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...

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.

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.

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.

Humans as Sensors: Assessing the Information Value of Qualitative Farmer's Crop Condition Surveys for Crop Yield Monitoring and Forecasting

NASA Astrophysics Data System (ADS)

Beguería, S.

2017-12-01

While large efforts are devoted to developing crop status monitoring and yield forecasting systems trough the use of Earth observation data (mostly remotely sensed satellite imagery) and observational and modeled weather data, here we focus on the information value of qualitative data on crop status from direct observations made by humans. This kind of data has a high value as it reflects the expert opinion of individuals directly involved in the development of the crop. However, they have issues that prevent their direct use in crop monitoring and yield forecasting systems, such as their non-spatially explicit nature, or most importantly their qualitative nature. Indeed, while the human brain is good at categorizing the status of physical systems in terms of qualitative scales (`very good', `good', `fair', etcetera), it has difficulties in quantifying it in physical units. This has prevented the incorporation of this kind of data into systems that make extensive use of numerical information. Here we show an example of using qualitative crop condition data to estimate yields of the most important crops in the US early in the season. We use USDA weekly crop condition reports, which are based on a sample of thousands of reporters including mostly farmers and people in direct contact with them. These reporters provide subjective evaluations of crop conditions, in a scale including five levels ranging from `very poor' to `excellent'. The USDA report indicates, for each state, the proportion of reporters fort each condition level. We show how is it possible to model the underlying non-observed quantitative variable that reflects the crop status on each state, and how this model is consistent across states and years. Furthermore, we show how this information can be used to monitor the status of the crops and to produce yield forecasts early in the season. Finally, we discuss approaches for blending this information source with other, more classical earth data sources such as remote sensing or weather data, in the context of hierarchical regression models.

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.

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.

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.

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.

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

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...

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.

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.

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.

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.

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.

Assessing COSMO-SkyMed capability for crops identification and monitoring

NASA Astrophysics Data System (ADS)

Guarini, R.; Dini, L.

2015-12-01

In the last decade, it has been possible to better understand the impact of agricultural human practices on the global environmental change at different spatial (from local to global) and time (from seasonal to decadal) scales. This has been achieved thanks to: big dataset continuously acquired by Earth Observation (EO) satellites; the improved capabilities of remote sensing techniques in extracting valuable information from the EO datasets; the new EO data policy which allowed unrestricted data usage; the net technologies which allowed to quickly and easily share national, international and market-derived information; an increasingly performing computing technology which allows to massively process large amount of data easier and at decreasing costs. To better understand the environmental impacts of agriculture and to monitor the consequences of human agricultural activities on the biosphere, scientists require to better identify crops and monitor crop conditions over time and space. Traditionally, NDVI time series maps derived from optical sensors have been used to this aim. As well-known this important source of information is conditioned by cloud cover. Unlike passive systems, synthetic aperture radar (SAR) ones are almost insensitive to atmospheric influences; thus, they are especially suitable for crop identification and condition monitoring. Among the other SAR systems currently in orbit, the Italian Space Agency (ASI) COSMO Sky-Med® (CSK®) constellation (X-band, frequency 9.6 GHz, wavelength 3.1 cm), especially for its peculiar high revisit capability (up to four images in 16 days with same acquisition geometry) seems to be particular suitable for providing information in addition and/or in alternative to other optical EO systems. To assess the capability of the CSK® constellation in identifying crops and in monitoring crops condition in 2013 ASI started the "AGRICIDOT" project. Some of the main project achievements will be presented at the congress.

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 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.

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 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.

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...

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.

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.

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.

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.

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.

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...

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.

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.

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

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.

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.

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.

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).

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.

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.

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.

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.

Strengthening Agricultural Decisions in Countries at Risk of Food Insecurity: The GEOGLAM Crop Monitor for Early Warning

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Barker, B.; McGaughey, K.; Humber, M. L.; Sanchez, A.; Justice, C. O.; Rembold, F.; Verdin, J. P.

2016-12-01

Timely, reliable information on crop conditions, and prospects at the subnational scale, is critical for making informed policy and agricultural decisions for ensuring food security, particularly for the most vulnerable countries. However, such information is often incomplete or lacking. As such, the Crop Monitor for Early Warning (CM for EW) was developed with the goal to reduce uncertainty and strengthen decision support by providing actionable information on a monthly basis to national, regional and global food security agencies through timely consensus assessments of crop conditions. This information is especially critical in recent years, given the extreme weather conditions impacting food supplies including the most recent El Nino event. This initiative brings together the main international food security monitoring agencies and organizations to develop monthly crop assessments based on satellite observations, meteorological information, field observations and ground reports, which reflect an international consensus. This activity grew out of the successful Crop Monitor for the G20 Agricultural Market Information System (AMIS), which provides operational monthly crop assessments of the main producing countries of the world. The CM for EW was launched in February 2016 and has already become a trusted source of information internationally and regionally. Its assessments have been featured in a large number of news articles, reports, and press releases, including a joint statement by the USAID's FEWS NET, UN World Food Program, European Commission Joint Research Center, and the UN Food and Agriculture Organziation, on the devastating impacts of the southern African drought due to El Nino. One of the main priorities for this activity going forward is to expand its partnership with regional and national monitoring agencies, and strengthen capacity for national crop condition assessments.

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.

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.

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.

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.

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.

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 ...

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.

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.

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...

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.

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.

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

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/

Mobile Phenotyping System Using an Aeromotively Stabilized Cable-Driven Robot

NASA Astrophysics Data System (ADS)

Newman, M. B.; Zygielbaum, A. I.

2017-12-01

Agricultural researchers are constantly attempting to generate superior agricultural crops. Whether this means creating crops with greater yield, crops that are more resilient to disease, or crops that can tolerate harsh environments with fewer failures, test plots of these experimental crops must be studied in real-world environments with minimal invasion to determine how they will perform in full-scale agricultural settings. To monitor these crops without interfering with their natural growth, a noninvasive sensor system has been implemented. This system, instituted by the College of Agricultural Sciences and Natural Resources at the University of Nebraska - Lincoln (UNL), uses a system of poles, cables, and winches to support and maneuver a sensor platform above the crops at an outdoor phenotyping site. In this work, we improve upon the UNL outdoor phenotyping system presenting the concept design for a mobile, cable-driven phenotyping system as opposed to a permanent phenotyping facility. One major challenge in large-scale, cable-driven robots is stability of the end-effector. As a result, this mobile system seeks to use a novel method of end-effector stabilization using an onboard rotor drive system, herein referred to as the Instrument Platform Aeromotive Stabilization System (IPASS). A prototype system is developed and analyzed to determine the viability of IPASS.

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).

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.

[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.

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.

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.

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

Integrating multiple satellite data for crop monitoring

USDA-ARS?s Scientific Manuscript database

Remote sensing provides a valuable data source for detecting crop types, monitoring crop condition and predicting crop yields from space. Routine and continuous remote sensing data are critical for agricultural research and operational applications. Since crop field dimensions tend to be relatively ...

Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

2014-12-01

The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are associated to derive food production estimates. Based on trends analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. CropWatch bulletin can be downloaded from the CropWatch website at http://www.cropwatch.com.cn.

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.

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.

Mapping Farming Practices in Belgian Intensive Cropping Systems from Sentinel-1 SAR Time Series

NASA Astrophysics Data System (ADS)

Chome, G.; Baret, P. V.; Defourny, P.

2016-08-01

The environmental impact of the so-called conventional farming system calls for new farming practices reducing negative externalities. Emerging farming practices such as no-till and new inter-cropping management are promising tracks. The development of methods to characterize crop management across an entire region and to understand their spatial dimension offers opportunities to accompany the transition towards a more sustainable agriculture.This research takes advantage of the unmatched polarimetric and temporal resolutions of Sentinel-1 SAR C- band to develop a method to identify farming practices at the parcel level. To this end, the detection of changes in backscattering due to surface roughness modification (tillage, inter-crop cover destruction ...) is used to detect the farming management. The final results are compared to a reference dataset collected through an intensive field campaign. Finally, the performances are discussed in the perspective of practices monitoring of cropping systems through remote sensing.

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.

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.

RF-CLASS: A Remote-sensing-based Interoperable Web service system for Flood Crop Loss Assessment

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Kang, L.

2014-12-01

Flood is one of the worst natural disasters in the world. Flooding often causes significant crop loss over large agricultural areas in the United States. Two USDA agencies, the National Agricultural Statistics Service (NASS) and Risk Management Agency (RMA), make decisions on flood statistics, crop insurance policy, and recovery management by collecting, analyzing, reporting, and utilizing flooded crop acreage and crop loss information. NASS has the mandate to report crop loss after all flood events. RMA manages crop insurance policy and uses crop loss information to guide the creation of the crop insurance policy and the aftermath compensation. Many studies have been conducted in the recent years on monitoring floods and assessing the crop loss due to floods with remote sensing and geographic information technologies. The Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS), being developed with NASA and USDA support, aims to significantly improve the post-flood agricultural decision-making supports in USDA by integrating and advancing the recently developed technologies. RF-CLASS will operationally provide information to support USDA decision making activities on collecting and archiving flood acreage and duration, recording annual crop loss due to flood, assessing the crop insurance rating areas, investigating crop policy compliance, and spot checking of crop loss claims. This presentation will discuss the remote sensing and GIS based methods for deriving the needed information to support the decision making, the RF-CLASS cybersystem architecture, the standards and interoperability arrangements in the system, and the current and planned capabilities of the system.

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.

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.

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.

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.

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...

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, ...

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

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.

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.

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.

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.

Monitoring and Characterizing Crop Root Systems Using Electrical Impedance Tomography (EIT)

NASA Astrophysics Data System (ADS)

Weigand, M.; Kemna, A.

2016-12-01

A better understanding of root-soil interactions and associated processes is essential to achieve progress in crop breeding and management, prompting the need for high-resolution and non-destructive characterization methods. Such methods are still lacking, in particular for characterizing root growth and function in the field. A promising technique in this respect is electrical impedance tomography (EIT), which provides images of the low-frequency electrical conduction and polarization properties and thus can be used to investigate polarization processes occurring within and in the direct vicinity of roots under the influence of an external alternating electric field. This approach takes advantage of the well-known polarization properties associated with electrical double layers forming at membranes of cells and cell clusters. However, upscaling these processes to the scale of an impedance, or complex conductivity, spectrum of the whole root system is not trivial given the lack of electrical root models, the complexity of root systems, and the occurrence of additional larger-scale, ion-selective, and therefore polarizable, structures such as the Casparian strip. We here present results from several EIT laboratory studies on rhizotrons with crop root systems in aqueous solutions. Based on optimized experimental and data analysis procedures, enabling the imaging of the weak signals encountered in our studies, we found systematic spatial and temporal changes of both the magnitude and the shape of the spectral polarization signatures during nutrient deprivation and in response to the decapitation of plants. Consistent, but relatively weak, spectral impedance changes were also observed over diurnal cycles. Our results provide evidence for the capability of EIT to non-invasively image and monitor root systems at the rhizotron scale. They further suggest that EIT is a promising tool for imaging, characterizing, and monitoring crop roots at the field scale.

Real-Time Blob-Wise Sugar Beets VS Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Milioto, A.; Lottes, P.; Stachniss, C.

2017-08-01

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

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.

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.

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.

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...

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.

Improving crop condition monitoring at field scale by using optimal Landsat and MODIS images

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data at coarse resolution (kilometers) have been widely used in monitoring crop condition for decades. However, crop condition monitoring at field scale requires high resolution data in both time and space. Although a large number of remote sensing instruments with different...

Instrumentation for full-year plot-scale runoff monitoring

USDA-ARS?s Scientific Manuscript database

Replicated 0.34 ha cropping systems plots have been in place since 1991 at the USDA-ARS Goodwater Creek Experimental Watershed in central Missouri. Recently, instrumentation has been installed at 18 of those plots for continuous runoff water quality and quantity monitoring. That installation require...

Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System for Dew Condensation Prevention

PubMed Central

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. PMID:22163813

Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems

NASA Astrophysics Data System (ADS)

Weigand, Maximilian; Kemna, Andreas

2017-02-01

A better understanding of root-soil interactions and associated processes is essential in achieving progress in crop breeding and management, prompting the need for high-resolution and non-destructive characterization methods. To date, such methods are still lacking or restricted by technical constraints, in particular the charactization and monitoring of root growth and function in the field. A promising technique in this respect is electrical impedance tomography (EIT), which utilizes low-frequency (< 1 kHz)- electrical conduction- and polarization properties in an imaging framework. It is well established that cells and cell clusters exhibit an electrical polarization response in alternating electric-current fields due to electrical double layers which form at cell membranes. This double layer is directly related to the electrical surface properties of the membrane, which in turn are influenced by nutrient dynamics (fluxes and concentrations on both sides of the membranes). Therefore, it can be assumed that the electrical polarization properties of roots are inherently related to ion uptake and translocation processes in the root systems. We hereby propose broadband (mHz to hundreds of Hz) multi-frequency EIT as a non-invasive methodological approach for the monitoring and physiological, i.e., functional, characterization of crop root systems. The approach combines the spatial-resolution capability of an imaging method with the diagnostic potential of electrical-impedance spectroscopy. The capability of multi-frequency EIT to characterize and monitor crop root systems was investigated in a rhizotron laboratory experiment, in which the root system of oilseed plants was monitored in a water-filled rhizotron, that is, in a nutrient-deprived environment. We found a low-frequency polarization response of the root system, which enabled the successful delineation of its spatial extension. The magnitude of the overall polarization response decreased along with the physiological decay of the root system due to the stress situation. Spectral polarization parameters, as derived from a pixel-based Debye decomposition analysis of the multi-frequency imaging results, reveal systematic changes in the spatial and spectral electrical response of the root system. In particular, quantified mean relaxation times (of the order of 10 ms) indicate changes in the length scales on which the polarization processes took place in the root system, as a response to the prolonged induced stress situation. Our results demonstrate that broadband EIT is a capable, non-invasive method to image root system extension as well as to monitor changes associated with the root physiological processes. Given its applicability on both laboratory and field scales, our results suggest an enormous potential of the method for the structural and functional imaging of root systems for various applications. This particularly holds for the field scale, where corresponding methods are highly desired but to date are lacking.

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.

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.

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.

Monitoring Global Crop Condition Indicators Using a Web-Based Visualization Tool

Treesearch

Bob Tetrault; Bob Baldwin

2006-01-01

Global crop condition information for major agricultural regions in the world can be monitored using the web-based application called Crop Explorer. With this application, U.S. and international producers, traders, researchers, and the public can access remote sensing information used by agricultural economists and scientists who predict crop production worldwide. For...

Comparison of Uncalibrated Rgbvi with Spectrometer-Based Ndvi Derived from Uav Sensing Systems on Field Scale

NASA Astrophysics Data System (ADS)

Bareth, G.; Bolten, A.; Gnyp, M. L.; Reusch, S.; Jasper, J.

2016-06-01

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

Wireless lysimeters for real-time online soil water monitoring

USDA-ARS?s Scientific Manuscript database

Identification of nitrate-nitrogen (NO3-N) in drainage water allows accessing the effectiveness of water quality management. A passive capillary wick-type lysimeter (PCAPs) was used to monitor water flux and NO3-N leached below the root zone under an irrigated cropping system. Wireless lysimeters we...

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.

Ion-Specific Nutrient Management in Closed Systems: The Necessity for Ion-Selective Sensors in Terrestrial and Space-Based Agriculture and Water Management Systems

PubMed Central

Bamsey, Matthew; Graham, Thomas; Thompson, Cody; Berinstain, Alain; Scott, Alan; Dixon, Michael

2012-01-01

The ability to monitor and control plant nutrient ions in fertigation solutions, on an ion-specific basis, is critical to the future of controlled environment agriculture crop production, be it in traditional terrestrial settings (e.g., greenhouse crop production) or as a component of bioregenerative life support systems for long duration space exploration. Several technologies are currently available that can provide the required measurement of ion-specific activities in solution. The greenhouse sector has invested in research examining the potential of a number of these technologies to meet the industry's demanding requirements, and although no ideal solution yet exists for on-line measurement, growers do utilize technologies such as high-performance liquid chromatography to provide off-line measurements. An analogous situation exists on the International Space Station where, technological solutions are sought, but currently on-orbit water quality monitoring is considerably restricted. This paper examines the specific advantages that on-line ion-selective sensors could provide to plant production systems both terrestrially and when utilized in space-based biological life support systems and how similar technologies could be applied to nominal on-orbit water quality monitoring. A historical development and technical review of the various ion-selective monitoring technologies is provided. PMID:23201999

Ion-specific nutrient management in closed systems: the necessity for ion-selective sensors in terrestrial and space-based agriculture and water management systems.

PubMed

Bamsey, Matthew; Graham, Thomas; Thompson, Cody; Berinstain, Alain; Scott, Alan; Dixon, Michael

2012-10-01

The ability to monitor and control plant nutrient ions in fertigation solutions, on an ion-specific basis, is critical to the future of controlled environment agriculture crop production, be it in traditional terrestrial settings (e.g., greenhouse crop production) or as a component of bioregenerative life support systems for long duration space exploration. Several technologies are currently available that can provide the required measurement of ion-specific activities in solution. The greenhouse sector has invested in research examining the potential of a number of these technologies to meet the industry's demanding requirements, and although no ideal solution yet exists for on-line measurement, growers do utilize technologies such as high-performance liquid chromatography to provide off-line measurements. An analogous situation exists on the International Space Station where, technological solutions are sought, but currently on-orbit water quality monitoring is considerably restricted. This paper examines the specific advantages that on-line ion-selective sensors could provide to plant production systems both terrestrially and when utilized in space-based biological life support systems and how similar technologies could be applied to nominal on-orbit water quality monitoring. A historical development and technical review of the various ion-selective monitoring technologies is provided.

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

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

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.

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 ...

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.

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.

NASA's Biomass Production Chamber: a testbed for bioregenerative life support studies

NASA Technical Reports Server (NTRS)

Wheeler, R. M.; Mackowiak, C. L.; Stutte, G. W.; Sager, J. C.; Yorio, N. C.; Ruffe, L. M.; Fortson, R. E.; Dreschel, T. W.; Knott, W. M.; Corey, K. A.

1996-01-01

The Biomass Production Chamber (BPC) located at Kennedy Space Center, FL, USA provides a large (20 m2 area, 113 m3 vol.), closed environment for crop growth tests for NASA's Controlled Ecological Life Support System (CELSS) program. Since the summer of 1988, the chamber has operated on a near-continuous basis (over 1200 days) without any major failures (excluding temporary power losses). During this time, five crops of wheat (64-86 days each), three crops of soybean (90 to 97 days), five crops of lettuce (28-30 days), and four crops of potato (90 to 105 days were grown, producing 481 kg of dry plant biomass, 196 kg edible biomass, 540 kg of oxygen, 94,700 kg of condensed water, and fixing 739 kg of carbon dioxide. Results indicate that total biomass yields were close to expected values for the given light input, but edible biomass yields and harvest indices were slightly lower than expected. Stand photosynthesis, respiration, transpiration, and nutrient uptake rates were monitored throughout growth and development of the different crops, along with the build-up of ethylene and other volatile organic compounds in the atmosphere. Data were also gathered on system hardware maintenance and repair, as well as person-hours required for chamber operation. Future tests will include long-term crop production studies, tests in which nutrients from waste treatment systems will be used to grow new crops, and multi-species tests.

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.

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.

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.

Soil quality monitoring in an area with land use change

NASA Astrophysics Data System (ADS)

Wilson, Marcelo; Gabioud, Emmanuel; Sasal, María Carolina; Oszust, José; Paz Gonzalez, Antonio

2013-04-01

The characterization of the soil quality through soil quality indicators (SQI), provides an effective method for the monitoring of the impacts to soil by use and management decisions. The key is to identify variables that are sensitive to changes in the soil functions and processes. The native forest area of Entre Ríos (Argentina) is associated with a constant change in land use, with an increase in recent years in agricultural use, especially for soybean crop. The aim was to monitor soil quality in three soils of an area of this area where native forest is being replaced by an agricultural system based in soybean crop, using a a minimum data set (MDS) previously selected for three soil type. The three soils selected were a Vertic Argiudoll, an Aquic Argiudoll and a Vertic Ocracualf. Treatments included plots with continuous cropping with different number of years under soybean crop, crop-pasture rotation, long-term pasture (PP), and uncropped land (UC) in pristine situation, which was taken as a reference. The crops were sowed under no tillage system and some plots were systematized with terraces contour to runoff management. The selection of a group of soil indicators in a MDS, was developed locally because it must be different for each soil type and each particular use. Total organic carbon (TOC), aggregate stability and pH were common indicators. Furthermore, it was assessed macroporosity, total porosity, cation exchange capacity two biological indicators (microbial biomass Carbon and potentially mineralizable Nitrogen) and A horizon soil mass, as a measure of the soil erosion. Statistical analysis, as linear regression analysis, ANOVA and cluster analysis were used. The soil indicators showed the changes caused by soil use, being more marked deterioration in the Vertic Ocracualf. TOC, microbial biomass Carbon and aggregate stability were the most sensitive SQI. However, positive changes were observed in potentially mineralizable Nitrogen, wiht PP. In the Vertic Argiudoll, the changes caused by agricultural use were significant in the plots with most years of continuous cropping as compared with UC and PP treatments, whereas in the Vertic Ocracualf with few years under agriculture, processes of soil deterioration started to be detected. The Aquic Argiudoll showed high resilience through all SQI. In the Vertic Ocracualf, we recommended that the period of crops rotation should be shorter than the period under pasture, to maintain the soil quality. The native forest should be the basis of sustainable production systems in the area. In addition, the agricultural use should be defined according to the soil limitations, and the dynamic soil qualities.

Is the soil quality monitoring an effective tool in consumers' protection of agricultural crops from cadmium soil contamination?-a case of the Silesia region (Poland).

PubMed

Piekut, Agata; Baranowska, Renata; Marchwińska-Wyrwał, Ewa; Ćwieląg-Drabek, Małgorzata; Hajok, Ilona; Dziubanek, Grzegorz; Grochowska-Niedworok, Elżbieta

2017-12-16

The monitoring of soil quality should be a control tool used to reduce the adverse health effects arising from exposure to toxic chemicals in soil through cultivated crop absorption. The aim of the study was to evaluate the effectiveness of the monitoring and control system of soil quality in Poland, in terms of consumer safety, for agricultural plants cultivated in areas with known serious cadmium contamination, such as Silesia Province. To achieve the objective, the contents of cadmium in soils and vegetables in the Silesia administrative area were examined. The obtained results were compared with the results of soil contamination from the quality monitoring of arable soil in Poland. The studies show a significant exceedance of the permissible values of cadmium in soil samples and the vegetables cultivated on that soil. The threat to consumer health is a valid concern, although this threat was not indicated by the results of the national monitoring of soil quality. The results indicated an unequal distribution of risk to consumers resulting from contaminated soil. Moreover, the monitoring systems should be designed at the local or regional scale to guarantee the safety of consumers of edible plants cultivated in the areas contaminated with cadmium.

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.

Advances in regional crop yield estimation over the United States using satellite remote sensing data

NASA Astrophysics Data System (ADS)

Johnson, D. M.; Dorn, M. F.; Crawford, C.

2015-12-01

Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a variety of data mining and modeling options and results strongly lean toward solutions of ensemble decision trees like Cubist and Random Forest. Those comparisons of what are seen as best will be also be shown. And finally, important model refinements accounting for temporal and spatial trends have also been considered and results will be presented.

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.

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.

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.

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.

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.

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.

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.

Incorporation of Monitoring Systems to Model Irrigated Cotton at a Landscape Level

USDA-ARS?s Scientific Manuscript database

Advances in computer speed, industry IT core capabilities, and available soils and weather information have resulted in the need for “cropping system models” that address in detail the spatial and temporal water, energy and carbon balance of the system at a landscape scale. Many of these models have...

Data Requirements to Assess Department of Defense (DOD) Investments in Law Enforcement in Southwest Asia

DTIC Science & Technology

2011-09-01

form similar organizational structures—loosely-connected webs of small, specialized cells, etc.28 Illicit networks form organizational structures...Activities SIMCI Sistema Integrado de Monitoreo de Cultivos Ilícitos (Integrated Crops Monitoring System) STRIDE System To Retrieve Information from Drug

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

AgroClimate: Simulating and Monitoring the Risk of Extreme Weather Events from a Crop Phenology Perspective

NASA Astrophysics Data System (ADS)

Fraisse, C.; Pequeno, D.; Staub, C. G.; Perry, C.

2016-12-01

Climate variability, particularly the occurrence of extreme weather conditions such as dry spells and heat stress during sensitive crop developmental phases can substantially increase the prospect of reduced crop yields. Yield losses or crop failure risk due to stressful weather conditions vary mainly due to stress severity and exposure time and duration. The magnitude of stress effects is also crop specific, differing in terms of thresholds and adaptation to environmental conditions. To help producers in the Southeast USA mitigate and monitor the risk of crop losses due to extreme weather events we developed a web-based tool that evaluates the risk of extreme weather events during the season taking into account the crop development stages. Producers can enter their plans for the upcoming season in a given field (e.g. crop, variety, planting date, acreage etc.), select or not a specific El Nino Southern Oscillation (ENSO) phase, and will be presented with the probabilities (ranging from 0 -100%) of extreme weather events occurring during sensitive phases of the growing season for the selected conditions. The DSSAT models CERES-Maize, CROPGRO-Soybean, CROPGRO-Cotton, and N-Wheat phenology models have been translated from FORTRAN to a standalone versions in R language. These models have been tested in collaboration with Extension faculty and producers during the 2016 season and their usefulness for risk mitigation and monitoring evaluated. A companion AgroClimate app was also developed to help producers track and monitor phenology development during the cropping season.

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production

PubMed Central

Domingues Franceschini, Marston Héracles; Bartholomeus, Harm; van Apeldoorn, Dirk; Suomalainen, Juha; Kooistra, Lammert

2017-01-01

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm−2), leaf area index (RMSE = 0.67 m2·m−2), canopy chlorophyll (RMSE = 0.24 g·m−2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm−2, 0.85 m2·m−2, 0.28 g·m−2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system. PMID:28629159

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production.

PubMed

Domingues Franceschini, Marston Héracles; Bartholomeus, Harm; van Apeldoorn, Dirk; Suomalainen, Juha; Kooistra, Lammert

2017-06-18

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm -2 ), leaf area index (RMSE = 0.67 m²·m -2 ), canopy chlorophyll (RMSE = 0.24 g·m -2 ) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm -2 , 0.85 m²·m -2 , 0.28 g·m -2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CI g provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system.

Tracking big and small agriculture with new satellite sensors

NASA Astrophysics Data System (ADS)

Lobell, D. B.; Azzari, G.; Jin, Z.

2017-12-01

New sensors from both the public and private sector are opening up exciting possibilities for monitoring agriculture and its use of water. This talk will present selected examples from recent work using data from Planet's Planetscope and Skysat sensors as well as Sentinel-1 and Sentinel-2 missions that are part of Europe's Copernicus program. Among other things, these satellites are now helping to track crop types and productivity for fields in rainfed cropping systems of East Africa and irrigated systems in South Asia. This information should contribute to understanding land and water use decisions throughout the world.

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.

Hand-held radiometer red and photographic infrared spectral measurements of agricultural crops

NASA Technical Reports Server (NTRS)

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

1978-01-01

Red and photographic infrared radiance data, collected under a variety of conditions at weekly intervals throughout the growing season using a hand-held radiometer, were used to monitor crop growth and development. The vegetation index transformation was used to effectively compensate for the different irradiational conditions encountered during the study period. These data, plotted against time, compared the different crops measured by comparing their green leaf biomass dynamics. This approach, based entirely upon spectral inputs, closely monitors crop growth and development and indicates the promise of ground-based hand-held radiometer measurements of crops.

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.

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.

Bioenergy Feedstock Development Program Status Report

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

Kszos, L.A.

2001-02-09

The U.S. Department of Energy's (DOE's) Bioenergy Feedstock Development Program (BFDP) at Oak Ridge National Laboratory (ORNL) is a mission-oriented program of research and analysis whose goal is to develop and demonstrate cropping systems for producing large quantities of low-cost, high-quality biomass feedstocks for use as liquid biofuels, biomass electric power, and/or bioproducts. The program specifically supports the missions and goals of DOE's Office of Fuels Development and DOE's Office of Power Technologies. ORNL has provided technical leadership and field management for the BFDP since DOE began energy crop research in 1978. The major components of the BFDP include energymore » crop selection and breeding; crop management research; environmental assessment and monitoring; crop production and supply logistics operational research; integrated resource analysis and assessment; and communications and outreach. Research into feedstock supply logistics has recently been added and will become an integral component of the program.« less

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.

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.

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

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.

A Portable Farmland Information Collection System with Multiple Sensors.

PubMed

Zhang, Jianfeng; Hu, Jinyang; Huang, Lvwen; Zhang, Zhiyong; Ma, Yimian

2016-10-22

Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture-efficient use of agricultural resources, and improving the crop yields and quality-some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops.

