Estimation of different data compositions for early-season crop type classification.

PubMed

Hao, Pengyu; Wu, Mingquan; Niu, Zheng; Wang, Li; Zhan, Yulin

2018-01-01

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer's accuracies (PAs) and user's accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

Estimation of different data compositions for early-season crop type classification

PubMed Central

Wu, Mingquan; Wang, Li; Zhan, Yulin

2018-01-01

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study. PMID:29868265

Remote sensing as a tool for estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Morgan, K. M.; Kiefer, R. W.

1979-01-01

The Universal Soil Loss Equation is a frequently used methodology for estimating soil erosion potential. The Universal Soil Loss Equation requires a variety of types of geographic information (e.g. topographic slope, soil erodibility, land use, crop type, and soil conservation practice) in order to function. This information is traditionally gathered from topographic maps, soil surveys, field surveys, and interviews with farmers. Remote sensing data sources and interpretation techniques provide an alternative method for collecting information regarding land use, crop type, and soil conservation practice. Airphoto interpretation techniques and medium altitude, multi-date color and color infrared positive transparencies (70mm) were utilized in this study to determine their effectiveness for gathering the desired land use/land cover data. Successful results were obtained within the test site, a 6136 hectare watershed in Dane County, Wisconsin.

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.

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.

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.

Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model

NASA Astrophysics Data System (ADS)

Hu, Q.; Friedl, M. A.; Wu, W.

2017-12-01

Accurate and timely information regarding the spatial distribution of crop types and their changes is essential for acreage surveys, yield estimation, water management, and agricultural production decision-making. In recent years, increasing population, dietary shifts and climate change have driven drastic changes in China's agricultural land use. However, no maps are currently available that document the spatial and temporal patterns of these agricultural land use changes. Because of its short revisit period, rich spectral bands and global coverage, MODIS time series data has been shown to have great potential for detecting the seasonal dynamics of different crop types. However, its inherently coarse spatial resolution limits the accuracy with which crops can be identified from MODIS in regions with small fields or complex agricultural landscapes. To evaluate this more carefully and specifically understand the strengths and weaknesses of MODIS data for crop-type mapping, we used MODIS time-series imagery to map the sub-pixel fractional crop area for four major crop types (rice, corn, soybean and wheat) at 500-m spatial resolution for Heilongjiang province, one of the most important grain-production regions in China where recent agricultural land use change has been rapid and pronounced. To do this, a random forest regression (RF-g) model was constructed to estimate the percentage of each sub-pixel crop type in 2006, 2011 and 2016. Crop type maps generated through expert visual interpretation of high spatial resolution images (i.e., Landsat and SPOT data) were used to calibrate the regression model. Five different time series of vegetation indices (155 features) derived from different spectral channels of MODIS land surface reflectance (MOD09A1) data were used as candidate features for the RF-g model. An out-of-bag strategy and backward elimination approach was applied to select the optimal spectra-temporal feature subset for each crop type. The resulting crop maps were assessed in two ways: (1) wall-to-wall pixel comparison with corresponding high spatial resolution reference maps; and (2) county-level comparison with census data. Based on these derived maps, changes in crop type, total area, and spatial patterns of change in Heilongjiang province during 2006-2016 were analyzed.

Machine-assisted analysis of Landsat data in the study of crop-soils relationships

USGS Publications Warehouse

Draeger, William C.

1976-01-01

To date, relatively few studies have dealt with crop-soil interactions as they affect the appearance of agricultural areas on Landsat imagery, and hence crop and soil classification or the analysis of agricultural land use.The Image 100, a computer-based data analysis system which allows an interpreter to interact directly and rapidly with Landsat computer compatible tape data, provided a tool to assist in the evaluation of the extent and significance of these interactions. Used with timely and accurate ground data, the system made possible a determination of the variability in crop spectral appearance, from soil type to soil type, as recorded on Landsat data. Information was provided in the form of spectral distribution histrograms for each crop-soil class on each Landsat band. Several crop categories in a test area in  rookings County, South Dakota, were classified using training fields that were selected to be representative of each major crop-soil class. Accuracies in each case, on a total acreage basis, were greater than 90 percent.

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.

Implementation of Sentinel-2 Data in the M4Land System for the Generation of Continuous Information Products in Agriculture

NASA Astrophysics Data System (ADS)

Klug, P.; Schlenz, F.; Hank, T.; Migdall, S.; Weiß, I.; Danner, M.; Bach, H.; Mauser, W.

2016-08-01

The analysis system developed in the frame of the M4Land project (Model based, Multi-temporal, Multi scale and Multi sensorial retrieval of continuous land management information) has proven its capabilities of classifying crop type and creating products on the intensity of agricultural production using optical remote sensing data from Landsat and RapidEye. In this study, Sentinel-2 data is used for the first time together with Landsat 7 ETM+ and 8 OLI data within the M4Land analysis system to derive continuously crop type and the agricultural intensity of fields in an area north of Munich, Germany and the year 2015.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Combined Analysis of SENTINEL-1 and Rapideye Data for Improved Crop Type Classification: AN Early Season Approach for Rapeseed and Cereals

NASA Astrophysics Data System (ADS)

Lussem, U.; Hütt, C.; Waldhoff, G.

2016-06-01

Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.

Sensitivity of crop cover to climate variability: insights from two Indian agro-ecoregions.

PubMed

Mondal, Pinki; Jain, Meha; DeFries, Ruth S; Galford, Gillian L; Small, Christopher

2015-01-15

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM - precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS - temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from -25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Agricultural pesticide emissions associated with common crops in the United States

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

Benjey, W.G.

Annual emissions for the year 1987 from the application of agricultural pesticides have been estimated by crop type by county for the United States using a geographic information system. The emissions estimates are based upon computed volatilization rates accounting for the properties of each pesticide, evaporation rates, mode of application (surface or soil incorporation) and percent of interception by leaves. Key pesticide properties include the Henry's Law constant, half-life in soil and the organic carbon partitioning coefficient. The volatilization rates are multiplied by the amount of pesticide applied by crop acreage in each county as determined from agricultural census andmore » pesticide sales data. The geographic distribution of the dominant emissions, such as atrazine and diazinon, etc. are presented by crop type and state. For a given pesticide, the geographic variability is controlled principally by amount applied and water availability as reflected in evaporation rates.« less

Integrating predictive information into an agro-economic model to guide agricultural planning

NASA Astrophysics Data System (ADS)

Block, Paul; Zhang, Ying; You, Liangzhi

2017-04-01

Seasonal climate forecasts can inform long-range planning, including water resources utilization and allocation, however quantifying the value of this information on the economy is often challenging. For rain-fed farmers, skillful season-ahead predictions may lead to superior planning, as compared to business as usual strategies, resulting in additional benefits or reduced losses. In this study, regional-level probabilistic precipitation forecasts of the major rainy season in Ethiopia are fed into an agro-economic model, adapted from the International Food Policy Research Institute, to evaluate economic outcomes (GDP, poverty rates, etc.) as compared with a no-forecast approach. Based on forecasted conditions, farmers can select various actions: adjusting crop area and crop type, purchasing drought resistant seed, or applying additional fertilizer. Preliminary results favor the forecast-based approach, particularly through crop area reallocation.

7 CFR 400.701 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... adjustment percentages, practices, particular types or varieties of the insurable crop or agricultural... consideration such factors as originality, the number and type of factual determinations necessary to establish... information is based, such data may include, but is not limited to, focus group results, market research...

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Mapping Human-Dominated Landscapes: the Distribution and Yield of Major Crops of the World

NASA Astrophysics Data System (ADS)

Monfreda, C.; Ramankutty, N.; Foley, J. A.

2005-12-01

Croplands cover 18 million km2, an area the size of South America, and provide ecosystem goods and services essential to human well-being. Most global land-cover classifications group the diversity of croplands into a single or very few categories, thereby excluding critical information to answer key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information on land-use practices is even more limited. The relative lack of information about agricultural landscapes results partly from difficulties in using satellite data to identify individual crop types and land-use practices at a global scale. We address limitations common to remote-sensing classifications by distributing national, state, and county level statistics across a recently updated global dataset of cropland cover at 5 minute resolution. The resulting datasets depict the fractional harvested area and yield of twenty distinct crop types: maize, wheat, rice, sorghum, millet, barley, oats, soybeans, sunflower, rapeseed/canola, pulses, groundnuts/peanuts, oil palm, cassava, potatoes, sugar cane, sugar beets, tobacco, coffee, and cotton. These datasets represent the state of agriculture circa the year 2000 and will be made available for applications in ecological analysis, modeling, visualization, and education.

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang*

PubMed Central

Fang, Bin; Wang, Guang-huo; Van den berg, Marrit; Roetter, Reimund

2005-01-01

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China’s Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops. PMID:16187411

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang.

PubMed

Fang, Bin; Wang, Guang-Huo; Van, Den Berg Marrit; Roetter, Reimund

2005-10-01

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China's Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops.

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.

Agriculture: Nurseries and Greenhouses

EPA Pesticide Factsheets

Nurseries and Greenhouses. Information about environmental requirements specifically relating to the production of many types of agricultural crops grown in nurseries and greenhouses, such as ornamental plants and specialty fruits and vegetables.

Current situation of pests targeted by Bt crops in Latin America.

PubMed

Blanco, C A; Chiaravalle, W; Dalla-Rizza, M; Farias, J R; García-Degano, M F; Gastaminza, G; Mota-Sánchez, D; Murúa, M G; Omoto, C; Pieralisi, B K; Rodríguez, J; Rodríguez-Maciel, J C; Terán-Santofimio, H; Terán-Vargas, A P; Valencia, S J; Willink, E

2016-06-01

Transgenic crops producing Bacillus thuringiensis- (Bt) insecticidal proteins (Bt crops) have provided useful pest management tools to growers for the past 20 years. Planting Bt crops has reduced the use of synthetic insecticides on cotton, maize and soybean fields in 11 countries throughout Latin America. One of the threats that could jeopardize the sustainability of Bt crops is the development of resistance by targeted pests. Governments of many countries require vigilance in measuring changes in Bt-susceptibility in order to proactively implement corrective measures before Bt-resistance is widespread, thus prolonging the usefulness of Bt crops. A pragmatic approach to obtain information on the effectiveness of Bt-crops is directly asking growers, crop consultants and academics about Bt-resistance problems in agricultural fields, first-hand information that not necessarily relies on susceptibility screens performed in laboratories. This type of information is presented in this report. Problematic pests of cotton and soybeans in five Latin American countries currently are effectively controlled by Bt crops. Growers that plant conventional (non-Bt) cotton or soybeans have to spray synthetic insecticides against multiple pests that otherwise are controlled by these Bt crops. A similar situation has been observed in six Latin American countries where Bt maize is planted. No synthetic insecticide applications are used to control corn pests because they are controlled by Bt maize, with the exception of Spodoptera frugiperda. While this insect in some countries is still effectively controlled by Bt maize, in others resistance has evolved and necessitates supplemental insecticide applications and/or the use of Bt maize cultivars that express multiple Bt proteins. Partial control of S. frugiperda in certain countries is due to its natural tolerance to the Bt bacterium. Of the 31 pests targeted and controlled by Bt crops in Latin America, only S. frugiperda has shown tolerance to certain Bt proteins in growers' fields, the most reliable indication of the status of Bt-susceptibility in most of the American continent. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

NASA Astrophysics Data System (ADS)

Chen, Yaoliang; Lu, Dengsheng; Moran, Emilio; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, Ieda Del'Arco; da Silva, Ramon Felipe Bicudo; Huang, Jingfeng; Luiz, Alfredo José Barreto; de Oliveira, Maria Antonia Falcão

2018-07-01

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

Meteorological risks are drivers of environmental innovation in agro-ecosystem management

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van de Vijver, Hans; Vanwindekens, Frédéric; de Frutos Cachorro, Julia; Verspecht, Ann; Planchon, Viviane; Buyse, Jeroen

2017-04-01

Agricultural crop production is to a great extent determined by weather conditions. The research hypothesis is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management. The methodology comprised five major parts: the hazard, its impact on different agro-ecosystems, vulnerability, risk management and risk communication. Generalized Extreme Value (GEV) theory was used to model annual maxima of meteorological variables based on a location-, scale- and shape-parameter that determine the center of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Spatial interpolation of GEV-derived return levels resulted in spatial temperature extremes, precipitation deficits and wet periods. The temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was realised using a bio-physically based modelling framework that couples phenology, a soil water balance and crop growth. 20-year return values for drought and waterlogging during different crop stages were related to arable yields. The method helped quantify agricultural production risks and rate both weather and crop-based agricultural insurance. The spatial extent of vulnerability is developed on different layers of geo-information to include meteorology, soil-landscapes, crop cover and management. Vulnerability of agroecosystems was mapped based on rules set by experts' knowledge and implemented by Fuzzy Inference System modelling and Geographical Information System tools. The approach was applied for cropland vulnerability to heavy rain and grassland vulnerability to drought. The level of vulnerability and resilience of an agro-ecosystem was also determined by risk management which differed across sectors and farm types. A calibrated agro-economic model demonstrated a marked influence of climate adapted land allocation and crop management on individual utility. The "chain of risk" approach allowed for investigating the hypothesis that meteorological risks act as drivers for agricultural innovation. Risk types were quantified in terms of probability and distribution, and further distinguished according to production type. Examples of strategies and options were provided at field, farm and policy level using different modelling methods.

Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

NASA Astrophysics Data System (ADS)

Kross, Angela; McNairn, Heather; Lapen, David; Sunohara, Mark; Champagne, Catherine

2015-02-01

Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.

Coupled Effects of Climatic and Socio-economic Factors on Winter Cropping in India

NASA Astrophysics Data System (ADS)

Jain, M.; Mondal, P.; Galford, G. L.; DeFries, R. S.

