Server (NTRS)

Woodhouse, R.; Heeb, M.; Berry, W.; Hoshizaki, T.; Wood, M.

1994-01-01

Remote non-contact reflection spectroscopy is examined as a method for detecting stress in Controlled Ecological Life Support System (CELSS) type crops. Lettuce (Latuca Sativa L. cv. Waldmans Green) and wheat (Triticum Aestivum L. cv. Yecora Rojo) were grown hydroponically. Copper and zinc treatments provided toxic conditions. Nitrogen, phosphorous, and potassium treatments were used for deficiency conditions. Water stress was also induced in test plants. Reflectance spectra were obtained in the visible and near infrared (400nm to 2600nm) wavebands. Numerous effects of stress conditions can be observed in the collected spectra and this technique appears to have promise as a remote monitor of plant health, but significant research remains to be conducted to realize the promise.

Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

USGS Publications Warehouse

Glenn, E.P.; Neale, C. M. U.; Hunsaker, D.J.; Nagler, P.L.

2011-01-01

Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil evaporation. Copyright ?? 2011 John Wiley & Sons, Ltd.

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

2009-09-01

With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

HyspIRI Measurements of Agricultural Systems in California: 2013-2015

NASA Astrophysics Data System (ADS)

Townsend, P. A.; Kruger, E. L.; Singh, A.; Jablonski, A. D.; Kochaver, S.; Serbin, S.

2015-12-01

During 2013-2015, NASA collected high-altitude AVIRIS hyperspectral and MASTER thermal infrared imagery across large swaths of California in support of the HyspIRI planning and prototyping activities. During these campaigns, we made extensive measurements of photosynthetic capacity—Vcmax and Jmax—and their temperature sensitivities across a range of sites, crop types and environmental conditions. Our objectives were to characterize the physiological diversity of agricultural vegetation in California and develop generalizable algorithms to map these physiological parameters across several image acquisitions, regardless of crop type and canopy temperatures. We employed AVIRIS imagery to scale and estimate the vegetation parameters and MASTER surface temperature to provide context, since physiology responds exponentially to leaf temperature. We demonstrate a segmentation approach to disentangling leaf and background soil temperature, and then illustrate our retrievals of Vcmax and Jmax during overflight conditions across a large number of the 2013-2015 HyspIRI acquisitions. Our results show >80% repeatability (R2) across split sample jack-knifing, with RMSEs within 15% of the range of our data. The approach was robust across crop types (e.g., grape, almond, pistachio, avocado, pomegranate, oats, peppers, citrus, date palm, alfalfa, melons, beets) and leaf temperatures. A global imaging spectroscopy system such as HyspIRI will offer unprecedented ability to monitor agricultural crop performance under widely varying surface conditions.

Laboratory measurements of emission factors of nonmethane volatile organic compounds from burning of Chinese crop residues

NASA Astrophysics Data System (ADS)

Inomata, Satoshi; Tanimoto, Hiroshi; Pan, Xiaole; Taketani, Fumikazu; Komazaki, Yuichi; Miyakawa, Takuma; Kanaya, Yugo; Wang, Zifa

2015-05-01

The emission factors (EFs) of nonmethane volatile organic compounds (NMVOCs) emitted during the burning of Chinese crop residue were investigated as a function of modified combustion efficiency in laboratory experiments. NMVOCs, including acetonitrile, aldehydes/ketones, furan, and aromatic hydrocarbons, were monitored by proton-transfer-reaction mass spectrometry. Rape plant was burned in dry conditions and wheat straw was burned in both wet and dry conditions to simulate the possible burning of damp crop residue in regions of high temperature and humidity. We compared the present data to field data reported by Kudo et al. (2014). Good agreement between field and laboratory data was obtained for aromatics under relatively more smoldering combustion of dry samples, but laboratory data were slightly overestimated compared to field data for oxygenated VOC (OVOC). When EFs from the burning of wet samples were investigated, the consistency between the field and laboratory data for OVOCs was stronger than for dry samples. This may be caused by residual moisture in crop residue that has been stockpiled in humid regions. Comparison of the wet laboratory data with field data suggests that Kudo et al. (2014) observed the biomass burning plumes under relatively more smoldering conditions in which approximately a few tens of percentages of burned fuel materials were wet.

Using NASA UAVSAR Datasets to Link Soil Moisture to Crop Conditions

NASA Astrophysics Data System (ADS)

Davitt, A. W. D.; McDonald, K. C.; Azarderakhsh, M.; Winter, J.

2015-12-01

California and The Central Valley are experiencing one of that region's worst, persistent droughts, which represents the continuation of a prolonged drought that started in the early 2000's. Due to the continued drought, many agricultural regions in The Central Valley have been experiencing water shortages, negatively impacting agricultural production and the socio-economics of the region. Due to these impacts, there has been an increased incentive to find new ways to conserve water for use in irrigation. Recent advances in remote sensing techniques provide the ability for end users to better understand field conditions so they may make more informed decisions on irrigation timing and amounts. However, a good understanding of soil moisture and its role in crop health and yield is lacking to support informed water management decisions. Though known to be important, a robust understanding of the role of the spatio-temporal patterns in soil moisture linked to crop health is lacking. Remote sensing platforms such as NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provide the capacity to obtain within-field measurements to estimate within-field and field-to-field variability in soil moisture. UAVSAR radar images acquired from 2010 to 2014 for Yolo County, California are being examined to determine the suitability of high resolution (field scale) multi-temporal L-band radar backscatter imagery for soil moisture assessment and crop conditions through the growing season. By using such data and linking to in-situ meteorology measurements, modeling (MIMICS), and other remote sensing derived datasets (Sentinel, Landsat, MODIS, and TOPS-SIMS), an integrated monitoring system can potentially support the assessment of agricultural field conditions. This allows growers to optimize the use of limited water supplies through informed water management practices, potentially improving crop conditions and yield in a water stressed region.

Root length densities of UK wheat and oilseed rape crops with implications for water capture and yield

PubMed Central

White, Charlotte A.; Sylvester-Bradley, Roger; Berry, Peter M.

2015-01-01

Root length density (RLD) was measured to 1 m depth for 17 commercial crops of winter wheat (Triticum aestivum) and 40 crops of winter oilseed rape [Brassica napus; oilseed rape (OSR)] grown in the UK between 2004 and 2013. Taking the critical RLD (cRLD) for water capture as 1cm cm–3, RLDs appeared inadequate for full water capture on average below a depth of 0.32 m for winter wheat and below 0.45 m for OSR. These depths compare unfavourably (for wheat) with average depths of ‘full capture’ of 0.86 m and 0.48 m, respectively, determined for three wheat crops and one OSR crop studied in the 1970s and 1980s, and treated as references here. A simple model of water uptake and yield indicated that these shortfalls in wheat and OSR rooting compared with the reference data might be associated with shortfalls of up to 3.5 t ha–1 and 1.2 t ha–1, respectively, in grain yields under water-limited conditions, as increasingly occur through climate change. Coupled with decreased summer rainfall, poor rooting of modern arable crops could explain much of the yield stagnation that has been observed on UK farms since the 1990s. Methods of monitoring and improving rooting under commercial conditions are reviewed and discussed. PMID:25750427

A data mining approach for sharpening satellite thermal imagery over land

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...

ADVANCES IN THE APPLICATION OF REMOTE SENSING TO PLANT INCORPORATED PROTECTANT CROP MONITORING

EPA Science Inventory

Current forecasts call for significant increases to the plantings of transgenic corn in the United States for the 2007 growing season and beyond. Transgenic acreage approaching 80% of the total corn plantings could be realized by 2009. These conditions call for a new approach to ...

The Potential of Small Satellites for Crop Monitoring in Emerging Economies

NASA Astrophysics Data System (ADS)

Bydekerke, L.; Meuleman, K.

2008-08-01

The use of low resolution data for monitoring of the overall vegetation condition and crops is nowadays wide spread in emerging economies. Various initiatives, global and local, have promoted the use of this type of imagery for assessing the progress of the growing season since the eighties. The normalized difference vegetation Index (NDVI), from various sensors with 250m to 8 km resolution, are used to identify potential anomalies in vegetation development which, in combination with other data, are used to identify emerging crisis situations in crop development and production before harvest time. Satellite data is analyzed by specialized centers and crop / vegetation assessments are summarized into bulletins, which are then used for communication with non-remote sensing specialists at the policy level. Satellite data is currently provided by large expensive space infrastructures and centrally distributed to the users. In this paper the current flow of information from satellite to information for agriculture is analyzed and the potential contribution of low cost small satellite in addressing the needs of the users is discussed. Two scenario's are presented: i. a centralized system whereby a few institutes have access to data generated by small satellites which process and analyze the data for use by analysts; ii. a decentralized system whereby a variety of users have direct access to data generated by small satellites who are capable of extracting, processing and analyzing information relevant for crop monitoring. The work shows that with affordable space infrastructure, as small satellites, the second scenario may become possible, but the complexity and the cost of the ground segment service remain limiting factors. Expertise and knowledge for processing, analysis and maintenance of IT/infrastructure is currently not enough, specifically in Institutions whose mandate is dealing with crop monitoring, such as the Ministries of Agriculture. However, in the short term, a limited number of specialized centers, can play a key role in gradually facilitating the integration of remote sensing information into the daily workflow, and gradually optimizing costs and efforts. The potential use of future small satellite missions such as e.g. SPOT-Vegetation continuity mission (Proba-V) is also addressed.

Global Monitoring RSEM System for Crop Production by Incorporating Satellite-based Photosynthesis Rates and Anomaly Data of Sea Surface Temperature

NASA Astrophysics Data System (ADS)

Kaneko, D.; Sakuma, H.

2014-12-01

The first author has been developing RSEM crop-monitoring system using satellite-based assessment of photosynthesis, incorporating meteorological conditions. Crop production comprises of several stages and plural mechanisms based on leaf photosynthesis, surface energy balance, and the maturing of grains after fixation of CO2, along with water exchange through soil vegetation-atmosphere transfer. Grain production in prime countries appears to be randomly perturbed regionally and globally. Weather for crop plants reflects turbulent phenomena of convective and advection flows in atmosphere and surface boundary layer. It has been difficult for scientists to simulate and forecast weather correctly for sufficiently long terms to crop harvesting. However, severely poor harvests related to continental events must originate from a consistent mechanism of abnormal energetic flow in the atmosphere through both land and oceans. It should be remembered that oceans have more than 100 times of energy storage compared to atmosphere and ocean currents represent gigantic energy flows, strongly affecting climate. Anomalies of Sea Surface Temperature (SST), globally known as El Niño, Indian Ocean dipole, and Atlantic Niño etc., affect the seasonal climate on a continental scale. The authors aim to combine monitoring and seasonal forecasting, considering such mechanisms through land-ocean biosphere transfer. The present system produces assessments for all continents, specifically monitoring agricultural fields of main crops. Historical regions of poor and good harvests are compared with distributions of SST anomalies, which are provided by NASA GSFC. Those comparisons fairly suggest that the Worst harvest in 1993 and the Best in 1994 relate to the offshore distribution of low temperature anomalies and high gaps in ocean surface temperatures. However, high-temperature anomalies supported good harvests because of sufficient solar radiation for photosynthesis, and poor harvests because of insufficient precipitation. Integrated rates of photosynthesis on prime grains with planted areas were compared with the SST anomalies in poor and good harvests years. Other factors for poor harvest such as rainfall, solar radiation in addition to the intensity of winds as a measure of pressure perturbations need to be studied.

Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean

USDA-ARS?s Scientific Manuscript database

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study ...

A greenhouse experiment for the identification of spectral indices for crop water and nitrogen status assessment

NASA Astrophysics Data System (ADS)

Marino Gallina, Pietro; Bechini, Luca; Cabassi, Giovanni; Cavalli, Daniele; Chiaradia, Enrico Antonio; Corti, Martina; Ferrante, Antonio; Martinetti, Livia; Masseroni, Daniele; Morgutti, Silvia; Nocito, Fabio Francesco; Facchi, Arianna

2015-04-01

Improvements in crop production depend on the correct adoption of agronomic and irrigation management strategies. The use of high spatial and temporal resolution monitoring methods may be used in precision agriculture to improve the efficiency in water and nutrient input management, guaranteeing the environmental sustainability of agricultural productions. In the last decades, many indices for the monitoring of water or nitrogen status of crops were developed by using multispectral images and, more recently, hyperspectral and thermal images acquired by satellite of airborne platforms. To date, however, comprehensive studies aimed at identifying indices as independent as possible for the management of the two types of stress are still scarce in the literature. Moreover, the chemometric approach for the statistical analysis of the acquired images is not yet widely experienced in this research area. In this context, this work presents the set-up of a greenhouse experiment that will start in February 2015 in Milan (Northern Italy), which aims to the objectives described above. The experiment will be carried out on two crops with a different canopy geometry (rice and spinach) subjected to four nitrogen treatments, for a total of 96 pots. Hyperspectral scanner and thermal images will be acquired at four phenological stages. At each phenological phase, acquisitions will be conducted on one-fourth of the pots, in the first instance in good water conditions and, subsequently, at different time steps after the cessation of irrigation. During the acquisitions, measurements of leaf area index and biomass, chlorophyll and nitrogen content in the plants, soil water content, stomatal conductance and leaf water potential will be performed. Moreover, on leaf samples, destructive biochemical analysis will be conducted to evaluate the physiological stress status of crops in the light of different irrigation and nutrient levels. Multivariate regression analysis between the acquired spectra and the chemical-physical properties of the crop determined with standard methods will be used to identify suitable models for the estimation of crop water and nitrogen status. The most significant wavelengths for the detection of water and nitrogen stress could be the subject of a future experimentation in open field conditions using multispectral systems.

The FAO/NASA/NLR Artemis system - An integrated concept for environmental monitoring by satellite in support of food/feed security and desert locust surveillance

NASA Technical Reports Server (NTRS)

Hielkema, J. U.; Howard, J. A.; Tucker, C. J.; Van Ingen Schenau, H. A.

1987-01-01

The African real time environmental monitoring using imaging satellites (Artemis) system, which should monitor precipitation and vegetation conditions on a continental scale, is presented. The hardware and software characteristics of the system are illustrated and the Artemis databases are outlined. Plans for the system include the use of hourly digital Meteosat data and daily NOAA/AVHRR data to study environmental conditions. Planned mapping activities include monthly rainfall anomaly maps, normalized difference vegetation index maps for ten day and monthly periods with a spatial resolution of 7.6 km, ten day crop/rangeland moisture availability maps, and desert locust potential breeding activity factor maps for a plague prevention program.

Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence.

PubMed

Guan, Kaiyu; Berry, Joseph A; Zhang, Yongguang; Joiner, Joanna; Guanter, Luis; Badgley, Grayson; Lobell, David B

2016-02-01

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change. © 2015 John Wiley & Sons Ltd.

Rice Crop Monitoring Using Microwave and Optical Remotely Sensed Image Data

NASA Astrophysics Data System (ADS)

Suga, Y.; Konishi, T.; Takeuchi, S.; Kitano, Y.; Ito, S.

Hiroshima Institute of Technology HIT is operating the direct down-links of microwave and optical satellite data in Japan This study focuses on the validation for rice crop monitoring using microwave and optical remotely sensed image data acquired by satellites referring to ground truth data such as height of crop ratio of crop vegetation cover and leaf area index in the test sites of Japan ENVISAT-1 ASAR data has a capability to capture regularly and to monitor during the rice growing cycle by alternating cross polarization mode images However ASAR data is influenced by several parameters such as landcover structure direction and alignment of rice crop fields in the test sites In this study the validation was carried out combined with microwave and optical satellite image data and ground truth data regarding rice crop fields to investigate the above parameters Multi-temporal multi-direction descending and ascending and multi-angle ASAR alternating cross polarization mode images were used to investigate rice crop growing cycle LANDSAT data were used to detect landcover structure direction and alignment of rice crop fields corresponding to the backscatter of ASAR As the result of this study it was indicated that rice crop growth can be precisely monitored using multiple remotely sensed data and ground truth data considering with spatial spectral temporal and radiometric resolutions

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

NASA Technical Reports Server (NTRS)

Guan, Kaiyu; Berry, Joseph A.; Zhang, Yongguang; Joiner, Joanna; Guanter, Luis; Badgley, Grayson; Lobell, David B.

2015-01-01

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Monitoring Crop Productivity over the U.S. Corn Belt using an Improved Light Use Efficiency Model

NASA Astrophysics Data System (ADS)

Wu, X.; Xiao, X.; Zhang, Y.; Qin, Y.; Doughty, R.

2017-12-01

Large-scale monitoring of crop yield is of great significance for forecasting food production and prices and ensuring food security. Satellite data that provide temporally and spatially continuous information that by themselves or in combination with other data or models, raises possibilities to monitor and understand agricultural productivity regionally. In this study, we first used an improved light use efficiency model-Vegetation Photosynthesis Model (VPM) to simulate the gross primary production (GPP). Model evaluation showed that the simulated GPP (GPPVPM) could well captured the spatio-temporal variation of GPP derived from FLUXNET sites. Then we applied the GPPVPM to further monitor crop productivity for corn and soybean over the U.S. Corn Belt and benchmarked with county-level crop yield statistics. We found VPM-based approach provides pretty good estimates (R2 = 0.88, slope = 1.03). We further showed the impacts of climate extremes on the crop productivity and carbon use efficiency. The study indicates the great potential of VPM in estimating crop yield and in understanding of crop yield responses to climate variability and change.

Regional crop yield forecasting: a probabilistic approach

NASA Astrophysics Data System (ADS)

de Wit, A.; van Diepen, K.; Boogaard, H.

2009-04-01

Information on the outlook on yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies with a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, and for commodity traders. Process-based mechanistic crop models are an important tool for providing such information, because they can integrate the effect of crop management, weather and soil on crop growth. When properly integrated in a yield forecasting system, the aggregated model output can be used to predict crop yield and production at regional, national and continental scales. Nevertheless, given the scales at which these models operate, the results are subject to large uncertainties due to poorly known weather conditions and crop management. Current yield forecasting systems are generally deterministic in nature and provide no information about the uncertainty bounds on their output. To improve on this situation we present an ensemble-based approach where uncertainty bounds can be derived from the dispersion of results in the ensemble. The probabilistic information provided by this ensemble-based system can be used to quantify uncertainties (risk) on regional crop yield forecasts and can therefore be an important support to quantitative risk analysis in a decision making process.

Combining Remote Sensing imagery of both fine and coarse spatial resolution to Estimate Crop Evapotranspiration and quantifying its Influence on Crop Growth Monitoring.

NASA Astrophysics Data System (ADS)

Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio; Duveiller, Gregory; Piccard, Isabelle; de Wit, Allard; Tychon, Bernard; Bakary, Djaby; Defourny, Pierre

2010-05-01

This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. Evapotranspiration (ET) is a valuable parameter in the crop monitoring context since it provides information on the plant water stress status, which strongly influences crop development and, by extension, crop yield. To assess crop evapotranspiration over the GLOBAM study areas (300x300 km sites in Northern Europe and Central Ethiopia), a Soil-Vegetation-Atmosphere Transfer (SVAT) model forced with remote sensing and numerical weather prediction data has been used. This model runs at pre-operational level in the framework of the EUMETSAT LSA-SAF (Land Surface Analysis Satellite Application Facility) using SEVIRI and ECMWF data, as well as the ECOCLIMAP database to characterize the vegetation. The model generates ET images at the Meteosat Second Generation (MSG) spatial resolution (3 km at subsatellite point),with a temporal resolution of 30 min and monitors the entire MSG disk which covers Europe, Africa and part of Sud America . The SVAT model was run for 2007 using two approaches. The first approach is at the standard pre-operational mode. The second incorporates remote sensing information at various spatial resolutions going from LANDSAT (30m) to SEVIRI (3-5 km) passing by AWIFS (56m) and MODIS (250m). Fine spatial resolution data consists of crop type classification which enable to identify areas where pure crop specific MODIS time series can be compiled and used to derive Leaf Area Index estimations for the most important crops (wheat and maize). The use of this information allowed to characterize the type of vegetation and its state of development in a more accurate way than using the ECOCLIMAP database. Finally, the CASA method was applied using the evapotranspiration images with FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) images from LSA-SAF to obtain Dry Matter Productivity (DMP) and crop yield. The potential of using evapotranspiration obtained from remote sensing in crop growth modeling is studied and discussed. Results of comparing the evapotranspiration obtained with ground truth data are shown as well as the influence of using high resolution information to characterize the vegetation in the evapotranspiration estimation. The values of DMP and yield obtained with the CASA method are compared with those obtained using crop growth modeling and field data, showing the potential of using this simplified remote sensing method for crop monitoring and yield forecasting. This methodology could be applied in an operative way to the entire MSG disk, allowing the continuous crop growth monitoring.

Evaluation of remote sensing in control of pink bollworm in cotton. [Imperial Valley, Coachella Valley, and Palo Verde Valley, California

NASA Technical Reports Server (NTRS)

Lewis, L. N. (Principal Investigator); Coleman, V. B.; Johnson, C. W.

1974-01-01

The author has identified the following significant results. This investigation is to evaluate the use of a satellite in monitoring the cotton production regulation program of the State of California as an aid in controlling pink bollworm infestation in the southern deserts of California. Color combined images of ERTS-1 multispectral images simulating color infrared are being used for crop identification. The status of each field (crop, bare, harvested, wet, plowed) is mapped from the imagery and is then compared to ground survey information taken at the time of ERTS-1 overflights. A computer analysis has been performed to compare field and satellite data to a crop calendar. Correlation to date has been 97% for field condition. Actual crop identification varies; cotton identification is only 63% due to lack of full season coverage.

Development of an Optical Device to Investigatechlorophyll Content of Tomato Leaves

NASA Astrophysics Data System (ADS)

Cui, Di; Li, Minzan; Li, Xiuhua

Chlorophyll content is an important indication for evaluating crop growth status and predicting crop yield. The NDVI (Normalized Difference Vegetation Index) is commonly used as an indicator in practical crop healthy monitoring. Hence, a spectroscopy-based device for indirectly measuring crop growth conditions in terms of NDVI is developed. This device consists of four channels: two are designed to measure the intensity of the sunlight and the other two are used to measure the reflected light from the crop canopy at the same time. An electronic control unit was designed to control the sensing and data recording processes, as well as to calculate the NDVI based on the sensed data. The measurable two wavelengths are 610 nm and 1220 nm. A series validation tests, comparing the measurement result against spectroradiometer readings, are conducted to evaluate the performance of the device. Leaf samples are collected to measure chlorophyll contents in laboratory. The correlation coefficient between the NDVI readings from the developed device and the chlorophyll content data measured by the UV-VIS Spectrophotometer reaches 0.81, which shows that the device can be used in practical crop management.

Determining the potential productivity of food crops in controlled environments

NASA Technical Reports Server (NTRS)

Bugbee, Bruce

1992-01-01

The quest to determine the maximum potential productivity of food crops is greatly benefitted by crop growth models. Many models have been developed to analyze and predict crop growth in the field, but it is difficult to predict biological responses to stress conditions. Crop growth models for the optimal environments of a Controlled Environment Life Support System (CELSS) can be highly predictive. This paper discusses the application of a crop growth model to CELSS; the model is used to evaluate factors limiting growth. The model separately evaluates the following four physiological processes: absorption of PPF by photosynthetic tissue, carbon fixation (photosynthesis), carbon use (respiration), and carbon partitioning (harvest index). These constituent processes determine potentially achievable productivity. An analysis of each process suggests that low harvest index is the factor most limiting to yield. PPF absorption by plant canopies and respiration efficiency are also of major importance. Research concerning productivity in a CELSS should emphasize: (1) the development of gas exchange techniques to continuously monitor plant growth rates and (2) environmental techniques to reduce plant height in communities.

iPot: Improved potato monitoring in Belgium using remote sensing and crop growth modelling

NASA Astrophysics Data System (ADS)

Piccard, Isabelle; Gobin, Anne; Curnel, Yannick; Goffart, Jean-Pierre; Planchon, Viviane; Wellens, Joost; Tychon, Bernard; Cattoor, Nele; Cools, Romain

2016-04-01

Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these. The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market. The iPot project, financed by the Belgian Science Policy Office (Belspo), aims at providing the Belgian potato processing sector, represented by Belgapom, with near real time information on field condition (weather-soil), crop development and yield estimates, derived from a combination of satellite images and crop growth models. During the cropping season regular UAV flights (RGB, 3x3 cm) and high resolution satellite images (DMC/Deimos, 22m pixel size) were combined to elucidate crop phenology and performance at variety trials. UAV images were processed using a K-means clustering algorithm to classify the crop according to its greenness at 5m resolution. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) on the DMC images. Both DMC and UAV-based cover maps showed similar patterns, and helped detect different crop stages during the season. A wide spread field monitoring campaign with crop observations and measurements allowed for further calibration of the satellite image derived vegetation indices. Curve fitting techniques and phenological models were developed and compared with the vegetation indices during the season, both at trials and farmers' fields. Understanding and predicting crop phenology and canopy development is important for timely crop management and ultimately for yield estimates. An intuitive web-based geo-information platform is developed to allow both the industry and the research centres to access, analyse and combine the data with their own field observations for improved decision-making.

[Distribution characteristics of soil profile nitrous oxide concentration in paddy fields with different rice-upland crop rotation systems].

PubMed

Liu, Ping-li; Zhang, Xiao-lin; Xiong, Zheng-qin; Huang, Tai-qing; Ding, Min; Wang, Jin-yang

2011-09-01

To investigate the dynamic distribution patterns of nitrous oxide (N2O) in the soil profiles in paddy fields with different rice-upland crop rotation systems, a special soil gas collection device was adopted to monitor the dynamics of N2O at the soil depths 7, 15, 30, and 50 cm in the paddy fields under both flooding and drainage conditions. Two rotation systems were installed, i.e., wheat-single rice and oilseed rape-double rice, each with or without nitrogen (N) application. Comparing with the control, N application promoted the N2O production in the soil profiles significantly (P < 0.01), and there existed significant correlations in the N2O concentration among the four soil depths during the whole observation period (P < 0.01). In the growth seasons of winter wheat and oilseed rape under drainage condition and with or without N application, the N2O concentrations at the soil depths 30 cm and 50 cm were significantly higher than those at the soil depths 7 cm and 15 cm; whereas in the early rice growth season under flooding condition and without N application, the N2O concentrations at the soil depth 7 cm and 15 cm were significantly higher than those at the soil depths 30 cm and 50 cm (P < 0.05). No significant differences were observed in the N2O concentrations at the test soil depths among the other rice cropping treatments. The soil N2O concentrations in the treatments without N application peaked in the transitional period from the upland crops cropping to rice planting, while those in the treatments with N application peaked right after the second topdressing N of upland crops. Relatively high soil N2O concentrations were observed at the transitional period from the upland crops cropping to rice planting.

Soil Carbon Budget During Establishment of Short Rotation Woody Crops

NASA Astrophysics Data System (ADS)

Coleman, M. D.

2003-12-01

Carbon budgets were monitored following forest harvest and during re-establishment of short rotation woody crops. Soil CO2 efflux was monitored using infared gas analyzer methods, fine root production was estimated with minirhizotrons, above ground litter inputs were trapped, coarse root inputs were estimated with developed allometric relationships, and soil carbon pools were measured in loblolly pine and cottonwood plantations. Our carbon budget allows evaluation of errors, as well as quantifying pools and fluxes in developing stands during non-steady-state conditions. Soil CO2 efflux was larger than the combined inputs from aboveground litter fall and root production. Fine-root production increased during stand development; however, mortality was not yet equivalent to production, showing the belowground carbon budget was not yet in equilibrium and root carbon standing crop was accruing. Belowground production was greater in cottonwood than pine, but the level of pine soil CO2 efflux was equal to or greater than that of cottonwood, indicating heterotrophic respiration was higher for pine. Comparison of unaccounted efflux with soil organic carbon changes provides verification of loss or accrual.

Multi-temporal UAV based data for mapping crop type and structure in smallholder dominated Tanzanian agricultural landscape

NASA Astrophysics Data System (ADS)

Nagol, J. R.; Chung, C.; Dempewolf, J.; Maurice, S.; Mbungu, W.; Tumbo, S.

2015-12-01

Timely mapping and monitoring of crops like Maize, an important food security crop in Tanzania, can facilitate timely response by government and non-government organizations to food shortage or surplus conditions. Small UAVs can play an important role in linking the spaceborne remote sensing data and ground based measurement to improve the calibration and validation of satellite based estimates of in-season crop metrics. In Tanzania most of the growing season is often obscured by clouds. UAV data, if collected within a stratified statistical sampling framework, can also be used to directly in lieu of spaceborne data to infer mid-season yield estimates at regional scales.Here we present an object based approach to estimate crop metrics like crop type, area, and height using multi-temporal UAV based imagery. The methods were tested at three 1km2 plots in Kilosa, Njombe, and Same districts in Tanzania. At these sites both ground based and UAV based data were collected on a monthly time-step during the year 2015 growing season. SenseFly eBee drone with RGB and NIR-R-G camera was used to collect data. Crop type classification accuracies of above 85% were easily achieved.

Rice crop risk map in Babahoyo canton (Ecuador)

NASA Astrophysics Data System (ADS)

Valverde Arias, Omar; Tarquis, Ana; Garrido, Alberto

2016-04-01

It is widely known that extreme climatic phenomena occur with more intensity and frequency. This fact has put more pressure over farming, making agricultural and livestock production riskier. In order to reduce hazards and economic loses that could jeopardize farmer's incomes and even its business continuity, it is very important to implement agriculture risk management plans by governments and institutions. One of the main strategies is transfer risk by agriculture insurance. Agriculture insurance based in indexes has a significant growth in the last decade. And consist in a comparison between measured index values with a defined threshold that triggers damage losses. However, based index insurance could not be based on an isolated measurement. It is necessary to be integrated in a complete monitoring system that uses many sources of information and tools. For example, index influence areas, crop production risk maps, crop yields, claim statistics, and so on. Crop production risk is related with yield variation of crops and livestock, due to weather, pests, diseases, and other factors that affect both the quantity and quality of commodities produced. This is the risk which farmers invest more time managing, and it is completely under their control. The aim of this study is generate a crop risk map of rice that can provide risk manager important information about the status of crop facing production risks. Then, based on this information, it will be possible to make best decisions to deal with production risk. The rice crop risk map was generated qualifying a 1:25000 scale soil and climatic map of Babahoyo canton, which is located in coast region of Ecuador, where rice is one of the main crops. The methodology to obtain crop risk map starts by establishing rice crop requirements and indentifying the risks associated with this crop. A second step is to evaluate soil and climatic conditions of the study area related to optimal crop requirements. Based on it, we can determinate which level of rice crop requirement is met. Finally we have established rice crop zones classified as: suitable, moderate suitable, marginal suitable and unsuitable. Several methods have been used to estimate the degree with which crop requirements are satisfied, pondering weights of limiting factors to adequate crop conditions. Better conditions for cropping in a specific area imply less risk in production. In this case, crop will be less affected by pests and disease, although this closely depends on crop management. Farmers have to invest less money to produce and could increase their benefit. Results are showed and discussed with the aim to study the efficiency and potential of this risk map.

Remote Sensing Technologies Mitigate Drought

NASA Technical Reports Server (NTRS)

2015-01-01

Ames Research Center has partnered with the California Department of Water Resources to develop satellite-based technologies to mitigate drought conditions. One project aims to help water managers adjust their irrigation to match the biological needs of each crop, and another involves monitoring areas where land is fallow so emergency relief can more quickly aid affected communities.

USDA/federal user of LANDSAT remote sensing

NASA Technical Reports Server (NTRS)

Allen, R.

1981-01-01

Developed and potential uses of remote sensing in crop condition and acreage assessment, renewable resources inventories, conservation practices, and water and forest management applications are described. Operational approaches, the adaptation of procedures to needs, and the agency's concern about data continuity and cost are discussed as well as support for future technology development for enhanced sensing capability. The use of improved camera systems for soil mapping and conservation monitoring from space shuttle, and of aerospace radar to improve soil moisture monitoring are mentioned.

Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

USGS Publications Warehouse

Tappan, G. Gray; Moore, Donald G.; Knauseberger, Walter I.

1991-01-01

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.

Agricultural Productivity Forecasts for Improved Drought Monitoring

NASA Technical Reports Server (NTRS)

Limaye, Ashutosh; McNider, Richard; Moss, Donald; Alhamdan, Mohammad

2010-01-01

Water stresses on agricultural crops during critical phases of crop phenology (such as grain filling) has higher impact on the eventual yield than at other times of crop growth. Therefore farmers are more concerned about water stresses in the context of crop phenology than the meteorological droughts. However the drought estimates currently produced do not account for the crop phenology. US Department of Agriculture (USDA) and National Oceanic and Atmospheric Administration (NOAA) have developed a drought monitoring decision support tool: The U.S. Drought Monitor, which currently uses meteorological droughts to delineate and categorize drought severity. Output from the Drought Monitor is used by the States to make disaster declarations. More importantly, USDA uses the Drought Monitor to make estimates of crop yield to help the commodities market. Accurate estimation of corn yield is especially critical given the recent trend towards diversion of corn to produce ethanol. Ethanol is fast becoming a standard 10% ethanol additive to petroleum products, the largest traded commodity. Thus the impact of large-scale drought will have dramatic impact on the petroleum prices as well as on food prices. USDA's World Agricultural Outlook Board (WAOB) serves as a focal point for economic intelligence and the commodity outlook for U.S. WAOB depends on Drought Monitor and has emphatically stated that accurate and timely data are needed in operational agrometeorological services to generate reliable projections for agricultural decision makers. Thus, improvements in the prediction of drought will reflect in early and accurate assessment of crop yields, which in turn will improve commodity projections. We have developed a drought assessment tool, which accounts for the water stress in the context of crop phenology. The crop modeling component is done using various crop modules within Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT is an agricultural crop simulation system, which integrates the effects of soil, crop phenotype, weather, and management options. It has been in use for more than 15 years by researchers, growers and has become a de-facto standard in crop modeling communities spanning over 100 countries. The meteorological forcings to DSSAT are provided by NASA s National Land Data Assimilation System (NLDAS) datasets. NLDAS is a framework that incorporates atmospheric forcing and land parameter values along with land surface models to diagnose and predict the state of the land surface.

Challenges in breeding for yield increase for drought.

PubMed

Sinclair, Thomas R

2011-06-01

Crop genetic improvement for environmental stress at the molecular and physiological level is very complex and challenging. Unlike the example of the current major commercial transgenic crops for which biotic stress tolerance is based on chemicals alien to plants, the complex, redundant and homeostatic molecular and physiological systems existing in plants must be altered for drought tolerance improvement. Sophisticated tools must be developed to monitor phenotype expression at the crop level to characterize variation among genotypes across a range of environments. Once stress-tolerant cultivars are developed, regional probability distributions describing yield response across years will be necessary. This information can then aid in identifying environmental conditions for positive and negative responses to genetic modification to guide farmer selection of stress-tolerant cultivars. Copyright © 2011 Elsevier Ltd. All rights reserved.

Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

NASA Astrophysics Data System (ADS)

B, Potgieter A.; D, Rodriguez; B, Power; J, Mclean; P, Davis

2014-02-01

Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (~1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible and 90m for thermal) satellite platforms. Results showed that spatial variations in crop yield were related to a satellite derived canopy stress index (CSIsat) and a moisture stress index (MSIsat). A weather station level canopy stress index (CSIws) calculated at midday was correlated to the CSIsat at late morning. In addition, a strong linear relationship was observed between EVI and LST at point scale throughout the crop growth period. Differences were smallest at anthesis when the canopy closure was highest. This suggests that LST imagery data around flowering could be used to calculate crop stress over large areas of the crop. The harvested yield was related (R2 = 0.67) to CSIsat using a fix date across all fields. This relationship improved (R2 = 0.92) using both indices from all five dates across all fields during the crop growth period. Here we successfully showed that satellite derived crop attributes (CSIsat and MSIsat) can account for most of the variability in final crop yield and that they can be used to predict crop yield at field scales. Applications of these results could enhance the ability of producers to hedge their financial on -farm crop production losses due to in-season water stress by taking crop insurance. This is likely to further improve their adaptive capacity and thus strengthening the long-term viability of the industry domestically and elsewhere.

The role of space borne imaging radars in environmental monitoring: Some shuttle imaging radar results in Asia

NASA Technical Reports Server (NTRS)

Imhoff, M.; Vermillion, C.

1986-01-01

The synoptic view afforded by orbiting Earth sensors can be extremely valuable for resource evaluation, environmental monitoring and development planning. For many regions of the world, however, cloud cover has prevented the acquisition of remotely sensed data during the most environmentally stressful periods of the year. This paper discusses how synthetic aperture imaging radar can be used to provide valuable data about the condition of the Earth's surface during periods of bad weather. Examples are given of applications using data from the Shuttle Imaging Radars (SIR) A and B for agriculture land use and crop condition assessment, monsoon flood boundary and flood damage assessment, water resource monitoring and terrain modeling, coastal forest mapping and vegetation penetration, and coastal development monitoring. Recent SIR-B results in Bangladesh are emphasized, radar system basics are reviewed and future SAR systems discussed.

The role of space borne imaging radars in environmental monitoring: Some shuttle imaging radar results in Asia

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Vermillion, C. H.

1986-01-01

The synoptic view afforded by orbiting Earth sensors can be extremely valuable for resource evaluation, environmental monitoring and development planning. For many regions of the world, however, cloud cover has prevented the acquisition of remotely sensed data during the most environmentally stressful periods of the year. How synthetic aperture imaging radar can be used to provide valuable data about the condition of the Earth's surface during periods of bad weather is discussed. Examples are given of applications using data from the Shuttle Imaging Radars (SIR) A and B for agricultural land use and crop condition assessment, monsoon flood boundary and flood damage assessment, water resource monitoring and terrain modeling, coastal forest mapping and vegetation penetration, and coastal development monitoring. Recent SIR-B results in Bangladesh are emphasized, radar system basics are reviewed and future SAR systems are discussed.

AgriSense-STARS: Advancing Methods of Agricultural Monitoring for Food Security in Smallholder Regions - the Case for Tanzania

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Becker-Reshef, I.; Nakalembe, C. L.; Tumbo, S.; Maurice, S.; Mbilinyi, B.; Ntikha, O.; Hansen, M.; Justice, C. J.; Adusei, B.; Kongo, V.

2015-12-01

In-season monitoring of crop conditions provides critical information for agricultural policy and decision making and most importantly for food security planning and management. Nationwide agricultural monitoring in countries dominated by smallholder farming systems, generally relies on extensive networks of field data collectors. In Tanzania, extension agents make up this network and report on conditions across the country, approaching a "near-census". Data is collected on paper which is resource and time intensive, as well as prone to errors. Data quality is ambiguous and there is a general lack of clear and functional feedback loops between farmers, extension agents, analysts and decision makers. Moreover, the data are not spatially explicit, limiting the usefulness for analysis and quality of policy outcomes. Despite significant advances in remote sensing and information communication technologies (ICT) for monitoring agriculture, the full potential of these new tools is yet to be realized in Tanzania. Their use is constrained by the lack of resources, skills and infrastructure to access and process these data. The use of ICT technologies for data collection, processing and analysis is equally limited. The AgriSense-STARS project is developing and testing a system for national-scale in-season monitoring of smallholder agriculture using a combination of three main tools, 1) GLAM-East Africa, an automated MODIS satellite image processing system, 2) field data collection using GeoODK and unmanned aerial vehicles (UAVs), and 3) the Tanzania Crop Monitor, a collaborative online portal for data management and reporting. These tools are developed and applied in Tanzania through the National Food Security Division of the Ministry of Agriculture, Food Security and Cooperatives (MAFC) within a statistically representative sampling framework (area frame) that ensures data quality, representability and resource efficiency.

Investigate the Capabilities of Remotely Sensed Crop Indicators for Agricultural Drought Monitoring in Kansas

NASA Astrophysics Data System (ADS)

Zhang, J.; Becker-Reshef, I.; Justice, C. O.

2013-12-01

Although agricultural production has been rising in the past years, drought remains the primary cause of crop failure, leading to food price instability and threatening food security. The recent 'Global Food Crisis' in 2008, 2011 and 2012 has put drought and its impact on crop production at the forefront, highlighting the need for effective agricultural drought monitoring. Satellite observations have proven a practical, cost-effective and dynamic tool for drought monitoring. However, most satellite based methods are not specially developed for agriculture and their performances for agricultural drought monitoring still need further development. Wheat is the most widely grown crop in the world, and the recent droughts highlight the importance of drought monitoring in major wheat producing areas. As the largest wheat producing state in the US, Kansas plays an important role in both global and domestic wheat markets. Thus, the objective of this study is to investigate the capabilities of remotely sensed crop indicators for effective agricultural drought monitoring in Kansas wheat-grown regions using MODIS data and crop yield statistics. First, crop indicators such as NDVI, anomaly and cumulative metrics were calculated. Second, the varying impacts of agricultural drought at different stages were explored by examining the relationship between the derived indicators and yields. Also, the starting date of effective agricultural drought early detection and the key agricultural drought alert period were identified. Finally, the thresholds of these indicators for agricultural drought early warning were derived and the implications of these indicators for agricultural drought monitoring were discussed. The preliminary results indicate that drought shows significant impacts from the mid-growing-season (after Mid-April); NDVI anomaly shows effective drought early detection from Late-April, and Late-April to Early-June can be used as the key alert period for agricultural drought early warning; and drought occurring in Early-May has the most significant agricultural impacts. This research intends to help prototype an agricultural drought alert system, which could alert crop analysts to agricultural drought vulnerable areas/periods and provide tools for assessing crop outlooks in these regions.

From LACIE to GEOGLAM: Integrating Earth Observations into Operational Agricultural Monitoring Systems

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.

2012-12-01

Earth observation data, owing to their synoptic, timely and repetitive coverage, have long been recognized as an indispensible tool for agricultural monitoring at local to global scales. Research and development over the past several decades in the field of agricultural remote sensing has led to considerable capacity for crop monitoring within the current operational monitoring systems. These systems are relied upon nationally and internationally to provide crop outlooks and production forecasts as the growing season progresses. This talk will discuss the legacy and current state of operational agricultural monitoring using earth observations. In the US, the National Aeronautics and Space Administration (NASA) and the US Department of Agriculture (USDA) have been collaborating to monitor global agriculture from space since the 1970s. In 1974, the USDA, NASA and National Oceanic and Atmospheric Administration (NOAA) initiated the Large Area Crop Inventory Experiment (LACIE) which demonstrated that earth observations could provide vital information on crop production, with unprecedented accuracy and timeliness, prior to harvest. This experiment spurred many agencies and researchers around the world to further develop and evaluate remote sensing technologies for timely, large area, crop monitoring. The USDA and NASA continue to closely collaborate. More recently they jointly initiated the Global Agricultural Monitoring Project (GLAM) to enhance the agricultural monitoring and the crop-production estimation capabilities of the USDA Foreign Agricultural Service by using the new generation of NASA satellite observations including from MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments. Internationally, in response to the growing calls for improved agricultural information, the Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative which was adopted by the G20 as part of the action plan on food price volatility and agriculture. The goal of GEOGLAM is to enhance agricultural production estimates through leveraging advances in the research domain and in satellite technologies, and integrating these into the existing operational monitoring systems.

FPGA-based smart sensor for drought stress detection in tomato plants using novel physiological variables and discrete wavelet transform.

PubMed

Duarte-Galvan, Carlos; Romero-Troncoso, Rene de J; Torres-Pacheco, Irineo; Guevara-Gonzalez, Ramon G; Fernandez-Jaramillo, Arturo A; Contreras-Medina, Luis M; Carrillo-Serrano, Roberto V; Millan-Almaraz, Jesus R

2014-10-09

Soil drought represents one of the most dangerous stresses for plants. It impacts the yield and quality of crops, and if it remains undetected for a long time, the entire crop could be lost. However, for some plants a certain amount of drought stress improves specific characteristics. In such cases, a device capable of detecting and quantifying the impact of drought stress in plants is desirable. This article focuses on testing if the monitoring of physiological process through a gas exchange methodology provides enough information to detect drought stress conditions in plants. The experiment consists of using a set of smart sensors based on Field Programmable Gate Arrays (FPGAs) to monitor a group of plants under controlled drought conditions. The main objective was to use different digital signal processing techniques such as the Discrete Wavelet Transform (DWT) to explore the response of plant physiological processes to drought. Also, an index-based methodology was utilized to compensate the spatial variation inside the greenhouse. As a result, differences between treatments were determined to be independent of climate variations inside the greenhouse. Finally, after using the DWT as digital filter, results demonstrated that the proposed system is capable to reject high frequency noise and to detect drought conditions.

WEBGIS based CropWatch online agriculture monitoring system

NASA Astrophysics Data System (ADS)

Zhang, X.; Wu, B.; Zeng, H.; Zhang, M.; Yan, N.

2015-12-01

CropWatch, which was developed by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), has achieved breakthrough results in the integration of methods, independence of the assessments and support to emergency response by periodically releasing global agricultural information. Taking advantages of the multi-source remote sensing data and the openness of the data sharing policies, CropWatch group reported their monitoring results by publishing four bulletins one year. In order to better analysis and generate the bulletin and provide an alternative way to access agricultural monitoring indicators and results in CropWatch, The CropWatch online system based on the WEBGIS techniques has been developed. Figure 1 shows the CropWatch online system structure and the system UI in Clustering mode. Data visualization is sorted into three different modes: Vector mode, Raster mode and Clustering mode. Vector mode provides the statistic value for all the indicators over each monitoring units which allows users to compare current situation with historical values (average, maximum, etc.). Users can compare the profiles of each indicator over the current growing season with the historical data in a chart by selecting the region of interest (ROI). Raster mode provides pixel based anomaly of CropWatch indicators globally. In this mode, users are able to zoom in to the regions where the notable anomaly was identified from statistic values in vector mode. Data from remote sensing image series at high temporal and low spatial resolution provide key information in agriculture monitoring. Clustering mode provides integrated information on different classes in maps, the corresponding profiles for each class and the percentage of area of each class to the total area of all classes. The time series data is categorized into limited types by the ISODATA algorithm. For each clustering type, pixels on the map, profiles, and percentage legend are all linked together. All the three visualization methods are applied to four scales including 65 monitoring and reporting units (MRUs), 7 major production zones (MPZs), 173 countries and sub-countries for 9 large countries. Agro-Climatic information, Agronomic information and indicators related with crop area, crop yield and crop production are provided.

Impact of switching crop type on water and solute fluxes in deep vadose zone

NASA Astrophysics Data System (ADS)

Turkeltaub, T.; Kurtzman, D.; Russak, E. E.; Dahan, O.

2015-12-01

Switching crop type and consequently changing irrigation and fertilization regimes lead to alterations in deep percolation and solute concentrations of pore water. Herein, observations from the deep vadose zone and model simulations demonstrate the changes in water, chloride, and nitrate fluxes under a commercial greenhouse following the change from tomato to lettuce cropping. The site, located above a phreatic aquifer, was monitored for 5 years. A vadose-zone monitoring system was implemented under the greenhouse and provided continuous data on both temporal variations in water content and chemical composition of the pore water at multiple depths in the deep vadose zone (up to 20 m). Following crop switching, a significant reduction in chloride concentration and dramatic increase in nitrate were observed across the unsaturated zone. The changes in chemical composition of the vadose-zone pore water appeared as sequential breakthroughs across the unsaturated zone, initiating at land surface and propagating down toward the water table. Today, 3 years after switching the crops, penetration of the impact exceeds 10 m depth. Variations in the isotopic composition of nitrate (18O and 15N) in water samples obtained from the entire vadose zone clearly support a fast leaching process and mobilization of solutes across the unsaturated zone following the change in crop type. Water flow and chloride transport models were calibrated to observations acquired during an enhanced infiltration experiment. Forward simulation runs were performed with the calibrated models, constrained to tomato and lettuce cultivation regimes as surface boundary conditions. Predicted chloride and nitrate concentrations were in agreement with the observed concentrations. The simulated water drainage and nitrogen leaching implied that the observed changes are an outcome of recommended agricultural management practices.

Influence of precipitation and crop germination on resource selection by mule deer (Odocoileus hemionus) in southwest Colorado

USGS Publications Warehouse

Carrollo, Emily M.; Johnson, Heather E.; Fischer, Justin W.; Hammond, Matthew; Dorsey, Patricia D.; Anderson, Charles; Vercauteren, Kurt C.; Walter, W. David

2017-01-01

Mule deer (Odocoileus hemionus) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km2, and for two summer seasons that ranged between 5.51 and 6.24 km2. Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

Influence of Precipitation and Crop Germination on Resource Selection by Mule Deer (Odocoileus hemionus) in Southwest Colorado.

PubMed

Carrollo, Emily M; Johnson, Heather E; Fischer, Justin W; Hammond, Matthew; Dorsey, Patricia D; Anderson, Charles; Vercauteren, Kurt C; Walter, W David

2017-11-09

Mule deer (Odocoileus hemionus) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km 2 , and for two summer seasons that ranged between 5.51 and 6.24 km 2 . Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

The Combination of Uav Survey and Landsat Imagery for Monitoring of Crop Vigor in Precision Agriculture

NASA Astrophysics Data System (ADS)

Lukas, V.; Novák, J.; Neudert, L.; Svobodova, I.; Rodriguez-Moreno, F.; Edrees, M.; Kren, J.

2016-06-01

Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 - 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

Characterization of Proteins in Filtrate from Biodegradation of Crop Residue

NASA Technical Reports Server (NTRS)

Horton, Wileatha; Trotman, A. A.

1997-01-01

Biodegradation of plant biomass is a feasible path for transformation of crop residue and recycling of nutrients for crop growth. The need to model the effects of factors associated with recycling of plant biomass resulting from hydroponic sweet potato production has led to investigation of natural soil isolates with the capacity for starch hydrolysis. This study sought to use nondenaturing gel electrophoresis to characterize the proteins present in filtered effluent from bioreactors seeded with starch hydrolyzing bacterial culture used in the biodegradation of senesced sweet potato biomass. The study determined the relative molecular weight of proteins in sampled effluent and the protein banding pattern was characterized. The protein profiles of effluent were similar for samples taken from independent runs under similar conditions of starch hydrolysis. The method can be used as a quality control tool for confirmation of starch hydrolysis of crop biomass. In addition, this method will allow monitoring for presence of contaminants within the system-protein profiles indicative of new enzymes in the bioreactors.

Continuous water quality monitoring for the hard clam industry in Florida, USA.

PubMed

Bergquist, Derk C; Heuberger, David; Sturmer, Leslie N; Baker, Shirley M

2009-01-01

In 2000, Florida's fast-growing hard clam aquaculture industry became eligible for federal agricultural crop insurance through the US Department of Agriculture, but the responsibility for identifying the cause of mortality remained with the grower. Here we describe the continuous water quality monitoring system used to monitor hard clam aquaculture areas in Florida and show examples of the data collected with the system. Systems recording temperature, salinity, dissolved oxygen, water depth, turbidity and chlorophyll at 30 min intervals were installed at 10 aquaculture lease areas along Florida's Gulf and Atlantic coasts. Six of these systems sent data in real-time to a public website, and all 10 systems provided data for web-accessible archives. The systems documented environmental conditions that could negatively impact clam survival and productivity and identified biologically relevant water quality differences among clam aquaculture areas. Both the real-time and archived data were used widely by clam growers and nursery managers to make management decisions and in filing crop loss insurance claims. While the systems were labor and time intensive, we recommend adjustments that could reduce costs and staff time requirements.

Towards Developing a Regional Drought Information System for Lower Mekong

NASA Astrophysics Data System (ADS)

Dutta, R.; Jayasinghe, S.; Basnayake, S. B.; Apirumanekul, C.; Pudashine, J.; Granger, S. L.; Andreadis, K.; Das, N. N.

2016-12-01

With the climate and weather patterns changing over the years, the Lower Mekong Basin have been experiencing frequent and prolonged droughts resulting in severe damage to the agricultural sector affecting food security and livelihoods of the farming community. However, the Regional Drought Information System (RDIS) for Lower Mekong countries would help prepare vulnerable communities from frequent and severe droughts through monitoring, assessing and forecasting of drought conditions and allowing decision makers to take effective decisions in terms of providing early warning, incentives to farmers, and adjustments to cropping calendars and so on. The RDIS is an integrated system that is being designed for drought monitoring, analysis and forecasting based on the need to meet the growing demand of an effective monitoring system for drought by the lower Mekong countries. The RDIS is being built on four major components that includes earth observation component, meteorological data component, database storage and Regional Hydrologic Extreme Assessment System (RHEAS) framework while the outputs from the system will be made open access to the public through a web-based user interface. The system will run on the RHEAS framework that allows both nowcasting and forecasting using hydrological and crop simulation models such as the Variable Infiltration Capacity (VIC) model and the Decision Support System for Agro-Technology Transfer (DSSAT) model respectively. The RHEAS allows for a tightly constrained observation based drought and crop yield information system that can provide customized outputs on drought that includes root zone soil moisture, Standard Precipitation Index (SPI), Standard Runoff Index (SRI), Palmer Drought Severity Index (PDSI) and Crop Yield and can integrate remote sensing products, along with evapotranspiration and soil moisture data. The anticipated outcomes from the RDIS is to improve the operational, technological and institutional capabilities of lower Mekong countries to prepare for and respond towards drought situations and providing policy makers with current and forecast drought indices for decision making on adjusting cropping calendars as well as planning short and long term mitigation measures.