A Portable Farmland Information Collection System with Multiple Sensors

PubMed Central

Zhang, Jianfeng; Hu, Jinyang; Huang, Lvwen; Zhang, Zhiyong; Ma, Yimian

2016-01-01

Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields and quality—some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops. PMID:27782076

The Crop Risk Zones Monitoring System for resilience to drought in the Sahel

NASA Astrophysics Data System (ADS)

Vignaroli, Patrizio; Rocchi, Leandro; De Filippis, Tiziana; Tarchiani, Vieri; Bacci, Maurizio; Toscano, Piero; Pasqui, Massimiliano; Rapisardi, Elena

2016-04-01

Food security is still one of the major concerns that Sahelian populations have to face. In the Sahel, agriculture is primarily based on rainfed crops and it is often structurally inadequate to manage the climatic variability. The predominantly rainfed cropping system of Sahel region is dependent on season quality on a year-to-year basis, and susceptible to weather extremes of droughts and extreme temperatures. Low water-storage capacity and high dependence on rainfed agriculture leave the agriculture sector even more vulnerable to climate risks. Crop yields may suffer significantly with either a late onset or early cessation of the rainy season, as well as with a high frequency of damaging dry spells. Early rains at the beginning of the season are frequently followed by dry spells which may last a week or longer. As the amount of water stored in the soil at this time of the year is negligible, early planted crops can suffer water shortage stresses during a prolonged dry spell. Therefore, the choice of the sowing date is of fundamental importance for farmers. The ability to estimate effectively the onset of the season and potentially dangerous dry spells becomes therefore vital for planning rainfed agriculture practices aiming to minimize risks and maximize yields. In this context, advices to farmers are key drivers for prevention allowing a better adaptation of traditional crop calendar to climatic variability. In the Sahel, particularly in CILSS (Permanent Interstates Committee for Drought Control in the Sahel) countries, national Early Warning System (EWS) for food security are underpinned by Multidisciplinary Working Groups (MWGs) lead by National Meteorological Services (NMS). The EWSs are mainly based on tools and models utilizing numeric forecasts and satellite data to outlook and monitor the growing season. This approach is focused on the early identification of risks and on the production of information within the prescribed time period for decision-making. Since the '90s, analysis tools and models based on meteorological satellites data have been developed within different regional and national initiatives to allow near-real-time monitoring of the cropping season. The software was in general stand-alone applications, transferred to MWGs without continuous user support and updates. Currently MWGs in the Sahel do not have any working operational tool for drought risk identification and forecast, because such tools are by now obsolete from the IT perspective. The challenge and the objective of this work is to provide to MWGs and local end-users an open access/source Crop Risk Zones Monitoring System (CRZ-MS) supporting decision making for drought risk reduction and resilience improvement. A first prototype has been developed for Niger and Mali NMSs, based on a coherent Open Source web-based infrastructure to treat all input and output data in a interoperable, platform-independent and uniform way. The System architecture and functions are based on a agro-meteorological model, running in two different modes: 1) diagnostic mode for the drought monitoring during the agro-pastoral campaign allowing MWGs to identify agricultural drought risk areas in order to support decision making at local and national level in agricultural drought management. This early warning information also represents an input for estimating the nutritional food insecurity, for the identification of potentially vulnerable populations and assessing food crises risks by National EWSs put in place by CILSS with EU, FAO and WFP. 2) predictive mode for "advisory-support" activities to the farmers by the Agricultural Extension Services, in order to implement the most appropriate strategies for minimizing drought risk on crops (i.e. identification of the optimal period of sowing, choice of varieties based on the expected length of the growing season, adoption of suitable cultural practices for soil water management) and to build farmers resilience. To increase the accessibility of appropriate and targeted drought risk information, it is essential to move from generic information to specific advises for end-users at different decision-making levels, bridging the gap between available technology and local users' needs. Thus, advices to farmers are a fundamental component of prevention allowing a better country's preparedness to cope with weather variability.

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.

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.

Global Drought Monitoring and Forecasting based on Satellite Data and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sheffield, J.; Lobell, D. B.; Wood, E. F.

2010-12-01

Monitoring drought globally is challenging because of the lack of dense in-situ hydrologic data in many regions. In particular, soil moisture measurements are absent in many regions and in real time. This is especially problematic for developing regions such as Africa where water information is arguably most needed, but virtually non-existent on the ground. With the emergence of remote sensing estimates of all components of the water cycle there is now the potential to monitor the full terrestrial water cycle from space to give global coverage and provide the basis for drought monitoring. These estimates include microwave-infrared merged precipitation retrievals, evapotranspiration based on satellite radiation, temperature and vegetation data, gravity recovery measurements of changes in water storage, microwave based retrievals of soil moisture and altimetry based estimates of lake levels and river flows. However, many challenges remain in using these data, especially due to biases in individual satellite retrieved components, their incomplete sampling in time and space, and their failure to provide budget closure in concert. A potential way forward is to use modeling to provide a framework to merge these disparate sources of information to give physically consistent and spatially and temporally continuous estimates of the water cycle and drought. Here we present results from our experimental global water cycle monitor and its African drought monitor counterpart (http://hydrology.princeton.edu/monitor). The system relies heavily on satellite data to drive the Variable Infiltration Capacity (VIC) land surface model to provide near real-time estimates of precipitation, evapotranspiraiton, soil moisture, snow pack and streamflow. Drought is defined in terms of anomalies of soil moisture and other hydrologic variables relative to a long-term (1950-2000) climatology. We present some examples of recent droughts and how they are identified by the system, including objective quantification and tracking of their spatial-temporal characteristics. Further we present strategies for merging various sources of information, including bias correction of satellite precipitation and assimilation of remotely sensed soil moisture, which can augment the monitoring in regions where satellite precipitation is most uncertain. Ongoing work is adding a drought forecast component based on a successful implementation over the U.S. and agricultural productivity estimates based on output from crop yield models. The forecast component uses seasonal global climate forecasts from the NCEP Climate Forecast System (CFS). These are merged with observed climatology in a Bayesian framework to produce ensemble atmospheric forcings that better capture the uncertainties. At the same time, the system bias corrects and downscales the monthly CFS data. We show some initial seasonal (up to 6-month lead) hydrologic forecast results for the African system. Agricultural monitoring is based on the precipitation, temperature and soil moisture from the system to force statistical and process based crop yield models. We demonstrate the feasibility of monitoring major crop types across the world and show a strategy for providing predictions of yields within our drought forecast mode.

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.

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.

Monitoring and modeling agricultural drought for famine early warning (Invited)

NASA Astrophysics Data System (ADS)

Verdin, J. P.; Funk, C.; Budde, M. E.; Lietzow, R.; Senay, G. B.; Smith, R.; Pedreros, D.; Rowland, J.; Artan, G. A.; Husak, G. J.; Michaelsen, J.; Adoum, A.; Galu, G.; Magadzire, T.; Rodriguez, M.

2009-12-01

The Famine Early Warning Systems Network (FEWS NET) makes quantitative estimates of food insecure populations, and identifies the places and periods during which action must be taken to assist them. Subsistence agriculture and pastoralism are the predominant livelihood systems being monitored, and they are especially drought-sensitive. At the same time, conventional climate observation networks in developing countries are often sparse and late in reporting. Consequently, remote sensing has played a significant role since FEWS NET began in 1985. Initially there was heavy reliance on vegetation index imagery from AVHRR to identify anomalies in landscape greenness indicative of drought. In the latter part of the 1990s, satellite rainfall estimates added a second, independent basis for identification of drought. They are used to force crop water balance models for the principal rainfed staple crops in twenty FEWS NET countries. Such models reveal seasonal moisture deficits associated with yield reduction on a spatially continuous basis. In 2002, irrigated crops in southwest Asia became a concern, and prompted the implementation of a gridded energy balance model to simulate the seasonal mountain snow pack, the main source of irrigation water. MODIS land surface temperature data are also applied in these areas to directly estimate actual seasonal evapotranspiration on the irrigated lands. The approach reveals situations of reduced irrigation water supply and crop production due to drought. The availability of MODIS data after 2000 also brought renewed interest in vegetation index imagery. MODIS NDVI data have proven to be of high quality, thanks to significant spectral and spatial resolution improvements over AVHRR. They are vital to producing rapid harvest assessments for drought-impacted countries in Africa and Asia. The global food crisis that emerged in 2008 has led to expansion of FEWS NET monitoring to over 50 additional countries. Unlike previous practice, these new countries have no local FEWS NET analysts, requiring increased reliance on remote sensing for detection of agricultural drought and potential food insecurity. USGS is increasing its cooperation with NASA, NOAA, and university partners to meet this challenge. New servers for near real time delivery of MODIS NDVI, satellite rainfall estimates, and gridded snow pack estimates are being established. A custom instance of NASA's Land Information System software is also being developed to create a land data assimilation system specifically for FEWS NET domains, data streams, and monitoring and forecast requirements. The system will take better advantage of remote sensing data, including promising new products from the Soil Moisture Active-Passive (SMAP) mission, by integrating them with surface observations for simulation of land surface processes. In this way, the continuous improvement of monitoring and modeling for famine early warning will advance to a new level of sophistication and effectiveness.

Developing a Satellite Based Automatic System for Crop Monitoring: Kenya's Great Rift Valley, A Case Study

NASA Astrophysics Data System (ADS)

Lucciani, Roberto; Laneve, Giovanni; Jahjah, Munzer; Mito, Collins

2016-08-01

The crop growth stage represents essential information for agricultural areas management. In this study we investigate the feasibility of a tool based on remotely sensed satellite (Landsat 8) imagery, capable of automatically classify crop fields and how much resolution enhancement based on pan-sharpening techniques and phenological information extraction, useful to create decision rules that allow to identify semantic class to assign to an object, can effectively support the classification process. Moreover we investigate the opportunity to extract vegetation health status information from remotely sensed assessment of the equivalent water thickness (EWT). Our case study is the Kenya's Great Rift valley, in this area a ground truth campaign was conducted during August 2015 in order to collect crop fields GPS measurements, leaf area index (LAI) and chlorophyll samples.

Applications of UAVs in row-crop agriculture: advantages and limitations

NASA Astrophysics Data System (ADS)

Basso, B.; Putnam, G.; Price, R.; Zhang, J.

2016-12-01

The application of Unmanned Aerial Vehicles (UAV) to monitor agricultural fields has increased over the last few years due to advances in the technology, sensors, post-processing software for image analysis, along with more favorable regulations that allowed UAVs to be flown for commercial use. UAV have several capabilities depending on the type of sensors that are mounted onboard. The most widely used application remains crop scouting to identify areas within fields where the crops underperform for various reasons (nutritional status and water stress, presence of weeds, poor stands etc). In this talk, we present the preliminary results of UAVs field based research to better understand spatial and temporal variability of crop yield. Their advantage in providing timely information is critical, but adaptive management requires a system approach to account for the interactions occurring between genetics, environment and management.

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.

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.

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.

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.

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.

The role of simulation models in monitoring soil organic carbon storage and greenhouse gas mitigation potential in bioenergy cropping systems

USDA-ARS?s Scientific Manuscript database

There is an increased demand on agricultural systems worldwide to provide food, fiber, and feedstock for the emerging bioenergy industry, raising legitimate concerns on the associated impacts of such intensification on the environment. Of the many ecosystem services that could be impacted by the la...

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.

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.

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...

Agricultural drought risk monitoring and yield loss forecast with remote sensing data

NASA Astrophysics Data System (ADS)

Nagy, Attila; Tamás, János; Fehér, János

2015-04-01

The World Meteorological Organization (WMO) and Global Water Partnership (GWP) have launched a joint Integrated Drought Management Programme (IDMP) to improve monitoring and prevention of droughts. In the frame of this project this study focuses on identification of agricultural drought characteristics and elaborates a monitoring method (with application of remote sensing data), which could result in appropriate early warning of droughts before irreversible yield loss and/or quality degradation occur. The spatial decision supporting system to be developed will help the farmers in reducing drought risk of the different regions by plant specific calibrated drought indexes. The study area was the Tisza River Basin, which is located in Central Europe within the Carpathian Basin. For the investigations normalized difference vegetation index (NDVI) was used calculated from 16 day moving average chlorophyll intensity and biomass quantity data. The results offer concrete identification of remote sensing and GIS data tools for agricultural drought monitoring and forecast, which eventually provides information on physical implementation of drought risk levels. In the first step, we statistically normalized the crop yield maps and the MODIS satellite data. Then the drought-induced crop yield loss values were classified. The crop yield loss data were validated against the regional meteorological drought index values (SPI), the water management and soil physical data. The objective of this method was to determine the congruency of data derived from spectral data and from field measurements. As a result, five drought risk levels were developed to identify the effect of drought on yields: Watch, Early Warning, Warning, Alert and Catastrophe. In the frame of this innovation such a data link and integration, missing from decision process of IDMP, are established, which can facilitate the rapid spatial and temporal monitoring of meteorological, agricultural drought phenomena and its economic relations, increasing the time factors effectiveness of decision support system. This methodology will be extendable for other Central European countries when country specific data are available and entered into the system. This new drought risk monitoring and forecasting method is an improvement for hydrologists, meteorologists and farmers, allowing to set up a complex drought monitoring system, where for a given period and respective catchment area the expected yield loss can be predicted, and the role of vegetation in the hydrological cycle could be more precisely quantified. Based on the results more water-saving agricultural land use alternatives could be planned on drought areas.

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

The CINMa index: assessing the potential impact of GM crop management across a heterogeneous landscape.

PubMed

Collier, Marcus J; Mullins, Ewen

2010-01-01

While significant progress has been made on the modification of crops for the benefit of producers, the same cannot be said in regards to eliciting the potential impact that these crops may have on the wider landscape and the diversity of life therein. Management impacts can create difficulties when making policy, regulation and licensing decisions in those countries where agriculture has a significant social and ecological position in the landscape. To begin to gauge the potential impacts of the management of a selection of GM crops on an agricultural landscape, four key biodiversity stressors (Chemicals, Introgression, Nutrients and Management: CINMa) were identified and a grading system developed using published data. Upon application to five selected GM crops in a case study area, CINMa identifies areas in the wider landscape where biodiversity is likely to be negatively or positively impacted, as well as agricultural zones which may benefit from the land use change associated with the management of GM crops and their associated post market environmental monitoring. © ISBR, EDP Sciences, 2011.

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.

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.

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.

The use of seasonal forecasts in a crop failure early warning system for West Africa

NASA Astrophysics Data System (ADS)

Nicklin, K. J.; Challinor, A.; Tompkins, A.

2011-12-01

Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

Population dynamics of caterpillars on three cover crops before sowing cotton in Mato Grosso (Brazil).

PubMed

Silvie, P J; Menzel, C A; Mello, A; Coelho, A G

2010-01-01

Direct seeding mulch-based cropping systems under a preliminary cover crop such as millet are common in some areas of Brazil. Lepidopteran pests that damage cotton, soybean and maize crops can proliferate on cover crops, so preventive chemical treatments are necessary. Very little data is available on these pests on cover crops. This paper presents the dynamics of Spodoptera frugiperda, S. eridania, Mocis latipes and Diatraea saccharalis caterpillars monitored at Primavera do Leste, Mato Grosso state (Brazil) during the of 2005/2006 and 2006/2007 cropping seasons on four cover crops, i.e. finger millet (Eleusine coracana), pearl millet (Pennisetum glaucum), sorghum (Sorghum bicolor) and ruzigrass (Brachiaria ruziziensis). The pests were visually counted on plants within a 1 m2 transect (wooden frame). Caterpillars were reared to facilitate identification of collected species and parasitoids. Many S. frugiperda caterpillars were observed on millet in 2005, with a maximum of 37 caterpillars/m2. On sorghum, we found 30 caterpillars/m2, or 0.83 caterpillars/plant. The Diatraea borer attacked sorghum later than the other pests. M. latipes was also observed on millet. The millet cover crop had to be dried for at least 1 month before direct drilling the main cotton crop in order to impede S. frugiperda infestations on cotton plantlets, thus avoiding the need for substantial resowing. The comparative methodological aspects are discussed.

[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.

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...

Increasing plant diversity with border crops reduces insecticide use and increases crop yield in urban agriculture

PubMed Central

Shen, Yan-Jun; Ji, Xiang-Yun; Wu, Xiang-Wen; Zheng, Xiang-Rong; Cheng, Wei; Li, Jun; Jiang, Yao-Pei; Chen, Xin; Weiner, Jacob; Nie, Ming; Ju, Rui-Ting; Yuan, Tao; Tang, Jian-Jun; Tian, Wei-Dong; Zhang, Hao

2018-01-01

Urban agriculture is making an increasing contribution to food security in large cities around the world. The potential contribution of biodiversity to ecological intensification in urban agricultural systems has not been investigated. We present monitoring data collected from rice fields in 34 community farms in mega-urban Shanghai, China, from 2001 to 2015, and show that the presence of a border crop of soybeans and neighboring crops (maize, eggplant and Chinese cabbage), both without weed control, increased invertebrate predator abundance, decreased the abundance of pests and dependence on insecticides, and increased grain yield and economic profits. Two 2 year randomized experiments with the low and high diversity practices in the same locations confirmed these results. Our study shows that diversifying farming practices can make an important contribution to ecological intensification and the sustainable use of associated ecosystem services in an urban ecosystem. PMID:29792597

Increasing plant diversity with border crops reduces insecticide use and increases crop yield in urban agriculture.

PubMed

Wan, Nian-Feng; Cai, You-Ming; Shen, Yan-Jun; Ji, Xiang-Yun; Wu, Xiang-Wen; Zheng, Xiang-Rong; Cheng, Wei; Li, Jun; Jiang, Yao-Pei; Chen, Xin; Weiner, Jacob; Jiang, Jie-Xian; Nie, Ming; Ju, Rui-Ting; Yuan, Tao; Tang, Jian-Jun; Tian, Wei-Dong; Zhang, Hao; Li, Bo

2018-05-24

Urban agriculture is making an increasing contribution to food security in large cities around the world. The potential contribution of biodiversity to ecological intensification in urban agricultural systems has not been investigated. We present monitoring data collected from rice fields in 34 community farms in mega-urban Shanghai, China, from 2001 to 2015, and show that the presence of a border crop of soybeans and neighboring crops (maize, eggplant and Chinese cabbage), both without weed control, increased invertebrate predator abundance, decreased the abundance of pests and dependence on insecticides, and increased grain yield and economic profits. Two 2 year randomized experiments with the low and high diversity practices in the same locations confirmed these results. Our study shows that diversifying farming practices can make an important contribution to ecological intensification and the sustainable use of associated ecosystem services in an urban ecosystem. © 2018, Wan et al.

Wireless canopy sensing network systems for automated control of irrigation and water use efficiency

USDA-ARS?s Scientific Manuscript database

Ground-based instrumentation for plant canopy sensing (infrared thermometry and spectral reflectance sensors) has been used extensively in agriculture to monitor crop status. Typically, measurements are accomplished with handheld or vehicle mounted instrumentation during limited periods of a day, an...

Criteria for Space-Based Sensor Applied to Bt Crop Monitoring

EPA Science Inventory

A joint agro-ecosystem research effort of NASA and USEPA has focused on the development of a decision support system designed to predict the development of insect pest resistance to transgenic toxins in maize. The use of NASA-developed remote sensing technologies that significant...

Regenerating Longleaf Pine Naturally

Treesearch

Thomas C. Croker; William D. Boyer

1975-01-01

Research has developed guides for consistent natural regeneration of longleaf pine by a shelterwood system. Key measures include hardwood control by fire and other means, timely preparatory and seed cuts, seed crop monitoring, seedbed preparation, protection of established seedlings, prompt removal of parent trees when reproduction is adequate, and control of...

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.

S.I.I.A for monitoring crop evolution and anomaly detection in Andalusia by remote sensing

NASA Astrophysics Data System (ADS)

Rodriguez Perez, Antonio Jose; Louakfaoui, El Mostafa; Munoz Rastrero, Antonio; Rubio Perez, Luis Alberto; de Pablos Epalza, Carmen

2004-02-01

A new remote sensing application was developed and incorporated to the Agrarian Integrated Information System (S.I.I.A), project which is involved on integrating the regional farming databases from a geographical point of view, adding new values and uses to the original information. The project is supported by the Studies and Statistical Service, Regional Government Ministry of Agriculture and Fisheries (CAP). The process integrates NDVI values from daily NOAA-AVHRR and monthly IRS-WIFS images, and crop classes location maps. Agrarian local information and meteorological information is being included in the working process to produce a synergistic effect. An updated crop-growing evaluation state is obtained by 10-days periods, crop class, sensor type (including data fusion) and administrative geographical borders. Last ten years crop database (1992-2002) has been organized according to these variables. Crop class database can be accessed by an application which helps users on the crop statistical analysis. Multi-temporal and multi-geographical comparative analysis can be done by the user, not only for a year but also for a historical point of view. Moreover, real time crop anomalies can be detected and analyzed. Most of the output products will be available on Internet in the near future by a on-line application.

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...

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.

Impact of vetch cover crop on runoff, soil loss, soil chemical properties and yield of chickpea in North Gondar, Ethiopia

NASA Astrophysics Data System (ADS)

Demelash, Nigus; Klik, Andreas; Holzmann, Hubert; Ziadat, Feras; Strohmeier, Stefan; Bayu, Wondimu; Zucca, Claudio; Abera, Atikilt

2016-04-01

Cover crops improve the sustainability and quality of both natural system and agro ecosystem. In Gumara-Maksegnit watershed which is located in Lake Tana basin, farmers usually use fallow during the rainy season for the preceding chickpea production system. The fallowing period can lead to soil erosion and nutrient losses. A field experiment was conducted during growing seasons 2014 and 2015 to evaluate the effect of cover crops on runoff, soil loss, soil chemical properties and yield of chickpea in North Gondar, Ethiopia. The plot experiment contained four treatments arranged in Randomized Complete Block Design with three replications: 1) Control plot (Farmers' practice: fallowing- without cover crop), 2) Chickpea planted with Di-ammonium phosphate (DAP) fertilizer with 46 k ha-1 P2O5 and 23 k ha-1 nitrogen after harvesting vetch cover crop, 3) Chick pea planted with vetch cover crop incorporated with the soil as green manure without fertilizer, 4) Chick pea planted with vetch cover crop and incorporated with the soil as green manure and with 23 k ha-1 P2O5 and 12.5 k ha-1 nitrogen. Each plot with an area of 36 m² was equipped with a runoff monitoring system. Vetch (Vicia sativa L.) was planted as cover crop at the onset of the rain in June and used as green manure. The results of the experiment showed statistically significant (P < 0.05) differences on the number of pods per plant, above ground biomass and grain yield of chick pea. However, there was no statistically significant difference (P > 0.05) on average plant height, average number of branches and hundred seed weight. Similarly, the results indicated that cover crop has a clear impact on runoff volume and sediment loss. Plots with vetch cover crop reduce the average runoff by 65% and the average soil loss decreased from 15.7 in the bare land plot to 8.6 t ha-1 with plots covered by vetch. In general, this result reveales that the cover crops, especially vetch, can be used to improve chickpea grain yield in addition to reduce soil erosion in the watershed.

Use of Satellite Remote Sensing of Cloud and Rainfall for Selected Operational Applications in the Fields of Applied Hydrology and Food Production.

NASA Astrophysics Data System (ADS)

Power, Clare

Available from UMI in association with The British Library. The material presented in this thesis takes the form of a series of discrete, but inter-related projects on subjects related to the use of satellite remote sensing techniques for selected applications in the fields of cloud, rainfall, vegetation and food production monitoring and assessment. Detailed literature reviews have been carried out on remote sensing techniques in these fields, in particular, for rainfall monitoring and the development of systems for food crop prediction from various rainfall, vegetation and crop monitoring algorithms. The second part of the thesis is devoted to a series of practical projects using five different and contrasting satellite rainfall monitoring techniques using visible and/or infrared imagery, three applied over the Sultanate of Oman and two over West Africa. The case studies applied over the Sultanate of Oman show a range of techniques from manual nephanalyses of Potential Rain Clouds and the derivation of a 20 year record of Tropical Cyclone tracks over the Arabian Sea, to the manual Bristol rainfall monitoring technique and its human-machine interactive successor BIAS, which are applicable to the analysis of short term extreme rainfall events. The remaining two techniques were developed simultaneously over West Africa. The first, namely, PERMIT (the Polar-orbiter Effective Rainfall Monitoring Technique), was developed by the Author, and the second, ADMIT (Agricultural Drought Monitoring Integrated Technique), by a colleague, Giles D'Souza. The development, testing on data from July and August 1985 and July 1986, and subsequent modification of the PERMIT technique is described. The 1986 Case Study results have been compared with the ADMIT results from the same data set, as part of a project funded by FAO to compare the performance of four Meteosat rainfall monitoring techniques (Snijders 1988). PERMIT was designed to be an economic, (in terms of satellite data and computer processing needs), automatic rainfall estimation technique suitable for use in environments where computer facilities are limited. Finally the PERMIT rainfall products have been compared with contemporaneous NOAA AVHRR Normalised Vegetation Index monthly composites. The relationships observed between these two satellite-derived products may contribute to the future development of a simple, low cost crop prediction scheme for developing countries. The main conclusion drawn from this research is that there is an urgent need for simple but effective rainfall and vegetation monitoring systems such as PERMIT, to be implemented operationally on low cost portable microcomputer systems which are readily installed in Developing Countries, where effective monitoring of such environmental elements can provide early warnings and reduce the impacts of drought inflicted famine disasters.

Plant cell wall-mediated immunity: cell wall changes trigger disease resistance responses.

PubMed

Bacete, Laura; Mélida, Hugo; Miedes, Eva; Molina, Antonio

2018-02-01

Plants have evolved a repertoire of monitoring systems to sense plant morphogenesis and to face environmental changes and threats caused by different attackers. These systems integrate different signals into overreaching triggering pathways which coordinate developmental and defence-associated responses. The plant cell wall, a dynamic and complex structure surrounding every plant cell, has emerged recently as an essential component of plant monitoring systems, thus expanding its function as a passive defensive barrier. Plants have a dedicated mechanism for maintaining cell wall integrity (CWI) which comprises a diverse set of plasma membrane-resident sensors and pattern recognition receptors (PRRs). The PRRs perceive plant-derived ligands, such as peptides or wall glycans, known as damage-associated molecular patterns (DAMPs). These DAMPs function as 'danger' alert signals activating DAMP-triggered immunity (DTI), which shares signalling components and responses with the immune pathways triggered by non-self microbe-associated molecular patterns that mediate disease resistance. Alteration of CWI by impairment of the expression or activity of proteins involved in cell wall biosynthesis and/or remodelling, as occurs in some plant cell wall mutants, or by wall damage due to colonization by pathogens/pests, activates specific defensive and growth responses. Our current understanding of how these alterations of CWI are perceived by the wall monitoring systems is scarce and few plant sensors/PRRs and DAMPs have been characterized. The identification of these CWI sensors and PRR-DAMP pairs will help us to understand the immune functions of the wall monitoring system, and might allow the breeding of crop varieties and the design of agricultural strategies that would enhance crop disease resistance. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Validation of Global EO Biophysical Products at JECAM Test Site in Ukraine

NASA Astrophysics Data System (ADS)

Skakun, Sergii; Kussul, Nataliia; Kravchenko, Oleksiy; Basarab, Ruslan; Ostapenko, Vadym; Yailymov, Bohdan; Shelestov, Andrii; Kolotii, Andrii; Mironov, Andrii

Efficient global agriculture monitoring requires appropriate validation of Earth observation (EO) products for different regions and cropping system. This problem is addressed within the Joint Experiment of Crop Assessment and Monitoring (JECAM) initiative which aims to develop monitoring and reporting protocols and best practices for a variety of global agricultural systems. Ukraine is actively involved into JECAM, and a JECAM Ukraine test site was officially established in 2011. The following problems are being solved within JECAM Ukraine: (i) crop identification and crop area estimation [1]; (ii) crop yield forecasting [2]; (iii) EO products validation. The following case study regions were selected for these purposes: (i) the whole Kyiv oblast (28,000 sq. km) indented for crop mapping and acreage estimation; (ii) intensive observation sub-site in Pshenichne which is a research farm from the National University of Life and Environmental Sciences of Ukraine and indented for crop biophysical parameters estimation; (iii) Lviv region for rape-seed identification and crop rotation control; (iv) Crimea region for crop damage assessment due to droughts, and illegial field detection. In 2013, Ukrainian JECAM test site was selected as one of the “Champion User” for the ESA Sentinel-2 for Agriculture project. The test site was observed with SPOT-4 and RapidEye satellites every 5 days. The collected images are then used to simulate Sentinel-2 images for agriculture purposes. JECAM Ukraine is responsible for collecting ground observation data for validation purposes, and is involved in providing user requirements for Sentinel-2 agriculture related products. In particular, three field campaigns to characterize the vegetation biophysical parameters at the Pshenichne test site were carried out: First campaign - 14th to 17th of May 2013; second campaign - 12th to 15th of June 2013; third campaign - 14th to 17th of July 2013. Digital Hemispheric Photographs (DHP) images were acquired with a NIKON D70 camera. The images acquired during the field campaign are processed with the CAN-EYE software to derive LAI, FAPAR and FCOVER. The in situ biophysical values were used for producing LAI, FCOVER and FAPAR maps from optical satellite images, and provide cross-validation, and validation of global remote sensing products. The following satellite data were used: SPOT-4, RapidEye and Landsat-8. Inter-comparison of the derived products is performed. The paper presents an insight on the general methodology used within JECAM test site, the results achieved so far and challenges, and future planned activities. 1. Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., Kussul, O. “Efficiency assessment of using satellite data for crop area estimation in Ukraine,” International Journal of Applied Earth Observation and Geoinformation, vol. 29, pp. 22-30, 2014. 2. Kogan, F., Kussul, N., Adamenko, T., Skakun, S., Kravchenko, O., Kryvobok, O., Shelestov, A., Kolotii, A., Kussul, O., Lavrenyuk, A., “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.

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.

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.

A new automated passive capillary lysimeter for logging real-time drainage water fluxes

USDA-ARS?s Scientific Manuscript database

Effective monitoring of chemical transport through the soil profile requires accurate and appropriate instrumentation to measure drainage water fluxes below the root zone of cropping system. The objectives of this study were to methodically describe in detail the construction and installation of a n...

Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems within the Bay watershed. Therefore, monitoring of crop production, agricultural water use and hydrologic connections betwee...

Benefit assessment of NASA space technology goals

NASA Technical Reports Server (NTRS)

1976-01-01

The socio-economic benefits to be derived from system applications of space technology goals developed by NASA were assessed. Specific studies include: electronic mail; personal telephone communications; weather and climate monitoring, prediction, and control; crop production forecasting and water availability; planetary engineering of the planet Venus; and planetary exploration.

Evaluating measures to assess soil health in long-term agroecosystem trials

USDA-ARS?s Scientific Manuscript database

Monitoring and assessing soil health is an important component of any land management system with a vision of sustaining soil resources. Soil organic matter(SOM)characteristics are key to soil health and responsive to tillage regime and crop management. As metrics of soil health, we evaluated surfac...

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...

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.