2015-12-01

India is predicted to be one of the most vulnerable regions in terms of agricultural sensitivity to future climate changes. Approximately 69% of India's population is rural, and over 55% of the working population relies on agriculture for sustenance and livelihoods. Indian smallholder farmers who own less than 2 ha of farmland represent 78% of the total Indian farmers and produce 41% of the country's food crops. These smallholder farmers are among some of the most vulnerable communities to climatic and economic changes due to limited access to technology, infrastructure, markets, and institutional or financial support in the case of adverse climatic events. Baseline information on agricultural sensitivity to climate variability will provide useful information for regional-level, and eventually state- and national-level, strategies and policies that promote adaption to climate variability. We use a decade of remote sensing analysis of cropping patterns and climatic factors along with census data for irrigation and demographic factors to understand winter cropping trajectories across agro-ecological zones in India. Findings from multiple agro-ecological zones indicate that there are three primary trajectories in winter cropping in India - increasing, fluctuating, and decreasing. In the Central Indian Highlands, for example, the most dominant trend is that of fluctuating cropped area, ranging between ~37,300 km2 in 2010 and ~21,100 km2 in 2013, which is associated with village-level access to irrigation and local labor dynamics. Clay soil type and increasing irrigation coverage were associated with intensification. Yet, suitable soil type and access to irrigation do not reduce vulnerability to high daytime temperatures that is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers would require additional strategies, such as access to fine-scale temperature forecasts ahead of the planting season and heat-tolerant winter crop varieties.

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.

Conjecture Regarding Posttranslational Modifications to the Arabidopsis Type I Proton-Pumping Pyrophosphatase (AVP1)

PubMed Central

Pizzio, Gaston A.; Hirschi, Kendal D.; Gaxiola, Roberto A.

2017-01-01

Agbiotechnology uses genetic engineering to improve the output and value of crops. Altering the expression of the plant Type I Proton-pumping Pyrophosphatase (H+-PPase) has already proven to be a useful tool to enhance crop productivity. Despite the effective use of this gene in translational research, information regarding the intracellular localization and functional plasticity of the pump remain largely enigmatic. Using computer modeling several putative phosphorylation, ubiquitination and sumoylation target sites were identified that may regulate Arabidopsis H+-PPase (AVP1- Arabidopsis Vacuolar Proton-pump 1) subcellular trafficking and activity. These putative regulatory sites will direct future research that specifically addresses the partitioning and transport characteristics of this pump. We posit that fine-tuning H+-PPases activity and cellular distribution will facilitate rationale strategies for further genetic improvements in crop productivity. PMID:28955362

Determining crop residue type and class using satellite acquired data. M.S. Thesis Progress Report, Jun. 1990

NASA Technical Reports Server (NTRS)

Zhuang, Xin

1990-01-01

LANDSAT Thematic Mapper (TM) data for March 23, 1987 with accompanying ground truth data for the study area in Miami County, IN were used to determine crop residue type and class. Principle components and spectral ratioing transformations were applied to the LANDSAT TM data. One graphic information system (GIS) layer of land ownership was added to each original image as the eighth band of data in an attempt to improve classification. Maximum likelihood, minimum distance, and neural networks were used to classify the original, transformed, and GIS-enhanced remotely sensed data. Crop residues could be separated from one another and from bare soil and other biomass. Two types of crop residue and four classes were identified from each LANDSAT TM image. The maximum likelihood classifier performed the best classification for each original image without need of any transformation. The neural network classifier was able to improve the classification by incorporating a GIS-layer of land ownership as an eighth band of data. The maximum likelihood classifier was unable to consider this eighth band of data and thus, its results could not be improved by its consideration.

77 FR 42433 - Difenoconazole; Pesticide Tolerances

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-19

... INFORMATION: I. General Information A. Does this action apply to me? You may be potentially affected by this... affected entities may include, but are not limited to those engaged in the following activities: Crop... provide a guide for readers regarding entities likely to be affected by this action. Other types of...

Evaluating the Usefulness of High-Temporal Resolution Vegetation Indices to Identify Crop Types

NASA Astrophysics Data System (ADS)

Hilbert, K.; Lewis, D.; O'Hara, C. G.

2006-12-01

The National Aeronautical and Space Agency (NASA) and the United States Department of Agriculture (USDA) jointly sponsored research covering the 2004 to 2006 South American crop seasons that focused on developing methods for the USDA's Foreign Agricultural Service's (FAS) Production Estimates and Crop Assessment Division (PECAD) to identify crop types using MODIS-derived, hyper-temporal Normalized Difference Vegetation Index (NDVI) images. NDVI images were composited in 8 day intervals from daily NDVI images and aggregated to create a hyper-termporal NDVI layerstack. This NDVI layerstack was used as input to image classification algorithms. Research results indicated that creating high-temporal resolution Normalized Difference Vegetation Index (NDVI) composites from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data products provides useful input to crop type classifications as well as potential useful input for regional crop productivity modeling efforts. A current NASA-sponsored Rapid Prototyping Capability (RPC) experiment will assess the utility of simulated future Visible Infrared Imager / Radiometer Suite (VIIRS) imagery for conducting NDVI-derived land cover and specific crop type classifications. In the experiment, methods will be considered to refine current MODIS data streams, reduce the noise content of the MODIS, and utilize the MODIS data as an input to the VIIRS simulation process. The effort also is being conducted in concert with an ISS project that will further evaluate, verify and validate the usefulness of specific data products to provide remote sensing-derived input for the Sinclair Model a semi-mechanistic model for estimating crop yield. The study area encompasses a large portion of the Pampas region of Argentina--a major world producer of crops such as corn, soybeans, and wheat which makes it a competitor to the US. ITD partnered with researchers at the Center for Surveying Agricultural and Natural Resources (CREAN) of the National University of Cordoba, Argentina, and CREAN personnel collected and continue to collect field-level, GIS-based in situ information. Current efforts involve both developing and optimizing software tools for the necessary data processing. The software includes the Time Series Product Tool (TSPT), Leica's ERDAS Imagine, and Mississippi State University's Temporal Map Algebra computational tools.

A satellite-driven, client-server hydro-economic model prototype for agricultural water management

NASA Astrophysics Data System (ADS)

Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

2017-04-01

Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because this product is only available for the conterminous United States, the framework is currently only applicable in this region. To obtain information on crop phenology, productivity and transpiration at adequate spatial and temporal frequencies we blend high spatial resolution Landsat information with high temporal fidelity MODIS imagery. The result is a 30 m, 8-day fused dataset of crop greenness that is subsequently transformed into productivity and transpiration by adapting existing forest productivity and transpiration algorithms for agricultural applications. To ensure all involved agencies work with identical information and that end-users are sheltered from the computational burden of storing and processing remote sensing data, this modeling framework is integrated in a client-server architecture based on the Hydra platform (www.hydraplatform.org). Assimilation and processing of resource-intensive remote sensing information, as well as hydrologic and other ancillary data, occur on the server side. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. This architecture ensures that all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Seed fates in crop-wild hybrid sunflower: crop allele and maternal effects.

PubMed

Pace, Brian A; Alexander, Helen M; Emry, Jason D; Mercer, Kristin L

2015-02-01

Domestication has resulted in selection upon seed traits found in wild populations, yet crop-wild hybrids retain some aspects of both parental phenotypes. Seed fates of germination, dormancy, and mortality can influence the success of crop allele introgression in crop-wild hybrid zones, especially if crop alleles or crop-imparted seed coverings result in out-of-season germination. We performed a seed burial experiment using crop, wild, and diverse hybrid sunflower (Helianthus annuus) cross types to test how a cross type's maternal parent and nuclear genetic composition might affect its fate under field conditions. We observed higher maladaptive fall germination in the crop- and F1- produced seeds than wild-produced seeds and, due to an interaction with percent crop alleles, fall germination was higher for cross types with more crop-like nuclear genetics. By spring, crop-produced cross types had the highest overwintering mortality, primarily due to higher fall germination. Early spring germination was identical across maternal types, but germination continued for F1-produced seeds. In conclusion, the more wild-like the maternal parent or the less proportion of the cross type's genome contributed by the crop, the greater likelihood a seed will remain ungerminated than die. Wild-like dormancy may facilitate introgression through future recruitment from the soil seed bank.

Benefits of an improved wheat crop information system

NASA Technical Reports Server (NTRS)

Kinne, I. L.

1976-01-01

The ECON work and the results of the independent reviews are summarized. Attempts are made to put this information into layman's terms and to present the benefits that can realistically be expected from a LANDSAT-type remote sensing system. Further the mechanisms by which these benefits can be expected to accrue are presented. The benefits are given including the nature of expected information improvements, how and why they can lead to benefits to society, and the estimated magnitude of the expected benefits. A brief description is presented of the ECON models, how they work, their results, and a summary of the pertinent aspects of each review. The ECON analyses show that substantial benefits will accrue from implementation of an improved wheat crop information system based on remote sensing.

Changing pollutants to green biogases for the crop food cycle chain.

PubMed

Zong, B Y; Xu, F J; Zong, B D; Zhang, Z G

2012-09-01

When fossil fuels on the Earth are used up, which kind of green energy can be used to replace them? Do every bioenergy generation or crop food chain results in environmental pollution? These questions are major concerns in a world facing restricted supplies of energy and food as well as environmental pollutions. To alleviate these issues, option biogases are explored in this paper. Two types of biogas generators were used for modifying the traditional crop food chain [viz. from atmospheric CO(2) photosynthesis to crops, crop stem/husk biowastes (burnt in cropland or as home fuels), to livestock droppings (dumping away), pork and people foods, then to CO(2)], via turning the biowaste pollutants into green bioenergies. By analyzing the traditional food chain via observation method, the drawbacks of by-product biowastes were revealed. Also, the whole cycle chain was further analyzed to assess its "greenness," using experimental data and other information, such as the material balance (e.g., the absorbed CO(2), investment versus generated food, energy, and wastes). The data show that by using the two types of biogas generators, clean renewable bioenergy, crop food, and livestock meat could be continuously produced without creating any waste to the world. The modification chain largely reduced CO(2) greenhouse gas and had a low-cost investment. The raw materials for the gas generators were only the wastes of crop stems and livestock droppings. Thus, the recommended CO(2) bioenergy cycle chain via the modification also greatly solved the environmental biowaste pollutions in the world. The described two type biogases effectively addressed the issues on energy, food, and environmental pollution. The green renewable bioenergy from the food cycle chain may be one of suitable alternatives to fossil and tree fuels for agricultural countries.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, p<0.05). The estimated crop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Accuracy Assessment of GAI Retrieval from SPOT5 Take According to Crop Type and Crop Development (BELCAM)

NASA Astrophysics Data System (ADS)

Delloye, C.; Weiss, M.; Baret, F.; Morin, D.; Defourny, P.

2016-08-01

The successful launch of Sentinel-2A equipped of the Multi Spectral Instrument is an exceptional opportunity to deliver regular information of high spatial and temporal resolution about the agricultural fields in Belgium. This research takes advantage of SPOT5 Take5 frequent acquisition over the Belgium in 2015 to realize an in-depth analysis of the Green Area Index (GAI) retrieval by inversion of a radiative transfer model at field scale over the whole Belgium for 2 crops: winter wheat and potato. The GAI is particularly relevant to derive the chlorophyll content at the canopy level (GAI × Cab) which is directly correlated to the Nitrogen content of the crops. This information is of crucial importance to advice farmers on the nitrogen fertilization genuinely required by the crops allowing the best yield and avoiding over fertilization and pollution of the groundwater table. The use of vegetation indexes seems promising to retrieve accurately the GAI (RRMSE =10.2%) during the period of the third Nitrogen application for the winter wheat. Further analyses have to be conducted for varieties of potato with a high level of biomass development (GAI > 4).

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.

Spatio-temporal trends in crop damage inform recent climate-mediated expansion of a large boreal herbivore into an agro-ecosystem.

PubMed

Laforge, Michel P; Michel, Nicole L; Brook, Ryan K

2017-11-09

Large-scale climatic fluctuations have caused species range shifts. Moose (Alces alces) have expanded their range southward into agricultural areas previously not considered moose habitat. We found that moose expansion into agro-ecosystems is mediated by broad-scale climatic factors and access to high-quality forage (i.e., crops). We used crop damage records to quantify moose presence across the Canadian Prairies. We regressed latitude of crop damage against North Atlantic Oscillation (NAO) and crop area to test the hypotheses that NAO-mediated wetland recharge and occurrence of more nutritious crop types would result in more frequent occurrences of crop damage by moose at southerly latitudes. We examined local-scale land use by generating a habitat selection model to test our hypothesis that moose selected for areas of high crop cover in agro-ecosystems. We found that crop damage by moose occurred farther south during dry winters and in years with greater coverage of oilseeds. The results of our analyses support our hypothesis that moose movement into cropland is mediated by high-protein crops, but not by thermoregulatory habitat at the scale examined. We conclude that broad-scale climate combined with changing land-use regimes are causal factors in species' range shifts and are important considerations when studying changing animal distributions.

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.

Land Use and Land Cover Change Modeling Using Remote Sensing and Soft Computing Approach to Assess Sugarcane Expansion Impacts in Tropical Agriculture

NASA Astrophysics Data System (ADS)

Vicente, L. E.; Koga-Vicente, A.; Friedel, M. J.; Victoria, D.; Zullo, J., Jr.; Gomes, D.; Bayma-Silva, G.

2014-12-01

Agriculture is related with land-use/cover changes (LUCC) over large areas and, in recent years, increase in demand of ethanol fuel has been influence in expansion of areas occupied with corn and sugar cane, raw material for ethanol production. Nevertheless, there´s a concern regarding the impacts on food security, such as, decrease in areas planted with food crops. Considering that the LUCC is highly dynamic, the use of Remote Sensing is a tool for monitoring changes quickly and precisely in order to provide information for agricultural planning. In this work, Remote Sensing techniques were used to monitor the LUCC occurred in municipalities of São Paulo state- Brazil related with sugarcane crops expansion in order to (i) evaluate and quantify the previous land cover in areas of sugarcane crop expansion, and (ii) provide information to elaborate a future land cover scenario based on Self Organizing Map (SOM) approach. The land cover classification procedure was based on Landsat 5 TM images, obtained from the Global Land Survey. The Landsat images were then segmented into homogeneous objects, with represent areas on the ground with similar spatial and spectral characteristics. These objects are related to the distinct land cover types that occur in each municipality. The segmentation procedure resulted in polygons over the three time periods along twenty years (1990-2010). The land cover for each object was visually identified, based on its shape, texture and spectral characteristics. Land cover types considered were: sugarcane plantations, pasture lands, natural cover, forest plantation, permanent crop, short cycle crop, water bodies and urban areas. SOM technique was used to estimate the values for the future land cover scenarios for the selected municipalities, using the information of land change provided by the remote sensing and data from official sources.