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

In silico analysis of decomposed reflectances of C3 and C4 plants aiming at the effective assessment of crop needs

NASA Astrophysics Data System (ADS)

Baranoski, Gladimir V. G.; Van Leeuwen, Spencer; Chen, Tenn F.

2017-04-01

By separating the surface and subsurface components of foliar hyperspectral signatures using polarization optics, it is possible to enhance the remote discrimination of different plant species and optimize the assessment of different factors associated with their health status. These initiatives, in turn, can lead to higher crop yield and lower environmental impact. It is important to consider, however, that the main varieties of crops, represented by C3 (e.g., soy) and C4 (e.g., maize) plants, have markedly distinct morphological characteristics. Accordingly, the influence of these characteristics on their interactions with impinging light may affect the selection of optimal probe wavelengths for specific applications making use of combined hyperspectral and polarization measurements. In this paper, we compare the sensitivity of the total (including surface and subsurface components) and subsurface reflectance responses of C3 and C4 plants to different spectral and geometrical light incidence conditions. This investigation is supported by measured biophysical data and predictive light transport simulations. The results of our comparisons indicate that the total and subsurface reflectance responses of C3 and C4 plants depict well-defined patterns of sensitivity for varying illumination conditions. We believe that these patterns should be considered in the design of high-fidelity crop discrimination and monitoring procedures.

Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test.

PubMed

Lebourgeois, Valentine; Bégué, Agnès; Labbé, Sylvain; Mallavan, Benjamin; Prévot, Laurent; Roux, Bruno

2008-11-17

The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces.

Development of a Global Agricultural Hotspot Detection and Early Warning System

NASA Astrophysics Data System (ADS)

Lemoine, G.; Rembold, F.; Urbano, F.; Csak, G.

2015-12-01

The number of web based platforms for crop monitoring has grown rapidly over the last years and anomaly maps and time profiles of remote sensing derived indicators can be accessed online thanks to a number of web based portals. However, while these systems make available a large amount of crop monitoring data to the agriculture and food security analysts, there is no global platform which provides agricultural production hotspot warning in a highly automatic and timely manner. Therefore a web based system providing timely warning evidence as maps and short narratives is currently under development by the Joint Research Centre. The system (called "HotSpot Detection System of Agriculture Production Anomalies", HSDS) will focus on water limited agricultural systems worldwide. The automatic analysis of relevant meteorological and vegetation indicators at selected administrative units (Gaul 1 level) will trigger warning messages for the areas where anomalous conditions are observed. The level of warning (ranging from "watch" to "alert") will depend on the nature and number of indicators for which an anomaly is detected. Information regarding the extent of the agricultural areas concerned by the anomaly and the progress of the agricultural season will complement the warning label. In addition, we are testing supplementary detailed information from other sources for the areas triggering a warning. These regard the automatic web-based and food security-tailored analysis of media (using the JRC Media Monitor semantic search engine) and the automatic detection of active crop area using Sentinel 1, upcoming Sentinel-2 and Landsat 8 imagery processed in Google Earth Engine. The basic processing will be fully automated and updated every 10 days exploiting low resolution rainfall estimates and satellite vegetation indices. Maps, trend graphs and statistics accompanied by short narratives edited by a team of crop monitoring experts, will be made available on the website on a monthly basis.

Projected climate change impacts and short term predictions on staple crops in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Mereu, V.; Spano, D.; Gallo, A.; Carboni, G.

2013-12-01

Agriculture in Sub-Saharan Africa (SSA) drives the economy of many African countries and it is mainly rain-fed agriculture used for subsistence. Increasing temperatures, changed precipitation patterns and more frequent droughts may lead to a substantial decrease of crop yields. The projected impacts of future climate change on agriculture are expected to be significant and extensive in the SSA due to the shortening of the growing seasons and the increasing of water-stress risk. Differences in Agro-Ecological Zones and geographical characteristics of SSA influence the diverse impacts of climate change, which can greatly differ across the continent and within countries. The vulnerability of African Countries to climate change is aggravated by the low adaptive capacity of the continent, due to the increasing of its population, the widespread poverty, and other social factors. In this contest, the assessment of climate change impact on agricultural sector has a particular interest to stakeholder and policy makers, in order to identify specific agricultural sectors and Agro-Ecological Zones that could be more vulnerable to changes in climatic conditions and to develop the most appropriate policies to cope with these threats. For these reasons, the evaluation of climate change impacts for key crops in SSA was made exploring climate uncertainty and focusing on short period monitoring, which is particularly useful for food security and risk management analysis. The DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5 was used for the analysis. Crop simulation models included in DSSAT-CSM are tools that allow to simulate physiological process of crop growth, development and production, by combining genetic crop characteristics and environmental (soil and weather) conditions. For each selected crop, the models were used, after a parameterization phase, to evaluate climate change impacts on crop phenology and production. Multiple combinations of soils and climate conditions, crop management and varieties were considered for the different Agro-Ecological Zones. The climate impact was assessed using future climate prediction, statistically and/or dynamically downscaled, for specific areas. Direct and indirect effects of different CO2 concentrations projected for the future periods were separately explored to estimate their effects on crops. Several adaptation strategies (e.g., introduction of full irrigation, shift of the ordinary sowing/planting date, changes in the ordinary fertilization management) were also evaluated with the aim to reduce the negative impact of climate change on crop production. The results of the study, analyzed at local, AEZ and country level, will be discussed.

A radarsat-2 quad-polarized time series for monitoring crop and soil conditions in Barrax, Spain

USDA-ARS?s Scientific Manuscript database

The European Space Agency (ESA) along with multiple university and agency investigators joined to conduct the AgriSAR Campaign in 2009. The main objective was to analyze a dense time series of RADARSAT-2 quad-pol data to define and quantify the performance of Sentinel-1 and other future ESA C-Band ...

Vertical farming monitoring system using the internet of things (IoT)

NASA Astrophysics Data System (ADS)

Chin, Yap Shien; Audah, Lukman

2017-09-01

Vertical farming had become a hot topic among peak development countries. However, vertical farming is hard to practice because minor changes on the surrounding would leave big impact to the productivity and quality of farming activity. Thus, the aim of this project is to provide a vertical farming monitoring system to help keeping track on the physical conditions of crops. In this system, varieties of sensors will be used to detect current physical conditions, and send the data to BeagleBone Black (BBB) microcontroller either in analog or digital input. Then, the data will be processed by BBB and upload to the Thingspeak Cloud. Furthermore, the system will record the position of equipment in used, which make it easier for maintenance when there is equipment broken down. The system also provide basic remote function where users could turn on/off the watering system, and the LED light via web-based application. The web-based application will also be designed to analyze and display data gathered in the form of graphs, charts or figures, for better understanding. With the improvement implemented on the vertical farming culture, it is expected that the productivity and quality of crops would increase significantly.

Environmental change challenges decision-making during post-market environmental monitoring of transgenic crops.

PubMed

Sanvido, Olivier; Romeis, Jörg; Bigler, Franz

2011-12-01

The ability to decide what kind of environmental changes observed during post-market environmental monitoring of genetically modified (GM) crops represent environmental harm is an essential part of most legal frameworks regulating the commercial release of GM crops into the environment. Among others, such decisions are necessary to initiate remedial measures or to sustain claims of redress linked to environmental liability. Given that consensus on criteria to evaluate 'environmental harm' has not yet been found, there are a number of challenges for risk managers when interpreting GM crop monitoring data for environmental decision-making. In the present paper, we argue that the challenges in decision-making have four main causes. The first three causes relate to scientific data collection and analysis, which have methodological limits. The forth cause concerns scientific data evaluation, which is controversial among the different stakeholders involved in the debate on potential impacts of GM crops on the environment. This results in controversy how the effects of GM crops should be valued and what constitutes environmental harm. This controversy may influence decision-making about triggering corrective actions by regulators. We analyse all four challenges and propose potential strategies for addressing them. We conclude that environmental monitoring has its limits in reducing uncertainties remaining from the environmental risk assessment prior to market approval. We argue that remaining uncertainties related to adverse environmental effects of GM crops would probably be assessed in a more efficient and rigorous way during pre-market risk assessment. Risk managers should acknowledge the limits of environmental monitoring programmes as a tool for decision-making.

A generalized approach to wheat yield forecasting using earth observations: Data considerations, application and relevance

NASA Astrophysics Data System (ADS)

Becker-Reshef, Inbal

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. The issue of food security has rapidly risen to the top of government agendas around the world as the recent lack of food access led to unprecedented food prices, hunger, poverty, and civil conflict. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production, for stabilizing food prices, developing effective agricultural policies, and for coordinating responses to regional food shortages. Earth Observations (EO) data offer a practical means for generating such information as they provide global, timely, cost-effective, and synoptic information on crop condition and distribution. Their utility for crop production forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions. Nevertheless it is widely acknowledged that EO data could be better utilized within the operational monitoring systems and thus there is a critical need for research focused on developing practical robust methods for agricultural monitoring. Within this context this dissertation focused on advancing EO-based methods for crop yield forecasting and on demonstrating the potential relevance for adopting EO-based crop forecasts for providing timely reliable agricultural intelligence. This thesis made contributions to this field by developing and testing a robust EO-based method for wheat production forecasting at state to national scales using available and easily accessible data. The model was developed in Kansas (KS) using coarse resolution normalized difference vegetation index (NDVI) time series data in conjunction with out-of-season wheat masks and was directly applied in Ukraine to assess its transferability. The model estimated yields within 7% in KS and 10% in Ukraine of final estimates 6 weeks prior to harvest. The relevance of adopting such methods to provide timely reliable information to crop commodity markets is demonstrated through a 2010 case study.

Agricultural water use, crop water footprints and irrigation strategies in the seasonally dry Guanacaste region in Costa Rica

NASA Astrophysics Data System (ADS)

Morillas, Laura; Johnson, Mark S.; Hund, Silja V.; Steyn, Douw G.

2017-04-01

Agriculture is the main productive sector and a major water-consuming sector in the seasonally-dry Guanacaste region of north-western Costa Rica. Agriculture in the region is intensifying at the same time that seasonal water scarcity is increasing. The climate of this region is characterized by a prolonged dry season from December to March, followed by a bimodal wet season from April to November. The wet season has historically experienced periodic oscillations in rainfall timing and amounts resulting from variations of several large-scale climatic features (El Niño Southern Oscillation, the Pacific Decadal Oscillation, the Atlantic Multidecadal Oscillation and the North Atlantic Oscillation). However, global circulation models now project more recurrent variations in total annual rainfall, changes in rainfall temporal distribution, and increased temperatures in this region. This may result in a lengthening of the dry season and an increase in water scarcity and water-related conflicts as water resources are already limited and disputed in this area. In fact, this region has just undergone a four-year drought over the 2012-2015 period, which has intensified water related conflicts and put agricultural production at risk. In turn, the recent drought has also increased awareness of the local communities regarding the regional threat of water scarcity and the need of a regional water planning. The overall goal of this research is to generate data to characterize water use by the agricultural sector in this region and asses its sustainability in the regional context. Towards this goal, eddy-covariance flux towers were deployed on two extensive farms growing regionally-representative crops (melon/rice rotation and sugarcane) to evaluate, monitor and quantify water use in large-scale farms. The two identically instrumented stations provide continuous measurements of evapotranspiration and CO2 fluxes, and are equipped with additional instrumentation to monitor micrometeorological variables, vegetative status, and soil conditions. In this presentation, we present measured crop water footprints (total crop water consumption as blue and green water), crop water use efficiencies (water used per unit of agricultural production), and crop physiological status (PRI and NDVI index) under drought conditions (2015) and under average rainfall conditions (2016). We will use these data to evaluate the resilience to drought of these crops, which is crucial for the economy of the region. We will also evaluate the impact of agricultural water use for the local water balance and implications of irrigation practices for catchment-scale hydrological processes. Finally, we will explore the feasibility and potential of using CROPWAT 8.0 modelling software to generate estimates of crops water footprint for regional water planning decision-making and farm irrigation planning. The implications of these findings will be discussed in the context of the regional socio-hydrological system that is facing a likely increase in water scarcity due to climate change and demand intensification.

Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

NASA Astrophysics Data System (ADS)

Parekh, R. A.; Mehta, R. L.; Vyas, A.

2016-10-01

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

Influences of climate on aflatoxin producing fungi and aflatoxin contamination.

PubMed

Cotty, Peter J; Jaime-Garcia, Ramon

2007-10-20

Aflatoxins are potent mycotoxins that cause developmental and immune system suppression, cancer, and death. As a result of regulations intended to reduce human exposure, crop contamination with aflatoxins causes significant economic loss for producers, marketers, and processors of diverse susceptible crops. Aflatoxin contamination occurs when specific fungi in the genus Aspergillus infect crops. Many industries frequently affected by aflatoxin contamination know from experience and anecdote that fluctuations in climate impact the extent of contamination. Climate influences contamination, in part, by direct effects on the causative fungi. As climate shifts, so do the complex communities of aflatoxin-producing fungi. This includes changes in the quantity of aflatoxin-producers in the environment and alterations to fungal community structure. Fluctuations in climate also influence predisposition of hosts to contamination by altering crop development and by affecting insects that create wounds on which aflatoxin-producers proliferate. Aflatoxin contamination is prevalent both in warm humid climates and in irrigated hot deserts. In temperate regions, contamination may be severe during drought. The contamination process is frequently broken down into two phases with the first phase occurring on the developing crop and the second phase affecting the crop after maturation. Rain and temperature influence the phases differently with dry, hot conditions favoring the first and warm, wet conditions favoring the second. Contamination varies with climate both temporally and spatially. Geostatistics and multiple regression analyses have shed light on influences of weather on contamination. Geostatistical analyses have been used to identify recurrent contamination patterns and to match these with environmental variables. In the process environmental conditions with the greatest impact on contamination are identified. Likewise, multiple regression analyses allow ranking of environmental variables based on relative influence on contamination. Understanding the impact of climate may allow development of improved management procedures, better allocation of monitoring efforts, and adjustment of agronomic practices in anticipation of global climate change.

An integrated approach to monitoring ecosystem services and agriculture: implications for sustainable agricultural intensification in Rwanda.

PubMed

Rosa, Melissa F; Bonham, Curan A; Dempewolf, Jan; Arakwiye, Bernadette

2017-01-01

Maintaining the long-term sustainability of human and natural systems across agricultural landscapes requires an integrated, systematic monitoring system that can track crop productivity and the impacts of agricultural intensification on natural resources. This study presents the design and practical implementation of a monitoring framework that combines satellite observations with ground-based biophysical measurements and household surveys to provide metrics on ecosystem services and agricultural production at multiple spatial scales, reaching from individual households and plots owned by smallholder farmers to 100-km 2 landscapes. We developed a set of protocols for monitoring and analyzing ecological and agricultural household parameters within two 10 × 10-km landscapes in Rwanda, including soil fertility, crop yield, water availability, and fuelwood sustainability. Initial results suggest providing households that rely on rainfall for crop irrigation with timely climate information and improved technical inputs pre-harvest could help increase crop productivity in the short term. The value of the monitoring system is discussed as an effective tool for establishing a baseline of ecosystem services and agriculture before further change in land use and climate, identifying limitations in crop production and soil fertility, and evaluating food security, economic development, and environmental sustainability goals set forth by the Rwandan government.

Real Time, On Line Crop Monitoring and Analysis with Near Global Landsat-class Mosaics

NASA Astrophysics Data System (ADS)

Varlyguin, D.; Hulina, S.; Crutchfield, J.; Reynolds, C. A.; Frantz, R.

2015-12-01

The presentation will discuss the current status of GDA technology for operational, automated generation of 10-30 meter near global mosaics of Landsat-class data for visualization, monitoring, and analysis. Current version of the mosaic combines Landsat 8 and Landsat 7. Sentinel-2A imagery will be added once it is operationally available. The mosaics are surface reflectance calibrated and are analysis ready. They offer full spatial resolution and all multi-spectral bands of the source imagery. Each mosaic covers all major agricultural regions of the world and 16 day time window. 2014-most current dates are supported. The mosaics are updated in real-time, as soon as GDA downloads Landsat imagery, calibrates it to the surface reflectances, and generates data gap masks (all typically under 10 minutes for a Landsat scene). The technology eliminates the complex, multi-step, hands-on process of data preparation and provides imagery ready for repetitive, field-to-country analysis of crop conditions, progress, acreages, yield, and production. The mosaics can be used for real-time, on-line interactive mapping and time series drilling via GeoSynergy webGIS platform. The imagery is of great value for improved, persistent monitoring of global croplands and for the operational in-season analysis and mapping of crops across the globe in USDA FAS purview as mandated by the US government. The presentation will overview operational processing of Landsat-class mosaics in support of USDA FAS efforts and will look into 2015 and beyond.

Land Surface Modeling Applications for Famine Early Warning

NASA Astrophysics Data System (ADS)

McNally, A.; Verdin, J. P.; Peters-Lidard, C. D.; Arsenault, K. R.; Wang, S.; Kumar, S.; Shukla, S.; Funk, C. C.; Pervez, M. S.; Fall, G. M.; Karsten, L. R.

2015-12-01

AGU 2015 Fall Meeting Session ID#: 7598 Remote Sensing Applications for Water Resources Management Land Surface Modeling Applications for Famine Early Warning James Verdin, USGS EROS Christa Peters-Lidard, NASA GSFC Amy McNally, NASA GSFC, UMD/ESSIC Kristi Arsenault, NASA GSFC, SAIC Shugong Wang, NASA GSFC, SAIC Sujay Kumar, NASA GSFC, SAIC Shrad Shukla, UCSB Chris Funk, USGS EROS Greg Fall, NOAA Logan Karsten, NOAA, UCAR Famine early warning has traditionally required close monitoring of agro-climatological conditions, putting them in historical context, and projecting them forward to anticipate end-of-season outcomes. In recent years, it has become necessary to factor in the effects of a changing climate as well. There has also been a growing appreciation of the linkage between food security and water availability. In 2009, Famine Early Warning Systems Network (FEWS NET) science partners began developing land surface modeling (LSM) applications to address these needs. With support from the NASA Applied Sciences Program, an instance of the Land Information System (LIS) was developed to specifically support FEWS NET. A simple crop water balance model (GeoWRSI) traditionally used by FEWS NET took its place alongside the Noah land surface model and the latest version of the Variable Infiltration Capacity (VIC) model, and LIS data readers were developed for FEWS NET precipitation forcings (NOAA's RFE and USGS/UCSB's CHIRPS). The resulting system was successfully used to monitor and project soil moisture conditions in the Horn of Africa, foretelling poor crop outcomes in the OND 2013 and MAM 2014 seasons. In parallel, NOAA created another instance of LIS to monitor snow water resources in Afghanistan, which are an early indicator of water availability for irrigation and crop production. These successes have been followed by investment in LSM implementations to track and project water availability in Sub-Saharan Africa and Yemen, work that is now underway. Adoption of LSM and data assimilation technology has enabled FEWS NET to take greater advantage of remote sensing observations to robustly estimate key agro-climatological states, like soil moisture and snow water equivalent, building confidence in our understanding of conditions in data sparse regions of the world.

Changes in water and solute fluxes in the vadose zone after switching crops

NASA Astrophysics Data System (ADS)

Turkeltaub, Tuvia; Dahan, Ofer; Kurtzman, Daniel

2015-04-01

Switching crop type and therefore changing irrigation and fertilization regimes leads to alternation in deep percolation and concentrations of solutes in pore water. Changes of fluxes of water, chloride and nitrate under a commercial greenhouse due to a change from tomato to green spices were observed. The site, located above the a coastal aquifer, was monitored for the last four years. A vadose-zone monitoring system (VMS) was implemented under the greenhouse and provided continuous data on both the temporal variation in water content and the chemical composition of pore water at multiple depths in the deep vadose zone (~20 m). Chloride and nitrate profiles, before and after the crop type switching, indicate on a clear alternation in soil water solutes concentrations. Before the switching of the crop type, the average chloride profile ranged from ~130 to ~210, while after the switching, the average profile ranged from ~34 to ~203 mg L-1, 22% reduction in chloride mass. Counter trend was observed for the nitrate concentrations, the average nitrate profile before switching ranged from ~11 to ~44 mg L-1, and after switching, the average profile ranged from ~500 to ~75 mg L-1, 400% increase in nitrate mass. A one dimensional unsaturated water flow and chloride transport model was calibrated to transient deep vadose zone data. A comparison between the simulation results under each of the surface boundary conditions of the vegetables and spices cultivation regime, clearly show a distinct alternation in the quantity and quality of groundwater recharge.

Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

NASA Astrophysics Data System (ADS)

Psomiadis, Emmanouil; Dercas, Nicholas; Dalezios, Nicolas R.; Spyropoulos, Nikolaos V.

2017-10-01

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

Early forecasting of crop condition using an integrative remote sensing method for corn and soybeans in Iowa and Illinois, USA

NASA Astrophysics Data System (ADS)

Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

2017-04-01

The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotely sense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE < 15.0%) and the average accuracy of the normal/bad year classification was well (> 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remote sensing method for crop condition and crop phenology contributes to reducing the growing risk of crop production in the Earth.

Rice crop growth and outlook monitoring using SAR in Asia

NASA Astrophysics Data System (ADS)

Hamamoto, K.; Sobue, S.; Oyoshi, K.; Ikehata, Y.

2016-12-01

The Asia-RiCE initiative (http://www.asia-rice.org) has been organized to enhance rice production estimates through the use of Earth observation satellites data, and seeks to ensure that Asian rice crops are appropriately represented within GEO Global Agriculture Monitoring (GEO-GLAM) to support FAO Agriculture Market Information System (FAO-AMIS). Asia-RiCE is composed of national teams that are actively contributing to the Crop Monitor for AMIS and developing technical demonstrations of rice crop monitoring activities using both Synthetic Aperture Radar (SAR) data (Radarsat-2 from 2013; Sentinel-1 and ALOS-2 from 2015; TerraSAR-X, Cosmo-SkyMed, RISAT, and others) and optical imagery (such as from MODIS, SPOT-5, Landsat, and Sentinel-2) for 100x100km Technical Demonstration Sites (TDS) as a phase 1 (2013-2015) in Asia. with satellite -based cultivated area and growing stage map. The Asia-RiCE teams are also developing satellite-based agro-met information for rice crop outlook, crop calendars and damage assessment in cooperation with ASEAN food security information system (AFSIS) for selected countries (currently Indonesia, Thailand, Vietnam, Philippine, and Japan; http://www.afsisnc.org/blog), using JAXA's Satellite-based MonItoring Network system as a contribution to the FAO AMIS outlook (JASMIN) with University of Tokyo (http://suzaku.eorc.jaxa.jp/cgi-bin/gcomw/jasm/jasm_top.cgi). Because of continous El Nino in South East Asia, there are less precipitation and rain fall pattern change in South East Asia, crop pattern has been changed and production may be decreased, especially for dry season crop. JAXA provides drought index (KBDI) and accumulated precipitation of Tak province, Thailand where main reservior is located, to AFSIS and national experts to assess rice crop outlook and NDVI time seriese to Ang Tong province where is main rice production area in downstream area of that reservior.From 2016 as a phase 2, Asia-RiCE initiative deploy up-scaling activity from a province (100x100km) to major crop areas or entire country to implement operational use for rice crop production information in low Mekong, Vietnam and top 10 provinces in Indonesia using space based technology. This paper reports this year activity of 2016 accomplishment and way forward.

Analysis of hyperspectral field radiometric data for monitoring nitrogen concentration in rice crops

NASA Astrophysics Data System (ADS)

Stroppiana, D.; Boschetti, M.; Confalonieri, R.; Bocchi, S.; Brivio, P. A.

2005-10-01

Monitoring crop conditions and assessing nutrition requirements is fundamental for implementing sustainable agriculture. Rational nitrogen fertilization is of particular importance in rice crops in order to guarantee high production levels while minimising the impact on the environment. In fact, the typical flooded condition of rice fields can be a significant source of greenhouse gasses. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies for a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field Northern Italy, in order to evaluate the potential of field radiometric measurements for the prediction of rice nitrogen concentration. The results indicate that rice reflectance is influenced by nitrogen supply at certain wavelengths although N concentration cannot be accurately predicted based on the reflectance measured at a given wavelength. Regression analysis highlighted that the visible region of the spectrum is most sensitive to plant nitrogen concentration when reflectance measures are combined into a spectral index. An automated procedure allowed the analysis of all the possible combinations into a Normalized Difference Index (NDI) of the narrow spectral bands derived by spectral resampling of field measurements. The derived index appeared to be least influenced by plant biomass and Leaf Area Index (LAI) providing a useful approach to detect rice nutritional status. The validation of the regressive model showed that the model is able to predict rice N concentration (R2=0.55 [p<0.01] RRMSE=29.4; modelling efficiency close to the optimum value).

Laboratory measurements of emissions of nonmethane volatile organic compounds from biomass burning in Chinese crop residues

NASA Astrophysics Data System (ADS)

Inomata, S.; Tanimoto, H.; PAN, X.; Taketani, F.; Komazaki, Y.; Miyakawa, T.; Kanaya, Y.; Wang, Z.

2014-12-01

The emission factors (EFs) of volatile organic compounds (VOCs) from the burning of Chinese crop residue were investigated as a function of modified combustion efficiency by the laboratory experiments. The VOCs including acetonitrile, aldehydes/ketones, furan, and aromatic hydrocarbons were monitored by proton-transfer-reaction mass spectrometry. Two samples, wheat straw and rape plant, were burned in dry conditions and for some experiments wheat straw was burned under wet conditions. We compared the present data to the field data reported by Kudo et al. [2014]. The agreement between the field and laboratory data was obtained for aromatics for relatively more smoldering data of dry samples but the field data were slightly underestimated compared with the laboratory data for oxygenated VOCs (OVOCs) and acetonitrile. When the EFs from the burning of wet samples were investigated, the underestimations for OVOCs and acetonitrile were improved compared with the data of dry samples. It may be a property of the burning of crop residue in the region of high temperature and high humidity that some inside parts of piled crop residue and/or the crop residue facing on the ground are still wet. But the ratios for acetic acid/glycolaldehyde was still lower than 1. This may suggest that strong loss processes of acetic acid/glycolaldehyde are present in the fresh plume.Kudo S., H. Tanimoto, S. Inomata, S. Saito, X. L. Pan, Y. Kanaya, F. Taketani, Z. F. Wang, H. Chen, H. Dong, M. Zhang, and K. Yamaji (2014), Emissions of nonmethane volatile organic compounds from open crop residue burning in Yangtze River Delta region, China, J. Geophys. Res. Atmos., 119, 7684-7698, doi: 10.1002/2013JD021044.

Plant health sensing

NASA Technical Reports Server (NTRS)

Manukian, Ara; Mckelvy, Colleen; Pearce, Michael; Syslo, Steph

1988-01-01

If plants are to be used as a food source for long term space missions, they must be grown in a stable environment where the health of the crops is continuously monitored. The sensor(s) to be used should detect any diseases or health problems before irreversible damage occurs. The method of analysis must be nondestructive and provide instantaneous information on the condition of the crop. In addition, the sensor(s) must be able to function in microgravity. This first semester, the plant health and disease sensing group concentrated on researching and consulting experts in many fields in attempts to find reliable plant health indicators. Once several indicators were found, technologies that could detect them were investigated. Eventually the three methods chosen to be implemented next semester were stimulus response monitoring, video image processing and chlorophyll level detection. Most of the other technologies investigated this semester are discussed here. They were rejected for various reasons but are included in the report because NASA may wish to consider pursuing them in the future.

The CSAICLAWPS project: a multi-scalar, multi-data source approach to providing climate services for both modelling of climate change impacts on crop yields and development of community-level adaptive capacity for sustainable food security

NASA Astrophysics Data System (ADS)

Forsythe, N. D.; Fowler, H. J.

2017-12-01

The "Climate-smart agriculture implementation through community-focused pursuit of land and water productivity in South Asia" (CSAICLAWPS) project is a research initiative funded by the (UK) Royal Society through its Challenge Grants programme which is part of the broader UK Global Challenges Research Fund (GCRF). CSAICLAWPS has three objectives: a) development of "added-value" - bias assessed, statistically down-scaled - climate projections for selected case study sites across South Asia; b) investigation of crop failure modes under both present (observed) and future (projected) conditions; and c) facilitation of developing local adaptive capacity and resilience through stakeholder engagement. At AGU we will be presenting both next steps and progress to date toward these three objectives: [A] We have carried out bias assessments of a substantial multi-model RCM ensemble (MME) from the CORDEX South Asia (CORDEXdomain for case studies in three countries - Pakistan, India and Sri Lanka - and (stochastically) produced synthetic time-series for these sites from local observations using a Python-based implementation of the principles underlying the Climate Research Unit Weather Generator (CRU-WG) in order to enable probabilistic simulation of current crop yields. [B] We have characterised present response of local crop yields to climate variability in key case study sites using AquaCrop simulations parameterised based on input (agronomic practices, soil conditions, etc) from smallholder farmers. [C] We have implemented community-based hydro-climatological monitoring in several case study "revenue villages" (panchayats) in the Nainital District of Uttarakhand. The purpose of this is not only to increase availability of meteorological data, but also has the aspiration of, over time, leading to enhanced quantitative awareness of present climate variability and potential future conditions (as projected by RCMs). Next steps in our work will include: 1) future crop yield simulations driven by "perturbation" of synthetic time-series using "change factors from the CORDEX-SA MME; 2) stakeholder dialogues critically evaluating potential strategies at the grassroots (implementation) level to mitigate impacts of climate variability and change on crop yields.

Irrigation scheduling and controlling crop water use efficiency with Infrared Thermometry

USDA-ARS?s Scientific Manuscript database

Scientific methods for irrigation scheduling include weather, soil and plant-based techniques. Infrared thermometers can be used a non-invasive practice to monitor canopy temperature and better manage irrigation scheduling. This presentation will discuss the theoretical basis for monitoring crop can...

Wheat growth monitoring with radar vegetation indices

USDA-ARS?s Scientific Manuscript database

Microwave remote sensing can help in the monitoring of crop growth. Many experiments have been carried out to investigate the sensitivity of microwave sensors to crop growth parameters. These have clearly shown that canopy structure and water content can greatly affect the measurements. For agricult...

Using ESAP Software for Predicting the Spatial Distributions of NDVI and Transpiration of Cotton

USDA-ARS?s Scientific Manuscript database

The normalized difference vegetation index (NDVI) has many applications in agricultural management, including monitoring real-time crop coefficients for estimating crop evapotranspiration (ET). However, frequent monitoring of NDVI as needed in such applications is generally not feasible from aerial ...

PLANT INCORPORATED PROTECTANT CROP MONITORING USING REMOTE SENSING

EPA Science Inventory

The extent of past and anticipated plantings of transgenic corn in the United States requires a new approach to monitor this important crop for the development of pest resistance. Remote sensing by aerial and/or satellite images may provide a method of identifying transgenic pest...

Strategic system development toward biofuel, desertification, and crop production monitoring in continental scales using satellite-based photosynthesis models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2013-10-01

The author regards fundamental root functions as underpinning photosynthesis activities by vegetation and as affecting environmental issues, grain production, and desertification. This paper describes the present development of monitoring and near real-time forecasting of environmental projects and crop production by approaching established operational monitoring step-by-step. The author has been developing a thematic monitoring structure (named RSEM system) which stands on satellite-based photosynthesis models over several continents for operational supports in environmental fields mentioned above. Validation methods stand not on FLUXNET but on carbon partitioning validation (CPV). The models demand continuing parameterization. The entire frame system has been built using Reanalysis meteorological data, but model accuracy remains insufficient except for that of paddy rice. The author shall accomplish the system that incorporates global environmental forces. Regarding crop production applications, industrialization in developing countries achieved through direct investment by economically developed nations raises their income, resulting in increased food demand. Last year, China began to import rice as it had in the past with grains of maize, wheat, and soybeans. Important agro-potential countries make efforts to cultivate new crop lands in South America, Africa, and Eastern Europe. Trends toward less food sustainability and stability are continuing, with exacerbation by rapid social and climate changes. Operational monitoring of carbon sequestration by herbaceous and bore plants converges with efforts at bio-energy, crop production monitoring, and socio-environmental projects such as CDM A/R, combating desertification, and bio-diversity.

Implementation of a global-scale operational data assimilation system for satellite-based soil moisture retrievals

NASA Astrophysics Data System (ADS)

Bolten, J.; Crow, W.; Zhan, X.; Reynolds, C.

2008-08-01

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.

A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops.

PubMed

Bengochea-Guevara, José M; Andújar, Dionisio; Sanchez-Sardana, Francisco L; Cantuña, Karla; Ribeiro, Angela

2017-12-24

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, "on ground crop inspection" potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. "On ground monitoring" is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows.

Agricultural land use and N losses to water: the case study of a fluvial park in northern Italy.

PubMed

Morari, F; Lugato, E; Borin, M

2003-01-01

An integrated water resource management programme has been under way since 1999 to reduce agricultural water pollution in the River Mincio fluvial park. The experimental part of the programme consisted of: a) a monitoring phase to evaluate the impact of conventional and environmentally sound techniques (Best Management Practices, BMPs) on water quality; this was done on four representative landscape units, where twelve fields were instrumented to monitor the soil, surface and subsurface water quality; b) a modelling phase to extend the results obtained at field scale to the whole territory of the Mincio watershed. For this purpose a GIS developed in the Arc/Info environment was integrated into the CropSyst model. The model had previously been calibrated to test its ability to describe the complexity of the agricultural systems. The first results showed a variable efficiency of the BMPs depending on the interaction between management and pedo-climatic conditions. In general though, the BMPs had positive effects in improving the surface and subsurface water quality. The CropSyst model was able to describe the agricultural systems monitored and its linking with the GIS represented a valuable tool for identifying the vulnerable areas within the watershed.

Application of SAR remote sensing and crop modeling for operational rice crop monitoring in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Maunahan, A. A.; Gatti, L.; Quicho, E. D.; Pazhanivelan, S.; Campos-Taberner, M.; Collivignarelli, F.; Haro, J. G.; Intrman, A.; Phuong, D.; Boschetti, M.; Prasadini, P.; Busetto, L.; Minh, V. Q.; Tuan, V. Q.

2017-12-01

This study uses multi-temporal SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations in South and South Asian countries and assimilate the information into ORYZA Crop Growth Simulation Model (CGSM) to monitor rice yield. The study demonstrates examples of operational application of this rice monitoring system in: (1) detecting drought impact on rice planting in Central Thailand and Tamil Nadu, India, (2) mapping heat stress impact on rice yield in Andhra Pradesh, India, and (3) generating historical rice yield data for districts in Red River Delta, Vietnam.

Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test

PubMed Central

Lebourgeois, Valentine; Bégué, Agnès; Labbé, Sylvain; Mallavan, Benjamin; Prévot, Laurent; Roux, Bruno

2008-01-01

The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces. PMID:27873930

A NEW APPROACH TO PIP CROP MONITORING USING REMOTE SENSING

EPA Science Inventory

Current plantings of 25+ million acres of transgenic corn in the United States require a new approach to monitor this important crop for the development of pest resistance. Remote sensing by aerial or satellite images may provide a method of identifying transgenic pesticidal cro...

Emerging pests and diseases of South-east Asian cassava: a comprehensive evaluation of geographic priorities, management options and research needs.

PubMed

Graziosi, Ignazio; Minato, Nami; Alvarez, Elizabeth; Ngo, Dung Tien; Hoat, Trinh Xuan; Aye, Tin Maung; Pardo, Juan Manuel; Wongtiem, Prapit; Wyckhuys, Kris Ag

2016-06-01

Cassava is a major staple, bio-energy and industrial crop in many parts of the developing world. In Southeast Asia, cassava is grown on >4 million ha by nearly 8 million (small-scale) farming households, under (climatic, biophysical) conditions that often prove unsuitable for many other crops. While SE Asian cassava has been virtually free of phytosanitary constraints for most of its history, a complex of invasive arthropod pests and plant diseases has recently come to affect local crops. We describe results from a region-wide monitoring effort in the 2014 dry season, covering 429 fields across five countries. We present geographic distribution and field-level incidence of the most prominent pest and disease invaders, introduce readily-available management options and research needs. Monitoring work reveals that several exotic mealybug and (red) mite species have effectively colonised SE Asia's main cassava-growing areas, occurring in respectively 70% and 54% of fields, at average field-level incidence of 27 ± 2% and 16 ± 2%. Cassava witches broom (CWB), a systemic phytoplasma disease, was reported from 64% of plots, at incidence levels of 32 ± 2%. Although all main pests and diseases are non-natives, we hypothesise that accelerating intensification of cropping systems, increased climate change and variability, and deficient crop husbandry are aggravating both organism activity and crop susceptibility. Future efforts need to consolidate local capacity to tackle current (and future) pest invaders, boost detection capacity, devise locally-appropriate integrated pest management (IPM) tactics, and transfer key concepts and technologies to SE Asia's cassava growers. Urgent action is needed to mobilise regional as well as international scientific support, to effectively tackle this phytosanitary emergency and thus safeguard the sustainability and profitability of one of Asia's key agricultural commodities. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Remote sensing to monitor cover crop adoption in southeastern Pennsylvania

USDA-ARS?s Scientific Manuscript database

In the Chesapeake Bay watershed, winter cereal cover crops are often planted in rotation with summer crops to reduce the loss of nutrients and sediment from agricultural systems. Cover crops can also improve soil health, control weeds and pests, supplement forage needs, and support resilient croppin...

Efficient Maize and Sunflower Multi-year Mapping with NDVI Time Series of HJ-1A/1B in Hetao Irrigation District of Inner Mongolia, China

NASA Astrophysics Data System (ADS)

Yu, B.; Shang, S.

2016-12-01

Food shortage is one of the major challenges that human beings are facing. It is urgent to improve the monitoring of the plantation and distribution of the main crops to solve the following economic and social issues. Recently, with the extensive use of remote sensing satellite data, it has provided favorable conditions for crop identification in large irrigation district with complex planting structure. Difference of different crop phenology is the main basis for crop identification, and the normalized difference vegetation index (NDVI) time-series could better delineate crop phenology cycle. Therefore, the key of crop identification is to obtain high quality NDVI time-series. MODIS and Landsat TM satellite images are the most frequently used, however, neither of them could guarantee high temporal and spatial resolutions at once. Accordingly, this paper makes use of NDVI time-series extracted from China Environment Satellites data, which has two-day-repeat temporal and 30m spatial resolutions. The NDVI time-series are fitted with an asymmetric logistic curve, the fitting effect is good and the correlation coefficient is greater than 0.9. The phonological parameters are derived from NDVI fitting curves, and crop identification is carried out by different relation ellipses between NDVI and its phonological parameters of different crops. This paper takes Hetao Irrigation District of Inner Mongolia as an example, to identify multi-year maize and sunflower in the district, and the identification result is good. Compared with the official statistics, the relative errors are both lower than 5%. The results show that the NDVI time-series dataset derived from HJ-1A/1B CCD could delineate the crop phenology cycle accurately and demonstrate its application in crop identification in irrigated district.

Backscattering and vegetation water content response of paddy crop at C-band using RISAT-1 satellite data

NASA Astrophysics Data System (ADS)

Kumar, Pradeep; Prasad, Rajendra; Choudhary, Arti; Gupta, Dileep Kumar; Narayan Mishra, Varun; Srivastava, Prashant K.

2016-04-01

The study about the temporal behaviour of vegetation water content (VWC) is essential for monitoring the growth of a crop to improve agricultural production. In agriculture, VWC could possibly provide information that can be used to infer water stress for irrigation decisions, vegetation health conditions, aid in yield estimation and assessment of drought conditions (Penuelas et al., 1993). The VWC is an important parameter for soil moisture retrieval in microwave remote sensing (Srivastava et al., 2014). In the present study, the backscattering and VWC response of paddy crop has been investigated using medium resolution (MRS) radar imaging satellite-1 (RISAT-1) synthetic aperture radar (SAR) data in Varanasi, India. The VWC of paddy crop was measured at its five different growth stages started from 15 July 2013 to 23 October 2013 from the transplanting to maturity stage during Kharif season. The whole life of paddy crop was divided into three different major growth stages like vegetative stage, reproductive stage and ripening stage. During vegetative stage, the backscattering coefficients were found increasing behaviour until the leaves became large and dense due to major contribution of stems and the interaction between the stems and water underneath the paddy crop. During reproductive stage, the backscattering coefficients were found to increase slowly due to random scattering by vertical leaves. The increase in the size of leaves cause to cover most of the spaces between plants resulted to quench the contributions from the stems and the water underneath. At the maturity stage, the backscattering showed its decreasing behaviour. The VWC of paddy crop was found increasing up to vegetative to reproductive stages (28 September 2013) and then started decreasing during the ripening (maturity) stage. Similar behaviour was obtained between backscattering coefficients and VWC that showed an increasing trend from vegetative to reproductive stage and then lowering down at ripening stage at HH- and HV- polarizations. It is concluded that HH- polarized backscattering coefficients using RISAT-1 data are more sensitive in comparison to HV- polarized backscattering coefficients. The C-band, RISAT-1 backscattering coefficients may be useful for the retrieval of VWC of paddy crop to monitor its growth stages. Keywords: SAR, C-band, dual polarimetric, RISAT-1, VWC, paddy References: Penuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The reflectance at the 950-970 mm region as an indicator of plant water status. International Journal of Remote Sensing, 14:1887-1905. Srivastava , P. K., Han, D., Rico-Ramirez, M. A., O'Neill, P., Islam, T., & Gupta, M. (2014). Assessment of SMOS soil moisture retrieval parameters using tau-omega algorithms for soil moisture deficit estimation. Journal of Hydrology 519:574-587

The iPot Project: improved potato monitoring in Belgium using remote sensing and crop growth modelling

NASA Astrophysics Data System (ADS)

Piccard, Isabelle; Nackaerts, Kris; Gobin, Anne; Goffart, Jean-Pierre; Planchon, Viviane; Curnel, Yannick; Tychon, Bernard; Wellens, Joost; Cools, Romain; Cattoor, Nele

2015-04-01

Belgian potato processors, traders and packers are increasingly working with potato contracts. The close follow up of contracted parcels on the land as well as from above is becoming an important tool to improve the quantity and quality of the potato crop and reduce risks in order to plan the storage, packaging or processing and as such to strengthen the competitiveness of the Belgian potato chain in a global market. At the same time, precision agriculture continues to gain importance and progress. Farmers are obligated to invest in new technologies. Between mid-May and the end of June 2014 potato fields in Gembloux were monitored from emergence till canopy closure. UAV images (RGB) and digital (hemispherical) photographs were taken at ten-daily intervals. Crop emergence maps show the time (date) and degree of crop emergence and crop closure (in terms of % cover). For three UAV flights during the growing season RGB images at 3 cm resolution were processed using a K-means clustering algorithm to classify the crop according to its greenness. Based on the greenness %cover and daily cover growth were derived for 5x5m pixels and 25x25m pixels. The latter resolution allowed for comparison with high resolution satellite imagery. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) from high resolution satellite images (DMC/Deimos, 22m pixel size). DMC based cover maps showed similar patterns as compared with the UAV-based cover maps, and allows for further applications of the data in crop management. Today the use of geo-information by the (private) agricultural sector in Belgium is rather limited, notwithstanding the great benefits this type of information may offer, as recognized by the sector. The iPot project, financed by the Belgian Science Policy Office (BELSPO), aims to provide the Belgian potato sector, represented by Belgapom, with near real time information on field condition (weather-soil) and crop development and with early yield estimates, derived from a combination of satellite images and crop growth models. An intuitive web based geo-information platform is being developed to allow both the Belgian potato industry and the potato research centres to access, analyse and combine the data with their own field observations in close collaboration with the farmers, for improved decision-making.

AN APPROACH TO TRANSGENIC CROP MONITORING

EPA Science Inventory

Remote sensing by aerial or satellite images may provide a method of identifying transgenic pesticidal crop distribution in the landscape. Genetically engineered crops containing bacterial gene(s) that express an insecticidal protein from Bacillus thuringiensis (Bt) are regulated...

Using the SPEI to Estimate Food Production in East Africa

NASA Astrophysics Data System (ADS)

Husak, G. J.; Hobbins, M.; Verdin, J. P.; Peterson, P.; Funk, C. C.

2015-12-01

The Famine Early Warning Systems Network (FEWS NET) monitors critical environmental variables that impact food production in developing countries. Due to a sparse network of observations in the developing world, many of these variables are estimated using remotely sensed data. As scientists develop new techniques to leverage available observations and remotely sensed information there are opportunities to create products that identify the environmental conditions that stress agriculture and reduce food production. FEWS NET pioneered the development of the Climate Hazards Group InfraRed Precipitation with stations (CHIRPS) dataset, to estimate precipitation and monitor growing conditions throughout the world. These data are used to drive land surface models, hydrologic models and basic crop models among others. A new dataset estimating the reference evapotranspiration (ET0) has been developed using inputs from the ERA-Interim GCM. This ET0 dataset stretches back to 1981, allowing for a long-term record, stretching many seasons and drought events. Combining the CHIRPS data to estimate water availability and the ET0 data to estimate evaporative demand, one can estimate the approximate water gap (surplus or deficit) over a specific time period. Normalizing this difference creates the Standardized Precipitation Evapotranspiration Index (SPEI), which presents these gaps in comparison to the historical record for a specific location and accumulation period. In this study we evaluate the SPEI as a tool to estimate crop yields for different regions of Kenya. Identifying the critical time of analysis for the SPEI is the first step in building a relationship between the water gap and food production. Once this critical period is identified, we look at the predictability of food production using the SPEI, and assess the utility of it for monitoring food security, with the goal of incorporating the SPEI in the standard monitoring suite of FEWS NET tools.

Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

NASA Technical Reports Server (NTRS)

Zhang, Ping; Anderson, Bruce; Tan, Bin; Barlow, Mathew; Myneni, Ranga

2011-01-01

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity.

Application of Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region

NASA Astrophysics Data System (ADS)

Jayasinghe, S.; Dutta, R.; Basnayake, S. B.; Granger, S. L.; Andreadis, K. M.; Das, N.; Markert, K. N.; Cutter, P. G.; Towashiraporn, P.; Anderson, E.

2017-12-01

The Lower Mekong Region has been experiencing frequent and prolonged droughts resulting in severe damage to agricultural production leading to food insecurity and impacts on livelihoods of the farming communities. Climate variability further complicates the situation by making drought harder to forecast. The Regional Drought and Crop Yield Information System (RDCYIS), developed by SERVIR-Mekong, helps decision makers to take effective measures through monitoring, analyzing and forecasting of drought conditions and providing early warnings to farmers to make adjustments to cropping calendars. The RDCYIS is built on regionally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. The RHEAS allows ingestion of numerus freely available earth observation and ground observation data to generate and customize drought related indices, variables and crop yield information for better decision making. The Lower Mekong region has experienced severe drought in 2016 encompassing the region's worst drought in 90 years. This paper presents the simulation of the 2016 drought event using RDCYIS based on its hindcast and forecast capabilities. The regionally calibrated RDCYIS can help capture salient features of drought through a variety of drought indices, soil variables, energy balance variables and water balance variables. The RDCYIS is capable of assimilating soil moisture data from different satellite products and perform ensemble runs to further reduce the uncertainty of it outputs. The calibrated results have correlation coefficient around 0.73 and NSE between 0.4-0.5. Based on the acceptable results of the retrospective runs, the system has the potential to generate reliable drought monitoring and forecasting information to improve decision-makings at operational, technological and institutional level of mandated institutes of lower Mekong countries. This is turn would help countries to prepare for and respond to drought situations by taking short and long-term risk mitigation measures such as adjusting cropping calendars, rainwater harvesting, and so on.

Crop classification using multidate/multifrequency radar data. [Colby, Kansas

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Shanmugam, K. S.; Narayanan, V.; Dobson, C.

1981-01-01

Both C- and L-band radar data acquired over a test site near Colby, Kansas during the summer of 1978 were used to identify three types of vegetation cover and bare soil. The effects of frequency, polarization, and the look angle on the overall accuracy of recognizing the four types of ground cover were analyzed. In addition, multidate data were used to study the improvement in recognition accuracy possible with the addition of temporal information. The soil moisture conditions had changed considerably during the temporal sequence of the data; hence, the effects of soil moisture on the ability to discriminate between cover types were also analyzed. The results provide useful information needed for selecting the parameters of a radar system for monitoring crops.

Comparison of multi- and hyperspectral imaging data of leaf rust infected wheat plants

NASA Astrophysics Data System (ADS)

Franke, Jonas; Menz, Gunter; Oerke, Erich-Christian; Rascher, Uwe

2005-10-01

In the context of precision agriculture, several recent studies have focused on detecting crop stress caused by pathogenic fungi. For this purpose, several sensor systems have been used to develop in-field-detection systems or to test possible applications of remote sensing. The objective of this research was to evaluate the potential of different sensor systems for multitemporal monitoring of leaf rust (puccinia recondita) infected wheat crops, with the aim of early detection of infected stands. A comparison between a hyperspectral (120 spectral bands) and a multispectral (3 spectral bands) imaging system shows the benefits and limitations of each approach. Reflectance data of leaf rust infected and fungicide treated control wheat stand boxes (1sqm each) were collected before and until 17 days after inoculation. Plants were grown under controlled conditions in the greenhouse and measurements were taken under consistent illumination conditions. The results of mixture tuned matched filtering analysis showed the suitability of hyperspectral data for early discrimination of leaf rust infected wheat crops due to their higher spectral sensitivity. Five days after inoculation leaf rust infected leaves were detected, although only slight visual symptoms appeared. A clear discrimination between infected and control stands was possible. Multispectral data showed a higher sensitivity to external factors like illumination conditions, causing poor classification accuracy. Nevertheless, if these factors could get under control, even multispectral data may serve a good indicator for infection severity.

Combining Landsat-8 and WorldView-3 data to assess crop residue cover

USDA-ARS?s Scientific Manuscript database

Crop residues on the soil surface contribute to soil quality and form the first line defense against the erosive forces of water and wind. Quantifying crop residue cover on the soil surface after crops are planted is crucial for monitoring soil tillage intensity and assessing the extent of conserva...

Rice crop growth monitoring using ENVISAT-1/ASAR AP mode

NASA Astrophysics Data System (ADS)

Konishi, Tomohisa; Suga, Yuzo; Omatu, Shigeru; Takeuchi, Shoji; Asonuma, Kazuyoshi

2007-10-01

Hiroshima Institute of Technology (HIT) is operating the direct down-links of microwave and optical earth observation satellite data in Japan. This study focuses on the validation for rice crop monitoring using microwave remotely sensed image data acquired by ENIVISAT-1 referring to ground truth data such as height of rice crop, vegetation cover rate and leaf area index in the test sites of Hiroshima district, the western part of Japan. ENVISAT-1/ASAR data has the capabilities for the monitoring of the rice crop growing cycle by using alternating cross polarization mode images. However, ASAR data is influenced by several parameters such as land cover structure, direction and alignment of rice crop fields in the test sites. In this study, the validation was carried out to be combined with microwave image data and ground truth data regarding rice crop fields to investigate the above parameters. Multi-temporal, multi-direction (descending and ascending) and multi-angle ASAR alternating cross polarization mode images were used to investigate during the rice crop growing cycle. On the other hand, LANDSAT-7/ETM+ data were used to detect land cover structure, direction and alignment of rice crop fields corresponding to the backscatter of ASAR. Finally, the extraction of rice planted area was attempted by using multi-temporal ASAR AP mode data such as VV/VH and HH/HV. As the result of this study, it is clear that the estimated rice planted area coincides with the existing statistical data for area of the rice crop field. In addition, HH/HV is more effective than VV/VH in the rice planted area extraction.

Gene Expression Biomarkers Provide Sensitive Indicators of in Planta Nitrogen Status in Maize[W][OA

PubMed Central

Yang, Xiaofeng S.; Wu, Jingrui; Ziegler, Todd E.; Yang, Xiao; Zayed, Adel; Rajani, M.S.; Zhou, Dafeng; Basra, Amarjit S.; Schachtman, Daniel P.; Peng, Mingsheng; Armstrong, Charles L.; Caldo, Rico A.; Morrell, James A.; Lacy, Michelle; Staub, Jeffrey M.

2011-01-01

Over the last several decades, increased agricultural production has been driven by improved agronomic practices and a dramatic increase in the use of nitrogen-containing fertilizers to maximize the yield potential of crops. To reduce input costs and to minimize the potential environmental impacts of nitrogen fertilizer that has been used to optimize yield, an increased understanding of the molecular responses to nitrogen under field conditions is critical for our ability to further improve agricultural sustainability. Using maize (Zea mays) as a model, we have characterized the transcriptional response of plants grown under limiting and sufficient nitrogen conditions and during the recovery of nitrogen-starved plants. We show that a large percentage (approximately 7%) of the maize transcriptome is nitrogen responsive, similar to previous observations in other plant species. Furthermore, we have used statistical approaches to identify a small set of genes whose expression profiles can quantitatively assess the response of plants to varying nitrogen conditions. Using a composite gene expression scoring system, this single set of biomarker genes can accurately assess nitrogen responses independently of genotype, developmental stage, tissue type, or environment, including in plants grown under controlled environments or in the field. Importantly, the biomarker composite expression response is much more rapid and quantitative than phenotypic observations. Consequently, we have successfully used these biomarkers to monitor nitrogen status in real-time assays of field-grown maize plants under typical production conditions. Our results suggest that biomarkers have the potential to be used as agronomic tools to monitor and optimize nitrogen fertilizer usage to help achieve maximal crop yields. PMID:21980173

Gene expression biomarkers provide sensitive indicators of in planta nitrogen status in maize.