Seasonal Soil Nitrogen Mineralization within an Integrated Crop and Livestock System in Western North Dakota, USA

NASA Astrophysics Data System (ADS)

Landblom, Douglas; Senturklu, Songul; Cihacek, Larry; Pfenning, Lauren; Brevik, Eric C.

2015-04-01

Protecting natural resources while maintaining or maximizing crop yield potential is of utmost importance for sustainable crop and livestock production systems. Since soil organic matter and its decomposition by soil organisms is at the very foundation of healthy productive soils, systems research at the North Dakota State University Dickinson Research Extension Center is evaluating seasonal soil nitrogen fertility within an integrated crop and livestock production system. The 5-year diverse crop rotation is: sunflower (SF) - hard red spring wheat (HRSW) - fall seeded winter triticale-hairy vetch (THV; spring harvested for hay)/spring seeded 7-species cover crop (CC) - Corn (C) (85-90 day var.) - field pea-barley intercrop (PBY). The HRSW and SF are harvested as cash crops and the PBY, C, and CC are harvested by grazing cattle. In the system, yearling beef steers graze the PBY and C before feedlot entry and after weaning, gestating beef cows graze the CC. Since rotation establishment, four crop years have been harvested from the crop rotation. All crops have been seeded using a JD 1590 no-till drill except C and SF. Corn and SF were planted using a JD 7000 no-till planter. The HRSW, PBY, and CC were seeded at a soil depth of 3.8 cm and a row width of 19.1 cm. Seed placement for the C and SF crops was at a soil depth of 5.1 cm and the row spacing was 0.762 m. The plant population goal/ha for C, SF, and wheat was 7,689, 50,587, and 7,244 p/ha, respectively. During the 3rd cropping year, soil bulk density was measured and during the 4th cropping year, seasonal nitrogen fertility was monitored throughout the growing season from June to October. Seasonal nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total season mineral nitrogen (NO3-N + NH4-N), cropping system NO3-N, and bulk density were measured in 3 replicated non-fertilized field plot areas within each 10.6 ha triple replicated crop fields. Within each plot area, 6 - 20.3 cm x 0.61 m aluminum irrigation pipes were pressed into the soil as enclosures to restrict root access to soil nitrogen. Soil samples were taken as close to 2-week intervals as possible from both inside and outside the enclosures. The crop rotation N values were also compared to triple replicated perennial native grassland plot areas (predominate sp. Western wheatgrass - Pascopyrum smithii, Blue grama - Bouteloua gracilis, Little bluestem - Schizachyrium scoparium, Switchgrass - Panicum virgatum). Trends identified for both NH4-N and NO3-N indicate that the values are relatively similar with respect to seasonal change over time. There was a greater amount of soil nitrogen accumulation inside the enclosures indicating that outside the enclosures roots scavenge nitrogen for plant growth and production. Seasonally, comparing the cropping system crops, NO3-N declined mid-July and then rebounded by mid-August and continued to increase until leveling off in September. Corn NO3-N, however, did not follow this pattern, but increased from early June to the end of June and remained high until the first of September. We will present the results of bulk density data and seasonal N fertility data providing evidence for the impact of previous CC on corn production. Probable explanation for the mid-summer nitrogen decline will be presented and justification for reduced fertilizer application 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.

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.

The method for detecting biological parameter of rice growth and early planting of paddy crop by using multi temporal remote sensing data

NASA Astrophysics Data System (ADS)

Domiri, D. D.

2017-01-01

Rice crop is the most important food crop for the Asian population, especially in Indonesia. During the growth of rice plants have four main phases, namely the early planting or inundation phase, the vegetative phase, the generative phase, and bare land phase. Monitoring the condition of the rice plant needs to be conducted in order to know whether the rice plants have problems or not in its growth. Application of remote sensing technology, which uses satellite data such as Landsat 8 and others which has a spatial and temporal resolution is high enough for monitoring the condition of crops such as paddy crop in a large area. In this study has been made an algorithm for monitoring rapidly of rice growth condition using Maximum of Vegetation Index (EVI Max). The results showed that the time of early planting can be estimated if known when EVI Max occurred. The value of EVI Max and when it occured can be known by trough spatial analysis of multitemporal EVI Landsat 8 or other medium spatial resolution satellites.

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.

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...

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...

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...

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).

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.

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.

Data Science Challenges at the Nexus of Food, Energy, and Water

NASA Astrophysics Data System (ADS)

Eftelioglu, E.; Shekhar, S.

2016-12-01

Food, energy and water (FEW) systems were traditionally analyzed and planned independently to address the challenges of population growth, climate change and urbanization. However, such piece-meal approaches (e.g., bio-fuel subsidy, fertilizers in agriculture) to solving problems in one system (e.g., energy, food) led to unanticipated harms to other systems (e.g., food price increase, water resource depletion and degradation). Thus, understanding the interdependent and interconnected nature of food, energy, and water systems (FEW nexus) is a societal priority. Data Science is crucial for understanding the problem, the interconnections, and the impacts withing FEW nexus. It is also needed for monitoring a variety of Earth resources (e.g., agriculture fields, fresh water lakes, energy needs for cooling or heating, etc.), and trends (e.g., deforestation, pollution, etc.) for timely detection and management of risks, such as impending crop failures and crop-stress anywhere in the world. It is also needed to reduce waste and to improve efficiency, e.g., amount of water and energy needed to produce food. Data Science success stories go beyond the cyber-infrastructure for simulations (e.g., GCMs, AgMIP ) to include precision agriculture and GEOGLAM. Precision agriculture uses cyber-physical systems and data science to increase yield, and reduce fertilizer and pesticide runoffs. The Global Agricultural Monitoring (GEOGLAM) , an international system, uses remotely sensed satellite imagery to monitor major crops for yield forecasts to enable timely interventions to reduce disruptions in global food supply. However, the FEW nexus presents new challenges and opportunities. For example, data science methods need to not only re-examine assumptions such as non-stationarity (e.g., climate change) but also address nexus challenges such as high cost of false positives, (social) feedback loops, and multiple spatio-temporal scale. Acknowledgements: This work was supported in part by the National Science Foundation, and the University of Minnesota. The talk presents outcomes of a recent NSF workshop to explore a research agenda for next generation data science to address the challenges of FEW nexus.

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...

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.

[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.

Cover crops and crop residue management under no-till systems improve soils and environmental quality

NASA Astrophysics Data System (ADS)

Kumar, Sandeep; Wegner, Brianna; Vahyala, Ibrahim; Osborne, Shannon; Schumacher, Thomas; Lehman, Michael

2015-04-01

Crop residue harvest is a common practice in the Midwestern USA for the ethanol production. However, excessive removal of crop residues from the soil surface contributes to the degradation of important soil quality indicators such as soil organic carbon (SOC). Addition of a cover crop may help to mitigate these negative effects. The present study was set up to assess the impacts of corn (Zea mays L.) residue removal and cover crops on various soil quality indicators and surface greenhouse gas (GHG) fluxes. The study was being conducted on plots located at the North Central Agricultural Research Laboratory (NCARL) in Brookings, South Dakota, USA. Three plots of a corn and soybean (Glycine max (L.) Merr.) rotation under a no-till (NT) system are being monitored for soils and surface gas fluxes. Each plot has three residue removal (high residue removal, HRR; medium residue removal, MRR; and low residue removal, LRR) treatments and two cover crops (cover crops and no cover crops) treatments. Both corn and soybean are represented every year. Gas flux measurements were taken weekly using a closed static chamber method. Data show that residue removal significantly impacted soil quality indicators while more time was needed for an affect from cover crop treatments to be noticed. The LRR treatment resulted in higher SOC concentrations, increased aggregate stability, and increased microbial activity. The LRR treatment also increased soil organic matter (SOM) and particulate organic matter (POM) concentrations. Cover crops used in HRR (high corn residue removal) improved SOC (27 g kg-1) by 6% compared to that without cover crops (25.4 g kg-1). Cover crops significantly impacted POM concentration directly after the residue removal treatments were applied in 2012. CO2 fluxes were observed to increase as temperature increased, while N2O fluxes increased as soil moisture increased. CH4 fluxes were responsive to both increases in temperature and moisture. On average, soils under cover crop management had lower N2O fluxes than soils that did not have a cover crop. Results from this study concluded that it is important to allow crop residues to return to the soil as they help to improve soil quality indicators. The presence of cover crops also will contribute to the improvement of these indicators once established and may help mitigate greenhouse gas emissions.

How agro-ecological research helps to address food security issues under new IPM and pesticide reduction policies for global crop production systems.

PubMed

E Birch, A Nicholas; Begg, Graham S; Squire, Geoffrey R

2011-06-01

Drivers behind food security and crop protection issues are discussed in relation to food losses caused by pests. Pests globally consume food estimated to feed an additional one billion people. Key drivers include rapid human population increase, climate change, loss of beneficial on-farm biodiversity, reduction in per capita cropped land, water shortages, and EU pesticide withdrawals under policies relating to 91/414 EEC. IPM (Integrated Pest Management) will be compulsory for all EU agriculture by 2014 and is also being widely adopted globally. IPM offers a 'toolbox' of complementary crop- and region-specific crop protection solutions to address these rising pressures. IPM aims for more sustainable solutions by using complementary technologies. The applied research challenge now is to reduce selection pressure on single solution strategies, by creating additive/synergistic interactions between IPM components. IPM is compatible with organic, conventional, and GM cropping systems and is flexible, allowing regional fine-tuning. It reduces pests below economic thresholds utilizing key 'ecological services', particularly biocontrol. A recent global review demonstrates that IPM can reduce pesticide use and increase yields of most of the major crops studied. Landscape scale 'ecological engineering', together with genetic improvement of new crop varieties, will enhance the durability of pest-resistant cultivars (conventional and GM). IPM will also promote compatibility with semiochemicals, biopesticides, precision pest monitoring tools, and rapid diagnostics. These combined strategies are urgently needed and are best achieved via multi-disciplinary research, including complex spatio-temporal modelling at farm and landscape scales. Integrative and synergistic use of existing and new IPM technologies will help meet future food production needs more sustainably in developed and developing countries, in an era of reduced pesticide availability. Current IPM research gaps are identified and discussed.

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.

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.

Monterey Bay study. [analysis of Landsat 1 multispectral band scanner data

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Wade, L. C.

1975-01-01

The multispectral scanner capabilities of LANDSAT 1 were tested over California's Monterey Bay area and portions of the San Joaquin Valley. Using both computer aided and image interpretive processing techniques, the LANDSAT 1 data were analyzed to determine their potential application in terms of land use and agriculture. Utilizing LANDSAT 1 data, analysts were able to provide the identifications and areal extent of the individual land use categories ranging from very general to highly specific levels (e.g., from agricultural lands to specific field crop types and even the different stages of growth). It is shown that the LANDSAT system is useful in the identification of major crop species and the delineation of numerous land use categories on a global basis and that repeated surveillance would permit the monitoring of changes in seasonal growth characteristics of crops as well as the assessment of various cultivation practices with a minimum of onsite observation. The LANDSAT system is demonstrated to be useful in the planning and development of resource programs on earth.

Mapping Rice Cropping Patterns Using Multi-temporal Sentinel-1A Data

NASA Astrophysics Data System (ADS)

Nguyen, S. T.; Chen, C. F.; Chen, C. R.; Chiang, S. H.; Khin, L. V.

2016-12-01

Rice is the world's third largest crop behind maize and wheat, providing food for more than half of the world's population. Rice agriculture has been a key driver of socioeconomic development in Vietnam as it provides food for more than 90 million people and is considered as a main source of income for the majority of rural populations. Vietnam has approximately 7.5 million ha, annually producing roughly 39 million tons of grain rice making this nation become one of the largest rice suppliers on earth with approximately 7.4 million tons of grain rice exported annually. Thus, monitoring rice-growing areas to meet people's food needs while safeguarding the environment is important to developing strategies for national food security and rice grain exports. Previous studies of rice crop monitoring are often carried using coarse resolution optical satellite data such as MODIS data. Because rice fields in Vietnam are generally small and fragmental, the use of coarse resolution optical satellite data reveals disadvantages due to mixed-pixel issues and data contamination caused by cloud cover. The Sentinel-1A satellite launched on 3 April 2014 provides opportunities to collectively map small patches of rice fields at different scales owing to its high spatial resolution of 10 m and temporal resolution of 12 days. The main objective of this study is to develop an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A VH data. We processed the data following four main steps: (1) data pre-processing, (2) constructing smooth time-series VH backscatter data, (3) rice crop classification using the support vector machines (SVM), and (4) accuracy assessment. The mapping results validated with the ground the ground reference data indicated that the overall accuracy and Kappa coefficient were 83.4% and 0.7, respectively. The mapping results also compared with the government's rice area statistics at the district level reaffirmed the consistency between these two datasets with the correlation coefficient (R2) of 0.93 and the relative error in area of 2.2%. This study demonstrates the potential application of time-series Sentinel-1A data for rice crop mapping and the methods are thus proposed for large-scale rice crop monitoring in the country.

Comparison of measured changes in seasonal soil water content by rainfed maize-bean intercrop and component cropping systems in a semi-arid region of southern Africa

NASA Astrophysics Data System (ADS)

Ogindo, H. O.; Walker, S.

Seasonal water content fluctuation within the effective root zone was monitored during the growing season for a maize-bean intercrop (IMB), sole maize (SM) and sole bean (SB) in Free State Province, Republic of South Africa. Comparisons were undertaken for progressive depths of extraction 0-300 mm; 300-600 mm and 600-900 mm respectively. These enabled the understanding of water extraction behavior of the cropping systems within the different soil layers including the topsoil surface normally influenced by soil surface evaporation. Additive intercrops have been known to conserve water, largely due to the early high leaf area index and the higher total leaf area. In this study, the combined effect of the intercrop components seemed to lower the total water demand by the intercrop compared to the sole crops. During the two seasons (2000/2001 and 2001/2002) the drained upper limit (DUL) and crop lower limits (CLL) were determined. The maize-bean intercrop, sole maize and sole bean had CLL of 141 mm/m, 149 mm/m and 159 mm/m respectively. The DUL was 262 mm/m for the site and therefore the potential plant extractable soil water for the cropping systems were: 121 mm/m (IMB); 114 mm/m (SM) and 103 mm/m (SB). Overall, the intercrop did not have significantly different total soil water extraction during both seasons, although it was additive, showing that it had higher water to biomass conversion.

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.

Make your trappings count: The mathematics of pest insect monitoring. Comment on “Multiscale approach to pest insect monitoring: Random walks, pattern formation, synchronization, and networks” by Petrovskii et al.

NASA Astrophysics Data System (ADS)

Blasius, Bernd

2014-09-01

Since the beginnings of agriculture the production of crops is characterized by an ongoing battle between farmers and pests [1]. Already during biblical times swarms of the desert locust, Schistocerca gregaria, were known as major pest that can devour a field of corn within an hour. Even today, harmful organisms have the potential to threaten food production worldwide. It is estimated that about 37% of all potential crops are destroyed by pests. Harmful insects alone destroy 13%, causing financial losses in the agricultural industry of millions of dollars each year [2-4]. These numbers emphasize the importance of pest insect monitoring as a crucial step of integrated pest management [1]. The main approach to gain information about infestation levels is based on trapping, which leads to the question of how to extrapolate the sparse population counts at singularly disposed traps to a spatial representation of the pest species distribution. In their review Petrovskii et al. provide a mathematical framework to tackle this problem [5]. Their analysis reveals that this seemingly inconspicuous problem gives rise to surprisingly deep mathematical challenges that touch several modern contemporary concepts of statistical physics and complex systems theory. The review does not aim for a collection of numerical recipes to support crop growers in the analysis of their trapping data. Instead the review identifies the relevant biological and physical processes that are involved in pest insect monitoring and it presents the mathematical techniques that are required to capture these processes.

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.

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.

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.

Global Food Security-support data at 30 m (GFSAD30)

NASA Astrophysics Data System (ADS)

Thenkabail, P. S.

2013-12-01

Monitoring global croplands (GCs) is imperative for ensuring sustainable water and food security to the people of the world in the Twenty-first Century. However, the currently available cropland products suffer from major limitations such as: (1) Absence of precise spatial location of the cropped areas; (b) Coarse resolution nature of the map products with significant uncertainties in areas, locations, and detail; (b) Uncertainties in differentiating irrigated areas from rainfed areas; (c) Absence of crop types and cropping intensities; and (e) Absence of a dedicated webdata portal for the dissemination of cropland products. Therefore, our project aims to close these gaps through a Global Food Security-support data at 30 m (GFSAD30) with 4 distinct products: 1. Cropland extentarea, 2. Crop types with focus on 8 crops that occupy 70% of the global cropland areas, 3. Irrigated versus rainfed, and 4. Cropping intensities: single, double, triple, and continuous cropping. The above 4 products will be generated for GFSAD for nominal year 2010 (GFSAD2010) based on Landsat 30m Global Land Survey 2010 (GLS2010) fused with Moderate Resolution Imaging Spectroradiometer (MODIS) 250m NDVI monthly maximum value composites (MVC) of 2009-2011 data, and suite of secondary data (e.g., long-term precipitation, temperature, GDEM elevation). GFSAD30 will be produced using three mature cropland mapping algorithms (CMAs): 1. Spectral matching techniques; 2. A cropland classification algorithm (ACCA) that is rule-based; and 3. Hierarchical segmentation (HSeg) algorithm. Funded by NASA MEaSUREs, GFSAD30 will make significant contributions to Earth System Data Records (ESDRs), Group on Earth Observations (GEO) Agriculture and Water Societal Beneficial Areas (GEO Ag. SBAs), GEO Global Agricultural Monitoring Initiative (GEO GLAM), and the recent 'Big Data' initiative by the White House. The project has the support of USGS Working Group on Global Croplands (https://powellcenter.usgs.gov/globalcroplandwater/).

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.

Improving irrigation management in L'Horta Nord (Valencia, Spain)

NASA Astrophysics Data System (ADS)

Pascual-Seva, Nuria; San Bautista, Alberto; López-Galarza, Salvador; Maroto, Jose Vicente; Pascual, Bernardo

2014-05-01

L'Horta Nord is an important irrigation district in Valencia (Spain), especially for vegetable crops. The traditional cropping pattern in the region consists of a rotation of chufa with crops such as potato, onion, lettuce, escarole and red cabbage, being all these crops furrow irrigated. Currently, the quality of the water used is acceptable, water is not expensive and there are no limitations on supply. Consequently, growers are not aware of the volumes of water used, application efficiencies, nor water productivity for any of the crops cited. The European Framework Directive 2000/60, based on the precautionary principle, considers preventive action for measures to be taken; moreover, drought periods are becoming more frequent and extended, and water is being diverted to other uses. Thus, water use is an issue to improve. In this sense, the current situation of the irrigation in the area is analysed using chufa (Cyperus esculentus L. var. sativus Boeck.) as representative of the crops, since most of the crops in the area have shallow root systems, as chufa, which are irrigated in similar patterns. In order to analyse the irrigation performance of the traditional chufa crop as well as to achieve more sustainable results, different studies have been carried out, during the last decade. Efforts have been directed to increase water productivity, increasing yield and minimising the volumes of water applied. Different planting configurations and different irrigation thresholds, not only in furrow irrigation but also in drip irrigation, are examples of how the irrigation performance could be improved. Herein is presented a two-year study, comparing, in both furrow and drip irrigation, two irrigation schedules based on the volumetric soil water content, which was continuously monitored using capacitance sensors. Yield was significantly affected by the growing season, the irrigation system and by the irrigation schedule, and by the second order interactions of the irrigation system with the other studied variables. Greater yields (p≤0.01) were obtained in the first growing season, drip irrigation and maintaining a higher soil moisture level. When considering the irrigation water use efficiency, the irrigation system showed significant differences (p≤0.01) with greater efficiencies for drip irrigation. Considering the homogeneity of the plots in the area and the similarities of the irrigation managements of chufa with the other crops, the results could be extended to most of the plots and crops in the area.

Using remote sensing to calculate plant available nitrogen needed by crops on swine factory farm sprayfields in North Carolina

NASA Astrophysics Data System (ADS)

Christenson, Elizabeth; Serre, Marc

2015-10-01

North Carolina (NC) is the second largest producer of hogs in the United States with Duplin county, NC having the densest population of hogs in the world. In NC, liquid swine manure is generally stored in open-air lagoons and sprayed onto sprayfields with sprinkler systems to be used as fertilizer for crops. Swine factory farms, termed concentrated animal feeding operations (CAFOs), are regulated by the Department of Environment and Natural Resources (DENR) based on nutrient management plans (NMPs) having balanced plant available nitrogen (PAN). The estimated PAN in liquid manure being sprayed must be less than the estimated PAN needed crops during irrigation. Estimates for PAN needed by crops are dependent on crop and soil types. Objectives of this research were to develop a new, time-efficient method to identify PAN needed by crops on Duplin county sprayfields for years 2010-2014. Using remote sensing data instead of NMP data to identify PAN needed by crops allowed calendar year identification of which crops were grown on sprayfields instead of a five-year range of values. Although permitted data have more detailed crop information than remotely sensed data, identification of PAN needed by crops using remotely sensed data is more time efficient, internally consistent, easily publically accessible, and has the ability to identify annual changes in PAN on sprayfields. Once PAN needed by crops is known, remote sensing can be used to quantify PAN at other spatial scales, such as sub-watershed levels, and can be used to inform targeted water quality monitoring of swine CAFOs.

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 %.

Comparison of commercial lures and food baits for early detection of fruit infestation risk by Drosophila suzukii

USDA-ARS?s Scientific Manuscript database

Drosophila suzukii is one of the most serious invasive pests of berries and cherries worldwide. Several adult monitoring systems are available to time foliar application of insecticides with the expectation of detecting the presence of D.suzukii before they infest susceptible crops. We tested this b...

Comparison of aerial imagery from manned and unmanned aircraft platforms for monitoring cotton growth

USDA-ARS?s Scientific Manuscript database

Unmanned aircraft systems (UAS) have emerged as a low-cost and versatile remote sensing platform in recent years, but little work has been done on comparing imagery from manned and unmanned platforms for crop assessment. The objective of this study was to compare imagery taken from multiple cameras ...

Simulating the fate of fall- and spring-applied poultry litter nitrogen in corn production

USDA-ARS?s Scientific Manuscript database

Monitoring the fate of N derived from manures applied to fertilize crops is difficult, time consuming, and relatively expensive. But computer simulation models can help understand the interactions among various N processes in the soil-plant system and determine the fate of applied N. The RZWQM2 was ...

Developing wireless sensor networks for monitoring crop canopy temperature using a moving sprinkler system as a platform

USDA-ARS?s Scientific Manuscript database

The objectives of this study were to characterize wireless sensor nodes that we developed in terms of power consumption and functionality, and compare the performance of mesh and non-mesh wireless sensor networks (WSNs) comprised mainly of infrared thermometer thermocouples located on a center pivot...

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...

Advanced Systems Map, Monitor, and Manage Earth's Resources

NASA Technical Reports Server (NTRS)

2007-01-01

SpecTIR LLC, headquartered in Reno, Nevada, is recognized for innovative sensor design, on-demand hyperspectral data collection, and image-generating products for business, academia, and national and international governments. SpecTIR's current vice president of business development has brought a wealth of NASA-related research experience to the company, as the former principal investigator on a NASA-sponsored hyperspectral crop-imaging project. This project, made possible through a Small Business Technology Transfer (STTR) contract with Goddard Space Flight Center, aimed to enhance airborne hyperspectral sensing and ground-truthing means for crop inspection in the Mid-Atlantic region of the United States. Areas of application for such technology include precision farming and irrigation; oil, gas, and mineral exploration; pollution and contamination monitoring; wetland and forestry characterization; water quality assessment; and submerged aquatic vegetation mapping. Today, SpecTIR maintains its relationship with Goddard through programs at the University of Maryland in College Park, Maryland, and at the U.S. Department of Agriculture campus in Beltsville, Maryland. Additionally, work continues on the integration of hyperspectral data with LIDAR systems and other commercial-off-the-shelf technologies.

Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation

NASA Astrophysics Data System (ADS)

Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.

2016-06-01

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

Evaluating Satellite Rainfall Estimates for Agro-hydrological Applications in Africa

NASA Astrophysics Data System (ADS)

Senay, G. B.; Verdin, J. P.; Korecha, D.; Asfaw, A.

2004-12-01

Regional water balance techniques are used to monitor and forecast crop performance and flooding potentials around the world. In the last few years, satellite rainfall estimates (RFE) have become available at continental scales, which made it possible to develop operational regional water balance models for the monitoring of crops performance and flooding potentials in Africa and other regions of the world as part of an environmental early warning system . The accuracy of RFE in absolute terms and importantly as it relates to agricultural and hydrological applications have not been evaluated systematically. This study evaluated a subset of the Africa-wide RFE product by comparing station-rainfall data and RFE from 1996 to 2002 using over 100 rain-gauge stations from Ethiopia at a dekadal (~10-day) time step. The results showed a general under-estimation of RFE compared to station rainfall values. The correlation between station rainfall data and RFE varied highly from place to place and between seasons. On the other hand, the correlation improved significantly when comparison was made between RFE-derived crop water satisfaction index (WRSI) and station-rainfall-derived WRSI, indicating the usefulness of the RFE for agro-hydrological applications.

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.

Seasonal Population Dynamics of Three Potato Pests in Washington State.

PubMed

D'Auria, Elizabeth M; Wohleb, Carrie H; Waters, Timothy D; Crowder, David W

2016-08-01

Pest phenology models allow producers to anticipate pest outbreaks and deploy integrated pest management (IPM) strategies. Phenology models are particularly useful for cropping systems with multiple economically damaging pests throughout a season. Potato (Solanum tuberosum L.) crops of Washington State, USA, are attacked by many insect pests including the potato tuberworm (Phthorimaea operculella Zeller), the beet leafhopper (Circulifer tenellus Baker), and the green peach aphid (Myzus persicae Sulzer). Each of these pests directly damages potato foliage or tubers; C. tenellus and M. persicae also transmit pathogens that can drastically reduce potato yields. We monitored the seasonal population dynamics of these pests by conducting weekly sampling on a network of commercial farms from 2007 to 2014. Using these data, we developed phenology models to characterize the seasonal population dynamics of each pest based on accumulated degree-days (DD). All three pests exhibited consistent population dynamics across seasons that were mediated by temperature. Of the three pests, C. tenellus was generally the first detected in potato crops, with 90% of adults captured by 936 DD. In contrast, populations of P. operculella and M. persicae built up more slowly over the course of the season, with 90% cumulative catch by 1,590 and 2,634 DD, respectively. Understanding these seasonal patterns could help potato producers plan their IPM strategies while allowing them to move away from calendar-based applications of insecticides. More broadly, our results show how long-term monitoring studies that explore dynamics of multiple pest species can aid in developing IPM strategies in crop systems. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Advanced nutrient root-feeding system for conveyor-type cylindrical plant growth facilities for microgravity

NASA Astrophysics Data System (ADS)

Berkovich, Yu. A.; Krivobok, N. M.; Krivobok, A. S.; Smolyanina, S. O.

2016-02-01

A compact and reliable automatic method for plant nutrition supply is needed to monitor and control space-based plant production systems. The authors of this study have designed a nutrient root-feeding system that minimizes and regulates nutrient and water supply without loss of crop yields in a space greenhouse. The system involves an ion-exchange fibrous artificial soil (AS) BIONA-V3TM as the root-inhabited medium; a pack with slow-release fertilizer as the main source of nitrogen, phosphorus, and potassium; and a cartridge with granular mineral-rich ionite (GMRI) as a source of calcium, magnesium, sulfur, and iron. A controller equipped with an electrical conductivity meter controls the solution flow and concentration of the solution in the mixing tank at specified values. Experiments showed that the fibrous AS-stabilized pH of the substrate solution within the range of 6.0-6.6 is favorable to the majority of crops. The experimental data confirmed that this technique allowed solution preparation for crops in space greenhouses by means of pumping water through the cartridge and minimization of the AS stock onboard the space vehicle.

A new attractant for monitoring western flower thrips, Frankliniella occidentalis in protected crops.

PubMed

Abdullah, Zayed S; Greenfield, Bethany Pj; Ficken, Katherine J; Taylor, James Wd; Wood, Martyn; Butt, Tariq M

2015-01-01

Monitoring of pest populations is an essential component of integrated pest management. An early warning system helps growers decide when best to take control measures, or when to alter them, should a control method prove inadequate. Studies have shown that adding chemical attractants to sticky cards can increase trap catch of western flower thrips, Frankliniella occidentalis, a global pest of agriculture and horticulture, giving more accurate accounts of population size and dynamics, thus leading to more efficient monitoring. We identify a novel semiochemical to the species, (S)-(-)-verbenone, showing that addition of this compound to sticky traps significantly increased F. occidentalis catch in two geographically distinct populations, infesting two unrelated crops of global economic importance. We validate through field trials that (S)-(-)-verbenone is highly attractive to F.occidentalis and can be used with blue sticky traps to enhance trap catch, leading to better estimations of pest population densities. The compound may be used in other control methods against F.occidentalis such as lure and kill, mass trapping and push-pull.

Plant stress analysis technology deployment

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

Ebadian, M.A.

1998-01-01

Monitoring vegetation is an active area of laser-induced fluorescence imaging (LIFI) research. The Hemispheric Center for Environmental Technology (HCET) at Florida International University (FIU) is assisting in the transfer of the LIFI technology to the agricultural private sector through a market survey. The market survey will help identify the key eco-agricultural issues of the nations that could benefit from the use of sensor technologies developed by the Office of Science and Technology (OST). The principal region of interest is the Western Hemisphere, particularly, the rapidly growing countries of Latin America and the Caribbean. The analysis of needs will assure thatmore » the focus of present and future research will center on economically important issues facing both hemispheres. The application of the technology will be useful to the agriculture industry for airborne crop analysis as well as in the detection and characterization of contaminated sites by monitoring vegetation. LIFI airborne and close-proximity systems will be evaluated as stand-alone technologies and additions to existing sensor technologies that have been used to monitor crops in the field and in storage.« less

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.

Development of a decision support system for crop disease monitoring, surveillance and prediction in Bomet county, Kenya

NASA Astrophysics Data System (ADS)

Otieno, O. M.