Assessing Climate Risk on Agricultural Production: Insights Using Retrospective Analysis of Crop Insurance and Climatic Trends

NASA Astrophysics Data System (ADS)

Reyes, J. J.; Elias, E.; Eischens, A.; Shilts, M.; Rango, A.; Steele, R.

2017-12-01

The collaborative synthesis of existing datasets, such as long-term climate observations and farmers' crop insurance payments, can increase their overall collective value and societal application. The U.S. Department of Agriculture (USDA) Climate Hubs were created to develop and deliver science-based information and technologies to agricultural and natural resource managers to enable climate-informed decision-making. As part of this mission, Hubs work across USDA and other climate service agencies to synthesize existing information. The USDA Risk Management Agency (RMA) is responsible for overseeing the Federal crop insurance program which currently insures over $100 billion in crops annually. RMA hosts data describing the cause for loss (e.g. drought, wind, irrigation failure) and indemnity amount (i.e. total cost of loss) at multiple spatio-temporal scales (i.e. state, county, year, month). The objective of this paper is to link climate information with indemnities, and their associated cause of loss, to assess climate risk on agricultural production and provide regionally-relevant information to stakeholders to promote resilient working landscapes. We performed a retrospective trend analysis at the state-level for the American Southwest (SW). First, we assessed indemnity-only trends by cause of loss and crop type at varying temporal scales. Historical monthly weather data (i.e. precipitation and temperature) and long-term drought indices (e.g. Palmer Drought Severity Index) were then linked with indemnities and grouped by different causes of loss. Climatological ranks were used to integrate historical comparative intensity of acute and long-term climatic events. Heat and drought as causes of loss were most correlated with temperature and drought indicators, respectively. Across all SW states increasing indemnities were correlated with warmer conditions. Multiple statistical trend analyses suggest a framework is necessary to appropriately measure the biophysical signals in crop insurance trends taking into account spatio-temporal characteristics. Based on stakeholder feedback, we also developed a web-based information browser to visualize and assess indemnity trends providing useful and usable knowledge to support informed land management decisions and ecosystem resilience.

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.

GEOGLAM Crop Assessment Tool: Adapting from global agricultural monitoring to food security monitoring

NASA Astrophysics Data System (ADS)

Humber, M. L.; Becker-Reshef, I.; Nordling, J.; Barker, B.; McGaughey, K.

2014-12-01

The GEOGLAM Crop Monitor's Crop Assessment Tool was released in August 2013 in support of the GEOGLAM Crop Monitor's objective to develop transparent, timely crop condition assessments in primary agricultural production areas, highlighting potential hotspots of stress/bumper crops. The Crop Assessment Tool allows users to view satellite derived products, best available crop masks, and crop calendars (created in collaboration with GEOGLAM Crop Monitor partners), then in turn submit crop assessment entries detailing the crop's condition, drivers, impacts, trends, and other information. Although the Crop Assessment Tool was originally intended to collect data on major crop production at the global scale, the types of data collected are also relevant to the food security and rangelands monitoring communities. In line with the GEOGLAM Countries at Risk philosophy of "foster[ing] the coordination of product delivery and capacity building efforts for national and regional organizations, and the development of harmonized methods and tools", a modified version of the Crop Assessment Tool is being developed for the USAID Famine Early Warning Systems Network (FEWS NET). As a member of the Countries at Risk component of GEOGLAM, FEWS NET provides agricultural monitoring, timely food security assessments, and early warnings of potential significant food shortages focusing specifically on countries at risk of food security emergencies. While the FEWS NET adaptation of the Crop Assessment Tool focuses on crop production in the context of food security rather than large scale production, the data collected is nearly identical to the data collected by the Crop Monitor. If combined, the countries monitored by FEWS NET and GEOGLAM Crop Monitor would encompass over 90 countries representing the most important regions for crop production and food security.

Information Use by PhD Students in Agriculture and Biology: A Dissertation Citation Analysis

ERIC Educational Resources Information Center

Kuruppu, Pali U.; Moore, Debra C.

2008-01-01

This article reports the findings of a study conducted to examine the types of information used by graduate students in the fields of biological and agricultural sciences at Iowa State University (ISU). The citations of doctoral dissertations submitted in nine agriculture and biological science subject fields (crop production and physiology;…

A study of the early detection of insect infestations and density/distribution of host plants. [Rio Grande

NASA Technical Reports Server (NTRS)

Hart, W. G. (Principal Investigator); Ingle, S. J.; Davis, M. R.

1975-01-01

The author has identified the following significant results. With comparative observations of film types and seasonal influences on reflectance characteristics, many crop varieties can be identified. This study shows that citrus, sugar cane, brush, some winter vegetables, and grain crops could be identified. Vegetative patterns in border areas can be detected. This information can be useful in detecting avenues of entry of pest species and areas of stress that require vigilance in stopping the spread of destructive species. Influence of some environmental factors on crops that may be confused with pest injury, or related factors, can be detected and identified with Skylab data (S-190B).

Coupling AVHRR imagery with biogeochemical models of methane emission from rice crops

NASA Astrophysics Data System (ADS)

Paliouras, Eleni Joyce

2000-10-01

Rice is a staple food source for much of the world and most of it is grown in paddies which remain flooded for a large part of the growing season. This anaerobic environment is ideal for the activities of methanogenic bacteria, that are responsible for the production of methane gas, some of which is released into the atmosphere. In order to better understand the role that rice cropping plays in the levels of atmospheric methane, several models have been developed to predict the methane flux from the paddies. These models generally utilize some type of nominal plant growth curve based on one or two pieces of ground truth data. Ideally, satellite data could be used instead to provide these models with an estimate of biomass change over the growing season, eliminating the need for related ground truth. A technique proposed to accomplish this is presented here, and results that demonstrate its success when applied to rice cropping areas of Texas are discussed. Also presented is a method for utilizing satellite data to map rice cropping areas that could eventually aid in a scheme for populating a GIS-type database with information on exact rice cropping areas. Such a database could then be directly tied to the methane emission models to obtain flux estimates for extensive regional areas.

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.

The pan-sharpening of satellite and UAV imagery for agricultural applications

NASA Astrophysics Data System (ADS)

Jenerowicz, Agnieszka; Woroszkiewicz, Malgorzata

2016-10-01

Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods. This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

Assessment of Climate Change Impacts on Agricultural Water Demands and Crop Yields in California's Central Valley

NASA Astrophysics Data System (ADS)

Tansey, M. K.; Flores-Lopez, F.; Young, C. A.; Huntington, J. L.

2012-12-01

Long term planning for the management of California's water resources requires assessment of the effects of future climate changes on both water supply and demand. Considerable progress has been made on the evaluation of the effects of future climate changes on water supplies but less information is available with regard to water demands. Uncertainty in future climate projections increases the difficulty of assessing climate impacts and evaluating long range adaptation strategies. Compounding the uncertainty in the future climate projections is the fact that most readily available downscaled climate projections lack sufficient meteorological information to compute evapotranspiration (ET) by the widely accepted ASCE Penman-Monteith (PM) method. This study addresses potential changes in future Central Valley water demands and crop yields by examining the effects of climate change on soil evaporation, plant transpiration, growth and yield for major types of crops grown in the Central Valley of California. Five representative climate scenarios based on 112 bias corrected spatially downscaled CMIP 3 GCM climate simulations were developed using the hybrid delta ensemble method to span a wide range future climate uncertainty. Analysis of historical California Irrigation Management Information System meteorological data was combined with several meteorological estimation methods to compute future solar radiation, wind speed and dew point temperatures corresponding to the GCM projected temperatures and precipitation. Future atmospheric CO2 concentrations corresponding to the 5 representative climate projections were developed based on weighting IPCC SRES emissions scenarios. The Land, Atmosphere, and Water Simulator (LAWS) model was used to compute ET and yield changes in the early, middle and late 21st century for 24 representative agricultural crops grown in the Sacramento, San Joaquin and Tulare Lake basins. Study results indicate that changes in ET and yield vary between crops due to plant specific sensitivities to temperature, solar radiation and the vapor pressure deficits. Shifts in the growth period to earlier in the year, shortened growth period for annual crops as well as extended fall growth can also exert important influences. Projected increases in CO2 concentrations in the late 21st century exert very significant influences on ET and yield for many crops. To characterize potential impacts and the range of uncertainty, changes in total agricultural water demands and yields were computed assuming that current crop types and acreages in 21 Central Valley regional planning areas remained constant throughout the 21st century for each of the 5 representative future climate scenarios.

Profiling agricultural land cover change in the North Central U.S. using ten years of the Cropland Data Layer

NASA Astrophysics Data System (ADS)

Sandborn, A.; Ebinger, L.

2016-12-01

The Cropland Data Layer (CDL), produced by the USDA/National Agricultural Statistics Service, provides annual, georeferenced crop specific land cover data over the contiguous United States. Several analyses were performed on ten years (2007-2016) of CDL data in order to visualize and quantify agricultural change over the North Central region (North Dakota, South Dakota, and Minnesota). Crop masks were derived from the CDL and layered to produce a ten-year time stack of corn, soybeans, and spring wheat at 30m spatial resolution. Through numerous image analyses, a temporal profile of each crop type was compiled and portrayed cartographically. For each crop, analyses included calculating the mean center of crop area over the ten year sequence, identifying the first and latest year the crop was grown on each pixel, and distinguishing crop rotation patterns and replacement statistics. Results show a clear north-western expansion trend for corn and soybeans, and a western migration trend for spring wheat. While some change may be due to commonly practiced crop rotation, this analysis shows that crop footprints have extended into areas that were previously other crops, idle cropland, and pasture/rangeland. Possible factors contributing to this crop migration pattern include profit advantages of row crops over small grains, improved crop genetics, climate change, and farm management program changes. Identifying and mapping these crop planting differences will better inform agricultural best practices, help to monitor the latest crop migration patterns, and present researchers with a way to quantitatively measure and forecast future agricultural trends.

Influence of crop type specification and spatial resolution on empirical modeling of field-scale Maize and Soybean carbon fluxes in the US Great Plains

NASA Astrophysics Data System (ADS)

McCombs, A. G.; Hiscox, A.; Wang, C.; Desai, A. R.

2016-12-01

A challenge in satellite land surface remote-sensing models of ecosystem carbon dynamics in agricultural systems is the lack of differentiation by crop type and management. This generalization can lead to large discrepancies between model predictions and eddy covariance flux tower observations of net ecosystem exchange of CO2 (NEE). Literature confirms that NEE varies remarkably among different crop types making the generalization of agriculture in remote sensing based models inaccurate. Here, we address this inaccuracy by identifying and mapping net ecosystem exchange (NEE) in agricultural fields by comparing bulk modeling and modeling by crop type, and using this information to develop empirical models for future use. We focus on mapping NEE in maize and soybean fields in the US Great Plains at higher spatial resolution using the fusion of MODIS and LandSAT surface reflectance. MODIS observed reflectance was downscaled using the ESTARFM downscaling methodology to match spatial scales to those found in LandSAT and that are more appropriate for carbon dynamics in agriculture fields. A multiple regression model was developed from surface reflectance of the downscaled MODIS and LandSAT remote sensing values calibrated against five FLUXNET/AMERIFLUX flux towers located on soybean and/or maize agricultural fields in the US Great Plains with multi-year NEE observations. Our new methodology improves upon bulk approximates to map and model carbon dynamics in maize and soybean fields, which have significantly different photosynthetic capacities.

Geology and Our Environment. Environmental Education Curriculum. Revised.

ERIC Educational Resources Information Center

Topeka Public Schools, KS.

Rocks, and the soil formed from rock, play a major role in determining such particulars as the type of crops that can be grown in a specific area and the type of housing that can be constructed. Also, rocks may supply fuel and building materials, and provide information about the history of an area. This unit is constructed to expose secondary…

A half-century analysis of landscape dynamics in southern Québec, Canada.

PubMed

Jobin, Benoît; Latendresse, Claudie; Baril, Alain; Maisonneuve, Charles; Boutin, Céline; Côté, Dominique

2014-04-01

We studied landscape dynamics for three time periods (<1950, 1965, and 1997) along a gradient of agricultural intensity from highly intensive agriculture to forested areas in southern Québec. Air photos were analyzed to obtain long-term information on land cover (crop and habitat types) and linear habitats (hedgerows and riparian habitats) and landscape metrics were calculated to quantify changes in habitat configuration. Anthropogenic areas increased in all types of landscapes but mostly occurred in the highly disturbed cash crop dominated landscape. Perennial crops (pasture and hayfields) were largely converted into annual crops (corn and soybean) between 1965 and 1997. The coalescence of annual crop fields resulted in a more homogeneous agricultural landscape. Old fields and forest cover was consistently low and forest fragmentation remained stable through time in the intensive agriculture landscapes. However, forest cover increased and forest fragmentation receded in the forest-dominated landscapes following farm abandonment and the transition of old fields into forests. Tree-dominated hedgerows and riparian habitats increased in areas with intensive agriculture. Observed changes in land cover classes are related to proximate factors, such as surficial deposits and topography. Agriculture intensification occurred in areas highly suitable for agriculture whereas farm abandonment was observed in poor-quality agriculture terrains. Large-scale conversion of perennial crops into annual crops along with continued urbanization exerts strong pressures on residual natural habitats and their inhabiting wildlife. The afforestation process occurring in the more forested landscapes along with the addition of tree-dominated hedgerows and riparian habitats in the agriculture-dominated landscapes should improve landscape ecological value.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Real-time PCR array as a universal platform for the detection of genetically modified crops and its application in identifying unapproved genetically modified crops in Japan.