PubMed

Yang, Xiaofeng S; Wu, Jingrui; Ziegler, Todd E; Yang, Xiao; Zayed, Adel; Rajani, M S; Zhou, Dafeng; Basra, Amarjit S; Schachtman, Daniel P; Peng, Mingsheng; Armstrong, Charles L; Caldo, Rico A; Morrell, James A; Lacy, Michelle; Staub, Jeffrey M

2011-12-01

Over the last several decades, increased agricultural production has been driven by improved agronomic practices and a dramatic increase in the use of nitrogen-containing fertilizers to maximize the yield potential of crops. To reduce input costs and to minimize the potential environmental impacts of nitrogen fertilizer that has been used to optimize yield, an increased understanding of the molecular responses to nitrogen under field conditions is critical for our ability to further improve agricultural sustainability. Using maize (Zea mays) as a model, we have characterized the transcriptional response of plants grown under limiting and sufficient nitrogen conditions and during the recovery of nitrogen-starved plants. We show that a large percentage (approximately 7%) of the maize transcriptome is nitrogen responsive, similar to previous observations in other plant species. Furthermore, we have used statistical approaches to identify a small set of genes whose expression profiles can quantitatively assess the response of plants to varying nitrogen conditions. Using a composite gene expression scoring system, this single set of biomarker genes can accurately assess nitrogen responses independently of genotype, developmental stage, tissue type, or environment, including in plants grown under controlled environments or in the field. Importantly, the biomarker composite expression response is much more rapid and quantitative than phenotypic observations. Consequently, we have successfully used these biomarkers to monitor nitrogen status in real-time assays of field-grown maize plants under typical production conditions. Our results suggest that biomarkers have the potential to be used as agronomic tools to monitor and optimize nitrogen fertilizer usage to help achieve maximal crop yields.

Evaluating Dense 3d Reconstruction Software Packages for Oblique Monitoring of Crop Canopy Surface

NASA Astrophysics Data System (ADS)

Brocks, S.; Bareth, G.

2016-06-01

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

THE POTENTIAL ROLE OF REMOTE SENSING IN TRANSGENIC CROP MONITORING PROGRAMS

EPA Science Inventory

Sustainable agriculture combines efficient production with wise stewardship of the earth's resources. Development of environmentally benign production techniques is one focus of sustainable agriculture. The new transgenic crops producing toxic proteins that target specific crop p...

Agroclimate.Org: Tools and Information for a Climate Resilient Agriculture in the Southeast USA

NASA Astrophysics Data System (ADS)

Fraisse, C.

2014-12-01

AgroClimate (http://agroclimate.org) is a web-based system developed to help the agricultural industry in the southeastern USA reduce risks associated with climate variability and change. It includes climate related information and dynamic application tools that interact with a climate and crop database system. Information available includes climate monitoring and forecasts combined with information about crop management practices that help increase the resiliency of the agricultural industry in the region. Recently we have included smartphone apps in the AgroClimate suite of tools, including irrigation management and crop disease alert systems. Decision support tools available in AgroClimate include: (a) Climate risk: expected (probabilistic) and historical climate information and freeze risk; (b) Crop yield risk: expected yield based on soil type, planting date, and basic management practices for selected commodities and historical county yield databases; (c) Crop diseases: disease risk monitoring and forecasting for strawberry and citrus; (d) Crop development: monitoring and forecasting of growing degree-days and chill accumulation; (e) Drought: monitoring and forecasting of selected drought indices, (f) Footprints: Carbon and water footprint calculators. The system also provides background information about the main drivers of climate variability and basic information about climate change in the Southeast USA. AgroClimate has been widely used as an educational tool by the Cooperative Extension Services in the region and also by producers. It is now being replicated internationally with version implemented in Mozambique and Paraguay.

Satellite-based monitoring of cotton evapotranspiration

NASA Astrophysics Data System (ADS)

Dalezios, Nicolas; Dercas, Nicholas; Tarquis, Ana Maria

2016-04-01

Water for agricultural use represents the largest share among all water uses. Vulnerability in agriculture is influenced, among others, by extended periods of water shortage in regions exposed to droughts. Advanced technological approaches and methodologies, including remote sensing, are increasingly incorporated for the assessment of irrigation water requirements. In this paper, remote sensing techniques are integrated for the estimation and monitoring of crop evapotranspiration ETc. The study area is Thessaly central Greece, which is a drought-prone agricultural region. Cotton fields in a small agricultural sub-catchment in Thessaly are used as an experimental site. Daily meteorological data and weekly field data are recorded throughout seven (2004-2010) growing seasons for the computation of reference evapotranspiration ETo, crop coefficient Kc and cotton crop ETc based on conventional data. Satellite data (Landsat TM) for the corresponding period are processed to estimate cotton crop coefficient Kc and cotton crop ETc and delineate its spatiotemporal variability. The methodology is applied for monitoring Kc and ETc during the growing season in the selected sub-catchment. Several error statistics are used showing very good agreement with ground-truth observations.

Exploring the Potential of TanDEM-X Data in Rice Monitoring

NASA Astrophysics Data System (ADS)

Erten, E.

2015-12-01

In this work, phenological parameters such as growth stage, calendar estimation, crop density and yield estimation for rice fields are estimated employing TanDEM-X data. Currently, crop monitoring is country-dependent. Most countries have databases based on cadastral information and annual farmer inputs. Inaccuracies are coming from wrong or missing farmer declarations and/or coarsely updated cadastral boundary definitions. This leads to inefficient regulation of the market, frauds as well as to ecological risks. An accurate crop calendar is also missing, since farmers provide estimations in advance and there is no efficient way to know the growth status over large plantations. SAR data is of particular interest for these purposes. The proposed method includes two step approach including field detection and phenological state estimation. In the context of precise farming it is substantial to define field borders which are usually changing every cultivation period. Linking the SAR inherit properties to transplanting practice such as irrigation, the spatial database of rice-planted agricultural crops can be updated. Boundaries of agricultural fields will be defined in the database, and assignments of crops and sowing dates will be continuously updated by our monitoring system considering that sowing practice variously changes depending on the field owner decision. To define and segment rice crops, the system will make use of the fact that rice fields are characterized as flooded parcels separated by path networks composed by soil or rare grass. This natural segmentation is well detectable by inspecting low amplitude and coherence values of bistatic acquisitions. Once the field borders are defined, the phenology estimation of crops monitored at any time is the key point of monitoring. In this aspect the wavelength and the polarization option of TanDEM-X are enough to characterize the small phenological changes. The combination of bistatic interferometry and Radiative Transfer Theory (RTT) with different polarization provides a realistic description of plants including their full morphology (stalks, tillers, leaves and panicles).

The ebb and flow of airborne pathogens: Monitoring and use in disease management decisions

USDA-ARS?s Scientific Manuscript database

Perhaps the earliest form of monitoring the regional spread of plant disease was a group of growers gathering together at the market and discussing what they see in their crops. This type of reporting continues to this day through regional extension blogs, by crop consultants and more formal scoutin...

The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

USDA-ARS?s Scientific Manuscript database

Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and rep...

LIFE CLIMATREE project: A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas

NASA Astrophysics Data System (ADS)

Stergiou, John; Tagaris, Efthimios; -Eleni Sotiropoulou, Rafaella

2016-04-01

Climate Change Mitigation is one of the most important objectives of the Kyoto Convention, and is mostly oriented towards reducing GHG emissions. However, carbon sink is retained only in the calculation of the forests capacity since agricultural land and farmers practices for securing carbon stored in soils have not been recognized in GHG accounting, possibly resulting in incorrect estimations of the carbon dioxide balance in the atmosphere. The agricultural sector, which is a key sector in the EU, presents a consistent strategic framework since 1954, in the form of Common Agricultural Policy (CAP). In its latest reform of 2013 (reg. (EU) 1305/13) CAP recognized the significance of Agriculture as a key player in Climate Change policy. In order to fill this gap the "LIFE ClimaTree" project has recently founded by the European Commission aiming to provide a novel method for including tree crop cultivations in the LULUCF's accounting rules for GHG emissions and removal. In the framework of "LIFE ClimaTree" project estimation of carbon sink within EU through the inclusion of the calculated tree crop capacity will be assessed for both current and future climatic conditions by 2050s using the GISS-WRF modeling system in a very fine scale (i.e., 9km x 9km) using RCP8.5 and RCP4.5 climate scenarios. Acknowledgement: LIFE CLIMATREE project "A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas" (LIFE14 CCM/GR/000635).

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

NASA Astrophysics Data System (ADS)

Shang, Jiali; Liu, Jiangui; Huffman, Ted; Qian, Budong; Pattey, Elizabeth; Wang, Jinfei; Zhao, Ting; Geng, Xiaoyuan; Kroetsch, David; Dong, Taifeng; Lantz, Nicholas

2014-01-01

This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R2) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.

Food and fitness: associations between crop yields and life-history traits in a longitudinally monitored pre-industrial human population.

PubMed

Hayward, Adam D; Holopainen, Jari; Pettay, Jenni E; Lummaa, Virpi

2012-10-22

Severe food shortage is associated with increased mortality and reduced reproductive success in contemporary and historical human populations. Studies of wild animal populations have shown that subtle variation in environmental conditions can influence patterns of mortality, fecundity and natural selection, but the fitness implications of such subtle variation on human populations are unclear. Here, we use longitudinal data on local grain production, births, marriages and mortality so as to assess the impact of crop yield variation on individual age-specific mortality and fecundity in two pre-industrial Finnish populations. Although crop yields and fitness traits showed profound year-to-year variation across the 70-year study period, associations between crop yields and mortality or fecundity were generally weak. However, post-reproductive individuals of both sexes, and individuals of lower socio-economic status experienced higher mortality when crop yields were low. This is the first longitudinal, individual-based study of the associations between environmental variation and fitness traits in pre-industrial humans, which emphasizes the importance of a portfolio of mechanisms for coping with low food availability in such populations. The results are consistent with evolutionary ecological predictions that natural selection for resilience to food shortage is likely to weaken with age and be most severe on those with the fewest resources.

Population Modeling Approach to Optimize Crop Harvest Strategy. The Case of Field Tomato.

PubMed

Tran, Dinh T; Hertog, Maarten L A T M; Tran, Thi L H; Quyen, Nguyen T; Van de Poel, Bram; Mata, Clara I; Nicolaï, Bart M

2017-01-01

In this study, the aim is to develop a population model based approach to optimize fruit harvesting strategies with regard to fruit quality and its derived economic value. This approach was applied to the case of tomato fruit harvesting under Vietnamese conditions. Fruit growth and development of tomato (cv. "Savior") was monitored in terms of fruit size and color during both the Vietnamese winter and summer growing seasons. A kinetic tomato fruit growth model was applied to quantify biological fruit-to-fruit variation in terms of their physiological maturation. This model was successfully calibrated. Finally, the model was extended to translate the fruit-to-fruit variation at harvest into the economic value of the harvested crop. It can be concluded that a model based approach to the optimization of harvest date and harvest frequency with regard to economic value of the crop as such is feasible. This approach allows growers to optimize their harvesting strategy by harvesting the crop at more uniform maturity stages meeting the stringent retail demands for homogeneous high quality product. The total farm profit would still depend on the impact a change in harvesting strategy might have on related expenditures. This model based harvest optimisation approach can be easily transferred to other fruit and vegetable crops improving homogeneity of the postharvest product streams.

Amma - Monitoring and Management Application

NASA Astrophysics Data System (ADS)

Rakesh Adapa, Swamy

Agriculture has been the part and parcel of the human life. It has been the major occupation around the world prior to the industrial revolution. Today many of the developing countries had their strong economical fundamentals from it. UN has been doing a great job in uplifting the rural population in terms of health and education. There is a need for uplifting the agricultural sector which has been lagging in the present economies. More than 70% of the rural employment comes from agricultural activities. So, there is a need for utilization of cutting edge technologies to make it more profitable. The problems that are being mainly faced by the farmers are illiteracy, climatic changes, improper data transfer, low technical skills, migration, natural disasters etc; AMMA helps the farmers to utilize the fruits of Geoinformatics ,tele-education, mobile technology etc; The farmer gets registered at our website through the Village Resource Centre (VRC), or AMMA Centre. The coordinator collects the data from the farmer about the past status of the crop; his economic status etc; now the process goes like this..,1.The coordinator sends the crop status through images and text data.2. The expert group sends the advice on viewing the crop status, satellite images on weekly basis.3.The coordinator on receiving the report explains it to the farmer about how to execute the recommendations that are made by the expert group.4.The farmer follows the recommendations that were said by his coordinator and takes appropriate steps. He gives back the feedback after the recommendations made by the agricultural supervisor are implemented.5.The feedback sent back by the farmer is verified by the agricultural geographer, whether the recommendations made by the experts are implemented or not.So, through the above process the crop is strictly monitored and extreme care is taken in the management of the crop. This cycle is repeated every week through internet. Here at AMMA a quantitative and calibrated map is generated each time the satellite data is collected. These maps indicate soil surface moisture, texture, organic matter, photosynthesis stress, water stress, insect stress, nutritional stress and other visible characteristics are used to monitor the crop. Salinity, alkalinity, crop condition assessment and soil wetness levels are found through VNIR and SWIR data that are used for long and short term management of the crop.This application can fulfill the dreams of many small farmers to have their plate full of meal.The hope of UN, ISRO, and MSSRF etc; that the space technology must reach the foot step of the rural people will become true by using our application. We conclude that our application could make the farming sector reach its new fame and heritage.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Space-Derived Phenology, Retrieval and Use for Drought and Food Security Monitoring

NASA Astrophysics Data System (ADS)

Meroni, M.; Kayitakire, F.; Rembold, F.; Urbano, F.; Schucknecht, A.; LEO, O.

2014-12-01

Monitoring vegetation conditions is a critical activity for assessing food security in Africa. Rural populations relying on rain-fed agriculture and livestock grazing are highly exposed to large seasonal and inter-annual fluctuations in water availability. Monitoring the state, evolution, and productivity of vegetation, crops and pastures in particular, is important to conduct food emergency responses and plan for a long-term, resilient, development strategy in this area. The timing of onset, the duration, and the intensity of vegetation growth can be retrieved from space observations and used for food security monitoring to assess seasonal vegetation development and forecast the likely seasonal outcome when the season is ongoing. In this contribution we present a set of phenology-based remote sensing studies in support to food security analysis. Key phenological indicators are retrieved using a model-fit approach applied to SOPT-VEGETATION FAPAR time series. Remote-sensing phenology is first used to estimate i) the impact of the drought in the Horn of Africa, ii) crop yield in Tunisia and, iii) rangeland biomass production in Niger. Then the impact of the start and length of vegetation growing period on the total biomass production is assessed over the Sahel. Finally, a probabilistic approach using phenological information to forecast the occurrence of an end-of-season biomass production deficit is applied over the Sahel to map hot-spots of drought-related risk.

Application of time-lapse ERT to Characterize Soil-Water-Disease Interactions of Citrus Orchard - Case Study

NASA Astrophysics Data System (ADS)

Peddinti, S. R.; Kbvn, D. P.; Ranjan, S.; Suradhaniwar, S.; J, P. A.; R M, G.

2015-12-01

Vidarbha region in Maharashtra, India (home for mandarin Orange) experience severe climatic uncertainties resulting in crop failure. Phytopthora are the soil-borne fungal species that accumulate in the presence of moisture, and attack the root / trunk system of Orange trees at any stage. A scientific understanding of soil-moisture-disease relations within the active root zone under different climatic, irrigation, and crop cycle conditions can help in practicing management activities for improved crop yield. In this study, we developed a protocol for performing 3-D time-lapse electrical resistivity tomography (ERT) at micro scale resolution to monitor the changes in resistivity distribution within the root zone of Orange trees. A total of 40 electrodes, forming a grid of 3.5 m x 2 m around each Orange tree were used in ERT survey with gradient and Wenner configurations. A laboratory test on un-disturbed soil samples of the region was performed to plot the variation of electrical conductivity with saturation. Curve fitting techniques were applied to get the modified Archie's model parameters. The calibrated model was further applied to generate the 3-D soil moisture profiles of the study area. The point estimates of soil moisture were validated using TDR probe measurements at 3 different depths (10, 20, and 40 cm) near to the root zone. In order to understand the effect of soil-water relations on plant-disease relations, we performed ERT analysis at two locations, one at healthy and other at Phytopthora affected Orange tree during the crop cycle, under dry and irrigated conditions. The degree to which an Orange tree is affected by Phytopthora under each condition is evaluated using 'grading scale' approach following visual inspection of the canopy features. Spatial-temporal distribution of moisture profiles is co-related with grading scales to comment on the effect of climatic and irrigation scenarios on the degree and intensity of crop disease caused by Phytopthora.

A concept for global crop forecasting. [using microwave radiometer satellites

NASA Technical Reports Server (NTRS)

Lovelace, U. M.; Wright, R. L.

1983-01-01

The mission, instrumentation, and design concepts for microwave radiometer satellites for continuous crop condition forecasting and monitoring on a global basis are described. Soil moisture affects both crop growth and the dielectric properties of the soil, and can be quantified by analysis of reflected radiance passively received by orbiting spacecraft. A dedicated satellite reading a swath 200 km across, with 1 km and 1 K temperature resolution, could track the time-varying changes of solid moisture, sea ice, and water surface temperature. Launched by the Shuttle into an interim orbit, a boost would place the satellite in a 400 or 700 km orbit. Resolution requirements indicate a 45-725 m diam antenna, with 70 dB gain, operating at frequencies of 1.08, 2.03, and 4.95 GHz to ensure atmospheric transparency. Alternative structural concepts include either double-layer tetrahedral or single-layer geodesic trusses as the basic structural members. An analysis of the electrostatic positioning of the parabolic antenna membrane is outlined.

Continuous cropping of endangered therapeutic plants via electron beam soil-treatment and neutron tomography.

PubMed

Sim, Cheul Muu; Seong, Bong Jae; Kim, Dong Won; Kim, Yong Bum; Wi, Seung Gon; Kim, Gyuil; Oh, Hwasuk; Kim, TaeJoo; Chung, Byung Yeoup; Song, Jeong Young; Kim, Hong Gi; Oh, Sang-Keun; Shin, Young Dol; Seok, Jea Hwan; Kang, Min Young; Lee, Yunhee; Radebe, Mabuti Jacob; Kardjilov, Nikolay; Honermeier, Bernd

2018-02-01

Various medicinal plants are threatened with extinction owing to their over-exploitation and the prevalence of soil borne pathogens. In this study, soils infected with root-rot pathogens, which prevent continuous-cropping, were treated with an electron beam. The level of soil-borne fungus was reduced to ≤0.01% by soil electron beam treatment without appreciable effects on the levels of antagonistic microorganism or on the physicochemical properties of the soil. The survival rate of 4-year-old plant was higher in electron beam-treated soil (81.0%) than in fumigated (62.5%), virgin (78%), or untreated-replanting soil (0%). Additionally, under various soils conditions, neutron tomography permitted the monitoring of plant health and the detection of root pathological changes over a period of 4-6 years by quantitatively measuring root water content in situ. These methods allow continual cropping on the same soil without pesticide treatment. This is a major step toward the environmentally friendly production of endangered therapeutic herbs.

Daily monitoring of 30 m crop condition over complex agricultural landscapes

NASA Astrophysics Data System (ADS)

Sun, L.; Gao, F.; Xie, D.; Anderson, M. C.; Yang, Y.

2017-12-01

Crop progress provides information necessary for efficient irrigation, scheduling fertilization and harvesting operations at optimal times for achieving higher yields. In the United States, crop progress reports are released online weekly by US Department of Agriculture (USDA) - National Agricultural Statistics Service (NASS). However, the ground data collection is time consuming and subjective, and these reports are provided at either district (multiple counties) or state level. Remote sensing technologies have been widely used to map crop conditions, to extract crop phenology, and to predict crop yield. However, for current satellite-based sensors, it is difficult to acquire both high spatial resolution and frequent coverage. For example, Landsat satellites are capable to capture 30 m resolution images, while the long revisit cycles, cloud contamination further limited their use in detecting rapid surface changes. On the other hand, MODIS can provide daily observations, but with coarse spatial resolutions range from 250 to 1000 m. In recent years, multi-satellite data fusion technology such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used to combine the spatial resolution of Landsat with the temporal frequency of MODIS. It has been found that this synthetic dataset could provide more valuable information compared to the images acquired from only one single sensor. However, accuracy of STARFM depends on heterogeneity of landscape and available clear image pairs of MODIS and Landsat. In this study, a new fusion method was developed using the crop vegetation index (VI) timeseries extracted from "pure" MODIS pixels and Landsat overpass images to generate daily 30 m VI for crops. The fusion accuracy was validated by comparing to the original Landsat images. Results show that the relative error in non-rapid growing period is around 3-5% and in rapid growing period is around 6-8% . The accuracy is much better than that of STARFM which is 4-9% in non-rapid growing period and 10-16% in rapid growing period based on 13 image pairs. The predicted VI from this approach looks consistent and smooth in the SLC-off gap stripes of Landsat 7 ETM+ image. The new fusion results will be used to map crop phenology and to predict crop yield at field scale in the complex agricultural landscapes.

Longleaf Pine Cone Crops and Climate: A Possible Link

Treesearch

Neil Pederson; John S. Kush; Ralph S. Meldahl; William D. Bayer

1999-01-01

The physiological development of longieaf pine seed extends over three calendar years. The duration of this process may explain the reason for infrequent seed crops. Infrequent crops cause problems for those interested in natural regeneration. Longleaf pine cone crops have been monitored on the Escambia Experimental Forest (EEF) in Brewton, AL since 1958. Weather data...

Combined Field Inoculations of Pseudomonas Bacteria, Arbuscular Mycorrhizal Fungi, and Entomopathogenic Nematodes and their Effects on Wheat Performance

PubMed Central

Imperiali, Nicola; Chiriboga, Xavier; Schlaeppi, Klaus; Fesselet, Marie; Villacrés, Daniela; Jaffuel, Geoffrey; Bender, S. Franz; Dennert, Francesca; Blanco-Pérez, Ruben; van der Heijden, Marcel G. A.; Maurhofer, Monika; Mascher, Fabio; Turlings, Ted C. J.; Keel, Christoph J.; Campos-Herrera, Raquel

2017-01-01

In agricultural ecosystems, pest insects, pathogens, and reduced soil fertility pose major challenges to crop productivity and are responsible for significant yield losses worldwide. Management of belowground pests and diseases remains particularly challenging due to the complex nature of the soil and the limited reach of conventional agrochemicals. Boosting the presence of beneficial rhizosphere organisms is a potentially sustainable alternative and may help to optimize crop health and productivity. Field application of single beneficial soil organisms has shown satisfactory results under optimal conditions. This might be further enhanced by combining multiple beneficial soil organisms, but this remains poorly investigated. Here, we inoculated wheat plots with combinations of three beneficial soil organisms that have different rhizosphere functions and studied their effects on crop performance. Plant beneficial Pseudomonas bacteria, arbuscular mycorrhizal fungi (AMF), and entomopathogenic nematodes (EPN), were inoculated individually or in combinations at seeding, and their effects on plant performance were evaluated throughout the season. We used traditional and molecular identification tools to monitor their persistence over the cropping season in augmented and control treatments, and to estimate the possible displacement of native populations. In three separate trials, beneficial soil organisms were successfully introduced into the native populations and readily survived the field conditions. Various Pseudomonas, mycorrhiza, and nematode treatments improved plant health and productivity, while their combinations provided no significant additive or synergistic benefits compared to when applied alone. EPN application temporarily displaced some of the native EPN, but had no significant long-term effect on the associated food web. The strongest positive effect on wheat survival was observed for Pseudomonas and AMF during a season with heavy natural infestation by the frit fly, Oscinella frit, a major pest of cereals. Hence, beneficial impacts differed between the beneficial soil organisms and were most evident for plants under biotic stress. Overall, our findings indicate that in wheat production under the test conditions the three beneficial soil organisms can establish nicely and are compatible, but their combined application provides no additional benefits. Further studies are required, also in other cropping systems, to fine-tune the functional interactions among beneficial soil organisms, crops, and the environment. PMID:29163562

Combined Field Inoculations of Pseudomonas Bacteria, Arbuscular Mycorrhizal Fungi, and Entomopathogenic Nematodes and their Effects on Wheat Performance.

PubMed

Imperiali, Nicola; Chiriboga, Xavier; Schlaeppi, Klaus; Fesselet, Marie; Villacrés, Daniela; Jaffuel, Geoffrey; Bender, S Franz; Dennert, Francesca; Blanco-Pérez, Ruben; van der Heijden, Marcel G A; Maurhofer, Monika; Mascher, Fabio; Turlings, Ted C J; Keel, Christoph J; Campos-Herrera, Raquel

2017-01-01

In agricultural ecosystems, pest insects, pathogens, and reduced soil fertility pose major challenges to crop productivity and are responsible for significant yield losses worldwide. Management of belowground pests and diseases remains particularly challenging due to the complex nature of the soil and the limited reach of conventional agrochemicals. Boosting the presence of beneficial rhizosphere organisms is a potentially sustainable alternative and may help to optimize crop health and productivity. Field application of single beneficial soil organisms has shown satisfactory results under optimal conditions. This might be further enhanced by combining multiple beneficial soil organisms, but this remains poorly investigated. Here, we inoculated wheat plots with combinations of three beneficial soil organisms that have different rhizosphere functions and studied their effects on crop performance. Plant beneficial Pseudomonas bacteria, arbuscular mycorrhizal fungi (AMF), and entomopathogenic nematodes (EPN), were inoculated individually or in combinations at seeding, and their effects on plant performance were evaluated throughout the season. We used traditional and molecular identification tools to monitor their persistence over the cropping season in augmented and control treatments, and to estimate the possible displacement of native populations. In three separate trials, beneficial soil organisms were successfully introduced into the native populations and readily survived the field conditions. Various Pseudomonas , mycorrhiza, and nematode treatments improved plant health and productivity, while their combinations provided no significant additive or synergistic benefits compared to when applied alone. EPN application temporarily displaced some of the native EPN, but had no significant long-term effect on the associated food web. The strongest positive effect on wheat survival was observed for Pseudomonas and AMF during a season with heavy natural infestation by the frit fly, Oscinella frit , a major pest of cereals. Hence, beneficial impacts differed between the beneficial soil organisms and were most evident for plants under biotic stress. Overall, our findings indicate that in wheat production under the test conditions the three beneficial soil organisms can establish nicely and are compatible, but their combined application provides no additional benefits. Further studies are required, also in other cropping systems, to fine-tune the functional interactions among beneficial soil organisms, crops, and the environment.

Monitoring the agricultural landscape for insect resistance

NASA Astrophysics Data System (ADS)

Casas, Joseph; Glaser, J. A.; Copenhaver, Ken

Farmers in 25 countries on six continents are using plant biotechnology to solve difficult crop production challenges and conserve the environment. In fact, 13.3 million farmers, which include 90 percent of the farming in developing countries, choose to plant biotech crops. Over the past decade, farmers increased area planted in genetically modified (GM) crops by more than 10 percent each year, thus increasing their farm income by more than 44 billion US dollars (1996-2007), and achieved economic, environmental and social benefits in crops such as soybeans, canola, corn and cotton. To date, total acres of biotech crops harvested exceed more than 2 billion with a proven 13-year history of safe use. Over the next decade, expanded adoption combined with current research on 57 crops in 63 countries will broaden the advantages of genetically modified foods for growers, consumers and the environment. Genetically modified (GM) crops with the ability to produce toxins lethal to specific insect pests are covering a larger percentage of the agricultural landscape every year. The United States department of Agriculture (USDA) estimated that 63 percent of corn and 65 percent of cotton contained these specific genetic traits in 2009. The toxins could protect billions of dollars of loss from insect damage for crops valued at greater than 165 billion US dollars in 2008. The stable and efficient production of these crops has taken on even more importance in recent years with their use, not only as a food source, but now also a source of fuel. It is in the best interest of the United States Environmental Protection Agency (USEPA) to ensure the continued efficacy of toxin producing GM crops as their use reduces pesticides harmful to humans and animals. However, population genetics models have indicated the risk of insect pests developing resistance to these toxins if a high percentage of acreage is grown in these crops. The USEPA is developing methods to monitor the agricultural landscape to ensure resistance is not developing. USEPA is teaming with NASA to perform this monitoring using models and NASA earth observation imagery from airborne and satellite platforms. Using multiple spatial, temporal and spectral resolutions, the project is monitoring the entire Midwestern "Corn Belt". By applying these methods, the project has successfully delineated insect infestations in genetically modified corn fields. Insect resistance development is expected to present itself as infestations thus indicating potential identification of resistance if it develops in genetically modified crops. The USEPA and NASA are currently considering the development of plans to potentially extend this aircraft research to other crops and develop a micro-satellite application.

Soil and water quality implications of production of herbaceous and woody energy crops

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tolbert, V.R.; Lindberg, J.E.; Green, T.H.

1997-10-01

Field-scale studies in three physiographic regions of the Tennessee Valley in the Southeastern US are being used to address the environmental effects of producing biomass energy crops on former agricultural lands. Comparison of erosion, surface water quality and quantity, and subsurface movement of water and nutrients from woody crops, switchgrass and agricultural crops began with crop establishment in 1994. Nutrient cycling, soil physical changes, and productivity of the different crops are also being monitored at the three sites.

Drought is Coming: Monitoring Vegetation Response to Water Scarcity through Variable Chlorophyll a Fluorescence

NASA Astrophysics Data System (ADS)

Guadagno, C. R.; Beverly, D.; Pleban, J. R.; Speckman, H. N.; Ewers, B. E.; Weinig, C.

2017-12-01

Aridity is one of the most pronounced environmental limits to plant survival, and understanding how plants respond to drought and recovery is crucial for predicting impacts on managed and natural ecosystems. Changes in soil moisture conditions induce a suite of physiological responses from the cell to ecosystem scale, complicating the assessment of drought effects. Characterizing early indicators of water scarcity across species can inform biophysical models with improved understanding of plant hydraulics. While indexes exist for drought monitoring across scales, many are unable to identify imminent vegetative drought. We explore a method of early diagnosis using leaf-level and kinetic imaging measures of variable chlorophyll a fluorescence. This is a fast and reliable tool capturing leaf physiological changes in advance of changes in NDVI or passive solar induced fluorescence. Both image and leaf level Pulse Amplitude Method (PAM) measurements illustrate the utility of variable chlorophyll a fluorescence for monitoring vegetative drought. Variable fluorescence was monitored across populations of crops, desert shrubs, montane conifers and riparian deciduous trees under variable water regimes. We found a strong correlation (R = 0.85) between the maximum efficiency of photosystem II measured using variable fluorescence (Fv'Fm') and leaf level electrolyte leakage, a proximal cause of drought stress induced by cellular damage in leaves. This association was confirmed in two gymnosperm species (Picea engelmannii and Pinus contorta) and for diverse varieties of the crop species Brassica rapa. The use of chlorophyll a fluorescence per image also allowed for early detection of drought in aspen (Populus tremuloides). These results provide evidence that variable chlorophyll fluorescence decreases between 25% and 70% in mild and severely droughted twigs with respect to ones collected from trees in wet soil conditions. While current systems for monitoring variable fluorescence are limited in scale, chlorophyll fluorescence comprises an indicator of drought stress across multiple spatial scales, from leaf to ecosystem level.

Effects on and recovery of microplankton and microbenthon to Gulf of Mexico oil spills impacting outer and mid shelfal, inner shelfal, and estuarine environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casey, R.E.; Wigley, C.R.; Fisco, P.

1982-01-01

During 1979, 1980, and 1981 3 major and different environments of human interest and economic well-being were impacted by 3 different and major Gulf of Mexico oil spills. All the studied spills had pre-spill data. This study revealed 3 conclusions useful in the monitoring of spill recovery. (1) Immediately or continually impacted areas exhibited a mass mortality for microplankton in pelagic systems, and an abnormally high concentration (collection) of nematodes in nearshore sands. (2) Impacted benthonic areas exhibited increases in nematode standing corps followed by increases in benthonic forminiferal standing crops. (3) Recovery to pre-spill conditions may be indicated bymore » termination of red tide condition or mortality of susceptible microplankton; return to pre-spill standing crops, taxonomic character, and diversity of microbenthon or microplankton; and return to pre-spill seasonal fluctuation of peaks and lows in microbenthon and microplankton.« less

Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

NASA Astrophysics Data System (ADS)

Jiao, Xianfeng; Kovacs, John M.; Shang, Jiali; McNairn, Heather; Walters, Dan; Ma, Baoluo; Geng, Xiaoyuan

2014-10-01

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude-Pottier and Freeman-Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude-Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman-Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.

Irrigation Trials for ET Estimation and Water Management in California Specialty Crops

NASA Astrophysics Data System (ADS)

Johnson, L.; Cahn, M.; Martin, F.; Lund, C.; Melton, F. S.

2012-12-01

Accurate estimation of crop evapotranspiration (ETc) can support efficient irrigation water management, which in turn brings benefits including surface water conservation, mitigation of groundwater depletion/degradation, energy savings, and crop quality assurance. Past research in California has revealed strong relationships between canopy fractional cover (Fc) and ETc of certain specialty crops, while additional research has shown the potential of monitoring Fc by satellite remote sensing. California's Central Coast is the leading region of cool season vegetable production in the U.S. Monterey County alone produces more than 80,000 ha of lettuce and broccoli (about half of U.S. production), valued at $1.5 billion in 2009. Under this study, we are conducting ongoing irrigation trials on these crops at the USDA Agricultural Research Station (Salinas) to compare irrigation scheduling via plant-based ETc approaches, by way of Fc, with current industry standard-practice. The following two monitoring approaches are being evaluated - 1) a remote sensing model employed by NASA's prototype Satellite Irrigation Management System, and 2) an online irrigation scheduling tool, CropManage, recently developed by U.C. Cooperative Extension. Both approaches utilize daily grass-reference ETo data as provided by the California Irrigation Management Irrigation System (CIMIS). A sensor network is deployed to monitor applied irrigation, volumetric soil water content, soil water potential, deep drainage, and standard meteorologic variables in order to derive ETc by a water balance approach. Evaluations of crop yield and crop quality are performed by the research team and by commercial growers. Initial results to-date indicate that applied water reductions based on Fc measurements are possible with little-to-no impact on yield of crisphead lettuce (Lactuca sativa). Additional results for both lettuce and broccoli trials, conducted during summer-fall 2012, are presented with respect to nutrient management and crop viability.

Electronic Field Data Collection in Support of Satellite-Based Food Security Monitoring in Tanzania

NASA Astrophysics Data System (ADS)

Nakalembe, C. L.; Dempewolf, J.; Justice, C. J.; Becker-Reshef, I.; Tumbo, S.; Maurice, S.; Mbilinyi, B.; Ibrahim, K.; Materu, S.

2016-12-01

In Tanzania agricultural extension agents traditionally collect field data on agriculture and food security on paper, covering most villages throughout the country. The process is expensive, slow and cumbersome and prone to data transcription errors when the data get entered at the district offices into electronic spreadsheets. Field data on the status and condition of agricultural crops, the population's nutritional status, food storage levels and other parameters are needed in near realtime for early warning to make critical but most importantly timely and appropriate decisions that are informed with verified data from the ground. With the ubiquitous distribution of cell phones, which are now used by the vast majority of the population in Tanzania including most farmers, new, efficient and cost-effective methods for field data collection have become available. Using smartphones and tablets data on crop conditions, pest and diseases, natural disasters and livelihoods can be collected and made available and easily accessible in near realtime. In this project we implemented a process for obtaining high quality electronic field data using the GeoODK application with a large network of field extension agents in Tanzania and Uganda. These efforts contribute to work being done on developing an advanced agriculture monitoring system for Tanzania, incorporating traditional data collection with satellite information and field data. The outcomes feed directly into the National Food Security Bulletin for Tanzania produced by the Ministry of Agriculture as well as a form a firm evidence base and field scale monitoring of the disaster risk financing in Uganda.

Biosolids, crop, and groundwater data for a biosolids-application area near Deer Trail, Colorado, 2009 and 2010

USGS Publications Warehouse

Yager, Tracy J.B.; Smith, David B.; Crock, James G.

2012-01-01

During 2009 and 2010, the U.S. Geological Survey monitored the chemical composition of biosolids, crops, and groundwater related to biosolids applications near Deer Trail, Colorado, in cooperation with the Metro Wastewater Reclamation District. This monitoring effort was a continuation of the monitoring program begun in 1999 in cooperation with the Metro Wastewater Reclamation District and the North Kiowa Bijou Groundwater Management District. The monitoring program addressed concerns from the public about potential chemical effects from applications of biosolids to farmland in the area near Deer Trail, Colo. This report presents chemical data from 2009 and 2010 for biosolids, crops, and alluvial and bedrock groundwater. The chemical data include the constituents of highest concern to the public (arsenic, cadmium, copper, lead, mercury, molybdenum, nickel, selenium, zinc, and plutonium) in addition to many other constituents. The groundwater section also includes data for precipitation, air temperature, and depth to groundwater at various groundwater-monitoring sites.

SiMon: Simulation Monitor for Computational Astrophysics

NASA Astrophysics Data System (ADS)

Xuran Qian, Penny; Cai, Maxwell Xu; Portegies Zwart, Simon; Zhu, Ming

2017-09-01

Scientific discovery via numerical simulations is important in modern astrophysics. This relatively new branch of astrophysics has become possible due to the development of reliable numerical algorithms and the high performance of modern computing technologies. These enable the analysis of large collections of observational data and the acquisition of new data via simulations at unprecedented accuracy and resolution. Ideally, simulations run until they reach some pre-determined termination condition, but often other factors cause extensive numerical approaches to break down at an earlier stage. In those cases, processes tend to be interrupted due to unexpected events in the software or the hardware. In those cases, the scientist handles the interrupt manually, which is time-consuming and prone to errors. We present the Simulation Monitor (SiMon) to automatize the farming of large and extensive simulation processes. Our method is light-weight, it fully automates the entire workflow management, operates concurrently across multiple platforms and can be installed in user space. Inspired by the process of crop farming, we perceive each simulation as a crop in the field and running simulation becomes analogous to growing crops. With the development of SiMon we relax the technical aspects of simulation management. The initial package was developed for extensive parameter searchers in numerical simulations, but it turns out to work equally well for automating the computational processing and reduction of observational data reduction.

Intra-seasonal risk of agriculturally-relevant weather extremes in West African Sudan Savanna

NASA Astrophysics Data System (ADS)

Boansi, David; Tambo, Justice A.; Müller, Marc

2018-01-01

Using household survey data and historical daily climate data for 29 communities across Upper East Ghana and Southwest Burkina Faso, we document climatic conditions deemed major threat to farming in the West African Sudan Savanna and assess risks posed by such conditions over the period 1997-2014. Based on farmers' perception, it is found that drought, low rainfall, intense precipitation, flooding, erratic rainfall pattern, extremely high temperatures, delayed rains, and early cessation of rains are the major threats farmers face. Using first-order Markov chain model and relevant indices for monitoring weather extremes, it is discovered that climatic risk is a general inherent attribute of the rainy season in the study area. Due to recent changes in onset of rains and length of the rainy season, some farmers have either resorted to early planting of drought-hardy crops, late planting of drought-sensitive crops, or spreading of planting across the first 3 months of the season to moderate harm. Each of these planting decisions however has some risk implications. The months of May, June, and October are found to be more susceptible to relatively longer duration of dry and hot spells, while July, August, and September are found to be more susceptible to intense precipitation and flooding. To moderate harm from anticipated weather extremes, farmers need to adjust their cropping calendar, adopt appropriate crop varieties, and implement soil and water management practices. For policy makers and other stakeholders, we recommend the supply of timely and accurate weather forecasts to guide farmers in their seasonal cropping decisions and investment in/installation of low cost irrigation facilities to enhance the practice of supplemental irrigation.

Tropical field performance of dual-pass PV tray dryer

NASA Astrophysics Data System (ADS)

Iskandar, A. Noor; Ya'acob, M. E.; Anuar, M. S.

2017-09-01

Solar Photovoltaic technology has become the preferable solution in many countries around the globe to solve the ever increasing energy demand of the consumers. In line with the consumer need, food processing technology has huge potentials of integration with the renewable energy resources especially in drying process which consumes the highest electricity loads. Traditionally, the solar dryer technology was applied in agriculture and food industries utilizing the sun's energy for drying process, but this is highly dependable on the weather condition and surrounding factors. This work shares some field performance of the new design of portable dual-pass PV tray dryer for drying crops in an enclosed system. The dual-pass PV tray dryer encompass a lightweight aluminium box structure with dimensions of 1.1m (L) x 0.6m (W) x 0.2m (H) and can hold a load capacity of 300g - 3kg of crop depending on the types of the crops. Experiments of field performance monitoring were conducted in October -November 2016 which justifies a considerable reduction in time and crops quality improvement when using the dual-pass PV tray dryer as compared to direct-sun drying.

Sugarcane Crop Extraction Using Object-Oriented Method from ZY-3 High Resolution Satellite Tlc Image

NASA Astrophysics Data System (ADS)

Luo, H.; Ling, Z. Y.; Shao, G. Z.; Huang, Y.; He, Y. Q.; Ning, W. Y.; Zhong, Z.

2018-04-01

Sugarcane is one of the most important crops in Guangxi, China. As the development of satellite remote sensing technology, more remotely sensed images can be used for monitoring sugarcane crop. With the help of Three Line Camera (TLC) images, wide coverage and stereoscopic mapping ability, Chinese ZY-3 high resolution stereoscopic mapping satellite is useful in attaining more information for sugarcane crop monitoring, such as spectral, shape, texture difference between forward, nadir and backward images. Digital surface model (DSM) derived from ZY-3 TLC images are also able to provide height information for sugarcane crop. In this study, we make attempt to extract sugarcane crop from ZY-3 images, which are acquired in harvest period. Ortho-rectified TLC images, fused image, DSM are processed for our extraction. Then Object-oriented method is used in image segmentation, example collection, and feature extraction. The results of our study show that with the help of ZY-3 TLC image, the information of sugarcane crop in harvest time can be automatic extracted, with an overall accuracy of about 85.3 %.

Heterogeneous Multi-Robot System for Mapping Environmental Variables of Greenhouses

PubMed Central

Roldán, Juan Jesús; Garcia-Aunon, Pablo; Garzón, Mario; de León, Jorge; del Cerro, Jaime; Barrientos, Antonio

2016-01-01

The productivity of greenhouses highly depends on the environmental conditions of crops, such as temperature and humidity. The control and monitoring might need large sensor networks, and as a consequence, mobile sensory systems might be a more suitable solution. This paper describes the application of a heterogeneous robot team to monitor environmental variables of greenhouses. The multi-robot system includes both ground and aerial vehicles, looking to provide flexibility and improve performance. The multi-robot sensory system measures the temperature, humidity, luminosity and carbon dioxide concentration in the ground and at different heights. Nevertheless, these measurements can be complemented with other ones (e.g., the concentration of various gases or images of crops) without a considerable effort. Additionally, this work addresses some relevant challenges of multi-robot sensory systems, such as the mission planning and task allocation, the guidance, navigation and control of robots in greenhouses and the coordination among ground and aerial vehicles. This work has an eminently practical approach, and therefore, the system has been extensively tested both in simulations and field experiments. PMID:27376297

REMOTE SENSING TECHNIQUES FOR MONITORING GENETICALLY ENGINEERED CROP CULTIVATION

EPA Science Inventory

Crops bioengineered to contain toxins derived from Bacillus thuringensis (Bt) are under regulatory scrutiny by USEPA under the FIFRA legislation. The agency has declared these crops to be "in the public good" based on the reduced use of pesticides required for management of these...

LARGE AREA MONITORING FOR PESTICIDAL TRANSGENIC CROPS: HOW SPECTRAL IMAGING MAY PLAY A ROLE

EPA Science Inventory

Crops genetically engineered to contain a bacterial gene that expresses an insecticidal protein from Bacillus thuringiensis are regulated by EPA under the Federal Insecticide Fungicide and Rodenticide Act (FIFRA). EPA has declared crops containing transgenic pesticidal traits to...

An approach for using AVHRR data to monitor U.S. great plains grasslands

USGS Publications Warehouse

Reed, B.C.; Loveland, Thomas R.; Tieszen, L.L.

1996-01-01

Environmental monitoring requires regular observations regarding the status of the landscape- The concept behind most monitoring efforts using satellite data involve deriving normalized difference vegetation index (NDVI) values or accumulating the NDVI over a specified time period. These efforts attempt to estimate the continuous growth of green biomass by using continuous additions of NDVI as a surrogate measure. To build upon this concept, this study proposes three refinements; 1) use an objective definition of the current growing season to adjust the time window during which the NDVI is accumulated, 2) accumulate only the NDVI values which are affected by green vegetation, and 3) base monitoring units upon land cover type. These refinements improve the sensitivity of detecting interannual vegetation variability, reduce the need for extensive and detailed knowledge of ground conditions and crop calendars, provide a framework in which several types of monitoring can take place over diverse land cover types, and provide an objective time frame during which monitoring takes place.

Radar remote sensing for crop classification and canopy condition assessment: Ground-data documentation

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Jung, B.; Gillespie, K.; Hemmat, M.; Aslam, A.; Brunfeldt, D.; Dobson, M. C.

1983-01-01

A vegetation and soil-moisture experiment was conducted in order to examine the microwave emission and backscattering from vegetation canopies and soils. The data-acquisition methodology used in conjunction with the mobile radar scatterometer (MRS) systems is described and associated ground-truth data are documented. Test fields were located in the Kansas River floodplain north of Lawrence, Kansas. Ten fields each of wheat, corn, and soybeans were monitored over the greater part of their growing seasons. The tabulated data summarize measurements made by the sensor systems and represent target characteristics. Target parameters describing the vegetation and soil characteristics include plant moisture, density, height, and growth stage, as well as soil moisture and soil-bulk density. Complete listings of pertinent crop-canopy and soil measurements are given.

Predicting the global warming potential of agro-ecosystems

NASA Astrophysics Data System (ADS)

Lehuger, S.; Gabrielle, B.; Larmanou, E.; Laville, P.; Cellier, P.; Loubet, B.

2007-04-01

Nitrous oxide, carbon dioxide and methane are the main biogenic greenhouse gases (GHG) contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate thus requires a capacity to predict the net exchanges of these gases in an integrated manner, as related to environmental conditions and crop management. Here, we used two year-round data sets from two intensively-monitored cropping systems in northern France to test the ability of the biophysical crop model CERES-EGC to simulate GHG exchanges at the plot-scale. The experiments involved maize and rapeseed crops on a loam and rendzina soils, respectively. The model was subsequently extrapolated to predict CO2 and N2O fluxes over an entire crop rotation. Indirect emissions (IE) arising from the production of agricultural inputs and from cropping operations were also added to the final GWP. One experimental site (involving a wheat-maize-barley rotation on a loamy soil) was a net source of GHG with a GWP of 350 kg CO2-C eq ha-1 yr-1, of which 75% were due to IE and 25% to direct N2O emissions. The other site (involving an oilseed rape-wheat-barley rotation on a rendzina) was a net sink of GHG for -250 kg CO2-C eq ha-1 yr-1, mainly due to a higher predicted C sequestration potential and C return from crops. Such modelling approach makes it possible to test various agronomic management scenarios, in order to design productive agro-ecosystems with low global warming impact.

Remote Sensing and Capacity Building to Improve Food Security

NASA Astrophysics Data System (ADS)

Husak, G. J.; Funk, C. C.; Verdin, J. P.; Rowland, J.; Budde, M. E.

2012-12-01

The Famine Early Warning Systems Network (FEWS NET) is a U.S. Agency for International Development (USAID) supported project designed to monitor and anticipate food insecurity in the developing world, primarily Africa, Central America, the Caribbean and Central Asia. This is done through a network of partners involving U.S. government agencies, universities, country representatives, and partner institutions. This presentation will focus on the remotely sensed data used in FEWS NET activities and capacity building efforts designed to expand and enhance the use of FEWS NET tools and techniques. Remotely sensed data are of particular value in the developing world, where ground data networks and data reporting are limited. FEWS NET uses satellite based rainfall and vegetation greenness measures to monitor and assess food production conditions. Satellite rainfall estimates also drive crop models which are used in determining yield potential. Recent FEWS NET products also include estimates of actual evapotranspiration. Efforts are currently underway to assimilate these products into a single tool which would indicate areas experiencing abnormal conditions with implications for food production. FEWS NET is also involved in a number of capacity building activities. Two primary examples are the development of software and training of institutional partners in basic GIS and remote sensing. Software designed to incorporate rainfall station data with existing satellite-derived rainfall estimates gives users the ability to enhance satellite rainfall estimates or long-term means, resulting in gridded fields of rainfall that better reflect ground conditions. Further, this software includes a crop water balance model driven by the improved rainfall estimates. Finally, crop parameters, such as the planting date or length of growing period, can be adjusted by users to tailor the crop model to actual conditions. Training workshops in the use of this software, as well as basic GIS and remote sensing tools, are routinely conducted by FEWS NET representatives at host country meteorological and agricultural services. These institutions are then able to produce information that can more accurately inform food security decision making. Informed decision making reduces the risk associated with a given hazard. In the case of FEWS NET, this involves identification of shocks to food availability, allowing for the pre-positioning of aid to be available when a hazard strikes. Developing tools to incorporate better information in food production estimates and working closely with local staff trained in state-of-the-practice techniques results in a more informed decision making process, reducing the impacts of food security hazards.

Modeling water scarcity over south Asia: Incorporating crop growth and irrigation models into the Variable Infiltration Capacity (VIC) model

NASA Astrophysics Data System (ADS)

Troy, Tara J.; Ines, Amor V. M.; Lall, Upmanu; Robertson, Andrew W.

2013-04-01

Large-scale hydrologic models, such as the Variable Infiltration Capacity (VIC) model, are used for a variety of studies, from drought monitoring to projecting the potential impact of climate change on the hydrologic cycle decades in advance. The majority of these models simulates the natural hydrological cycle and neglects the effects of human activities such as irrigation, which can result in streamflow withdrawals and increased evapotranspiration. In some parts of the world, these activities do not significantly affect the hydrologic cycle, but this is not the case in south Asia where irrigated agriculture has a large water footprint. To address this gap, we incorporate a crop growth model and irrigation model into the VIC model in order to simulate the impacts of irrigated and rainfed agriculture on the hydrologic cycle over south Asia (Indus, Ganges, and Brahmaputra basin and peninsular India). The crop growth model responds to climate signals, including temperature and water stress, to simulate the growth of maize, wheat, rice, and millet. For the primarily rainfed maize crop, the crop growth model shows good correlation with observed All-India yields (0.7) with lower correlations for the irrigated wheat and rice crops (0.4). The difference in correlation is because irrigation provides a buffer against climate conditions, so that rainfed crop growth is more tied to climate than irrigated crop growth. The irrigation water demands induce hydrologic water stress in significant parts of the region, particularly in the Indus, with the streamflow unable to meet the irrigation demands. Although rainfall can vary significantly in south Asia, we find that water scarcity is largely chronic due to the irrigation demands rather than being intermittent due to climate variability.

Direct and indirect effects of climate change on herbicide leaching--a regional scale assessment in Sweden.

PubMed

Steffens, Karin; Jarvis, Nicholas; Lewan, Elisabet; Lindström, Bodil; Kreuger, Jenny; Kjellström, Erik; Moeys, Julien

2015-05-01

Climate change is not only likely to improve conditions for crop production in Sweden, but also to increase weed pressure and the need for herbicides. This study aimed at assessing and contrasting the direct and indirect effects of climate change on herbicide leaching to groundwater in a major crop production region in south-west Sweden with the help of the regional pesticide fate and transport model MACRO-SE. We simulated 37 out of the 41 herbicides that are currently approved for use in Sweden on eight major crop types for the 24 most common soil types in the region. The results were aggregated accounting for the fractional coverage of the crop and the area sprayed with a particular herbicide. For simulations of the future, we used projections of five different climate models as model driving data and assessed three different future scenarios: (A) only changes in climate, (B) changes in climate and land-use (altered crop distribution), and (C) changes in climate, land-use, and an increase in herbicide use. The model successfully distinguished between leachable and non-leachable compounds (88% correctly classified) in a qualitative comparison against regional-scale monitoring data. Leaching was dominated by only a few herbicides and crops under current climate and agronomic conditions. The model simulations suggest that the direct effects of an increase in temperature, which enhances degradation, and precipitation which promotes leaching, cancel each other at a regional scale, resulting in a slight decrease in leachate concentrations in a future climate. However, the area at risk of groundwater contamination doubled when indirect effects of changes in land-use and herbicide use, were considered. We therefore concluded that it is important to consider the indirect effects of climate change alongside the direct effects and that effective mitigation strategies and strict regulation are required to secure future (drinking) water resources. Copyright © 2014 Elsevier B.V. All rights reserved.

Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data

NASA Astrophysics Data System (ADS)

Kim, H. O.; Yeom, J. M.