2015-12-01

The study proposes to use Geographic Information Systems and Remote Sensing techniques to spatially model Maize Lethal Necrosis (MLN) disease in maize growing areas in Kenya. Results from this work will be used for prediction, monitoring and to guide intervention on MLN. This will minimize maize yield losses resulting from MLN infestation and thus safeguard the livelihoods of maize farmers in Kenya. MLN was first reported in Kenya in September 2011 in Bomet county. It then subsequently spread to other parts in Kenya. Maize crops are susceptible to MLN at all growth stages. Once infected the only option left for the farmers is to burn their maize plantations. Infection rate and damage is very high affecting yields and sometimes causing complete loss of maize yield.The modelling exercise will cover the period prior to and after the incidence of MLN. Specifically, the analysis will integrate spatio-temporal information on maize phenology and field surveys with the intention of delineating the extent of MLN infestation and the degree of damage as a result of MLN. Additionally, the task will identify potential predisposing factors leading to MLN resurgence and spread and to predict potential areas where MLN is likely to spread and to estimate the potential impact of MLN on the farm holders. The area of study for this task will be Bomet County. Historical and current environmental and spatial indicators including temperature, rainfall, soil moisture, vegetation health and crop cover will be fed into a model in order to determine the main factors that aide the occurrence and the spread of MLN. Multi-spectral image processing will be used to produce indices to study maize crop health whilst image classification techniques will be used to identify crop cover clusters by differentiating the variations in spectral signatures in the area of study and hence distinguish infected, unaffected maize crops and other crop cover classes. Variables from these indicators will then be weighted in a spatial model and be used as a basis for generating site-specific MLN prediction maps that will guide policy on MLN management in Kenya. The broaderobjective is to document a model that can be up-scaled and replicated in other maize producing areas in Kenya affected by MLN.

Prediction of seasonal climate-induced variations in global food production

NASA Astrophysics Data System (ADS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-10-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop--rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

Remote sensing to monitor cover crop adoption in southeastern Pennsylvania

USGS Publications Warehouse

Hively, Wells; Sjoerd Duiker,; Greg McCarty,; Prabhakara, Kusuma

2015-01-01

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 cropping systems. In southeastern Pennsylvania, cover crops can be successfully established following corn (Zea mays L.) silage harvest and are strongly promoted for use in this niche. They are also planted following corn grain, soybean (Glycine max L.), and vegetable harvest. In Pennsylvania, the use of winter cover crops for agricultural conservation has been supported through a combination of outreach, regulation, and incentives. On-farm implementation is thought to be increasing, but the actual extent of cover crops is not well quantified. Satellite imagery can be used to map green winter cover crop vegetation on agricultural fields and, when integrated with additional remote sensing data products, can be used to evaluate wintertime vegetative groundcover following specific summer crops. This study used Landsat and SPOT (System Probatoire d’ Observation de la Terre) satellite imagery, in combination with the USDA National Agricultural Statistics Service Cropland Data Layer, to evaluate the extent and amount of green wintertime vegetation on agricultural fields in four Pennsylvania counties (Berks, Lebanon, Lancaster, and York) from 2010 to 2013. In December of 2010, a windshield survey was conducted to collect baseline data on winter cover crop implementation, with particular focus on identifying corn harvested for silage (expected earlier harvest date and lower levels of crop residue), versus for grain (expected later harvest date and higher levels of crop residue). Satellite spectral indices were successfully used to detect both the amount of green vegetative groundcover and the amount of crop residue on the surveyed fields. Analysis of wintertime satellite imagery showed consistent increases in vegetative groundcover over the four-year study period and determined that trends did not result from annual weather variability, indicating that farmers are increasing adoption of practices such as cover cropping that promote wintertime vegetation. Between 2010 and 2013, the occurrence of wintertime vegetation on agricultural fields increased from 36% to 67% of corn fields in Berks County, from 53% to 75% in Lancaster County, from 42% to 65% in Lebanon County, and from 26% to 52% in York County. Apparently, efforts to promote cover crop use in the Chesapeake Bay Watershed have coincided with a rapid increase in the occurrence of wintertime vegetation following corn harvest in southeastern Pennsylvania. However, despite these increases, between 25% and 48% of corn fields remained without substantial green vegetation over the wintertime, indicating further opportunity for cover crop adoption.

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.

Using daily field-scale evapotranspiration (ET) derived with multi-sensor data fusion for monitoring crop condition and yield in central Iowa, United States

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 pr...

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

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.

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.

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.

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...

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.

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.

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.

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.

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.

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

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)

[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.

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.

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

The Combined Application of the Caco-2 Cell Bioassay Coupled with In Vivo (Gallus gallus) Feeding Trial Represents an Effective Approach to Predicting Fe Bioavailability in Humans

PubMed Central

Tako, Elad; Bar, Haim; Glahn, Raymond P.

2016-01-01

Research methods that predict Fe bioavailability for humans can be extremely useful in evaluating food fortification strategies, developing Fe-biofortified enhanced staple food crops and assessing the Fe bioavailability of meal plans that include such crops. In this review, research from four recent poultry (Gallus gallus) feeding trials coupled with in vitro analyses of Fe-biofortified crops will be compared to the parallel human efficacy studies which used the same varieties and harvests of the Fe-biofortified crops. Similar to the human studies, these trials were aimed to assess the potential effects of regular consumption of these enhanced staple crops on maintenance or improvement of iron status. The results demonstrate a strong agreement between the in vitro/in vivo screening approach and the parallel human studies. These observations therefore indicate that the in vitro/Caco-2 cell and Gallus gallus models can be integral tools to develop varieties of staple food crops and predict their effect on iron status in humans. The cost-effectiveness of this approach also means that it can be used to monitor the nutritional stability of the Fe-biofortified crop once a variety has released and integrated into the food system. These screening tools therefore represent a significant advancement to the field for crop development and can be applied to ensure the sustainability of the biofortification approach. PMID:27869705

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....

Plastic cup traps equipped with light-emitting diodes for monitoring adult Bemisia tabaci (Homoptera: Aleyrodidae).

PubMed

Chu, Chang-Chi; Jackson, Charles G; Alexander, Patrick J; Karut, Kamil; Henneberry, Thomas J

2003-06-01

Equipping the standard plastic cup trap, also known as the CC trap, with lime-green light-emitting diodes (LED-plastic cup trap) increased its efficacy for catching Bemisia tabaci by 100%. Few Eretmocerus eremicus Rose and Zolnerowich and Encarsia formosa Gahan were caught in LED-plastic cup traps. The LED-plastic cup traps are less expensive than yellow sticky card traps for monitoring adult whiteflies in greenhouse crop production systems and are more compatible with whitefly parasitoids releases for Bemisia nymph control.

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.

Crop Surveillance Demonstration Using a Near-Daily MODIS Derived Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Ryan, Robert E.; Blonski, Slawomir; Prados, Don

2005-01-01

Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.

Connecting Digital Repeat Photography to Ecosystem Fluxes in Inland Pacific Northwest, US Cropping Systems

NASA Astrophysics Data System (ADS)

Russell, E.; Chi, J.; Waldo, S.; Pressley, S. N.; Lamb, B. K.; Pan, W.

2017-12-01

Diurnal and seasonal gas fluxes vary by crop growth stage. Digital cameras are increasingly being used to monitor inter-annual changes in vegetation phenology in a variety of ecosystems. These cameras are not designed as scientific instruments but the information they gather can add value to established measurement techniques (i.e. eddy covariance). This work combined deconstructed digital images with eddy covariance data from five agricultural sites (1 fallow, 4 cropped) in the inland Pacific Northwest, USA. The data were broken down with respect to crop stage and management activities. The fallow field highlighted the camera response to changing net radiation, illumination, and rainfall. At the cropped sites, the net ecosystem exchange, gross primary production, and evapotranspiration were correlated with the greenness and redness values derived from the images over the growing season. However, the color values do not change quickly enough to respond to day-to-day variability in the flux exchange as the two measurement types are based on different processes. The management practices and changes in phenology through the growing season were not visible within the camera data though the camera did capture the general evolution of the ecosystem fluxes.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

PubMed Central

Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

2017-01-01

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. PMID:28492515

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

PubMed

Calera, Alfonso; Campos, Isidro; Osann, Anna; D'Urso, Guido; Menenti, Massimo

2017-05-11

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Global Crop Area Monitoring at High Resolution Exploiting Complementary Use of Free and Open SAR and VSNIR/SWIR Sensor Data Sets

NASA Astrophysics Data System (ADS)

Lemoine, G.; LEO, O.

2015-12-01

Earth Observation imaging sensors with spatial resolutions in the 10-30 m range allow for separation of the area and crop status contributions to the radiometric signatures, typically at parcel level for a wide range of arable crop production systems. These sensors complement current monitoring efforts that deploy low (100-1000 m) resolution VSNIR/SWIR sensors like MODIS, METOP or PROBA-V, which provide denser time series, but with aggregated and mixed radiometric information for cropped areas. "Free and Open" access to US Landsat imagery has recently been complemented by the European Union's Copernicus program with access to Sentinel-1A C-band SAR and Sentinel-2A visual, near and short-ware infrared (VSNIR/SWIR) sensor data in the 10-20 m resolution range. Sentinel-1A has already proven that consistent time series can be generated at its 12 day revisit frequency. The density of Sentinel-2 time series will greatly expand the availability of [partially cloud covered] VSNIR/SWIR imagery. The release of this large new data flow coincides with wider availability of "big data" processing capacity, the public release of ever more detailed ancillary data sets that support extraction of georeferenced and robust indicators on crop production and their spatial and temporal statistics and developments in crowd-sourced mobile data collection for data validation purposes. We will illustrate the use of hybrid SAR and VSNIR/SWIR data sets from Sentinel-1 and Landsat-8 (and initially released Sentinel-2 imagery) for a number of selected examples. These include crop area delineation and classification in the Netherlands with the support of detailed parcel delineation sets for validation, detection of winter cereal cultivation in Ukraine, impact of the Syrian civil war on irrigated summer crop cultivation and recent examples in support to crop anomaly detection in food insecure areas (North Korea, Sub-Saharan Africa). We discuss method implementation, operational issues and outline the needs for further research in support to our crop "knowledge inference" framework. Most of our work is based on the use of Google Earth Engine (GEE) and the first batch of 12,000 geocoded Sentinel-1A images that was recently included.

Developing an operational rangeland water requirement satisfaction index

USGS Publications Warehouse

Senay, Gabriel B.; Verdin, James P.; Rowland, James

2011-01-01

Developing an operational water requirement satisfaction index (WRSI) for rangeland monitoring is an important goal of the famine early warning systems network. An operational WRSI has been developed for crop monitoring, but until recently a comparable WRSI for rangeland was not successful because of the extremely poor performance of the index when based on published crop coefficients (K c) for rangelands. To improve the rangeland WRSI, we developed a simple calibration technique that adjusts the K c values for rangeland monitoring using long-term rainfall distribution and reference evapotranspiration data. The premise for adjusting the K c values is based on the assumption that a viable rangeland should exhibit above-average WRSI (values >80%) during a normal year. The normal year was represented by a median dekadal rainfall distribution (satellite rainfall estimate from 1996 to 2006). Similarly, a long-term average for potential evapotranspiration was used as input to the famine early warning systems network WRSI model in combination with soil-water-holding capacity data. A dekadal rangeland WRSI has been operational for east and west Africa since 2005. User feedback has been encouraging, especially with regard to the end-of-season WRSI anomaly products that compare the index's performance to ‘normal’ years. Currently, rangeland WRSI products are generated on a dekadal basis and posted for free distribution on the US Geological Survey early warning website at http://earlywarning.usgs.gov/adds/

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.

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

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...

Estimating yield gaps at the cropping system level.

PubMed

Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G

2017-05-01

Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems ( e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.

CropEx Web-Based Agricultural Monitoring and Decision Support

NASA Technical Reports Server (NTRS)

Harvey. Craig; Lawhead, Joel

2011-01-01

CropEx is a Web-based agricultural Decision Support System (DSS) that monitors changes in crop health over time. It is designed to be used by a wide range of both public and private organizations, including individual producers and regional government offices with a vested interest in tracking vegetation health. The database and data management system automatically retrieve and ingest data for the area of interest. Another stores results of the processing and supports the DSS. The processing engine will allow server-side analysis of imagery with support for image sub-setting and a set of core raster operations for image classification, creation of vegetation indices, and change detection. The system includes the Web-based (CropEx) interface, data ingestion system, server-side processing engine, and a database processing engine. It contains a Web-based interface that has multi-tiered security profiles for multiple users. The interface provides the ability to identify areas of interest to specific users, user profiles, and methods of processing and data types for selected or created areas of interest. A compilation of programs is used to ingest available data into the system, classify that data, profile that data for quality, and make data available for the processing engine immediately upon the data s availability to the system (near real time). The processing engine consists of methods and algorithms used to process the data in a real-time fashion without copying, storing, or moving the raw data. The engine makes results available to the database processing engine for storage and further manipulation. The database processing engine ingests data from the image processing engine, distills those results into numerical indices, and stores each index for an area of interest. This process happens each time new data is ingested and processed for the area of interest, and upon subsequent database entries, the database processing engine qualifies each value for each area of interest and conducts a logical processing of results indicating when and where thresholds are exceeded. Reports are provided at regular, operator-determined intervals that include variances from thresholds and links to view raw data for verification, if necessary. The technology and method of development allow the code base to easily be modified for varied use in the real-time and near-real-time processing environments. In addition, the final product will be demonstrated as a means for rapid draft assessment of imagery.

Advanced nutrient root-feeding system for conveyor-type cylindrical plant growth facilities for microgravity.

PubMed

Berkovich, Yu A; Krivobok, N M; Krivobok, A S; Smolyanina, S O

2016-02-01

A compact and reliable automatic method for plant nutrition supply is needed to monitor and control space-based plant production systems. The authors of this study have designed a nutrient root-feeding system that minimizes and regulates nutrient and water supply without loss of crop yields in a space greenhouse. The system involves an ion-exchange fibrous artificial soil (AS) BIONA-V3(TM) as the root-inhabited medium; a pack with slow-release fertilizer as the main source of nitrogen, phosphorus, and potassium; and a cartridge with granular mineral-rich ionite (GMRI) as a source of calcium, magnesium, sulfur, and iron. A controller equipped with an electrical conductivity meter controls the solution flow and concentration of the solution in the mixing tank at specified values. Experiments showed that the fibrous AS-stabilized pH of the substrate solution within the range of 6.0-6.6 is favorable to the majority of crops. The experimental data confirmed that this technique allowed solution preparation for crops in space greenhouses by means of pumping water through the cartridge and minimization of the AS stock onboard the space vehicle. Copyright © 2015 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

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.

How well do meteorological indicators represent agricultural and forest drought across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, S.; Tanguy, M.; Hannaford, J.; Stahl, K.

2018-03-01

Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs’ representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

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...

N use efficiencies and N2O emissions in two contrasting, biochar amended soils under winter wheat—cover crop—sorghum rotation

NASA Astrophysics Data System (ADS)

Hüppi, Roman; Neftel, Albrecht; Lehmann, Moritz F.; Krauss, Maike; Six, Johan; Leifeld, Jens

2016-08-01

Biochar, a carbon-rich, porous pyrolysis product of organic residues, is evaluated as an option to tackle major problems of the global food system. Applied to soil, biochar can sequester carbon and have beneficial effects on nitrogen (N) cycling, thereby enhancing crop yields and reducing nitrous oxide (N2O) emissions. There is little understanding of the underlying mechanisms, but many experiments indicated increased yields and manifold changes in N transformation, suggesting an increase in N use efficiency. Biochar’s effects can be positive in extensively managed tropical agriculture, however less is known about its use in temperate soils with intensive fertilisation. We tested the effect of slow pyrolysis wood chip biochar on N use efficiency, crop yields and N2O emissions in a lysimeter system with two soil types (sandy loamy Cambisol and silty loamy Luvisol) in a winter wheat—cover crop—sorghum rotation. 15N-labelled ammonium nitrate fertiliser (170 kg N ha-1 in 3 doses, 10% 15N) was applied to the first crop to monitor its fate in three ecosystem components (plants, soil, leachate). Green rye was sown as cover crop to keep the first year’s fertiliser N for the second year’s sorghum crop (fertilised with 110 kg N ha-1 in two doses and natural abundance 15N). We observed no effects of biochar on N fertiliser use efficiency, yield or N uptake for any crop. Biochar reduced leaching by 43 ± 19% but only towards the end of the experiment with leaching losses being generally low. For both soils N2O emissions were reduced by 15 ± 4% with biochar compared to the control treatments. Our results indicate that application of the chosen biochar induces environmental benefits in terms of N2O emission and N leaching but does not substantially affect the overall N cycle and hence crop performance in the analyzed temperate crop rotation.

Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

NASA Astrophysics Data System (ADS)

dela Torre, D. M.; Perez, G. J. P.

2016-12-01

Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

Construction of an unmanned aerial vehicle remote sensing system for crop monitoring

NASA Astrophysics Data System (ADS)

Jeong, Seungtaek; Ko, Jonghan; Kim, Mijeong; Kim, Jongkwon

2016-04-01

We constructed a lightweight unmanned aerial vehicle (UAV) remote sensing system and determined the ideal method for equipment setup, image acquisition, and image processing. Fields of rice paddy (Oryza sativa cv. Unkwang) grown under three different nitrogen (N) treatments of 0, 50, or 115 kg/ha were monitored at Chonnam National University, Gwangju, Republic of Korea, in 2013. A multispectral camera was used to acquire UAV images from the study site. Atmospheric correction of these images was completed using the empirical line method, and three-point (black, gray, and white) calibration boards were used as pseudo references. Evaluation of our corrected UAV-based remote sensing data revealed that correction efficiency and root mean square errors ranged from 0.77 to 0.95 and 0.01 to 0.05, respectively. The time series maps of simulated normalized difference vegetation index (NDVI) produced using the UAV images reproduced field variations of NDVI reasonably well, both within and between the different N treatments. We concluded that the UAV-based remote sensing technology utilized in this study is potentially an easy and simple way to quantitatively obtain reliable two-dimensional remote sensing information on crop growth.

Synthetic Aperture Radar (SAR)-based paddy rice monitoring system: Development and application in key rice producing areas in Tropical Asia

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Quicho, E.; Collivignarelli, F.; Maunahan, A.; Gatti, L.; Romuga, G. C.

2017-01-01

Reliable and regular rice information is essential part of many countries’ national accounting process but the existing system may not be sufficient to meet the information demand in the context of food security and policy. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland paddy rice, especially in tropical region where pervasive cloud cover in the rainy seasons limits the use of optical imagery. This study uses multi-temporal X-band and C-band SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations across Tropical Asia and assimilate the information into ORYZA Crop Growth Simulation model (CGSM) to generate high resolution yield maps. The resulting cultivated rice area maps had classification accuracies above 85% and yield estimates were within 81-93% agreement against district level reported yields. The study sites capture much of the diversity in water management, crop establishment and rice maturity durations and the study demonstrates the feasibility of rice detection, yield monitoring, and damage assessment in case of climate disaster at national and supra-national scales using multi-temporal SAR imagery combined with CGSM and automated methods.

Model-data integration for developing the Cropland Carbon Monitoring System (CCMS)

NASA Astrophysics Data System (ADS)

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

2017-12-01

The Cropland Carbon Monitoring System (CCMS) has been initiated to improve regional estimates of carbon fluxes from croplands in the conterminous United States through integration of terrestrial ecosystem modeling, use of remote-sensing products and publically available datasets, and development of improved landscape and management databases. In order to develop these improved carbon flux estimates, experimental datasets are essential for evaluating the skill of estimates, characterizing the uncertainty of these estimates, characterizing parameter sensitivities, and calibrating specific modeling components. Experiments were sought that included flux tower measurement of CO2 fluxes under production of major agronomic crops. Currently data has been collected from 17 experiments comprising 117 site-years from 12 unique locations. Calibration of terrestrial ecosystem model parameters using available crop productivity and net ecosystem exchange (NEE) measurements resulted in improvements in RMSE of NEE predictions of between 3.78% to 7.67%, while improvements in RMSE for yield ranged from -1.85% to 14.79%. Model sensitivities were dominated by parameters related to leaf area index (LAI) and spring growth, demonstrating considerable capacity for model improvement through development and integration of remote-sensing products. Subsequent analyses will assess the impact of such integrated approaches on skill of cropland carbon flux estimates.

An inventory of irrigated lands for selected counties within the state of California based on LANDSAT and supporting aircraft data

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1977-01-01

The author has identified the following significant results: (1) Goals of the irrigated lands project were addressed by the design and implementation of a multiphase sampling scheme that was founded on the utilization of a LANDSAT-based remote sensing system. (2) The synoptic coverage of LANDSAT and the eighteen day orbit cycle allowed the project to study agricultural test sites in a variety of environmental regions and monitor the development of crops throughout the major growing season. (3) The capability to utilize multidate imagery is crucial to the reliable estimation of irrigated acreage in California where multiple cropping is widespread and current estimation systems must rely on single data survey techniques. (4) In addition, the magnitude of agricultural acreage in California makes estimation by conventional methods impossible.

Use of thermal and visible imagery for estimating crop water status of irrigated grapevine.

PubMed

Möller, M; Alchanatis, V; Cohen, Y; Meron, M; Tsipris, J; Naor, A; Ostrovsky, V; Sprintsin, M; Cohen, S

2007-01-01

Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates are discussed. Future research should aim at developing thermal imaging into an irrigation scheduling tool applicable to different crops.

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.

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.

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.

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.

Greenhouse Gas Emissions Increase Following the Termination of a Perennial Legume Phase of an Annual Crop Rotation within the Red River Valley, Manitoba

NASA Astrophysics Data System (ADS)

Hanis, K. L.; Tenuta, M.; Amiro, B. D.; Glenn, A. J.; Maas, S.; Gervais, M.

2013-12-01

Perennial legume forages may have the potential to increase soil carbon sequestration and decrease nitrous oxide (N2O) emissions to the atmosphere when introduced into annual cropping systems. However, little is known about what short-term effect the return to annual cropping following termination of perennial legume forage would have on carbon dioxide (CO2) and N2O emissions. Furthermore, there are few quantitative measurements about this impact on the Canadian Prairies. A long-term field experiment to continuously measure CO2 and N2O fluxes was established at the Trace Gas Manitoba (TGAS-MAN) Long Term Greenhouse Gas Monitoring Site at Glenlea, Manitoba using the flux gradient micrometeorlogical technique with a tunable diode laser analyzer. The soil is poorly drained clay in the Red River Valley. The field experiment consisted of four 4-hectare plots planted to corn in 2006 and faba bean in 2007. In 2008, grass-alfalfa forage was introduced to two plots (annual - perennial) and grown until 2011 whereas the other two plots (annual) were planted to annual crops: spring wheat, rapeseed, barley and spring wheat in 2008, 2009, 2010 and 2011, respectively. In late September of 2011 the grass-alfalfa forage was killed and in 2012 all four plots were planted with corn. Termination of the grass-alfalfa forage resulted in greater fall CO2 emissions in 2011, greater spring melt CO2 emissions and net annual N2O emissions in 2012 from the annual-perennial plots when compared to the annual plots. Over seven crop years (2006-2012), the annual - perennial system increased carbon uptake by 3.4 Mg C ha-1 and reduced N2O emissions by 3.0 Mg CO2-eq ha-1 compared to the annual system. However after accounting for harvest removals both the annual and annual-perennial systems were net carbon sources of 5.7 and 2.5 Mg C ha-1 and net GHG sources of 38 and 24 Mg CO2-eq ha-1 respectively. We are currently following the long-term impacts of inclusion of perennial forages in an annual cropping system.

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.

Illinois drainage water management demonstration project

USGS Publications Warehouse

Pitts, D.J.; Cooke, R.; Terrio, P.J.; ,

2004-01-01

Due to naturally high water tables and flat topography, there are approximately 4 million ha (10 million ac) of farmland artificially drained with subsurface (tile) systems in Illinois. Subsurface drainage is practiced to insure trafficable field conditions for farm equipment and to reduce crop stress from excess water within the root zone. Although drainage is essential for economic crop production, there have been some significant environmental costs. Tile drainage systems tend to intercept nutrient (nitrate) rich soil-water and shunt it to surface water. Data from numerous monitoring studies have shown that a significant amount of the total nitrate load in Illinois is being delivered to surface water from tile drainage systems. In Illinois, these drainage systems are typically installed without control mechanisms and allow the soil to drain whenever the water table is above the elevation of the tile outlet. An assessment of water quality in the tile drained areas of Illinois showed that approximately 50 percent of the nitrate load was being delivered through the tile systems during the fallow period when there was no production need for drainage to occur. In 1998, a demonstration project to introduce drainage water management to producers in Illinois was initiated by NRCS4 An initial aspect of the project was to identify producers that were willing to manage their drainage system to create a raised water table during the fallow (November-March) period. Financial assistance from two federal programs was used to assist producers in retrofitting the existing drainage systems with control structures. Growers were also provided guidance on the management of the structures for both water quality and production benefits. Some of the retrofitted systems were monitored to determine the effect of the practice on water quality. This paper provides background on the water quality impacts of tile drainage in Illinois, the status of the demonstration project, preliminary monitoring results, and other observations.

Crop and cattle production responses to tillage and cover crop management in an integrated crop-livestock system in the southeastern USA

USDA-ARS?s Scientific Manuscript database

Integrated crop-livestock systems can help achieve greater environmental quality from disparate crop and livestock systems by recycling nutrients and taking advantage of synergies between systems. We investigated crop and animal production responses in integrated crop-livestock systems with two typ...

Procedure to select test organisms for environmental risk assessment of genetically modified crops in aquatic systems.

PubMed

Hilbeck, Angelika; Bundschuh, Rebecca; Bundschuh, Mirco; Hofmann, Frieder; Oehen, Bernadette; Otto, Mathias; Schulz, Ralf; Trtikova, Miluse

2017-11-01

For a long time, the environmental risk assessment (ERA) of genetically modified (GM) crops focused mainly on terrestrial ecosystems. This changed when it was scientifically established that aquatic ecosystems are exposed to GM crop residues that may negatively affect aquatic species. To assist the risk assessment process, we present a tool to identify ecologically relevant species usable in tiered testing prior to authorization or for biological monitoring in the field. The tool is derived from a selection procedure for terrestrial ecosystems with substantial but necessary changes to adequately consider the differences in the type of ecosystems. By using available information from the Water Framework Directive (2000/60/EC), the procedure can draw upon existing biological data on aquatic systems. The proposed procedure for aquatic ecosystems was tested for the first time during an expert workshop in 2013, using the cultivation of Bacillus thuringiensis (Bt) maize as the GM crop and 1 stream type as the receiving environment in the model system. During this workshop, species executing important ecological functions in aquatic environments were identified in a stepwise procedure according to predefined ecological criteria. By doing so, we demonstrated that the procedure is practicable with regard to its goal: From the initial long list of 141 potentially exposed aquatic species, 7 species and 1 genus were identified as the most suitable candidates for nontarget testing programs. Integr Environ Assess Manag 2017;13:974-979. © 2017 SETAC. © 2017 SETAC.

Modeling Energy and Mass Fluxes Over a Vineyard Using the Acasa Model

NASA Astrophysics Data System (ADS)

Marras, S.; Bellucco, V.; Pyles, D.; Falk, M.; Sirca, C.; Duce, P.; Snyder, R. L.; Paw U, K.; Spano, D.

2012-12-01

Energy and mass fluxes are widely monitored over natural ecosystems by the Eddy Covariance (EC) towers within the FLUXNET monitoring network. Only a few studies focused on EC measurements over tree crops and vines, and there is a lack of information useful to parameterize crop and flux models over such systems. The aim of this study was to improve our knowledge about the performance of the land surface model ACASA (Advanced Canopy-Atmosphere-Soil Algorithm) in estimating energy, water, and carbon fluxes over a typical Mediterranean vineyard located in Southern Sardinia (Italy). ACASA estimates turbulent fluxes per 20 canopy layers (10 layers within and 10 above the canopy) and 15 soil layers, using third-order closure equations. CO2 fluxes are estimated using a combination of Ball-Berry and Farquhar equations. The model parameters derived from literature, from a previous work conducted in Tuscany (Italy) and from direct measurements collected in the experimental site of this study. An Eddy Covariance measurement tower was installed to continuously monitor sensible and latent heat, and CO2 fluxes, in conjunction with a net radiometer, and soil heat flux plates from June 2009. A meteorological station was also set up for ancillary measurements. Model performance was evaluated by RMSE and linear regression statistics. Results for the energy balance components and CO2 exchanges will be presented. Detailed analysis was devoted to evaluate the model ability in estimating the vineyard evapotranspiration. This term of the energy balance is, in fact, important for farmers since they are mainly interested in quantify crop water requirements for a better irrigation management.

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.

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.

Pests, diseases and crop protection practices in the smallholder sweetpotato production system of the highlands of Papua New Guinea

PubMed Central

Liu, Jian; Johnson, Anne C.; Woruba, Deane N.; Kirchhof, Gunnar; Fujinuma, Ryosuke; Sirabis, William; Jeffery, Yapo; Akkinapally, Ramakrishna

2016-01-01

Sweetpotato (Ipomea batatans) is a food crop of global significance. The storage roots and foliage of crop are attacked by a wide range of pests and diseases. Whilst these are generally well controlled in developed countries using approaches such as clean planting material and monitoring with pheromone traps to guide insecticide use, research into methods suitable for developing countries has lagged. In Papua New Guinea (PNG), sweetpotato is grown extensively as a subsistence crop and commercial production as a cash crop is developing. We report results from a survey of 33 smallholder producers located in the Highlands of PNG where the crop is of particular importance. Surveys of interviewees’ crops showed high levels of pest and disease impact to foliage, stems and storage roots, especially in crops that were several years old. Weevils (Curculionidae) were reportedly the most damaging pests and scab (caused by the fungus Elisnoe batatus) the most damaging disease. Most producers reported root damage from the former and foliar damage from the latter but the general level of knowledge of pest and disease types was low. Despite the apparency of pest and disease signs and symptoms and recognition of their importance by farmers, a large majority of producers reported practiced no active pest or disease management. This was despite low numbers of farmers reporting use of traditional cultural practices including phytosanitary measures and insecticidal plants that had the scope for far wider use. Only one respondent reported use of insecticide though pesticides were available in nearby cities. This low level of pest and disease management in most cases, likely due to paucity in biological and technical knowledge among growers, hampers efforts to establish food security and constrains the development of sweetpotato as a cash crop. PMID:27957387

Pests, diseases and crop protection practices in the smallholder sweetpotato production system of the highlands of Papua New Guinea.