PubMed

Mano, Junichi; Shigemitsu, Natsuki; Futo, Satoshi; Akiyama, Hiroshi; Teshima, Reiko; Hino, Akihiro; Furui, Satoshi; Kitta, Kazumi

2009-01-14

We developed a novel type of real-time polymerase chain reaction (PCR) array with TaqMan chemistry as a platform for the comprehensive and semiquantitative detection of genetically modified (GM) crops. Thirty primer-probe sets for the specific detection of GM lines, recombinant DNA (r-DNA) segments, endogenous reference genes, and donor organisms were synthesized, and a 96-well PCR plate was prepared with a different primer-probe in each well as the real-time PCR array. The specificity and sensitivity of the array were evaluated. A comparative analysis with the data and publicly available information on GM crops approved in Japan allowed us to assume the possibility of unapproved GM crop contamination. Furthermore, we designed a Microsoft Excel spreadsheet application, Unapproved GMO Checker version 2.01, which helps process all the data of real-time PCR arrays for the easy assumption of unapproved GM crop contamination. The spreadsheet is available free of charge at http://cse.naro.affrc.go.jp/jmano/index.html .

Development of the crop residue and rangeland burning in the 2014 National Emissions Inventory using information from multiple sources.

PubMed

Pouliot, George; Rao, Venkatesh; McCarty, Jessica L; Soja, Amber

2017-05-01

Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. One component of the biomass burning inventory, crop residue burning, has been poorly characterized in the National Emissions Inventory (NEI). In the 2011 NEI, wildland fires, prescribed fires, and crop residue burning collectively were the largest source of PM 2.5 . This paper summarizes our 2014 NEI method to estimate crop residue burning emissions and grass/pasture burning emissions using remote sensing data and field information and literature-based, crop-specific emission factors. We focus on both the postharvest and pre-harvest burning that takes place with bluegrass, corn, cotton, rice, soybeans, sugarcane and wheat. Estimates for 2014 indicate that over the continental United States (CONUS), crop residue burning excluding all areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay occurred over approximately 1.5 million acres of land and produced 19,600 short tons of PM 2.5 . For areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay, biomass burning emissions occurred over approximately 1.6 million acres of land and produced 30,000 short tons of PM 2.5 . This estimate compares with the 2011 NEI and 2008 NEI as follows: 2008: 49,650 short tons and 2011: 141,180 short tons. Note that in the previous two NEIs rangeland burning was not well defined and so the comparison is not exact. The remote sensing data also provided verification of our existing diurnal profile for crop residue burning emissions used in chemical transport modeling. In addition, the entire database used to estimate this sector of emissions is available on EPA's Clearinghouse for Inventories and Emission Factors (CHIEF, http://www3.epa.gov/ttn/chief/index.html ). Estimates of crop residue burning and rangeland burning emissions can be improved by using satellite detections. Local information is helpful in distinguishing crop residue and rangeland burning from all other types of fires.

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

Nisbet, A.F.; Woodman, R.F.M.

A database of soil-to-plant transfer factors for radiocesium and radiostrontium has been compiled for arable crops from published and unpublished sources. The database is more extensive than previous compilations of data published by the International Union of Radioecologists, containing new information for Scandinavia and Greece in particular. It also contains ancillary data on important soil characteristics. The database is sub-divided into 28 soil-crop combinations, covering four soil types and seven crop groups. Statistical analyses showed that transfer factors for radiocesium could not generally be predicted as a function of climatic region, type of experiment, age of contamination, or silt characteristics.more » However, significant relationships accounting for more than 30% of the variability in transfer factor were identified between transfer factors for radiostrontium and soil pH/organic matter status for a few soil-crop combinations. Best estimate transfer factors for radiocesium and radiostrontium were calculated for 28 soil-crop combinations, based on their geometric means: only the edible parts were considered. To predict the likely value of future individual transfer factors, 95% confidence intervals were also derived. A comparison of best estimate transfer factors derived in this study with recommended values published by the International Union of Radioecologists in 1989 and 1992 was made for comparable soil-crop groupings. While there were no significant differences between the best estimate values derived in this study and the 1992 data, radiological assessments that still use 1989 data may be unnecessarily cautious.« less

Predicting optimum crop designs using crop models and seasonal climate forecasts.

PubMed

Rodriguez, D; de Voil, P; Hudson, D; Brown, J N; Hayman, P; Marrou, H; Meinke, H

2018-02-02

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

From forest to farm: systematic review of cultivar feeding by chimpanzees--management implications for wildlife in anthropogenic landscapes.

PubMed

Hockings, Kimberley J; McLennan, Matthew R

2012-01-01

Crop-raiding is a major source of conflict between people and wildlife globally, impacting local livelihoods and impeding conservation. Conflict mitigation strategies that target problematic wildlife behaviours such as crop-raiding are notoriously difficult to develop for large-bodied, cognitively complex species. Many crop-raiders are generalist feeders. In more ecologically specialised species crop-type selection is not random and evidence-based management requires a good understanding of species' ecology and crop feeding habits. Comprehensive species-wide studies of crop consumption by endangered wildlife are lacking but are important for managing human-wildlife conflict. We conducted a comprehensive literature search of crop feeding records by wild chimpanzees (Pan troglodytes), a ripe-fruit specialist. We assessed quantitatively patterns of crop selection in relation to species-specific feeding behaviour, agricultural exposure, and crop availability. Crop consumption by chimpanzees is widespread in tropical Africa. Chimpanzees were recorded to eat a considerable range of cultivars (51 plant parts from 36 species). Crop part selection reflected a species-typical preference for fruit. Crops widely distributed in chimpanzee range countries were eaten at more sites than sparsely distributed crops. We identified 'high' and 'low' conflict crops according to their attractiveness to chimpanzees, taking account of their importance as cash crops and/or staple foods to people. Most (86%) high conflict crops were fruits, compared to 13% of low conflict crops. Some widely farmed cash or staple crops were seldom or never eaten by chimpanzees. Information about which crops are most frequently consumed and which are ignored has enormous potential for aiding on-the-ground stakeholders (i.e. farmers, wildlife managers, and conservation and agricultural extension practitioners) develop sustainable wildlife management schemes for ecologically specialised and protected species in anthropogenic habitats. However, the economic and subsistence needs of local people, and the crop-raiding behaviour of sympatric wildlife, must be considered when assessing suitability of particular crops for conflict prevention and mitigation.

Estimating irrigation water use in the humid eastern United States

USGS Publications Warehouse

Levin, Sara B.; Zarriello, Phillip J.

2013-01-01

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. Only through additional collection of quality data and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern United States.

Using a geographic information system and scanning technology to create high-resolution land-use data sets

USGS Publications Warehouse

Harvey, Craig A.; Kolpin, Dana W.; Battaglin, William A.

1996-01-01

A geographic information system (GIS) procedure was developed to compile low-altitude aerial photography, digitized data, and land-use data from U.S. Department of Agriculture Consolidated Farm Service Agency (CFSA) offices into a high-resolution (approximately 5 meters) land-use GIS data set. The aerial photography consisted of 35-mm slides which were scanned into tagged information file format (TIFF) images. These TIFF images were then imported into the GIS where they were registered into a geographically referenced coordinate system. Boundaries between land use were delineated from these GIS data sets using on-screen digitizing techniques. Crop types were determined using information obtained from the U.S. Department of Agriculture CFSA offices. Crop information not supplied by the CFSA was attributed by manual classification procedures. Automated methods to provide delineation of the field boundaries and land-use classification were investigated. It was determined that using these data sources, automated methods were less efficient and accurate than manual methods of delineating field boundaries and classifying land use.

Economic evaluation: Concepts, selected studies, system costs, and a proposed program

NASA Technical Reports Server (NTRS)

Osterhoudt, F. H. (Principal Investigator)

1979-01-01

The more usual approaches to valuing crop information are reviewed and an integrated approach is recommended. Problems associated with implementation are examined. What has already been accomplished in the economic evaluation of LACIE-type information is reported including various studies of benefits. The costs of the existing and proposed systems are considered. A method and approach is proposed for further studies.

CROP type analysis using Landsat digital data

NASA Technical Reports Server (NTRS)

Brown, C. E.; Thomas, R. W.; Wall, S. L.

1981-01-01

Classification and statistical sampling techniques for crop type discrimination using Landsat digital data have been developed by the University of California in cooperation with NASA and the California Department of Water Resources. Ratioed bands (MSS 7/5 and 5/4) and a sun-angle corrected Euclidean albedo band were prepared from data for the Sacramento Valley for five different dates. The test area was stratified into general crop groupings based on the particular patterns of irrigation timing for each crop. Data classified within each stratum were used to produce a crop type map. Comparison with ground data indicates that certain crops and crop groups are discernable. Small grains and rice are easily identifiable, as are deciduous fruit varieties as a group. However, it is not feasible to separate various fruit and nut varieties, or separate vegetable crops with these techniques at present.

Food for Thought: Crop Yields in the Columbia River Basin in an Altered Future

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K.; Nelson, R.; Stockle, C.; Kruger, C.; Brady, M.; Adam, J. C.

2013-12-01

Growth of global population and food consumption in the next several decades is expected to result in a food security challenge. Strategies to address this challenge, such as enhancing agricultural productivity and resiliency, need to be considered within the context of a full range of plausible consequences so as to identify investments that create win-win-win scenarios for the environment, economy, and society. Regional earth systems models can provide the necessary scale-appropriate framework to inform the decision making context for adaptation strategies, especially in the context of global change. In an altered future, changes to climate, technology and socioeconomics affect regional agriculture both directly and indirectly. These effects are not independent and an integrated process-based model may better capture unanticipated non-linear and non-monotonic responses and feedbacks over time . BioEarth is a research initiative designed to explore the coupling of multiple stand-alone earth systems models to generate usable information for agricultural and natural resource decision making at the regional scale at decadal time-steps. This project focuses on the U.S. Pacific Northwest (PNW) region and is a framework that integrates atmospheric, terrestrial, aquatic, and economic models. We apply component models of BioEarth to the Columbia River basin in the PNW to study the direct and indirect impacts of climate change on regional irrigated and dryland crop yields for a variety of annual and perennial crops. Results indicate that the net effect of climate change on crop yields is dependent on the crop type. There is a negative effect of temperature on yields for most crops. Dryland winter wheat is a notable exception. With warming, although the available growing season increases, faster thermal accumulation results in a shorter time to maturity. Precipitation changes in the region have a positive impact on dryland agriculture. Carbon dioxide (CO2) fertilization has a positive impact on crop yields for most crops. This positive impact is minimal for corn which is a C4 crop that is already CO2 efficient. The net response is an increase in yields for dryland agriculture and depends on the crop type for irrigated agriculture. Although, climate change results in increased water shortages and water rights curtailment in the region, this does not translate into an increased negative effect on yields. This could be attributed to higher water use efficiency under elevated CO2 levels as well crops getting through growth stages earlier in the season with wetter spring conditions. The non linear and non monotonic nature of the response of climate change on crop yields is discussed. In accounting for biophysical effects of climate change on crop yields, socio-economic effects cannot be ignored because biophysical effects are nested with the framework of human decision making. We also discuss our results in the context of socioeconomic factors . Current results assume no adaptation strategies and incorporating this is our next step.

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.

Using Landsat satellite data to support pesticide exposure assessment in California.

PubMed

Maxwell, Susan K; Airola, Matthew; Nuckols, John R

2010-09-16

The recent U.S. Geological Survey policy offering Landsat satellite data at no cost provides researchers new opportunities to explore relationships between environment and health. The purpose of this study was to examine the potential for using Landsat satellite data to support pesticide exposure assessment in California. We collected a dense time series of 24 Landsat 5 and 7 images spanning the year 2000 for an agricultural region in Fresno County. We intersected the Landsat time series with the California Department of Water Resources (CDWR) land use map and selected field samples to define the phenological characteristics of 17 major crop types or crop groups. We found the frequent overpass of Landsat enabled detection of crop field conditions (e.g., bare soil, vegetated) over most of the year. However, images were limited during the winter months due to cloud cover. Many samples designated as single-cropped in the CDWR map had phenological patterns that represented multi-cropped or non-cropped fields, indicating they may have been misclassified. We found the combination of Landsat 5 and 7 image data would clearly benefit pesticide exposure assessment in this region by 1) providing information on crop field conditions at or near the time when pesticides are applied, and 2) providing information for validating the CDWR map. The Landsat image time-series was useful for identifying idle, single-, and multi-cropped fields. Landsat data will be limited during the winter months due to cloud cover, and for years prior to the Landsat 7 launch (1999) when only one satellite was operational at any given time. We suggest additional research to determine the feasibility of integrating CDWR land use maps and Landsat data to derive crop maps in locations and time periods where maps are not available, which will allow for substantial improvements to chemical exposure estimation.

Modeling crop residue burning experiments to evaluate smoke emissions and plume transport.

PubMed

Zhou, Luxi; Baker, Kirk R; Napelenok, Sergey L; Pouliot, George; Elleman, Robert; O'Neill, Susan M; Urbanski, Shawn P; Wong, David C

2018-06-15

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment in the Pacific Northwest focused on cropland residue burning was used to evaluate model performance in capturing surface and aloft impacts from the burning events. The Community Multiscale Air Quality (CMAQ) model was used to simulate multiple crop residue burns with 2 km grid spacing using field-specific information and also more general assumptions traditionally used to support National Emission Inventory based assessments. Field study specific information, which includes area burned, fuel consumption, and combustion completeness, resulted in increased biomass consumption by 123 tons (60% increase) on average compared to consumption estimated with default methods in the National Emission Inventory (NEI) process. Buoyancy heat flux, a key parameter for model predicted fire plume rise, estimated from fuel loading obtained from field measurements can be 30% to 200% more than when estimated using default field information. The increased buoyancy heat flux resulted in higher plume rise by 30% to 80%. This evaluation indicates that the regulatory air quality modeling system can replicate intensity and transport (horizontal and vertical) features for crop residue burning in this region when region-specific information is used to inform emissions and plume rise calculations. Further, previous vertical emissions allocation treatment of putting all cropland residue burning in the surface layer does not compare well with measured plume structure and these types of burns should be modeled more similarly to prescribed fires such that plume rise is based on an estimate of buoyancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

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

Hilker, Frank M.; Allen, Linda J. S.; Bokil, Vrushali A.