2014-12-01

Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

Monitoring Agricultural Drought Using Geographic Information Systems and Remote Sensing on the Primary Corn and Soybean Belt in the United States

NASA Astrophysics Data System (ADS)

Al-Shomrany, Adel

The study aims to evaluate various remote sensing drought indices to assess those most fitting for monitoring agricultural drought. The objectives are (1) to assess and study the impact of drought effect on (corn and soybean) crop production by crop mapping information and GIS technology; (2) to use Geographical Weighted Regression (GWR) as a technical approach to evaluate the spatial relationships between precipitation vs. irrigated and non-irrigated corn and soybean yield, using a Nebraska county-level case study; (3) to assess agricultural drought indices derived from remote sensing (NDVI, NMDI, NDWI, and NDII6); (4) to develop an optimal approach for agricultural drought detection based on remote sensing measurements to determine the relationship between US county-level yields versus relatively common variables collected. Extreme drought creates low corn and soybean production where irrigation systems are not implemented. This results in a lack of moisture in soil leading to dry land and stale crop yields. When precipitation and moisture is found across all states, corn and soybean production flourishes. For Kansas, Nebraska, and South Dakota, irrigation management methods assist in strong crop yields throughout SPI monthly averages. The data gathered on irrigation consisted of using drought indices gathered by the national agricultural statistics service website. For the SPI levels ranging between one-month and nine-months, Kansas and Nebraska performed the best out of all 12-states contained in the Midwestern primary Corn and Soybean Belt. The reasoning behind Kansas and Nebraska's results was due to a more efficient and sustainable irrigation system, where upon South Dakota lacked. South Dakota was leveled by strong correlations throughout all SPI periods for corn only. Kansas showed its strongest correlations for the two-month and three-month averages, for both corn and soybean. Precipitation regression with irrigated and non-irrigated maize (corn) and soybean levels show yields as a function of precipitation. The GWR models predicted that yields were significantly better than OLS performances for maize (corn) and soybean. The OLS regression model when used showed a general trend of correlation between observed yields and long-term mean precipitation totals, with 84% and 63% of the variability in mean yield explained by the mean annual precipitation for the non-irrigated crops. The GWR technique performance in predicting yields was significantly better than OLS performances. For instance in the months of June, July, and August precipitations had greater impacts on maize (corn) yields than soybeans under non-irrigated conditions as a result of the greater sensitivity maize (corn) had to water stress. SPI is capable of offering various time-scales enabling it to show initial warning signs of drought conditions and accompanying severity levels. SPI calculation techniques used for various locations are reflected upon the precipitation records acquired during those periods. Over the 3, 6, and 9-month periods, NDII6 performed the best out of all of the MODIS indices as shown in its results in monitoring vegetation moisture and drought detection. NDII6 performed the best due to its detection abilities. The 9-month SPI provides an indication of inter-seasonal precipitation patterns over medium timescale duration. A new approach used is to average corn and soybean yields for all counties of the study area in comparison with average anomalies of the MODIS indices for the growing season between May through September from 2006-2012. There was a strong correlation between average corn yields versus MODIS NDII6 averages for these years with R2 equaling 0.62. That means NDII6 is the best indicator to show drought conditions and vegetation moisture monitoring. There was a weak correlation with R2 = 0.16 between averages of soybean yields and averages of precipitation. Irrigation and management systems, technological improvements from hybrids, producer management techniques, and other management practices have an impact on crop yield productions. (Abstract shortened by ProQuest.).

Assessment and Monitoring of Nutrient Management in Irrigated Agriculture for Groundwater Quality Protection

NASA Astrophysics Data System (ADS)

Harter, T.; Davis, R.; Smart, D. R.; Brown, P. H.; Dzurella, K.; Bell, A.; Kourakos, G.

2017-12-01

Nutrient fluxes to groundwater have been subject to regulatory assessment and control only in a limited number of countries, including those in the European Union, where the Water Framework Directive requires member countries to manage groundwater basis toward achieving "good status", and California, where irrigated lands will be subject to permitting, stringent nutrient monitoring requirements, and development of practices that are protective of groundwater. However, research activities to rigorously assess agricultural practices for their impact on groundwater have been limited and instead focused on surface water protection. For groundwater-related assessment of agricultural practices, a wide range of modeling tools has been employed: vulnerability studies, nitrogen mass balance assessments, crop-soil-system models, and various statistical tools. These tools are predominantly used to identify high risk regions, practices, or crops. Here we present the development of a field site for rigorous in-situ evaluation of water and nutrient management practices in an irrigated agricultural setting. Integrating groundwater monitoring into agricultural practice assessment requires large research plots (on the order of 10s to 100s of hectares) and multi-year research time-frames - much larger than typical agricultural field research plots. Almonds are among the most common crops in California with intensive use of nitrogen fertilizer and were selected for their high water quality improvement potential. Availability of an orchard site with relatively vulnerable groundwater conditions (sandy soils, water table depth less than 10 m) was also important in site selection. Initial results show that shallow groundwater concentrations are commensurate with nitrogen leaching estimates obtained by considering historical, long-term field nitrogen mass balance and groundwater dynamics.

Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems

PubMed Central

Junker, Astrid; Muraya, Moses M.; Weigelt-Fischer, Kathleen; Arana-Ceballos, Fernando; Klukas, Christian; Melchinger, Albrecht E.; Meyer, Rhonda C.; Riewe, David; Altmann, Thomas

2015-01-01

Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications. PMID:25653655

THE ROLE OF SPECTRAL IMAGERY FOR MONITORING & MODELING TRANSGENIC CROP-PEST INTERACTIONS

EPA Science Inventory

Crops bioengineered to contain toxins derived from Bacillus thuringensis (Bt) are under regulatory scrutiny by USEPA under the FIFRA legislation. The agency has declared these crops to be "in the public good" based on the reduced use of pesticides required for management of these...

Estimating maize water stress by standard deviation of canopy temperature in thermal imagery

USDA-ARS?s Scientific Manuscript database

A new crop water stress index using standard deviation of canopy temperature as an input was developed to monitor crop water status. In this study, thermal imagery was taken from maize under various levels of deficit irrigation treatments in different crop growing stages. The Expectation-Maximizatio...

Comparison of Satellite-based Basal and Adjusted Evapotranspiration for Several California Crops

NASA Astrophysics Data System (ADS)

Johnson, L.; Lund, C.; Melton, F. S.

2013-12-01

There is a continuing need to develop new sources of information on agricultural crop water consumption in the arid Western U.S. Pursuant to the California Water Conservation Act of 2009, for instance, the stakeholder community has developed a set of quantitative indicators involving measurement of evapotranspiration (ET) or crop consumptive use (Calif. Dept. Water Resources, 2012). Fraction of reference ET (or, crop coefficients) can be estimated from a biophysical description of the crop canopy involving green fractional cover (Fc) and height as per the FAO-56 practice standard of Allen et al. (1998). The current study involved 19 fields in California's San Joaquin Valley and Central Coast during 2011-12, growing a variety of specialty and commodity crops: lettuce, raisin, tomato, almond, melon, winegrape, garlic, peach, orange, cotton, corn and wheat. Most crops were on surface or subsurface drip, though micro-jet, sprinkler and flood were represented as well. Fc was retrospectively estimated every 8-16 days by optical satellite data and interpolated to a daily timestep. Crop height was derived as a capped linear function of Fc using published guideline maxima. These variables were used to generate daily basal crop coefficients (Kcb) per field through most or all of each respective growth cycle by the density coefficient approach of Allen & Pereira (2009). A soil water balance model for both topsoil and root zone, based on FAO-56 and using on-site measurements of applied irrigation and precipitation, was used to develop daily soil evaporation and crop water stress coefficients (Ke, Ks). Key meteorological variables (wind speed, relative humidity) were extracted from the California Irrigation Management Information System (CIMIS) for climate correction. Basal crop ET (ETcb) was then derived from Kcb using CIMIS reference ET. Adjusted crop ET (ETc_adj) was estimated by the dual coefficient approach involving Kcb, Ke, and incorporating Ks. Cumulative ETc_adj throughout each monitoring period was lower than cumulative ETb for most crops, indicating that effect of water stress tended to exceed that of soil evaporation relative to basal conditions. We present results from the analysis and discuss implications for operational use of satellite-based Kcb and ETcb estimates for agricultural water resource management.

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System.

PubMed

Ni, Jun; Yao, Lili; Zhang, Jingchao; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

2017-03-03

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R²) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

PubMed Central

Ni, Jun; Yao, Lili; Zhang, Jingchao; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

2017-01-01

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively. PMID:28273815

Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

NASA Astrophysics Data System (ADS)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty. The results indicate that uncertainty estimates provide a valuable addition to traditional accuracy assessments and helps the user to allocate error in crop maps.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

Quantifying agricultural drought impacts using soil moisture model and drought indices in South Korea

NASA Astrophysics Data System (ADS)

Nam, W. H.; Bang, N.; Hong, E. M.; Pachepsky, Y. A.; Han, K. H.; Cho, H.; Ok, J.; Hong, S. Y.

2017-12-01

Agricultural drought is defined as a combination of abnormal deficiency of precipitation, increased crop evapotranspiration demands from high-temperature anomalies, and soil moisture deficits during the crop growth period. Soil moisture variability and their spatio-temporal trends is a key component of the hydrological balance, which determines the crop production and drought stresses in the context of agriculture. In 2017, South Korea has identified the extreme drought event, the worst in one hundred years according to the South Korean government. The objective of this study is to quantify agricultural drought impacts using observed and simulated soil moisture, and various drought indices. A soil water balance model is used to simulate the soil water content in the crop root zone under rain-fed (no irrigation) conditions. The model used includes physical process using estimated effective rainfall, infiltration, redistribution in soil water zone, and plant water uptake in the form of actual crop evapotranspiration. Three widely used drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Self-Calibrated Palmer Drought Severity Index (SC-PDSI) are compared with the observed and simulated soil moisture in the context of agricultural drought impacts. These results demonstrated that the soil moisture model could be an effective tool to provide improved spatial and temporal drought monitoring for drought policy.

Satellite-guided hydro-economic analysis for integrated management and prediction of the impact of droughts on agricultural regions

NASA Astrophysics Data System (ADS)

Maneta, M. P.; Howitt, R.; Kimball, J. S.

2013-12-01

Agricultural activity can exacerbate or buffer the impact of climate variability, especially droughts, on the hydrologic and socioeconomic conditions of rural areas. Potential negative regional impacts of droughts include impoverishment of agricultural regions, deterioration or overuse of water resources, risk of monoculture, and regional dependence on external food markets. Policies that encourage adequate management practices in the face of adverse climatic events are critical to preserve rural livelihoods and to ensure a sustainable future for agriculture. Diagnosing and managing drought effects on agricultural production, on the social and natural environment, and on limited water resources, is highly complex and interdisciplinary. The challenges that decision-makers face to mitigate the impact of water shortage are social, agronomic, economic and environmental in nature and therefore must be approached from an integrated multidisciplinary point of view. Existing observation technologies, in conjunction with models and assimilation methods open the opportunity for novel interdisciplinary analysis tools to support policy and decision making. We present an integrated modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, management decisions and socioeconomic policy. The core of this framework is a hydroeconomic model of agricultural production that assimilates remote sensing inputs to quantify the amount of land, water, fertilizer and labor farmers allocate for each crop they choose to grow on a seasonal basis in response to changing climatic conditions, including drought. A regional hydroclimatologic model provides biophysical constraints to an economic model of agricultural production based on a class of models referred to as positive mathematical programming (PMP). A recursive Bayesian update method is used to adjust the model parameters by assimilating information on crop acreage, production, and crop evapotranspiration estimated from high-spatial resolution satellite remote sensing. We are developing new land parameter records adapted for agricultural application by merging relatively fine scale, calibrated spectral reflectance time series with similar spectral information from coarser scale and more temporally continuous global satellite data records. These new products will be used to generate field scale estimates of LAI and FPAR, which will be used with regional surface meteorology and biophysical data to estimate crop production including C4 crop types. This integrated framework provides an operational means to monitor and forecast what crops will be grown and how farmers will allocate land, water and other agricultural resources under expected adverse conditions, and the resulting consequences for other water users. It will also permit evaluation of impacts of water policy and changes in food prices on rural community livelihoods. The Bayesian update framework constitutes an efficient method for the identification of the production function parameters and provides valuable information on the associated uncertainty of the forecasts.

Evaluation of two evapotranspiration approaches simulated with the CSM-CERES-Maize model under different irrigation strategies and the impact on maize growth, development and soil moisture content for semi-arid conditions

USDA-ARS?s Scientific Manuscript database

Water deficit is the most common adverse environmental condition that can seriously reduce crop productivity. Crop simulation models could assist in determining alternate crop management scenarios to deal with water-limited conditions. However, prior to the application of crop models, the appropriat...

Detecting crop population growth using chlorophyll fluorescence imaging.

PubMed

Wang, Heng; Qian, Xiangjie; Zhang, Lan; Xu, Sailong; Li, Haifeng; Xia, Xiaojian; Dai, Liankui; Xu, Liang; Yu, Jingquan; Liu, Xu

2017-12-10

For both field and greenhouse crops, it is challenging to evaluate their growth information on a large area over a long time. In this work, we developed a chlorophyll fluorescence imaging-based system for crop population growth information detection. Modular design was used to make the system provide high-intensity uniform illumination. This system can perform modulated chlorophyll fluorescence induction kinetics measurement and chlorophyll fluorescence parameter imaging over a large area of up to 45  cm×34  cm. The system can provide different lighting intensity by modulating the duty cycle of its control signal. Results of continuous monitoring of cucumbers in nitrogen deficiency show the system can reduce the judge error of crop physiological status and improve monitoring efficiency. Meanwhile, the system is promising in high throughput application scenarios.

Integrated Exposure Assessment Monitoring.

ERIC Educational Resources Information Center

Behar, Joseph V.; And Others

1979-01-01

Integrated Exposure Assessment Monitoring is the coordination of environmental (air, water, land, and crops) monitoring networks to collect systematically pollutant exposure data for a specific receptor, usually man. (Author/BB)

Remote-sensing-based rapid assessment of flood crop loss to support USDA flooding decision-making

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Yang, Z.; Hipple, J.; Shrestha, R.

2016-12-01

Floods often cause significant crop loss in the United States. Timely and objective assessment of flood-related crop loss is very important for crop monitoring and risk management in agricultural and disaster-related decision-making in USDA. Among all flood-related information, crop yield loss is particularly important. Decision on proper mitigation, relief, and monetary compensation relies on it. Currently USDA mostly relies on field surveys to obtain crop loss information and compensate farmers' loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. Recent studies have demonstrated that Earth observation (EO) data are useful in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. There are three stages of flood damage assessment, including rapid assessment, early recovery assessment, and in-depth assessment. EO-based flood assessment methods currently rely on the time-series of vegetation index to assess the yield loss. Such methods are suitable for in-depth assessment but are less suitable for rapid assessment since the after-flood vegetation index time series is not available. This presentation presents a new EO-based method for the rapid assessment of crop yield loss immediately after a flood event to support the USDA flood decision making. The method is based on the historic records of flood severity, flood duration, flood date, crop type, EO-based both before- and immediate-after-flood crop conditions, and corresponding crop yield loss. It hypotheses that a flood of same severity occurring at the same pheonological stage of a crop will cause the similar damage to the crop yield regardless the flood years. With this hypothesis, a regression-based rapid assessment algorithm can be developed by learning from historic records of flood events and corresponding crop yield loss. In this study, historic records of MODIS-based flood and vegetation products and USDA/NASS crop type and crop yield data are used to train the regression-based rapid assessment algorithm. Validation of the rapid assessment algorithm indicates it can predict the yield loss at 90% accuracy, which is accurate enough to support USDA on flood-related quick response and mitigation.

The Development of a Remote Sensor System and Decision Support Systems Architecture to Monitor Resistance Development in Transgenic Crops

NASA Technical Reports Server (NTRS)

Cacas, Joseph; Glaser, John; Copenhaver, Kenneth; May, George; Stephens, Karen

2008-01-01

The United States Environmental Protection Agency (EPA) has declared that "significant benefits accrue to growers, the public, and the environment" from the use of transgenic pesticidal crops due to reductions in pesticide usage for crop pest management. Large increases in the global use of transgenic pesticidal crops has reduced the amounts of broad spectrum pesticides used to manage pest populations, improved yield and reduced the environmental impact of crop management. A significant threat to the continued use of this technology is the evolution of resistance in insect pest populations to the insecticidal Bt toxins expressed by the plants. Management of transgenic pesticidal crops with an emphasis on conservation of Bt toxicity in field populations of insect pests is important to the future of sustainable agriculture. A vital component of this transgenic pesticidal crop management is establishing the proof of concept basic understanding, situational awareness, and monitoring and decision support system tools for more than 133650 square kilometers (33 million acres) of bio-engineered corn and cotton for development of insect resistance . Early and recent joint NASA, US EPA and ITD remote imagery flights and ground based field experiments have provided very promising research results that will potentially address future requirements for crop management capabilities.

Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

NASA Astrophysics Data System (ADS)

Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

Integrated study of biomass index in La Herreria (Sierra de Guadarrama)

NASA Astrophysics Data System (ADS)

Hernandez Díaz-Ambrona, Carlos G.

2016-04-01

Drought severity has many implications for society, including its impacts on the water supply, water pollution, reservoir management and ecosystem. There have been many attempts to characterize its severity, resulting in the numerous drought indices that have been developed (Niemeyer 2008). The'biomass index', based on satellite image derived Normalized Difference Vegetation Index (NDVI) has been used in several countries for pasture and forage crops for some years (Rao, 2010; Escribano-Rodriguez et al., 2014). NDVI generally provides a broad overview of the vegetation condition and spatial vegetation distribution in a region. Vegetative drought is closely related with weather impacts. However, in NDVI, the weather component gets subdued by the strong ecological component. Another vegetation index is Vegetation Condition Index (VCI) that separates the short-term weather-related NDVI fluctuations from the long-term ecosystem changes (Kogan, 1990). Therefore, while NDVI shows seasonal vegetation dynamics, VCI rescales vegetation dynamics between 0 and 100 to reflect relative changes in the vegetation condition from extremely bad to optimal (Kogan et al., 2003). In this work a pasture area at La Herreria (Sierra de Guadarrama, Spain) has been delimited. Then, NDVI historical data are reconstructed based on remote sensing imaging MODIS, with 500x500m2 resolution. From the closest meteorological station (Santolaria-Canales, 2015) records of weekly precipitation, temperature and evapotranspiration from 2001 till 2012 were obtained. Standard Precipitation Index (SPI), Crop Moisture Index (CMI) (Palmer, 1968) and Evapotranspiration-Precipitation Ratio (EPR) are calculated in an attempt to relate them to several vegetation indexes: NDVI, VCI and NDVI Change Ratio to Median (RMNDVI). The results are discussed in the context of pasture index insurance. References Escribano Rodriguez, J.Agustín, Carlos Gregorio Hernández Díaz-Ambrona and Ana María Tarquis Alfonso. Selection of vegetation indices to estimate pasture production in Dehesas. PASTOS, 44(2), 6-18, 2014. Kogan, F. N., 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int. J. Remote Sensing, 11(8), pp. 1405-1419. Kogan, F. N., Gitelson, A., Edige, Z., Spivak, l., and Lebed, L., 2003. AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation. Photogrammetric Engineering & Remote Sensing, 69(8), pp. 899-906. Niemeyer, S., 2008. New drought indices. First Int. Conf. on Drought Management: Scientific and Technological Innovations, Zaragoza, Spain, Joint Research Centre of the European Commission. Palmer, W.C., 1968. Keeping track of crop moisture conditions, nationwide: The new Crop Moisture Index. Weatherwise 21, 156-161. Rao, K.N. 2010. Index based Crop Insurance. Agriculture and Agricultural Science Procedia 1, 193-203. Santolaria-Canales, Edmundo and the GuMNet Consortium Team (2015). GuMNet - Guadarrama Monitoring Network. Installation and set up of a high altitude monitoring network, north of Madrid. Spain. Geophysical Research Abstracts, 17, EGU2015-13989-2 Web: http://www.ucm.es/gumnet/

NASA's NI-SAR Observing Strategy and Data Availability for Agricultural Monitoring and Assessment

NASA Astrophysics Data System (ADS)

Siqueira, P.; Dubayah, R.; Kellndorfer, J. M.; Saatchi, S. S.; Chapman, B. D.

2014-12-01

The monitoring and characterization of global crop development by remote sensing is a complex task, in part, because of the time varying nature of the target and the diversity of crop types and agricultural practices that vary worldwide. While some of these difficulties are overcome with the availability of national and market-derived resources (e.g. publication of crop statistics by the USDA and FAO), monitoring by remote sensing has the ability of augmenting those resources to better identify changes over time, and to provide timely assessments for the current year's production. Of the remote sensing techniques that are used for agricultural applications, optical observations of NDVI from Landsat, AVHRR, MODIS and similar sensors have historically provided the majority of data that is used by the community. In addition, radiometer and radar sensors, are often used for estimating soil moisture and structural information for these agricultural regions. The combination of these remote sensing datasets and national resources constitutes the state of the art for crop monitoring and yield forecasts. To help improve these crop monitoring efforts in the future, the joint NASA-ISRO SAR mission known as NI-SAR is being planned for launch in 2020, and will have L- and S-band fully polarimetric radar systems, a fourteen day repeat period, and a swath width on the order of several hundred kilometers. To address the needs of the science and applications communities that NI-SAR will support, the systems observing strategy is currently being planned such that data rate and the system configuration will address the needs of the community. In this presentation, a description of the NI-SAR system will be given along with the currently planned observing strategy and derived products that will be relevant to the overall GEOGLAM initiative.

THE USE OF AIR QUALITY FORECASTS TO ASSESS IMPACTS OF AIR POLLUTION ON CROPS

EPA Science Inventory

Assessing O₃ damage to crops is challenging due to the difficulties in determining the reduction in crop yield that results from exposure to surface O₃, for which monitors are limited and deployed mostly in non-rural areas. This work explores the potential b...

Coopers Rock Crop Tree Demonstration Area—20-year results

Treesearch

Arlyn W. Perkey; Gary W. Miller; David L. Feicht

2011-01-01

During the 1988/1989 dormant season, the Coopers Rock Crop Tree Demonstration Area was established in a 55-year-old central Appalachian hardwood forest in north-central West Virginia. After treatment, 89 northern red oak (Quercus rubra L.) and 147 yellow-poplar (Liriodentron tulipifera L.) crop trees were monitored for 20 years....

Solutions Network Formulation Report. Using NASA Sensors to Perform Crop Type Assessment for Monitoring Insect Resistance in Corn

NASA Technical Reports Server (NTRS)

Lewis, David; Copenhaver, Ken; Anderson, Daniel; Hilbert, Kent

2007-01-01

The EPA (U.S. Environmental Protection Agency) is tasked to monitor for insect pest resistance to transgenic crops. Several models have been developed to understand the resistance properties of insects. The Population Genetics Simulator model is used in the EPA PIRDSS (Pest Infestation and Resistance Decision Support System). The EPA Office of Pesticide Programs uses the DSS to help understand the potential for insect pest resistance development and the likelihood that insect pest resistance will negatively affect transgenic corn. Once the DSS identifies areas of concern, crews are deployed to collect insect pest samples, which are tested to identify whether they have developed resistance to the toxins in transgenic corn pesticides. In this candidate solution, VIIRS (Visible/Infrared Imager/Radiometer Suite) vegetation index products will be used to build hypertemporal layerstacks for crop type and phenology assessment. The current phenology attribute is determined by using the current time of year to index the expected growth stage of the crop. VIIRS might provide more accurate crop type assessment and also might give a better estimate on the crop growth stage.

Fusion of multi-source remote sensing data for agriculture monitoring tasks

NASA Astrophysics Data System (ADS)

Skakun, S.; Franch, B.; Vermote, E.; Roger, J. C.; Becker Reshef, I.; Justice, C. O.; Masek, J. G.; Murphy, E.

2016-12-01

Remote sensing data is essential source of information for enabling monitoring and quantification of crop state at global and regional scales. Crop mapping, state assessment, area estimation and yield forecasting are the main tasks that are being addressed within GEO-GLAM. Efficiency of agriculture monitoring can be improved when heterogeneous multi-source remote sensing datasets are integrated. Here, we present several case studies of utilizing MODIS, Landsat-8 and Sentinel-2 data along with meteorological data (growing degree days - GDD) for winter wheat yield forecasting, mapping and area estimation. Archived coarse spatial resolution data, such as MODIS, VIIRS and AVHRR, can provide daily global observations that coupled with statistical data on crop yield can enable the development of empirical models for timely yield forecasting at national level. With the availability of high-temporal and high spatial resolution Landsat-8 and Sentinel-2A imagery, course resolution empirical yield models can be downscaled to provide yield estimates at regional and field scale. In particular, we present the case study of downscaling the MODIS CMG based generalized winter wheat yield forecasting model to high spatial resolution data sets, namely harmonized Landsat-8 - Sentinel-2A surface reflectance product (HLS). Since the yield model requires corresponding in season crop masks, we propose an automatic approach to extract winter crop maps from MODIS NDVI and MERRA2 derived GDD using Gaussian mixture model (GMM). Validation for the state of Kansas (US) and Ukraine showed that the approach can yield accuracies > 90% without using reference (ground truth) data sets. Another application of yearly derived winter crop maps is their use for stratification purposes within area frame sampling for crop area estimation. In particular, one can simulate the dependence of error (coefficient of variation) on the number of samples and strata size. This approach was used for estimating the area of winter crops in Ukraine for 2013-2016. The GMM-GDD approach is further extended for HLS data to provide automatic winter crop mapping at 30 m resolution for crop yield model and area estimation. In case of persistent cloudiness, addition of Sentinel-1A synthetic aperture radar (SAR) images is explored for automatic winter crop mapping.

Encounter risk analysis of rainfall and reference crop evapotranspiration in the irrigation district

NASA Astrophysics Data System (ADS)

Zhang, Jinping; Lin, Xiaomin; Zhao, Yong; Hong, Yang

2017-09-01

Rainfall and reference crop evapotranspiration are random but mutually affected variables in the irrigation district, and their encounter situation can determine water shortage risks under the contexts of natural water supply and demand. However, in reality, the rainfall and reference crop evapotranspiration may have different marginal distributions and their relations are nonlinear. In this study, based on the annual rainfall and reference crop evapotranspiration data series from 1970 to 2013 in the Luhun irrigation district of China, the joint probability distribution of rainfall and reference crop evapotranspiration are developed with the Frank copula function. Using the joint probability distribution, the synchronous-asynchronous encounter risk, conditional joint probability, and conditional return period of different combinations of rainfall and reference crop evapotranspiration are analyzed. The results show that the copula-based joint probability distributions of rainfall and reference crop evapotranspiration are reasonable. The asynchronous encounter probability of rainfall and reference crop evapotranspiration is greater than their synchronous encounter probability, and the water shortage risk associated with meteorological drought (i.e. rainfall variability) is more prone to appear. Compared with other states, there are higher conditional joint probability and lower conditional return period in either low rainfall or high reference crop evapotranspiration. For a specifically high reference crop evapotranspiration with a certain frequency, the encounter risk of low rainfall and high reference crop evapotranspiration is increased with the decrease in frequency. For a specifically low rainfall with a certain frequency, the encounter risk of low rainfall and high reference crop evapotranspiration is decreased with the decrease in frequency. When either the high reference crop evapotranspiration exceeds a certain frequency or low rainfall does not exceed a certain frequency, the higher conditional joint probability and lower conditional return period of various combinations likely cause a water shortage, but the water shortage is not severe.

Bill Would Expand U.S. Drought Monitoring

NASA Astrophysics Data System (ADS)

Zielinski, Sarah

2006-05-01

The collection and dissemination of drought information would be centralized within the U.S. National Oceanic and Atmospheric Administration (NOAA) under a newly proposed bill, which received support at a 4 May hearing before the U.S. House of Representatives Science Subcommittee on Environment, Technology, and Standards. The economic costs of drought average $6 to $8 billion each year in the United States, according to NOAA. The effects of prolonged drought include extreme wildfire conditions, water restrictions, and reduced crop yields.

Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

USDA-ARS?s Scientific Manuscript database

Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evalu...

Silver-gelatine bionanocomposites for qualitative detection of a pesticide by SERS.

PubMed

Fateixa, S; Soares, S F; Daniel-da-Silva, A L; Nogueira, H I S; Trindade, T

2015-03-07

The controlled release of pesticides using hydrogel vehicles is an important procedure to limit the amount of these compounds in the environment, providing an effective way for crop protection. A key-step in the formulation of new materials for these purposes encompasses the monitoring of available pesticides in the gel matrix under variable working conditions. In this work, we report a series of bionanocomposites made of Ag nanoparticles (NPs) and gelatine A for the surface enhanced Raman scattering (SERS) detection of sodium diethyldithiocarbamate (EtDTC) as a pesticide model. These studies demonstrate the effectiveness of these substrates for the detection of EtDTC in aqueous solutions in a concentration as low as 10(-5) M. We have monitored the Raman signal enhancement of this analyte in bionanocomposites having an increasing amount of gelatine due to their relevance in formulating hydrogels of variable gel strengths. Under these conditions, the bionanocomposites have shown an effective SERS activity using EtDTC, demonstrating their effectiveness in the qualitative detection of this analyte. Finally, experiments involving the release of EtDTC from Ag/gelatine samples have been monitored by SERS, which attest the potential of this spectroscopic method in the laboratorial monitoring of hydrogels for pesticide release.

Hydrological Responses of Weather Conditions and Crop Change of Agricultural Area in the Rincon Valley, New Mexico

NASA Astrophysics Data System (ADS)

Ahn, S.; Sheng, Z.; Abudu, S.

2017-12-01

Hydrologic cycle of agricultural area has been changing due to the impacts of climate and land use changes (crop coverage changes) in an arid region of Rincon Valley, New Mexico. This study is to evaluate the impacts of weather condition and crop coverage change on hydrologic behavior of agricultural area in Rincon Valley (2,466km2) for agricultural watershed management using a watershed-scale hydrologic model, SWAT (Soil and Water Assessment Tool). The SWAT model was developed to incorporate irrigation of different crops using auto irrigation function. For the weather condition and crop coverage change evaluation, three spatial crop coverages including a normal (2008), wet (2009), and dry (2011) years were prepared using USDA crop data layer (CDL) for fourteen different crops. The SWAT model was calibrated for the period of 2001-2003 and validated for the period of 2004-2006 using daily-observed streamflow data. Scenario analysis was performed for wet and dry years based on the unique combinations of crop coverages and releases from Caballo Reservoir. The SWAT model simulated the present vertical water budget and horizontal water transfer considering irrigation practices in the Rincon Valley. Simulation results indicated the temporal and spatial variability for irrigation and non-irrigation seasons of hydrologic cycle in agricultural area in terms of surface runoff, evapotranspiration, infiltration, percolation, baseflow, soil moisture, and groundwater recharge. The water supply of the dry year could not fully cover whole irrigation period due to dry weather conditions, resulting in reduction of crop acreage. For extreme weather conditions, the temporal variation of water budget became robust, which requires careful irrigation management of the agricultural area. The results could provide guidelines for farmers to decide crop patterns in response to different weather conditions and water availability.

The Asia-RiCE activity with data cube

NASA Astrophysics Data System (ADS)

Oyoshi, K.; Sobue, S.; LE Toan, T.; Lam, N. D.

2017-12-01

The Asia-RiCE initiative (http://www.asia-rice.org) has been organized to enhance rice production estimates through the use of Earth observation satellites data, and seeks to ensure that Asian rice crops are appropriately represented within GEO Global Agriculture Monitoring (GEO-GLAM) to support FAO Agriculture Market Information System (FAO-AMIS). Asia-RiCE is composed of national teams that are actively contributing to the Crop Monitor for AMIS and developing technical demonstrations of rice crop monitoring activities using both Synthetic Aperture Radar (SAR) data (Radarsat-2 from 2013; Sentinel-1 and ALOS-2 from 2015.From 2016 after the successful rice crop area and growing estimation using SAR in a technical demonstration site (provincial level), wall-to-wall (national scale) excurse as phase 2 has been implemented in Vietnam and Indonesia in cooperation with ministry of agriculture and space agencies. This paper reports this year activity of 2017 accomplishment and way forward, especially for analysis ready data (ARD) definition of SAR to ingest to CEOS data cube to provide national scale service in Vietnam and Indonesia.

ARC-2010-ACD10-0243-002

NASA Image and Video Library

2010-12-22

Wireless crop water monitoring project: Dr. Chris Lund and Forrest Melton, California State University Monterey Bay research scientists who work at NASA Ames Research Center, check data being returned from a wireless soil moisture monitoring network, installed in an agricultural field. Data from the soil moisture sensor network will be used to assist in interpretation of the satellite estimates of crop water demand. Image of courtesy of Forrest S. Melton

Summer cover crops reduce atrazine leaching to shallow groundwater in southern Florida.

PubMed

Potter, Thomas L; Bosch, David D; Joo, Hyun; Schaffer, Bruce; Muñoz-Carpena, Rafael

2007-01-01

At Florida's southeastern tip, sweet corn (Zea Mays) is grown commercially during winter months. Most fields are treated with atrazine (6-chloro-N-ethyl-N'-[1-methylethyl]-1,3,5-triazine-2,4-diamine). Hydrogeologic conditions indicate a potential for shallow groundwater contamination. This was investigated by measuring the parent compound and three degradates--DEA (6-chloro-N-[1-methylethyl]-1,3,5-triazine-2,4-diamine), DIA (6-chloro-N-ethyl)-1,3,5-triazine-2,4-diamine, and HA (6-hydroxy-N-[1-methylethyl]-1,3,5-triazine-2,4-diamine)--in water samples collected beneath sweet corn plots treated annually with the herbicide. During the study, a potential mitigation measure (i.e., the use of a cover crop, Sunn Hemp [Crotalaria juncea L.], during summer fallow periods followed by chopping and turning the crop into soil before planting the next crop) was evaluated. Over 3.5 yr and production of four corn crops, groundwater monitoring indicated leaching of atrazine, DIA, and DEA, with DEA accounting for more than half of all residues in most samples. Predominance of DEA, which increased after the second atrazine application, was interpreted as an indication of rapid and extensive atrazine degradation in soil and indicated that an adapted community of atrazine degrading organisms had developed. A companion laboratory study found a sixfold increase in atrazine degradation rate in soil after three applications. Groundwater data also revealed that atrazine and degradates concentrations were significantly lower in samples collected beneath cover crop plots when compared with concentrations below fallow plots. Together, these findings demonstrated a relatively small although potentially significant risk for leaching of atrazine and its dealkylated degradates to groundwater and that the use of a cover crop like Sunn Hemp during summer months may be an effective mitigation measure.

Production of polyhydroxybutyrate in switchgrass, a value-added co-product in an important lignocellulosic biomass crop.

PubMed

Somleva, Maria N; Snell, Kristi D; Beaulieu, Julie J; Peoples, Oliver P; Garrison, Bradley R; Patterson, Nii A

2008-09-01

Polyhydroxyalkanoate bio-based plastics made from renewable resources can reduce petroleum consumption and decrease plastic waste disposal issues as they are inherently biodegradable in soil, compost and marine environments. In this paper, the successful engineering of the biomass crop switchgrass (Panicum virgatum L.) for the synthesis of polyhydroxybutyrate (PHB) is reported. Polymer production was monitored in more than 400 primary transformants grown under in vitro and glasshouse conditions. Plants containing up to 3.72% dry weight of PHB in leaf tissues and 1.23% dry weight of PHB in whole tillers were obtained. Results from the analysis of the polymer distribution at the cellular and whole plant levels are presented, and target areas for the improvement of PHB production are highlighted. Polymer accumulation was also analysed in the T(1) generation obtained from controlled crosses of transgenic plants. This study presents the first successful expression of a functional multigene pathway in switchgrass, and demonstrates that this high-yielding biomass crop is amenable to the complex metabolic engineering strategies necessary to produce high-value biomaterials with lignocellulose-derived biofuels.

Long term plant biomonitoring in the vicinity of waste incinerators in The Netherlands.

PubMed

van Dijk, Chris; van Doorn, Wim; van Alfen, Bert

2015-03-01

Since the mid-nineties new waste incineration plants have come into operation in the Netherlands. Burning of waste can result in the emission of potentially toxic compounds. Although the incineration plants must comply with strict conditions concerning emission control, public concern on the possible impact on human health and the environment still exists. Multiple year (2004-2013) biomonitoring programs were set up around three waste incinerators for early detection of possible effects of stack emissions on the quality of crops and agricultural products. The results showed that the emissions did not affect the quality of crops and cow milk. Concentrations of heavy metals, PAHs and dioxins/PCBs were generally similar to background levels and did not exceed standards for maximum allowable concentrations in foodstuffs (e.g. vegetables and cow milk). Some exceedances of the fluoride standard for cattle feed were found almost every year in the maximum deposition areas of two incinerators. Biomonitoring with leafy vegetables can be used to monitor the real impact of these emissions on agricultural crops and to communicate with all stakeholders. Copyright © 2014 Elsevier Ltd. All rights reserved.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

Crop tree release increases growth of red oak sawtimber in southern New England: 12-year results

Treesearch

Jeffrey S. Ward

2008-01-01

In winter 1995, five crop tree thinning plots were established in central Connecticut. Stands were mature red oak sawtimber (74-94 years old) with no history of prior management. Crop trees were upper canopy red oaks (northern red, black, and scarlet) with a potential grade 1 or 2 butt log. Growth of crop trees was monitored for the next 12 years. Diameter, cubic-foot...

Assessing environmental impacts of constructed wetland effluents for vegetable crop irrigation.

PubMed

Castorina, A; Consoli, S; Barbagallo, S; Branca, F; Farag, A; Licciardello, F; Cirelli, G L

2016-01-01

The objective of this study was to monitor and assess environmental impacts of reclaimed wastewater (RW), used for irrigation of vegetable crops, on soil, crop quality and irrigation equipment. During 2013, effluents of a horizontal sub-surface flow constructed treatment wetland (TW) system, used for tertiary treatment of sanitary wastewater from a small rural municipality located in Eastern Sicily (Italy), were reused by micro-irrigation techniques to irrigate vegetable crops. Monitoring programs, based on in situ and laboratory analyses were performed for assessing possible adverse effects on water-soil-plant systems caused by reclaimed wastewater reuse. In particular, experimental results evidenced that Escherichia coli content found in RW would not present a risk for rotavirus infection following WHO (2006) standards. Irrigated soil was characterized by a certain persistence of microbial contamination and among the studied vegetable crops, lettuce responds better, than zucchini and eggplants, to the irrigation with low quality water, evidencing a bettering of nutraceutical properties and production parameters.

Spatio-temporal dynamic climate model for Neoleucinodes elegantalis using CLIMEX

NASA Astrophysics Data System (ADS)

da Silva, Ricardo Siqueira; Kumar, Lalit; Shabani, Farzin; da Silva, Ezio Marques; da Silva Galdino, Tarcisio Visintin; Picanço, Marcelo Coutinho

2017-05-01

Seasonal variations are important components in understanding the ecology of insect population of crops. Ecological studies through modeling may be a useful tool for enhancing knowledge of seasonal patterns of insects on field crops as well as seasonal patterns of favorable climatic conditions for species. Recently CLIMEX, a semi-mechanistic niche model, was upgraded and enhanced to consider spatio-temporal dynamics of climate suitability through time. In this study, attempts were made to determine monthly variations of climate suitability for Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae) in five commercial tomato crop localities through the latest version of CLIMEX. We observed that N. elegantalis displays seasonality with increased abundance in tomato crops during summer and autumn, corresponding to the first 6 months of the year in monitored areas in this study. Our model demonstrated a strong accord between the CLIMEX weekly growth index (GIw) and the density of N. elegantalis for this period, thus indicating a greater confidence in our model results. Our model shows a seasonal variability of climatic suitability for N. elegantalis and provides useful information for initiating methods for timely management, such as sampling strategies and control, during periods of high degree of suitability for N. elegantalis. In this study, we ensure that the simulation results are valid through our verification using field data.

Plant pathogen nanodiagnostic techniques: forthcoming changes?

PubMed Central

Khiyami, Mohammad A.; Almoammar, Hassan; Awad, Yasser M.; Alghuthaymi, Mousa A.; Abd-Elsalam, Kamel A.

2014-01-01

Plant diseases are among the major factors limiting crop productivity. A first step towards managing a plant disease under greenhouse and field conditions is to correctly identify the pathogen. Current technologies, such as quantitative polymerase chain reaction (Q-PCR), require a relatively large amount of target tissue and rely on multiple assays to accurately identify distinct plant pathogens. The common disadvantage of the traditional diagnostic methods is that they are time consuming and lack high sensitivity. Consequently, developing low-cost methods to improve the accuracy and rapidity of plant pathogens diagnosis is needed. Nanotechnology, nano particles and quantum dots (QDs) have emerged as essential tools for fast detection of a particular biological marker with extreme accuracy. Biosensor, QDs, nanostructured platforms, nanoimaging and nanopore DNA sequencing tools have the potential to raise sensitivity, specificity and speed of the pathogen detection, facilitate high-throughput analysis, and to be used for high-quality monitoring and crop protection. Furthermore, nanodiagnostic kit equipment can easily and quickly detect potential serious plant pathogens, allowing experts to help farmers in the prevention of epidemic diseases. The current review deals with the application of nanotechnology for quicker, more cost-effective and precise diagnostic procedures of plant diseases. Such an accurate technology may help to design a proper integrated disease management system which may modify crop environments to adversely affect crop pathogens. PMID:26740775

Stimulating innovation for global monitoring of agriculture and its impact on the environment in support of GEOGLAM

NASA Astrophysics Data System (ADS)

Bydekerke, Lieven; Gilliams, Sven; Gobin, Anne

2015-04-01

There is an urgent need to ensure food supply for a growing global population. To enable a sustainable growth of agricultural production, effective and timely information is required to support decision making and to improve management of agricultural resources. This requires innovative ways and monitoring methods that will not only improve short-term crop production forecasts, but also allow to assess changes in cultivation practices, agricultural areas, agriculture in general and, its impact on the environment. The G20 launched in June 2011 the "GEO Global Agricultural Monitoring initiative (GEOGLAM), requesting the GEO (Group on Earth Observations) Agricultural Community of Practice to implement GEOGLAM with the main objective to improve crop yield forecasts as an input to the Agricultural Market Information System (AMIS), in order to foster stabilisation of markets and increase transparency on agricultural production. In response to this need, the European Commission decided in 2013 to fund an international partnership to contribute to GEOGLAM and its research agenda. The resulting SIGMA project (Stimulating Innovation for Global Monitoring of Agriculture), a partnership of 23 globally distributed expert organisations, focusses on developing datasets and innovative techniques in support of agricultural monitoring and its impact on the environment in support of GEOGLAM. SIGMA has 3 generic objectives which are: (i) develop and test methods to characterise cropland and assess its changes at various scales; (ii) develop and test methods to assess changes in agricultural production levels; and; (iii) study environmental impacts of agriculture. Firstly, multi-scale remote sensing data sets, in combination with field and other ancillary data, will be used to generate an improved (global) agro-ecological zoning map and crop mask. Secondly, a combination of agro-meteorological models, satellite-based information and long-term time series will be explored to assess crop yield gaps and shifts in cultivation. The third research topic entails the development of best practices for assessing the impact of crop land and cropping system change on the environment. In support of the GEO JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative, SIGMA has selected case studies in Ukraine, Russia, Europe, Africa, Latin America and China, coinciding with the JECAM sites in these area, to explore possible methodological synergies and particularities according to different cropping systems. In combination with research conducted at regional and global scale, it is one of the goals to improve the understanding of dynamics, interactions and validity of the developed methods at the various scales. In addition, specific activities will be dedicated to raising awareness and strengthening capacity for what concerns agro-environmental monitoring, data accessibility and interoperability in line with the GEOSS Data-core principles. The SIGMA project will also anticipate on the availability of the SENTINEL satellites for agricultural applications as open-data in the near future. References http://proba-v.vgt.vito.be/ http://www.geoglam-sigma.info/

Regionalizing land use impacts on farmland birds.

PubMed

Glemnitz, Michael; Zander, Peter; Stachow, Ulrich

2015-06-01

The environmental impacts of land use vary regionally. Differences in geomorphology, climate, landscape structure, and biotope inventories are regarded as the main causes of this variation. We present a methodological approach for identifying regional responses in land use type to large-scale changes and the implications for the provision of habitat for farmland birds. The methodological innovations of this approach are (i) the coupling of impact assessments with economic models, (ii) the linking of cropping techniques at the plot scale with the regional distribution of land use, and (iii) the integration of statistical or monitoring data on recent states. This approach allows for the regional differentiation of farmers' responses to changing external conditions and for matching the ecological impacts of land use changes with regional environmental sensitivities. An exemplary scenario analysis was applied for a case study of an area in Germany, assessing the impacts of increased irrigation and the promotion of energy cropping on farmland birds, evaluated as a core indicator for farmland biodiversity. The potential effects on farmland birds were analyzed based on the intrinsic habitat values of the crops and cropping techniques. The results revealed that the strongest decrease in habitat availability for farmland birds occurred in regions with medium-to-low agricultural yields. As a result of the limited cropping alternatives, the increase in maize production was highest in marginal regions for both examined scenarios. Maize production replaced many crops with good-to-medium habitat suitability for birds. The declines in habitat quality were strongest in regions that are not in focus for conservation efforts for farmland birds.

Developing an automatic classification system of vegetation anomalies for early warning with the ASAP (Anomaly hot Spots of Agricultural Production) system

NASA Astrophysics Data System (ADS)

Meroni, M.; Rembold, F.; Urbano, F.; Lemoine, G.

2016-12-01

Anomaly maps and time profiles of remote sensing derived indicators relevant to monitor crop and vegetation stress can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for SDG 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide monthly warning of production deficits in water-limited agriculture worldwide. The first step is fully automated and aims at classifying each administrative unit (1st sub-national level) into a number of possible warning levels, ranging from "none" to "watch" and up to "extended alarm". The second step involves the verification of the automatic warnings and integration into a short national level analysis by agricultural analysts. In this paper we describe the methodological development of the automatic vegetation anomaly classification system. Warnings are triggered only during the crop growing season, defined by a remote sensing based phenology. The classification takes into consideration the fraction of the agricultural and rangelands area for each administrative unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index (SPI), computed at 1 and 3-month scale) and one biophysical indicator (the cumulative NDVI from the start of the growing season). The severity of the warning thus depends on the timing, the nature and the number of indicators for which an anomaly is detected. The prototype system is using global NDVI images of the METOP sensor, while a second version is being developed based on 1km Modis NDVI with temporal smoothing and near real time filtering. Also a specific water balance model is under development to include agriculture water stress information in addition to the SPI. The monthly warning classification and crop condition assessment will be made available on a website and will strengthen the JRC support to information products based on consensus assessment such as the GEOGLAM Crop Monitor for Early Warning.

Protocol for monitoring standing crop in grasslands using visual obstruction

Treesearch

Lakhdar Benkobi; Daniel W. Uresk; Greg Schenbeck; Rudy M. King

2000-01-01

Assessment of standing crop on grasslands using a visual obstruction technique provides valuable information to help plan livestock grazing management and indicate the status of wildlife habitat. The objectives of this study were to: (1) develop a simple regression model using easily measured visual obstruction to estimate standing crop on sandy lowland range sites in...

Identification and discrimination of herbicide residues using a conducting polymer electronic nose

Treesearch

Alphus Dan Wilson

2016-01-01

The identification of herbicide residues on crop foliage is necessary to make crop-management decisions for weed pest control and to monitor pesticide residue levels on food crops. Electronic-nose (e-nose) methods were tested as a cheaper, alternative means of discriminating between herbicide residue types (compared with conventional chromatography methods), by...

Steady-state chlorophyll fluorescence (Fs) as a tool to monitor plant heat and drought stress

NASA Astrophysics Data System (ADS)

Cendrero Mateo, M.; Carmo-Silva, A.; Salvucci, M.; Moran, S. M.; Hernandez, M.

2012-12-01

Crop yield decreases when photosynthesis is limited by heat or drought conditions. Yet farmers do not monitor crop photosynthesis because it is difficult to measure at the field scale in real time. Steady-state chlorophyll fluorescence (Fs) can be used at the field level as an indirect measure of photosynthetic activity in both healthy and physiologically-perturbed vegetation. In addition, Fs can be measured by satellite-based sensors on a regular basis over large agricultural regions. In this study, plants of Camelina sativa grown under controlled conditions were subjected to heat and drought stress. Gas exchange and Fs were measured simultaneously with a portable photosynthesis system under light limiting and saturating conditions. Results showed that Fs was directly correlated with net CO2 assimilation (A) and inversely correlated with non-photochemical quenching (NPQ). Analysis of the relationship between Fs and Photosynthetically Active Radiation (PAR) revealed significant differences between control and stressed plants that could be used to track the status, resilience, and recovery of photochemical processes. In summary, the results provide evidence that Fs measurements, even without normalization, are an easy means to monitor changes in plant photosynthesis, and therefore, provide a rapid assessment of plant stress to guide farmers in resource applications. Figure1. Net CO2 assimilation rate (A) of Camelina sativa plants under control conditions and after heat stress exposure for 1 or 3 days (1d-HS and 3d-HS, respectively) (right) and control, drought and re-watering conditions (left). Conditions for infra-red gas analysis were: reference CO2 = 380 μmol mol-1, PPFD = 500 μmol m-2 s-1 and Tleaf set to 25°C (control, drought and re-water) or 35°C (HS). Different letters denote significant differences at the α=0.05 level. Values are means±SEM (n=10). Figure 2. Stable chlorophyll fluorescence (Fs) of Camelina sativa plants under control conditions and after heat stress exposure for 1 or 3 days (1d-HS and 3d-HS, respectively) (right) and control, drought and re-watering conditions (left). Conditions for infra-red gas analysis were: reference CO2 = 380 μmol mol-1, PPFD = 500 μmol m-2 s-1 and Tleaf set to 25°C (control, drought and re-water) or 35°C (HS). Different letters denote significant differences at the α=0.05 level. Values are means±SEM (n=10).

A quality assessment of the MARS crop yield forecasting system for the European Union

NASA Astrophysics Data System (ADS)

van der Velde, Marijn; Bareuth, Bettina

2015-04-01

Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

Actual Condition of Paddy Field Levee Maintenance by Various Farm Households including Large-scale Farming in the Developed Land Renting Area

NASA Astrophysics Data System (ADS)

Sakata, Yasuyo

The survey of interview, resource acquisition, photographic operation, and questionnaire were carried out in the “n” Community in the “y” District in Hakusan City in Ishikawa Prefecture to investigate the actual condition of paddy field levee maintenance in the area where land-renting market was proceeding, large-scale farming was dominant, and the problems of geographically scattered farm-land existed. In the study zone, 1) an agricultural production legal person rent-cultivated some of the paddy fields and maintained the levees, 2) another agricultural production legal person rent-cultivated some of the soy bean fields for crop changeover and land owners maintained the levees. The results indicated that sufficient maintenance was executed on the levees of the paddy fields cultivated by the agricultural production legal person, the soy bean fields for crop changeover, and the paddy fields cultivated by the land owners. Each reason is considered to be the managerial strategy, the economic incentive, the mutual monitoring and cross-regulatory mechanism, etc.

The Lower Sevier River Basin Crop Monitor and Forecast Decision Support System: Exploiting Landsat Imagery to Provide Continuous Information to Farmers and Water Managers

NASA Astrophysics Data System (ADS)

Torres-Rua, A. F.; Walker, W. R.; McKee, M.

2013-12-01

The last century has seen a large number of innovations in agriculture such as better policies for water control and management, upgraded water conveyance, irrigation, distribution, and monitoring systems, and better weather forecasting products. In spite of this, irrigation management and irrigation water deliveries by farmers/water managers is still based on factors like water share amounts, tradition, and past experience on irrigation. These factors are not necessarily related to the actual crop water use; they are followed because of the absence of related information provided in a timely manner at an affordable cost. Thus, it is necessary to develop means to deliver continuous and personalized information about crop water requirements to water users/managers at the field and irrigation system levels so managers at these levels can better quantify the required versus available water for irrigation during the irrigation season. This study presents a new decision support system (DSS) platform that addresses the absence of information on actual crop water requirements and crop performance by providing continuous updated farm-based crop water use along with other farm performance indicators such as crop yield and farm management to irrigators and water managers. This DSS exploits the periodicity of the Landsat Satellite Mission (8 to 16 days, depending on the period of interest) to provide remote monitoring at the individual field and irrigation system levels. The Landsat satellite images are converted into information about crop water use, yield performance and field management through application of state-of-the-art semi-physical and statistical algorithms that provide this information at a pixel basis that are ultimately aggregated to field and irrigation system levels. A version of the DSS has been implemented for the agricultural lands in the Lower Sevier River, Utah, and has been operational since the beginning of the 2013 irrigation season. The main goal of this DSS implementation is to provide continuous and personalized information to farmers and water managers regarding crops in fields and the irrigation delivery system throughout the irrigation season so that decisions related to agricultural water use can result in water savings while not diminishing crop yields.

Satellite Based Cropland Carbon Monitoring System

NASA Astrophysics Data System (ADS)

Bandaru, V.; Jones, C. D.; Sedano, F.; Sahajpal, R.; Jin, H.; Skakun, S.; Pnvr, K.; Kommareddy, A.; Reddy, A.; Hurtt, G. C.; Izaurralde, R. C.