PubMed

Gurr, Geoff M; Liu, Jian; Johnson, Anne C; Woruba, Deane N; Kirchhof, Gunnar; Fujinuma, Ryosuke; Sirabis, William; Jeffery, Yapo; Akkinapally, Ramakrishna

2016-01-01

Sweetpotato ( Ipomea batatans ) is a food crop of global significance. The storage roots and foliage of crop are attacked by a wide range of pests and diseases. Whilst these are generally well controlled in developed countries using approaches such as clean planting material and monitoring with pheromone traps to guide insecticide use, research into methods suitable for developing countries has lagged. In Papua New Guinea (PNG), sweetpotato is grown extensively as a subsistence crop and commercial production as a cash crop is developing. We report results from a survey of 33 smallholder producers located in the Highlands of PNG where the crop is of particular importance. Surveys of interviewees' crops showed high levels of pest and disease impact to foliage, stems and storage roots, especially in crops that were several years old. Weevils (Curculionidae) were reportedly the most damaging pests and scab (caused by the fungus Elisnoe batatus ) the most damaging disease. Most producers reported root damage from the former and foliar damage from the latter but the general level of knowledge of pest and disease types was low. Despite the apparency of pest and disease signs and symptoms and recognition of their importance by farmers, a large majority of producers reported practiced no active pest or disease management. This was despite low numbers of farmers reporting use of traditional cultural practices including phytosanitary measures and insecticidal plants that had the scope for far wider use. Only one respondent reported use of insecticide though pesticides were available in nearby cities. This low level of pest and disease management in most cases, likely due to paucity in biological and technical knowledge among growers, hampers efforts to establish food security and constrains the development of sweetpotato as a cash crop.

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.

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).

Does Passive Sampling Accurately Reflect the Bee (Apoidea: Anthophila) Communities Pollinating Apple and Sour Cherry Orchards?

PubMed

Gibbs, Jason; Joshi, Neelendra K; Wilson, Julianna K; Rothwell, Nikki L; Powers, Karen; Haas, Mike; Gut, Larry; Biddinger, David J; Isaacs, Rufus

2017-06-01

During bloom of spring orchard crops, bees are the primary providers of pollination service. Monitoring these insects for research projects is often done by timed observations or by direct aerial netting, but there has been increasing interest in blue vane traps as an efficient passive approach to collecting bees. Over multiple spring seasons in Michigan and Pennsylvania, orchards were monitored for wild bees using timed netting from crop flowers and blue vane traps. This revealed a distinctly different community of wild bees captured using the two methods, suggesting that blue vane traps can complement but cannot replace direct aerial netting. The bee community in blue vane traps was generally composed of nonpollinating species, which can be of interest for broader biodiversity studies. In particular, blue vane traps caught Eucera atriventris (Smith), Eucera hamata (Bradley), Bombus fervidus (F.), and Agapostemon virescens (F.) that were never collected from the orchard crop flowers during the study period. Captures of bee species in nets was generally stable across the 3 yr, whereas we observed significant declines in the abundance of Lasioglossum pilosum (Smith) and Eucera spp. trapped using blue vane traps during the project, suggesting local overtrapping of reproductive individuals. We conclude that blue vane traps are a useful tool for expanding insights into bee communities within orchard crop systems, but they should be used with great caution to avoid local extirpation of these important insects. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Folksong based appraisal of bioecocultural heritage of sorghum (Sorghum bicolor (L.) Moench): A new approach in ethnobiology

PubMed Central

Mekbib, Firew

2009-01-01

Background Sorghum is one of the main staple crops for the world's poorest and most food insecure people. As Ethiopia is the centre of origin and diversity for sorghum, the crop has been cultivated for thousands of years and hence the heritage of the crop is expected to be rich. Folksong based appraisal of bioecocultural heritage has not been done before. Methods In order to assess the bioecocultural heritage of sorghum by folksongs various research methods were employed. These included focus group discussions with 360 farmers, direct on-farm participatory monitoring and observation with 120 farmers, and key informant interviews with 60 farmers and development agents. Relevant secondary data was also collected from the museum curators and historians. Results The crop is intimately associated with the life of the farmers. The association of sorghum with the farmers from seed selection to utilization is presented using folksongs. These include both tune and textual (ballad stories or poems) types. Folksongs described how farmers maintain a number of varieties on-farm for many biological, socio-economic, ecological, ethnological and cultural reasons. Farmers describe sorghum as follows: Leaf number is less than twenty; Panicle hold a thousand seeds; a clever farmer takes hold of it. In addition, they described the various farmers' varieties ethnobotanically by songs. The relative importance of sorghum vis-à-vis others crops is similarly explained in folksong terms. Conclusion The qualitative description of farmers' characterisation of the crop systems based on folksongs is a new system of appraising farmers' bioecocultural heritage. Hence, researchers, in addition to formal and quantitative descriptions, should use the folksong system for enhanced characterisation and utilization of bioecocultural heritages. In general, the salient characteristics of the folksongs used in describing the bioecocultural heritages are their oral traditions, varied function, communal or individual recreation and message transmissions. PMID:19575802

4D Near Real-Time Environmental Monitoring Using Highly Temporal LiDAR

NASA Astrophysics Data System (ADS)

Höfle, Bernhard; Canli, Ekrem; Schmitz, Evelyn; Crommelinck, Sophie; Hoffmeister, Dirk; Glade, Thomas

2016-04-01

The last decade has witnessed extensive applications of 3D environmental monitoring with the LiDAR technology, also referred to as laser scanning. Although several automatic methods were developed to extract environmental parameters from LiDAR point clouds, only little research has focused on highly multitemporal near real-time LiDAR (4D-LiDAR) for environmental monitoring. Large potential of applying 4D-LiDAR is given for landscape objects with high and varying rates of change (e.g. plant growth) and also for phenomena with sudden unpredictable changes (e.g. geomorphological processes). In this presentation we will report on the most recent findings of the research projects 4DEMON (http://uni-heidelberg.de/4demon) and NoeSLIDE (https://geomorph.univie.ac.at/forschung/projekte/aktuell/noeslide/). The method development in both projects is based on two real-world use cases: i) Surface parameter derivation of agricultural crops (e.g. crop height) and ii) change detection of landslides. Both projects exploit the "full history" contained in the LiDAR point cloud time series. One crucial initial step of 4D-LiDAR analysis is the co-registration over time, 3D-georeferencing and time-dependent quality assessment of the LiDAR point cloud time series. Due to the high amount of datasets (e.g. one full LiDAR scan per day), the procedure needs to be performed fully automatically. Furthermore, the online near real-time 4D monitoring system requires to set triggers that can detect removal or moving of tie reflectors (used for co-registration) or the scanner itself. This guarantees long-term data acquisition with high quality. We will present results from a georeferencing experiment for 4D-LiDAR monitoring, which performs benchmarking of co-registration, 3D-georeferencing and also fully automatic detection of events (e.g. removal/moving of reflectors or scanner). Secondly, we will show our empirical findings of an ongoing permanent LiDAR observation of a landslide (Gresten, Austria) and an agricultural maize crop stand (Heidelberg, Germany). This research demonstrates the potential and also limitations of fully automated, near real-time 4D LiDAR monitoring in geosciences.

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.

Midwest Climate and Agriculture - Monitoring Tillage Practices with NASA Remote Sensors

NASA Astrophysics Data System (ADS)

Makar, N. I.; Archer, S.; Rooks, K.; Sparks, K.; Trigg, C.; Lourie, J.; Wilkins, K.

2011-12-01

Concerns about climate change have driven efforts to reduce or offset greenhouse gas emissions. Agricultural activity has drawn considerable attention because it accounts for nearly twelve percent of total anthropogenic emissions. Depending on the type of tillage method utilized, farm land can be either a source or a sink of carbon. Conventional tillage disturbs the soil and can release greenhouse gases into the atmosphere. Conservational tillage practices have been advocated for their ability to sequester carbon, reduce soil erosion, maintain soil moisture, and increase long-term productivity. If carbon credit trading systems are implemented, a cost-effective, efficient tillage monitoring system is needed to enforce offset standards. Remote sensing technology can expedite the process and has shown promising results in distinguishing crop residue from soil. Agricultural indices such as the CAI, SINDRI, and LCA illuminate the unique reflectance spectra of crop residue and are thus able to classify fields based on percent crop cover. The CAI requires hyperspectral data, as it relies on narrow bands within the shortwave infrared portion of the electromagnetic spectrum. Although limited in availability, hyperspectral data has been shown to produce the most accurate results for detecting crop residue on the soil. A new approach to using the CAI was the focus of this study. Previously acquired field data was located in a region covered by a Hyperion swath and is thus the primary study area. In previous studies, ground-based data were needed for each satellite swath to correctly calibrate the linear relationship between the index values and the fraction of residue cover. We hypothesized that there should be a standard method which is able to convert index values into residue classifications without ground data analysis. To do this, end index values for a particular data set were assumed to be associated with end values of residue cover percentages. This method may prove to be more practical for end-users such as the USDA to quickly assess residue cover in a given region.

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/

Evaluation of a monitoring program for assessing the effects of management practices on the quantity and quality of drainwater from the Panoche Water District, western San Joaquin Valley, California

USGS Publications Warehouse

Leighton, David A.; Fio, John L.

1995-01-01

An evaluation was made of an existing monitoring program in the Panoche Water District for 1986-93. The Panoche Water District is an agricultural area located in the western San Joaquin Valley of California. Because irrigation drainage from this area has high concentrations of dissolved solids and selenium, management strategies have been developed to improve the quality of drainwater discharge. The purpose of the Panoche Water District's monitoring program is to assess the effects of water- and land-use practices on local ground water and drain flow from the district. Drainflow from the district consists of the discharge from 50 separate on-farm underground tile-drainage systems. The Panoche Water District maintains information on water deliveries, planned and actual crop types, and planned and actual acreages planted each year. In addition, the water district monitors ground-water and drainage-system discharges using a variety of data-collection methods. A total of 62 observation well sites are used to monitor ground-water level and quality. A total of 42 sites were monitored for drainflow quantity, and drain flow quality samples were collected from the outlets of each of the 50 drainage systems. However, these data were collected inconsistently and (or) intermittently during the period studied. All data obtained from the water district were compiled and stored in a geographic information system database. Water delivered for irrigation by the Panoche Water District is a mix of imported water and local ground water pumped directly into delivery canals. Although delivered water is a mix, information on the proportion of water from the two sources is not reported. Also, individual growers pump directly to their crops unknown quantities of ground water, the total of which could be greater than 60 percent of total applications during years when water district deliveries are greatly reduced (for example, the years during and following a drought). To evaluate the effects of irrigation on ground-water and drainflow quality, data on the combined chemical characteristics and the volume of water applied to crops are needed as part of the district's monitoring program. For example, without these data, this study could estimate only the effects of irrigation on ground-water recharge for 1986 (60.4 106 m3/y), 1987 (74.2 106 m3/y), and 1988 (56.0 106 m3/y) in the Panoche Water District water years when the amount of ground water pumped by individual growers was probably small. Water-level data show a significant decline of the water table in the upslope, undrained parts of the study area, and little or no significant change in the down slope, drained parts of the study area. Pumping from productions wells, most of which are located in the upslope part of the study area, may have contributed to the decline of the water table in the upslope area. The quantities of drainflow, dissolved solids, and selenium discharged from the study area decreased during the study period. However, drainflow, dissolved solids, and selenium discharged from individual on-farm drainage systems did not decrease. These data also illustrate the need for consistent and regular monitoring of the factors that affect drainage in the western San Joaquin Valley.

Continuously Monocropped Jerusalem Artichoke Changed Soil Bacterial Community Composition and Ammonia-Oxidizing and Denitrifying Bacteria Abundances.

PubMed

Zhou, Xingang; Wang, Zhilin; Jia, Huiting; Li, Li; Wu, Fengzhi

2018-01-01

Soil microbial communities have profound effects on the growth, nutrition and health of plants in agroecosystems. Understanding soil microbial dynamics in cropping systems can assist in determining how agricultural practices influence soil processes mediated by microorganisms. In this study, soil bacterial communities were monitored in a continuously monocropped Jerusalem artichoke (JA) system, in which JA was successively monocropped for 3 years in a wheat field. Soil bacterial community compositions were estimated by amplicon sequencing of the 16S rRNA gene. Abundances of ammonia-oxidizing and denitrifying bacteria were estimated by quantitative PCR analysis of the amoA , nirS , and nirK genes. Results showed that 1-2 years of monocropping of JA did not significantly impact the microbial alpha diversity, and the third cropping of JA decreased the microbial alpha diversity ( P < 0.05). Principal coordinates analysis and permutational multivariate analysis of variance analyses revealed that continuous monocropping of JA changed soil bacterial community structure and function profile ( P < 0.001). At the phylum level, the wheat field was characterized with higher relative abundances of Latescibacteria , Planctomycetes , and Cyanobacteria , the first cropping of JA with Actinobacteria , the second cropping of JA with Acidobacteria , Armatimonadetes , Gemmatimonadetes , and Proteobacteria . At the genus level, the first cropping of JA was enriched with bacterial species with pathogen-antagonistic and/or plant growth promoting potentials, while members of genera that included potential denitrifiers increased in the second and third cropping of JA. The first cropping of JA had higher relative abundances of KO terms related to lignocellulose degradation and phosphorus cycling, the second cropping of JA had higher relative abundances of KO terms nitrous-oxide reductase and nitric-oxide reductase, and the third cropping of JA had higher relative abundances of KO terms nitrate reductase and nitrite reductase. The abundances of amoA genes decreased while nirK increased in the third cropping of JA, nirS continuously increased in the second and third cropping of JA ( P < 0.05). Redundancy analysis and Mantel test found that soil organic carbon and Olsen phosphorus contents played important roles in shaping soil bacterial communities. Overall, our results revealed that continuous monocropping of JA changed soil bacterial community composition and its functional potentials.

Diversified cropping systems support greater microbial cycling and retention of carbon and nitrogen

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

King, Alison E.; Hofmockel, Kirsten S.

2017-03-01

Diversifying biologically simple cropping systems often entails altering other management practices, such as tillage regime or nitrogen (N) source. We hypothesized that the interaction of crop rotation, N source, and tillage in diversified cropping systems would promote microbially-mediated soil C and N cycling while attenuating inorganic N pools. We studied a cropping systems trial in its 10th year in Iowa, USA, which tested a 2-yr cropping system of corn (Zea mays L.)/soybean [Glycine max (L.) Merr.] managed with conventional fertilizer N inputs and conservation tillage, a 3-yr cropping system of corn/soybean/small grain + red clover (Trifolium pratense L.), and amore » 4-yr cropping system of corn/soybean/small grain + alfalfa (Medicago sativa L.)/alfalfa. Three year and 4-yr cropping systems were managed with composted manure, reduced N fertilizer inputs, and periodic moldboard ploughing. We assayed soil microbial biomass carbon (MBC) and N (MBN), soil extractable NH4 and NO3, gross proteolytic activity of native soil, and potential activity of six hydrolytic enzymes eight times during the growing season. At the 0-20cm depth, native protease activity in the 4-yr cropping system was greater than in the 2-yr cropping system by a factor of 7.9, whereas dissolved inorganic N pools did not differ between cropping systems (P = 0.292). At the 0-20cm depth, MBC and MBN the 4-yr cropping system exceeded those in the 2-yr cropping system by factors of 1.51 and 1.57. Our findings suggest that diversified crop cropping systems, even when periodically moldboard ploughed, support higher levels of microbial biomass, greater production of bioavailable N from SOM, and a deeper microbially active layer than less diverse cropping systems.« less

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.

Synthetic aperture radar for a crop information system: A multipolarization and multitemporal approach

NASA Astrophysics Data System (ADS)

Ban, Yifang

Acquisition of timely information is a critical requirement for successful management of an agricultural monitoring system. Crop identification and crop-area estimation can be done fairly successfully using satellite sensors operating in the visible and near-infrared (VIR) regions of the spectrum. However, data collection can be unreliable due to problems of cloud cover at critical stages of the growing season. The all-weather capability of synthetic aperture radar (SAR) imagery acquired from satellites provides data over large areas whenever crop information is required. At the same time, SAR is sensitive to surface roughness and should be able to provide surface information such as tillage-system characteristics. With the launch of ERS-1, the first long-duration SAR system became available. The analysis of airborne multipolarization SAR data, multitemporal ERS-1 SAR data, and their combinations with VIR data, is necessary for the development of image-analysis methodologies that can be applied to RADARSAT data for extracting agricultural crop information. The overall objective of this research is to evaluate multipolarization airborne SAR data, multitemporal ERS-1 SAR data, and combinations of ERS-1 SAR and satellite VIR data for crop classification using non-conventional algorithms. The study area is situated in Norwich Township, an agricultural area in Oxford County, southern Ontario, Canada. It has been selected as one of the few representative agricultural 'supersites' across Canada at which the relationships between radar data and agriculture are being studied. The major field crops are corn, soybeans, winter wheat, oats, barley, alfalfa, hay, and pasture. Using airborne C-HH and C-HV SAR data, it was found that approaches using contextual information, texture information and per-field classification for improving agricultural crop classification proved to be effective, especially the per-field classification method. Results show that three of the four best per-field classification accuracies (\\ K=0.91) are achieved using combinations of C-HH and C-VV SAR data. This confirms the strong potential of multipolarization data for crop classification. The synergistic effects of multitemporal ERS-1 SAR and Landsat TM data are evaluated for crop classification using an artificial neural network (ANN) approach. The results show that the per-field approach using a feed-forward ANN significantly improves the overall classification accuracy of both single-date and multitemporal SAR data. Using the combination of TM3,4,5 and Aug. 5 SAR data, the best per-field ANN classification of 96.8% was achieved. It represents an 8.5% improvement over a single TM3,4,5 classification alone. Using multitemporal ERS-1 SAR data acquired during the 1992 and 1993 growing seasons, the radar backscatter characteristics of crops and their underlying soils are analyzed. The SAR temporal backscatter profiles were generated for each crop type and the earliest times of the year for differentiation of individual crop types were determined. Orbital (incidence-angle) effects were also observed on all crops. The average difference between the two orbits was about 3 dB. Thus attention should be given to the local incidence-angle effects when using ERS-1 SAR data, especially when comparing fields from different scenes or different areas within the same scene. Finally, early- and mid-season multitemporal SAR data for crop classification using sequential-masking techniques are evaluated, based on the temporal backscatter profiles. It was found that all crops studied could be identified by July 21.

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.

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.

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.).

Monitoring growth condition of spring maize in Northeast China using a process-based model

NASA Astrophysics Data System (ADS)

Wang, Peijuan; Zhou, Yuyu; Huo, Zhiguo; Han, Lijuan; Qiu, Jianxiu; Tan, Yanjng; Liu, Dan

2018-04-01

Early and accurate assessment of the growth condition of spring maize, a major crop in China, is important for the national food security. This study used a process-based Remote-Sensing-Photosynthesis-Yield Estimation for Crops (RS-P-YEC) model, driven by satellite-derived leaf area index and ground-based meteorological observations, to simulate net primary productivity (NPP) of spring maize in Northeast China from the first ten-day (FTD) of May to the second ten-day (STD) of August during 2001-2014. The growth condition of spring maize in 2014 in Northeast China was monitored and evaluated spatially and temporally by comparison with 5- and 13-year averages, as well as 2009 and 2013. Results showed that NPP simulated by the RS-P-YEC model, with consideration of multi-scattered radiation inside the crop canopy, could reveal the growth condition of spring maize more reasonably than the Boreal Ecosystem Productivity Simulator. Moreover, NPP outperformed other commonly used vegetation indices (e.g., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) for monitoring and evaluating the growth condition of spring maize. Compared with the 5- and 13-year averages, the growth condition of spring maize in 2014 was worse before the STD of June and after the FTD of August, and it was better from the third ten-day (TTD) of June to the TTD of July across Northeast China. Spatially, regions with slightly worse and worse growth conditions in the STD of August 2014 were concentrated mainly in central Northeast China, and they accounted for about half of the production area of spring maize in Northeast China. This study confirms that NPP is a good indicator for monitoring and evaluating growth condition because of its capacity to reflect the physiological characteristics of crops. Meanwhile, the RS-P-YEC model, driven by remote sensing and ground-based meteorological data, is effective for monitoring crop growth condition over large areas in a near real time.

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...

[Main interspecific competition and land productivity of fruit-crop intercropping in Loess Region of West Shauxi].

PubMed

Yun, Lei; Bi, Hua-Xing; Tian, Xiao-Ling; Cui, Zhe-Wei; Zhou, Hui-Zi; Gao, Lu-Bo; Liu, Li-Xia

2011-05-01

Taking the four typical fruit-crop intercropping models, i.e., walnut-peanut, walnut-soybean, apple-peanut, and apple-soybean, in the Loess Region of western Shanxi Province as the objects, this paper analyzed the crop (peanut and soybean) photosynthetic active radiation (PAR), net photosynthetic rate (P(n)), yield, and soil moisture content. Comparing with crop monoculture, fruit-crop intercropping decreased the crop PAR and P(n). The smaller the distance from tree rows, the smaller the crop PAR and P(n). There was a significantly positive correlation between the P(n) and crop yield, suggesting that illumination was one of the key factors affecting crop yield. From the whole trend, the 0-100 cm soil moisture content had no significant differences between walnut-crop intercropping systems and corresponding monoculture cropping systems, but had significant differences between apple-crop intercropping systems and corresponding monoculture cropping systems, indicating that the competition for soil moisture was more intense in apple-crop intercropping systems than in walnut-crop intercropping systems. Comparing with monoculture, fruit-crop intercropping increased the land use efficiency and economic benefit averagely by 70% and 14%, respectively, and walnut-crop intercropping was much better than apple-crop intercropping. To increase the crop yield in fruit-crop intercropping systems, the following strategies should be taken: strengthening the management of irrigation and fertilization, increasing the distances or setting root barriers between crop and tree rows, regularly and properly pruning, and planting shade-tolerant crops in intercropping.

Cover crops support ecological intensification of arable cropping systems

NASA Astrophysics Data System (ADS)

Wittwer, Raphaël A.; Dorn, Brigitte; Jossi, Werner; van der Heijden, Marcel G. A.

2017-02-01

A major challenge for agriculture is to enhance productivity with minimum impact on the environment. Several studies indicate that cover crops could replace anthropogenic inputs and enhance crop productivity. However, so far, it is unclear if cover crop effects vary between different cropping systems, and direct comparisons among major arable production systems are rare. Here we compared the short-term effects of various cover crops on crop yield, nitrogen uptake, and weed infestation in four arable production systems (conventional cropping with intensive tillage and no-tillage; organic cropping with intensive tillage and reduced tillage). We hypothesized that cover cropping effects increase with decreasing management intensity. Our study demonstrated that cover crop effects on crop yield were highest in the organic system with reduced tillage (+24%), intermediate in the organic system with tillage (+13%) and in the conventional system with no tillage (+8%) and lowest in the conventional system with tillage (+2%). Our results indicate that cover crops are essential to maintaining a certain yield level when soil tillage intensity is reduced (e.g. under conservation agriculture), or when production is converted to organic agriculture. Thus, the inclusion of cover crops provides additional opportunities to increase the yield of lower intensity production systems and contribute to ecological intensification.

Direct and indirect impacts of crop-livestock organization on mixed crop-livestock systems sustainability: a model-based study.

PubMed

Sneessens, I; Veysset, P; Benoit, M; Lamadon, A; Brunschwig, G

2016-11-01

Crop-livestock production is claimed more sustainable than specialized production systems. However, the presence of controversial studies suggests that there must be conditions of mixing crop and livestock productions to allow for higher sustainable performances. Whereas previous studies focused on the impact of crop-livestock interactions on performances, we posit here that crop-livestock organization is a key determinant of farming system sustainability. Crop-livestock organization refers to the percentage of the agricultural area that is dedicated to each production. Our objective is to investigate if crop-livestock organization has both a direct and an indirect impact on mixed crop-livestock (MC-L) sustainability. In that objective, we build a whole-farm model parametrized on representative French sheep and crop farming systems in plain areas (Vienne, France). This model permits simulating contrasted MC-L systems and their subsequent sustainability through the following indicators of performance: farm income, production, N balance, greenhouse gas (GHG) emissions (/kg product) and MJ consumption (/kg product). Two MC-L systems were simulated with contrasted crop-livestock organizations (MC20-L80: 20% of crops; MC80-L20: 80% of crops). A first scenario - constraining no crop-livestock interactions in both MC-L systems - permits highlighting that crop-livestock organization has a significant direct impact on performances that implies trade-offs between objectives of sustainability. Indeed, the MC80-L20 system is showing higher performances for farm income (+44%), livestock production (+18%) and crop GHG emissions (-14%) whereas the MC20-L80 system has a better N balance (-53%) and a lower livestock MJ consumption (-9%). A second scenario - allowing for crop-livestock interactions in both MC20-L80 and MC80-L20 systems - stated that crop-livestock organization has a significant indirect impact on performances. Indeed, even if crop-livestock interactions permit improving performances, crop-livestock organization influences the capacity of MC-L systems to benefit from crop-livestock interactions. As a consequence, we observed a decreasing performance trade-off between MC-L systems for farm income (-4%) and crop GHG emissions (-10%) whereas the gap increases for nitrogen balance (+23%), livestock production (+6%) - MJ consumption (+16%) - GHG emissions (+5%) and crop MJ consumption (+5%). However, the indirect impact of crop-livestock organization doesn't reverse the trend of trade-offs between objectives of sustainability determined by the direct impact of crop-livestock organization. As a conclusion, crop-livestock organization is a key factor that has to be taken into account when studying the sustainability of mixed crop-livestock systems.

NASA Advanced Life Support Technology Testing and Development

NASA Technical Reports Server (NTRS)

Wheeler, Raymond M.

2012-01-01

Prior to 2010, NASA's advanced life support research and development was carried out primarily under the Exploration Life Support Project of NASA's Exploration Systems Mission Directorate. In 2011, the Exploration Life Support Project was merged with other projects covering Fire Prevention/Suppression, Radiation Protection, Advanced Environmental Monitoring and Control, and Thermal Control Systems. This consolidated project was called Life Support and Habitation Systems, which was managed under the Exploration Systems Mission Directorate. In 2012, NASA re-organized major directorates within the agency, which eliminated the Exploration Systems Mission Directorate and created the Office of the Chief Technologist (OCT). Life support research and development is currently conducted within the Office of the Chief Technologist, under the Next Generation Life Support Project, and within the Human Exploration Operation Missions Directorate under several Advanced Exploration System projects. These Advanced Exploration Systems projects include various themes of life support technology testing, including atmospheric management, water management, logistics and waste management, and habitation systems. Food crop testing is currently conducted as part of the Deep Space Habitation (DSH) project within the Advanced Exploration Systems Program. This testing is focused on growing salad crops that could supplement the crew's diet during near term missions.

Development of High Yield Feedstocks and Biomass Conversion Technology for Renewable Energy

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

Hashimoto, Andrew G.; Crow, Susan; DeBeryshe, Barbara

2015-04-09

This project had two main goals. The first goal was to evaluate several high yielding tropical perennial grasses as feedstock for biofuel production, and to characterize the feedstock for compatible biofuel production systems. The second goal was to assess the integration of renewable energy systems for Hawaii. The project focused on high-yield grasses (napiergrass, energycane, sweet sorghum, and sugarcane). Field plots were established to evaluate the effects of elevation (30, 300 and 900 meters above sea level) and irrigation (50%, 75% and 100% of sugarcane plantation practice) on energy crop yields and input. The test plots were extensive monitored including:more » hydrologic studies to measure crop water use and losses through seepage and evapotranspiration; changes in soil carbon stock; greenhouse gas flux (CO 2, CH 4, and N 2O) from the soil surface; and root morphology, biomass, and turnover. Results showed significant effects of environment on crop yields. In general, crop yields decrease as the elevation increased, being more pronounced for sweet sorghum and energycane than napiergrass. Also energy crop yields were higher with increased irrigation levels, being most pronounced with energycane and less so with sweet sorghum. Daylight length greatly affected sweet sorghum growth and yields. One of the energy crops (napiergrass) was harvested at different ages (2, 4, 6, and 8 months) to assess the changes in feedstock characteristics with age and potential to generate co-products. Although there was greater potential for co-products from younger feedstock, the increased production was not sufficient to offset the additional cost of harvesting multiple times per year. The feedstocks were also characterized to assess their compatibility with biochemical and thermochemical conversion processes. The project objectives are being continued through additional support from the Office of Naval Research, and the Biomass Research and Development Initiative. Renewable energy assessments included: biomass feedstocks currently being produced by Hawaiian Commercial & Sugar Co., and possibilities of producing methane from agricultural and livestock wastes and the potential of photovoltaic systems for irrigation pumping at HC&S. Finally, the impact of a micro-hydroelectric system on a small-farm economics and the local community was assessed.« less

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.

The Benchmark Farm Program : a method for estimating irrigation water use in southwest Florida

USGS Publications Warehouse

Duerr, A.D.; Trommer, J.T.

1982-01-01

Irrigation water-use data are summarized in this report for 74 farms in the Southwest Florida Water Management District. Most data are for 1978-90, but 18 farms have data extending back to the early 1970's. Data include site number and location, season and year, crop type, irrigation system, monitoring method, and inches of water applied per acre. Crop types include citrus, cucumbers, pasture, peanuts, sod, strawberries, and tropical fish farms are also included. Water-application rates per growing season ranged from 0 inches per acre for several citrus and pasture sites to 239.7 inches per acre for a nursery site. The report also includes rainfall data for 12 stations throughout the study area. (USGS)

Remotely sensing wheat maturation with radar

NASA Technical Reports Server (NTRS)

Bush, T. F.; Ulaby, F. T.