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with largemore » holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.« less

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

DOE PAGES

Hilker, Frank M.; Allen, Linda J. S.; Bokil, Vrushali A.; ...

2017-08-01

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with largemore » holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.« less

Beta vulgaris crop types: Genomic signatures of selection (GSS) using next generation sequencing of pooled samples

USDA-ARS?s Scientific Manuscript database

Beta vulgaris crop types represent highly diverged populations with distinct phenotypes resulting from long-term selection. Differential end use in the crop types includes: leaf quality (chard/leaf beet), root enlargement and biomass, (table beet, fodder beet, sugar beet), and secondary metabolite a...

Assessment of time-series MODIS data for cropland mapping in the U.S. central Great Plains

NASA Astrophysics Data System (ADS)

Masialeti, Iwake

This study had three general objectives. First, to explore ways of creating and refining a reference data set when reference data set is unobtainable. Second, extend work previously done in Kansas by Wardlow et al. (2007) to Nebraska, several exploratory approaches were used to further investigate the potential of MODIS NDVI 250-m data in agricultural-related land cover research other parts of the Great Plains. The objective of this part of the research was to evaluate the applicability of time-series MODIS 250-m NDVI data for crop-type discrimination by spectrally characterizing and discriminating major crop types in Nebraska using the reference data set collected and refined under research performed for the first objective. Third, conduct an initial investigation into whether time-series NDVI response curves for crops over a growing season for one year could be used to classify crops for a different year. In this case, time-series NDVI response curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 data set could be used to classify crops for 2005. GIS operations, and reference data refinement using clustering and visual assessment of each crop's NDVI cluster profiles in Nebraska, demonstrated that it is possible to devise an alternative reference data set and refinement plan that redresses the unexpected loss of training and validation data. The analysis enabled the identification and removal of crop pattern outliers and sites atypical of crop phenology under consideration, and after editing, a total of 1,288 field sites remained, which were used as a reference data set for classification of Nebraska crop types. A pixel-level analysis of the time-series MODIS 250-m NDVI for 1,288 field sites representing each of the eight cover types under investigation across Nebraska found that each crop type had a distinctive MODIS 250-m NDVI profile corresponding to the crop calendar. A visual and statistical comparison of the average NDVI profiles showed that the crop types were separable at different times of the growing season based on their phenology-driven spectral-temporal differences. Winter wheat and alfalfa, winter wheat and summer crops, and alfalfa and summer crops were clearly separable. Specific summer crop types were not easily distinguishable from each other due to their similar crop calendars. Their greatest separability however occurred during the initial spring green up and/or senescence plant growth phases. In Kansas, an initial investigation revealed that there was near-complete agreement between the winter wheat crop profiles but that there were some minor differences in the crop profiles for alfalfa and summer crops between 2001 and 2005. However, the profiles of summer crops---corn, grain sorghum, and soybeans---displayed a shift to the right by at least 1 composite date, indicative of possible late crop planting and emergence. Alfalfa and summer crops, seem to suggest that time series NDVI response curves for crops over a growing period for one year of valid ground reference data may not be used to map crops for a different year without taking into account the climatic and/or environmental conditions of each year.

Preliminary Evaluation of TM for Soils Information

NASA Technical Reports Server (NTRS)

Thompson, D. R.; Henderson, K. E.; Houston, A. G.; Pitts, D. E.

1984-01-01

Thematic mapper data acquired over Mississippi County, Arkansas, were examined for utility in separating soil associations within generally level alluvium deposited by the Mississippi River. The 0.76 to 0.90 micron (Band 4) and the 1.55 to 1.75 micron (Band 5) were found to separate the different soil associations fairly well when compared to the USDA-SCS general soil map. The thermal channel also appeared to provide information at this level. A detailed soil survey was available at the field level along with ground observations of crop type, plant height, percent cover and growth stage. Soils within the fields ranged from uniform to soils that occur as patches of sand that stand out strongly against the intermingled areas of dark soil. Examination of the digital values of individual TM bands at the field level indicates that the influence of the soil is greater in TM than it was in MSS bands. The TM appears to provide greater detail of within field variability caused by soils than MSS and thus should provide improved information relating to crop and soil properties. However, this soil influence may cause crop identification classification procedures to have to account for the soil in their algorithms.

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.

New Microwave-Based Missions Applications for Rainfed Crops Characterization

NASA Astrophysics Data System (ADS)

Sánchez, N.; Lopez-Sanchez, J. M.; Arias-Pérez, B.; Valcarce-Diñeiro, R.; Martínez-Fernández, J.; Calvo-Heras, J. M.; Camps, A.; González-Zamora, A.; Vicente-Guijalba, F.

2016-06-01

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

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.

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.

Plant/soil concentration ratios for paired field and garden crops, with emphasis on iodine and the role of soil adhesion.

PubMed

Sheppard, S C; Long, J M; Sanipelli, B

2010-12-01

In the effort to predict the risks associated with contaminated soils, considerable reliance is placed on plant/soil concentration ratio (CR) values measured at sites other than the contaminated site. This inevitably results in the need to extrapolate among the many soil and plant types. There are few studies that compare CR among plant types that encompass both field and garden crops. Here, CRs for 40 elements were measured for 25 crops from farm and garden sites chosen so the grain crops were in close proximity to the gardens. Special emphasis was placed on iodine (I) because data for this element are sparse. For many elements, there were consistent trends among CRs for the various crop types, with leafy crops > root crops ≥ fruit crops ≈ seed crops. Exceptions included CR values for As, K, Se and Zn which were highest in the seed crops. The correlation of CRs from one plant type to another was evident only when there was a wide range in soil concentrations. In comparing CRs between crop types, it became apparent that the relationships differed for the rare earth elements (REE), which also had very low CR values. The CRs for root and leafy crops of REE converged to a minimum value. This was attributed to soil adhesion, despite the samples being washed, and the average soil adhesion for root crops was 500 mg soil kg⁻¹ dry plant and for leafy crops was 5 g kg⁻¹. Across elements, the log CR was negatively correlated with log Kd (the soil solid/liquid partition coefficient), as expected. Although, this correlation is expected, measures of correlation coefficients suitable for stochastic risk assessment are not frequently reported. The results suggest that r ≈ -0.7 would be appropriate for risk assessment. Copyright © 2010 Elsevier Ltd. All rights reserved.

Exploring the Influence of Smallholders' Perceptions Regarding Water Availability on Crop Choice and Water Allocation Through Socio-Hydrological Modeling

NASA Astrophysics Data System (ADS)

Kuil, L.; Evans, T.; McCord, P. F.; Salinas, J. L.; Blöschl, G.

2018-04-01

While it is known that farmers adopt different decision-making behaviors to cope with stresses, it remains challenging to capture this diversity in formal model frameworks that are used to advance theory and inform policy. Guided by cognitive theory and the theory of bounded rationality, this research develops a novel, socio-hydrological model framework that can explore how a farmer's perception of water availability impacts crop choice and water allocation. The model is informed by a rich empirical data set at the household level collected during 2013 in Kenya's Upper Ewaso Ng'iro basin that shows that the crop type cultivated is correlated with water availability. The model is able to simulate this pattern and shows that near-optimal or "satisficing" crop patterns can emerge also when farmers were to make use of simple decision rules and have diverse perceptions on water availability. By focusing on farmer decision making it also captures the rebound effect, i.e., as additional water becomes available through the improvement of crop efficiencies it will be reallocated on the farm instead of flowing downstream, as a farmer will adjust his (her) water allocation and crop pattern to the new water conditions. This study is valuable as it is consistent with the theory of bounded rationality, and thus offers an alternative, descriptive model in addition to normative models. The framework can be used to understand the potential impact of climate change on the socio-hydrological system, to simulate and test various assumptions regarding farmer behavior and to evaluate policy interventions.

Development and implementation of a GEOGLAM Crop Monitor web interface

NASA Astrophysics Data System (ADS)

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

2016-12-01

Beginning in September 2013, the GEOGLAM Crop Monitor activity has provided earth observation (EO) data to a network of partners and collected crop assessments on a subnational basis through a web interface known as the Crop Assessment Tool. Based on the collection of monthly crop assessments, a monthly crop condition bulletin is published in the Agricultural Market Information System (AMIS) Market Monitor report. This workflow has been successfully applied to food security applications through the Early Warning Crop Monitor activity. However, a lack of timely and accurate information on crop conditions and prospects at the national scale is a critical issue in the majority of southern and eastern African countries and some South American countries. Such information is necessary for informed and prompt decision making in the face of emergencies, food insecurity and planning requirements for agricultural markets. This project addresses these needs through the development of relevant, user-friendly remote sensing monitor systems, collaborative internet technology, and collaboration with national and regional agricultural monitoring networks. By building on current projects and relationships established through the various GEOGLAM Crop Monitor activities, this project aims to ultimately provide EO-informed crop condition maps and charts designed for economics and policy oriented audiences, thereby providing quick and easy to understand products on crop conditions as the season progresses. Integrating these data and assessments vertically throughout the system provides a basis for regional sharing and collaboration in food security applications.

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Evaluating the economics of biomass energy production in the Watts Bar region

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

Alexander, R.R.; English, B.C.; Bhat, M.G.

1993-12-31

While the commercial potential of biofuel technology is becoming more feasible, it is not clear whether the supply of biomass feedstock will be available in competitive markets. In order to exploit the potential of biomass crops as a reliable source of biofuels, a significant commitment on the part of farmers to convert large amounts of cropland would be required. Dedicated energy crops have to compete with conventional crops which could result in significant interregional shifts in crop production. Those changes could further affect overall agricultural production, food prices, consumer spending, and government spending on farm programs. Evaluating these economic impactsmore » provides important information for the ongoing debate. This research is a case study incorporating an existing power plant. The objective of this project is to evaluate the potential of short rotation woody crops as a fuel source in the Watts Bar facility located in eastern Tennessee. The appraisal includes estimates of environmental impacts as well as of economic feasibility. This is achieved by estimating the amounts of biomass that would be supplied at a predetermined price. By changing prices of biomass at the plant in an incremental fashion, a regional supply curve for biomass is estimated. The model incorporates current agricultural production possibilities in the region along with the proposed short rotation woody crop production activities. In order to adequately model the landscape, several variables are considered. These variables include soil type, crop production, government policy, land use conversion to crop land, and distance from the plant. Environmental issues including erosion, chemical usage, and potential leaching are also incorporated within the modeling framework; however, only estimates on erosion are available in this analysis. Output from the model provides insight on where and what types of land should shift from current land use to biomass production.« less

Remote sensing based crop type mapping and evapotranspiration estimates at the farm level in arid regions of the globe

NASA Astrophysics Data System (ADS)

Ozdogan, M.; Serrat-Capdevila, A.; Anderson, M. C.

2017-12-01

Despite increasing scarcity of freshwater resources, there is dearth of spatially explicit information on irrigation water consumption through evapotranspiration, particularly in semi-arid and arid geographies. Remote sensing, either alone or in combination with ground surveys, is increasingly being used for irrigation water management by quantifying evaporative losses at the farm level. Increased availability of observations, sophisticated algorithms, and access to cloud-based computing is also helping this effort. This presentation will focus on crop-specific evapotranspiration estimates at the farm level derived from remote sensing in a number of water-scarce regions of the world. The work is part of a larger effort to quantify irrigation water use and improve use efficiencies associated with several World Bank projects. Examples will be drawn from India, where groundwater based irrigation withdrawals are monitored with the help of crop type mapping and evapotranspiration estimates from remote sensing. Another example will be provided from a northern irrigation district in Mexico, where remote sensing is used for detailed water accounting at the farm level. These locations exemplify the success stories in irrigation water management with the help of remote sensing with the hope that spatially disaggregated information on evapotranspiration can be used as inputs for various water management decisions as well as for better water allocation strategies in many other water scarce regions.

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

NASA Astrophysics Data System (ADS)

Sun, Liang; Anderson, Martha C.; Gao, Feng; Hain, Christopher; Alfieri, Joseph G.; Sharifi, Amirreza; McCarty, Gregory W.; Yang, Yun; Yang, Yang; Kustas, William P.; McKee, Lynn

2017-07-01

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. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step - an improvement from 27% errors using standard Landsat-only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed-scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

Anopheline larval habitats seasonality and species distribution: a prerequisite for effective targeted larval habitats control programmes.

PubMed

Kweka, Eliningaya J; Zhou, Guofa; Munga, Stephen; Lee, Ming-Chieh; Atieli, Harrysone E; Nyindo, Mramba; Githeko, Andrew K; Yan, Guiyun

2012-01-01

Larval control is of paramount importance in the reduction of malaria vector abundance and subsequent disease transmission reduction. Understanding larval habitat succession and its ecology in different land use managements and cropping systems can give an insight for effective larval source management practices. This study investigated larval habitat succession and ecological parameters which influence larval abundance in malaria epidemic prone areas of western Kenya. A total of 51 aquatic habitats positive for anopheline larvae were surveyed and visited once a week for a period of 85 weeks in succession. Habitats were selected and identified. Mosquito larval species, physico-chemical parameters, habitat size, grass cover, crop cycle and distance to nearest house were recorded. Polymerase chain reaction revealed that An. gambiae s.l was the most dominant vector species comprised of An.gambiae s.s (77.60%) and An.arabiensis (18.34%), the remaining 4.06% had no amplification by polymerase chain reaction. Physico-chemical parameters and habitat size significantly influenced abundance of An. gambiae s.s (P = 0.024) and An. arabiensis (P = 0.002) larvae. Further, larval species abundance was influenced by crop cycle (P≤0.001), grass cover (P≤0.001), while distance to nearest houses significantly influenced the abundance of mosquito species larvae (r = 0.920;P≤0.001). The number of predator species influenced mosquito larval abundance in different habitat types. Crop weeding significantly influenced with the abundance of An.gambiae s.l (P≤0.001) when preceded with fertilizer application. Significantly higher anopheline larval abundance was recorded in habitats in pasture compared to farmland (P = 0.002). When habitat stability and habitat types were considered, hoof print were the most productive followed by disused goldmines. These findings suggest that implementation of effective larval control programme should be targeted with larval habitats succession information when larval habitats are fewer and manageable. Crop cycles and distance from habitats to household should be considered as effective information in planning larval control.