2017-12-01

Agricultural croplands act as both sources and sinks of atmospheric carbon dioxide (CO2); absorbing CO2 through photosynthesis, releasing CO2 through autotrophic and heterotrophic respiration, and sequestering CO2 in vegetation and soils. Part of the carbon captured in vegetation can be transported and utilized elsewhere through the activities of food, fiber, and energy production. As well, a portion of carbon in soils can be exported somewhere else by wind, water, and tillage erosion. Thus, it is important to quantify how land use and land management practices affect the net carbon balance of croplands. To monitor the impacts of various agricultural activities on carbon balance and to develop management strategies to make croplands to behave as net carbon sinks, it is of paramount importance to develop consistent and high resolution cropland carbon flux estimates. Croplands are typically characterized by fine scale heterogeneity; therefore, for accurate carbon flux estimates, it is necessary to account for the contribution of each crop type and their spatial distribution. As part of NASA CMS funded project, a satellite based Cropland Carbon Monitoring System (CCMS) was developed to estimate spatially resolved crop specific carbon fluxes over large regions. This modeling framework uses remote sensing version of Environmental Policy Integrated Climate Model and satellite derived crop parameters (e.g. leaf area index (LAI)) to determine vertical and lateral carbon fluxes. The crop type LAI product was developed based on the inversion of PRO-SAIL radiative transfer model and downscaled MODIS reflectance. The crop emergence and harvesting dates were estimated based on MODIS NDVI and crop growing degree days. To evaluate the performance of CCMS framework, it was implemented over croplands of Nebraska, and estimated carbon fluxes for major crops (i.e. corn, soybean, winter wheat, grain sorghum, alfalfa) grown in 2015. Key findings of the CCMS framework will be presented and discussed some of which include 1) comparison of remote sensing based crop type LAI and crop phenology estimates with observed field scale data 2) comparison of carbon flux estimates from CCMS framework with measured fluxes at flux tower sites 3) regional scale differences in carbon fluxes among various crops in Nebraska.

The application of dam break monitoring based on BJ-2 images

NASA Astrophysics Data System (ADS)

Cui, Yan; Li, Suju; Wu, Wei; Liu, Ming

2018-03-01

Flood is one of the major disasters in China. There are heavy intensity and wide range rainstorm during flood season in eastern part of China, and the flood control capacity of rivers is lower somewhere, so the flood disaster is abrupt and caused lots of direct economic losses. In this paper, based on BJ-2 Spatio-temporal resolution remote sensing data, reference image, 30-meter Global Land Cover Dataset(GlobeLand 30) and basic geographic data, forming Dam break monitoring model which including BJ-2 date processing sub-model, flood inundation range monitoring sub-model, dam break change monitoring sub-model and crop inundation monitoring sub-model. Case analysis in Poyang County Jiangxi province in 20th, Jun, 2016 show that the model has a high precision and could monitoring flood inundation range, crops inundation range and breach.

Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production

USGS Publications Warehouse

Leslie, Colin R.; Serbina, Larisa O.; Miller, Holly M.

2017-03-29

Executive SummaryThe use of Landsat satellite imagery for global agricultural monitoring began almost immediately after the launch of Landsat 1 in 1972, making agricultural monitoring one of the longest-standing operational applications for the Landsat program. More recently, Landsat imagery has been used in domestic agricultural applications as an input for field-level production management. The enactment of the U.S. Geological Survey’s free and open data policy in 2008 and the launch of Landsat 8 in 2013 have both influenced agricultural applications. This report presents two primary sets of case studies on the applications and benefits of Landsat imagery use in agriculture. The first set examines several operational applications within the U.S. Department of Agriculture (USDA) and the second focuses on private sector applications for agronomic management.  Information on the USDA applications is provided in the U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring section of the report in the following subsections:Estimating Crop Production.—Provides an overview of how Landsat satellite imagery is used to estimate crop production, including the spectral bands most frequently utilized in this application.Monitoring Consumptive Water Use.—Highlights the role of Landsat imagery in monitoring consumptive water use for agricultural production. Globally, a significant amount of agricultural production relies on irrigation, so monitoring water resources is a critical component of agricultural monitoring. National Agricultural Statistics Service—Cropland Data Layer.—Highlights the use of Landsat imagery in developing the annual Cropland Data Layer, a crop-specific land cover classification product that provides information on more than 100 crop categories grown in the United States. Foreign Agricultural Service—Global Agricultural Monitoring.—Highlights Landsat’s role in monitoring global agricultural production. The USDA has been using Landsat imagery to monitor global agricultural production since the launch of Landsat 1 in 1972. Landsat imagery provides objective, global input for a number of USDA agricultural programs and plays an important role in economic and food security forecasting.U.S. Department of Agriculture—Satellite Imagery Archive.—Highlights a number of the experiences of the USDA in acquiring, sharing, and managing moderate resolution imagery to support the diversity of USDA operational programs. Private sector applications using Landsat imagery for agricultural management are discussed in the Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management section of the report in the following subsections:Field-Level Management.—Provides an introduction to what field-level production management is and how it can be applied to agricultural management. This section explores the concept of zone mapping and how Landsat imagery can be used to identify different conditions within a field. The section also provides a case study of zone-mapping software, developed by GK Technology, Inc., that is used by numerous agricultural consultants.Putting Zone Maps to Work.—Highlights several case studies of private agricultural consultants who have been using Landsat imagery to develop zone maps for farmers. Landsat imagery is helping consultants and farmers optimize agricultural inputs, including fertilizer and seed, which leads to higher yield and economic return for the farmer.Increasing Yield.—Highlights the primary benefit of zone mapping using Landsat imagery. Using 5-year market average prices for a number of commodities, this section provides examples of how yield increases translate into higher returns for farmers.

Assessing the levels of food shortage using the traffic light metaphor by analyzing the gathering and consumption of wild food plants, crop parts and crop residues in Konso, Ethiopia

PubMed Central

2012-01-01

Background Humanitarian relief agencies use scales to assess levels of critical food shortage to efficiently target and allocate food to the neediest. These scales are often labor-intensive. A lesser used approach is assessing gathering and consumption of wild food plants. This gathering per se is not a reliable signal of emerging food stress. However, the gathering and consumption of some specific plant species could be considered markers of food shortage, as it indicates that people are compelled to eat very poor or even health-threatening food. Methods We used the traffic light metaphor to indicate normal (green), alarmingly low (amber) and fully depleted (red) food supplies and identified these conditions for Konso (Ethiopia) on the basis of wild food plants (WFPs), crop parts (crop parts not used for human consumption under normal conditions; CPs) and crop residues (CRs) being gathered and consumed. Plant specimens were collected for expert identification and deposition in the National Herbarium. Two hundred twenty individual households free-listed WFPs, CPs, and CRs gathered and consumed during times of food stress. Through focus group discussions, the species list from the free-listing that was further enriched through key informants interviews and own field observations was categorized into species used for green, amber and red conditions. Results The study identified 113 WFPs (120 products/food items) whose gathering and consumption reflect the three traffic light metaphors: red, amber and green. We identified 25 food items for the red, 30 food items for the amber and 65 food items for the green metaphor. We also obtained reliable information on 21 different products/food items (from 17 crops) normally not consumed as food, reflecting the red or amber metaphor and 10 crop residues (from various crops), plus one recycled stuff which are used as emergency foods in the study area clearly indicating the severity of food stress (red metaphor) households are dealing with. Our traffic light metaphor proved useful to identify and closely monitor the types of WFPs, CPs, and CRs collected and consumed and their time of collection by subsistence households in rural settings. Examples of plant material only consumed under severe food stress included WFPs with health-threatening features like Dobera glabra (Forssk.) Juss. ex Poir. and inkutayata, parts of 17 crops with 21 food items conventionally not used as food (for example, maize tassels, husks, empty pods), ten crop residues (for example bran from various crops) and one recycled food item (tata). Conclusions We have complemented the conventional seasonal food security assessment tool used by humanitarian partners by providing an easy, cheap tool to scale food stress encountered by subsistence farmers. In cognizance of environmental, socio-cultural differences in Ethiopia and other parts of the globe, we recommend analogous studies in other parts of Ethiopia and elsewhere in the world where recurrent food stress also occurs and where communities intensively use WFPs, CPs, and CRs to cope with food stress. PMID:22871123

A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops

PubMed Central

Andújar, Dionisio; Sanchez-Sardana, Francisco L.; Cantuña, Karla

2017-01-01

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, “on ground crop inspection” potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. “On ground monitoring” is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows. PMID:29295536

Remote Sensing Data Fusion to Detect Illicit Crops and Unauthorized Airstrips

NASA Astrophysics Data System (ADS)

Pena, J. A.; Yumin, T.; Liu, H.; Zhao, B.; Garcia, J. A.; Pinto, J.

2018-04-01

Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic information. This methodology emerges as a complementary and effective strategy to control and eradicate illicit crops.

Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

NASA Astrophysics Data System (ADS)

Yeo, I.-Y.; Lee, S.; Sadeghi, A. M.; Beeson, P. C.; Hively, W. D.; McCarty, G. W.; Lang, M. W.

2014-12-01

Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay watershed (CBW), which is located in the mid-Atlantic US, winter cover crop use has been emphasized, and federal and state cost-share programs are available to farmers to subsidize the cost of cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops to improve water quality at the watershed scale (~ 50 km2) and to identify critical source areas of high nitrate export. A physically based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data to simulate hydrological processes and agricultural nutrient cycling over the period of 1990-2000. To accurately simulate winter cover crop biomass in relation to growing conditions, a new approach was developed to further calibrate plant growth parameters that control the leaf area development curve using multitemporal satellite-based measurements of species-specific winter cover crop performance. Multiple SWAT scenarios were developed to obtain baseline information on nitrate loading without winter cover crops and to investigate how nitrate loading could change under different winter cover crop planting scenarios, including different species, planting dates, and implementation areas. The simulation results indicate that winter cover crops have a negligible impact on the water budget but significantly reduce nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading from agricultural lands was approximately 14 kg ha-1, but decreased to 4.6-10.1 kg ha-1 with cover crops resulting in a reduction rate of 27-67% at the watershed scale. Rye was the most effective species, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of cover crops (~ 30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~ 2 kg ha-1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implementation of cover crop programs, in part by helping to target critical pollution source areas for cover crop implementation.

Sustainable biochar effects for low carbon crop production: A 5-crop season field experiment on a low fertility soil from Central China

NASA Astrophysics Data System (ADS)

Liu, X.

2014-12-01

Biochar's effects on improving soil fertility, enhancing crop productivity and reducing greenhouse gases (GHGs) emission from croplands had been well addressed in numerous short-term experiments with biochar soil amendment (BSA) mostly in a single crop season / cropping year. However, the persistence of these effects, after a single biochar application, has not yet been well known due to limited long-term field studies so far. Large scale BSA in agriculture is often commented on the high cost due to large amount of biochar in a single application. Here, we try to show the persistence of biochar effects on soil fertility and crop productivity improvement as well as GHGs emission reduction, using data from a field experiment with BSA for 5 crop seasons in central North China. A single amendment of biochar was performed at rates of 0 (C0), 20 (C20) and 40 t ha-1 (C40) before sowing of the first crop season. Emissions of CO2, CH4 and N2O were monitored with static closed chamber method throughout the crop growing season for the 1st, 2nd and 5th cropping. Crop yield was measured and topsoil samples were collected at harvest of each crop season. BSA altered most of the soil physic-chemical properties with a significant increase over control in soil organic carbon (SOC) and available potassium (K) content. The increase in SOC and available K was consistent over the 5 crop seasons after BSA. Despite a significant yield increase in the first maize season, enhancement of crop yield was not consistent over crop seasons without corresponding to the changes in soil nutrient availability. BSA did not change seasonal total CO2 efflux but greatly reduced N2O emissions throughout the five seasons. This supported a stable nature of biochar carbon in soil, which played a consistent role in reducing N2O emission, which showed inter-annual variation with changes in temperature and soil moisture conditions. The biochar effect was much more consistent under C40 than under C20 and with GHGs emission than with soil property and crop yield. Thus, our study suggested that biochar amended in dry land could sustain a low carbon production both of maize and wheat in terms of its efficient carbon sequestration, lower GHGs emission intensity and soil improvement over 5 crop seasons after a single amendment.

Development of electrical conductivity measurement technology for key plant physiological information using microneedle sensor

NASA Astrophysics Data System (ADS)

Jeon, Eunyong; Choi, Seungyul; Yeo, Kyung-Hwan; Park, Kyoung Sub; Rathod, Mitesh L.; Lee, Junghoon

2017-08-01

Impedance measurement is a widely used technique for monitoring ion species in various applications. In plant cultivation, the impedance system is used to measure the electrical conductivity (EC) of nutrient solutions. Recent research has shown that the quality and quantity of horticultural crops, e.g. tomato, can be optimized by controlling the salinity of nutrient solutions. However, understanding the detailed response of a plant to a nutrient solution is not possible until the fruit is fully grown or by sacrificing the stem. To overcome this issue, horticultural crop cultivation requires real-time monitoring of the EC inside the stem. Using this data, the growth model of a plant could be constructed, and the response of the plant to external environment determined. In this paper, we propose an implantable microneedle device equipped with a micro-patterned impedance measurement system for direct measurement of the EC inside the tomato stem. The fabrication process includes silicon-based steps such as microscale deposition, photolithography, and a deep etching process. Further, microscale fabrication enables all functional elements to fulfill the area budget and be very accurate with minimal plant invasion. A two-electrode geometry is used to match the measurement condition of the tomato stem. Real-time measurement of local sap condition inside the plant in which real-time data for tomato sap EC is obtained after calibration at various concentrations of standard solution demonstrate the efficacy of the proposed device.

Assessing cover crop management under actual and climate change conditions.

PubMed

Alonso-Ayuso, María; Quemada, Miguel; Vanclooster, Marnik; Ruiz-Ramos, Margarita; Rodriguez, Alfredo; Gabriel, José Luis

2018-04-15

The termination date is recognized as a key management factor to enhance cover crops for multiple benefits and to avoid competition with the following cash crop. However, the optimum date depends on annual meteorological conditions, and climate variability induces uncertainty in a decision that needs to be taken every year. One of the most important cover crop benefits is reducing nitrate leaching, a major concern for irrigated agricultural systems and highly affected by the termination date. This study aimed to determine the effects of cover crops and their termination date on the water and N balances of an irrigated Mediterranean agroecosystem under present and future climate conditions. For that purpose, two field experiments were used for inverse calibration and validation of the WAVE model (Water and Agrochemicals in the soil and Vadose Environment), based on continuous soil water content data, soil nitrogen content and crop measurements. The calibrated and validated model was subsequently used in advanced scenario analysis under present and climate change conditions. Under present conditions, a late termination date increased cover crop biomass and subsequently soil water and N depletion. Hence, preemptive competition risk with the main crop was enhanced, but a reduction of nitrate leaching also occurred. The hypothetical planting date of the following cash crop was also an important tool to reduce preemptive competition. Under climate change conditions, the simulations showed that the termination date will be even more important to reduce preemptive competition and nitrate leaching. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Drought Monitoring with VegDRI

USGS Publications Warehouse

Brown, Jesslyn F.

2010-01-01

Drought strikes somewhere in the United States every year, turning green landscapes brown as precipitation falls below normal levels and water supplies dwindle. Drought is typically a temporary climatic aberration, but it is also an insidious natural hazard. It might last for weeks, months, or years and may have many negative effects. Drought can threaten crops, livestock, and livelihoods, stress wildlife and habitats, and increase wildfire risks and threats to human health. Drought conditions can vary tremendously from place to place and week to week. Accurate drought monitoring is essential to understand a drought's progression and potential effects, and to provide information necessary to support drought mitigation decisions. It is also crucial in light of climate change where droughts could become more frequent, severe, and persistent.

Biogenic Volatile Organic Compound (BVOC) emissions from agricultural crop species: is guttation a possible source for methanol emissions following light/dark transition ?

NASA Astrophysics Data System (ADS)

Mozaffar, Ahsan; Amelynck, Crist; Bachy, Aurélie; Digrado, Anthony; Delaplace, Pierre; du Jardin, Patrick; Fauconnier, Marie-Laure; Schoon, Niels; Aubinet, Marc; Heinesch, Bernard

2015-04-01

In the framework of the CROSTVOC (CROp STress VOC) project, the exchange of biogenic volatile organic compounds (BVOCs) between two important agricultural crop species, maize and winter wheat, and the atmosphere has recently been measured during an entire growing season by using the eddy covariance technique. Because of the co-variation of BVOC emission drivers in field conditions, laboratory studies were initiated in an environmental chamber in order to disentangle the responses of the emissions to variations of the individual environmental parameters (such as PPFD and temperature) and to diverse abiotic stress factors. Young plants were enclosed in transparent all-Teflon dynamic enclosures (cuvettes) through which BVOC-free and RH-controlled air was sent. BVOC enriched air was subsequently sampled from the plant cuvettes and an empty cuvette (background) and analyzed for BVOCs in a high sensitivity Proton Transfer Reaction Mass Spectrometer (hs-PTR-MS) and for CO2 in a LI-7000 non-dispersive IR gas analyzer. Emissions were monitored at constant temperature (25 °C) and at a stepwise varying PPFD pattern (0-650 µmol m-2 s-1). For maize plants, sudden light/dark transitions at the end of the photoperiod were accompanied by prompt and considerable increases in methanol (m/z 33) and water vapor (m/z 39) emissions. Moreover, guttation droplets appeared on the sides and the tips of the leaves within a few minutes after light/dark transition. Therefore the assumption has been raised that methanol is also coming out with guttation fluid from the leaves. Consequently, guttation fluid was collected from young maize and wheat plants, injected in an empty enclosure and sampled by PTR-MS. Methanol and a large number of other compounds were observed from guttation fluid. Recent studies have shown that guttation from agricultural crops frequently occurs in field conditions. Further research is required to find out the source strength of methanol emissions by this guttation phenomenon in real environmental conditions.

Analysis of proteins involved in biodegradation of crop biomass

NASA Technical Reports Server (NTRS)

Crawford, Kamau; Trotman, Audrey

1998-01-01

The biodegradation of crop biomass for re-use in crop production is part of the bioregenerative life support concept proposed by the National Aeronautics and Space Administration (NASA) for long duration, manned space exploration. The current research was conducted in the laboratory to evaluate the use of electrophoretic analysis as a means of rapidly assaying for constitutive and induced proteins associated with the bacterial degradation of crop residue. The proteins involved in crop biomass biodegradation are either constitutive or induced. As a result, effluent and cultures were examined to investigate the potential of using electrophoretic techniques as a means of monitoring the biodegradation process. Protein concentration for optimum banding patterns was determined using the Bio-Rad Protein Assay kit. Four bacterial soil isolates were obtained from the G.W. Carver research Farm at Tuskegee University and used in the decomposition of components of plant biomass. The culture, WDSt3A was inoculated into 500 mL of either Tryptic Soy Broth or Nutrient Broth. Incubation, with shaking of each flask was for 96 hours at 30 C. The cultures consistently gave unique banding patterns under denaturing protein electrophoresis conditions, The associated extracellular enzymes also yielded characteristic banding patterns over a 14-day period, when native electrophoresis techniques were used to examine effluent from batch culture bioreactors. The current study evaluated sample preparation and staining protocols to determine the ease of use, reproducibility and reliability, as well as the potential for automation.

Drought Early Warning and Agro-Meteorological Risk Assessment using Earth Observation Rainfall Datasets and Crop Water Budget Modelling

NASA Astrophysics Data System (ADS)

Tarnavsky, E.

2016-12-01

The water resources satisfaction index (WRSI) model is widely used in drought early warning and food security analyses, as well as in agro-meteorological risk management through weather index-based insurance. Key driving data for the model is provided from satellite-based rainfall estimates such as ARC2 and TAMSAT over Africa and CHIRPS globally. We evaluate the performance of these rainfall datasets for detecting onset and cessation of rainfall and estimating crop production conditions for the WRSI model. We also examine the sensitivity of the WRSI model to different satellite-based rainfall products over maize growing regions in Tanzania. Our study considers planting scenarios for short-, medium-, and long-growing cycle maize, and we apply these for 'regular' and drought-resistant maize, as well as with two different methods for defining the start of season (SOS). Simulated maize production estimates are compared against available reported production figures at the national and sub-national (province) levels. Strengths and weaknesses of the driving rainfall data, insights into the role of the SOS definition method, and phenology-based crop yield coefficient and crop yield reduction functions are discussed in the context of space-time drought characteristics. We propose a way forward for selecting skilled rainfall datasets and discuss their implication for crop production monitoring and the design and structure of weather index-based insurance products as risk transfer mechanisms implemented across scales for smallholder farmers to national programmes.

A Phenology-based Approach for Rice Crop Mapping from Multi-temporal Sentinel-1A Data in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. F.; Chen, J. B.; Nguyen, S. T.; Chen, C. R.; Chiang, S. H.

2016-12-01

Rice is the most important food crop in Taiwan, accounting for approximately 5% (166,616 ha) of the total cultivated area. Besides its nutritional value, rice agriculture remains the primary source of livelihood for the majority of rural populations in the country. Rice monitoring is a crucial activity due to official initiatives to ensure the national food security. Because the size of rice fields in Taiwan is relatively small, rice monitoring is traditionally implemented through time-consuming and costly visual interpretation of aerial photos. The Sentinel-1A launched on 3 April 2014 provides the data that have sufficient spatial and temporal resolutions (i.e., 10 m resolution and 12-day revisit cycle) for monitoring small patches of rice fields in the country. This study aimed to develop a phenology-based approach to map rice-growing areas in Taiwan from multi-temporal descending Sentinel-1A VH and VV data. The data were processed for the second rice cropping season (July‒December) in 2015, consisting four main steps: (1) data pre-processing, including radiometric and geometric corrections, and speckle noise filtering of the VH and VV backscattering coefficient data, (2) normalization difference sigma-naught index (NDSI) calculation based on the sowing and heading periods obtained from the analysis of rice crop phenology in the region, (3) threshold-based rice classification using the expectation-maximization method, and (4) accuracy assessment of the mapping results. The mapping results compared with the ground reference data indicated that the overall accuracies and Kappa coefficients achieved for the VH data were 92.0% and 0.84, while the values for the VV data were 81.1% and 0.62, respectively. The mapping results further verified with the government's rice area statistics reaffirmed the consistency between these two datasets with the root mean square error (RMSE) less than 1%, in both cases. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop monitoring in Taiwan using information of rice crop phenology. The methods were thus proposed for rice monitoring in the country and other regions around the world.

A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields

USGS Publications Warehouse

Senay, G.B.; Budde, Michael; Verdin, J.P.; Melesse, Assefa M.

2007-01-01

Accurate crop performance monitoring and production estimation are critical for timely assessment of the food balance of several countries in the world. Since 2001, the Famine Early Warning Systems Network (FEWS NET) has been monitoring crop performance and relative production using satellite-derived data and simulation models in Africa, Central America, and Afghanistan where ground-based monitoring is limited because of a scarcity of weather stations. The commonly used crop monitoring models are based on a crop water-balance algorithm with inputs from satellite-derived rainfall estimates. These models are useful to monitor rainfed agriculture, but they are ineffective for irrigated areas. This study focused on Afghanistan, where over 80 percent of agricultural production comes from irrigated lands. We developed and implemented a Simplified Surface Energy Balance (SSEB) model to monitor and assess the performance of irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water-use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water-balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields. ?? 2007 by MDPI.

A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields

PubMed Central

Senay, Gabriel B.; Budde, Michael; Verdin, James P.; Melesse, Assefa M.

2007-01-01

Accurate crop performance monitoring and production estimation are critical for timely assessment of the food balance of several countries in the world. Since 2001, the Famine Early Warning Systems Network (FEWS NET) has been monitoring crop performance and relative production using satellite-derived data and simulation models in Africa, Central America, and Afghanistan where ground-based monitoring is limited because of a scarcity of weather stations. The commonly used crop monitoring models are based on a crop water-balance algorithm with inputs from satellite-derived rainfall estimates. These models are useful to monitor rainfed agriculture, but they are ineffective for irrigated areas. This study focused on Afghanistan, where over 80 percent of agricultural production comes from irrigated lands. We developed and implemented a Simplified Surface Energy Balance (SSEB) model to monitor and assess the performance of irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water-use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water-balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields.

Crops Models for Varying Environmental Conditions

NASA Technical Reports Server (NTRS)

Jones, Harry; Cavazzoni, James; Keas, Paul

2001-01-01

New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.

Mobile Phone-Based Field Monitoring for Satsuma Mandarin and Its Application to Watering Advice System

NASA Astrophysics Data System (ADS)

Kamiya, Toshiyuki; Numano, Nagisa; Yagyu, Hiroyuki; Shimazu, Hideo

This paper describes a mobile phone-based data logging system for monitoring the growing status of Satsuma mandarin, a type of citrus fruit, in the field. The system can provide various feedback to the farm producers with collected data, such as visualization of related data as a timeline chart or advice on the necessity of watering crops. It is important to collect information on environment conditions, plant status and product quality, to analyze it and to provide it as feedback to the farm producers to aid their operations. This paper proposes a novel framework of field monitoring and feedback for open-field farming. For field monitoring, it combines a low-cost plant status monitoring method using a simple apparatus and a Field Server for environment condition monitoring. Each field worker has a simple apparatus to measure fruit firmness and records data with a mobile phone. The logged data are stored in the database of the system on the server. The system analyzes stored data for each field and is able to show the necessity of watering to the user in five levels. The system is also able to show various stored data in timeline chart form. The user and coach can compare or analyze these data via a web interface. A test site was built at a Satsuma mandarin field at Kumano in Mie Prefecture, Japan using the framework, and farm workers monitor in the area used and evaluated the system.

Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time

PubMed Central

Neilson, E. H.; Edwards, A. M.; Blomstedt, C. K.; Berger, B.; Møller, B. Lindberg; Gleadow, R. M.

2015-01-01

The use of high-throughput phenotyping systems and non-destructive imaging is widely regarded as a key technology allowing scientists and breeders to develop crops with the ability to perform well under diverse environmental conditions. However, many of these phenotyping studies have been optimized using the model plant Arabidopsis thaliana. In this study, The Plant Accelerator® at The University of Adelaide, Australia, was used to investigate the growth and phenotypic response of the important cereal crop, Sorghum bicolor L. Moench and related hybrids to water-limited conditions and different levels of fertilizer. Imaging in different spectral ranges was used to monitor plant composition, chlorophyll, and moisture content. Phenotypic image analysis accurately measured plant biomass. The data set obtained enabled the responses of the different sorghum varieties to the experimental treatments to be differentiated and modelled. Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index. Analysis of colour images revealed that leaf ‘greenness’ correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content. It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits. R scripts for robust, parsimonious models are provided to allow other users of phenomic imaging systems to extract useful data readily, and thus relieve a bottleneck in phenotypic screening of multiple genotypes of key crop plants. PMID:25697789

Frost Damage Detection in Sugarcane Crop Using Modis Images and Srtm Data

NASA Astrophysics Data System (ADS)

Rudorff, B.; Alves de Aguiar, D.; Adami, M.

2011-12-01

Brazil is the largest world producer of sugarcane which is used to produce almost equal proportions of either sugar (food) or ethanol (biofuel). In recent years sugarcane crop production has increased fast to meet the growing market demand for sugar and ethanol. This increase has been mainly due to expansion in crop area, but sugarcane production is also subjected to several factors that influence both the agricultural crop yield (tons of stalks/ha) and the industrial yield (kg of sugar/ton of stalks). Sugarcane is a semi-perennial crop that experiences major growth during spring and summer seasons with large demands for water and high temperatures to produce good stalk formation (crop yield). The harvest is performed mainly during fall and winter seasons when water availability and temperature should be low in order to accumulate sucrose in the stalks (industrial yield). These favorable climatic conditions for sugarcane crop are found in several regions in Brazil, particularly in São Paulo state, which is the major sugarcane producer in Brazil being responsible for almost 60% of its production. Despite the favorable climate in São Paulo state there is a certain probability of frost occurrence from time to time that has a negative impact on sugarcane crop, particularly on industrial yield, reducing the amount of sugar in the stalks; having consequences on price increase and product shortage. To evaluate the impact of frost on sugarcane crop, in the field, on a state level, is not a trivial task; however, this information is relevant due to its direct impact on the consumer market. Remote sensing images allow a synoptic view and present great potential to monitor large sugarcane plantations as has been done since 2003 in São Paulo state by the Canasat Project with Landsat type images (http://www.dsr.inpe.br/laf/canasat/en/). Images acquired from sensors with high temporal resolution such as MODIS (Moderate-Resolution Imaging Spectroradiometer) present the potential to detect the impact of climatic effects, such as frost, on crop growth, which is relevant information to evaluate the negative impact on sugarcane production. Thus, the objective of the present study is to detect the impact of the frost occurred on 28 June 2011 in the sugarcane production region of São Paulo state, using MODIS images acquired on board of Terra and Aqua satellites before and after the frost event. Also, Landsat type images were used to map the harvested sugarcane fields up to the frost event based on a sugarcane crop map for year 2011. The remaining sugarcane fields available for harvest in 2011 were monitored with the MODIS images acquired on 17, 19, 27, 28 June and 8 and 9 July, to detect frost damage. Field work was conducted shortly after frost occurrence to identify sugarcane fields with frost damage for training and validation purposes. MODIS images transformed to vegetation indices and morphometric variables extracted from SRTM (Shuttle Radar Topography Mission) data are being analyzed to detect and quantify the damage of the frost from 28 July 2011 on sugarcane crop.

TEMPO Specific Photochemical Reflectance Index for Monitoring Crop Productivity

NASA Astrophysics Data System (ADS)

Wulamu, A.; Fishman, J.; Maimaitiyiming, M.

2016-12-01

Chlorophyll fluorescence and Photochemical Reflectance Index (PRI) are two key indicators of plant functional status used for early stress detection. With its less than one nanometer hyperspectral resolution and hourly revisit capabilities, NASA's Tropospheric Emissions: Monitoring of Pollution (TEMPO) sensor provides new opportunities for monitoring regional food security. Chlorophyll fluorescence can be retrieved by TEMPO using Oxygen B (O2-B) absorption region at 687 nm. The Photochemical Reflectance Index (PRI) is calculated from spectral reflectance at 531 and 570. However, TEMPO spectral range covers from 290 mm - 490 nm and 540 nm -740 nm, does not provide the 531 nm measurement band for PRI. It is imperative to develop alternate wavelengths within the TEMPO spectral range for these early stress indicators so that regional crop health can be observed by TEMPO with unparalleled spectral and temporal resolutions to address food security. Combining field and airborne remote sensing experiments and radiative transfer simulations, this work proposes a TEMPO specific PRI and demonstrates that TEMPO offers a new set of high-resolution spectral data for crop monitoring.

Crop suitability monitoring for improved yield estimations with 100m PROBA-V data

NASA Astrophysics Data System (ADS)

Özüm Durgun, Yetkin; Gilliams, Sven; Gobin, Anne; Duveiller, Grégory; Djaby, Bakary; Tychon, Bernard

2015-04-01

This study has been realised within the framework of a PhD targeting to advance agricultural monitoring with improved yield estimations using SPOT VEGETATION remotely sensed data. For the first research question, the aim was to improve dry matter productivity (DMP) for C3 and C4 plants by adding a water stress factor. Additionally, the relation between the actual crop yield and DMP was studied. One of the limitations was the lack of crop specific maps which leads to the second research question on 'crop suitability monitoring'. The objective of this work is to create a methodological approach based on the spectral and temporal characteristics of PROBA-V images and ancillary data such as meteorology, soil and topographic data to improve the estimation of annual crop yields. The PROBA-V satellite was launched on 6th May 2013, and was designed to bridge the gap in space-borne vegetation measurements between SPOT-VGT (March 1998 - May 2014) and the upcoming Sentinel-3 satellites scheduled for launch in 2015/2016. PROBA -V has products in four spectral bands: BLUE (centred at 0.463 µm), RED (0.655 µm), NIR (0.845 µm), and SWIR (1.600 µm) with a spatial resolution ranging from 1km to 300m. Due to the construction of the sensor, the central camera can provide a 100m data product with a 5 to 8 days revisiting time. Although the 100m data product is still in test phase a methodology for crop suitability monitoring was developed. The multi-spectral composites, NDVI (Normalised Difference Vegetation Index) (NIR_RED/NIR+RED) and NDII (Normalised Difference Infrared Index) (NIR-SWIR/NIR+SWIR) profiles are used in addition to secondary data such as digital elevation data, precipitation, temperature, soil types and administrative boundaries to improve the accuracy of crop yield estimations. The methodology is evaluated on several FP7 SIGMA test sites for the 2014 - 2015 period. Reference data in the form of vector GIS with boundaries and cover type of agricultural fields are available through the SIGMA site partners. References http://proba-v.vgt.vito.be/ http://www.geoglam-sigma.info/

Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

NASA Astrophysics Data System (ADS)

Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

2017-04-01

Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed to evaluate the influence of each parameter mentioned above on the winter wheat yield formation. Finally, six parameters that sensitivity index more than 0.1 as sensitivity factors were chose, which are TSUM1, SLATB1, SLATB2, SPAN, EFFTB3 and TMPF4. To other parameters, we confirmed them via practical measurement and calculation, available literature or WOFOST default. Eventually, we completed the regulation of WOFOST parameters. (3) Look-up table algorithm was used to realize single-point yield estimation through the assimilation of the WOFOST model and the retrieval LAI. This simulation achieved a high accuracy which perfectly meet the purpose of assimilation (R2=0.941 and RMSE=194.58kg/hm2). In this paper, the optimum value of sensitivity parameters were confirmed and the estimation of single-point yield were finished. Key words: yield estimation of winter wheat, LAI, WOFOST crop growth model, assimilation

Towards a Quantitative Use of Satellite Remote Sensing in Crop Growth Models for Large Scale Agricultural Production Estimate (Invited)

NASA Astrophysics Data System (ADS)

Defourny, P.

2013-12-01

The development of better agricultural monitoring capabilities is clearly considered as a critical step for strengthening food production information and market transparency thanks to timely information about crop status, crop area and yield forecasts. The documentation of global production will contribute to tackle price volatility by allowing local, national and international operators to make decisions and anticipate market trends with reduced uncertainty. Several operational agricultural monitoring systems are currently operating at national and international scales. Most are based on the methods derived from the pioneering experiences completed some decades ago, and use remote sensing to qualitatively compare one year to the others to estimate the risks of deviation from a normal year. The GEO Agricultural Monitoring Community of Practice described the current monitoring capabilities at the national and global levels. An overall diagram summarized the diverse relationships between satellite EO and agriculture information. There is now a large gap between the current operational large scale systems and the scientific state of the art in crop remote sensing, probably because the latter mainly focused on local studies. The poor availability of suitable in-situ and satellite data over extended areas hampers large scale demonstrations preventing the much needed up scaling research effort. For the cropland extent, this paper reports a recent research achievement using the full ENVISAT MERIS 300 m archive in the context of the ESA Climate Change Initiative. A flexible combination of classification methods depending to the region of the world allows mapping the land cover as well as the global croplands at 300 m for the period 2008 2012. This wall to wall product is then compared with regards to the FP 7-Geoland 2 results obtained using as Landsat-based sampling strategy over the IGADD countries. On the other hand, the vegetation indices and the biophysical variables such the Green Area Index (GAI), fAPAR and fcover usually retrieved from MODIS, MERIS, SPOT-Vegetation described the quality of the green vegetation development. The GLOBAM (Belgium) and EU FP-7 MOCCCASIN projects (Russia) improved the standard products and were demonstrated over large scale. The GAI retrieved from MODIS time series using a purity index criterion depicted successfully the inter-annual variability. Furthermore, the quantitative assimilation of these GAI time series into a crop growth model improved the yield estimate over years. These results showed that the GAI assimilation works best at the district or provincial level. In the context of the GEO Ag., the Joint Experiment of Crop Assessment and Monitoring (JECAM) was designed to enable the global agricultural monitoring community to compare such methods and results over a variety of regional cropping systems. For a network of test sites around the world, satellite and field measurements are currently collected and will be made available for collaborative effort. This experiment should facilitate international standards for data products and reporting, eventually supporting the development of a global system of systems for agricultural crop assessment and monitoring.

Large Area Crop Inventory Experiment (LACIE). An overview of the Large Area Crop Inventory Experiment and the outlook for a satellite crop inventory. [Great Plains Corridor (North America), Canada, U.S.S.R., Brazil, China, India, and Australia

NASA Technical Reports Server (NTRS)

Erb, R. B. (Principal Investigator)

1979-01-01

The author has identified the following significant results. The most important LACIE finding was that the technology worked very well in estimating wheat production in important geographic locations. Based on working through the many successes and shortcomings of LACIE, it can be stated with confidence that: (1) the current technology can successfully monitor what production in regions having similar characteristics to those of the U.S.S.R. wheat areas and the U.S. hard red winter wheat areas; (2) with additional applied research, significant improvements in capabilities to monitor wheat in these and other important production regions can be expected in the near future; (3) the remote sensing and weather effects modeling technology approached used by LACIE is generally applicable to other major crops and crop-producing regions of the world; and (4) with suitable effort, this technology can now advance rapidly and could be widespread use in the late 1980's.

Monitoring crop gross primary productivity using Landsat data (Invited)

NASA Astrophysics Data System (ADS)

Gitelson, A. A.; Peng, Y.; Keydan, G. P.; Masek, J.; Rundquist, D. C.; Verma, S. B.; Suyker, A. E.

2009-12-01

There is a growing interest in monitoring the gross primary productivity (GPP) of crops due mostly to their carbon sequestration potential. We presented a new technique for GPP estimation in irrigated and rainfed maize and soybeans based on the close and consistent relationship between GPP and crop chlorophyll content, and entirely on remotely sensed data. A recently proposed Green Chlorophyll Index (Green CI), which employs the green and the NIR spectral bands, was used to retrieve daytime GPP from Landsat ETM+ data. Due to its high spatial resolution (i.e., 30x30m/pixel), this satellite system is particularly appropriate for detecting not only between but also within field GPP variability during the growing season. The Green CI obtained using atmospherically corrected Landsat ETM+ data was found to be linearly related with crop GPP explaining about 90% of GPP variation. Green CI constitutes an accurate surrogate measure for GPP estimation. For comparison purposes, other vegetation indices were also tested. These results open new possibilities for analyzing the spatio-temporal variation of the GPP of crops using the extensive archive of Landsat imagery acquired since the early 1980s.

Extraction and Analysis of Major Autumn Crops in Jingxian County Based on Multi - Temporal gf - 1 Remote Sensing Image and Object-Oriented

NASA Astrophysics Data System (ADS)

Ren, B.; Wen, Q.; Zhou, H.; Guan, F.; Li, L.; Yu, H.; Wang, Z.

2018-04-01

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.

Coupling sensing to crop models for closed-loop plant production in advanced life support systems

NASA Astrophysics Data System (ADS)

Cavazzoni, James; Ling, Peter P.

1999-01-01

We present a conceptual framework for coupling sensing to crop models for closed-loop analysis of plant production for NASA's program in advanced life support. Crop status may be monitored through non-destructive observations, while models may be independently applied to crop production planning and decision support. To achieve coupling, environmental variables and observations are linked to mode inputs and outputs, and monitoring results compared with model predictions of plant growth and development. The information thus provided may be useful in diagnosing problems with the plant growth system, or as a feedback to the model for evaluation of plant scheduling and potential yield. In this paper, we demonstrate this coupling using machine vision sensing of canopy height and top projected canopy area, and the CROPGRO crop growth model. Model simulations and scenarios are used for illustration. We also compare model predictions of the machine vision variables with data from soybean experiments conducted at New Jersey Agriculture Experiment Station Horticulture Greenhouse Facility, Rutgers University. Model simulations produce reasonable agreement with the available data, supporting our illustration.

Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment.

PubMed

Papadopoulos, Antonis; Kalivas, Dionissios; Theocharopoulos, Sid

2017-07-01

Multispectral sensor capability of capturing reflectance data at several spectral channels, together with the inherent reflectance responses of various soils and especially plant surfaces, has gained major interest in crop production. In present study, two multispectral sensing systems, a ground-based and an aerial-based, were applied for the multispatial and temporal monitoring of two cotton fields in central Greece. The ground-based system was Crop Circle ACS-430, while the aerial consisted of a consumer-level quadcopter (Phantom 2) and a modified Hero3+ Black digital camera. The purpose of the research was to monitor crop growth with the two systems and investigate possible interrelations between the derived well-known normalized difference vegetation index (NDVI). Five data collection campaigns were conducted during the cultivation period and concerned scanning soil and plants with the ground-based sensor and taking aerial photographs of the fields with the unmanned aerial system. According to the results, both systems successfully monitored cotton growth stages in terms of space and time. The mean values of NDVI changes through time as retrieved by the ground-based system were satisfactorily modelled by a second-order polynomial equation (R 2 0.96 in Field 1 and 0.99 in Field 2). Further, they were highly correlated (r 0.90 in Field 1 and 0.74 in Field 2) with the according values calculated via the aerial-based system. The unmanned aerial system (UAS) can potentially substitute crop scouting as it concerns a time-effective, non-destructive and reliable way of soil and plant monitoring.

Shortleaf pine seed production in natural stands in the Ouachita and Ozark mountains

Treesearch

Michael G. Shelton; Robert F. Wittwer

1996-01-01

Seed production of shortleaf pine (Pinus echinata Mill.) was monitored from 1965 to 1974 to determine the periodicity qf seed crops in both woods-run stands and seed-production areas. One bumper and two good seed crops occurred during the 9-yr period. The two largest crops occurred in successive years, then seed production was low for 4 yr before...

Uncertainty functions of modelled soil organic carbon changes in response to crop management derived from a French long term experiments dataset

NASA Astrophysics Data System (ADS)

Dimassi, Bassem; Guenet, Bertrand; Mary, Bruno; Trochard, Robert; Bouthier, Alain; Duparque, Annie; Sagot, Stéphanie; Houot, Sabine; Morel, Christian; Martin, Manuel

2016-04-01

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.

Efficiency of chlorophyll in gross primary productivity: A proof of concept and application in crops.

PubMed

Gitelson, Anatoly A; Peng, Yi; Viña, Andrés; Arkebauer, Timothy; Schepers, James S

2016-08-20

One of the main factors affecting vegetation productivity is absorbed light, which is largely governed by chlorophyll. In this paper, we introduce the concept of chlorophyll efficiency, representing the amount of gross primary production per unit of canopy chlorophyll content (Chl) and incident PAR. We analyzed chlorophyll efficiency in two contrasting crops (soybean and maize). Given that they have different photosynthetic pathways (C3 vs. C4), leaf structures (dicot vs. monocot) and canopy architectures (a heliotrophic leaf angle distribution vs. a spherical leaf angle distribution), they cover a large spectrum of biophysical conditions. Our results show that chlorophyll efficiency in primary productivity is highly variable and responds to various physiological and phenological conditions, and water availability. Since Chl is accessible through non-destructive, remotely sensed techniques, the use of chlorophyll efficiency for modeling and monitoring plant optimization patterns is practical at different scales (e.g., leaf, canopy) and under widely-varying environmental conditions. Through this analysis, we directly related a functional characteristic, gross primary production with a structural characteristic, canopy chlorophyll content. Understanding the efficiency of the structural characteristic is of great interest as it allows explaining functional components of the plant system. Copyright © 2016 Elsevier GmbH. All rights reserved.

Three examples of applied remote sensing of vegetation

NASA Technical Reports Server (NTRS)

Rouse, J. W., Jr.; Benton, A. R., Jr.; Toler, R. W.; Haas, R. H.

1975-01-01

Cause studies in which remote sensing techniques were adapted to assist in the solution of particular problem situations in Texas involving vegetation are described. In each case, the final sensing technique developed for operational use by the concerned organizations employed photographic sensors which were optimized through studies of the spectral reflectance characteristics of the vegetation species and background conditions unique to the problem being considered. The three examples described are: (1) Assisting Aquatic Plant Monitoring and Control; (2) Improving Vegetation Utilization in Urban Planning; and (3) Enforcing the Quarantine of Diseased Crops.

Summary of the 2017 South Southeast Research Initiative (SARI) Agricultural Workshop

NASA Technical Reports Server (NTRS)

Vadrevu, Krishna Prasad; Justice, Chris

2017-01-01

South/Southeast Asian countries are growing rapidly in terms of population, industrialization, andurbanization. As a result of this growth, one of the key policy challenges facing the region is foodsecurity—that is, those conditions “…when all people, at all times, have physical and economic access tosufficient, safe and nutritious food that meets their dietary needs and food preferences for an active andhealthy life”.1 Although total food production has increased in the region since 1960 due to land areahaving been converted to agricultural use, more recently it has decreased, mostly due to loss ofproductive agricultural land due to urbanization and industrial development. Furthermore, the region isexperiencing variability in the timing of the monsoon and extreme weather events, resulting in droughtor flooding, which impact agricultural production. Monitoring crop production in a timely manner isessential to predict and prepare for disruptions in the food supply. To achieve such timely monitoringrequires improved and up-to-date information on agricultural land-use practices.Although there has been significant progress in remote sensing and geospatial technologies over thepast few decades, there has been little emphasis placed on developing robust methods for operationalmapping and monitoring of areas devoted to crops. In South/Southeast Asia generally, most mappingefforts to date have focused on the broader classification of land cover types and generalized croplandareas into a single or limited number of thematic classes. Only a few countries have access to up-todatecrop type information. There is an urgent need to make this near-real-time information morereadily available to stakeholders and to enhance national and regional operational systems formonitoring agricultural crops..

Thiamethoxam: Assessing flight activity of honeybees foraging on treated oilseed rape using radio frequency identification technology.

PubMed

Thompson, Helen; Coulson, Mike; Ruddle, Natalie; Wilkins, Selwyn; Harkin, Sarah

2016-02-01

The present study was designed to assess homing behavior of bees foraging on winter oilseed rape grown from seed treated with thiamethoxam (as Cruiser OSR), with 1 field drilled with thiamethoxam-treated seed and 2 control fields drilled with fungicide-only-treated seed. Twelve honeybee colonies were used per treatment group, 4 each located at the field edge (on-field site), at approximately 500 m and 1000 m from the field. A total of nearly 300 newly emerged bees per colony were fitted (tagged) with Mic3 radio frequency identification (RFID) transponders and introduced into each of the 36 study hives. The RFID readers fitted to the entrances of the test colonies were used to monitor the activity of the tagged bees for the duration of the 5-wk flowering period of the crop. These activity data were analyzed to assess any impact on flight activity of bees foraging on the treated compared with untreated crops. Honeybees were seen to be actively foraging within all 3 treatment groups during the exposure period. The data for the more than 3000 RFID-tagged bees and more than 90 000 foraging flights monitored throughout the exposure phase for the study follow the same trends across the treatment and controls and at each of the 3 apiary distances, indicating that there were no effects from foraging on the treated crop. Under the experimental conditions, there was no effect of foraging on thiamethoxam-treated oilseed rape on honeybee flight activity or on their ability to return to the hive. © 2015 SETAC.

Mobile open-source plant-canopy monitoring system

USDA-ARS?s Scientific Manuscript database

Many agricultural applications, including improved crop production, precision agriculture, and phenotyping, rely on detailed field and crop information to detect and react to spatial variabilities. Mobile farm vehicles, such as tractors and sprayers, have the potential to operate as mobile sensing ...

Long-Term Monitoring of Field Trial Sites with Genetically Modified Oilseed Rape (Brassica napus L.) in Saxony-Anhalt, Germany. Fifteen Years Persistence to Date but No Spatial Dispersion

PubMed Central

Belter, Anke

2016-01-01

Oilseed rape is known to persist in arable fields because of its ability to develop secondary seed dormancy in certain agronomic and environmental conditions. If conditions change, rapeseeds are able to germinate up to 10 years later to build volunteers in ensuing crops. Extrapolations of experimental data acted on the assumption of persistence periods for more than 20 years after last harvest of rapeseed. Genetically-modified oilseed rape—cultivated widely in Northern America since 1996—is assumed not to differ from its conventional form in this property. Here, experimental data are reported from official monitoring activities that verify these assumptions. At two former field trial sites in Saxony-Anhalt genetically-modified herbicide-resistant oilseed rape volunteers are found up to fifteen years after harvest. Nevertheless, spatial dispersion or establishment of GM plants outside of the field sites was not observed within this period. PMID:26784233

Exploring Agro-Climatic Trends in Ethiopia Using CHIRPS

NASA Astrophysics Data System (ADS)

Pedreros, D. H.; Funk, C. C.; Brown, M. E.; Korecha, D.; Seid, Y. M.

2015-12-01

The Famine Early Warning Systems Network (FEWS NET) uses the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) to monitor agricultural food production in different regions of the world. CHIRPS is a 1981-present, 5 day, approximately 5km resolution, rainfall product based on a combination of geostationary satellite observations, a high resolution climatology and in situ station observations. Furthermore, FEWS NET has developed a gridded implementation of the Water Requirement Satisfaction Index (WRSI), a water balance measurement indicator of crop performance. This study takes advantage of the CHIRPS' long term period of record and high spatial and temporal resolution to examine agro-climatic trends in Ethiopia. We use the CHIRPS rainfall dataset to calculate the WRSI for the boreal spring and summer crop seasons, as well as for spring-summer rangelands conditions. We find substantial long term rainfall declines in the spring and summer seasons across southeastern and northeastern Ethiopia. Crop Model results indicate that rainfall declines in the cropped regions have been associated with water deficits during the critical grain filling periods in well populated and/or highly vulnerable parts of eastern Ethiopia. WRSI results in the pastoral areas indicate substantial reductions in rangeland health during the later part of the growing seasons. These health declines correspond to the regions of Somaliland and Afar that have experienced chronic severe food insecurity since 2010. Key words: CHIRPS, satellite estimated rainfall, agricultural production

Quasi 3D modelling of water flow in the sandy soil

NASA Astrophysics Data System (ADS)

Rezaei, Meisam; Seuntjens, Piet; Joris, Ingeborg; Boënne, Wesley; De Pue, Jan; Cornelis, Wim

2016-04-01

Monitoring and modeling tools may improve irrigation strategies in precision agriculture. Spatial interpolation is required for analyzing the effects of soil hydraulic parameters, soil layer thickness and groundwater level on irrigation management using hydrological models at field scale. We used non-invasive soil sensor, a crop growth (LINGRA-N) and a soil hydrological model (Hydrus-1D) to predict soil-water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. In the first step, the sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models at one soil column. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. In the second step, to predict Ks over the whole field, the spatial distributions of Ks and its relationship between co-located soil ECa measured by a DUALEM-21S sensor were investigated. Measured groundwater levels and soil layer thickness were interpolated using ordinary point kriging (OK) to a 0.5 by 0.5 m in aim of digital elevation maps. In the third step, a quasi 3D modelling approach was conducted using interpolated data as input hydraulic parameter, geometric information and boundary conditions in the integrated model. In addition, three different irrigation scenarios namely current, no irrigation and optimized irrigations were carried out to find out the most efficient irrigation regime. In this approach, detailed field scale maps of soil water stress, water storage and crop yield were produced at each specific time interval to evaluate the best and most efficient distribution of water using standard gun sprinkler irrigation. The results show that the effect of the position of the groundwater level was dominant in soil-water content prediction and associated water stress. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity Ks in a two-layered soil. Results demonstrated the large spatial variability of Ks (CV = 86.21%). A significant negative correlation was found between ln Ks and ECa (r = 0.83; P≤0.01). This site-specific relation between ln Ks and ECa was used to predict Ks for the whole field after validation using an independent dataset of measured Ks. Result showed that this approach can accurately determine the field scale irrigation requirements, taking into account variations in boundary conditions and spatial variations of model parameters across the field. We found that uniform distribution of water using standard gun sprinkler irrigation is not an efficient approach since at locations with shallow groundwater, the amount of water applied will be excessive as compared to the crop requirements, while in locations with a deeper groundwater table, the crop irrigation requirements will not be met during crop water stress. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to ~25% irrigation water as compared to the current irrigation regime. This resulted in a yield increase of ~7%, simulated by the crop growth model.

Satellite mapping of crop water demand in California

USDA-ARS?s Scientific Manuscript database

Surface delivery of irrigation water in the San Joaquin Valley is becoming increasingly restricted due to urbanization and environmental regulation, and the strain is projected to worsen under most climate change scenarios. Remote sensing technology offers the potential to monitor crop evapotranspi...

75 FR 65995 - Biomass Crop Assistance Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-27

... practices approved through conservation planning would be periodically monitored by USDA to determine the... negative impacts, through reduced purchases of inputs for traditional farming, within a region ranging from... changes in land management associated with the adoption of dedicated biomass energy cropping practices and...

Monitoring Agricultural Cropping Patterns in the Great Lakes Basin Using MODIS-NDVI Time Series Data

EPA Science Inventory

This research examined changes in agricultural cropping patterns across the Great Lakes Basin (GLB) using the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. Specific research objectives were to characterize the distribut...