1975-01-01

The scattering properties of wheat were studied in the 8-18 GHz band as a function of frequency, polarization, incidence angle, and crop maturity. Supporting ground truth was collected at the time of measurement. The data indicate that the radar backscattering coefficient is sensitive to both radar system parameters and crop characteristics particularly at incidence angles near nadir. Linear regression analyses of the radar backscattering coefficient on both time and plant moisture content result in rather good correlation. Furthermore, by calculating the average time rate of change of the radar backscattering coefficient it is found that it undergoes rapid variations shortly before and after the wheat is harvested. Both of these analyses suggest methods for estimating wheat maturity and for monitoring the progress of harvest.

Eight years of Conservation Agriculture-based cropping systems research in Eastern Africa to conserve soil and water and mitigate effects of climate change

NASA Astrophysics Data System (ADS)

Araya, Tesfay; Nyssen, Jan; Govaerts, Bram; Lanckriet, Sil; Baudron, Frédéric; Deckers, Jozef; Cornelis, Wim

2014-05-01

In Ethiopia, repeated plowing, complete removal of crop residues at harvest, aftermath grazing of crop fields and occurrence of repeated droughts have reduced the biomass return to the soil and aggravated cropland degradation. Conservation Agriculture (CA)-based resource conserving cropping systems may reduce runoff and soil erosion, and improve soil quality, thereby increasing crop productivity. Thus, a long-term tillage experiment has been carried out (2005 to 2012) on a Vertisol to quantify - among others - changes in runoff and soil loss for two local tillage practices, modified to integrate CA principles in semi-arid northern Ethiopia. The experimental layout was a randomized complete block design with three replications on permanent plots of 5 m by 19 m. The tillage treatments were (i) derdero+ (DER+) with a furrow and permanent raised bed planting system, ploughed only once at planting by refreshing the furrow from 2005 to 2012 and 30% standing crop residue retention, (ii) terwah+ (TER+) with furrows made at 1.5 m interval, plowed once at planting, 30% standing crop residue retention and fresh broad beds, and (iii) conventional tillage (CT) with a minimum of three plain tillage operations and complete removal of crop residues. All the plowing and reshaping of the furrows was done using the local ard plough mahresha and wheat, teff, barley and grass pea were grown. Glyphosate was sprayed starting from the third year onwards (2007) at 2 l ha-1 before planting to control pre-emergent weeds in CA plots. Runoff and soil loss were measured daily. Soil water content was monitored every 6 days. Significantly different (p<0.05) runoff coefficients averaged over 8 years were 14, 20 and 27% for DER+, TER+ and CT, respectively. Mean soil losses were 4 t ha-1 y-1 in DER+, 13 in TER+ and 18 in CT. Soil water storage during the growing season was constantly higher in CA-based systems compared with CT. A period of at least three years of cropping was required before improvements in crop yield became significant. Further, modeling of the sediment budgets shows that total soil loss due to sheet and rill erosion in cropland, when CA would be practiced at large scale in a 180 ha catchment, would reduce to 581 t y-1, instead of 1109 t y-1 under the current farmer practice. Using NASA/GISS Model II precipitation projections of IPCC scenario A1FI, CA is estimated to reduce soil loss and runoff and mitigate the effect of increased rainfall due to climate change. For smallholder farmers in semi-arid agro-ecosystems, CA-based systems constitute a field rainwater and soil conservation improvement strategy that enhances crop and economic productivity and reduces siltation of reservoirs, especially under changing climate. The reduction in draught power requirement would enable a reduction in oxen density and crop residue demand for livestock feed, which would encourage smallholder farmers to increase biomass return to the soil. Adoption of CA-based systems in the study area requires further work to improve smallholder farmers' awareness on benefits, to guarantee high standards during implementation and to design appropriate weed management strategies.

Irrigation Water Quality for Leafy Crops: A Perspective of Risks and Potential Solutions

PubMed Central

Allende, Ana; Monaghan, James

2015-01-01

There is increasing evidence of the contribution of irrigation water in the contamination of produce leading to subsequent outbreaks of foodborne illness. This is a particular risk in the production of leafy vegetables that will be eaten raw without cooking. Retailers selling leafy vegetables are increasingly targeting zero-risk production systems and the associated requirements for irrigation water quality have become more stringent in regulations and quality assurance schemes (QAS) followed by growers. Growers can identify water sources that are contaminated with potential pathogens through a monitoring regime and only use water free of pathogens, but the low prevalence of pathogens makes the use of faecal indicators, particularly E. coli, a more practical approach. Where growers have to utilise water sources of moderate quality, they can reduce the risk of contamination of the edible portion of the crop (i.e., the leaves) by treating irrigation water before use through physical or chemical disinfection systems, or avoid contact between the leaves and irrigation water through the use of drip or furrow irrigation, or the use of hydroponic growing systems. This study gives an overview of the main problems in the production of leafy vegetables associated with irrigation water, including microbial risk and difficulties in water monitoring, compliance with evolving regulations and quality standards, and summarises the current alternatives available for growers to reduce microbial risks. PMID:26151764

Irrigation Water Quality for Leafy Crops: A Perspective of Risks and Potential Solutions.

PubMed

Allende, Ana; Monaghan, James

2015-07-03

There is increasing evidence of the contribution of irrigation water in the contamination of produce leading to subsequent outbreaks of foodborne illness. This is a particular risk in the production of leafy vegetables that will be eaten raw without cooking. Retailers selling leafy vegetables are increasingly targeting zero-risk production systems and the associated requirements for irrigation water quality have become more stringent in regulations and quality assurance schemes (QAS) followed by growers. Growers can identify water sources that are contaminated with potential pathogens through a monitoring regime and only use water free of pathogens, but the low prevalence of pathogens makes the use of faecal indicators, particularly E. coli, a more practical approach. Where growers have to utilise water sources of moderate quality, they can reduce the risk of contamination of the edible portion of the crop (i.e., the leaves) by treating irrigation water before use through physical or chemical disinfection systems, or avoid contact between the leaves and irrigation water through the use of drip or furrow irrigation, or the use of hydroponic growing systems. This study gives an overview of the main problems in the production of leafy vegetables associated with irrigation water, including microbial risk and difficulties in water monitoring, compliance with evolving regulations and quality standards, and summarises the current alternatives available for growers to reduce microbial risks.

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.

First typology of cacao (Theobroma cacao L.) systems in Colombian Amazonia, based on tree species richness, canopy structure and light availability.

PubMed

Suárez Salazar, Juan Carlos; Ngo Bieng, Marie Ange; Melgarejo, Luz Marina; Di Rienzo, Julio A; Casanoves, Fernando

2018-01-01

We present a typology of cacao agroforest systems in Colombian Amazonia. These systems had yet to be described in the literature, especially their potential in terms of biodiversity conservation. The systems studied are located in a post-conflict area, and a deforestation front in Colombian Amazonia. Cacao cropping systems are of key importance in Colombia: cacao plays a prime role in post conflict resolution, as cacao is a legal crop to replace illegal crops; cacao agroforests are expected to be a sustainable practice, promoting forest-friendly land use. We worked in 50 x 2000 m2 agroforest plots, in Colombian Amazonia. A cluster analysis was used to build a typology based on 28 variables characterised in each plot, and related to diversity, composition, spatial structure and light availability for the cacao trees. We included variables related to light availability to evaluate the amount of transmitted radiation to the cacao trees in each type, and its suitability for cacao ecophysiological development. We identified 4 types of cacao agroforests based on differences concerning tree species diversity and the impact of canopy spatial structure on light availability for the cacao trees in the understorey. We found 127 tree species in the dataset, with some exclusive species in each type. We also found that 3 out of the 4 types identified displayed an erosion of tree species diversity. This reduction in shade tree species may have been linked to the desire to reduce shade, but we also found that all the types described were compatible with good ecophysiological development of the cacao trees. Cacao agroforest systems may actually be achieving biodiversity conservation goals in Colombian Amazonia. One challenging prospect will be to monitor and encourage the conservation of tree species diversity in cacao agroforest systems during the development of these cropping systems, as a form of forest-friendly management enhancing sustainable peace building in Colombia.

High clearance phenotyping systems for season-long measurement of corn, sorghum and other row crops to complement unmanned aerial vehicle systems

NASA Astrophysics Data System (ADS)

Murray, Seth C.; Knox, Leighton; Hartley, Brandon; Méndez-Dorado, Mario A.; Richardson, Grant; Thomasson, J. Alex; Shi, Yeyin; Rajan, Nithya; Neely, Haly; Bagavathiannan, Muthukumar; Dong, Xuejun; Rooney, William L.

2016-05-01

The next generation of plant breeding progress requires accurately estimating plant growth and development parameters to be made over routine intervals within large field experiments. Hand measurements are laborious and time consuming and the most promising tools under development are sensors carried by ground vehicles or unmanned aerial vehicles, with each specific vehicle having unique limitations. Previously available ground vehicles have primarily been restricted to monitoring shorter crops or early growth in corn and sorghum, since plants taller than a meter could be damaged by a tractor or spray rig passing over them. Here we have designed two and already constructed one of these self-propelled ground vehicles with adjustable heights that can clear mature corn and sorghum without damage (over three meters of clearance), which will work for shorter row crops as well. In addition to regular RGB image capture, sensor suites are incorporated to estimate plant height, vegetation indices, canopy temperature and photosynthetically active solar radiation, all referenced using RTK GPS to individual plots. These ground vehicles will be useful to validate data collected from unmanned aerial vehicles and support hand measurements taken on plots.

U.S National cropland soil moisture monitoring using SMAP

USDA-ARS?s Scientific Manuscript database

Crop condition information is critical for public and private sector decision making that concerns agricultural policy, food production, food security, and food commodity prices. Crop conditions change quickly due to various growing condition events, such as temperature extremes, soil moisture defic...

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...

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.

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.

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

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.

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.

Monitoring and control technologies for bioregenerative life support systems/CELSS

NASA Technical Reports Server (NTRS)

Knott, William M.; Sager, John C.

1991-01-01

The development of a controlled Ecological Life Support System (CELSS) will require NASA to develop innovative monitoring and control technologies to operate the different components of the system. Primary effort over the past three to four years has been directed toward the development of technologies to operate a biomass production module. Computer hardware and software required to operate, collect, and summarize environmental data for a large plant growth chamber facility were developed and refined. Sensors and controls required to collect information on such physical parameters as relative humidity, temperature, irradiance, pressure, and gases in the atmosphere; and PH, dissolved oxygen, fluid flow rates, and electrical conductivity in the nutrient solutions are being developed and tested. Technologies required to produce high artificial irradiance for plant growth and those required to collect and transport natural light into a plant growth chamber are also being evaluated. Significant effort was directed towards the development and testing of a membrane nutrient delivery system required to manipulate, seed, and harvest crops, and to determine plant health prior to stress impacting plant productivity are also being researched. Tissue culture technologies are being developed for use in management and propagation of crop plants. Though previous efforts have focussed on development of technologies required to operate a biomass production module for a CELSS, current efforts are expanding to include technologies required to operate modules such as food preparation, biomass processing, and resource (waste) recovery which are integral parts of the CELSS.

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

NASA Technical Reports Server (NTRS)

Gat, N.; Subramanian, S.; Barhen, J.; Toomarian, N.

1996-01-01

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

Comparison of multispectral remote-sensing techniques for monitoring subsurface drain conditions. [Imperial Valley, California

NASA Technical Reports Server (NTRS)

Goettelman, R. C.; Grass, L. B.; Millard, J. P.; Nixon, P. R.

1983-01-01

The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.

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

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

PubMed Central

Ammad Uddin, Mohammad; Mansour, Ali; Le Jeune, Denis; Ayaz, Mohammad; Aggoune, el-Hadi M.

2018-01-01

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use. PMID:29439496

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring.

PubMed

Uddin, Mohammad Ammad; Mansour, Ali; Jeune, Denis Le; Ayaz, Mohammad; Aggoune, El-Hadi M

2018-02-11

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.

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.

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.

Impacts on Water Management and Crop Production of Regional Cropping System Adaptation to Climate Change

NASA Astrophysics Data System (ADS)

Zhong, H.; Sun, L.; Tian, Z.; Liang, Z.; Fischer, G.

2014-12-01

China is one of the most populous and fast developing countries, also faces a great pressure on grain production and food security. Multi-cropping system is widely applied in China to fully utilize agro-climatic resources and increase land productivity. As the heat resource keep improving under climate warming, multi-cropping system will also shifting northward, and benefit crop production. But water shortage in North China Plain will constrain the adoption of new multi-cropping system. Effectiveness of multi-cropping system adaptation to climate change will greatly depend on future hydrological change and agriculture water management. So it is necessary to quantitatively express the water demand of different multi-cropping systems under climate change. In this paper, we proposed an integrated climate-cropping system-crops adaptation framework, and specifically focused on: 1) precipitation and hydrological change under future climate change in China; 2) the best multi-cropping system and correspondent crop rotation sequence, and water demand under future agro-climatic resources; 3) attainable crop production with water constraint; and 4) future water management. In order to obtain climate projection and precipitation distribution, global climate change scenario from HADCAM3 is downscaled with regional climate model (PRECIS), historical climate data (1960-1990) was interpolated from more than 700 meteorological observation stations. The regional Agro-ecological Zone (AEZ) model is applied to simulate the best multi-cropping system and crop rotation sequence under projected climate change scenario. Finally, we use the site process-based DSSAT model to estimate attainable crop production and the water deficiency. Our findings indicate that annual land productivity may increase and China can gain benefit from climate change if multi-cropping system would be adopted. This study provides a macro-scale view of agriculture adaptation, and gives suggestions to national agriculture adaptation strategy decisions.

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.

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.

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.

Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review.

PubMed

Jawad, Haider Mahmood; Nordin, Rosdiadee; Gharghan, Sadik Kamel; Jawad, Aqeel Mahmood; Ismail, Mahamod

2017-08-03

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.

Automated observation of diurnal solar-induced chlorophyll fluorescence for better understanding of crop photosynthesis

NASA Astrophysics Data System (ADS)

Huang, C.; Zhang, L.; Wang, S.; Qiao, N.

2016-12-01

Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been considered an ideal probe in monitoring vegetation photosynthesis. However, numerous challenges have greatly limited its wide applications, including accurate estimate of faint SIF from the observed apparent reflected radiation, uncertainties in inferring the vegetation photosynthesis as well as lack of validation. These difficulties should be resolved at ground-based controlled scales before the launch of SIF satellite platforms such as ESA's FLEX (to be launched 2021). Currently, increasing continuous and long-term automated SIF measurement systems have been reported for better understanding the diurnal and seasonal changes of vegetation photosynthesis. This study introduces a newly developed automated SIF field measurement system (Auto-SIF, 500-800 nm, FWHM=0.74 nm, SSI=0.38 nm, SNR=1000:1, see figure 1) in China and its initial results for inferring photosynthesis of different crops including soybean (three types), maize (two types) and rice (two types). The experiments were conducted at the test crop field affiliated to the Institute of Genetics and Development Biology, Chinese Academy of Sciences. The Auto-SIF incorporates two observation modes, i.e., reference panel mode and target mode (see figure 1), and the two modes can be switched very quickly through an electrical motor. All diurnal super-spectra data and SIFs of crops were collected in clear days and with a finer time interval of 1minute, therefore they can be easily resampled to different time intervals (see figure 2) in order for convenient comparisons with other data from different observation platforms, like 30-minute tower-flux GPP data. For better understanding of crop photosynthesis, Li-6400 XT (LI-COR, Inc.) and TES-1339R light meter were respectively used in this study to simultaneously obtain diurnal dynamics of leaf-level SIFs and sun incoming flux. Due to the availability of wide spectral range of Auto-SIF (500-800 nm), the photochemical reflectance index (PRI) and NDVI were also calculated to assess the diurnal SIFs and photosynthesis performances among different crops. This study presents a primary analyses of field diurnal canopy/leaf SIFs, PRI, NDVI of different crops , and may provide a better understanding of crop photosynthesis.

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.

Design of components for the NASA OCEAN project

NASA Technical Reports Server (NTRS)

Wright, Jenna (Editor); Clift, James; Dumais, Bryan; Gardner, Shannon; Hernandez, Juan Carlos; Nolan, Laura; Park, Mia; Peoples, Don; Phillips, Elizabeth; Tillman, Mark

1993-01-01

The goal of the Fall 1993 semester of the EGM 4000 class was to design, fabricate, and test components for the 'Ocean CELSS Experimental Analog NASA' Project (OCEAN Project) and to aid in the future development of NASA's Controlled Ecological Life Support System (CELSS). The OCEAN project's specific aims are to place a human, Mr. Dennis Chamberland from NASA's Life Science Division of Research, into an underwater habitat off the shore of Key Largo, FL for three months. During his stay, he will monitor the hydroponic growth of food crops and evaluate the conditions necessary to have a successful harvest of edible food. The specific designs chosen to contribute to the OCEAN project by the EGM 4000 class are in the areas of hydroponic habitat monitoring, human health monitoring, and production of blue/green algae. The hydroponic monitoring system focused on monitoring the environment of the plants. This included the continuous sensing of the atmospheric and hydroponic nutrient solution temperatures. Methods for monitoring the continuous flow of the hydroponic nutrient solution across the plants and the continuous supply of power for these sensing devices were also incorporated into the design system. The human health monitoring system concentrated on continuously monitoring various concerns of the occupant in the underwater living habitat of the OCEAN project. These concerns included monitoring the enclosed environment for dangerous levels of carbon monoxide and smoke, high temperatures from fire, and the ceasing of the continuous airflow into the habitat. The blue/green algae project emphasized both the production and harvest of a future source of food. This project did not interact with any part of the OCEAN project. Rather, it was used to show the possibility of growing this kind of algae as a supplemental food source inside a controlled ecological life support system.

Earth Observations for Early Detection of Agricultural Drought: Contributions of the Famine Early Warning Systems Network (FEWS NET)

NASA Astrophysics Data System (ADS)

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

2016-12-01

The U.S. Geological Survey (USGS) has a long history of supporting the use of Earth observation data for food security monitoring through its role as an implementing partner of the Famine Early Warning Systems Network (FEWS NET) program. The use of remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and changing climatic regimes has been a core activity in monitoring FEWS NET countries. In recent years, it has become a requirement that FEWS NET apply monitoring and modeling frameworks at global scales to assess emerging crises in regions that FEWS NET does not traditionally monitor. USGS FEWS NET, in collaboration with the University of California, Santa Barbara, has developed a number of new global applications of satellite observations, derived products, and efficient tools for visualization and analyses to address these requirements. (1) A 35-year quasi-global (+/- 50 degrees latitude) time series of gridded rainfall estimates, the Climate Hazards Infrared Precipitation with Stations (CHIRPS) dataset, based on infrared satellite imagery and station observations. Data are available as 5-day (pentadal) accumulations at 0.05 degree spatial resolution. (2) Global actual evapotranspiration data based on application of the Simplified Surface Energy Balance (SSEB) model using 10-day MODIS Land Surface Temperature composites at 1-km resolution. (3) Production of global expedited MODIS (eMODIS) 10-day NDVI composites updated every 5 days. (4) Development of an updated Early Warning eXplorer (EWX) tool for data visualization, analysis, and sharing. (5) Creation of stand-alone tools for enhancement of gridded rainfall data and trend analyses. (6) Establishment of an agro-climatology analysis tool and knowledge base for more than 90 countries of interest to FEWS NET. In addition to these new products and tools, FEWS NET has partnered with the GEOGLAM community to develop a Crop Monitor for Early Warning (CM4EW) which brings together global expertise in agricultural monitoring to reach consensus on growing season status of "countries at risk". Such engagements will result in enhanced capabilities for extending our monitoring efforts globally.

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.

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

NASA Astrophysics Data System (ADS)

Intrieri, Emanuele; Bardi, Federica; Fanti, Riccardo; Gigli, Giovanni; Fidolini, Francesco; Casagli, Nicola; Costanzo, Sandra; Raffo, Antonio; Di Massa, Giuseppe; Capparelli, Giovanna; Versace, Pasquale

2017-10-01

A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

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].

More Yield with Less Water: Increasing Water Use Efficiency by Capitalizing on the Adaptation of Native Shrubs in the Sudano-Sahel

NASA Astrophysics Data System (ADS)

Bogie, Nathaniel; Bayala, Roger; Diedhiou, Ibrahima; Dick, Richard; Ghezzehei, Teamrat

2016-04-01

A changing climate along with human and animal population pressure can have a devastating effect on crop yields and food security in the Sudano-Sahel. Agricultural solutions to address soil degradation and crop water stress are needed to combat this increasingly difficult situation. Significant differences in crop success have been observed in peanut and millet grown in association with two native evergreen shrubs Piliostigma reticulatum, and Guiera senegalensis at the sites of Nioro du Rip and Keur Matar, respectively. We investigate how farmers can increase crop productivity by capitalizing on the evolutionary adaptation of native shrubs to the harsh Sudano-Sahelian environment as well as the physical mechanisms at work in the system that can lead to more robust yields. Research plots at Keur Matar Arame with no fertilizer added were monitored in 2013 using two soil moisture sensor networks at depths of 10, 20, 40, 60, 100, 200, and 300cm. Cropping season water use total calculated based on beginning and end of season soil moisture and seasonal precipitation data revealed that crop-only plot used 411±32 mm of water, and the crop and shrub plot used 439±42 mm of water. Taking into account the quantity of crop biomass produced and neglecting the shrub biomass produced, the crop and shrub plot had a water use efficiency of 1.60 kg ha-1 mm-1 and the crop only plot had 0.269 kg ha-1 mm-1. Water status was measured three times throughout the season on millet leaves and revealed no significant trends. Handheld NDVI readings revealed significantly higher NDVI values in crop and shrub plots at all measurement dates. These findings build on work that was completed in 2004 at the site, but further increases in crop yields have been shown. Increasing water use efficiency by over 500% can be a great advantage in years of limited water availability such as 2013. Using even the limited resources that farmers possess, this agroforestry technique can be expanded over wide swaths of the Sahel.

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.

The Ebb and Flow of Airborne Pathogens: Monitoring and Use in Disease Management Decisions.

PubMed

Mahaffee, Walter F; Stoll, Rob

2016-05-01

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 scouting of sentential plots in the IPM PIPE network (http://www.ipmpipe.org/). As our knowledge of plant disease epidemiology has increased, we have also increased our ability to detect and monitor the presence of pathogens and use this information to make management decisions in commercial production systems. The advent of phylogenetics, next-generation sequencing, and nucleic acid amplification technologies has allowed for development of sensitive and accurate assays for pathogen inoculum detection and quantification. The application of these tools is beginning to change how we manage diseases with airborne inoculum by allowing for the detection of pathogen movement instead of assuming it and by targeting management strategies to the early phases of the epidemic development when there is the greatest opportunity to reduce the rate of disease development. While there are numerous advantages to using data on inoculum presence to aid management decisions, there are limitations in what the data represent that are often unrecognized. In addition, our understanding of where and how to effectively monitor airborne inoculum is limited. There is a strong need to improve our knowledge of the mechanisms that influence inoculum dispersion across scales as particles move from leaf to leaf, and everything in between.

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

Validated environmental and physiological data from the CELSS Breadboard Projects Biomass Production Chamber. BWT931 (Wheat cv. Yecora Rojo)

NASA Technical Reports Server (NTRS)

Stutte, G. W.; Mackowiak, C. L.; Markwell, G. A.; Wheeler, R. M.; Sager, J. C.

1993-01-01

This KSC database is being made available to the scientific research community to facilitate the development of crop development models, to test monitoring and control strategies, and to identify environmental limitations in crop production systems. The KSC validated dataset consists of 17 parameters necessary to maintain bioregenerative life support functions: water purification, CO2 removal, O2 production, and biomass production. The data are available on disk as either a DATABASE SUBSET (one week of 5-minute data) or DATABASE SUMMARY (daily averages of parameters). Online access to the VALIDATED DATABASE will be made available to institutions with specific programmatic requirements. Availability and access to the KSC validated database are subject to approval and limitations implicit in KSC computer security policies.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series.

PubMed

Zhao, Yan; Bai, Linyan; Feng, Jianzhong; Lin, Xiaosong; Wang, Li; Xu, Lijun; Ran, Qiyun; Wang, Kui

2016-04-19

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series

PubMed Central

Zhao, Yan; Bai, Linyan; Feng, Jianzhong; Lin, Xiaosong; Wang, Li; Xu, Lijun; Ran, Qiyun; Wang, Kui

2016-01-01

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province. PMID:27104536

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...

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 ...

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide

PubMed Central

Johnson, Melissa Anne; Hollingsworth, Robert; Fortna, Samuel; Aristizábal, Luis F.; Manoukis, Nicholas C.

2018-01-01

Coffee berry borer (CBB) is the most devastating insect pest for coffee crops worldwide. We developed a scientific monitoring protocol that is aimed at capturing and quantifying the dynamics and impact of this invasive insect pest as well as the development of its host plant across a heterogeneous landscape. The cornerstone of this comprehensive monitoring system is timely georeferenced data collection on CBB movement, coffee berry infestation, mortality by the fungus Beauveria bassiana, and coffee plant phenology via a mobile electronic data recording application. This electronic data collection system allows field records to be georeferenced through built-in global positioning systems, and is backed by a network of weather stations and records of farm management practices. Comprehensive monitoring of CBB and host plant dynamics is an essential part of an area-wide project in Hawaii to aggregate landscape-level data for research to improve management practices. Coffee agroecosystems in other parts of the world that experience highly variable environmental and socioeconomic factors will also benefit from implementing this protocol, in that it will drive the development of customized integrated pest management (IPM) to manage CBB populations. PMID:29608152

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide.

PubMed

Johnson, Melissa Anne; Hollingsworth, Robert; Fortna, Samuel; Aristizábal, Luis F; Manoukis, Nicholas C

2018-03-19

Coffee berry borer (CBB) is the most devastating insect pest for coffee crops worldwide. We developed a scientific monitoring protocol that is aimed at capturing and quantifying the dynamics and impact of this invasive insect pest as well as the development of its host plant across a heterogeneous landscape. The cornerstone of this comprehensive monitoring system is timely georeferenced data collection on CBB movement, coffee berry infestation, mortality by the fungus Beauveria bassiana, and coffee plant phenology via a mobile electronic data recording application. This electronic data collection system allows field records to be georeferenced through built-in global positioning systems, and is backed by a network of weather stations and records of farm management practices. Comprehensive monitoring of CBB and host plant dynamics is an essential part of an area-wide project in Hawaii to aggregate landscape-level data for research to improve management practices. Coffee agroecosystems in other parts of the world that experience highly variable environmental and socioeconomic factors will also benefit from implementing this protocol, in that it will drive the development of customized integrated pest management (IPM) to manage CBB populations.

Observing Crop-Height Dynamics Using a UAV

NASA Astrophysics Data System (ADS)

Ziliani, M. G.; Parkes, S. D.; McCabe, M.

2017-12-01

Retrieval of vegetation height during a growing season is a key indicator for monitoring crop status, offering insight to the forecast yield relative to previous planting cycles. Improvement in Unmanned Aerial Vehicle (UAV) technologies, supported by advances in computer vision and photogrammetry software, has enabled retrieval of crop heights with much higher spatial resolution and coverage. These methodologies retrieve a Digital Surface Map (DSM), which combine terrain and crop elements to obtain a Crop Surface Map (CSM). Here we describe an automated method for deriving high resolution CSMs from a DSM, using RGB imagery from a UAV platform. Importantly, the approach does not require the need for a digital terrain map (DTM). The method involves distinguishing between vegetation and bare-ground cover pixels, using vegetation index maps from the RGB orthomosaic derived from the same flight as the DSM. We show that the absolute crop height can be extracted to within several centimeters, exploiting the data captured from a single UAV flight. In addition, the method is applied across five surveys during a maize growing cycle and compared against a terrain map constructed from a baseline UAV survey undertaken prior to crop growth. Results show that the approach is able to reproduce the observed spatial variability of the crop height within the maize field throughout the duration of the growing season. This is particularly valuable since it may be employed to detect intra-field problems (i.e. fertilizer variability, inefficiency in the irrigation system, salinity etc.) at different stages of the season, from which remedial action can be initiated to mitigate against yield loss. The method also demonstrates that UAV imagery combined with commercial photogrammetry software can determine a CSM from a single flight without the requirement of a prior DTM. This, together with the dynamic crop height estimation, provide useful information with which to inform precision agricultural management at the local scale.

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.

Optical fluorescence biosensor for plant water stress detection

NASA Astrophysics Data System (ADS)

Chong, Jenny P. C.; Liew, O. W.; Li, B. Q.; Asundi, A. K.

2007-05-01

Precision farming in arable agriculture and horticulture allows conservative use of resources that are applied according to plant needs. The growing concern for sustainability in crop production has accentuated the significance of our work to develop a rapid, sensitive and non-destructive spectroscopic method for real-time monitoring of plant water stress. Elucidation of crop water status before the onset of irreversible cellular damage is critical for effective water management to ensure maximum crop yield and profit margin. A two-component bio-sensing system comprising transgenic 'Indicator Plants' and a spectrometer-linked stereoscopic microscope was developed to detect early signs of water stress before the permanent wilting point is reached. The 'Indicator Plants' are transgenic Petunia hybrida genetically engineered with a drought-responsive promoter-linked enhanced green fluorescent protein marker gene (EGFP). No EGFP fluorescence was detected prior to induction of dehydration stress. Fluorescence emission intensity increased with dehydration period and was found mainly in the stems, leaf veins and leaf tips. While fluorescence emission above endogenous background was detectable after 2 hours of water stress treatment, the plants reached permanent wilting point after 6 hours, showing that our system was able to detect water stress prior to plant entry into the stage of irreversible damage. Future work will be geared towards overcoming biological and instrument-related difficulties encountered in our initial detection system.

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.

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.

Land-use legacies regulate decomposition dynamics following bioenergy crop conversion

DOE PAGES

Kallenbach, Cynthia M.; Stuart Grandy, A.