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..

Information extraction with object based support vector machines and vegetation indices

NASA Astrophysics Data System (ADS)

Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

2016-07-01

Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

7 CFR 1221.6 - Crop year.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false Crop year. 1221.6 Section 1221.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... INFORMATION ORDER Sorghum Promotion, Research, and Information Order Definitions § 1221.6 Crop year. Crop year...

7 CFR 1221.6 - Crop year.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 10 2011-01-01 2011-01-01 false Crop year. 1221.6 Section 1221.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... INFORMATION ORDER Sorghum Promotion, Research, and Information Order Definitions § 1221.6 Crop year. Crop year...

Life history traits and phenotypic selection among sunflower crop-wild hybrids and their wild counterpart: implications for crop allele introgression.

PubMed

Kost, Matthew A; Alexander, Helen M; Jason Emry, D; Mercer, Kristin L

2015-06-01

Hybridization produces strong evolutionary forces. In hybrid zones, selection can differentially occur on traits and selection intensities may differ among hybrid generations. Understanding these dynamics in crop-wild hybrid zones can clarify crop-like traits likely to introgress into wild populations and the particular hybrid generations through which introgression proceeds. In a field experiment with four crop-wild hybrid Helianthus annuus (sunflower) cross types, we measured growth and life history traits and performed phenotypic selection analysis on early season traits to ascertain the likelihood, and routes, of crop allele introgression into wild sunflower populations. All cross types overwintered, emerged in the spring, and survived until flowering, indicating no early life history barriers to crop allele introgression. While selection indirectly favored earlier seedling emergence and taller early season seedlings, direct selection only favored greater early season leaf length. Further, there was cross type variation in the intensity of selection operating on leaf length. Thus, introgression of multiple early season crop-like traits, due to direct selection for greater early season leaf length, should not be impeded by any cross type and may proceed at different rates among generations. In sum, alleles underlying early season sunflower crop-like traits are likely to introgress into wild sunflower populations.

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.

Mapping Cropland and Crop-type Distribution Using Time Series MODIS Data

NASA Astrophysics Data System (ADS)

Lu, D.; Chen, Y.; Moran, E. F.; Batistella, M.; Luo, L.; Pokhrel, Y.; Deb, K.

2016-12-01

Mapping regional and global cropland distribution has attracted great attention in the past decade, but the separation of crop types is challenging due to the spectral confusion and cloud cover problems during the growing season in Brazil. The objective of this study is to develop a new approach to identify crop types (including soybean, cotton, maize) and planting patterns (soybean-maize, soybean-cotton, and single crop) in Mato Grosso, Goias and Tocantins States, Brazil. The time series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (MOD13Q1) in 2015/2016 were used in this research and field survey data were collected in May 2016. The major steps include: (1) reconstruct time series NDVI data contaminated by noise and clouds using the temporal interpolation algorithm; (2) identify the best periods and develop temporal indices and phenology parameters to distinguish cropland from other land cover types based on time series NDVI data; (3) develop a crop temporal difference index (CTDI) to extract crop types and patterns using time series NDVI data. This research shows that (1) the cropland occupied approximately 16.85% of total land in these three states; (2) soybean-maize and soybean-cotton were two major crop patterns which occupied 54.80% and 19.30% of total cropland area. This research indicates that the proposed approach is promising for accurately and rapidly mapping cropland and crop-type distribution in these three states of Brazil.

Biological Implications in Cassava for the Production of Amylose-Free Starch: Impact on Root Yield and Related Traits

PubMed Central

Karlström, Amanda; Calle, Fernando; Salazar, Sandra; Morante, Nelson; Dufour, Dominique; Ceballos, Hernán

2016-01-01

Cassava (Manihot esculenta, Crantz) is an important food security crop, but it is becoming an important raw material for different industrial applications. Cassava is the second most important source of starch worldwide. Novel starch properties are of interest to the starch industry, and one them is the recently identified amylose-free (waxy) cassava starch. Waxy mutants have been found in different crops and have been often associated with a yield penalty. There are ongoing efforts to develop commercial cassava varieties with amylose-free starch. However, little information is available regarding the biological and agronomic implications of starch mutations in cassava, nor in other root and tuber crops. In this study, siblings from eight full-sib families, segregating for the waxy trait, were used to determine if the mutation has implications for yield, dry matter content (DMC) and harvest index in cassava. A total of 87 waxy and 87 wild-type starch genotypes from the eight families were used in the study. The only significant effect of starch type was on DMC (p < 0.01), with waxy clones having a 0.8% lower content than their wild type counterparts. There was no effect of starch type on fresh root yield (FRY), adjusted FRY and harvest index. It is not clear if lower DMC is a pleiotropic effect of the waxy starch mutation or else the result of linked genes introgressed along with the mutation. It is expected that commercial waxy cassava varieties will have competitive FRYs but special efforts will be required to attain adequate DMCs. This study contributes to the limited knowledge available of the impact of starch mutations on the agronomic performance of root and tuber crops. PMID:27242813

Crop Frequency Mapping for Land Use Intensity Estimation During Three Decades

NASA Astrophysics Data System (ADS)

Schmidt, Michael; Tindall, Dan

2016-08-01

Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, food security and sustainability of agricultural practices. Such spatial datasets are likely to support decisions on natural resource management, planning and policy. The complete Landsat Time Series (LTS) archive for 23 Landsat footprints in western Queensland from 1987 to 2015 was used in a multi-temporal mapping approach. Spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by on ground training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons for each year were summarised, and combined with various vegetation indices and band ratios computed from a mid-season spectral-composite image. All available temporal information was spatially aggregated to the scale of image segments in the mid- season composite for each growing season and used to train a random forest classifier for a Crop and No- Crop classification. Validation revealed that the predictive accuracy varied by growing season and region to be within k = 0.88 to 0.97 and are thus suitable for mapping current and historic cropping activity. Crop frequency maps were produced for all regions at different time intervals. The crop frequency maps were validated separately with a historic crop information time series. Different land use intensities and conversions e.g. from agricultural to pastures are apparent and potential drivers of these conversions are discussed.

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.

USSR Report, Consumer Goods and Domestic Trade, No. 65

DTIC Science & Technology

1983-06-08

types of oil-bearing raw materials— grape seeds , fruit and tree-fruit pits, corn germs and others. In recent years the ties of the workers of oils and...brief, indicate how the original information was processed. Where no processing indicator is given, the information was summarized or extracted ...good crop. More than 2 million tons of grapes were harvested for the first time. Public education, science and culture underwent further development

Drought Effects on Agricultural Yield: Comparison Across Regions and Crop Types

NASA Astrophysics Data System (ADS)

Daryanto, S.; Wang, L.; Jacinthe, P. A.

2014-12-01

Global agricultural production is dominated by rainfed agriculture, and is therefore prone to disruption from climate extreme weathers. These uncertainties become more problematic when considering the projection of increased drought frequency suggested by several climate models for various world regions. Curiously, few regional analyses of drought impact of food production have been attempted. We collated and analyzed data from the last 25 years to disentangle the effects of drought (i.e. timing, intensity and duration) on agricultural production in different eco-regions and with varying crop types. Our preliminary results suggested greater yield reduction in annual (-21.5%) than perennial plants (-16%), in C4 (-21%) compared to C3 crops (-17%), and when drought occurred during generative (i.e. flowering until maturity; -16.5%) than vegetative stage (-15.5%). Although drought caused similar amounts of yield reduction in both tropical and subtropical regions (i.e. -17%), it carries a greater food security risk in the tropics due to inherently low productivity (i.e. less than half than in the subtropical regions). Consequently, cultivating drought-resistant C3 perennial plants (e.g. sweet potato and cassava) in the tropics could prove a viable adaptive strategy to mitigate the effects of climate variability. In addition, these crops have limited input requirements, are well adapted to nutrient-poor Oxisols and Ultisols of the tropics, and generally outyield cereal crops in the region. Our analysis is ongoing and needs to take into account agronomic traits (e.g. water requirement), as well as the energy and nutritional values (e.g. protein, minerals) of alternative crops. Our results could inform the selection and development of new cultivars for the drought-prone regions of the world.

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.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Knowlton, D. J.; Dean, M. E.

1981-01-01

Supervised and cluster block training statistics were used to analyze the thematic mapper simulation MSS data (both 1979 and 1980 data sets). Cover information classes identified on SAR imagery include: hardwood, pine, mixed pine hardwood, clearcut, pasture, crops, emergent crops, bare soil, urban, and water. Preliminary analysis of the HH and HV polarized SAR data indicate a high variance associated with each information class except for water and bare soil. The large variance for most spectral classes suggests that while the means might be statistically separable, an overlap may exist between the classes which could introduce a significant classification error. The quantitative values of many cover types are much larger on the HV polarization than on the HH, thereby indicating the relative nature of the digitized data values. The mean values of the spectral classes in the areas with larger look angles are greater than the means of the same cover type in other areas having steeper look angles. Difficulty in accurately overlaying the dual polarization of the SAR data was resolved.

United States benefits of improved worldwide wheat crop information from a LANDSAT system

NASA Technical Reports Server (NTRS)

Heiss, K. P.; Sand, F.; Seidel, A.; Warner, D.; Sheflin, N.; Bhattacharyya, R.; Andrews, J.

1975-01-01

The value of worldwide information improvements on wheat crops, promised by LANDSAT, is measured in the context of world wheat markets. These benefits are based on current LANDSAT technical goals and assume that information is made available to all (United States and other countries) at the same time. A detailed empirical sample demonstration of the effect of improved information is given; the history of wheat commodity prices for 1971-72 is reconstructed and the price changes from improved vs. historical information are compared. The improved crop forecasting from a LANDSAT system assumed include wheat crop estimates of 90 percent accuracy for each major wheat producing region. Accurate, objective worldwide wheat crop information using space systems may have a very stabilizing influence on world commodity markets, in part making possible the establishment of long-term, stable trade relationships.

Analysis of Brassica oleracea early stage abiotic stress responses reveals tolerance in multiple crop types and for multiple sources of stress.

PubMed

Beacham, Andrew M; Hand, Paul; Pink, David Ac; Monaghan, James M

2017-12-01

Brassica oleracea includes a number of important crop types such as cabbage, cauliflower, broccoli and kale. Current climate conditions and weather patterns are causing significant losses in these crops, meaning that new cultivars with improved tolerance of one or more abiotic stress types must be sought. In this study, genetically fixed B. oleracea lines belonging to a Diversity Fixed Foundation Set (DFFS) were assayed for their response to seedling stage-imposed drought, flood, salinity, heat and cold stress. Significant (P ≤ 0.05) variation in stress tolerance response was found for each stress, for each of four measured variables (relative fresh weight, relative dry weight, relative leaf number and relative plant height). Lines tolerant to multiple stresses were found to belong to several different crop types. There was no overall correlation between the responses to the different stresses. Abiotic stress tolerance was identified in multiple B. oleracea crop types, with some lines exhibiting resistance to multiple stresses. For each stress, no one crop type appeared significantly more or less tolerant than others. The results are promising for the development of more environmentally robust lines of different B. oleracea crops by identifying tolerant material and highlighting the relationship between responses to different stresses. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya.

PubMed

Hilker, Frank M; Allen, Linda J S; Bokil, Vrushali A; Briggs, Cheryl J; Feng, Zhilan; Garrett, Karen A; Gross, Louis J; Hamelin, Frédéric M; Jeger, Michael J; Manore, Carrie A; Power, Alison G; Redinbaugh, Margaret G; Rúa, Megan A; Cunniffe, Nik J

2017-10-01

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production.

PubMed

Kukal, Meetpal S; Irmak, Suat

2018-02-22

Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.

Application of modified VICAR/IBIS GIS to analysis of July 1991 Flevoland AIRSAR data

NASA Technical Reports Server (NTRS)

Norikane, L.; Broek, B.; Freeman, A.

1992-01-01

Three overflights of the Flevoland calibration/agricultural site were made by the JPL Airborne Synthetic Aperture Radar (AIRSAR) on 3, 12, and 28 July 1991 as part of MAC-Europe '92. A polygon map was generated at TNO-FEL which overlayed the slant range projected July 3 data set. Each polygon was identified by a sequence of points and a crop label. The polygon map was composed of 452 uniquely identified polygons and 15 different crop types. Analysis of the data was done using our modified Video Image Communication and Retrieval/Image Based Information System Geographic Information System (VICAR/IBIS GIS). This GIS is an extension of the VICAR/IBIS GIS first developed by Bryant in the 1970's which is itself an extension of the VICAR image processing system also developed at JPL.

Heavy Metal Contamination of Vegetables Irrigated by Urban Stormwater: A Matter of Time?

PubMed Central

Tom, Minna; Fletcher, Tim D.; McCarthy, David T.

2014-01-01

Urban stormwater is a crucial resource at a time when climate change and population growth threaten freshwater supplies; but there are health risks from contaminants, such as toxic metals. It is vitally important to understand how to use this resource safely and responsibly. Our study investigated the extent of metal contamination in vegetable crops irrigated with stormwater under short- and long-term conditions. We created artificially aged gardens by adding metal-contaminated sediment to soil, simulating accumulation of metals in the soil from irrigation with raw stormwater over zero, five and ten years. Our crops - French bean (Phaseolus vulgaris), kale (Brassica oleracea var. acephala), and beetroot (Beta vulgaris) - were irrigated twice a week for 11 weeks, with either synthetic stormwater or potable water. They were then tested for concentrations of Cd, Cr, Pb, Cu and Zn. An accumulation of Pb was the most marked sign of contamination, with six of nine French bean and seven of nine beetroot leaf samples breaching Australia's existing guidelines. Metal concentration in a crop tended to increase with the effective age of the garden; but importantly, its rate of increase did not match the rate of increase in the soil. Our study also highlighted differences in sensitivity between different crop types. French bean demonstrated the highest levels of uptake, while kale displayed restrictive behaviour. Our study makes it clear: irrigation with stormwater is indeed feasible, as long as appropriate crops are selected and media are frequently turned over. We have also shown that an understanding of such risks yields meaningful information on appropriate safeguards. A holistic approach is needed - to account for all routes to toxic metal exposure, including especially Pb. A major outcome of our study is critical information for minimising health risks from stormwater irrigation of crops. PMID:25426946

Heavy metal contamination of vegetables irrigated by urban stormwater: a matter of time?