Diurnal Solar Energy Conversion and Photoprotection in Rice Canopies1[OPEN

PubMed Central

Quick, W. Paul; von Caemmerer, Susanne; Furbank, Robert

2017-01-01

Genetic improvement of photosynthetic performance of cereal crops and increasing the efficiency with which solar radiation is converted into biomass has recently become a major focus for crop physiologists and breeders. The pulse amplitude modulated chlorophyll fluorescence technique (PAM) allows quantitative leaf level monitoring of the utilization of energy for photochemical light conversion and photoprotection in natural environments, potentially over the entire crop lifecycle. Here, the diurnal relationship between electron transport rate (ETR) and irradiance was measured in five cultivars of rice (Oryza sativa) in canopy conditions with PAM fluorescence under natural solar radiation. This relationship differed substantially from that observed for conventional short term light response curves measured under controlled actinic light with the same leaves. This difference was characterized by a reduced curvature factor when curve fitting was used to model this diurnal response. The engagement of photoprotective processes in chloroplast electron transport in leaves under canopy solar radiation was shown to be a major contributor to this difference. Genotypic variation in the irradiance at which energy flux into photoprotective dissipation became greater than ETR was observed. Cultivars capable of higher ETR at midrange light intensities were shown to produce greater leaf area over time, estimated by noninvasive imaging. PMID:27895208

Characterizing Agricultural Impacts of Recent Large-Scale US Droughts and Changing Technology and Management

NASA Technical Reports Server (NTRS)

Elliott, Joshua; Glotter, Michael; Ruane, Alex C.; Boote, Kenneth J.; Hatfield, Jerry L.; Jones, James W.; Rosenzweig, Cynthia; Smith, Leonard A.; Foster, Ian

2017-01-01

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for a total, adjusted for inflation, of $9 billion in 1988 and $21.6 billion in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This work suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.

Non-invasive monitoring of below ground cassava storage root bulking by ground penetrating radar technology

NASA Astrophysics Data System (ADS)

Ruiz Vera, U. M.; Larson, T. H.; Mwakanyamale, K. E.; Grennan, A. K.; Souza, A. P.; Ort, D. R.; Balikian, R. J.

2017-12-01

Agriculture needs a new technological revolution to be able to meet the food demands, to overcome weather and natural hazards events, and to monitor better crop productivity. Advanced technologies used in other fields have recently been applied in agriculture. Thus, imagine instrumentation has been applied to phenotype above-ground biomass and predict yield. However, the capability to monitor belowground biomass is still limited. There are some existing technologies available, for example the ground penetrating radar (GPR) which has been used widely in the area of geology and civil engineering to detect different kind of formations under the ground without the disruption of the soil. GPR technology has been used also to monitor tree roots but as yet not crop roots. Some limitation are that the GPR cannot discern roots smaller than 2 cm in diameter, but it make it feasible for application in tuber crops like Cassava since harvest diameter is greater than 4 cm. The objective of this research is to test the availability to use GPR technology to monitor the growth of cassava roots by testing this technique in the greenhouse and in the field. So far, results from the greenhouse suggest that GPR can detect mature roots of cassava and this data could be used to predict biomass.

Global Agricultural Monitoring (GLAM) using MODAPS and LANCE Data Products

NASA Astrophysics Data System (ADS)

Anyamba, A.; Pak, E. E.; Majedi, A. H.; Small, J. L.; Tucker, C. J.; Reynolds, C. A.; Pinzon, J. E.; Smith, M. M.

2012-12-01

The Global Inventory Modeling and Mapping Studies / Global Agricultural Monitoring (GIMMS GLAM) system is a web-based geographic application that offers Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and user interface tools to data query and plot MODIS NDVI time series. The system processes near real-time and science quality Terra and Aqua MODIS 8-day composited datasets. These datasets are derived from the MOD09 and MYD09 surface reflectance products which are generated and provided by NASA/GSFC Land and Atmosphere Near Real-time Capability for EOS (LANCE) and NASA/GSFC MODIS Adaptive Processing System (MODAPS). The GIMMS GLAM system is developed and provided by the NASA/GSFC GIMMS group for the U.S. Department of Agriculture / Foreign Agricultural Service / International Production Assessment Division (USDA/FAS/IPAD) Global Agricultural Monitoring project (GLAM). The USDA/FAS/IPAD mission is to provide objective, timely, and regular assessment of the global agricultural production outlook and conditions affecting global food security. This system was developed to improve USDA/FAS/IPAD capabilities for making operational quantitative estimates for crop production and yield estimates based on satellite-derived data. The GIMMS GLAM system offers 1) web map imagery including Terra & Aqua MODIS 8-day composited NDVI, NDVI percent anomaly, and SWIR-NIR-Red band combinations, 2) web map overlays including administrative and 0.25 degree Land Information System (LIS) shape boundaries, and crop land cover masks, and 3) user interface tools to select features, data query, plot, and download MODIS NDVI time series.

Monitoring meteorological spatial variability in viticulture using a low-cost Wireless Sensor Network

NASA Astrophysics Data System (ADS)

Matese, Alessandro; Crisci, Alfonso; Di Gennaro, Filippo; Primicerio, Jacopo; Tomasi, Diego; Guidoni, Silvia

2014-05-01

In a long-term perspective, the current global agricultural scenario will be characterize by critical issues in terms of water resource management and environmental protection. The concept of sustainable agriculture would become crucial at reducing waste, optimizing the use of pesticides and fertilizers to crops real needs. This can be achieved through a minimum-scale monitoring of the crop physiologic status and the environmental parameters that characterize the microclimate. Viticulture is often subject to high variability within the same vineyard, thus becomes important to monitor this heterogeneity to allow a site-specific management and maximize the sustainability and quality of production. Meteorological variability expressed both at vineyard scale (mesoclimate) and at single plant level (microclimate) plays an important role during the grape ripening process. The aim of this work was to compare temperature, humidity and solar radiation measurements at different spatial scales. The measurements were assessed for two seasons (2011, 2012) in two vineyards of the Veneto region (North-East Italy), planted with Pinot gris and Cabernet Sauvignon using a specially designed and developed Wireless Sensor Network (WSN). The WSN consists of various levels: the Master/Gateway level coordinates the WSN and performs data aggregation; the Farm/Server level takes care of storing data on a server, data processing and graphic rendering. Nodes level is based on a network of peripheral nodes consisting of a sensor board equipped with sensors and wireless module. The system was able to monitor the agrometeorological parameters in the vineyard: solar radiation, air temperature and air humidity. Different sources of spatial variation were studied, from meso-scale to micro-scale. A widespread investigation was conducted, building a factorial design able to evidence the role played by any factor influencing the physical environment in the vineyard, such as the surrounding climate effect, canopy management and relative position inside the vineyard. The results highlighted that the impact of agrometeorological parameters variability is predominantly determined by differences between within-field and external-field. These results may provide support for the composition of crop production and disease model simulations where data are usually taken from an agrometeorological station not representative of actual field conditions. Finally, the WSN performances, in terms of monitoring and reliability of the system, have been evaluated considering: its handiness, cost-effective, non-invasive dimensions and low power.

Development, implementation and evaluation of satellite-aided agricultural monitoring systems

NASA Technical Reports Server (NTRS)

Cicone, R. C.; Crist, E. P.; Metzler, M.; Nuesch, D.

1982-01-01

Research activities in support of AgRISTARS Inventory Technology Development Project in the use of aerospace remote sensing for agricultural inventory described include: (1) corn and soybean crop spectral temporal signature characterization; (2) efficient area estimation techniques development; and (3) advanced satellite and sensor system definition. Studies include a statistical evaluation of the impact of cultural and environmental factors on crop spectral profiles, the development and evaluation of an automatic crop area estimation procedure, and the joint use of SEASAT-SAR and LANDSAT MSS for crop inventory.

Impact of the reusing of food manufacturing wastewater for irrigation in a closed system on the microbiological quality of the food crops.

PubMed

Beneduce, Luciano; Gatta, Giuseppe; Bevilacqua, Antonio; Libutti, Angela; Tarantino, Emanuele; Bellucci, Micol; Troiano, Eleonora; Spano, Giuseppe

2017-11-02

In order to evaluate if the reuse of food industry treated wastewater is compatible for irrigation of food crops, without increased health risk, in the present study a cropping system, in which ground water and treated wastewater were used for irrigation of tomato and broccoli, during consecutive crop seasons was monitored. Water, crop environment and final products were monitored for microbial indicators and pathogenic bacteria, by conventional and molecular methods. The microbial quality of the irrigation waters influenced sporadically the presence of microbial indicators in soil. No water sample was found positive for pathogenic bacteria, independently from the source. Salmonella spp. and Listeria monocytogenes were detected in soil samples, independently from the irrigation water source. No pathogen was found to contaminate tomato plants, while Listeria monocytogenes and E. coli O157:H7 were detected on broccoli plant, but when final produce were harvested, no pathogen was detected on edible part. The level of microbial indicators and detection of pathogenic bacteria in field and plant was not dependent upon wastewater used. Our results, suggest that reuse of food industry wastewater for irrigation of agricultural crop can be applied without significant increase of potential health risk related to microbial quality. Copyright © 2017 Elsevier B.V. All rights reserved.

Overview and highlights of Early Warning and Crop Condition Assessment project

NASA Technical Reports Server (NTRS)

Boatwright, G. O.; Whitehead, V. S.

1985-01-01

Work of the Early Warning and Crop Condition Assessment (EW/CCA) project, one of eight projects in the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS), is reviewed. Its mission, to develop and test remote sensing techniques that enhance operational methodologies for crop condition assessment, was in response to initiatives issued by the Secretary of Agriculture. Meteorologically driven crop stress indicator models have been developed or modified for wheat, maize, grain sorghum, and soybeans. These models provide early warning alerts of potential or actual crop stresses due to water deficits, adverse temperatures, and water excess that could delay planting or harvesting operations. Recommendations are given for future research involving vegetative index numbers and the NOAA and Landsat satellites.

Finding the Balance: Fertility Control for the Management of Fragmented Populations of a Threatened Rock-Wallaby Species

PubMed Central

Willers, Nicole; Martin, Graeme B.; Matson, Phill; Mawson, Peter R.; Morris, Keith; Bencini, Roberta

2015-01-01

Simple Summary Black-flanked rock-wallabies (Petrogale lateralis lateralis) can reach high numbers in fragmented populations in the West Australian wheat-belt, where they can damage crops and cause habitat degradation. As they are threatened, we wanted a non-permanent control method that did not adversely affect the body condition of treated females compared to untreated females, using body condition as an indicator of general health and fitness. We gave adult female rock-wallabies deslorelin contraceptive implants to suppress their fertility and monitored the impact for three years. Treated females did not conceive new young for over two years. We did not detect any negative effects on body condition, suggesting that deslorelin may be an effective tool for managing overabundant populations of marsupials. Abstract Populations of Australian marsupials can become overabundant, resulting in detrimental impacts on the environment. For example, the threatened black-flanked rock-wallaby (Petrogale lateralis lateralis) has previously been perceived as overabundant and thus ‘unwanted’ when they graze crops and cause habitat degradation. Hormonally-induced fertility control has been increasingly used to manage population size in other marsupials where alternative management options are not viable. We tested whether deslorelin, a superagonist of gonadotropin-releasing hormone (GnRH), would suppress reproduction in free-living adult female rock-wallabies without adversely impacting body condition. We trapped, synchronised reproduction and allocated female rock-wallabies to a placebo implant (control, n = 22), one (n = 22) or two (n = 20) subcutaneous implants of deslorelin. Females were then recaptured over the following 36 months to monitor reproduction, including Luteinising Hormone levels, and body condition. Following treatment, diapaused blastocysts reactivated in five females and the resulting young were carried through to weaning. No wallabies treated with deslorelin, conceivede a new young for at least 27 months. We did not observe adverse effects on body condition on treated females. We conclude that deslorelin implants are effective for the medium-term suppression of reproduction in female black-flanked rock-wallabies and for managing overabundant populations of some marsupials. PMID:26694471

Locus-dependent selection in crop-wild hybrids of lettuce under field conditions and its implication for GM crop development

PubMed Central

Hooftman, Danny A P; Flavell, Andrew J; Jansen, Hans; den Nijs, Hans C M; Syed, Naeem H; Sørensen, Anker P; Orozco-ter Wengel, Pablo; van de Wiel, Clemens C M

2011-01-01

Gene escape from crops has gained much attention in the last two decades, as transgenes introgressing into wild populations could affect the latter's ecological characteristics. However, different genes have different likelihoods of introgression. The mixture of selective forces provided by natural conditions creates an adaptive mosaic of alleles from both parental species. We investigated segregation patterns after hybridization between lettuce (Lactuca sativa) and its wild relative, L. serriola. Three generations of hybrids (S1, BC1, and BC1S1) were grown in habitats mimicking the wild parent's habitat. As control, we harvested S1 seedlings grown under controlled conditions, providing very limited possibility for selection. We used 89 AFLP loci, as well as more recently developed dominant markers, 115 retrotransposon markers (SSAP), and 28 NBS loci linked to resistance genes. For many loci, allele frequencies were biased in plants exposed to natural field conditions, including over-representation of crop alleles for various loci. Furthermore, Linkage disequilibrium was locally changed, allegedly by selection caused by the natural field conditions, providing ample opportunity for genetic hitchhiking. Our study indicates that when developing genetically modified crops, a judicious selection of insertion sites, based on knowledge of selective (dis)advantages of the surrounding crop genome under field conditions, could diminish transgene persistence. PMID:25568012

Locus-dependent selection in crop-wild hybrids of lettuce under field conditions and its implication for GM crop development.

PubMed

Hooftman, Danny A P; Flavell, Andrew J; Jansen, Hans; den Nijs, Hans C M; Syed, Naeem H; Sørensen, Anker P; Orozco-Ter Wengel, Pablo; van de Wiel, Clemens C M

2011-09-01

Gene escape from crops has gained much attention in the last two decades, as transgenes introgressing into wild populations could affect the latter's ecological characteristics. However, different genes have different likelihoods of introgression. The mixture of selective forces provided by natural conditions creates an adaptive mosaic of alleles from both parental species. We investigated segregation patterns after hybridization between lettuce (Lactuca sativa) and its wild relative, L. serriola. Three generations of hybrids (S1, BC1, and BC1S1) were grown in habitats mimicking the wild parent's habitat. As control, we harvested S1 seedlings grown under controlled conditions, providing very limited possibility for selection. We used 89 AFLP loci, as well as more recently developed dominant markers, 115 retrotransposon markers (SSAP), and 28 NBS loci linked to resistance genes. For many loci, allele frequencies were biased in plants exposed to natural field conditions, including over-representation of crop alleles for various loci. Furthermore, Linkage disequilibrium was locally changed, allegedly by selection caused by the natural field conditions, providing ample opportunity for genetic hitchhiking. Our study indicates that when developing genetically modified crops, a judicious selection of insertion sites, based on knowledge of selective (dis)advantages of the surrounding crop genome under field conditions, could diminish transgene persistence.

Hyperspectral remote sensing of paddy crop using insitu measurement and clustering technique

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2014-11-01

Rice Agriculture, mainly cultivated in South Asia regions, is being monitored for extracting crop parameter, crop area, crop growth profile, crop yield using both optical and microwave remote sensing. Hyperspectral data provide more detailed information of rice agriculture. The present study was carried out at the experimental station of the Regional Rainfed Low land Rice Research Station, Assam, India (26.1400° N, 91.7700° E) and the overall climate of the study area comes under Lower Brahmaputra Valley (LBV) Agro Climatic Zones. The hyperspectral measurements were made in the year 2009 from 72 plots that include eight rice varieties along with three different level of nitrogen treatments (50, 100, 150 kg/ha) covering rice transplanting to the crop harvesting period. With an emphasis to varieties, hyperspectral measurements were taken in the year 2014 from 24 plots having 24 rice genotypes with different crop developmental ages. All the measurements were performed using a spectroradiometer with a spectral range of 350-1050 nm under direct sunlight of a cloud free sky and stable condition of the atmosphere covering more than 95 % canopy. In this study, reflectance collected from canopy of rice were expressed in terms of waveforms. Furthermore, generated waveforms were analysed for all combinations of nitrogen applications and varieties. A hierarchical clustering technique was employed to classify these waveforms into different groups. By help of agglomerative clustering algorithm a few number of clusters were finalized for different rice varieties along with nitrogen treatments. By this clustering approach, observational error in spectroradiometer reflectance was also nullified. From this hierarchical clustering, appropriate spectral signature for rice canopy were identified and will help to create rice crop classification accurately and therefore have a prospect to make improved information on rice agriculture at both local and regional scales. From this hierarchical clustering, spectral signature library for rice canopy were identified which will help to create rice crop classification maps and critical wave bands like green (519,559 nm), red (649 nm), red edge (729 nm) and NIR region (779,819 nm) were marked sensitive to nitrogen which will further help in nitrogen mapping of paddy agriculture over therefore have the prospect to make improved informed decisions.

Mixed cropping regimes promote the soil fungal community under zero tillage.

PubMed

Silvestro, L B; Biganzoli, F; Stenglein, S A; Forjan, H; Manso, L; Moreno, M V

2018-07-01

Fungi of yield soils represent a significant portion of the microbial biomass and reflect sensitivity to changes in the ecosystem. Our hypothesis was that crops included in cropping regimes under the zero tillage system modify the structure of the soil fungi community. Conventional and molecular techniques provide complementary information for the analysis of diversity of fungal species and successful information to accept our hypothesis. The composition of the fungal community varied according to different crops included in the cropping regimes. However, we detected other factors as sources of variation among them, season and sampling depth. The mixed cropping regimes including perennial pastures and one crop per year promote fungal diversity and species with potential benefit to soil and crop. The winter season and 0-5 cm depth gave the largest evenness and fungal diversity. Trichoderma aureoviride and Rhizopus stolonifer could be used for monitoring changes in soil under zero tillage.

Sampling Simulations for Assessing the Accuracy of U.S. Agricultural Crop Mapping from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Dwyer, Linnea; Yadav, Kamini; Congalton, Russell G.

2017-04-01

Providing adequate food and water for a growing, global population continues to be a major challenge. Mapping and monitoring crops are useful tools for estimating the extent of crop productivity. GFSAD30 (Global Food Security Analysis Data at 30m) is a program, funded by NASA, that is producing global cropland maps by using field measurements and remote sensing images. This program studies 8 major crop types, and includes information on cropland area/extent, if crops are irrigated or rainfed, and the cropping intensities. Using results from the US and the extensive reference data available, CDL (USDA Crop Data Layer), we will experiment with various sampling simulations to determine optimal sampling for thematic map accuracy assessment. These simulations will include varying the sampling unit, the sampling strategy, and the sample number. Results of these simulations will allow us to recommend assessment approaches to handle different cropping scenarios.

Agronomic responses to late-seeded cover crops in a semiarid region

USDA-ARS?s Scientific Manuscript database

Intensification of cropping systems in the Great Plains beyond annual cropping practices may be limited by inadequate precipitation, short growing seasons, and highly variable climatic conditions. Inclusion of cover crops in dryland cropping systems may serve as an effective intensification strateg...

Land Cover Monitoring for Water Resources Management in Angola

NASA Astrophysics Data System (ADS)

Miguel, Irina; Navarro, Ana; Rolim, Joao; Catalao, Joao; Silva, Joel; Painho, Marco; Vekerdy, Zoltan

2016-08-01

The aim of this paper is to assess the impact of improved temporal resolution and multi-source satellite data (SAR and optical) on land cover mapping and monitoring for efficient water resources management. For that purpose, we developed an integrated approach based on image classification and on NDVI and SAR backscattering (VV and VH) time series for land cover mapping and crop's irrigation requirements computation. We analysed 28 SPOT-5 Take-5 images with high temporal revisiting time (5 days), 9 Sentinel-1 dual polarization GRD images and in-situ data acquired during the crop growing season. Results show that the combination of images from different sources provides the best information to map agricultural areas. The increase of the images temporal resolution allows the improvement of the estimation of the crop parameters, and then, to calculate of the crop's irrigation requirements. However, this aspect was not fully exploited due to the lack of EO data for the complete growing season.

Fluorescence of crop residue: postmortem analysis of crop conditions

NASA Astrophysics Data System (ADS)

McMurtrey, James E., III; Kim, Moon S.; Daughtry, Craig S. T.; Corp, Lawrence A.; Chappelle, Emmett W.

1997-07-01

Fluorescence of crop residues at the end of the growing season may provide an indicator of the past crop's vegetative condition. Different levels of nitrogen (N) fertilization were applied to field grown corn and wheat at Beltsville, Maryland. The N fertilizer treatments produce a range of physiological conditions, pigment concentrations, biomass levels, and grain yields that resulted in varying growth and stress conditions in the living crops. After normal harvesting procedures the crop residues remained. The fluorescence spectral characteristics of the plant residues from crops grown under different levels of N nutrition were analyzed. The blue-green fluorescence response of in-vitro residue biomass of the N treated field corn had different magnitudes. A blue-green- yellow algorithm, (460/525)*600 nm, gave the best separations between prior corn growth conditions at different N fertilization levels. Relationships between total dry biomass, the grain yield, and fluorescence properties in the 400 - 670 nm region of the spectrum were found in both corn and wheat residues. The wheat residue was analyzed to evaluate the constituents responsible for fluorescence. A ratio of the blue-green, 450/550 nm, images gave the best separation among wheat residues at different N fertilization levels. Fluorescence of extracts from wheat residues showed inverse fluorescence intensities as a function of N treatments compared to that of the intact wheat residue or ground residue samples. The extracts also had an additional fluorescence emission peak in the red, 670 nm. Single band fluorescence intensity in corn and wheat residues is due mostly to the quantity of the material on the soil surface. Ratios of fluorescence bands varied as a result of the growth conditions created by the N treatments and are thought to be indicative of the varying concentrations of the plant residues fluorescing constituents. Estimates of the amount and cost effectiveness of N fertilizers to satisfy optimal plant growth condition for specific areas of the field for the next growing season may be useful indicators for crop management. Analysis of plant constituent qualities and quantities of dead crop materials during the harvesting practice or after harvest could be useful indicators of the previous crop's conditions. These measures could be used as a tool in determining precision farming management practices for site specific areas in a field.

Agricultural crop harvest progress monitoring by fully polarimetric synthetic aperture radar imagery

NASA Astrophysics Data System (ADS)

Yang, Hao; Zhao, Chunjiang; Yang, Guijun; Li, Zengyuan; Chen, Erxue; Yuan, Lin; Yang, Xiaodong; Xu, Xingang

2015-01-01

Dynamic mapping and monitoring of crop harvest on a large spatial scale will provide critical information for the formulation of optimal harvesting strategies. This study evaluates the feasibility of C-band polarimetric synthetic aperture radar (PolSAR) for monitoring the harvesting progress of oilseed rape (Brassica napus L.) fields. Five multitemporal, quad-pol Radarsat-2 images and one optical ZY-1 02C image were acquired over a farmland area in China during the 2013 growing season. Typical polarimetric signatures were obtained relying on polarimetric decomposition methods. Temporal evolutions of these signatures of harvested fields were compared with the ones of unharvested fields in the context of the entire growing cycle. Significant sensitivity was observed between the specific polarimetric parameters and the harvest status of oilseed rape fields. Based on this sensitivity, a new method that integrates two polarimetric features was devised to detect the harvest status of oilseed rape fields using a single image. The validation results are encouraging even for the harvested fields covered with high residues. This research demonstrates the capability of PolSAR remote sensing in crop harvest monitoring, which is a step toward more complex applications of PolSAR data in precision agriculture.

Monitoring Agricultural Production in Primary Export Countries within the framework of the GEOGLAM Initiative

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.; Vermote, E.

2012-12-01

Up to date, reliable, global, information on crop production prospects is indispensible for informing and regulating grain markets and for instituting effective agricultural policies. The recent price surges in the global grain markets were in large part triggered by extreme weather events in primary grain export countries. These events raise important questions about the accuracy of current production forecasts and their role in market fluctuations, and highlight the deficiencies in the state of global agricultural monitoring. Satellite-based earth observations are increasingly utilized as a tool for monitoring agricultural production as they offer cost-effective, daily, global information on crop growth and extent and their utility for crop production forecasting has long been demonstrated. Within this context, the Group on Earth Observations developed the Global Agricultural Monitoring (GEOGLAM) initiative which was adopted by the G20 as part of the action plan on food price volatility and agriculture. The goal of GEOGLAM is to enhance agricultural production estimates through the use of Earth observations. This talk will explore the potential contribution of EO-based methods for improving the accuracy of early production estimates of main export countries within the framework of GEOGLAM.

Closed Ecological Life Support Systems (CELSS) Test Facility

NASA Technical Reports Server (NTRS)

Macelroy, Robert D.

1992-01-01

The CELSS Test Facility (CTF) is being developed for installation on Space Station Freedom (SSF) in August 1999. It is designed to conduct experiments that will determine the effects of microgravity on the productivity of higher (crop) plants. The CTF will occupy two standard SSF racks and will accommodate approximately one square meter of growing area and a canopy height of 80 cm. The growth volume will be isolated from the external environment, allowing stringent control of environmental conditions. Temperature, humidity, oxygen, carbon dioxide, and light levels will all be closely controlled to prescribed set points and monitored. This level of environmental control is needed to prevent stress and allow accurate assessment of microgravity effect (10-3 to 10-6 x g). Photosynthetic rates and respiration rates, calculated through continuous recording of gas concentrations, transpiration, and total and edible biomass produced will be measured. Toxic byproducts will be monitored and scrubbed. Transpiration water will be collected within the chamber and recycled into the nutrient solution. A wide variety of crop plants, e.g., wheat, soy beans, lettuce, potatoes, can be accommodated and various nutrient delivery systems and light delivery systems will be available. In the course of its development, the CTF will exploit fully, and contribute importantly, to the state-of-art in closed system technology and plant physiology.

Model-based coefficient method for calculation of N leaching from agricultural fields applied to small catchments and the effects of leaching reducing measures

NASA Astrophysics Data System (ADS)

Kyllmar, K.; Mårtensson, K.; Johnsson, H.

2005-03-01

A method to calculate N leaching from arable fields using model-calculated N leaching coefficients (NLCs) was developed. Using the process-based modelling system SOILNDB, leaching of N was simulated for four leaching regions in southern Sweden with 20-year climate series and a large number of randomised crop sequences based on regional agricultural statistics. To obtain N leaching coefficients, mean values of annual N leaching were calculated for each combination of main crop, following crop and fertilisation regime for each leaching region and soil type. The field-NLC method developed could be useful for following up water quality goals in e.g. small monitoring catchments, since it allows normal leaching from actual crop rotations and fertilisation to be determined regardless of the weather. The method was tested using field data from nine small intensively monitored agricultural catchments. The agreement between calculated field N leaching and measured N transport in catchment stream outlets, 19-47 and 8-38 kg ha -1 yr -1, respectively, was satisfactory in most catchments when contributions from land uses other than arable land and uncertainties in groundwater flows were considered. The possibility of calculating effects of crop combinations (crop and following crop) is of considerable value since changes in crop rotation constitute a large potential for reducing N leaching. When the effect of a number of potential measures to reduce N leaching (i.e. applying manure in spring instead of autumn; postponing ploughing-in of ley and green fallow in autumn; undersowing a catch crop in cereals and oilseeds; and increasing the area of catch crops by substituting winter cereals and winter oilseeds with corresponding spring crops) was calculated for the arable fields in the catchments using field-NLCs, N leaching was reduced by between 34 and 54% for the separate catchments when the best possible effect on the entire potential area was assumed.

[Continuous remediation of heavy metal contaminated soil by co-cropping system enhanced with chelator].

PubMed

Wei, Ze-Bin; Guo, Xiao-Fang; Wu, Qi-Tang; Long, Xin-Xian

2014-11-01

In order to elucidate the continuous effectiveness of co-cropping system coupling with chelator enhancement in remediating heavy metal contaminated soils and its environmental risk towards underground water, soil lysimeter (0.9 m x 0.9 m x 0.9 m) experiments were conducted using a paddy soil affected by Pb and Zn mining in Lechang district of Guangdong Province, 7 successive crops were conducted for about 2.5 years. The treatments included mono-crop of Sedum alfredii Hance (Zn and Cd hyperaccumulator), mono-crop of corn (Zea mays, cv. Yunshi-5, a low-accumulating cultivar), co-crop of S. alfredii and corn, and co-crop + MC (Mixture of Chelators, comprised of citric acid, monosodium glutamate waste liquid, EDTA and KCI with molar ratio of 10: 1:2:3 at the concentration of 5 mmol x kg(-1) soil). The changes of heavy metal concentrations in plants, soil and underground water were monitored. Results showed that the co-cropping system was suitable only in spring-summer seasons and significantly increased Zn and Cd phytoextraction. In autumn-winter seasons, the growth of S. alfredii and its phytoextraction of Zn and Cd were reduced by co-cropping and MC application. In total, the mono-crops of S. alfredii recorded a highest phytoextraction of Zn and Cd. However, the greatest reduction of soil Zn, Cd and Pb was observed with the co-crop + MC treatment, the reduction rates were 28%, 50%, and 22%, respectively, relative to the initial soil metal content. The reduction of this treatment was mainly attributed to the downwards leaching of metals to the subsoil caused by MC application. The continuous monitoring of leachates during 2. 5 year's experiment also revealed that the addition of MC increased heavy metal concentrations in the leaching water, but they did not significantly exceed the III grade limits of the underground water standard of China.

Simulation of crop yield variability by improved root-soil-interaction modelling

NASA Astrophysics Data System (ADS)

Duan, X.; Gayler, S.; Priesack, E.

2009-04-01

Understanding the processes and factors that govern the within-field variability in crop yield has attached great importance due to applications in precision agriculture. Crop response to environment at field scale is a complex dynamic process involving the interactions of soil characteristics, weather conditions and crop management. The numerous static factors combined with temporal variations make it very difficult to identify and manage the variability pattern. Therefore, crop simulation models are considered to be useful tools in analyzing separately the effects of change in soil or weather conditions on the spatial variability, in order to identify the cause of yield variability and to quantify the spatial and temporal variation. However, tests showed that usual crop models such as CERES-Wheat and CERES-Maize were not able to quantify the observed within-field yield variability, while their performance on crop growth simulation under more homogeneous and mainly non-limiting conditions was sufficent to simulate average yields at the field-scale. On a study site in South Germany, within-field variability in crop growth has been documented since years. After detailed analysis and classification of the soil patterns, two site specific factors, the plant-available-water and the O2 deficiency, were considered as the main causes of the crop growth variability in this field. Based on our measurement of root distribution in the soil profile, we hypothesize that in our case the insufficiency of the applied crop models to simulate the yield variability can be due to the oversimplification of the involved root models which fail to be sensitive to different soil conditions. In this study, the root growth model described by Jones et al. (1991) was adapted by using data of root distributions in the field and linking the adapted root model to the CERES crop model. The ability of the new root model to increase the sensitivity of the CERES crop models to different enviromental conditions was then evaluated by means of comparison of the simualtion results with measured data and by scenario calculations.

Root zone soil water dynamics and its effects on above ground biomass in cellulosic and grain based bioenergy crops of Midwest USA

NASA Astrophysics Data System (ADS)

Bhardwaj, A. K.; Hamilton, S. K.; van Dam, R. L.; Diker, K.; Basso, B.; Glbrc-Sustainability Thrust-4. 3 Biogeochemistry

2010-12-01

Root-zone soil moisture constitutes an important variable for hydrological and agronomic models. In agriculture, crop yields are directly related to soil moisture, levels that are most important in the root zone area of the soil. One of the most accurate in-situ methods that has established itself as a recognized standard around the world uses Time Domain Reflectometry (TDR) to determine volumetric water content of the soil. We used automated field-to-desk TDR based systems to monitor temporal (1-hr interval) soil moisture variability in 10 different bioenergy cropping systems at the Great Lakes Bioenergy Research Center’s (GLBRC) sustainability research site in south western Michigan, U.S.A. These crops range from high-diversity, low-input grass mixes to low-diversity, high-input crop monocultures. We equipped the 28 x 40 m vegetation plots with 30 cm long TDR probes at seven depths from 10 cm to 1.25 m below surface. The parent material at the site consists of coarse sandy glacial tills in which a soil with an approximately 50cm thick A-Bt horizon has developed. Additional equipment permanently installed for each system includes soil moisture access tubes, multi-depth temperature sensors, and multi-electrode resistivity arrays. The access tubes were monitored using a portable TDR system at bi-weekly intervals. 2D dipole-dipole electrical resistivity tomography (ERT) data are collected in 4-week intervals, while a subset of the electrodes is used for bi-hourly monitoring. The continuous scans (1 hr) provided us the real time changes in water content, replenishment and depletion, providing indications of water uptake by plant roots and potential seasonal water limitation of biomass accumulation. The results show significant seasonal variations between the crops and cropping systems. Significant relationships were observed between soil moisture stress, above-ground biomass and rooting characteristics. The overall goal of the study is to quantify the components of water balance, and identify water quality and water use implications of these cropping systems.Key Words

Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops

NASA Astrophysics Data System (ADS)

Betbeder, Julie; Fieuzal, Remy; Philippets, Yannick; Ferro-Famil, Laurent; Baup, Frederic

2016-04-01

This paper aims to evaluate the contribution of multitemporal polarimetric synthetic aperture radar (SAR) data for winter wheat and rapeseed crops parameters [height, leaf area index, and dry biomass (DB)] estimation, during their whole vegetation cycles in comparison to backscattering coefficients and optical data. Angular sensitivities and dynamics of polarimetric indicators were also analyzed following the growth stages of these two common crop types using, in total, 14 radar images (Radarsat-2), 16 optical images (Formosat-2, Spot-4/5), and numerous ground data. The results of this study show the importance of correcting the angular effect on SAR signals especially for copolarized signals and polarimetric indicators associated to single-bounce scattering mechanisms. The analysis of the temporal dynamic of polarimetric indicators has shown their high potential to detect crop growth changes. Moreover, this study shows the high interest of using SAR parameters (backscattering coefficients and polarimetric indicators) for crop parameters estimation during the whole vegetation cycle instead of optical vegetation index. They particularly revealed their high potential for rapeseed height and DB monitoring [i.e., Shannon entropy polarimetry (r2=0.70) and radar vegetation index (r2=0.80), respectively].

Non-Crop Host Sampling Yields Insights into Small-Scale Population Dynamics of Drosophila suzukii (Matsumura)

PubMed Central

Loeb, Gregory M.

2018-01-01

Invasive, polyphagous crop pests subsist on a number of crop and non-crop resources. While knowing the full range of host species is important, a seasonal investigation into the use of non-crop plants adjacent to cropping systems provide key insights into some of the factors determining local population dynamics. This study investigated the infestation of non-crop plants by the invasive Drosophila suzukii (Matsumura), a pest of numerous economically important stone and small fruit crops, by sampling fruit-producing non-crop hosts adjacent to commercial plantings weekly from June through November in central New York over a two-year period. We found D. suzukii infestation rates (number of flies emerged/kg fruit) peaked mid-August through early September, with Rubus allegheniensis Porter and Lonicera morrowii Asa Gray showing the highest average infestation in both years. Interannual infestation patterns were similar despite a lower number of adults caught in monitoring traps the second year, suggesting D. suzukii host use may be density independent. PMID:29301358

Are Adult Crambid Snout Moths (Crambinae) and Larval Stages of Lepidoptera Suitable Tools for an Environmental Monitoring of Transgenic Crops? — Implications of a Field Test

PubMed Central

Lang, Andreas; Dolek, Matthias; Theißen, Bernhard; Zapp, Andreas

2011-01-01

Butterflies and moths (Lepidoptera) have been suggested for the environmental monitoring of genetically modified (GM) crops due to their suitability as ecological indicators, and because of the possible adverse impact of the cultivation of current transgenic crops. The German Association of Engineers (VDI) has developed guidelines for the standardized monitoring of Lepidoptera describing the use of light traps for adult moths, transect counts for adult butterflies, and visual search for larvae. The guidelines suggest recording adults of Crambid Snout Moths during transect counts in addition to butterflies, and present detailed protocols for the visual search of larvae. In a field survey in three regions of Germany, we tested the practicability and effort-benefit ratio of the latter two VDI approaches. Crambid Snout Moths turned out to be suitable and practical indicators, which can easily be recorded during transect counts. They were present in 57% of the studied field margins, contributing a substantial part to the overall Lepidoptera count, thus providing valuable additional information to the monitoring results. Visual search of larvae generated results in an adequate effort-benefit ratio when searching for lepidopteran larvae of common species feeding on nettles. Visual search for larvae living on host plants other than nettles was time-consuming and yielded much lower numbers of recorded larvae. Beating samples of bushes and trees yielded a higher number of species and individuals. This method is especially appropriate when hedgerows are sampled, and was judged to perform intermediate concerning the relationship between invested sampling effort and obtained results for lepidopteran larvae. In conclusion, transect counts of adult Crambid Moths and recording of lepidopteran larvae feeding on nettles are feasible additional modules for an environmental monitoring of GM crops. Monitoring larvae living on host plants other than nettles and beating samples of bushes and trees can be used as a supplementary tool if necessary or desired. PMID:26467735

Monitoring Agricultural Cropping Patterns across the Laurentian Great Lakes Basin Using MODIS-NDVI Data

EPA Science Inventory

The Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composite data product (MOD12Q) was used to develop annual cropland and crop-specific map products (corn, soybeans, and wheat) for the Laurentian Great Lakes Basin (GLB). Th...

Mapping and monitoring potato cropping systems in Maine: geospatial methods and land use assessments

USDA-ARS?s Scientific Manuscript database

Geospatial frameworks and GIS-based approaches were used to assess current cropping practices in potato production systems in Maine. Results from the geospatial integration of remotely-sensed cropland layers (2008-2011) and soil datasets for Maine revealed a four-year potato systems footprint estima...

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

DTIC Science & Technology

1993-01-01

during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on...well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote - sensing techniques with crop

DEVELOPMENT OF MOLECULAR MONITORING TECHNOLOGIES TO MEASURE TRANSGENE FLOW AND INTROGRESSION IN CROP AND NON-CROP PLANT SPECIES

EPA Science Inventory

The Gene Flow Project at the US Environmental Protection Agency, Western Ecology Division is developing methodologies for ecological risk assessments of transgene flow using Agrostis and Brassica engineered with CP4 EPSPS genes that confer resistance to glyphosate herbicide. In ...

Performance of a wireless sensor network for crop monitoring and irrigation control

USDA-ARS?s Scientific Manuscript database

Robust automatic irrigation scheduling has been demonstrated using wired sensors and sensor network systems with subsurface drip and moving irrigation systems. However, there are limited studies that report on crop yield and water use efficiency resulting from the use of wireless networks to automat...

Simulating crop growth with Expert-N-GECROS under different site conditions in Southwest Germany

NASA Astrophysics Data System (ADS)

Poyda, Arne; Ingwersen, Joachim; Demyan, Scott; Gayler, Sebastian; Streck, Thilo

2016-04-01

When feedbacks between the land surface and the atmosphere are investigated by Atmosphere-Land surface-Crop-Models (ALCM) it is fundamental to accurately simulate crop growth dynamics as plants directly influence the energy partitioning at the plant-atmosphere interface. To study both the response and the effect of intensive agricultural crop production systems on regional climate change in Southwest Germany, the crop growth model GECROS (YIN & VAN LAAR, 2005) was calibrated based on multi-year field data from typical crop rotations in the Kraichgau and Swabian Alb regions. Additionally, the SOC (soil organic carbon) model DAISY (MÜLLER et al., 1998) was implemented in the Expert-N model tool (ENGEL & PRIESACK, 1993) and combined with GECROS. The model was calibrated based on a set of plant (BBCH, LAI, plant height, aboveground biomass, N content of biomass) and weather data for the years 2010 - 2013 and validated with the data of 2014. As GECROS adjusts the root-shoot partitioning in response to external conditions (water, nitrogen, CO2), it is suitable to simulate crop growth dynamics under changing climate conditions and potentially more frequent stress situations. As C and N pools and turnover rates in soil as well as preceding crop effects were expected to considerably influence crop growth, the model was run in a multi-year, dynamic way. Crop residues and soil mineral N (nitrate, ammonium) available for the subsequent crop were accounted for. The model simulates growth dynamics of winter wheat, winter rape, silage maize and summer barley at the Kraichgau and Swabian Alb sites well. The Expert-N-GECROS model is currently parameterized for crops with potentially increasing shares in future crop rotations. First results will be shown.

Mapping and monitoring of crop intensity, calendar and irrigation using multi-temporal MODIS data

NASA Astrophysics Data System (ADS)

Xiao, X.; Boes, S.; Mulukutla, G.; Proussevitch, A.; Routhier, M.

2005-12-01

Agriculture is the most extensive land use and water use on the Earth. Because of the diverse range of natural environments and human needs, agriculture is also the most complicated land use and water use system, which poses an enormous challenge to the scientific community, the public and decision-makers. Updated and geo-referenced information on crop intensity (number of crops per year), calendar (planting date, harvesting date) and irrigation is critically needed to better understand the impacts of agriculture on biogeochemical cycles (e.g., carbon, nitrogen, trace gases), water and climate dynamics. Here we present an effort to develop a novel approach for mapping and monitoring crop intensity, calendar and irrigation, using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) image data. Our algorithm employed three vegetation indices that are sensitive to the seasonal dynamics of leaf area index, light absorption by leaf chlorophyll and land surface water content. Our objective is to generate geospatial databases of crop intensity, calendar and irrigation at 500-m spatial resolution and at 8-day temporal resolution. In this presentation, we report a preliminary geospatial dataset of paddy rice crop intensity, calendar and irrigation in Asia, which is developed from the 8-day composite images of MODIS in 2002. The resultant dataset could be used in many applications, including hydrological and climate modeling.

Colonization dynamic of various crop residues by Fusarium graminearum monitored through real-time PCR measurements.

PubMed

Leplat, J; Heraud, C; Gautheron, E; Mangin, P; Falchetto, L; Steinberg, C

2016-11-01

To evaluate the effect of the type of crop residues on the colonization dynamic of Fusarium graminearum in soil. The ability of F. graminearum to survive in the presence of various crop residues was assessed on Petri dishes and in microcosms. These microcosms comprised soil that had or had not been previously disinfested with or without amendment with various crop residues. The colonization dynamic of F. graminearum was monitored through real-time PCR. Fusarium graminearum development was higher in disinfested soil than in non-disinfested one. The fungal growth was enhanced to various extents according to the type of crop residues, except for mustard residues which inhibited it. The biochemical and physical properties of the residues were likely to account for the differences in the survival of F. graminearum. Fusarium graminearum is a poor competitor in soil but it can use maize, wheat, and rape residues to ensure its survival. Conversely alfalfa, which is assimilated by micro-organisms very easily, avoids long-lasting survival of the fungus. And finally, mustard producing glucosinolates could be used as an intermediate crop to reduce the inoculum amount. This study is contributing to the knowledge about F. graminearum saprotophic abilities and proposes interesting paths to limit its survival in soil. © 2016 The Society for Applied Microbiology.

Honey Bee Colonies Remote Monitoring System.

PubMed

Gil-Lebrero, Sergio; Quiles-Latorre, Francisco Javier; Ortiz-López, Manuel; Sánchez-Ruiz, Víctor; Gámiz-López, Victoria; Luna-Rodríguez, Juan Jesús

2016-12-29

Bees are very important for terrestrial ecosystems and, above all, for the subsistence of many crops, due to their ability to pollinate flowers. Currently, the honey bee populations are decreasing due to colony collapse disorder (CCD). The reasons for CCD are not fully known, and as a result, it is essential to obtain all possible information on the environmental conditions surrounding the beehives. On the other hand, it is important to carry out such information gathering as non-intrusively as possible to avoid modifying the bees' work conditions and to obtain more reliable data. We designed a wireless-sensor networks meet these requirements. We designed a remote monitoring system (called WBee) based on a hierarchical three-level model formed by the wireless node, a local data server, and a cloud data server. WBee is a low-cost, fully scalable, easily deployable system with regard to the number and types of sensors and the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication. In addition, the nodes include a backup battery, which allows for further data acquisition and storage in the event of a power outage. Unlike other systems that monitor a single point of a hive, the system we present monitors and stores the temperature and relative humidity of the beehive in three different spots. Additionally, the hive is continuously weighed on a weighing scale. Real-time weight measurement is an innovation in wireless beehive-monitoring systems. We designed an adaptation board to facilitate the connection of the sensors to the node. Through the Internet, researchers and beekeepers can access the cloud data server to find out the condition of their hives in real time.

Honey Bee Colonies Remote Monitoring System

PubMed Central

Gil-Lebrero, Sergio; Quiles-Latorre, Francisco Javier; Ortiz-López, Manuel; Sánchez-Ruiz, Víctor; Gámiz-López, Victoria; Luna-Rodríguez, Juan Jesús

2016-01-01

Bees are very important for terrestrial ecosystems and, above all, for the subsistence of many crops, due to their ability to pollinate flowers. Currently, the honey bee populations are decreasing due to colony collapse disorder (CCD). The reasons for CCD are not fully known, and as a result, it is essential to obtain all possible information on the environmental conditions surrounding the beehives. On the other hand, it is important to carry out such information gathering as non-intrusively as possible to avoid modifying the bees’ work conditions and to obtain more reliable data. We designed a wireless-sensor networks meet these requirements. We designed a remote monitoring system (called WBee) based on a hierarchical three-level model formed by the wireless node, a local data server, and a cloud data server. WBee is a low-cost, fully scalable, easily deployable system with regard to the number and types of sensors and the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication. In addition, the nodes include a backup battery, which allows for further data acquisition and storage in the event of a power outage. Unlike other systems that monitor a single point of a hive, the system we present monitors and stores the temperature and relative humidity of the beehive in three different spots. Additionally, the hive is continuously weighed on a weighing scale. Real-time weight measurement is an innovation in wireless beehive—monitoring systems. We designed an adaptation board to facilitate the connection of the sensors to the node. Through the Internet, researchers and beekeepers can access the cloud data server to find out the condition of their hives in real time. PMID:28036061

A comparison of numerical and machine-learning modeling of soil water content with limited input data

NASA Astrophysics Data System (ADS)

Karandish, Fatemeh; Šimůnek, Jiří

2016-12-01

Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a better choice for predicting SWCs with lower uncertainties when required data are available, and thus for designing water saving strategies for agriculture and for other environmental applications requiring estimates of SWCs.

The biospeckle method for the investigation of agricultural crops: A review

NASA Astrophysics Data System (ADS)

Zdunek, Artur; Adamiak, Anna; Pieczywek, Piotr M.; Kurenda, Andrzej

2014-01-01

Biospeckle is a nondestructive method for the evaluation of living objects. It has been applied to medicine, agriculture and microbiology for monitoring processes related to the movement of material particles. Recently, this method is extensively used for evaluation of quality of agricultural crops. In the case of botanical materials, the sources of apparent biospeckle activity are the Brownian motions and biological processes such as cyclosis, growth, transport, etc. Several different applications have been shown to monitor aging and maturation of samples, organ development and the detection and development of defects and diseases. This review will focus on three aspects: on the image analysis and mathematical methods for biospeckle activity evaluation, on published applications to botanical samples, with special attention to agricultural crops, and on interpretation of the phenomena from a biological point of view.

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

NASA Astrophysics Data System (ADS)

Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

2015-04-01

Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

Ecoclimatic indicators to study climate suitability of areas for the cultivation of specific crops

NASA Astrophysics Data System (ADS)

Caubel, J.; Garcia de Cortazar Atauri, I.; Cufi, J.; Huard, F.; Launay, M.; Ripoche, D.; Graux, A.; deNoblet, N.

2013-12-01

Climatic conditions play a fundamental role in the suitability of geographical areas for cropping. In the context of climate change, we could expect changes in overall climatic conditions and so, on the suitability for cropping. Therefore, assessing the future climate suitability of areas for cropping is decisive for anticipating agriculture in a given area. Moreover, it is crucial to have access to the split up information concerning the effect of climate on the achievement of the main ecophysiological processes and cultural practices taking place during the crop cycle. In this way, stakeholders can envisage land use adaptations under climate change conditions, such as changes in cultural practices or development of new varieties for example. We proposed an aggregation tool of ecoclimatic indicators to design evaluation trees of climate suitability of areas for cropping, GETARI (Generic Evaluation Tool of Ecoclimatic Indicators). It calculates an overall climate suitability index at the annual scale, from a designed evaluation tree. This aggregation tool allows to characterize climate suitability according to crop ecophysiology, grain/fruit quality or crop management. GETARI proposes the major ecophysiological processes and cultural practices taking place during phenological periods, together with the climatic effects that are known to affect their achievement. The climatic effects on the ecophysiological processes (or cultural practices) during phenological periods are captured by the ecoclimatic indicators, which are agroclimatic indicators calculated over phenological periods. They give information about crop response to climate through ecophysiological or agronomic thresholds. Those indices of suitability are normalized and aggregated according to aggregation rules in order to compute an overall climate index. In order to illustrate how GETARI can be used, we designed evaluation trees in order to study the climate suitability for maize cropping regarding ecophysiology, for wheat cropping regarding its management and for grape cropping regarding its quality. The designed evaluation trees were developed in accordance with expert assessment and were applied in some past climatic conditions in France to verify their consistence. To conclude, the use of indicators does not replace models but represent additional tools for understanding and spatializing some results obtained by models. Their use can provide information about suitability of geographical areas for cropping in future climatic conditions and can enable to minimize the risk of crop failure. This work is carried out under the research program ORACLE (Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France (and Europe).

Cultural and environmental influences on temporal-spectral development patterns of corn and soybeans

NASA Technical Reports Server (NTRS)

Crist, E. P.

1982-01-01

A technique for evaluating crop temporal-spectral development patterns is described and applied to the analysis of cropping practices and environmental conditions as they affect reflectance characteristics of corn and soybean canopies. Typical variations in field conditions are shown to exert significant influences on the spectral development patterns, and thereby to affect the separability of the two crops.

Influence of soil management on water erosion and hydrological responses in semiarid agrosystems

NASA Astrophysics Data System (ADS)

De Alba, Saturnino; Alcazar, María; Ivón Cermeño, F.

2014-05-01

In Europe, in the Mediterranean area, water erosion is very severe, moderately to seriously affecting 50% to 70% of the agricultural land. However, it is remarkable the lack of field data of water erosion rates for agricultural areas of semiarid Mediterranean climate. Moreover, this lack of field data is even more severe regarding the hydrological and erosive responses of soils managed with organic farming compared to those with conventional managements or others under conservation agriculture. This paper describes an experimental field station (La Higueruela Station) for the continuous monitoring of water erosion that was set up in 1992 in Central Spain (Toledo, Castilla-La Mancha). In the study area, the annual precipitation is around 450 mm with a very irregular inter-annual and seasonal distribution, which includes a strong drought in summer. The geology is characterised by non-consolidated Miocene materials, mostly arcosics. The area presents a low relief and gentle slopes, generally less than 15%. At the experimental field, the soil is a Typic Haploxeralf (USDA, 1990). The land-uses are rainfed crops mainly herbaceous crops, vineyard and olive trees. The hydrological response and soil losses by water erosion under natural rainfall conditions are monitored in a total of 28 experimental plots of the USLE type. The plots have a total area of 33.7 m2, (22.5 m long downslope and 3 m wide) and presented a slope gradient of 9%. Detailed descriptions of the experimental field facilities and the automatic station for monitoring runoff and sediment productions, as well as of the meteorological station, are presented. The land uses and treatments applied on the experimental plots are for different soil management systems for cereals crops (barley): 1) Organic farming, 2) Minimum tillage of moderate tillage intensity, 3) No-tillage, and 4) Conventional tillage; five alternatives of fallow: 1) Traditional fallow (white fallow) with conventional tillage, 2) Traditional fallow (white fallow) with minimum tillage, 3) Organic fallow (Green fallow), 4) Delayed fallow, and 5) Chemical fallow with a no-tillage management. Additionally, there is an experimental plot presenting a simulation of abandonment and natural re-vegetation. This paper presents the main results, for a data series of 20 years (1993-2013) with special attention to the organic farming management results, regarding to the following research objectives: 1) Monitoring the hydrological and erosive responses of the different management systems; 2) Study of the role of key factors in soil erodibility affected by the management as soil physics and chemistry, surface cover and roughness, and soil and surface initial conditions (soil water content, surface roughness…); and, 3) Characterizing the seasonal variability of the rainfall erosivity.

Sentinel-1 backscatter sensitivity to vegetation dynamics at the field scale.

NASA Astrophysics Data System (ADS)

Vreugdenhil, Mariette; Eder, Alexander; Bauer-Marschallinger, Bernhard; Cao, Senmao; Naeimi, Vahid; Oismueller, Markus; Strauss, Peter; Wagner, Wolfgang

2017-04-01

Vegetation monitoring is pivotal to improve our understanding of the role vegetation dynamics play in the global carbon-, energy- and hydrological cycle. And with the increasing stress on food supply due to the growing world populating and changing climate, vegetation monitoring is of great importance in agricultural areas. By closely tracking crop conditions, droughts and subsequent crop losses could be mitigated. Sensors operating in the microwave domain are sensitive to several surface characteristics, including soil moisture and vegetation. Hence, spaceborne microwave remote sensing provides the means to monitor vegetation and soil conditions on different scales, ranging from field scale to global scale. However, it also presents a challenge since multiple combinations of soil and vegetation characteristics can lead to a similar measurement. Copernicus Sentinel-1 (S-1) is a series of two satellites, developed by the European Space Agency (ESA) , which carry C-band Synthetic Aperture Radars. The C-SAR sensors provide VV, HH, VH and HV backscatter at a 5 m by 20 m spatial resolution. The temporal revisit time of the two satellites is 3-6 days. With their unique capacity for temporally dense and spatially detailed data, the S-1 satellite series provides for the first time the chance to investigate vegetation dynamics at high temporal and spatial resolution. The aim of this study is to assess the sensitivity of Sentinel-1 backscatter to vegetation dynamics. The study is performed in the Hydrological Open Air Laboratory (HOAL), which is a 66 hectare large catchment located in Petzenkirchen, Austria. In the HOAL several vegetation parameters were measured during the course of the growing season (2016) at the overpass time of S-1a. Vegetation height was obtained ten times for the whole catchment, using georeferenced photos made by a motorized paraglider and a Land Surface Model. In addition, vegetation water content, Leaf Area Index and soil moisture were measured in four different cropfields. An in situ soil moisture network provides continuous soil moisture measurements at 31 locations within the catchment. Different polarizations and ratios thereof were calculated and compared, both spatially and temporally, to the in situ measurements of vegetation height, LAI, vegetation water content and soil moisture. Preliminary results show a clear spatial pattern in cross-polarized backscatter, which is related to different crop types. Time series analysis suggests that a ratio between cross- and co-polarized backscatter is affected by both vegetation water content and vegetation structure. This presentation will provide a comprehensive assessment of Sentinel-1's capability for monitoring of vegetation over croplands, using in situ reference data obtained over a full growing season.

Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

NASA Astrophysics Data System (ADS)

Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

2015-12-01

Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.

Applying Earth Observation Data to agriculture risk management: a public-private collaboration to develop drought maps in North-East China

NASA Astrophysics Data System (ADS)

Surminski, S.; Holt Andersen, B.; Hohl, R.; Andersen, S.

2012-04-01

Earth Observation Data (EO) can improve climate risk assessment particularly in developing countries where densities of weather stations are low. Access to data that reflects exposure to weather and climate risks is a key condition for any successful risk management approach. This is of particular importance in the context of agriculture and drought risk, where historical data sets, accurate current data about crop growth and weather conditions, as well as information about potential future changes based on climate projections and socio-economic factors are all relevant, but often not available to stakeholders. Efforts to overcome these challenges in using EO data have so far been predominantly focused on developed countries, where satellite-derived Normalized Difference Vegetation Indexes (NDVI) and the MERIS Global Vegetation Indexes (MGVI), are already used within the agricultural sector for assessing and managing crop risks and to parameterize crop yields. This paper assesses how public-private collaboration can foster the application of these data techniques. The findings are based on a pilot project in North-East China where severe droughts frequently impact the country's largest corn and soybeans areas. With support from the European Space Agency (ESA), a consortium of meteorological experts, mapping firms and (re)insurance experts has worked to explore the potential use and value of EO data for managing crop risk and assessing exposure to drought for four provinces in North-East China (Heilongjiang, Jilin, Inner Mongolia and Liaoning). Combining NDVI and MGVI data with meteorological observations to help alleviate shortcomings of NDVI specific to crop types and region has resulted in the development of new drought maps for the time 2000-2011 in digital format at a high resolution (1x1 km). The observed benefits of this data application range from improved risk management to cost effective drought monitoring and claims verification for insurance purposes. This paper concludes by exploring the potential of replicating such a partnership approach to climate risk assessment in other regions. Authors of the paper: Surminski, Swenja (London School of Economics); Holt Andersen, Birgitte (CWare); Hohl, Roman (Swiss Re); Andersen, Søren (COWI)

Safety and Certification Considerations for Expanding the Use of UAS in Precision Agriculture

NASA Technical Reports Server (NTRS)

Hayhurst, Kelly J.; Maddalon, Jeffrey M.; Neogi, Natasha A.; Vertstynen, Harry A.

2016-01-01

The agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans. In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot. A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricultural inputs based on the results of remote sensing. Larger UAS with substantial payload capacity could provide an option for site-specific application of agricultural inputs in a timely fashion, without substantive damage to the crops or soil. A recent study by the National Aeronautics and Space Administration (NASA) investigated certification requirements needed to enable the use of larger UAS to support the precision agriculture industry. This paper provides a brief introduction to aircraft certification relevant to agricultural UAS, an overview of and results from the NASA study, and a discussion of how those results might affect the precision agriculture community. Specific topics of interest include business model considerations for unmanned aerial applicators and a comparison with current means of variable rate application. The intent of the paper is to inform the precision agriculture community of evolving technologies that will enable broader use of unmanned vehicles to reduce costs, reduce environmental impacts, and enhance yield, especially for specialty crops that are grown on small to medium size farms.

Characterization of a new fertilizer during field trials by hyperspectral imaging

NASA Astrophysics Data System (ADS)

Bonifazi, Giuseppe; Serranti, Silvia; Trella, Agata; Garcia Izquierdo, Carlos

2016-05-01

This work was carried out in the framework of the LIFE RESAFE Project (LIFE12 ENV/IT/000356) "Innovative fertilizer from urban waste, bio-char and farm residues as substitute of chemical fertilizers". The aim of RESAFE project is the production of a new fertilizer from waste for agricultural practices. The new fertilizer was tested on 5 different crops during field trials carried out in Spain: barley, corn, tomato, potato and melon. For each crop six different treatments were applied and compared to verify the quality of RESAFE fertilizer. Soil samples were collected at the beginning and at the end of the experiment. The possibility to apply hyperspectral imaging (HSI) to perform soil evolution monitoring and characterization in respect to the fertilizer utilization and quality of the resulting crops was investigated. Soil samples were acquired by HSI in the near infrared field (1000-1700 nm) and on the same samples classical chemical analyses were carried out with reference to total nitrogen, total organic carbon, C/N ratio, total organic matter. Hyperspectral data were analyzed adopting a chemometric approach through application of Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares Analysis (PLS) for estimation of chemical parameters. The results showed as the proposed hardware and software integrated architecture allows to implement low cost and easy to use analytical procedures able to quantitatively assess soil chemical-physical attributes according to different fertilization strategies, in respect of different environmental conditions and selected crops.

The effect of different initial densities of nematode (Meloidogyne javanica) on the build-up of Pasteuria penetrans population.

PubMed

Darban, Daim Ali; Pathan, Mumtaz Ali; Bhatti, Abdul Ghaffar; Maitelo, Sultan Ahmed

2005-02-01

Pasteuria penetrans will build-up faster where there is a high initial nematode density and can suppress root-knot nematode populations in the roots of tomato plants. The effect of different initial densities of nematode (Meloidogyne javanica) (150, 750, 1500, 3000) and P. penetrans infected females (F1, F3) densities (F0=control and AC=absolute control without nematode or P. penetrans inoculum) on the build-up of Pasteuria population was investigated over four crop cycles. Two major points of interest were highlighted. First, that within a confined soil volume, densities of P. penetrans can increase >100 times within 2 or 3 crop cycles. Second, from a relatively small amount of spore inoculum, infection of the host is very high. There were more infected females in the higher P. penetrans doses. The root growth data confirms the greater number of females in the controls particularly at the higher inoculum densities in the third and fourth crops. P. penetrans generally caused the fresh root weights to be higher than those in the control. P. penetrans has shown greater reduction of egg masses per plant at most densities. The effects of different initial densities of M. javanica and P. penetrans on the development of the pest and parasite populations were monitored. And no attempt was made to return the P. penetrans spores to the pots after each crop so the build-up in actual numbers of infected females and spores under natural conditions may be underestimated.

1-Aminocyclopropane-1-carboxylic acid (ACC) deaminase-containing rhizobacteria protect Ocimum sanctum plants during waterlogging stress via reduced ethylene generation.

PubMed

Barnawal, Deepti; Bharti, Nidhi; Maji, Deepamala; Chanotiya, Chandan Singh; Kalra, Alok

2012-09-01

Ocimum sanctum grown as rain-fed crop, is known to be poorly adapted to waterlogged conditions. Many a times the crop suffers extreme damages because of anoxia and excessive ethylene generation due to waterlogging conditions present under heavy rain. The usefulness of 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase-containing plant growth promoting rhizobacteria was investigated under waterlogging stress. The comparison of herb yield and stress induced biochemical changes of waterlogged and non-waterlogged plants with and without ACC deaminase-containing microbiological treatments were monitored in this study. Ten plant growth promoting rhizobacteria strains containing ACC-deaminase were isolated and characterized. Four selected isolates Fd2 (Achromobacter xylosoxidans), Bac5 (Serratia ureilytica), Oci9 (Herbaspirillum seropedicae) and Oci13 (Ochrobactrum rhizosphaerae) had the potential to protect Ocimum plants from flood induced damage under waterlogged glass house conditions. Pot experiments were conducted to evaluate the potential of these ACC deaminase-containing selected strains for reducing the yield losses caused by waterlogging conditions. Bacterial treatments protected plants from waterlogging induced detrimental changes like stress ethylene production, reduced chlorophyll concentration, higher lipid peroxidation, proline concentration and reduced foliar nutrient uptake. Fd2 (A. xylosoxidans) induced maximum waterlogging tolerance as treated waterlogged plants recorded maximum growth and herb yield (46.5% higher than uninoculated waterlogged plants) with minimum stress ethylene levels (53% lower ACC concentration as compared to waterlogged plants without bacterial inoculation) whereas under normal non-waterlogged conditions O. rhizosphaerae was most effective in plant growth promotion. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Winter Cover Crop Effects on Nitrate Leaching in Subsurface Drainage as Simulated by RZWQM-DSSAT

NASA Astrophysics Data System (ADS)

Malone, R. W.; Chu, X.; Ma, L.; Li, L.; Kaspar, T.; Jaynes, D.; Saseendran, S. A.; Thorp, K.; Yu, Q.

2007-12-01

Planting winter cover crops such as winter rye (Secale cereale L.) after corn and soybean harvest is one of the more promising practices to reduce nitrate loss to streams from tile drainage systems without negatively affecting production. Because availability of replicated tile-drained field data is limited and because use of cover crops to reduce nitrate loss has only been tested over a few years with limited environmental and management conditions, estimating the impacts of cover crops under the range of expected conditions is difficult. If properly tested against observed data, models can objectively estimate the relative effects of different weather conditions and agronomic practices (e.g., various N fertilizer application rates in conjunction with winter cover crops). In this study, an optimized winter wheat cover crop growth component was integrated into the calibrated RZWQM-DSSAT hybrid model and then we compare the observed and simulated effects of a winter cover crop on nitrate leaching losses in subsurface drainage water for a corn-soybean rotation with N fertilizer application rates over 225 kg N ha-1 in corn years. Annual observed and simulated flow-weighted average nitrate concentration (FWANC) in drainage from 2002 to 2005 for the cover crop treatments (CC) were 8.7 and 9.3 mg L-1 compared to 21.3 and 18.2 mg L-1 for no cover crop (CON). The resulting observed and simulated FWANC reductions due to CC were 59% and 49%. Simulations with the optimized model at various N fertilizer rates resulted in average annual drainage N loss differences between CC and CON to increase exponentially from 12 to 34 kg N ha-1 for rates of 11 to 261 kg N ha-1. The results suggest that RZWQM-DSSAT is a promising tool to estimate the relative effects of a winter crop under different conditions on nitrate loss in tile drains and that a winter cover crop can effectively reduce nitrate losses over a range of N fertilizer levels.

Omics Approach to Identify Factors Involved in Brassica Disease Resistance.

PubMed

Francisco, Marta; Soengas, Pilar; Velasco, Pablo; Bhadauria, Vijai; Cartea, Maria E; Rodríguez, Victor M

2016-01-01

Understanding plant's defense mechanisms and their response to biotic stresses is of fundamental meaning for the development of resistant crop varieties and more productive agriculture. The Brassica genus involves a large variety of economically important species and cultivars used as vegetable source, oilseeds, forage and ornamental. Damage caused by pathogens attack affects negatively various aspects of plant growth, development, and crop productivity. Over the last few decades, advances in plant physiology, genetics, and molecular biology have greatly improved our understanding of plant responses to biotic stress conditions. In this regard, various 'omics' technologies enable qualitative and quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. In this review, we have described advances in 'omic' tools (genomics, transcriptomics, proteomics and metabolomics) in the view of conventional and modern approaches being used to elucidate the molecular mechanisms that underlie Brassica disease resistance.

Toward an automated low-cost three-dimensional crop surface monitoring system using oblique stereo imagery from consumer-grade smart cameras

NASA Astrophysics Data System (ADS)

Brocks, Sebastian; Bendig, Juliane; Bareth, Georg

2016-10-01

Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually. This system was set up on a barley field experiment with nine different barley cultivars in the growing period of 2014. Images were acquired three times a day for a period of two months. CSMs were successfully generated for 95 out of 98 acquisitions between May 2 and June 30. The best linear regressions of the CSM-derived plot-wise averaged plant-heights compared to manual plant height measurements taken at four dates resulted in a coefficient of determination R2 of 0.87 and a root-mean-square error (RMSE) of 0.08 m, with Willmott's refined index of model performance dr equaling 0.78. In total, 103 mean plot heights were used in the regression based on the noon acquisition time. The presented system succeeded in semiautomatedly monitoring crop height on a plot scale to field scale.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

A sustainable path to food security.

PubMed

Xuan, V T

1996-01-01

This paper summarizes remarks made by Vo-Tong Xuan, professor of agronomy at the University of Can Tho. He states that agricultural production affects government market systems of supply and demand. The aim of world food production is to supply more food with fewer resources to meet the needs of a growing global population, which may reach 8 billion by 2025. Global food production needs to increase by 2% annually. Developing country food production needs to increase by 3% annually. There are needs for new land use patterns, improved crop choices, and market options and responsiveness. Better national and regional food monitoring systems are needed, as well as appropriate farming systems. Sustainability entails appropriate receipts for producer costs and affordable costs for consumers. Yields must be increased while lowering production costs. This may be achieved through the use of labor-intensive, low-input technology, increases in non-rice food crops, and changes in livestock and fishery production. Food for livestock must not compete with human food demand. Sustainable food production is dependent upon efficient use of irrigation systems, less consumption of rain water, integrated pest and nutrient management for reducing soil and water degradation, and high-yield, disease-resistant crop varieties suitable for a variety of land conditions. Crop loss must be reduced and better weed management implemented. Parliamentarians are important political resources for assuring the political will to make changes. Several delegations were concerned about the low prices for rice. Professor Xuan recommended reducing overproduction of rice, diversifying crops, and providing ready access to markets for food not consumed at home. Individual subsidies were discouraged in favor of better land use planning. Most delegates agreed that rice should be excluded from international trade agreements.

An automated multi-model based evapotranspiration estimation framework for understanding crop-climate interactions in India

NASA Astrophysics Data System (ADS)

Bhattarai, N.; Jain, M.; Mallick, K.

2017-12-01

A remote sensing based multi-model evapotranspiration (ET) estimation framework is developed using MODIS and NASA Merra-2 reanalysis data for data poor regions, and we apply this framework to the Indian subcontinent. The framework eliminates the need for in-situ calibration data and hence estimates ET completely from space and is replicable across all regions in the world. Currently, six surface energy balance models ranging from widely-used SEBAL, METRIC, and SEBS to moderately-used S-SEBI, SSEBop, and a relatively new model, STIC1.2 are being integrated and validated. Preliminary analysis suggests good predictability of the models for estimating near- real time ET under clear sky conditions from various crop types in India with coefficient of determination 0.32-0.55 and percent bias -15%-28%, when compared against Bowen Ratio based ET estimates. The results are particularly encouraging given that no direct ground input data were used in the analysis. The framework is currently being extended to estimate seasonal ET across the Indian subcontinent using a model-ensemble approach that uses all available MODIS 8-day datasets since 2000. These ET products are being used to monitor inter-seasonal and inter-annual dynamics of ET and crop water use across different crop and irrigation practices in India. Particularly, the potential impacts of changes in precipitation patterns and extreme heat (e.g., extreme degree days) on seasonal crop water consumption is being studied. Our ET products are able to locate the water stress hotspots that need to be targeted with water saving interventions to maintain agricultural production in the face of climate variability and change.

Network Candidate Genes in Breeding for Drought Tolerant Crops

PubMed Central

Krannich, Christoph Tim; Maletzki, Lisa; Kurowsky, Christina; Horn, Renate

2015-01-01

Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance. PMID:26193269

Network Candidate Genes in Breeding for Drought Tolerant Crops.

PubMed

Krannich, Christoph Tim; Maletzki, Lisa; Kurowsky, Christina; Horn, Renate

2015-07-17

Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance.

Altered pesticide use on transgenic crops and the associated general impact from an environmental perspective.

PubMed

Kleter, Gijs A; Bhula, Raj; Bodnaruk, Kevin; Carazo, Elizabeth; Felsot, Allan S; Harris, Caroline A; Katayama, Arata; Kuiper, Harry A; Racke, Kenneth D; Rubin, Baruch; Shevah, Yehuda; Stephenson, Gerald R; Tanaka, Keiji; Unsworth, John; Wauchope, R Donald; Wong, Sue-Sun

2007-11-01

The large-scale commercial cultivation of transgenic crops has undergone a steady increase since their introduction 10 years ago. Most of these crops bear introduced traits that are of agronomic importance, such as herbicide or insect resistance. These traits are likely to impact upon the use of pesticides on these crops, as well as the pesticide market as a whole. Organizations like USDA-ERS and NCFAP monitor the changes in crop pest management associated with the adoption of transgenic crops. As part of an IUPAC project on this topic, recent data are reviewed regarding the alterations in pesticide use that have been observed in practice. Most results indicate a decrease in the amounts of active ingredients applied to transgenic crops compared with conventional crops. In addition, a generic environmental indicator -- the environmental impact quotient (EIQ) -- has been applied by these authors and others to estimate the environmental consequences of the altered pesticide use on transgenic crops. The results show that the predicted environmental impact decreases in transgenic crops. With the advent of new types of agronomic trait and crops that have been genetically modified, it is useful to take also their potential environmental impacts into account.

Rice Crop Monitoring and Yield Assessment with MODIS 250m Gridded Vegetation Products: A Case Study of Sa Kaeo Province, Thailand

NASA Astrophysics Data System (ADS)

Wijesingha, J. S. J.; Deshapriya, N. L.; Samarakoon, L.

2015-04-01

Billions of people in the world depend on rice as a staple food and as an income-generating crop. Asia is the leader in rice cultivation and it is necessary to maintain an up-to-date rice-related database to ensure food security as well as economic development. This study investigates general applicability of high temporal resolution Moderate Resolution Imaging Spectroradiometer (MODIS) 250m gridded vegetation product for monitoring rice crop growth, mapping rice crop acreage and analyzing crop yield, at the province-level. The MODIS 250m Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series data, field data and crop calendar information were utilized in this research in Sa Kaeo Province, Thailand. The following methodology was used: (1) data pre-processing and rice plant growth analysis using Vegetation Indices (VI) (2) extraction of rice acreage and start-of-season dates from VI time series data (3) accuracy assessment, and (4) yield analysis with MODIS VI. The results show a direct relationship between rice plant height and MODIS VI. The crop calendar information and the smoothed NDVI time series with Whittaker Smoother gave high rice acreage estimation (with 86% area accuracy and 75% classification accuracy). Point level yield analysis showed that the MODIS EVI is highly correlated with rice yield and yield prediction using maximum EVI in the rice cycle predicted yield with an average prediction error 4.2%. This study shows the immense potential of MODIS gridded vegetation product for keeping an up-to-date Geographic Information System of rice cultivation.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

The use of Landsat digital data to detect and monitor vegetation water deficiencies

NASA Technical Reports Server (NTRS)

Thompson, D. R.; Wehmanen, O. A.

1977-01-01

In the Large Area Crop Inventory Experiment a technique was devised using a vector transformation of Landsat digital data to indicate when vegetation is undergoing moisture stress. A relation was established between the remote-sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index).

Development of an irrigation scheduling software based on model predicted crop water stress

USDA-ARS?s Scientific Manuscript database

Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress which is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling software based on RZWQM2 model pr...

ASSESSING POSSIBLE ECOLOGICAL RISKS OF GENETICALLY MODIFIED CROPS: GENE EXPRESSION ASSAYS AND GENETIC MONITORING OF NON-TARGET ORGANISMS

EPA Science Inventory

Widespread planting of genetically modified crops with the Bt transgene pesticide has led to concern over non-target effects of Bt compounds in agroecosystems. While some research suggests that non-target organisms exposed to Bt toxin exhibit reduced fecundity and increased morta...

Implications of observed and simulated soil carbon sequestration for management options in corn-based rotations

USDA-ARS?s Scientific Manuscript database

Managing cropping systems to sequester soil organic carbon (SOC) improves soil health and a system’s resiliency to impacts of changing climate. Our objectives were to 1) monitor SOC from a bio-energy cropping study in central Pennsylvania that included a corn-soybean-alfalfa rotation, switchgrass, a...

Implications of observed and simulated soil carbon sequestration for management options in corn-based rotations

USDA-ARS?s Scientific Manuscript database

Managing cropping systems to sequester soil organic carbon (SOC) improves soil health and a system’s resiliency to impacts of changing climate. Our objectives were to 1) monitor SOC from a bio-energy cropping study in central Pennsylvania that included a corn-soybean-alfalfa rotation, switchgrass, ...

Sensitivity analysis of the STICS-MACRO model to identify cropping practices reducing pesticides losses.

PubMed

Lammoglia, Sabine-Karen; Makowski, David; Moeys, Julien; Justes, Eric; Barriuso, Enrique; Mamy, Laure

2017-02-15

STICS-MACRO is a process-based model simulating the fate of pesticides in the soil-plant system as a function of agricultural practices and pedoclimatic conditions. The objective of this work was to evaluate the influence of crop management practices on water and pesticide flows in contrasted environmental conditions. We used the Morris screening sensitivity analysis method to identify the most influential cropping practices. Crop residues management and tillage practices were shown to have strong effects on water percolation and pesticide leaching. In particular, the amount of organic residues added to soil was found to be the most influential input. The presence of a mulch could increase soil water content so water percolation and pesticide leaching. Conventional tillage was also found to decrease pesticide leaching, compared to no-till, which is consistent with many field observations. The effects of the soil, crop and climate conditions tested in this work were less important than those of cropping practices. STICS-MACRO allows an ex ante evaluation of cropping systems and agricultural practices, and of the related pesticides environmental impacts. Copyright © 2016 Elsevier B.V. All rights reserved.

Decision Support system- DSS- for irrigation management in greenhouses: a case study in Campania Region

NASA Astrophysics Data System (ADS)

Monaco, Eugenia; De Mascellis, Roberto; Riccardi, Maria; Basile, Angelo; D'Urso, Guido; Magliulo, Vincenzo; Tedeschi, Anna

2016-04-01

In Mediterranean Countries the proper management of water resources is important for the preservation of actual production systems. The possibility to manage water resources is possible especially in the greenhouses systems. The challenge to manage the soil in greenhouse farm can be a strategy to maintain both current production systems both soil conservation. In Campania region protected crops (greenhouses and tunnels) have a considerable economic importance both for their extension in terms of surface harvested and also for their production in terms of yields. Agricultural production in greenhouse is closely related to the micro-climatic condition but also to the physical and agronomic characteristics of the soil-crop system. The protected crops have an high level of technology compare to the other production systems, but the irrigation management is still carried out according to empirical criteria. The rational management of the production process requires an appropriate control of climatic parameters (temperature, humidity, wind) and agronomical inputs (irrigation, fertilization,). All these factors need to be monitored as well is possible, in order to identify the optimal irrigation schedule. The aim of this work is to implement a Decision Support system -DSS- for irrigation management in greenhouses focused on a smart irrigation control based on observation of the agro-climatic parameters monitored with an advanced wireless sensors network. The study is conducted in a greenhouse farm of 6 ha located in the district of Salerno were seven plots were cropped with rocket. Preliminary a study of soils proprieties was conducted in order to identify spatial variability of the soil in the farm. So undisturbed soil samples were collected to define chemical and physical proprieties; moreover soil hydraulic properties were determined for two soils profiles deemed representation of the farm. Then the wireless sensors, installed at different depth in the soils, determined volumetric water content (VWC) by measuring the dielectric constant of the soil using frequency domain technology (FDR). The data acquired real time were used to determine water balance with a physically based model Hydrus 1D. The results show how the model is able to identify the optimal irrigation schedule as function of soil proprieties and crop needs. Keywords: irrigation, DSS, rocket, water content

Study on an agricultural environment monitoring server system using Wireless Sensor Networks.

PubMed

Hwang, Jeonghwan; Shin, Changsun; Yoe, Hyun

2010-01-01

This paper proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural environment monitoring server system collects environmental and soil information on the outdoors through WSN-based environmental and soil sensors, collects image information through CCTVs, and collects location information using GPS modules. This collected information is converted into a database through the agricultural environment monitoring server consisting of a sensor manager, which manages information collected from the WSN sensors, an image information manager, which manages image information collected from CCTVs, and a GPS manager, which processes location information of the agricultural environment monitoring server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural environment monitoring server system could even monitor the environmental information on the outdoors remotely, and it could be expected that the use of such a system could contribute to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information.

Diurnal Solar Energy Conversion and Photoprotection in Rice Canopies.

PubMed

Meacham, Katherine; Sirault, Xavier; Quick, W Paul; von Caemmerer, Susanne; Furbank, Robert

2017-01-01

Genetic improvement of photosynthetic performance of cereal crops and increasing the efficiency with which solar radiation is converted into biomass has recently become a major focus for crop physiologists and breeders. The pulse amplitude modulated chlorophyll fluorescence technique (PAM) allows quantitative leaf level monitoring of the utilization of energy for photochemical light conversion and photoprotection in natural environments, potentially over the entire crop lifecycle. Here, the diurnal relationship between electron transport rate (ETR) and irradiance was measured in five cultivars of rice (Oryza sativa) in canopy conditions with PAM fluorescence under natural solar radiation. This relationship differed substantially from that observed for conventional short term light response curves measured under controlled actinic light with the same leaves. This difference was characterized by a reduced curvature factor when curve fitting was used to model this diurnal response. The engagement of photoprotective processes in chloroplast electron transport in leaves under canopy solar radiation was shown to be a major contributor to this difference. Genotypic variation in the irradiance at which energy flux into photoprotective dissipation became greater than ETR was observed. Cultivars capable of higher ETR at midrange light intensities were shown to produce greater leaf area over time, estimated by noninvasive imaging. © 2017 American Society of Plant Biologists. All Rights Reserved.

Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management

DOE PAGES

Elliott, Joshua; Glotter, Michael; Ruane, Alex C.; ...

2018-01-01

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for amore » total, adjusted for inflation, of 9 billion USD in 1988 and 21.6 billion USD in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This study suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.« less

Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elliott, Joshua; Glotter, Michael; Ruane, Alex C.

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for amore » total, adjusted for inflation, of $9 billion in 1988 and $21.6 billion in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This work suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.« less

Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elliott, Joshua; Glotter, Michael; Ruane, Alex C.

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for amore » total, adjusted for inflation, of 9 billion USD in 1988 and 21.6 billion USD in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This study suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.« less

Biological Control Outcomes Using the Generalist Aphid Predator Aphidoletes aphidimyza under Multi-Prey Conditions.

PubMed

Jandricic, Sarah E; Wraight, Stephen P; Gillespie, Dave R; Sanderson, John P

2016-12-14

The aphidophagous midge Aphidoletes aphidimyza (Diptera: Cecidomyiidae) is used in biological control programs against aphids in many crops. Short-term trials with this natural enemy demonstrated that that females prefer to oviposit among aphids colonizing the new growth of plants, leading to differential attack rates for aphid species that differ in their within-plant distributions. Thus, we hypothesized that biological control efficacy could be compromised when more than one aphid species is present. We further hypothesized that control outcomes may be different at different crop stages if aphid species shift their preferred feeding locations. Here, we used greenhouse trials to determine biological control outcomes using A. aphidimyza under multi-prey conditions and at different crop stages. At all plant stages, aphid species had a significant effect on the number of predator eggs laid. More eggs were found on M. persicae versus A. solani -infested plants, since M. persicae consistently colonized plant meristems across plant growth stages. This translated to higher numbers of predatory larvae on M. periscae -infested plants in two out of our three experiments, and more consistent control of this pest (78%-95% control across all stages of plant growth). In contrast, control of A. solani was inconsistent in the presence of M. persicae , with 36%-80% control achieved. An additional experiment demonstrated control of A. solani by A. aphidimyza was significantly greater in the absence of M. persicae than in its presence. Our study illustrates that suitability of a natural enemy for pest control may change over a crop cycle as the position of prey on the plant changes, and that prey preference based on within-plant prey location can negatively influence biological control programs in systems with pest complexes. Careful monitoring of the less-preferred pest and its relative position on the plant is suggested.

Early Warning of El Nino Impacts on Food Security

NASA Astrophysics Data System (ADS)

Rowland, J.; Verdin, J. P.; Hillbruner, C.; Budde, M. E.

2016-12-01

Before and during the El Niño of 2015-2016, regular and frequent application of climate monitoring and seasonal forecasts enabled early warning of food insecurity in Africa, Central America, and the Caribbean. As it happened, drought associated with the quasi-El Niño of 2014 had already adversely impacted harvests in Central America, Haiti, and Southern Africa, so the effects of the El Niño of 2015-2016 were especially hard-hitting and particularly devastating to crop conditions and food security. In the case of Ethiopia, 2014 conditions were normal but there were record rainfall deficits in 2015, with consequent crop failure, inadequate forage, and sharply curtailed water availability. Combining such agro-climatological information with knowledge of household economies, livelihood systems, markets & trade, and health & nutrition, FEWS NET constructed scenarios of food insecurity eight months into the future, with monthly updates. These scenarios informed assistance programming by USAID and partners. Overall, FEWS NET estimates that at least 18 million people will be severely food insecure during 2015/16 as a direct result of the impact of El Nino on rainfall. However, in Ethiopia, the contrast with the 1982-1983 El Niño is dramatic; though the two events were climatically similar, the human impacts of the 2015-2016 El Niño are much less, thanks not only to well-functioning early warning systems and large scale emergency response, but also improved social safety nets and lack of ongoing armed conflict. In southern Africa, El Nino resulted in extensive failed crops, with some areas of South Africa and Zimbabwe having insufficient rain to plant crops. Remote sensing products provided relevant information to depict the severity of rainfall and vegetation deficits. Likewise, in Central America and the Caribbean (Hispaniola), rainfall deficits were portrayed in the perspective of 30+ years of data.

Crop improvement using life cycle datasets acquired under field conditions.

PubMed

Mochida, Keiichi; Saisho, Daisuke; Hirayama, Takashi

2015-01-01

Crops are exposed to various environmental stresses in the field throughout their life cycle. Modern plant science has provided remarkable insights into the molecular networks of plant stress responses in laboratory conditions, but the responses of different crops to environmental stresses in the field need to be elucidated. Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops. The interdisciplinary efforts of plant science and data science enable us to explore factors that affect crop productivity and identify stress tolerance-related genes and alleles. Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction. Integration of the outcomes from these technologies will accelerate our understanding of crop phenology under practical field situations and identify key characteristics to represent crop stress status. These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.

An integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes.

PubMed

Chiaradia, Enrico Antonio; Facchi, Arianna; Masseroni, Daniele; Ferrari, Daniele; Bischetti, Gian Battista; Gharsallah, Olfa; Cesari de Maria, Sandra; Rienzner, Michele; Naldi, Ezio; Romani, Marco; Gandolfi, Claudio

2015-09-01

The cultivation of rice, one of the most important staple crops worldwide, has very high water requirements. A variety of irrigation practices are applied, whose pros and cons, both in terms of water productivity and of their effects on the environment, are not completely understood yet. The continuous monitoring of irrigation and rainfall inputs, as well as of soil water dynamics, is a very important factor in the analysis of these practices. At the same time, however, it represents a challenging and costly task because of the complexity of the processes involved, of the difference in nature and magnitude of the driving variables and of the high variety of field conditions. In this paper, we present the prototype of an integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes. The system consists of the following: (1) flow measurement devices for the monitoring of irrigation supply and tailwater drainage; (2) piezometers for groundwater level monitoring; (3) level gauges for monitoring the flooding depth; (4) multilevel tensiometers and moisture sensor clusters to monitor soil water status; (5) eddy covariance station for the estimation of evapotranspiration fluxes and (6) wireless transmission devices and software interface for data transfer, storage and control from remote computer. The system is modular and it is replicable in different field conditions. It was successfully applied over a 2-year period in three experimental plots in Northern Italy, each one with a different water management strategy. In the paper, we present information concerning the different instruments selected, their interconnections and their integration in a common remote control scheme. We also provide considerations and figures on the material and labour costs of the installation and management of the system.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

PubMed

Jayaraman, Prem Prakash; Yavari, Ali; Georgakopoulos, Dimitrios; Morshed, Ahsan; Zaslavsky, Arkady

2016-11-09

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

A method for mapping corn using the US Geological Survey 1992 National Land Cover Dataset

USGS Publications Warehouse

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.

2006-01-01

Long-term exposure to elevated nitrate levels in community drinking water supplies has been associated with an elevated risk of several cancers including non-Hodgkin's lymphoma, colon cancer, and bladder cancer. To estimate human exposure to nitrate, specific crop type information is needed as fertilizer application rates vary widely by crop type. Corn requires the highest application of nitrogen fertilizer of crops grown in the Midwest US. We developed a method to refine the US Geological Survey National Land Cover Dataset (NLCD) (including map and original Landsat images) to distinguish corn from other crops. Overall average agreement between the resulting corn and other row crops class and ground reference data was 0.79 kappa coefficient with individual Landsat images ranging from 0.46 to 0.93 kappa. The highest accuracies occurred in Regions where corn was the single dominant crop (greater than 80.0%) and the crop vegetation conditions at the time of image acquisition were optimum for separation of corn from all other crops. Factors that resulted in lower accuracies included the accuracy of the NLCD map, accuracy of corn areal estimates, crop mixture, crop condition at the time of Landsat overpass, and Landsat scene anomalies.

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

NASA Astrophysics Data System (ADS)

Chemura, Abel; Mutanga, Onisimo; Dube, Timothy

2017-05-01

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention.

Capabilities of the new “Universal” AC-DC monitor for electropenetrography (EPG)

USDA-ARS?s Scientific Manuscript database

Electropenetrography (EPG), invented over 50 years ago, is the most rigorous and important means of studying the feeding of piercing-sucking crop pests. The 1st-generation monitor (or AC monitor) used AC applied signal voltage and had fixed amplifier sensitivity (input resistor or Ri) of 106 Ohms. T...

Testing an Irrigation Decision Support Tool for California Specialty Crops

NASA Astrophysics Data System (ADS)

Johnson, L.; Cahn, M.; Benzen, S.; Zaragoza, I.; Murphy, L.; Melton, F. S.; Martin, F.; Quackenbush, A.; Lockhart, T.

2015-12-01

Estimation of crop evapotranspiration supports efficiency of irrigation water management, which in turn can mitigate nitrate leaching, groundwater depletion, and provide energy savings. Past research in California and elsewhere has revealed strong relationships between photosynthetically active vegetation fraction (Fc) and crop evapotranspiration (ETc). Additional research has shown the potential of monitoring Fc by satellite remote sensing. The U.C. Cooperative Extension developed and operates CropManage (CM) as on-line database irrigation (and nitrogen) scheduling tool. CM accounts for the rapid growth and typically brief cycle of cool-season vegetables, where Fc and fraction of reference ET can change daily during canopy development. The model automates crop water requirement calculations based on reference ET data collected by California Dept. Water Resources. Empirically-derived equations are used to estimate daily Fc time-series for a given crop type primarily as a function of planting date and expected harvest date. An application programming interface (API) is under development to provide a check on modeled Fc of current crops and facilitate CM expansion to new crops. The API will enable CM to extract field scale Fc observations from NASA's Satellite Irrigation Management Support (SIMS). SIMS is mainly Landsat based and currently monitors Fc over about 8 million irrigation acres statewide, with potential for adding data from ESA/Sentinel for improved temporal resolution. In the current study, a replicated irrigation trial was performed on romaine lettuce at the USDA Agricultural Research Station in Salinas, CA. CropManage recommendations were used to guide water treatments by drip irrigation at 50%, 75%, 100% ETc replacement levels, with an added treatment at 150% ET representing grower standard practice. Experimental results indicate that yields from the 100% and 150% treatments were not significantly different and were in-line with industry average, while yields from the 75% and 50% treatments were significantly lower. Additional results will be presented with respect to a subsequent cabbage trial harvested October 2015.

Development of a wireless crop growth monitor based on optical principle

NASA Astrophysics Data System (ADS)

Li, Xihua; Li, Minzan; Cui, Di

2008-12-01

In order to detect the plant's nitrogen content in real-time, a wireless crop growth monitor is developed. It is made up of a sensor and a controller. The sensor consists of an optical part and a circuit part. The optical part is made up of 4 optical channels and 4 photo-detectors. 2 channels receive the sunlight and the other 2 receive the reflected light from the crop canopy. The intensity of sunlight and the reflected light is measured at the wavebands of 610 nm and 1220 nm respectively. The circuit part is made up of power supply unit, 4 amplifiers and a wireless module. The controller has functions such as keyboard input, LCD display, data storage, data upload and so on. Both hardware and software are introduced in this report. Calibration tests show that the optical part has a high accuracy and the wireless transmission also has a good performance.

Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention.

PubMed

Park, Dae-Heon; Park, Jang-Woo

2011-01-01

Dew condensation on the leaf surface of greenhouse crops can promote diseases caused by fungus and bacteria, affecting the growth of the crops. In this paper, we present a WSN (Wireless Sensor Network)-based automatic monitoring system to prevent dew condensation in a greenhouse environment. The system is composed of sensor nodes for collecting data, base nodes for processing collected data, relay nodes for driving devices for adjusting the environment inside greenhouse and an environment server for data storage and processing. Using the Barenbrug formula for calculating the dew point on the leaves, this system is realized to prevent dew condensation phenomena on the crop's surface acting as an important element for prevention of diseases infections. We also constructed a physical model resembling the typical greenhouse in order to verify the performance of our system with regard to dew condensation control.

Identifying the Impact of Natural Hazards on Food Security in Africa: Crop Monitoring Using MODIS NDVI Time-Series

NASA Astrophysics Data System (ADS)

Freund, J. T.; Husak, G.; Funk, C.; Brown, M. E.; Galu, G.

2005-12-01

Most developing countries rely primarily on the successful cultivation of staple crops to ensure food security. Climatic hazards like drought and flooding often negatively impact economically vulnerable economies such as those in Eastern Africa. Effective tracking of food production is required in this area. Production is typically quantified as the simple product of a planted area and its corresponding crop yield. To date, crop yields have been estimated with reasonable accuracy using grid-cell techniques and a Water Requirement Satisfaction Index (WRSI), which draw from remotely sensed data. However, planted area and hence production estimation remains an arduous manual technique fraught with inevitable inaccuracies. In this study we present ongoing efforts to use MODIS NDVI time-series data as a surrogate for greenness, exploiting phenological contrast between cropland and other land cover types. In regions with small field sizes, variations in land cover can impose uncertainty in food production figures, resulting in a lack of consensus in the donor community as to the amount and type of food aid required during an emergency. To concentrate on this issue, statistical methods were employed to produce sub-pixel estimation, addressing the challenges in a monitoring system for use in subsistence-farmed areas. We will discuss two key results. Firstly, we established an inter-annual evaluation of crop health in primary agricultural areas in Kenya. These estimates will greatly improve our ability to anticipate and prevent famine in risk-prone regions through the FEWS NET early warning system. A primary goal is to build capacity in high-risk areas through the transfer of these results to local entities in the form of an operational tool. The low cost and accessibility of MODIS data lends itself well to this objective. Monitoring of crop health will be instituted for use on a yearly basis, and will draw on MODIS data analysis, ground sampling and valuable local expertise. Secondly, a baseline map of cropped areas was established, utilizing MODIS time-series data, Landsat ETM+ data and a custom dot-grid sampling method. This product aids in disaggregating crop location and density, and establishes a nominal quantitative assessment of farming practices. The techniques used to generate these results for Kenya can be expanded for use throughout developing Africa and beyond.

Assessment of Climate Suitability of Maize in South Korea

NASA Astrophysics Data System (ADS)

Hyun, S.; Choi, D.; Seo, B.

2017-12-01

Assessing suitable areas for crops would be useful to design alternate cropping systems as an adaptation option to climate change adaptation. Although suitable areas could be identified by using a crop growth model, it would require a number of input parameters including cultivar and soil. Instead, a simple climate suitability model, e.g., EcoCrop model, could be used for an assessment of climate suitability for a major grain crop. The objective of this study was to assess of climate suitability for maize using the EcoCrop model under climate change conditions in Korea. A long term climate data from 2000 - 2100 were compiled from weather data source. The EcoCrop model implemented in R was used to determine climate suitability index at each grid cell. Overall, the EcoCrop model tended to identify suitable areas for maize production near the coastal areas whereas the actual major production areas located in inland areas. It is likely that the discrepancy between assessed and actual crop production areas would result from the socioeconomic aspects of maize production. Because the price of maize is considerably low, maize has been grown in an area where moisture and temperature conditions would be less than optimum. In part, a simple algorithm to predict climate suitability for maize would caused a relatively large error in climate suitability assessment under the present climate conditions. In 2050s, the climate suitability for maize increased in a large areas in southern and western part of Korea. In particular, the plain areas near the coastal region had considerably greater suitability index in the future compared with mountainous areas. The expansion of suitable areas for maize would help crop production policy making such as the allocation of rice production area for other crops due to considerably less demand for the rice in Korea.

COSMO-SkyMed potentiality to identify crop-specific behavior and monitor phenological parameters

NASA Astrophysics Data System (ADS)

Guarini, Rocchina; Segalini, Federica; Mastronardi, Giovanni; Notarnicola, Claudia; Vuolo, Francesco; Dini, Luigi

2014-10-01

This work aims at investigating the capability of COSMO-SkyMed® (CSK®) constellation of Synthetic Aperture Radar (SAR) system to monitor the Leaf Area Index (LAI) of different crops. The experiment was conducted in the Marchfeld Region, an agricultural Austrian area, and focused on five crop species: sugar beet, soybean, potato, pea and corn. A linear regression analysis was carried out to assess the sensitivity of CSK® backscattering coefficients to crops changes base on LAI values. CSK® backscattering coefficients were averaged at a field scale (<σ°dB>) and were compared to the DEIMOS-1 derived values of estimated LAI. LAI were as well averaged over the corresponding fields (). CSK® data acquired at three polarizations (HH, VV and VH), four incidence angles (23°, 33°, 40° and 57°) and at different pixel spacings (2.5 m and 10 m) were tested to assess whether spatial resolution may influence results at a field scale and to find the best combination of polarizations and CSK® acquisition beams which indicate the highest sensitivity to crop LAI values. The preliminary results show that sugar beet can be well monitored (r = 0.72 - 0.80) by CSK® by using any of the polarization acquisition modes, at moderate to shallow incidence angles (33° - 57°). Slightly weaker correlations were found, at VH polarization only, between CSK® < σ°dB> and for potato (r = 0.65), pea (r = 0.65) and soybean (r = -0.83). Shallower view incidence angles seem to be preferable to steep ones in most cases. CSK® backscattering coefficients were no sensitive at all to LAI changes for already developed corn fields.

CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture.

PubMed

Gommes, René; Wu, Bingfang; Zhang, Ning; Feng, Xueliang; Zeng, Hongwei; Li, Zhongyuan; Chen, Bo

2017-02-01

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; www.cropwatch.com.cn , Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture

NASA Astrophysics Data System (ADS)

Gommes, René; Wu, Bingfang; Zhang, Ning; Feng, Xueliang; Zeng, Hongwei; Li, Zhongyuan; Chen, Bo

2017-02-01

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; http://www.cropwatch.com.cn, Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

Crop yield responses to a hardwood biochar across varied soils and climate conditions

USDA-ARS?s Scientific Manuscript database

Biochars applied to soil for crop yield improvements have produced mixed results. The assorted crop yield responses may be linked to employing biochars with diverse chemical and physical characteristics. To clarify if biochars can improve crop yields, it may be prudent to evaluate one biochar type...

Drought impacts and resilience on crops via evapotranspiration estimations

NASA Astrophysics Data System (ADS)

Timmermans, Joris; Asadollahi Dolatabad, Saeid

2015-04-01

Currently, the global needs for food and water is at a critical level. It has been estimated that 12.5 % of the global population suffers from malnutrition and 768 million people still do not have access to clean drinking water. This need is increasing because of population growth but also by climate change. Changes in precipitation patterns will result either in flooding or droughts. Consequently availability, usability and affordability of water is becoming challenge and efficient use of water and water management is becoming more important, particularly during severe drought events. Drought monitoring for agricultural purposes is very hard. While meteorological drought can accurately be monitored using precipitation only, estimating agricultural drought is more difficult. This is because agricultural drought is dependent on the meteorological drought, the impacts on the vegetation, and the resilience of the crops. As such not only precipitation estimates are required but also evapotranspiration at plant/plot scale. Evapotranspiration (ET) describes the amount of water evaporated from soil and vegetation. As 65% of precipitation is lost by ET, drought severity is highly linked with this variable. In drought research, the precise quantification of ET and its spatio-temporal variability is therefore essential. In this view, remote sensing based models to estimate ET, such as SEBAL and SEBS, are of high value. However the resolution of current evapotranspiration products are not good enough for monitoring the impact of the droughts on the specific crops. This limitation originates because plot scales are in general smaller than the resolution of the available satellite ET products. As such remote sensing estimates of evapotranspiration are always a combination of different land surface types and cannot be used for plant health and drought resilience studies. The goal of this research is therefore to enable adequate resolutions of daily evapotranspiration estimates for monitoring crop health during the severe drought events. The presentation will provide results of the investigation into Droughts using time series of coarse resolution daily evapotranspiration produced from the SEBS remote sensing model, on basis of MODIS data. The evapotranspiration will be converted into drought severity using the evapotranspiration deficit index (ETDI). Afterwards the disaggregation to plot scale will be investigated. This disaggregation will be performed as a weighted filtering on basis of crop-coefficient at high resolution. These growth stage of the vegeation (needed for the estimation of the crop coefficients) are estimated on basis of Normalized Difference Vegetation Index (NDVI) using Landsat 5,7 and 8 observations. The final result of the research provides good statistical information about drought resilience and crop health.

Regenerative Life Support Systems Test Bed performance - Lettuce crop characterization

NASA Technical Reports Server (NTRS)

Barta, Daniel J.; Edeen, Marybeth A.; Eckhardt, Bradley D.

1992-01-01

System performance in terms of human life support requirements was evaluated for two crops of lettuce (Lactuca sative cv. Waldmann's Green) grown in the Regenerative Life Support Systems Test Bed. Each crop, grown in separate pots under identical environmental and cultural conditions, was irrigated with half-strength Hoagland's nutrient solution, with the frequency of irrigation being increased as the crop aged over the 30-day crop tests. Averaging over both crop tests, the test bed met the requirements of 2.1 person-days of oxygen production, 2.4 person-days of CO2 removal, and 129 person-days of potential potable water production. Gains in the mass of water and O2 produced and CO2 removed could be achieved by optimizing environmental conditions to increase plant growth rate and by optimizing cultural management methods.

Increased occurrence of pesticide residues on crops grown in protected environments compared to crops grown in open field conditions.

PubMed

Allen, Gina; Halsall, Crispin J; Ukpebor, Justina; Paul, Nigel D; Ridall, Gareth; Wargent, Jason J

2015-01-01

Crops grown under plastic-clad structures or in greenhouses may be prone to an increased frequency of pesticide residue detections and higher concentrations of pesticides relative to equivalent crops grown in the open field. To test this we examined pesticide data for crops selected from the quarterly reports (2004-2009) of the UK's Pesticide Residue Committee. Five comparison crop pairs were identified whereby one crop of each pair was assumed to have been grown primarily under some form of physical protection ('protected') and the other grown primarily in open field conditions ('open'). For each pair, the number of detectable pesticide residues and the proportion of crop samples containing pesticides were statistically compared (n=100 s samples for each crop). The mean concentrations of selected photolabile pesticides were also compared. For the crop pairings of cabbage ('open') vs. lettuce ('protected') and 'berries' ('open') vs. strawberries ('protected') there was a significantly higher number of pesticides and proportion of samples with multiple residues for the protected crops. Statistically higher concentrations of pesticides, including cypermethrin, cyprodinil, fenhexamid, boscalid and iprodione were also found in the protected crops compared to the open crops. The evidence here demonstrates that, in general, the protected crops possess a higher number of detectable pesticides compared to analogous crops grown in the open. This may be due to different pesticide-use regimes, but also due to slower rates of pesticide removal in protected systems. The findings of this study raise implications for pesticide management in protected-crop systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Genome editing using CRISPR/Cas9-targeted mutagenesis: An opportunity for yield improvements of crop plants grown under environmental stresses.

PubMed

Abdelrahman, Mostafa; Al-Sadi, Abdullah M; Pour-Aboughadareh, Alireza; Burritt, David J; Tran, Lam-Son Phan

2018-03-12

Developing more crops able to sustainably produce high yields when grown under biotic/abiotic stresses is an important goal, if crop production and food security are to be guaranteed in the face of ever-increasing human population and unpredictable global climatic conditions. However, conventional crop improvement, through random mutagenesis or genetic recombination, is time-consuming and cannot keep pace with increasing food demands. Targeted genome editing (GE) technologies, especially clustered regularly interspaced short palindromic repeats (CRISPR)/(CRISPR)-associated protein 9 (Cas9), have great potential to aid in the breeding of crops that are able to produce high yields under conditions of biotic/abiotic stress. This is due to their high efficiency, accuracy and low risk of off-target effects, compared with conventional random mutagenesis methods. The use of CRISPR/Cas9 system has grown very rapidly in recent years with numerous examples of targeted mutagenesis in crop plants, including gene knockouts, modifications, and the activation and repression of target genes. The potential of the GE approach for crop improvement has been clearly demonstrated. However, the regulation and social acceptance of GE crops still remain a challenge. In this review, we evaluate the recent applications of the CRISPR/Cas9-mediated GE, as a means to produce crop plants with greater resilience to the stressors they encounter when grown under increasing stressful environmental conditions. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Changes in rainfed and irrigated crop yield response to climate in the western US

NASA Astrophysics Data System (ADS)

Li, X.; Troy, T. J.

2018-06-01

As the global population increases and the climate changes, ensuring a secure food supply is increasingly important. One strategy is irrigation, which allows for crops to be grown outside their optimal climate growing regions and which buffers against climate variability. Although irrigation is a positive climate adaptation mechanism for agriculture, it has a potentially negative effect on water resources as it can lead to groundwater depletion and diminished surface water supplies. This study quantifies how crop yields are affected by climate variability and extremes and the impact of irrigation on crop yield increases under various growing-season climate conditions. To do this, we use historical climate data and county-level rainfed and irrigated crop yields for maize, soybean, winter and spring wheat over the US to analyze the relationship between climate, crop yields, and irrigation. We find that there are optimal climates, specific to each crop, where irrigation provides a benefit and other conditions where irrigation proves to have marginal, if any, benefits. Furthermore, the relationship between crop yields and climate has changed over the last decades, with a changing sensitivity in the relationship of soybean and winter wheat yields to certain climate variables, like crop reference evapotranspiration. These two conclusions have important implications for agricultural and water resource system planning, as it implies there are more optimal climate conditions where irrigation is particularly productive and regions where irrigation should be reconsidered as there is not a significant agricultural benefit and the water could be used more productively.

Leaf wetness distribution within a potato crop

NASA Astrophysics Data System (ADS)

Heusinkveld, B. G.

2010-07-01

The Netherlands has a mild maritime climate and therefore the major interest in leaf wetness is associated with foliar plant diseases. During moist micrometeorological conditions (i.e. dew, fog, rain), foliar fungal diseases may develop quickly and thereby destroy a crop quickly. Potato crop monocultures covering several hectares are especially vulnerable to such diseases. Therefore understanding and predicting leaf wetness in potato crops is crucial in crop disease control strategies. A field experiment was carried out in a large homogeneous potato crop in the Netherlands during the growing season of 2008. Two innovative sensor networks were installed as a 3 by 3 grid at 3 heights covering an area of about 2 hectares within two larger potato crops. One crop was located on a sandy soil and one crop on a sandy peat soil. In most cases leaf wetting starts in the top layer and then progresses downward. Leaf drying takes place in the same order after sunrise. A canopy dew simulation model was applied to simulate spatial leaf wetness distribution. The dew model is based on an energy balance model. The model can be run using information on the above-canopy wind speed, air temperature, humidity, net radiation and within canopy air temperature, humidity and soil moisture content and temperature conditions. Rainfall was accounted for by applying an interception model. The results of the dew model agreed well with the leaf wetness sensors if all local conditions were considered. The measurements show that the spatial correlation of leaf wetness decreases downward.

Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring

PubMed Central

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2016-01-01

Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change. PMID:27941704

Synergy between optical and microwave remote sensing to derive soil and vegetation parameters from MAC Europe 1991 Experiment

NASA Technical Reports Server (NTRS)

Taconet, O.; Benallegue, M.; Vidal, A.; Vidal-Madjar, D.; Prevot, L.; Normand, M.