2014-07-14

Land-use conversion into bioenergy crop production can alter litter decomposition processes tightly coupled to soil carbon and nutrient dynamics. Yet, litter decomposition has been poorly described in bioenergy production systems, especially following land-use conversion. Predicting decomposition dynamics in postconversion bioenergy production systems is challenging because of the combined influence of land-use legacies with current management and litter quality. To evaluate how land-use legacies interact with current bioenergy crop management to influence litter decomposition in different litter types, we conducted a landscape-scale litterbag decomposition experiment. We proposed land-use legacies regulate decomposition, but their effects are weakened under higher quality litter andmore » when current land use intensifies ecosystem disturbance relative to prior land use. We compared sites left in historical land uses of either agriculture (AG) or Conservation Reserve Program grassland (CRP) to those that were converted to corn or switchgrass bioenergy crop production. Enzyme activities, mass loss, microbial biomass, and changes in litter chemistry were monitored in corn stover and switchgrass litter over 485 days, accompanied by similar soil measurements. Across all measured variables, legacy had the strongest effect (P < 0.05) relative to litter type and current management, where CRP sites maintained higher soil and litter enzyme activities and microbial biomass relative to AG sites. Decomposition responses to conversion depended on legacy but also current management and litter type. Within the CRP sites, conversion into corn increased litter enzymes, microbial biomass, and litter protein and lipid abundances, especially on decomposing corn litter, relative to nonconverted CRP. However, conversion into switchgrass from CRP, a moderate disturbance, often had no effect on switchgrass litter decomposition parameters. Thus, legacies shape the direction and magnitude of decomposition responses to bioenergy crop conversion and therefore should be considered a key influence on litter and soil C cycling under bioenergy crop management.« less

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.

Implementation of Wireless Sensor Networks Based Pig Farm Integrated Management System in Ubiquitous Agricultural Environments

NASA Astrophysics Data System (ADS)

Hwang, Jeonghwan; Lee, Jiwoong; Lee, Hochul; Yoe, Hyun

The wireless sensor networks (WSN) technology based on low power consumption is one of the important technologies in the realization of ubiquitous society. When the technology would be applied to the agricultural field, it can give big change in the existing agricultural environment such as livestock growth environment, cultivation and harvest of agricultural crops. This research paper proposes the 'Pig Farm Integrated Management System' based on WSN technology, which will establish the ubiquitous agricultural environment and improve the productivity of pig-raising farmers. The proposed system has WSN environmental sensors and CCTV at inside/outside of pig farm. These devices collect the growth-environment related information of pigs, such as luminosity, temperature, humidity and CO2 status. The system collects and monitors the environmental information and video information of pig farm. In addition to the remote-control and monitoring of the pig farm facilities, this system realizes the most optimum pig-raising environment based on the growth environmental data accumulated for a long time.

Dynamic cropping systems: Holistic approach for dryland agricultural systems in the northern Great Plains of North America

USDA-ARS?s Scientific Manuscript database

Cropping systems over the past century have developed greater crop specialization, more effectively conserve our soil and water resources, and are more resilient. The purpose of this chapter is to discuss the evolution of cropping systems in the Northern Great Plains and provide an approach to crop...

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.

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...

Utility and Value of Satellite-Based Frost Forecasting for Kenya's Tea Farming Sector

NASA Astrophysics Data System (ADS)

Morrison, I.

2016-12-01

Frost damage regularly inflicts millions of dollars of crop losses in the tea-growing highlands of western Kenya, a problem that the USAID/NASA Regional Visualization and Monitoring System (SERVIR) program is working to mitigate through a frost monitoring and forecasting product that uses satellite-based temperature and soil moisture data to generate up to three days of advanced warning before frost events. This paper presents the findings of a value of information (VOI) study assessing the value of this product based on Kenyan tea farmers' experiences with frost and frost-damage mitigation. Value was calculated based on historic trends of frost frequency, severity, and extent; likelihood of warning receipt and response; and subsequent frost-related crop-loss aversion. Quantification of these factors was derived through inferential analysis of survey data from 400 tea-farming households across the tea-growing regions of Kericho and Nandi, supplemented with key informant interviews with decision-makers at large estate tea plantations, historical frost incident and crop-loss data from estate tea plantations and agricultural insurance companies, and publicly available demographic and economic data. At this time, the product provides a forecasting window of up to three days, and no other frost-prediction methods are used by the large or small-scale farmers of Kenya's tea sector. This represents a significant opportunity for preemptive loss-reduction via Earth observation data. However, the tea-growing community has only two realistic options for frost-damage mitigation: preemptive harvest of available tea leaves to minimize losses, or skiving (light pruning) to facilitate fast recovery from frost damage. Both options are labor-intensive and require a minimum of three days of warning to be viable. As a result, the frost forecasting system has a very narrow margin of usefulness, making its value highly dependent on rapid access to the warning messages and flexible access to harvesting labor for mitigation activities. These findings show that the Frost monitoring product has the potential for real monetary benefit to members of the frost-vulnerable tea growing community but realization of that value needs direct collaboration with the tea-farming community to ensure effective product utilization.

Dynamics of CO2-exchange and C-budgets due to soil erosion: Insights from a 4 years observation period

NASA Astrophysics Data System (ADS)

Hoffmann, Mathias; Albiac Borraz, Elisa; Garcia Alba, Juana; Augustin, Jürgen; Sommer, Michael

2015-04-01

Agriculture in the hummocky ground moraine landscape of NE-Germany is characterized by an increase in energy crop cultivation, like maize or sorghum. Both enhance lateral C fluxes by erosion and induce feedbacks on C dynamics of agroecosystems as a result of reduced wintertime plant cover and vigorous crop growth during summer. However, the actual impact of these phenomena on the CO2-sink/-source function of agricultural landscapes, is still not clear. Therefore, the interdisciplinary project "CarboZALF" was established in Dedelow/Prenzlau (NE-Germany) in 2009. Within the field experiment CarboZALF-D, CO2 fluxes for the soil-plant systems were monitored, covering typical landscape relevant soil states in respect to erosion and deposition, like Calcic Cutanic Luvisol and Endogleyic Colluvic Regosol. Automated chamber systems, each consisting of four transparent chambers (2.5 m height, basal area 2.25 m2), were placed along gradients at both measurement sites. Monitored CO2 fluxes were gap-filled on a high-temporal resolution by modelling ecosystem respiration (Reco), gross primary productivity (GPP) and net ecosystem exchange (NEE) based on parallel and continuous measurements of the CO2 exchange, soil and air temperatures as well as photosynthetic active radiation (PAR). Gap-filling was e.g. needed in case of chamber malfunctions and abrupt disturbances by farming practice. The monitored crop rotation was corn-winter wheat (2 a), sorghum-winter triticale and alfalfa (1.5 a). In our presentation we would like to show insights from a 4 years observation period, with prounounced differences between the eroded and the colluvial soil: The Endogleyic Colluvic Regosol showed higher flux rates for Reco, GPP and NEE compared to the Calcic Cutanic Luvisol. Site-specific NEE and C-balances were positively related to soil C-stocks as well as biomass production, and generated a minor C-sink in case of the Calcic Cutanic Luvisol and a highly variable C-source in case of the Endogleyic Colluvic Regosol. Moreover, obtained high local variability in CO2 fluxes and C-balances at both sites, can be interpreted in terms of relevant drivers.

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.

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.

Preliminary validation of leaf area index sensor in Huailai

NASA Astrophysics Data System (ADS)

Cai, Erli; Li, Xiuhong; Liu, Qiang; Dou, Baocheng; Chang, Chongyan; Niu, Hailin; Lin, Xingwen; Zhang, Jialin

2015-12-01

Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.

Soil fertilization with wastewater biosolids - monitoring changes in the 'soil-fertilizer-plant' system and phosphorus recovery options.

PubMed

Kathijotes, Nicholas; Zlatareva, Elena; Marinova, Svetla; Petrova, Vera

2016-09-01

The aim of this study is to establish changes that may occur after a prolonged application of wastewater sludge treated to biosolids, in the 'soil-fertilizer-plant' system. Thirteen experimental plots with different soil types planted with experimental crops were investigated in order to evaluate the suitability of these biosolids as soil conditioners and fertilizers. The biosolids were incorporated in soil starting in 2006 in different quantities (from 6 tons per ha) for various arrays. The rate of application was calculated on the basis of imported nitrogen and was consistent with the characteristics of the sludge, soil diversity, growing crop requirements, and other factors. In 2013 (after 7 years of land use) average soil samples from the same arrays were taken and analyzed. No chemical fertilizer was applied during the experimental period. The results show that the use of sewage biosolids as a soil improver in accordance with local legislation does not pose any serious environmental risks but can maintain and improve soil fertility and crop yield. A slight increase in Cu and Zn in plants was detected, however the content of heavy metals in all soil samples was below maximum allowable limits and no signs of phytotoxicity were observed.

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.

Reducing dissolved inorganic nitrogen in surface runoff water from sugarcane production systems.

PubMed

Webster, A J; Bartley, R; Armour, J D; Brodie, J E; Thorburn, P J

2012-01-01

Nitrogen (N) lost from farms, especially as the highly bioavailable dissolved inorganic form, may be damaging Australia's Great Barrier Reef (GBR). As sugarcane is the dominant cropping system in GBR catchments, its N management practises are coming under increasing scrutiny. This study measured dissolved inorganic N lost in surface runoff water and sugarcane productivity over 3 years. The experiment compared the conventional fertiliser N application rate to sugarcane (average 180kg N/ha/year) and a rate based on replacing N exported in the previous crop (average 94kg N/ha/year). Dissolved inorganic N losses in surface water were 72%, 48% and 66% lower in the three monitored years in the reduced N fertiliser treatment. There was no significant difference in sugarcane yield between the two fertiliser N treatments, nor any treatment difference in soil mineral N - both of these results are indicators of the sustainability of the lower fertiliser N applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

A bioenergy feedstock/vegetable double-cropping system

USDA-ARS?s Scientific Manuscript database

Certain warm-season vegetable crops may lend themselves to bioenergy double-cropping systems, which involve growing a winter annual bioenergy feedstock crop followed by a summer annual crop. The objective of the study was to compare crop productivity and weed communities in different pumpkin product...

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).

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...

Classification and soil moisture determination of agricultural fields

NASA Technical Reports Server (NTRS)

Vandenbroek, A. C.; Groot, J. S.

1993-01-01

During the Mac-Europe campaign of 1991 several SAR (Synthetic Aperature Radar) experiments were carried out in the Flevoland test area in the Netherlands. The test site consists of a forested and an agricultural area with more than 15 different crop types. The experiments took place in June and July (mid to late growing season). The area was monitored by the spaceborne C-band VV polarized ERS-1, the Dutch airborne PHARS with similar frequency and polarization and the three-frequency PP-, L-, and C-band) polarimetric AIRSAR system of NASA/JPL. The last system passed over on June 15, 3, 12, and 28. The last two dates coincided with the overpasses of the PHARS and the ERS-1. Comparison of the results showed that backscattering coefficients from the three systems agree quite well. In this paper we present the results of a study of crop type classification (section 2) and soil moisture determination in the agricultural area (section 3). For these studies we used field averaged Stokes matrices extracted from the AIRSAR data (processor version 3.55 or 3.56).

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.

Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone.

PubMed

García-Tejero, Iván Francisco; Ortega-Arévalo, Carlos José; Iglesias-Contreras, Manuel; Moreno, José Manuel; Souza, Luciene; Tavira, Simón Cuadros; Durán-Zuazo, Víctor Hugo

2018-03-31

Different tools are being implemented in order to improve the water management in agricultural irrigated areas of semiarid environments. Thermography has been progressively introduced as a promising technique for irrigation scheduling and the assessing of crop-water status, especially when deficit irrigation is being implemented. However, an important limitation is related to the cost of the actual cameras, this being a severe limitation to its practical usage by farmers and technicians. This work evaluates the potential and the robustness of a thermal imaging camera that is connected to smartphone (Flir One) recently developed by Flir Systems Inc. as a first step to assess the crop water status. The trial was developed in mature almond ( Prunus dulcis Mill.) trees that are subjected to different irrigation treatments. Thermal information obtained by the Flir One camera was deal with the thermal information obtained with a conventional Thermal Camera (Flir SC660) with a high resolution, and subsequently, confronted with other related plant physiological parameters (leaf water potential, Ψ leaf , and stomatal conductance, g s ). Thermal imaging camera connected to smartphone provided useful information in estimating the crop-water status in almond trees, being a potential promising tool to accelerate the monitoring process and thereby enhance water-stress management of almond orchards.

Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone

PubMed Central

García-Tejero, Iván Francisco; Ortega-Arévalo, Carlos José; Iglesias-Contreras, Manuel; Moreno, José Manuel; Souza, Luciene; Tavira, Simón Cuadros; Durán-Zuazo, Víctor Hugo

2018-01-01

Different tools are being implemented in order to improve the water management in agricultural irrigated areas of semiarid environments. Thermography has been progressively introduced as a promising technique for irrigation scheduling and the assessing of crop-water status, especially when deficit irrigation is being implemented. However, an important limitation is related to the cost of the actual cameras, this being a severe limitation to its practical usage by farmers and technicians. This work evaluates the potential and the robustness of a thermal imaging camera that is connected to smartphone (Flir One) recently developed by Flir Systems Inc. as a first step to assess the crop water status. The trial was developed in mature almond (Prunus dulcis Mill.) trees that are subjected to different irrigation treatments. Thermal information obtained by the Flir One camera was deal with the thermal information obtained with a conventional Thermal Camera (Flir SC660) with a high resolution, and subsequently, confronted with other related plant physiological parameters (leaf water potential, Ψleaf, and stomatal conductance, gs). Thermal imaging camera connected to smartphone provided useful information in estimating the crop-water status in almond trees, being a potential promising tool to accelerate the monitoring process and thereby enhance water-stress management of almond orchards. PMID:29614740

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

Replacing fallow with continuous cropping reduces crop water productivity of semiarid wheat

USDA-ARS?s Scientific Manuscript database

Water supply frequently limits crop yield in semiarid cropping systems; water deficits can restrict yields in drought-affected subhumid regions. In semiarid wheat (Triticum aestivumL.)-based cropping systems, replacing an uncropped fallow period with a crop can increase precipitation use efficiency ...

New horizons. [assessment of technology developed and utilized under various NASA programs

NASA Technical Reports Server (NTRS)

1975-01-01

The contribution of space exploration and space related research to the future of man and the accomplishments of the space program are assessed. Topics discussed include: the role of applications satellites in crop surveillance, land use surveys, weather forecasting, education, communications, and pollution monitoring; planetary studies which examine the origin and evolution of the solar system, including dynamic processes that bear directly on earth's environment; and fuel conservation and development of new energy sources.

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.

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.

Effects of Applied Land Use Strategies on Farmland Soils in the Southwestern Siberian Kulunda-Steppe

NASA Astrophysics Data System (ADS)

Grunwald, Lars-Christian; Illiger, Patrick; Stephan, Eckart; Frühauf, Manfred

2014-05-01

The Kulunda steppe in southwestern Siberia is one of the most intensely used agricultural regions in the world. The study area of the KULUNDA project is the Kulunda steppe, which is a part of the conversion region created during the so called "virgin land campaign" in the soviet era. Nowadays it is characterized by widespread soil degradation. Despite the fact that agriculture is the basis of existence, land use practice is maladjusted to the local conditions. The widespread soil degradation and accordingly the decreased soil humus content have negative effects on crop yields in this region. With respect to climate change, the current study analyses the cause effect relationship between land use practice and soil properties. In particular, different methods of soil cultivation will be tested and for each of the cases the soil humus content, soil water, soil solute balance will be measured and compared. In addition, the possibilities of soil carbon sequestration capacity will be analyzed. Furthermore, the study aims to achieve properly adapted sustainable cropping systems to stabilize the yields and to increase the productivity of plant production per spatial unit in this high vulnerable dry farming region. In 2012 the long term field trials started at three test farms in different steppe biomes, containing different soil types from chernozems to kastanozems. Each of them is characterized by a negative water balance. Successfully running cropping models, such as crop rotation, tilling intensity, plant protection and nutrition strategies from south Canadian steppe regions were adapted to regional agronomic needs. The traditional Russian cultivation system will be compared with two modern systems, including no-tillage methods on specially randomized test plots. Additionally, these plots are equipped with soil moisture monitoring systems to analyze the soil water content in different depths under the different cropping methods. The expected results will not only deepen the understanding of the impact of agricultural land use practice on field scale, but also largely contribute to the research on sustainable land management, rural development and climate change and connect applied science with capacity building for local stakeholders.

Soil quality and the solar corridor crop system

USDA-ARS?s Scientific Manuscript database

The solar corridor crop system (SCCS) is designed for improved crop productivity based on highly efficient use of solar radiation by integrating row crops with drilled or solid-seeded crops in broad strips (corridors) that also facilitate establishment of cover crops for year-round soil cover. The S...

Soil Quality and the Solar Corridor Crop System

USDA-ARS?s Scientific Manuscript database

The solar corridor crop system (SCCS) is designed for improved crop productivity based on highly efficient use of solar radiation by integrating row crops with drilled or solid-seeded crops in broad strips (corridors) that also facilitate establishment of cover crops for year-round soil cover. The S...

First typology of cacao (Theobroma cacao L.) systems in Colombian Amazonia, based on tree species richness, canopy structure and light availability

PubMed Central

Suárez Salazar, Juan Carlos; Melgarejo, Luz Marina; Di Rienzo, Julio A.; Casanoves, Fernando

2018-01-01

Aim and background We present a typology of cacao agroforest systems in Colombian Amazonia. These systems had yet to be described in the literature, especially their potential in terms of biodiversity conservation. The systems studied are located in a post-conflict area, and a deforestation front in Colombian Amazonia. Cacao cropping systems are of key importance in Colombia: cacao plays a prime role in post conflict resolution, as cacao is a legal crop to replace illegal crops; cacao agroforests are expected to be a sustainable practice, promoting forest-friendly land use. Material and methods We worked in 50 x 2000 m2 agroforest plots, in Colombian Amazonia. A cluster analysis was used to build a typology based on 28 variables characterised in each plot, and related to diversity, composition, spatial structure and light availability for the cacao trees. We included variables related to light availability to evaluate the amount of transmitted radiation to the cacao trees in each type, and its suitability for cacao ecophysiological development. Main results We identified 4 types of cacao agroforests based on differences concerning tree species diversity and the impact of canopy spatial structure on light availability for the cacao trees in the understorey. We found 127 tree species in the dataset, with some exclusive species in each type. We also found that 3 out of the 4 types identified displayed an erosion of tree species diversity. This reduction in shade tree species may have been linked to the desire to reduce shade, but we also found that all the types described were compatible with good ecophysiological development of the cacao trees. Main conclusions and prospects Cacao agroforest systems may actually be achieving biodiversity conservation goals in Colombian Amazonia. One challenging prospect will be to monitor and encourage the conservation of tree species diversity in cacao agroforest systems during the development of these cropping systems, as a form of forest-friendly management enhancing sustainable peace building in Colombia. PMID:29401499

Reduced soil cultivation and organic fertilization on organic farms: effects on crop yield and soil physical traits

NASA Astrophysics Data System (ADS)

Surböck, Andreas; Gollner, Gabriele; Klik, Andreas; Freyer, Bernhard; Friedel, Jürgen K.

2017-04-01

A continuous investment in soil fertility is necessary to achieve sustainable yields in organic arable farming. Crucial factors here besides the crop rotation are organic fertilization and the soil tillage system. On this topic, an operational group (Project BIOBO*) was established in the frame of an European Innovation Partnership in 2016 consisting of organic farmers, consultants and scientists in the farming region of eastern Austria. The aim of this group is the development and testing of innovative, reduced soil cultivation, green manure and organic fertilization systems under on-farm and on-station conditions to facilitate the sharing and transfer of experience and knowledge within and outside the group. Possibilities for optimization of the farm-specific reduced soil tillage system in combination with green manuring are being studied in field trials on six organic farms. The aim is to determine, how these measures contribute to an increase in soil organic matter contents, yields and income, to an improved nitrogen and nutrient supply to the crops, as well as support soil fertility in general. Within a long-term monitoring project (MUBIL), the effects of different organic fertilization systems on plant and soil traits have been investigated since 2003, when the farm was converted to organic management. The examined organic fertilization systems, i.e. four treatments representing stockless and livestock keeping systems, differ in lucerne management and the supply of organic manure (communal compost, farmyard manure, digestate from a biogas plant). Previous results of this on-station experiment have shown an improvement of some soil properties, especially soil physical properties, since 2003 in all fertilization systems and without differences between them. The infiltration rate of rainwater has increased because of higher hydraulic conductivity. The aggregate stability has shown also positive trends, which reduces the susceptibility to soil erosion by wind and water. The improvements are attributed to the crop rotation with two-year lucerne and other crops with a dense root system. In autumn 2015, the soil tillage in the trial was converted from an intensive use of the plough to a reduced tillage system with a chisel. With this change, further improvements in soil properties, especially in connection with organic fertilizers, are expected and are further examined. Plots in which the previous tillage with the plough is continued, allow a comparison of the effects of the different soil tillage systems. * The project BIOBO is supported by the Austrian Federal Government, Austrian Federal Provinces and the European Union.

A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

NASA Astrophysics Data System (ADS)

Cong, Z.

2015-12-01

In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting and other information about the irrigation. This system can be expanded in other irrigation districts. In future, it is even possible to upgrade the system for the mobile user.

Farm-scale costs and returns for second generation bioenergy cropping systems in the US Corn Belt

NASA Astrophysics Data System (ADS)

Manatt, Robert K.; Hallam, Arne; Schulte, Lisa A.; Heaton, Emily A.; Gunther, Theo; Hall, Richard B.; Moore, Ken J.

2013-09-01

While grain crops are meeting much of the initial need for biofuels in the US, cellulosic or second generation (2G) materials are mandated to provide a growing portion of biofuel feedstocks. We sought to inform development of a 2G crop portfolio by assessing the profitability of novel cropping systems that potentially mitigate the negative effects of grain-based biofuel crops on food supply and environmental quality. We analyzed farm-gate costs and returns of five systems from an ongoing experiment in central Iowa, USA. The continuous corn cropping system was most profitable under current market conditions, followed by a corn-soybean rotation that incorporated triticale as a 2G cover crop every third year, and a corn-switchgrass system. A novel triticale-hybrid aspen intercropping system had the highest yields over the long term, but could only surpass the profitability of the continuous corn system when biomass prices exceeded foreseeable market values. A triticale/sorghum double cropping system was deemed unviable. We perceive three ways 2G crops could become more cost competitive with grain crops: by (1) boosting yields through substantially greater investment in research and development, (2) increasing demand through substantially greater and sustained investment in new markets, and (3) developing new schemes to compensate farmers for environmental benefits associated with 2G crops.

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

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...

Mixed crop-livestock systems: an economic and environmental-friendly way of farming?

PubMed

Ryschawy, J; Choisis, N; Choisis, J P; Joannon, A; Gibon, A

2012-10-01

Intensification and specialisation of agriculture in developed countries enabled productivity to be improved but had detrimental impacts on the environment and threatened the economic viability of a huge number of farms. The combination of livestock and crops, which was very common in the past, is assumed to be a viable alternative to specialised livestock or cropping systems. Mixed crop-livestock systems can improve nutrient cycling while reducing chemical inputs and generate economies of scope at farm level. Most assumptions underlying these views are based on theoretical and experimental evidence. Very few assessments of their environmental and economic advantages have nevertheless been undertaken in real-world farming conditions. In this paper, we present a comparative assessment of the environmental and economic performances of mixed crop-livestock farms v. specialised farms among the farm population of the French 'Coteaux de Gascogne'. In this hilly region, half of the farms currently use a mixed crop-livestock system including beef cattle and cash crops, the remaining farms being specialised in either crops or cattle. Data were collected through an exhaustive survey of farms located in our study area. The economic performances of farming systems were assessed on 48 farms on the basis of (i) overall gross margin, (ii) production costs and (iii) analysis of the sensitivity of gross margins to fluctuations in the price of inputs and outputs. The environmental dimension was analysed through (i) characterisation of farmers' crop management practices, (ii) analysis of farm land use diversity and (iii) nitrogen farm-gate balance. Local mixed crop-livestock farms did not have significantly higher overall gross margins than specialised farms but were less sensitive than dairy and crop farms to fluctuations in the price of inputs and outputs considered. Mixed crop-livestock farms had lower costs than crop farms, while beef farms had the lowest costs as they are grass-based systems. Concerning crop management practices, our results revealed an intensification gradient from low to high input farming systems. Beyond some general trends, a wide range of management practices and levels of intensification were observed among farms with a similar production system. Mixed crop-livestock farms were very heterogeneous with respect to the use of inputs. Nevertheless, our study revealed a lower potential for nitrogen pollution in mixed crop-livestock and beef production systems than in dairy and crop farming systems. Even if a wide variability exists within system, mixed crop-livestock systems appear to be a way for an environmental and economical sustainable agriculture.

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.

Variation of Bacterial Community Diversity in Rhizosphere Soil of Sole-Cropped versus Intercropped Wheat Field after Harvest.

PubMed

Yang, Zhenping; Yang, Wenping; Li, Shengcai; Hao, Jiaomin; Su, Zhifeng; Sun, Min; Gao, Zhiqiang; Zhang, Chunlai

2016-01-01

As the major crops in north China, spring crops are usually planted from April through May every spring and harvested in fall. Wheat is also a very common crop traditionally planted in fall or spring and harvested in summer year by year. This continuous cropping system exhibited the disadvantages of reducing the fertility of soil through decreasing microbial diversity. Thus, management of microbial diversity in the rhizosphere plays a vital role in sustainable crop production. In this study, ten common spring crops in north China were chosen sole-cropped and four were chosen intercropped with peanut in wheat fields after harvest. Denaturing gradient gel electrophoresis (DGGE) and DNA sequencing of one 16S rDNA fragment were used to analyze the bacterial diversity and species identification. DGGE profiles showed the bacterial community diversity in rhizosphere soil samples varied among various crops under different cropping systems, more diverse under intercropping system than under sole-cropping. Some intercropping-specific bands in DGGE profiles suggested that several bacterial species were stimulated by intercropping systems specifically. Furthermore, the identification of these dominant and functional bacteria by DNA sequencing indicated that intercropping systems are more beneficial to improve soil fertility. Compared to intercropping systems, we also observed changes in microbial community of rhizosphere soil under sole-crops. The rhizosphere bacterial community structure in spring crops showed a strong crop species-specific pattern. More importantly, Empedobacter brevis, a typical plant pathogen, was only found in the carrot rhizosphere, suggesting carrot should be sown prudently. In conclusion, our study demonstrated that crop species and cropping systems had significant effects on bacterial community diversity in the rhizosphere soils. We strongly suggest sorghum, glutinous millet and buckwheat could be taken into account as intercropping crops with peanut; while hulled oat, mung bean or foxtail millet could be considered for sowing in wheat fields after harvest in North China.

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.

A Wireless Sensor Network-Based Ubiquitous Paprika Growth Management System

PubMed Central

Hwang, Jeonghwan; Shin, Changsun; Yoe, Hyun

2010-01-01

Wireless Sensor Network (WSN) technology can facilitate advances in productivity, safety and human quality of life through its applications in various industries. In particular, the application of WSN technology to the agricultural area, which is labor-intensive compared to other industries, and in addition is typically lacking in IT technology applications, adds value and can increase the agricultural productivity. This study attempts to establish a ubiquitous agricultural environment and improve the productivity of farms that grow paprika by suggesting a ‘Ubiquitous Paprika Greenhouse Management System’ using WSN technology. The proposed system can collect and monitor information related to the growth environment of crops outside and inside paprika greenhouses by installing WSN sensors and monitoring images captured by CCTV cameras. In addition, the system provides a paprika greenhouse environment control facility for manual and automatic control from a distance, improves the convenience and productivity of users, and facilitates an optimized environment to grow paprika based on the growth environment data acquired by operating the system. PMID:22163543

Multiyear high-resolution carbon exchange over European croplands from the integration of observed crop yields into CarbonTracker Europe

NASA Astrophysics Data System (ADS)

Combe, Marie; Vilà-Guerau de Arellano, Jordi; de Wit, Allard; Peters, Wouter

2016-04-01

Carbon exchange over croplands plays an important role in the European carbon cycle over daily-to-seasonal time scales. Not only do crops occupy one fourth of the European land area, but their photosynthesis and respiration are large and affect CO2 mole fractions at nearly every atmospheric CO2 monitoring site. A better description of this crop carbon exchange in our CarbonTracker Europe data assimilation system - which currently treats crops as unmanaged grasslands - could strongly improve its ability to constrain terrestrial carbon fluxes. Available long-term observations of crop yield, harvest, and cultivated area allow such improvements, when combined with the new crop-modeling framework we present. This framework can model the carbon fluxes of 10 major European crops at high spatial and temporal resolution, on a 12x12 km grid and 3-hourly time-step. The development of this framework is threefold: firstly, we optimize crop growth using the process-based WOrld FOod STudies (WOFOST) agricultural crop growth model. Simulated yields are downscaled to match regional crop yield observations from the Statistical Office of the European Union (EUROSTAT) by estimating a yearly regional parameter for each crop species: the yield gap factor. This step allows us to better represent crop phenology, to reproduce the observed multiannual European crop yields, and to construct realistic time series of the crop carbon fluxes (gross primary production, GPP, and autotrophic respiration, Raut) on a fine spatial and temporal resolution. Secondly, we combine these GPP and Raut fluxes with a simple soil respiration model to obtain the total ecosystem respiration (TER) and net ecosystem exchange (NEE). And thirdly, we represent the horizontal transport of carbon that follows crop harvest and its back-respiration into the atmosphere during harvest consumption. We distribute this carbon using observations of the density of human and ruminant populations from EUROSTAT. We assess the model's ability to represent the seasonal GPP, TER and NEE fluxes using observations at 6 European FluxNet winter wheat and grain maize sites and compare it with the fluxes of the current terrestrial carbon cycle model of CarbonTracker Europe: the Simple Biosphere - Carnegie-Ames-Stanford Approach (SiBCASA) model. We find that the new model framework provides a detailed, realistic, and strongly observation-driven estimate of carbon exchange over European croplands. Its products will be made available to the scientific community through the ICOS Carbon Portal, and serve as a new cropland component in CarbonTracker Europe flux estimates.