PubMed

Tom, Minna; Fletcher, Tim D; McCarthy, David T

2014-01-01

Urban stormwater is a crucial resource at a time when climate change and population growth threaten freshwater supplies; but there are health risks from contaminants, such as toxic metals. It is vitally important to understand how to use this resource safely and responsibly. Our study investigated the extent of metal contamination in vegetable crops irrigated with stormwater under short- and long-term conditions. We created artificially aged gardens by adding metal-contaminated sediment to soil, simulating accumulation of metals in the soil from irrigation with raw stormwater over zero, five and ten years. Our crops--French bean (Phaseolus vulgaris), kale (Brassica oleracea var. acephala), and beetroot (Beta vulgaris)--were irrigated twice a week for 11 weeks, with either synthetic stormwater or potable water. They were then tested for concentrations of Cd, Cr, Pb, Cu and Zn. An accumulation of Pb was the most marked sign of contamination, with six of nine French bean and seven of nine beetroot leaf samples breaching Australia's existing guidelines. Metal concentration in a crop tended to increase with the effective age of the garden; but importantly, its rate of increase did not match the rate of increase in the soil. Our study also highlighted differences in sensitivity between different crop types. French bean demonstrated the highest levels of uptake, while kale displayed restrictive behaviour. Our study makes it clear: irrigation with stormwater is indeed feasible, as long as appropriate crops are selected and media are frequently turned over. We have also shown that an understanding of such risks yields meaningful information on appropriate safeguards. A holistic approach is needed--to account for all routes to toxic metal exposure, including especially Pb. A major outcome of our study is critical information for minimising health risks from stormwater irrigation of crops.

Spatial estimation from remotely sensed data via empirical Bayes models

NASA Technical Reports Server (NTRS)

Hill, J. R.; Hinkley, D. V.; Kostal, H.; Morris, C. N.

1984-01-01

Multichannel satellite image data, available as LANDSAT imagery, are recorded as a multivariate time series (four channels, multiple passovers) in two spatial dimensions. The application of parametric empirical Bayes theory to classification of, and estimating the probability of, each crop type at each of a large number of pixels is considered. This theory involves both the probability distribution of imagery data, conditional on crop types, and the prior spatial distribution of crop types. For the latter Markov models indexed by estimable parameters are used. A broad outline of the general theory reveals several questions for further research. Some detailed results are given for the special case of two crop types when only a line transect is analyzed. Finally, the estimation of an underlying continuous process on the lattice is discussed which would be applicable to such quantities as crop yield.

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...

Forecasting model for Pea seed-borne mosaic virus epidemics in field pea crops in a Mediterranean-type environment.

PubMed

Congdon, B S; Coutts, B A; Jones, R A C; Renton, M

2017-09-15

An empirical model was developed to forecast Pea seed-borne mosaic virus (PSbMV) incidence at a critical phase of the annual growing season to predict yield loss in field pea crops sown under Mediterranean-type conditions. The model uses pre-growing season rainfall to calculate an index of aphid abundance in early-August which, in combination with PSbMV infection level in seed sown, is used to forecast virus crop incidence. Using predicted PSbMV crop incidence in early-August and day of sowing, PSbMV transmission from harvested seed was also predicted, albeit less accurately. The model was developed so it provides forecasts before sowing to allow sufficient time to implement control recommendations, such as having representative seed samples tested for PSbMV transmission rate to seedlings, obtaining seed with minimal PSbMV infection or of a PSbMV-resistant cultivar, and implementation of cultural management strategies. The model provides a disease forecast risk indication, taking into account predicted percentage yield loss to PSbMV infection and economic factors involved in field pea production. This disease risk forecast delivers location-specific recommendations regarding PSbMV management to end-users. These recommendations will be delivered directly to end-users via SMS alerts with links to web support that provide information on PSbMV management options. This modelling and decision support system approach would likely be suitable for use in other world regions where field pea is grown in similar Mediterranean-type environments. Copyright © 2017 Elsevier B.V. All rights reserved.

Tillage practices in the conterminous United States, 1989-2004-Datasets Aggregated by Watershed

USGS Publications Warehouse

Baker, Nancy T.

2011-01-01

This report documents the methods used to aggregate county-level tillage practices to the 8-digit hydrologic unit (HU) watershed. The original county-level data were collected by the Conservation Technology Information Center (CTIC). The CTIC collects tillage data by conducting surveys about tillage systems for all counties in the United States. Tillage systems include three types of conservation tillage (no-till, ridge-till, and mulch-till), reduced tillage, and intensive tillage. Total planted acreage for each tillage practice for each crop grown is reported to the CTIC. The dataset includes total planted acreage by tillage type for selected crops (corn, cotton, grain sorghum, soybeans, fallow, forage, newly established permanent pasture, spring and fall seeded small grains, and 'other' crops) for 1989-2004. Two tabular datasets, based on the 1992 enhanced and 2001 National Land Cover Data (NLCD), are provided as part of this report and include the land-cover area-weighted interpolation and aggregation of acreage for each tillage practice in each 8-digit HU watershed in the conterminous United States for each crop. Watershed aggregations were done by overlying the 8-digit HU polygons with a raster of county boundaries and a raster of either the enhanced 1992 or the 2001 NLCD for cultivated land to derive a county/land-cover area weighting factor. The weighting factor then was applied to the county-level tillage data for the counties within each 8-digit HU and summed to yield the total acreage of each tillage type within each 8-digit HU watershed.

Orphan Crops Browser: a bridge between model and orphan crops.

PubMed

Kamei, Claire Lessa Alvim; Severing, Edouard I; Dechesne, Annemarie; Furrer, Heleen; Dolstra, Oene; Trindade, Luisa M

2016-01-01

Many important crops have received little attention by the scientific community, either because they are not considered economically important or due to their large and complex genomes. De novo transcriptome assembly, using next-generation sequencing data, is an attractive option for the study of these orphan crops. In spite of the large amount of sequencing data that can be generated, there is currently a lack of tools which can effectively help molecular breeders and biologists to mine this type of information. Our goal was to develop a tool that enables molecular breeders, without extensive bioinformatics knowledge, to efficiently study de novo transcriptome data from any orphan crop (http://www.bioinformatics.nl/denovobrowser/db/species/index). The Orphan Crops Browser has been designed to facilitate the following tasks (1) search and identification of candidate transcripts based on phylogenetic relationships between orthologous sequence data from a set of related species and (2) design specific and degenerate primers for expression studies in the orphan crop of interest. To demonstrate the usability and reliability of the browser, it was used to identify the putative orthologues of 17 known lignin biosynthetic genes from maize and sugarcane in the orphan crop Miscanthus sinensis . Expression studies in miscanthus stem internode tissue differing in maturation were subsequently carried out, to follow the expression of these genes during lignification. Our results showed a negative correlation between lignin content and gene expression. The present data are in agreement with recent findings in maize and other crops, and it is further discussed in this paper.

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.

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

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 ...

Multivariate ordination identifies vegetation types associated with spider conservation in brassica crops

PubMed Central

Saqib, Hafiz Sohaib Ahmed; You, Minsheng

2017-01-01

Conservation biological control emphasizes natural and other non-crop vegetation as a source of natural enemies to focal crops. There is an unmet need for better methods to identify the types of vegetation that are optimal to support specific natural enemies that may colonize the crops. Here we explore the commonality of the spider assemblage—considering abundance and diversity (H)—in brassica crops with that of adjacent non-crop and non-brassica crop vegetation. We employ spatial-based multivariate ordination approaches, hierarchical clustering and spatial eigenvector analysis. The small-scale mixed cropping and high disturbance frequency of southern Chinese vegetation farming offered a setting to test the role of alternate vegetation for spider conservation. Our findings indicate that spider families differ markedly in occurrence with respect to vegetation type. Grassy field margins, non-crop vegetation, taro and sweetpotato harbour spider morphospecies and functional groups that are also present in brassica crops. In contrast, pumpkin and litchi contain spiders not found in brassicas, and so may have little benefit for conservation biological control services for brassicas. Our findings also illustrate the utility of advanced statistical approaches for identifying spatial relationships between natural enemies and the land uses most likely to offer alternative habitats for conservation biological control efforts that generates testable hypotheses for future studies. PMID:29085741

Analysis of thematic mapper simulator data collected over eastern North Dakota

NASA Technical Reports Server (NTRS)

Anderson, J. E. (Principal Investigator)

1982-01-01

The results of the analysis of aircraft-acquired thematic mapper simulator (TMS) data, collected to investigate the utility of thematic mapper data in crop area and land cover estimates, are discussed. Results of the analysis indicate that the seven-channel TMS data are capable of delineating the 13 crop types included in the study to an overall pixel classification accuracy of 80.97% correct, with relative efficiencies for four crop types examined between 1.62 and 26.61. Both supervised and unsupervised spectral signature development techniques were evaluated. The unsupervised methods proved to be inferior (based on analysis of variance) for the majority of crop types considered. Given the ground truth data set used for spectral signature development as well as evaluation of performance, it is possible to demonstrate which signature development technique would produce the highest percent correct classification for each crop type.

MaizeGDB: The Maize Genetics and Genomics Database.

PubMed

Harper, Lisa; Gardiner, Jack; Andorf, Carson; Lawrence, Carolyn J

2016-01-01

MaizeGDB is the community database for biological information about the crop plant Zea mays. Genomic, genetic, sequence, gene product, functional characterization, literature reference, and person/organization contact information are among the datatypes stored at MaizeGDB. At the project's website ( http://www.maizegdb.org ) are custom interfaces enabling researchers to browse data and to seek out specific information matching explicit search criteria. In addition, pre-compiled reports are made available for particular types of data and bulletin boards are provided to facilitate communication and coordination among members of the community of maize geneticists.

Annual crop type classification of the U.S. Great Plains for 2000 to 2011

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.

2014-01-01

The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophysical environmental characteristics. This study accounted for the most prevalent crop types in the region, including, corn, soybeans, winter wheat, spring wheat, cotton, sorghum, and alfalfa. Annual CTM crop maps of the US Great Plains were created for 2000 to 2011 at a spatial resolution of 250 meters. The CTM achieved an 87 percent classification success rate on 1.8 million observation points that were withheld from model training. Product validation was performed on greater than 15,000 county records with a coefficient of determination of R2 = 0.76.

Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2018-01-01

Switchgrass (Panicum virgatum) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e. it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e. NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario‐based (‘A1B’) land use and land cover map for 2050, the US Geological Survey crop type and Compound Topographic Index (CTI) maps, and long‐term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2528 km2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4232 km2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development.

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.

A generic probability based model to derive regional patterns of crops in time and space

NASA Astrophysics Data System (ADS)

Wattenbach, Martin; Luedtke, Stefan; Redweik, Richard; van Oijen, Marcel; Balkovic, Juraj; Reinds, Gert Jan

2015-04-01

Croplands are not only the key to human food supply, they also change the biophysical and biogeochemical properties of the land surface leading to changes in the water cycle, energy portioning, they influence soil erosion and substantially contribute to the amount of greenhouse gases entering the atmosphere. The effects of croplands on the environment depend on the type of crop and the associated management which both are related to the site conditions, economic boundary settings as well as preferences of individual farmers. The method described here is designed to predict the most probable crop to appear at a given location and time. The method uses statistical crop area information on NUTS2 level from EUROSTAT and the Common Agricultural Policy Regionalized Impact Model (CAPRI) as observation. These crops are then spatially disaggregated to the 1 x 1 km grid scale within the region, using the assumption that the probability of a crop appearing at a given location and a given year depends on a) the suitability of the land for the cultivation of the crop derived from the MARS Crop Yield Forecast System (MCYFS) and b) expert knowledge of agricultural practices. The latter includes knowledge concerning the feasibility of one crop following another (e.g. a late-maturing crop might leave too little time for the establishment of a winter cereal crop) and the need to combat weed infestations or crop diseases. The model is implemented in R and PostGIS. The quality of the generated crop sequences per grid cell is evaluated on the basis of the given statistics reported by the joint EU/CAPRI database. The assessment is given on NUTS2 level using per cent bias as a measure with a threshold of 15% as minimum quality. The results clearly indicates that crops with a large relative share within the administrative unit are not as error prone as crops that allocate only minor parts of the unit. However, still roughly 40% show an absolute per cent bias above the 15% threshold. This highlights the discrepancy between the best practice given the soil properties within an administrative unit and the effectively cultivated crops.

Manual LANDSAT data analysis for crop type identification

NASA Technical Reports Server (NTRS)

Hay, C. M. (Principal Investigator)

1979-01-01

The process of manual identification of crop type by human analysts and problems associated in LACIE that were associated with manual crop identification measurement procedures are described. Research undertaken in cooperation with LACIE operations by the supporting research community to effect solutions to, or obtain greater understanding of the problems is discussed.

Planter closing wheel effects on cotton emergence in a conservation tillage system

USDA-ARS?s Scientific Manuscript database

Closing wheels on a row crop planter help provide good seed-soil contact during planting and can influence emergence and crop stand. Various types of closing wheels are available to producers for use on planters. Seven closing wheel types were used on a row crop planter planting cotton in a conser...