1993-01-01

The ability of remote sensing for monitoring vegetation density and soil moisture for agricultural applications is extensively studied. In optical bands, vegetation indices (NDVI, WDVI) in visible and near infrared reflectances are related to biophysical quantities as the leaf area index, the biomass. In active microwave bands, the quantitative assessment of crop parameters and soil moisture over agricultural areas by radar multiconfiguration algorithms remains prospective. Furthermore the main results are mostly validated on small test sites, but have still to be demonstrated in an operational way at a regional scale. In this study, a large data set of radar backscattering has been achieved at a regional scale on a French pilot watershed, the Orgeval, along two growing seasons in 1988 and 1989 (mainly wheat and corn). The radar backscattering was provided by the airborne scatterometer ERASME, designed at CRPE, (C and X bands and HH and VV polarizations). Empirical relationships to estimate water crop and soil moisture over wheat in CHH band under actual field conditions and at a watershed scale are investigated. Therefore, the algorithms developed in CHH band are applied for mapping the surface conditions over wheat fields using the AIRSAR and TMS images collected during the MAC EUROPE 1991 experiment. The synergy between optical and microwave bands is analyzed.

ARC-2010-ACD10-0243-001

NASA Image and Video Library

2010-12-22

Wireless crop water monitoring project: Dr. Chris Lund, a scientist at the California State University Monterey Bay who is working on the NASA project at NASA Ames installs soil mositure probes in an agricultural field. The soil mositure measurements will be used to assist in interpretation of the satelite estimates of crop water deamand. Image of courtesy of Forrest S. Melton

Bamboo vs. crops: An integrated emergy and economic evaluation of using bamboo to replace crops in south Sichuan Province, China

EPA Science Inventory

Based on long-term monitoring conducted in Chang-ning county, a pilot site of the ‘Grain for Green Program’ (GFGP), an integrated emergy and economic method was applied to evaluate the dynamic ecological-economic performance of 3 kinds of bamboo systems planted on slo...

Field Evaluation of Open System Chambers for Measuring Whole Canopy Gas Exchanges

USDA-ARS?s Scientific Manuscript database

The ability to monitor whole canopy CO2 and H2O fluxes of crop plants in the field is needed for many research efforts ranging from plant breeding to the study of Climate Change effects on crops. Four portable, transparent, open system chambers for measuring canopy gas exchanges were field tested on...

Development and assessment of Transpirative Deficit Index (D-TDI) for agricultural drought monitoring

NASA Astrophysics Data System (ADS)

Borghi, Anna; Rienzner, Michele; Gandolfi, Claudio; Facchi, Arianna

2017-04-01

Drought is a major cause of crop yield loss, both in rainfed and irrigated agroecosystems. In past decades, many approaches have been developed to assess agricultural drought, usually based on the monitoring or modelling of the soil water content condition. All these indices show weaknesses when applied for a real time drought monitoring and management at the local scale, since they do not consider explicitly crops and soil properties at an adequate spatial resolution. This work describes a newly developed agricultural drought index, called Transpirative Deficit Index (D-TDI), and assesses the results of its application over a study area of about 210 km2 within the Po River Plain (northern Italy). The index is based on transforming the interannual distribution of the transpirative deficit (potential crop transpiration minus actual transpiration), calculated daily by means of a spatially distributed conceptual hydrological model and cumulated over user-selected time-steps, to a standard normal distribution (following the approach proposed by the meteorological index SPI - Standard Precipitation Index). For the application to the study area a uniform maize crop cover (maize is the most widespread crop in the area) and 22-year (1993-2014) meteorological data series were considered. Simulation results consist in maps of the index cumulated over 10-day time steps over a mesh with cells of 250 m. A correlation analysis was carried out (1) to study the characteristics and the memory of D-TDI and to assess its intra- and inter-annual variability, (2) to assess the response of the agricultural drought (i.e., the information provided by D-TDI) to the meteorological drought computed through the SPI over different temporal steps. The D-TDI is positively auto-correlated with a persistence of 30 days, and positively cross-correlated to the SPI with a persistence of 40 days, demonstrating that D-TDI responds to meteorological forcing. Correlation analyses demonstrate that soils characterized by high available water content (AWC) can more easily compensate for a short-term variability in the precipitation pattern, while soils with low AWC are more strictly linked to the SPI variability. Since D-TDI relies both on climate and fine-resolution soil and land cover data, it provides a reliable measure of the evolution of agricultural drought over the territory with respect to that achieved through meteorological drought indices. The accumulation of the index over a 10-day period considering a mesh with cells of 250 m allows to capture the response of the territory to drought at time and spatial scales of interest for stakeholders. Modelling efforts utilizing the D-TDI have potential to shed light on the vulnerability of agricultural areas to drought; future work using the D-TDI as a tool to map drought prone areas could therefore improve the ability of farmers and irrigation district managers to cope with agricultural droughts and set up adaptation actions. Despite D-TDI was used in this study on historical data series, the index has the potential to be applied for real-time or provisional monitoring by incorporating real time or provisional meteorological data, giving the opportunity to stakeholders to promptly cope with droughts.

Human health risk from heavy metal via food crops consumption with wastewater irrigation practices in Pakistan.

PubMed

Khan, Muhammad Usman; Malik, Riffat Naseem; Muhammad, Said

2013-11-01

The current study was designed to investigate the potential human health risks associated with consumption of food crops contaminated with toxic heavy metals. Cadmium (Cd) concentration in surface soils; Cd, lead (Pb) and chromium (Cr) in the irrigation water and food crops were above permissible limits. The accumulation factor (AF) was >1 for manganese (Mn) and Pb in different food crops. The Health Risk Index (HRI) was >1 for Pb in all food crops irrigated with wastewater and tube well water. HRI >1 was also recorded for Cd in all selected vegetables; and for Mn in Spinacia oleracea irrigated with wastewater. All wastewater irrigated samples (soil and food crops) exhibited high relative contamination level as compared to samples irrigated with tube well water. Our results emphasized the need for pretreatment of wastewater and routine monitoring in order to avoid contamination of food crops from the wastewater irrigation system. Copyright © 2013 Elsevier Ltd. All rights reserved.

Use of the Budyko Framework to Estimate the Virtual Water Content in Shijiazhuang Plain, North China

NASA Astrophysics Data System (ADS)

Zhang, E.; Yin, X.

2017-12-01

One of the most challenging steps in implementing analysis of virtual water content (VWC) of agricultural crops is how to properly assess the volume of consumptive water use (CWU) for crop production. In practice, CWU is considered equivalent to the crop evapotranspiration (ETc). Following the crop coefficient method, ETc can be calculated under standard or non-standard conditions by multiplying the reference evapotranspiration (ET0) by one or a few coefficients. However, when current crop growing conditions deviate from standard conditions, accurately determining the coefficients under non-standard conditions remains to be a complicated process and requires lots of field experimental data. Based on regional surface water-energy balance, this research integrates the Budyko framework into the traditional crop coefficient approach to simplify the coefficients determination. This new method enables us to assess the volume of agricultural VWC only based on some hydrometeorological data and agricultural statistic data in regional scale. To demonstrate the new method, we apply it to the Shijiazhuang Plain, which is an agricultural irrigation area in the North China Plain. The VWC of winter wheat and summer maize is calculated and we further subdivide VWC into blue and green water components. Compared with previous studies in this study area, VWC calculated by the Budyko-based crop coefficient approach uses less data and agrees well with some of the previous research. It shows that this new method may serve as a more convenient tool for assessing VWC.

Quantifying yield gaps in wheat production in Russia

NASA Astrophysics Data System (ADS)

Schierhorn, Florian; Faramarzi, Monireh; Prishchepov, Alexander V.; Koch, Friedrich J.; Müller, Daniel

2014-08-01

Crop yields must increase substantially to meet the increasing demands for agricultural products. Crop yield increases are particularly important for Russia because low crop yields prevail across Russia’s widespread and fertile land resources. However, reliable data are lacking regarding the spatial distribution of potential yields in Russia, which can be used to determine yield gaps. We used a crop growth model to determine the yield potentials and yield gaps of winter and spring wheat at the provincial level across European Russia. We modeled the annual yield potentials from 1995 to 2006 with optimal nitrogen supplies for both rainfed and irrigated conditions. Overall, the results suggest yield gaps of 1.51-2.10 t ha-1, or 44-52% of the yield potential under rainfed conditions. Under irrigated conditions, yield gaps of 3.14-3.30 t ha-1, or 62-63% of the yield potential, were observed. However, recurring droughts cause large fluctuations in yield potentials under rainfed conditions, even when the nitrogen supply is optimal, particularly in the highly fertile black soil areas of southern European Russia. The highest yield gaps (up to 4 t ha-1) under irrigated conditions were detected in the steppe areas in southeastern European Russia along the border of Kazakhstan. Improving the nutrient and water supply and using crop breeds that are adapted to the frequent drought conditions are important for reducing yield gaps in European Russia. Our regional assessment helps inform policy and agricultural investors and prioritize research that aims to increase crop production in this important region for global agricultural markets.

Remote sensing applied to crop disease control, urban planning, and monitoring aquatic plants, oil spills, rangelands, and soil moisture

NASA Technical Reports Server (NTRS)

1975-01-01

The application of remote sensing techniques to land management, urban planning, agriculture, oceanography, and environmental monitoring is discussed. The results of various projects are presented along with cost effective considerations.

7 CFR 205.203 - Soil fertility and crop nutrient management practice standard.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 3 2014-01-01 2014-01-01 false Soil fertility and crop nutrient management practice... Requirements § 205.203 Soil fertility and crop nutrient management practice standard. (a) The producer must..., and biological condition of soil and minimize soil erosion. (b) The producer must manage crop...

7 CFR 205.203 - Soil fertility and crop nutrient management practice standard.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 3 2013-01-01 2013-01-01 false Soil fertility and crop nutrient management practice... Requirements § 205.203 Soil fertility and crop nutrient management practice standard. (a) The producer must..., and biological condition of soil and minimize soil erosion. (b) The producer must manage crop...

7 CFR 205.203 - Soil fertility and crop nutrient management practice standard.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 3 2012-01-01 2012-01-01 false Soil fertility and crop nutrient management practice... Requirements § 205.203 Soil fertility and crop nutrient management practice standard. (a) The producer must..., and biological condition of soil and minimize soil erosion. (b) The producer must manage crop...

7 CFR 205.203 - Soil fertility and crop nutrient management practice standard.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 3 2011-01-01 2011-01-01 false Soil fertility and crop nutrient management practice... Requirements § 205.203 Soil fertility and crop nutrient management practice standard. (a) The producer must..., and biological condition of soil and minimize soil erosion. (b) The producer must manage crop...

Assessing above-and below-ground traits of disparate peanut genotypes for determining adaptability to soil hydrologic conditions

USDA-ARS?s Scientific Manuscript database

Crop water deficit stress contributes to more global crop loss than any other abiotic or biotic stress. To help achieve greater crop production under water scarcity, much emphasis has been placed on identifying irrigation management practices and crop genotypes for improving water stress resilience ...

Assessing crop residue cover as scene moisture conditions change

USDA-ARS?s Scientific Manuscript database

Crop residue or plant litter is the portion of a crop left in the field after harvest. Crop residues on the soil surface provide a first line of defense against water and wind erosion and reduce the amounts of soil, nutrients, and pesticides that reach streams and rivers. Thus, quantification of cro...

Impacts of elevated atmospheric CO2 on nutrient content of important food crops

NASA Astrophysics Data System (ADS)

Dietterich, Lee H.; Zanobetti, Antonella; Kloog, Itai; Huybers, Peter; Leakey, Andrew D. B.; Bloom, Arnold J.; Carlisle, Eli; Fernando, Nimesha; Fitzgerald, Glenn; Hasegawa, Toshihiro; Holbrook, N. Michele; Nelson, Randall L.; Norton, Robert; Ottman, Michael J.; Raboy, Victor; Sakai, Hidemitsu; Sartor, Karla A.; Schwartz, Joel; Seneweera, Saman; Usui, Yasuhiro; Yoshinaga, Satoshi; Myers, Samuel S.

2015-07-01

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Impacts of elevated atmospheric CO₂ on nutrient content of important food crops.

PubMed

Dietterich, Lee H; Zanobetti, Antonella; Kloog, Itai; Huybers, Peter; Leakey, Andrew D B; Bloom, Arnold J; Carlisle, Eli; Fernando, Nimesha; Fitzgerald, Glenn; Hasegawa, Toshihiro; Holbrook, N Michele; Nelson, Randall L; Norton, Robert; Ottman, Michael J; Raboy, Victor; Sakai, Hidemitsu; Sartor, Karla A; Schwartz, Joel; Seneweera, Saman; Usui, Yasuhiro; Yoshinaga, Satoshi; Myers, Samuel S

2015-01-01

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Impacts of elevated atmospheric CO2 on nutrient content of important food crops

PubMed Central

Dietterich, Lee H.; Zanobetti, Antonella; Kloog, Itai; Huybers, Peter; Leakey, Andrew D. B.; Bloom, Arnold J.; Carlisle, Eli; Fernando, Nimesha; Fitzgerald, Glenn; Hasegawa, Toshihiro; Holbrook, N. Michele; Nelson, Randall L.; Norton, Robert; Ottman, Michael J.; Raboy, Victor; Sakai, Hidemitsu; Sartor, Karla A.; Schwartz, Joel; Seneweera, Saman; Usui, Yasuhiro; Yoshinaga, Satoshi; Myers, Samuel S.

2015-01-01

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients. PMID:26217490

Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification.

PubMed

Lati, Ran N; Filin, Sagi; Aly, Radi; Lande, Tal; Levin, Ilan; Eizenberg, Hanan

2014-07-01

Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crop's leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models. © 2013 Society of Chemical Industry.

Soil-plant water status and wine quality: the case study of Aglianico wine (the ZOViSA project)

NASA Astrophysics Data System (ADS)

Bonfante, Antonello; Manna, Piero; Albrizio, Rossella; Basile, Angelo; Agrillo, Antonietta; De Mascellis, Roberto; Caputo, Pellegrina; Delle Cave, Aniello; Gambuti, Angelita; Giorio, Pasquale; Guida, Gianpiero; Minieri, Luciana; Moio, Luigi; Orefice, Nadia; Terribile, Fabio

2014-05-01

The terroir analysis, aiming to achieve a better use of environmental features with respect to plant requirement and wine production, needs to be strongly rooted on hydropedology. In fact, the relations between wine quality and soil moisture regime during the cropping season is well established. The ZOViSA Project (Viticultural zoning at farm scale) tests a new physically oriented approach to terroir analysis based on the relations between the soil-plant water status and wine quality. The project is conducted in southern Italy in the farm Quintodecimo of Mirabella Eclano (AV) located in the Campania region, devoted to quality Aglianico red wine production (DOC). The soil spatial distribution of study area (about 3 ha) was recognized by classical soil survey and geophysics scan by EM38DD; then the soil-plant water status was monitored for three years in two experimental plots from two different soils (Cambisol and Calcisol). Daily climate variables (temperature, solar radiation, rainfall, wind), daily soil water variables (through TDR probes and tensiometers), crop development (biometric and physiological parameters), and grape must and wine quality were monitored. The agro-hydrological model SWAP was calibrated and applied in the two experimental plots to estimate soil-plant water status in different crop phenological stages. The effects of crop water status on crop response and wine quality was evaluated in two different pedo-systems, comparing the crop water stress index with both: crop physiological measurements (leaf gas exchange, leaf water potential, chlorophyll content, LAI measurement), grape bunches measurements (berry weight, sugar content, titratable acidity, etc.) and wine quality (aromatic response). Finally a "spatial application" of the model was carried out and different terroirs defined.

Probabilistic estimates of drought impacts on agricultural production

NASA Astrophysics Data System (ADS)

Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

2017-08-01

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

Comparison of Soil Respiration in Typical Conventional and New Alternative Cereal Cropping Systems on the North China Plain

PubMed Central

Gao, Bing; Ju, Xiaotang; Su, Fang; Gao, Fengbin; Cao, Qingsen; Oenema, Oene; Christie, Peter; Chen, Xinping; Zhang, Fusuo

2013-01-01

We monitored soil respiration (Rs), soil temperature (T) and volumetric water content (VWC%) over four years in one typical conventional and four alternative cropping systems to understand Rs in different cropping systems with their respective management practices and environmental conditions. The control was conventional double-cropping system (winter wheat and summer maize in one year - Con.W/M). Four alternative cropping systems were designed with optimum water and N management, i.e. optimized winter wheat and summer maize (Opt.W/M), three harvests every two years (first year, winter wheat and summer maize or soybean; second year, fallow then spring maize - W/M-M and W/S-M), and single spring maize per year (M). Our results show that Rs responded mainly to the seasonal variation in T but was also greatly affected by straw return, root growth and soil moisture changes under different cropping systems. The mean seasonal CO2 emissions in Con.W/M were 16.8 and 15.1 Mg CO2 ha−1 for summer maize and winter wheat, respectively, without straw return. They increased significantly by 26 and 35% in Opt.W/M, respectively, with straw return. Under the new alternative cropping systems with straw return, W/M-M showed similar Rs to Opt.W/M, but total CO2 emissions of W/S-M decreased sharply relative to Opt.W/M when soybean was planted to replace summer maize. Total CO2 emissions expressed as the complete rotation cycles of W/S-M, Con.W/M and M treatments were not significantly different. Seasonal CO2 emissions were significantly correlated with the sum of carbon inputs of straw return from the previous season and the aboveground biomass in the current season, which explained 60% of seasonal CO2 emissions. T and VWC% explained up to 65% of Rs using the exponential-power and double exponential models, and the impacts of tillage and straw return must therefore be considered for accurate modeling of Rs in this geographical region. PMID:24278340

Comparison of soil respiration in typical conventional and new alternative cereal cropping systems on the North China plain.

PubMed

Gao, Bing; Ju, Xiaotang; Su, Fang; Gao, Fengbin; Cao, Qingsen; Oenema, Oene; Christie, Peter; Chen, Xinping; Zhang, Fusuo

2013-01-01

We monitored soil respiration (Rs), soil temperature (T) and volumetric water content (VWC%) over four years in one typical conventional and four alternative cropping systems to understand Rs in different cropping systems with their respective management practices and environmental conditions. The control was conventional double-cropping system (winter wheat and summer maize in one year--Con.W/M). Four alternative cropping systems were designed with optimum water and N management, i.e. optimized winter wheat and summer maize (Opt.W/M), three harvests every two years (first year, winter wheat and summer maize or soybean; second year, fallow then spring maize--W/M-M and W/S-M), and single spring maize per year (M). Our results show that Rs responded mainly to the seasonal variation in T but was also greatly affected by straw return, root growth and soil moisture changes under different cropping systems. The mean seasonal CO2 emissions in Con.W/M were 16.8 and 15.1 Mg CO2 ha(-1) for summer maize and winter wheat, respectively, without straw return. They increased significantly by 26 and 35% in Opt.W/M, respectively, with straw return. Under the new alternative cropping systems with straw return, W/M-M showed similar Rs to Opt.W/M, but total CO2 emissions of W/S-M decreased sharply relative to Opt.W/M when soybean was planted to replace summer maize. Total CO2 emissions expressed as the complete rotation cycles of W/S-M, Con.W/M and M treatments were not significantly different. Seasonal CO2 emissions were significantly correlated with the sum of carbon inputs of straw return from the previous season and the aboveground biomass in the current season, which explained 60% of seasonal CO2 emissions. T and VWC% explained up to 65% of Rs using the exponential-power and double exponential models, and the impacts of tillage and straw return must therefore be considered for accurate modeling of Rs in this geographical region.

Disaster Emergency Rapid Assessment Based on Remote Sensing and Background Data

NASA Astrophysics Data System (ADS)

Han, X.; Wu, J.

2018-04-01

The period from starting to the stable conditions is an important stage of disaster development. In addition to collecting and reporting information on disaster situations, remote sensing images by satellites and drones and monitoring results from disaster-stricken areas should be obtained. Fusion of multi-source background data such as population, geography and topography, and remote sensing monitoring information can be used in geographic information system analysis to quickly and objectively assess the disaster information. According to the characteristics of different hazards, the models and methods driven by the rapid assessment of mission requirements are tested and screened. Based on remote sensing images, the features of exposures quickly determine disaster-affected areas and intensity levels, and extract key disaster information about affected hospitals and schools as well as cultivated land and crops, and make decisions after emergency response with visual assessment results.

Backscatter Analysis Using Multi-Temporal SENTINEL-1 SAR Data for Crop Growth of Maize in Konya Basin, Turkey

NASA Astrophysics Data System (ADS)

Abdikan, S.; Sekertekin, A.; Ustunern, M.; Balik Sanli, F.; Nasirzadehdizaji, R.

2018-04-01

Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80 % accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

Long-Term Monitoring of Rainfed Wheat Yield and Soil Water at the Loess Plateau Reveals Low Water Use Efficiency

PubMed Central

Qin, Wei; Chi, Baoliang; Oenema, Oene

2013-01-01

Increasing crop yield and water use efficiency (WUE) in dryland farming requires a quantitative understanding of relationships between crop yield and the water balance over many years. Here, we report on a long-term dryland monitoring site at the Loess Plateau, Shanxi, China, where winter wheat was grown for 30 consecutive years and soil water content (0–200 cm) was measured every 10 days. The monitoring data were used to calibrate the AquaCrop model and then to analyse the components of the water balance. There was a strong positive relationship between total available water and mean cereal yield. However, only one-third of the available water was actually used by the winter wheat for crop transpiration. The remaining two-thirds were lost by soil evaporation, of which 40 and 60% was lost during the growing and fallow seasons, respectively. Wheat yields ranged from 0.6 to 3.9 ton/ha and WUE from 0.3 to 0.9 kg/m3. Results of model experiments suggest that minimizing soil evaporation via straw mulch or plastic film covers could potentially double wheat yields and WUE. We conclude that the relatively low wheat yields and low WUE were mainly related to (i) limited rainfall, (ii) low soil water storage during fallow season due to large soil evaporation, and (iii) poor synchronisation of the wheat growing season to the rain season. The model experiments suggest significant potential for increased yields and WUE. PMID:24302987

Influence of agricultural activities, forest fires and agro-industries on air quality in Thailand.

PubMed

Phairuang, Worradorn; Hata, Mitsuhiko; Furuuchi, Masami

2017-02-01

Annual and monthly-based emission inventories in northern, central and north-eastern provinces in Thailand, where agriculture and related agro-industries are very intensive, were estimated to evaluate the contribution of agricultural activity, including crop residue burning, forest fires and related agro-industries on air quality monitored in corresponding provinces. The monthly-based emission inventories of air pollutants, or, particulate matter (PM), NOx and SO 2 , for various agricultural crops were estimated based on information on the level of production of typical crops: rice, corn, sugarcane, cassava, soybeans and potatoes using emission factors and other parameters related to country-specific values taking into account crop type and the local residue burning period. The estimated monthly emission inventory was compared with air monitoring data obtained at monitoring stations operated by the Pollution Control Department, Thailand (PCD) for validating the estimated emission inventory. The agro-industry that has the greatest impact on the regions being evaluated, is the sugar processing industry, which uses sugarcane as a raw material and its residue as fuel for the boiler. The backward trajectory analysis of the air mass arriving at the PCD station was calculated to confirm this influence. For the provinces being evaluated which are located in the upper northern, lower northern and northeast in Thailand, agricultural activities and forest fires were shown to be closely correlated to the ambient PM concentration while their contribution to the production of gaseous pollutants is much less. Copyright © 2016. Published by Elsevier B.V.

Genetically modified crops: success, safety assessment, and public concern.

PubMed

Singh, Om V; Ghai, Shivani; Paul, Debarati; Jain, Rakesh K

2006-08-01

With the emergence of transgenic technologies, new ways to improve the agronomic performance of crops for food, feed, and processing applications have been devised. In addition, ability to express foreign genes using transgenic technologies has opened up options for producing large quantities of commercially important industrial or pharmaceutical products in plants. Despite this high adoption rate and future promises, there is a multitude of concerns about the impact of genetically modified (GM) crops on the environment. Potential contamination of the environment and food chains has prompted detailed consideration of how such crops and the molecules that they produce can be effectively isolated and contained. One of the reasonable steps after creating a transgenic plant is to evaluate its potential benefits and risks to the environment and these should be compared to those generated by traditional agricultural practices. The precautionary approach in risk management of GM plants may make it necessary to monitor significant wild and weed populations that might be affected by transgene escape. Effective risk assessment and monitoring mechanisms are the basic prerequisites of any legal framework to adequately address the risks and watch out for new risks. Several agencies in different countries monitor the release of GM organisms or frame guidelines for the appropriate application of recombinant organisms in agro-industries so as to assure the safe use of recombinant organisms and to achieve sound overall development. We feel that it is important to establish an internationally harmonized framework for the safe handling of recombinant DNA organisms within a few years.

Occurrence of pesticide residues in fruiting vegetables from production farms in south-eastern region of Poland

PubMed

Słowik-Borowiec, Magdalena; Szpyrka, Ewa; Rupar, Julian; Podbielska, Magdalena; Matyaszek, Aneta

Considering the fact that pesticides are commonly used in agriculture, continuous monitoring of these substances in food products is of great significance. Residues of these substances can be present in crops after harvest. The aim of this study was to evaluate presence of pesticide residues in fruiting vegetables from production farms in south-eastern region of Poland in 2012–2015. 138 samples were tested using accredited test methods. The monitoring programme covered determination of 242 pesticides. The tests covered tomato, cucumber and pepper crops. The test results were interpreted in accordance with criteria included in the European Commission recommendations published in the document SANCO/12571/2013 (now superseded by Document SANTE 2015), as well as on a basis of the maximum residue levels in force in the EU Member States. Pesticide residues were found in 47 samples, representing 34% of all tested samples. 17 active substances were found, belonging to fungicides and insecticides. Azoxystrobin (38%), boscalid (28%) and chlorothalonil (21%) were most commonly found in fruiting vegetables testing samples. Non-compliances related to use of plant protection product not authorized for protection of a given crop were observed in 6% of analysed samples. However, pesticide residues of fruiting vegetables in quantities that exceed the maximum residue levels (NDP, ang. MRLs), as well as substances which use for plant protection is forbidden were no found. Crops monitoring is used to determine to what extent such products are contaminated with pesticide residues, and ensures protection of customer health.

Application of MODIS images for modeling the energy balance components in the semi-arid conditions of Brazil

NASA Astrophysics Data System (ADS)

H. de C. Teixeira, Antônio; Sherer-Warren, Morris; Lopes, Hélio L.; Hernandez, Fernando B. T.; Andrade, Ricardo G.; Neale, Christopher M. U.

2013-10-01

In the semi-arid areas of Petrolina municipality, Northeast Brazil, irrigated agriculture has replaced the natural vegetation, being important the quantification of the energy exchanges between the plants and the low atmosphere. MODIS satellite images and agro-meteorological data for the years of 2010 and 2011 were used together, for modelling the energy balance components under these conditions. Surface albedo (α0), NDVI and surface temperature (T0) were the remote sensing parameters necessary to calculate the latent heat flux (λE) and the surface resistance to evapotranspiration (rs) on a large scale. The daily net radiation (Rn) was retrieved from α0, air temperature (Ta) and transmissivity (τsw), allowing the quantification of the sensible heat flux (H) by residual in the energy balance. With threshold values for rs, it was possible to do a simplified vegetation classification. The incident solar radiation (RS↓) partitioned as Rn ranged from 0.40 to 0.51, corresponding respectively to periods after the rainy season and the driest conditions of the year, with the differences between irrigated crops and natural ecosystem not significant. Considering all periods along the year the averaged fractions of Rn partitioned as H, were 31 and 78%, for irrigated crops and natural vegetation, respectively, while as λE the corresponding ratios were 69 and 22%. It was observed heat advection from the dry areas to irrigated plots, with λE exceeding Rn by 9% during the coldest periods. The models tested here can be used for monitoring the energy exchanges in agro-ecosystems under conditions of land use and climate changes.

Waves and Crops

ERIC Educational Resources Information Center

Bennett, J.

1973-01-01

Discusses wave patterns on the surfaces of ripening wheat and barley crops when the wind is moderately strong. Examines the structure of the turbulence over such natural surfaces and conditions under which the crop may be damaged by the wind. (JR)

Large-scale monitoring of effects of clothianidin dressed oilseed rape seeds on pollinating insects in Northern Germany: implementation of the monitoring project and its representativeness.

PubMed

Heimbach, Fred; Russ, Anja; Schimmer, Maren; Born, Katrin

2016-11-01

Monitoring studies at the landscape level are complex, expensive and difficult to conduct. Many aspects have to be considered to avoid confounding effects which is probably the reason why they are not regularly performed in the context of risk assessments of plant protection products to pollinating insects. However, if conducted appropriately their contribution is most valuable. In this paper we identify the requirements of a large-scale monitoring study for the assessment of side-effects of clothianidin seed-treated winter oilseed rape on three species of pollinating insects (Apis mellifera, Bombus terrestris and Osmia bicornis) and present how these requirements were implemented. Two circular study sites were delineated next to each other in northeast Germany and comprised almost 65 km 2 each. At the reference site, study fields were drilled with clothianidin-free OSR seeds while at the test site the oilseed rape seeds contained a coating with 10 g clothianidin and 2 g beta-cyfluthrin per kg seeds (Elado®). The comparison of environmental conditions at the study sites indicated that they are as similar as possible in terms of climate, soil, land use, history and current practice of agriculture as well as in availability of oilseed rape and non-crop bee forage. Accordingly, local environmental conditions were considered not to have had any confounding effect on the results of the monitoring of the bee species. Furthermore, the study area was found to be representative for other oilseed rape cultivation regions in Europe.

Effects of long-term continuous cropping on soil nematode community and soil condition associated with replant problem in strawberry habitat.

PubMed

Li, Xingyue; Lewis, Edwin E; Liu, Qizhi; Li, Heqin; Bai, Chunqi; Wang, Yuzhu

2016-08-10

Continuous cropping changes soil physiochemical parameters, enzymes and microorganism communities, causing "replant problem" in strawberry cultivation. We hypothesized that soil nematode community would reflect the changes in soil conditions caused by long-term continuous cropping, in ways that are consistent and predictable. To test this hypothesis, we studied the soil nematode communities and several soil parameters, including the concentration of soil phenolic acids, organic matter and nitrogen levels, in strawberry greenhouse under continuous-cropping for five different durations. Soil pH significantly decreased, and four phenolic acids, i.e., p-hydroxybenzoic acid, ferulic acid, cinnamic acid and p-coumaric acid, accumulated with time under continuous cropping. The four phenolic acids were highly toxic to Acrobeloides spp., the eudominant genus in non-continuous cropping, causing it to reduce to a resident genus after seven-years of continuous cropping. Decreased nematode diversity indicated loss of ecosystem stability and sustainability because of continuous-cropping practice. Moreover, the dominant decomposition pathway was altered from bacterial to fungal under continuous cropping. Our results suggest that along with the continuous-cropping time in strawberry habitat, the soil food web is disturbed, and the available plant nutrition as well as the general health of the soil deteriorates; these changes can be indicated by soil nematode community.

Effects of long-term continuous cropping on soil nematode community and soil condition associated with replant problem in strawberry habitat

NASA Astrophysics Data System (ADS)

Li, Xingyue; Lewis, Edwin E.; Liu, Qizhi; Li, Heqin; Bai, Chunqi; Wang, Yuzhu

2016-08-01

Continuous cropping changes soil physiochemical parameters, enzymes and microorganism communities, causing “replant problem” in strawberry cultivation. We hypothesized that soil nematode community would reflect the changes in soil conditions caused by long-term continuous cropping, in ways that are consistent and predictable. To test this hypothesis, we studied the soil nematode communities and several soil parameters, including the concentration of soil phenolic acids, organic matter and nitrogen levels, in strawberry greenhouse under continuous-cropping for five different durations. Soil pH significantly decreased, and four phenolic acids, i.e., p-hydroxybenzoic acid, ferulic acid, cinnamic acid and p-coumaric acid, accumulated with time under continuous cropping. The four phenolic acids were highly toxic to Acrobeloides spp., the eudominant genus in non-continuous cropping, causing it to reduce to a resident genus after seven-years of continuous cropping. Decreased nematode diversity indicated loss of ecosystem stability and sustainability because of continuous-cropping practice. Moreover, the dominant decomposition pathway was altered from bacterial to fungal under continuous cropping. Our results suggest that along with the continuous-cropping time in strawberry habitat, the soil food web is disturbed, and the available plant nutrition as well as the general health of the soil deteriorates; these changes can be indicated by soil nematode community.

A Home-Made Trap Baited With Sex Pheromone for Monitoring Spodoptera Frugiperda Males (Lepidoptera: Noctuidae) in Corn crops in Mexico.

PubMed

Malo, Edi A; Cruz-Esteban, Samuel; González, Francisco J; Rojas, Julio C

2018-05-15

Fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith), populations are monitored with a variety of commercial sex pheromone-baited traps. However, a number of trap-related variables may affect the number of FAW males captured. In this study, we tested the effect of trap design, trap size, and trap color for monitoring FAW males in corn crops in Mexico. We found that plastic jug trap (a home-made trap), captured significantly more FAW males than a commercial trap (Scentry Heliothis) and water bottle trap (another home-made trap). We also found that size of plastic jug traps (3.78, 10, or 20 liters) did not affect the captures of FAW males. Our results indicated that plastic yellow jug traps captured significantly more males than blue and black traps. Plastic jug white, red, and green traps captured a similar number of FAW males than plastic jug yellow, blue, and black traps. Plastic jug blue, white, and yellow traps captured more nontarget insects compared to black traps. The number of nontarget insects captured by green and red traps was similar and not significantly different to that caught by blue, white, yellow, and black traps. Traps captured more individuals from Diptera than Coleoptera and Hymenoptera. Overall, the results suggest that yellow plastic jug may be used for monitoring FAW males in corn and sorghum crops in Mexico.

Fitness of Crop-Wild Hybrid Sunflower under Competitive Conditions: Implications for Crop-to-Wild Introgression

PubMed Central

Mercer, Kristin L.; Emry, D. Jason; Snow, Allison A.; Kost, Matthew A.; Pace, Brian A.; Alexander, Helen M.

2014-01-01

Understanding the likelihood and extent of introgression of novel alleles in hybrid zones requires comparison of lifetime fitness of parents and hybrid progeny. However, fitness differences among cross types can vary depending on biotic conditions, thereby influencing introgression patterns. Based on past work, we predicted that increased competition would enhance introgression between cultivated and wild sunflower (Helianthus annuus) by reducing fitness advantages of wild plants. To test this prediction, we established a factorial field experiment in Kansas, USA where we monitored the fitness of four cross types (Wild, F1, F2, and BCw hybrids) under different levels of interspecific and intraspecific competition. Intraspecific manipulations consisted both of density of competitors and of frequency of crop-wild hybrids. We recorded emergence of overwintered seeds, survival to reproduction, and numbers of seeds produced per reproductive plant. We also calculated two compound fitness measures: seeds produced per emerged seedling and seeds produced per planted seed. Cross type and intraspecific competition affected emergence and survival to reproduction, respectively. Further, cross type interacted with competitive treatments to influence all other fitness traits. More intense competition treatments, especially related to density of intraspecific competitors, repeatedly reduced the fitness advantage of wild plants when considering seeds produced per reproductive plant and per emerged seedling, and F2 plants often became indistinguishable from the wilds. Wild fitness remained superior when seedling emergence was also considered as part of fitness, but the fitness of F2 hybrids relative to wild plants more than quadrupled with the addition of interspecific competitors and high densities of intraspecific competitors. Meanwhile, contrary to prediction, lower hybrid frequency reduced wild fitness advantage. These results emphasize the importance of taking a full life cycle perspective. Additionally, due to effects of exogenous selection, a given hybrid generation may be especially well-suited to hastening introgression under particular environmental conditions. PMID:25295859

Fitness of crop-wild hybrid sunflower under competitive conditions: implications for crop-to-wild introgression.

PubMed

Mercer, Kristin L; Emry, D Jason; Snow, Allison A; Kost, Matthew A; Pace, Brian A; Alexander, Helen M

2014-01-01

Understanding the likelihood and extent of introgression of novel alleles in hybrid zones requires comparison of lifetime fitness of parents and hybrid progeny. However, fitness differences among cross types can vary depending on biotic conditions, thereby influencing introgression patterns. Based on past work, we predicted that increased competition would enhance introgression between cultivated and wild sunflower (Helianthus annuus) by reducing fitness advantages of wild plants. To test this prediction, we established a factorial field experiment in Kansas, USA where we monitored the fitness of four cross types (Wild, F1, F2, and BCw hybrids) under different levels of interspecific and intraspecific competition. Intraspecific manipulations consisted both of density of competitors and of frequency of crop-wild hybrids. We recorded emergence of overwintered seeds, survival to reproduction, and numbers of seeds produced per reproductive plant. We also calculated two compound fitness measures: seeds produced per emerged seedling and seeds produced per planted seed. Cross type and intraspecific competition affected emergence and survival to reproduction, respectively. Further, cross type interacted with competitive treatments to influence all other fitness traits. More intense competition treatments, especially related to density of intraspecific competitors, repeatedly reduced the fitness advantage of wild plants when considering seeds produced per reproductive plant and per emerged seedling, and F2 plants often became indistinguishable from the wilds. Wild fitness remained superior when seedling emergence was also considered as part of fitness, but the fitness of F2 hybrids relative to wild plants more than quadrupled with the addition of interspecific competitors and high densities of intraspecific competitors. Meanwhile, contrary to prediction, lower hybrid frequency reduced wild fitness advantage. These results emphasize the importance of taking a full life cycle perspective. Additionally, due to effects of exogenous selection, a given hybrid generation may be especially well-suited to hastening introgression under particular environmental conditions.

Indexes of Land Use Change to Predict Aggregate Stability in a Mollisol and a Vertisol of Argentina

NASA Astrophysics Data System (ADS)

Novelli, L. E.; Caviglia, O. P.; Wilson, M. G.; Sasal, M. C.

2012-04-01

In several areas of South America, the extensive cropping systems in traditional agricultural lands have increase the area cropped with soybean, mainly as a single annual crop. Also nowadays agriculture has a progressive expansion toward more environmentally fragile areas that were traditionally occupied by livestock or native forests. This change of land use may be characterized through different indexes as the length of the growth period or the frequency of a particular crop in the cropping sequence. On the other hand the consequences of land-use changes on soil physical condition may be monitored through the aggregate stability, which is directly related to soil functionality. However, there are different methods for aggregate stability analysis, which may vary in their potential for prediction. The aim of our work was to assess different quantitative indexes of change in the land use on aggregate stability through two methods in two soils differing in the main agents of aggregation. The study was conducted in a Mollisol and a Vertisol from Argentina. Eleven fields (agricultural and crop-pasture rotation) under no-tillage and one natural grassland were selected in each soil type. The fraction of annual time with vegetal cover (as a measure of the intensification in the land use - ISI) and the frequency of a given crop (soybean - SCF; wheat - WCF; and wheat plus maize - CCF) in the cropping sequence over a 6-year period were calculated. Samples were collected at 0-5 and 5-15 cm depths from each soil. The mean weight diameter (MWD) of the soil aggregates where determined by two methods: Le Bissonnais with three pretreatment (fast wetting, slow wetting and stirring after prewetting) and by wet sieving using an instrument similar to the Yoder apparatus. The MWD by wet-sieving was affected by ISI and SCF, but the impact only was recorded in 0-5cm depth of the Mollisol. The MWD by fast and slow wetting and the means of three pretreatments (MWDm) were directly related to ISI, SCF and WCF in both depth of the Mollisol. Although in the Vertisol, the aggregate stability in natural grassland was higher than under agricultural use, indexes did not show the change in the land use for any pretreatment or depth, except by ISI and SCF in the slow wetting pretreatment at 5-15 cm depth. The method of Le Bissonnais was more sensitive to predict changes in the land use driven by the frequency of a given crop in the cropping sequence that the wet-sieving, mainly in the Mollisol.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

PubMed Central

Jayaraman, Prem Prakash; Yavari, Ali; Georgakopoulos, Dimitrios; Morshed, Ahsan; Zaslavsky, Arkady

2016-01-01

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations. PMID:27834862

Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

NASA Astrophysics Data System (ADS)

Xie, Qiaoyun; Huang, Wenjiang; Dash, Jadunandan; Song, Xiaoyu; Huang, Linsheng; Zhao, Jinling; Wang, Renhong

2015-12-01

Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices.

Bt-transgenic oilseed rape hybridization with its weedy relative, Brassica rapa.

PubMed

Halfhill, Matthew D; Millwood, Reginald J; Raymer, Paul L; Stewart, C Neal

2002-10-01

The movement of transgenes from crops to weeds and the resulting consequences are concerns of modern agriculture. The possible generation of "superweeds" from the escape of fitness-enhancing transgenes into wild populations is a risk that is often discussed, but rarely studied. Oilseed rape, Brassica napus (L.), is a crop with sexually compatible weedy relatives, such as birdseed rape (Brassica rapa (L.)). Hybridization of this crop with weedy relatives is an extant risk and an excellent interspecific gene flow model system. In laboratory crosses, T3 lines of seven independent transformation events of Bacillus thuringiensis (Bt) oilseed rape were hybridized with two weedy accessions of B. rapa. Transgenic hybrids were generated from six of these oilseed rape lines, and the hybrids exhibited an intermediate morphology between the parental species. The Bt transgene was present in the hybrids, and the protein was synthesized at similar levels to the corresponding independent oilseed rape lines. Insect bioassays were performed and confirmed that the hybrid material was insecticidal. The hybrids were backcrossed with the weedy parent, and only half the oilseed rape lines were able to produce transgenic backcrosses. After two backcrosses, the ploidy level and morphology of the resultant plants were indistinguishable from B. rapa. Hybridization was monitored under field conditions (Tifton, GA, USA) with four independent lines of Bt oilseed rape with a crop to wild relative ratio of 1200:1. When B. rapa was used as the female parent, hybridization frequency varied among oilseed rape lines and ranged from 16.9% to 0.7%.

Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

USDA-ARS?s Scientific Manuscript database

Precision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress ind...

Educational Software for Illustration of Drainage, Evapotranspiration, and Crop Yield.

ERIC Educational Resources Information Center

Khan, A. H.; And Others

1996-01-01

Describes a study that developed a software package for illustrating drainage, evapotranspiration, and crop yield as influenced by water conditions. The software is a tool for depicting water's influence on crop production in western Kansas. (DDR)

A dual indicator set to help farms achieve more sustainable crop protection.

PubMed

Wustenberghs, Hilde; Delcour, Ilse; D'Haene, Karoline; Lauwers, Ludwig; Marchand, Fleur; Steurbaut, Walter; Spanoghe, Pieter

2012-08-01

Farmers are being called to use plant protection products (PPPs) more consciously and adopt more sustainable crop protection strategies. Indicators will help farmers to monitor their progress towards sustainability and will support their learning process. Talking the indicators through in farmers' discussion groups and the resulting peer encouragement will foster knowledge acquirement and can lead to changes in attitudes, norms, perception and behaviour. Using a participatory approach, a conceptual framework for on-farm sustainable crop protection practices was created. The same participatory approach was used to design a dual indicator set, which pairs a pesticide impact assessment system (PIAS) with a farm inquiry. The PIAS measures the risk for human health and the environment exerted by chemical crop protection. The inquiry reveals the farmers' response to this risk, both in terms of the actions they take and their knowledge, awareness and attitude. The dual indicator set allows for implementation in four tiers, each representing increased potential for monitoring and social learning. The indicator set can be adjusted on the basis of new findings, and the participatory approach can be extrapolated to other situations. Copyright © 2012 Society of Chemical Industry.

Identifying Decision Support Tools to Bridge Climate and Agricultural Needs in the Midwest

NASA Astrophysics Data System (ADS)

Hall, B. L.; Kluck, D. R.; Hatfield, J.; Black, C.; Kellner, O.; Woloszyn, M.; Timlin, M. S.

2015-12-01

Climate monitoring tools designed to help stakeholders reduce climate impacts have been developed for the primary Midwest field crops of corn and soybean. However, the region also produces vital livestock and specialty crops that currently lack similar climate monitoring and projection tools. In autumn 2015, the National Oceanic and Atmospheric Administration's (NOAA's) National Integrated Drought Information System (NIDIS) and Midwestern Regional Climate Center (MRCC) partnered with the US Department of Agriculture's Midwest Climate Hub to convene agriculture stakeholders, climate scientists, and climate service specialists to discuss climate impacts and needs for these two, often under-represented, sectors. The goals of this workshop were to (1) identify climate impacts that specialty crops and livestock producers face within the Midwest, (2) develop an understanding of the types of climate and weather information and tools currently available in the Midwest that could be applied to decision making, and (3) discover the types of climate and weather information and tools needed to address concerns of specialty crop and livestock commodities across the Midwest. This presentation will discuss the workshop and provide highlights of the outcomes that developed into strategic plans for the future to better serve these sectors of agriculture in the Midwest.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

How to Create Conditioned Taste Aversion for Grazing Ground Covers in Woody Crops with Small Ruminants

PubMed Central

Manuelian, Carmen L.; Albanell, Elena; Rovai, Maristela; Caja, Gerardo

2016-01-01

Conditioned taste aversion (CTA) is a learning behavior process where animals are trained to reject certain feed after gastrointestinal discomfort has been produced. Lithium chloride (LiCl) is the preferred agent used in livestock to induce CTA because it specifically stimulates the vomit center. In addition, LiCl is commercially available, and easy to prepare and administer using a drenching gun. Nevertheless, some factors have to be considered to obtain an effective long-lasting CTA, which allows small ruminants to graze during the cropping season. A key aspect is to use animals with no previous contact with the target plant (the plant chosen to be avoided; new feed). Due to their native neophobic feeding behavior, small ruminants can easily associate the negative feedback effects with the new feed, resulting in a strong and persistent CTA. The recommended doses are 200 and 225 mg LiCl/kg body weight (BW) for goats and sheep, respectively. To induce CTA, 100 g of the target plant should be individually offered for at least 30 min, and LiCl administered thereafter if the intake is greater than 10 g. Each time the animal eats the target plant without negative consequences, the CTA becomes weaker. Consequently, to minimize the risk of target plant consumption, it is essential to have sufficient palatable ground cover available. The presence of an alternative feed (of quality and quantity) prevents the accidental consumption of the target plant. A close monitoring of the flock is recommended to remove and re-dose any animal consuming more than 4 bites or 10 g of the target plant. At the beginning of each grazing season, check the CTA status of each animal before moving them to the crop. PMID:27167860

Characterizing drought stress and trait influence on maize yield under current and future conditions.

PubMed

Harrison, Matthew T; Tardieu, François; Dong, Zhanshan; Messina, Carlos D; Hammer, Graeme L

2014-03-01

Global climate change is predicted to increase temperatures, alter geographical patterns of rainfall and increase the frequency of extreme climatic events. Such changes are likely to alter the timing and magnitude of drought stresses experienced by crops. This study used new developments in the classification of crop water stress to first characterize the typology and frequency of drought-stress patterns experienced by European maize crops and their associated distributions of grain yield, and second determine the influence of the breeding traits anthesis-silking synchrony, maturity and kernel number on yield in different drought-stress scenarios, under current and future climates. Under historical conditions, a low-stress scenario occurred most frequently (ca. 40%), and three other stress types exposing crops to late-season stresses each occurred in ca. 20% of cases. A key revelation shown was that the four patterns will also be the most dominant stress patterns under 2050 conditions. Future frequencies of low drought stress were reduced by ca. 15%, and those of severe water deficit during grain filling increased from 18% to 25%. Despite this, effects of elevated CO2 on crop growth moderated detrimental effects of climate change on yield. Increasing anthesis-silking synchrony had the greatest effect on yield in low drought-stress seasonal patterns, whereas earlier maturity had the greatest effect in crops exposed to severe early-terminal drought stress. Segregating drought-stress patterns into key groups allowed greater insight into the effects of trait perturbation on crop yield under different weather conditions. We demonstrate that for crops exposed to the same drought-stress pattern, trait perturbation under current climates will have a similar impact on yield as that expected in future, even though the frequencies of severe drought stress will increase in future. These results have important ramifications for breeding of maize and have implications for studies examining genetic and physiological crop responses to environmental stresses. © 2013 John Wiley & Sons Ltd.

Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

NASA Astrophysics Data System (ADS)

Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

2015-12-01

The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast. The period of analysis is from 1985-2010 (over which the reforecasts of GEFS is available) and the focus season is October-November-December. We examine the improvement (if any) in long-term skill, and present results for several recent drought events in the region.

Characterization of yield reduction in Ethiopia using a GIS-based crop water balance model

USGS Publications Warehouse

Senay, G.B.; Verdin, J.

2003-01-01

In many parts of sub-Saharan Africa, subsistence agriculture is characterized by significant fluctuations in yield and production due to variations in moisture availability to staple crops. Widespread drought can lead to crop failures, with associated deterioration in food security. Ground data collection networks are sparse, so methods using geospatial rainfall estimates derived from satellite and gauge observations, where available, have been developed to calculate seasonal crop water balances. Using conventional crop production data for 4 years in Ethiopia (1996-1999), it was found that water-limited and water-unlimited growing regions can be distinguished. Furthermore, maize growing conditions are also indicative of conditions for sorghum. However, another major staple, teff, was found to behave sufficiently differently from maize to warrant studies of its own.

Characterization of Soil Moisture Level for Rice and Maize Crops using GSM Shield and Arduino Microcontroller

NASA Astrophysics Data System (ADS)

Gines, G. A.; Bea, J. G.; Palaoag, T. D.

2018-03-01

Soil serves a medium for plants growth. One factor that affects soil moisture is drought. Drought has been a major cause of agricultural disaster. Agricultural drought is said to occur when soil moisture is insufficient to meet crop water requirements, resulting in yield losses. In this research, it aimed to characterize soil moisture level for Rice and Maize Crops using Arduino and applying fuzzy logic. System architecture for soil moisture sensor and water pump were the basis in developing the equipment. The data gathered was characterized by applying fuzzy logic. Based on the results, applying fuzzy logic in validating the characterization of soil moisture level for Rice and Maize crops is accurate as attested by the experts. This will help the farmers in monitoring the soil moisture level of the Rice and Maize crops.

A model of plant canopy polarization

NASA Technical Reports Server (NTRS)

Vanderbilt, V. C.

1980-01-01

A model for the amount of linearly polarized light reflected by the shiny leaves of grain crops is based on the morphological and phenological characteristics of the plant canopy and upon the Fresnel equations which describe the light reflection process at the smooth boundary separating two dielectrics. The theory used demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops such as wheat and barley, two crops which are so spectrally similar that they are misclassified with unacceptable frequency. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The information content of these measurements is needed to evaluate the proposed polarization sensor for the satellite-borne multispectral resource sampler.

Using a water-food-energy nexus approach for optimal irrigation management during drought events in Nebraska

NASA Astrophysics Data System (ADS)

Campana, P. E.; Zhang, J.; Yao, T.; Melton, F. S.; Yan, J.

2017-12-01

Climate change and drought have severe impacts on the agricultural sector affecting crop yields, water availability, and energy consumption for irrigation. Monitoring, assessing and mitigating the effects of climate change and drought on the agricultural and energy sectors are fundamental challenges that require investigation for water, food, and energy security issues. Using an integrated water-food-energy nexus approach, this study is developing a comprehensive drought management system through integration of real-time drought monitoring with real-time irrigation management. The spatially explicit model developed, GIS-OptiCE, can be used for simulation, multi-criteria optimization and generation of forecasts to support irrigation management. To demonstrate the value of the approach, the model has been applied to one major corn region in Nebraska to study the effects of the 2012 drought on crop yield and irrigation water/energy requirements as compared to a wet year such as 2009. The water-food-energy interrelationships evaluated show that significant water volumes and energy are required to halt the negative effects of drought on the crop yield. The multi-criteria optimization problem applied in this study indicates that the optimal solutions of irrigation do not necessarily correspond to those that would produce the maximum crop yields, depending on both water and economic constraints. In particular, crop pricing forecasts are extremely important to define the optimal irrigation management strategy. The model developed shows great potential in precision agriculture by providing near real-time data products including information on evapotranspiration, irrigation volumes, energy requirements, predicted crop growth, and nutrient requirements.

Within-Crop Air Temperature and Humidity Outcomes on Spatio-Temporal Distribution of the Key Rose Pest Frankliniella occidentalis.

PubMed

Fatnassi, Hicham; Pizzol, Jeannine; Senoussi, Rachid; Biondi, Antonio; Desneux, Nicolas; Poncet, Christine; Boulard, Thierry

2015-01-01

Frankliniella occidentalis (Pergande) is a key pest of various crops worldwide. In this study, we analyse the dependence of the infestation of this pest on spatially distributed micro climatic factors in a rose greenhouse. Despite the importance of this subject, the few existing studies have been realized in laboratory rather than in greenhouse conditions. However, recent progress on greenhouse microclimate characterisation has highlighted the strong indoor climate heterogeneity that may influence the within-crop pest distribution. In this study, both microclimate (air temperature and humidity) and thrips distribution were simultaneously mapped in a rose greenhouse. The measurements were sensed in a horizontal plane situated at mid-height of the rose crop inside the greenhouse. Simultaneously, thrips population dynamics were assessed after an artificial and homogeneous infestation of the rose crop. The spatio-temporal distribution of climate and thrips within the greenhouse were compared, and links between thrips infestation and climatic conditions were investigated. A statistical model was used to define the favourable climate conditions for thrips adults and larvae. Our results showed that (i) the air temperature and air humidity were very heterogeneously distributed within the crop, (ii) pest populations aggregated in the most favourable climatic areas and (iii) the highest population density of thrips adults and larvae were recorded at 27°C and 22°C for temperature and 63% and 86% for humidity, respectively. These findings confirm, in real rose cropping conditions, previous laboratory studies on the F. occidentalis climatic optimum and provide a solid scientific support for climatic-based control methods against this pest.