Remote Sensing of Vineyard FPAR, with Implications for Irrigation Scheduling

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Scholasch, Thibaut

2004-01-01

Normalized difference vegetation index (NDVI) data, acquired at two-meter resolution by an airborne ADAR System 5500, were compared with fraction of photosynthetically active radiation (FPAR) absorbed by commercial vineyards in Napa Valley, California. An empirical line correction was used to transform image digital counts to surface reflectance. "Apparent" NDVI (generated from digital counts) and "corrected" NDVI (from reflectance) were both strongly related to FPAR of range 0.14-0.50 (both r(sup 2) = 0.97, P < 0.01). By suppressing noise, corrected NDVI should form a more spatially and temporally stable relationship with FPAR, reducing the need for repeated field support. Study results suggest the possibility of using optical remote sensing to monitor the transpiration crop coefficient, thus providing an enhanced spatial resolution component to crop water budget calculations and irrigation management.

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.

Trade-Offs between Economic and Environmental Impacts of Introducing Legumes into Cropping Systems

PubMed Central

Reckling, Moritz; Bergkvist, Göran; Watson, Christine A.; Stoddard, Frederick L.; Zander, Peter M.; Walker, Robin L.; Pristeri, Aurelio; Toncea, Ion; Bachinger, Johann

2016-01-01

Europe's agriculture is highly specialized, dependent on external inputs and responsible for negative environmental impacts. Legume crops are grown on less than 2% of the arable land and more than 70% of the demand for protein feed supplement is imported from overseas. The integration of legumes into cropping systems has the potential to contribute to the transition to a more resource-efficient agriculture and reduce the current protein deficit. Legume crops influence the production of other crops in the rotation making it difficult to evaluate the overall agronomic effects of legumes in cropping systems. A novel assessment framework was developed and applied in five case study regions across Europe with the objective of evaluating trade-offs between economic and environmental effects of integrating legumes into cropping systems. Legumes resulted in positive and negative impacts when integrated into various cropping systems across the case studies. On average, cropping systems with legumes reduced nitrous oxide emissions by 18 and 33% and N fertilizer use by 24 and 38% in arable and forage systems, respectively, compared to systems without legumes. Nitrate leaching was similar with and without legumes in arable systems and reduced by 22% in forage systems. However, grain legumes reduced gross margins in 3 of 5 regions. Forage legumes increased gross margins in 3 of 3 regions. Among the cropping systems with legumes, systems could be identified that had both relatively high economic returns and positive environmental impacts. Thus, increasing the cultivation of legumes could lead to economic competitive cropping systems and positive environmental impacts, but achieving this aim requires the development of novel management strategies informed by the involvement of advisors and farmers. PMID:27242870

Trade-Offs between Economic and Environmental Impacts of Introducing Legumes into Cropping Systems.

PubMed

Reckling, Moritz; Bergkvist, Göran; Watson, Christine A; Stoddard, Frederick L; Zander, Peter M; Walker, Robin L; Pristeri, Aurelio; Toncea, Ion; Bachinger, Johann

2016-01-01

Europe's agriculture is highly specialized, dependent on external inputs and responsible for negative environmental impacts. Legume crops are grown on less than 2% of the arable land and more than 70% of the demand for protein feed supplement is imported from overseas. The integration of legumes into cropping systems has the potential to contribute to the transition to a more resource-efficient agriculture and reduce the current protein deficit. Legume crops influence the production of other crops in the rotation making it difficult to evaluate the overall agronomic effects of legumes in cropping systems. A novel assessment framework was developed and applied in five case study regions across Europe with the objective of evaluating trade-offs between economic and environmental effects of integrating legumes into cropping systems. Legumes resulted in positive and negative impacts when integrated into various cropping systems across the case studies. On average, cropping systems with legumes reduced nitrous oxide emissions by 18 and 33% and N fertilizer use by 24 and 38% in arable and forage systems, respectively, compared to systems without legumes. Nitrate leaching was similar with and without legumes in arable systems and reduced by 22% in forage systems. However, grain legumes reduced gross margins in 3 of 5 regions. Forage legumes increased gross margins in 3 of 3 regions. Among the cropping systems with legumes, systems could be identified that had both relatively high economic returns and positive environmental impacts. Thus, increasing the cultivation of legumes could lead to economic competitive cropping systems and positive environmental impacts, but achieving this aim requires the development of novel management strategies informed by the involvement of advisors and farmers.

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.

Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review

PubMed Central

Jawad, Haider Mahmood; Nordin, Rosdiadee; Gharghan, Sadik Kamel; Jawad, Aqeel Mahmood

2017-01-01

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data. PMID:28771214

Characterization of successional changes in bacterial community composition during bioremediation of used motor oil-contaminated soil in a boreal climate.

PubMed

Yan, Lijuan; Sinkko, Hanna; Penttinen, Petri; Lindström, Kristina

2016-01-15

The widespread use of motor oil makes it a notable risk factor to cause scattered contamination in soil. The monitoring of microbial community dynamics can serve as a comprehensive tool to assess the ecological impact of contaminants and their disappearance in the ecosystem. Hence, a field study was conducted to monitor the ecological impact of used motor oil under different perennial cropping systems (fodder galega, brome grass, galega-brome grass mixture and bare fallow) in a boreal climate zone. Length heterogeneity PCR characterized a successional pattern in bacterial community following oil contamination over a four-year bioremediation period. Soil pH and electrical conductivity were associated with the shifts in bacterial community composition. Crops had no detectable effect on bacterial community composition or complexity. However, the legume fodder galega increased soil microbial biomass, expressed as soil total DNA. Oil contamination induced an abrupt change in bacterial community composition at the early stage, yet the effect did not last as long as the oil in soil. The successional variation in bacterial community composition can serve as a sensitive ecological indicator of oil contamination and remediation in situ. Copyright © 2015 Elsevier B.V. All rights reserved.

Enhanced monitoring of the temporal and spatial relationships between water demand and water availability

NASA Astrophysics Data System (ADS)

Schneider, C. A.; Aggett, G. R.; Hattendorf, M. J.

2007-12-01

Better information on evapotranspiration (ET) is essential to better understanding of consumptive use of water by crops. RTi is using NASA Earth-sun System research results and METRIC (Mapping ET at high Resolution with Internalized Calibration) to increase the repeatability and accuracy of consumptive use estimates. METRIC, an image-processing model for calculating ET as a residual of the surface energy balance, utilizes the thermal band on various satellite remote sensors. Calculating actual ET from satellites can avoid many of the assumptions driving other methods of calculating ET over a large area. Because it is physically based and does not rely on explicit knowledge of crop type in the field, a large potential source of error should be eliminated. This paper assesses sources of error in current operational estimates of ET for an area of the South Platte irrigated lands of Colorado, and benchmarks potential improvements in the accuracy of ET estimates gained using METRIC, as well as the processing efficiency of consumptive use demand for large irrigated lands. Examples highlighting how better water planning decisions and water management can be achieved via enhanced monitoring of the temporal and spatial relationships between water demand and water availability are provided.

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

Development, implementation and evaluation of satellite-aided agricultural monitoring systems

NASA Technical Reports Server (NTRS)

Cicone, R. (Principal Investigator); Crist, E.; Metzler, M.; Parris, T.

1982-01-01

Research supporting the use of remote sensing for inventory and assessment of agricultural commodities is summarized. Three task areas are described: (1) corn and soybean crop spectral/temporal signature characterization; (2) efficient area estimation technology development; and (3) advanced satellite and sensor system definition. Studies include an assessment of alternative green measures from MSS variables; the evaluation of alternative methods for identifying, labeling or classification targets in an automobile procedural context; a comparison of MSS, the advanced very high resolution radiometer and the coastal zone color scanner, as well as a critical assessment of thematic mapper dimensionally and spectral structure.

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...

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

A rule-based smart automated fertilization and irrigation systems

NASA Astrophysics Data System (ADS)

Yousif, Musab El-Rashid; Ghafar, Khairuddin; Zahari, Rahimi; Lim, Tiong Hoo

2018-04-01

Smart automation in industries has become very important as it can improve the reliability and efficiency of the systems. The use of smart technologies in agriculture have increased over the year to ensure and control the production of crop and address food security. However, it is important to use proper irrigation systems avoid water wastage and overfeeding of the plant. In this paper, a Smart Rule-based Automated Fertilization and Irrigation System is proposed and evaluated. We propose a rule based decision making algorithm to monitor and control the food supply to the plant and the soil quality. A build-in alert system is also used to update the farmer using a text message. The system is developed and evaluated using a real hardware.

Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China

DOE PAGES

Ye, Qing; Yang, Xiaoguang; Dai, Shuwei; ...

2015-06-05

Here, we discuss that rice is one of the main crops grown in southern China. Global climate change has significantly altered the local water availability and temperature regime for rice production. In this study, we explored the influence of climate change on suitable rice cropping areas, rice cropping systems and crop water requirements (CWRs) during the growing season for historical (from 1951 to 2010) and future (from 2011 to 2100) time periods. The results indicated that the land areas suitable for rice cropping systems shifted northward and westward from 1951 to 2100 but with different amplitudes.

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.

The century experiment: the first twenty years of UC Davis' Mediterranean agroecological experiment.

PubMed

Wolf, Kristina M; Torbert, Emma E; Bryant, Dennis; Burger, Martin; Denison, R Ford; Herrera, Israel; Hopmans, Jan; Horwath, Will; Kaffka, Stephen; Kong, Angela Y Y; Norris, R F; Six, Johan; Tomich, Thomas P; Scow, Kate M

2018-02-01

The Century Experiment at the Russell Ranch Sustainable Agriculture Facility at the University of California, Davis provides long-term agroecological data from row crop systems in California's Central Valley starting in 1993. The Century Experiment was initially designed to study the effects of a gradient of water and nitrogen availability on soil properties and crop performance in ten different cropping systems to measure tradeoffs and synergies between agricultural productivity and sustainability. Currently systems include 11 different cropping systems-consisting of four different crops and a cover crop mixture-and one native grass system. This paper describes the long-term core data from the Century Experiment from 1993-2014, including crop yields and biomass, crop elemental contents, aerial-photo-based Normalized Difference Vegetation Index data, soil properties, weather, chemical constituents in irrigation water, winter weed populations, and operational data including fertilizer and pesticide application amounts and dates, planting dates, planting quantity and crop variety, and harvest dates. This data set represents the only known long-term set of data characterizing food production and sustainability in irrigated and rainfed Mediterranean annual cropping systems. There are no copyright restrictions associated with the use of this dataset. © 2018 by the Ecological Society of America.

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.

Rice crop mapping and change prediction using multi-temporal satellite images in the Mekong Delta, Vietnam

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.

2014-12-01

The rice cropping systems in the Vietnamese Mekong Delta (VMD) has been undergoing major changes to cope with developing agro-economics, increasing population and changing climate. Information on rice cropping practices and changes in cropping systems is critical for policymakers to devise successful strategies to ensure food security and rice grain exports for the country. The primary objective of this research is to map rice cropping systems and predict future dynamics of rice cropping systems using the MODIS time-series data of 2002, 2006, and 2010. First, a phenology-based classification approach was applied for the classification and assessment of rice cropping systems in study region. Second, the Cellular Automata-Markov (CA-Markov) models was used to simulate the rice-cropping system map of VMD for 2010. The comparisons between the classification maps and the ground reference data indicated satisfactory results with overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2010. The simulated map of rice cropping system for 2010 was extrapolated by CA-Markov model based on the trend of rice cropping systems during 2002~2006. The comparison between predicted scenario and classification map for 2010 presents a reasonably closer agreement. In conclusion, the CA-Markov model performs a powerful tool for the dynamic modeling of changes in rice cropping systems, and the results obtained demonstrate that the approach produces satisfactory results in terms of accuracy, quantitative forecast and spatial pattern changes. Meanwhile, the projections of the future changes would provide useful inputs to the agricultural policy for effective management of the rice cropping practices in VMD.

Solutions Network Formulation Report. Visible/Infrared Imager/Radiometer Suite and Advanced Microwave Scanning Radiometer Data Products for National Drought Monitor Decision Support

NASA Technical Reports Server (NTRS)

Estep, Leland

2007-01-01

Drought effects are either direct or indirect depending on location, population, and regional economic vitality. Common direct effects of drought are reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat. Indirect impacts follow on the heels of direct impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agribusiness, increased prices for food and timber, unemployment, reduced tax revenues, increased crime, foreclosures on bank loans to farmers and businesses, migration, and disaster relief programs. In the United States alone, drought is estimated to result in annual losses of between $6 - 8 billion. Recent sustained drought in the United States has made decision-makers aware of the impacts of climate change on society and environment. The eight major droughts that occurred in the United States between 1980 and 1999 accounted for the largest percentage of weather-related monetary losses. Monitoring drought and its impact that occurs at a variety of scales is an important government activity -- not only nationally but internationally as well. The NDMC (National Drought Mitigation Center) and the USDA (U.S. Department of Agriculture) RMA (Risk Management Agency) have partnered together to develop a DM-DSS (Drought Monitoring Decision Support System). This monitoring system will be an interactive portal that will provide users the ability to visualize and assess drought at all levels. This candidate solution incorporates atmospherically corrected VIIRS data products, such as NDVI (Normalized Difference Vegetation Index) and Ocean SST (sea surface temperature), and AMSR-E soil moisture data products into two NDMC vegetation indices -- VegDRI (Vegetation Drought Response Index) and VegOUT (Vegetation Outlook) -- which are then input into the DM-DSS.

Localizing drought monitoring products to support agricultural climate service advisories in South Asia

NASA Astrophysics Data System (ADS)

Qamer, F. M.; Matin, M. A.; Yadav, N. K.; Bajracharya, B.; Zaitchik, B. F.; Ellenburg, W. L.; Krupnik, T. J.; Hussain, G.

2017-12-01

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change identifies drought as one of the major climate risks in South Asia. During past two decades, a large amount of climate data have been made available by the scientific community, but the deployment of climate information for local level and agricultural decision making remains less than optimal. The provisioning of locally calibrated, easily accessible, decision-relevant and user-oriented information, in the form of drought advisory service could help to prepare communities to reduce climate vulnerability and increase resilience. A collaborative effort is now underway to strengthen existing and/or establish new drought monitoring and early warning systems in Afghanistan, Bangladesh, Nepal and Pakistan by incorporating standard ground-based observations, earth observation datasets, and numerical forecast models. ICT-based agriculture drought monitoring platforms, hosted at national agricultural and meteorological institutions, are being developed and coupled with communications and information deployment strategies to enable the rapid and efficient deployment of information that farmers can understand, interpret, and act on to adapt to anticipated droughts. Particular emphasis is being placed on the calibration and validation of data products through retrospective analysis of time series data, in addition to the installation of automatic weather station networks. In order to contextualize monitoring products to that they may be relevant for farmers' primary cropping systems, district level farming practices calendars are being compiled and validated through focus groups and surveys to identify the most important times and situations during which farmers can adapt to drought. High-resolution satellite crop distribution maps are under development and validation to add value to these efforts. This programme also aims to enhance capacity of agricultural extension staff to better understand climate information, probabilistic forecasts, related technologies, and adaptation strategies, in addition to equipping them with increased capacity to convey drought risks to farmers and improve climate related decision making.

Pesticide use in the wheat-maize double cropping systems of the North China Plain: Assessment, field study, and implications.

PubMed

Brauns, Bentje; Jakobsen, Rasmus; Song, Xianfang; Bjerg, Poul L

2018-03-01

In the North China Plain (NCP), rising inputs of pesticides have intensified the environmental impact of farming activities in recent decades by contributing to surface water and groundwater contamination. In response to this, the Chinese government imposed stricter regulations on pesticide approval and application, and better monitoring strategies are being developed. However, sufficient and well-directed research on the accumulation and impact of different pesticides is needed for informed decision-making. In this study, current pesticide use, and recent and current research on water contamination by pesticides in the NCP are reviewed and assessed. Additionally, a small-scale field study was performed to determine if residuals from currently-used pesticides in the NCP can be detected in surface water, and in connected shallow groundwater. The contaminants of interest were commonly used pesticides on winter wheat-summer maize fields (the dominant cropping system in the NCP), such as 2,4-D and atrazine. Sampling took place in May, July, and October 2013; and March 2014. Results from our literature research showed that sampling is biased towards surface water monitoring. Furthermore, most studies focus on organic chlorinated pesticides (OCPs) like the isomers of dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH), which were banned in China in 1983. However, currently-used herbicides like 2,4-D and atrazine were detected in river water and groundwater in all samplings of our field study. The highest concentrations of 2,4-D and atrazine were found in the river water, ranging up to 3.00 and 0.96μg/L, respectively. The monitoring of banned compounds was found to be important because several studies indicate that they are still accumulating in the environment and/or are still illegally in use. However, supported by our own data, we find that the monitoring in groundwater and surface water of currently permitted pesticides in China needs equal attention, and should therefore be increased. Copyright © 2017 Elsevier B.V. All rights reserved.

AgMIP: Next Generation Models and Assessments

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2014-12-01

Next steps in developing next-generation crop models fall into several categories: significant improvements in simulation of important crop processes and responses to stress; extension from simplified crop models to complex cropping systems models; and scaling up from site-based models to landscape, national, continental, and global scales. Crop processes that require major leaps in understanding and simulation in order to narrow uncertainties around how crops will respond to changing atmospheric conditions include genetics; carbon, temperature, water, and nitrogen; ozone; and nutrition. The field of crop modeling has been built on a single crop-by-crop approach. It is now time to create a new paradigm, moving from 'crop' to 'cropping system.' A first step is to set up the simulation technology so that modelers can rapidly incorporate multiple crops within fields, and multiple crops over time. Then the response of these more complex cropping systems can be tested under different sustainable intensification management strategies utilizing the updated simulation environments. Model improvements for diseases, pests, and weeds include developing process-based models for important diseases, frameworks for coupling air-borne diseases to crop models, gathering significantly more data on crop impacts, and enabling the evaluation of pest management strategies. Most smallholder farming in the world involves integrated crop-livestock systems that cannot be represented by crop modeling alone. Thus, next-generation cropping system models need to include key linkages to livestock. Livestock linkages to be incorporated include growth and productivity models for grasslands and rangelands as well as the usual annual crops. There are several approaches for scaling up, including use of gridded models and development of simpler quasi-empirical models for landscape-scale analysis. On the assessment side, AgMIP is leading a community process for coordinated contributions to IPCC AR6 that involves the key modeling groups from around the world including North America, Europe, South America, Sub-Saharan Africa, South Asia, East Asia, and Australia and Oceania. This community process will lead to mutually agreed protocols for coordinated global and regional assessments.

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...

Soil microbiome characteristics and soilborne disease development associated with long-term potato cropping system practices

USDA-ARS?s Scientific Manuscript database

Potato cropping system practices substantially affect soil microbial communities and the development of soilborne diseases. Cropping systems incorporating soil health management practices, such as longer rotations, disease-suppressive crops, reduced tillage, and/or organic amendments can potentially...

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.

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.

Towards the Development and Validation of a Global Field Size and Irrigation Map using Crowdsourcing, Mobile Apps and Google Earth Engine in support of GEOGLAM

NASA Astrophysics Data System (ADS)

Fritz, S.; Nordling, J.; See, L. M.; McCallum, I.; Perger, C.; Becker-Reshef, I.; Mucher, S.; Bydekerke, L.; Havlik, P.; Kraxner, F.; Obersteiner, M.

2014-12-01

The International Institute for Applied Systems Analysis (IIASA) has developed a global cropland extent map, which supports the monitoring and assessment activities of GEOGLAM (Group on Earth Observations Global Agricultural Monitoring Initiative). Through the European-funded SIGMA (Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM) project, IIASA is continuing to support GEOGLAM by providing cropland projections in the future and modelling environmental impacts on agriculture under various scenarios. In addition, IIASA is focusing on two specific elements within SIGMA: the development of a global field size and irrigation map; and mobile app development for in-situ data collection and validation of remotely-sensed products. Cropland field size is a very useful indicator for agricultural monitoring yet the information we have at a global scale is currently very limited. IIASA has already created a global map of field size at a 1 km resolution using crowdsourced data from Geo-Wiki as a first approximation. Using automatic classification of Landsat imagery and algorithms contained within Google Earth Engine, initial experimentation has shown that circular fields and landscape structures can easily be extracted. Not only will this contribute to improving the global map of field size, it can also be used to create a global map that contains a large proportion of the world's irrigated areas, which will be another useful contribution to GEOGLAM. The field size map will also be used to stratify and develop a global crop map in SIGMA. Mobile app development in support of in-situ data collection is another area where IIASA is currently working. An Android app has been built using the Open Data Toolkit (ODK) and extended further with spatial mapping capabilities called GeoODK. The app allows users to collect data on different crop types and delineate fields on the ground, which can be used to validate the field size map. The app can also cache map data so that high resolution satellite imagery and reference data from the users can be viewed in the field without the need for an internet connection. This app will be used for calibration and validation of the data products in SIGMA, e.g. data collection at JECAM (Joint Experiment of Crop Assessment and Monitoring) sites.

Agricultural conversion of floodplain ecosystems: implications for groundwater quality.

PubMed

Schilling, Keith E; Jacobson, Peter J; Vogelgesang, Jason A

2015-04-15

With current trends of converting grasslands to row crop agriculture in vulnerable areas, there is a critical need to evaluate the effects of land use on groundwater quality in large river floodplain systems. In this study, groundwater hydrology and nutrient dynamics associated with three land cover types (grassland, floodplain forest and cropland) were assessed at the Cedar River floodplain in southeastern Iowa. The cropland site consisted of newly-converted grassland, done specifically for our study. Our objectives were to evaluate spatial and temporal variations in groundwater hydrology and quality, and quantify changes in groundwater quality following land conversion from grassland to row crop in a floodplain. We installed five shallow and one deep monitoring wells in each of the three land cover types and recorded water levels and quality over a three year period. Crop rotations included soybeans in year 1, corn in year 2 and fallow with cover crops during year 3 due to river flooding. Water table levels behaved nearly identically among the sites but during the second and third years of our study, NO₃-N concentrations in shallow floodplain groundwater beneath the cropped site increased from 0.5 mg/l to more than 25 mg/l (maximum of 70 mg/l). The increase in concentration was primarily associated with application of liquid N during June of the second year (corn rotation), although site flooding may have exacerbated NO₃-N leaching. Geophysical investigation revealed differences in ground conductivity among the land cover sites that related significantly to variations in groundwater quality. Study results provide much-needed information on the effects of different land covers on floodplain groundwater and point to challenges ahead for meeting nutrient reduction goals if row crop land use expands into floodplains. Copyright © 2015 Elsevier Ltd. All rights reserved.

Crop diversity effects on productivity and economic returns under dryland agriculture

USDA-ARS?s Scientific Manuscript database

Increasing crop diversity has been identified as a method to improve agronomic performance of cropping systems and increase provision of ecosystem services. However, there is a need to understand the economic performance of more diverse cropping systems. Crop productivity and economic net returns we...

Statistical modeling of yield and variance instability in conventional and organic cropping systems

USDA-ARS?s Scientific Manuscript database

Cropping systems research was undertaken to address declining crop diversity and verify competitiveness of alternatives to the predominant conventional cropping system in the northern Corn Belt. To understand and capitalize on temporal yield variability within corn and soybean fields, we quantified ...

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.

Tolerance of interseeded annual ryegrass and red clover cover crops to residual herbicides in Mid-Atlantic corn cropping systems

USDA-ARS?s Scientific Manuscript database

In the Mid-Atlantic region, there is increasing interest in the use of relay-cropping strategies to establish cover crops in corn cropping systems. Recent studies have demonstrated the potential to establish annual ryegrass and red clover cover crops at the V5 corn growth stage using a high-clearan...

Perspectives on genetically modified crops and food detection.

PubMed

Lin, Chih-Hui; Pan, Tzu-Ming

2016-01-01

Genetically modified (GM) crops are a major product of the global food industry. From 1996 to 2014, 357 GM crops were approved and the global value of the GM crop market reached 35% of the global commercial seed market in 2014. However, the rapid growth of the GM crop-based industry has also created controversies in many regions, including the European Union, Egypt, and Taiwan. The effective detection and regulation of GM crops/foods are necessary to reduce the impact of these controversies. In this review, the status of GM crops and the technology for their detection are discussed. As the primary gap in GM crop regulation exists in the application of detection technology to field regulation, efforts should be made to develop an integrated, standardized, and high-throughput GM crop detection system. We propose the development of an integrated GM crop detection system, to be used in combination with a standardized international database, a decision support system, high-throughput DNA analysis, and automated sample processing. By integrating these technologies, we hope that the proposed GM crop detection system will provide a method to facilitate comprehensive GM crop regulation. Copyright © 2015. Published by Elsevier B.V.

Monitoring and Analysis of Nonpoint Source Pollution - Case study on terraced paddy fields in an agricultural watershed

NASA Astrophysics Data System (ADS)

Chen, Shih-Kai; Jang, Cheng-Shin; Yeh, Chun-Lin

2013-04-01

The intensive use of chemical fertilizer has negatively impacted environments in recent decades, mainly through water pollution by nitrogen (N) and phosphate (P) originating from agricultural activities. As a main crop with the largest cultivation area about 0.25 million ha per year in Taiwan, rice paddies account for a significant share of fertilizer consumption among agriculture crops. This study evaluated the fertilization of paddy fields impacting return flow water quality in an agricultural watershed located at Hsinchu County, northern Taiwan. Water quality monitoring continued for two crop-periods in 2012, around subject to different water bodies, including the irrigation water, drainage water, and shallow groundwater. The results indicated that obviously increasing of ammonium-N, nitrate-N and TP concentrations in the surface drainage water were observed immediately following three times of fertilizer applications (including basal, tillering, and panicle fertilizer application), but reduced to relatively low concentrations after 7-10 days after each fertilizer application. Groundwater quality monitoring showed that the observation wells with the more shallow water depth, the more significant variation of concentrations of ammonium-N, nitrate-N and TP could be observed, which means that the contamination potential of nutrient of groundwater is related not only to the impermeable plow sole layer but also to the length of percolation route in this area. The study also showed that the potential pollution load of nutrient could be further reduced by well drainage water control and rational fertilizer management, such as deep-water irrigation, reuse of return flow, the rational application of fertilizers, and the SRI (The System of Rice Intensification) method. The results of this study can provide as an evaluation basis to formulate effective measures for agricultural non-point source pollution control and the reuse of agricultural return flow. Keywords:Chemical fertilizer, Nitrogen, Phosphorus, Paddy field, Non-point source pollution.

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...

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

Wolf, Julie; West, Tristram O.; Le Page, Yannick LB

Quantification of biogenic carbon fluxes from agricultural lands is needed to generate comprehensive bottom-up estimates of net carbon exchange for global and regional carbon monitoring. We estimated global agricultural carbon fluxes associated with annual crop net primary production (NPP), harvested biomass, and consumption of biomass by humans and livestock. These estimates were combined for a single estimate of net carbon exchange (NCE) and spatially distributed to 0.05 degree resolution using MODIS satellite land cover data. Global crop NPP in 2011 was estimated at 5.25 ± 0.46 Pg C yr-1, of which 2.05 ± 0.05 Pg C yr-1 was harvested andmore » 0.54 Pg C yr-1 was collected from crop residues for livestock fodder. Total livestock feed intake in 2011 was 2.42 ± 0.21 Pg C yr-1, of which 2.31 ± 0.21 Pg C yr-1 was emitted as CO2, 0.07 ± 0.01 Pg C yr-1 was emitted as CH4, and 0.04 Pg C yr-1 was contained within milk and egg production. Livestock grazed an estimated 1.27 Pg C yr-1 in 2011, which constituted 52.4% of total feed intake. Global human food intake was 0.57 ± 0.03 Pg C yr-1 in 2011, the majority of which is respired as CO2. Completed global cropland carbon budgets accounted for the ultimate use of ca. 80% of harvested biomass. The spatial distribution of these fluxes may be used for global carbon monitoring, estimation of regional uncertainty, and for use as input to Earth system models.« less

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..

Effect of Nutrient Management Planning on Crop Yield, Nitrate Leaching and Sediment Loading in Thomas Brook Watershed

NASA Astrophysics Data System (ADS)

Amon-Armah, Frederick; Yiridoe, Emmanuel K.; Ahmad, Nafees H. M.; Hebb, Dale; Jamieson, Rob; Burton, David; Madani, Ali

2013-11-01

Government priorities on provincial Nutrient Management Planning (NMP) programs include improving the program effectiveness for environmental quality protection, and promoting more widespread adoption. Understanding the effect of NMP on both crop yield and key water-quality parameters in agricultural watersheds requires a comprehensive evaluation that takes into consideration important NMP attributes and location-specific farming conditions. This study applied the Soil and Water Assessment Tool (SWAT) to investigate the effects of crop and rotation sequence, tillage type, and nutrient N application rate on crop yield and the associated groundwater leaching and sediment loss. The SWAT model was applied to the Thomas Brook Watershed, located in the most intensively managed agricultural region of Nova Scotia, Canada. Cropping systems evaluated included seven fertilizer application rates and two tillage systems (i.e., conventional tillage and no-till). The analysis reflected cropping systems commonly managed by farmers in the Annapolis Valley region, including grain corn-based and potato-based cropping systems, and a vegetable-horticulture system. ANOVA models were developed and used to assess the effects of crop management choices on crop yield and two water-quality parameters (i.e., leaching and sediment loading). Results suggest that existing recommended N-fertilizer rate can be reduced by 10-25 %, for grain crop production, to significantly lower leaching ( P > 0.05) while optimizing the crop yield. The analysis identified the nutrient N rates in combination with specific crops and rotation systems that can be used to manage leaching while balancing impacts on crop yields within the watershed.

Integrated crop-livestock systems and cover crop grazing in the Northern Great Plains

USDA-ARS?s Scientific Manuscript database

Integrating crops and livestock has been identified as an approach to sustainably intensify agricultural systems, increasing production while reducing the need for external inputs, building soil health, and increasing economic returns. Cover crops and grazing these cover crops are a natural fit with...