United States benefits of improved worldwide wheat crop information from a LANDSAT system overview

NASA Technical Reports Server (NTRS)

1976-01-01

The value of improvements in worldwide information on wheat crops provided by LANDSAT was measured in the context of world wheat markets. These benefits were based on exiting LANDSAT technical goals and assumed that information would be made available to the United States and other countries at the same time. The benefits to the United States of such public LANDSAT information on wheat crops were found to be 174 million dollars a year on the average. The benefits from improved wheat crop information compare favorably with the annual system's cost of about $62 million. A detailed empirical sample demonstration of the effect of improved information was developed. The history of wheat commodity prices for 1971-72 was reconstructed and the price changes from improved vs. historical information were compared.

A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, Katherine M.; Bell, Andrew M.; Barber, Quinn E.; Kourakos, George; Harter, Thomas

2018-05-01

This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Nitrogen loading rates for several crop types have been measured based on field-scale experiments, and recent research has calculated nitrogen loading rates for crops throughout the Central Valley based on a mass balance approach. However, research is lacking to infer nitrogen loading rates for the broad diversity of crop and land-use types directly from groundwater nitrate measurements. Relating groundwater nitrate measurements to specific crops must account for the uncertainty about and multiplicity in contributing crops (and other land uses) to individual well measurements, and for the variability of nitrogen loading within farms and from farm to farm for the same crop type. In this study, we developed a Bayesian regression model that allowed us to estimate land-use-specific groundwater nitrogen loading rate probability distributions for 15 crop and land-use groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water and natural, rice, and alfalfa and pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kg N ha-1 yr-1. Confined animal feeding operations (dairies) and citrus and subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kg N ha-1 yr-1, respectively. In general, our probability-based estimates compare favorably with previous direct measurements and with mass-balance-based estimates of nitrogen loading. Nitrogen mass-balance-based estimates are larger than our groundwater nitrate derived estimates for manured and nonmanured forage, nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to be due to groundwater age mixing, dilution from infiltrating river water, or denitrification between the time when nitrogen leaves the root zone (point of reference for mass-balance-derived loading) and the time and location of groundwater measurement.

Identifying sources of groundwater nitrate contamination in a large alluvial groundwater basin with highly diversified intensive agricultural production

NASA Astrophysics Data System (ADS)

Lockhart, K. M.; King, A. M.; Harter, T.

2013-08-01

Groundwater quality is a concern in alluvial aquifers underlying agricultural areas worldwide. Nitrate from land applied fertilizers or from animal waste can leach to groundwater and contaminate drinking water resources. The San Joaquin Valley, California, is an example of an agricultural landscape with a large diversity of field, vegetable, tree, nut, and citrus crops, but also confined animal feeding operations (CAFOs, here mostly dairies) that generate, store, and land apply large amounts of liquid manure. As in other such regions around the world, the rural population in the San Joaquin Valley relies almost exclusively on shallow domestic wells (≤ 150 m deep), of which many have been affected by nitrate. Variability in crops, soil type, and depth to groundwater contribute to large variability in nitrate occurrence across the underlying aquifer system. The role of these factors in controlling groundwater nitrate contamination levels is examined. Two hundred domestic wells were sampled in two sub-regions of the San Joaquin Valley, Stanislaus and Merced (Stan/Mer) and Tulare and Kings (Tul/Kings) Counties. Forty six percent of well water samples in Tul/Kings and 42% of well water samples in Stan/Mer exceeded the MCL for nitrate (10 mg/L NO3-N). For statistical analysis of nitrate contamination, 78 crop and landuse types were considered by grouping them into ten categories (CAFO, citrus, deciduous fruits and nuts, field crops, forage, native, pasture, truck crops, urban, and vineyards). Vadose zone thickness, soil type, well construction information, well proximity to dairies, and dominant landuse near the well were considered. In the Stan/Mer area, elevated nitrate levels in domestic wells most strongly correlate with the combination of very shallow (≤ 21 m) water table and the presence of either CAFO derived animal waste applications or deciduous fruit and nut crops (synthetic fertilizer applications). In Tulare County, statistical data indicate that elevated nitrate levels in domestic well water are most strongly associated with citrus orchards when located in areas with a very shallow (≤ 21 m) water table. Kings County had relatively few nitrate MCL exceedances in domestic wells, probably due to the deeper water table in Kings County.

AIRBORNE REMOTELY SENSED INFORMATION FOR PESTICIDAL TRANSGENIC CROPS: HOW SPECTRAL IMAGING MAY PLAY A ROLE

EPA Science Inventory

The importance of sustainability to Bt crops. Resistance management as a sustainability strategy. The importance of data quality to sustainability and regulation of Bt Crops. Where information improvement can be useful across the globe.

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.

When should irrigators invest in more water-efficient technologies as an adaptation to climate change?

NASA Astrophysics Data System (ADS)

Malek, K.; Adam, J. C.; Stockle, C.; Brady, M.; Yoder, J.

2015-12-01

The western US is expected to experience more frequent droughts with higher magnitudes and persistence due to the climate change, with potentially large impacts on agricultural productivity and the economy. Irrigated farmers have many options for minimizing drought impacts including changing crops, engaging in water markets, and switching irrigation technologies. Switching to more efficient irrigation technologies, which increase water availability in the crop root zone through reduction of irrigation losses, receives significant attention because of the promise of maintaining current production with less. However, more efficient irrigation systems are almost always more capital-intensive adaptation strategy particularly compared to changing crops or trading water. A farmer's decision to switch will depend on how much money they project to save from reducing drought damages. The objective of this study is to explore when (and under what climate change scenarios) it makes sense economically for farmers to invest in a new irrigation system. This study was performed over the Yakima River Basin (YRB) in Washington State, although the tools and information gained from this study are transferable to other watersheds in the western US. We used VIC-CropSyst, a large-scale grid-based modeling framework that simulates hydrological processes while mechanistically capturing crop water use, growth and development. The water flows simulated by VIC-CropSyst were used to run the RiverWare river system and water management model (YAK-RW), which simulates river processes and calculates regional water availability for agricultural use each day (i.e., the prorationing ratio). An automated computational platform has been developed and programed to perform the economic analysis for each grid cell, crop types and future climate projections separately, which allows us to explore whether or not implementing a new irrigation system is economically viable. Results of this study indicate that climate change could justify the investment in new irrigation systems during this century, but the timing of a farmer's response is likely to depend on a variety of factors, including changes in the frequency and magnitude of drought events, current irrigation systems, climatological characteristics within the basin, and crop type.

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

NASA Astrophysics Data System (ADS)

Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine

2017-03-01

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.

Comparing soil functions for a wide range of agriculture soils focusing on production for bioenergy using a combined isotope-based observation and modelling approach

NASA Astrophysics Data System (ADS)

Leistert, Hannes; Herbstritt, Barbara; Weiler, Markus

2017-04-01

Increase crop production for bioenergy will result in changes in land use and the resulting soil functions and may generate new chances and risks. However, detailed data and information are still missing how soil function may be altered under changing crop productions for bioenergy, in particular for a wide range of agricultural soils since most data are currently derived from individual experimental sites studying different bioenergy crops at one location. We developed a new, rapid measurement approach to investigate the influence of bioenergy plants on the water cycle and different soil functions (filter and buffer of water and N-cycling). For this approach, we drilled 89 soil cores (1-3 m deep) in spring and fall at 11 sites with different soil properties and climatic conditions comparing different crops (grass, corn, willow, poplar, and other less common bioenergy crops) and analyzing 1150 soil samples for water content, nitrate concentration and stable water isotopes. We benchmarked a soil hydrological model (1-D numerical Richards equation, ADE, water isotope fractionation including liquid and vapor composition of isotopes) using longer-term climate variables and water isotopes in precipitation to derive crop specific parameterization and to specifically validate the differences in water transport and water partitioning into evaporation, transpiration and groundwater recharge among the sites and crops using the water isotopes in particular. The model simulation were in good agreement with the observed isotope profiles and allowed us to differentiate among the different crops. We defined different indicators for the soil functions considered in this study. These indicators included the proportion of groundwater recharge, transit time of water (different percentiles) though the upper 2m and nutrient leaching potential (e.g. nitrate) during the dormant season from the rooting zone. The parameterized model was first used to calculate the indicators for the sampled locations and to derive the changes in soil functions by altering the land cover among the different bioenergy crops in comparison to the grassland as a reference. We could show that percolation is strongly influenced by the crops and climate, the transit time is influenced by a combination of soil type, climate and land use, but the effect of soil type is very strong and the nitrate leaching is strongly influenced by soil type. The high variability of transit times and nitrate leaching are due to high variability of the temporal distribution of precipitation. Finally, the model was used to regionalized the indicators to a wide range of soils in the state of Baden-Württemberg and to assess if there are locations where bioenergy crops may improve the considered soil function. Our idea behind this was to propose location where specific bioenergy crops may be highly suitable to improve the current soil function to increase for example the protection of groundwater for drinking water, reduce erosion risk or increase water availability. The proposed method allows to assess the influence of different bioenergy crops on soil functions without costly multi-year measurement systems for assessing the soil functions using soil water content measurements or/and soil water suction devices.

Separation of man-made and natural patterns in high-altitude imagery of agricultural areas

NASA Technical Reports Server (NTRS)

Samulon, A. S.

1975-01-01

A nonstationary linear digital filter is designed and implemented which extracts the natural features from high-altitude imagery of agricultural areas. Essentially, from an original image a new image is created which displays information related to soil properties, drainage patterns, crop disease, and other natural phenomena, and contains no information about crop type or row spacing. A model is developed to express the recorded brightness in a narrow-band image in terms of man-made and natural contributions and which describes statistically the spatial properties of each. The form of the minimum mean-square error linear filter for estimation of the natural component of the scene is derived and a suboptimal filter is implemented. Nonstationarity of the two-dimensional random processes contained in the model requires a unique technique for deriving the optimum filter. Finally, the filter depends on knowledge of field boundaries. An algorithm for boundary location is proposed, discussed, and implemented.

Emergent insect production in post-harvest flooded agricultural fields used by waterbirds

USGS Publications Warehouse

Moss, Richard C.; Blumenshine, Steven C.; Yee, Julie; Fleskes, Joseph P.

2009-01-01

California’s Tulare Lake Basin (TLB) is one of the most important waterbird areas in North America even though most wetlands there have been converted to cropland. To guide management programs promoting waterbird beneficial agriculture, which includes flooding fields between growing periods, we measured emergence rates of insects, an important waterbird food, in three crop types (tomato, wheat, alfalfa) in the TLB relative to water depth and days flooded during August–October, 2003 and 2004. We used corrected Akaike’s Information Criterion values to compare a set of models that accounted for our repeated measured data. The best model included crop type and crop type interacting with days flooded and depth flooded. Emergence rates (mg m−2 day−1) were greater in tomato than wheat or alfalfa fields, increased with days flooded in alfalfa and tomato but not wheat fields, and increased with water depth in alfalfa and wheat but not tomato fields. To investigate the relationship between the range of diel water temperatures and insect emergence rates, we rearedChironomus dilutus larvae in environmental chambers under high (15–32°C) and low fluctuation (20–26°C) temperature regimes that were associated with shallow and deep (respectively) sampling sites in our fields. Larval survival (4×) and biomass (2×) were greater in the low thermal fluctuation treatment suggesting that deeply flooded areas would support greater insect production.

Corn blight review: Sampling model and ground data measurements program

NASA Technical Reports Server (NTRS)

Allen, R. D.

1972-01-01

The sampling plan involved the selection of the study area, determination of the flightline and segment sample design within the study area, and determination of a field sample design. Initial interview survey data consisting of crop species acreage and land use were collected. On all corn fields, additional information such as seed type, row direction, population, planting date, ect. were also collected. From this information, sample corn fields were selected to be observed through the growing season on a biweekly basis by county extension personnel.

Satellite Remote Sensing of Cropland Characteristics in 30m Resolution: The First North American Continental-Scale Classification on High Performance Computing Platforms

NASA Astrophysics Data System (ADS)

Massey, Richard

Cropland characteristics and accurate maps of their spatial distribution are required to develop strategies for global food security by continental-scale assessments and agricultural land use policies. North America is the major producer and exporter of coarse grains, wheat, and other crops. While cropland characteristics such as crop types are available at country-scales in North America, however, at continental-scale cropland products are lacking at fine sufficient resolution such as 30m. Additionally, applications of automated, open, and rapid methods to map cropland characteristics over large areas without the need of ground samples are needed on efficient high performance computing platforms for timely and long-term cropland monitoring. In this study, I developed novel, automated, and open methods to map cropland extent, crop intensity, and crop types in the North American continent using large remote sensing datasets on high-performance computing platforms. First, a novel method was developed in this study to fuse pixel-based classification of continental-scale Landsat data using Random Forest algorithm available on Google Earth Engine cloud computing platform with an object-based classification approach, recursive hierarchical segmentation (RHSeg) to map cropland extent at continental scale. Using the fusion method, a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010 was produced. In this map, the total cropland area for North America was estimated at 275.2 million hectares (Mha). This map was assessed for accuracy using randomly distributed samples derived from United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-Food Canada (AAFC) annual crop inventory (ACI), Servicio de Informacion Agroalimentaria y Pesquera (SIAP), Mexico's agricultural boundaries, and photo-interpretation of high-resolution imagery. The overall accuracies of the map are 93.4% with a producer's accuracy for crop class at 85.4% and user's accuracy of 74.5% across the continent. The sub-country statistics including state-wise and county-wise cropland statistics derived from this map compared well in regression models resulting in R2 > 0.84. Secondly, an automated phenological pattern matching (PPM) method to efficiently map cropping intensity was also developed in this study. This study presents a continental-scale cropping intensity map for the North American continent at 250m spatial resolution for 2010. In this map, the total areas for single crop, double crop, continuous crop, and fallow were estimated to be 123.5 Mha, 11.1 Mha, 64.0 Mha, and 83.4 Mha, respectively. This map was assessed using limited country-level reference datasets derived from United States Department of Agriculture cropland data layer and Agriculture and Agri-Food Canada annual crop inventory with overall accuracies of 79.8% and 80.2%, respectively. Third, two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification were developed. The classification approaches use similarities and dissimilarities in crop type phenology derived from NDVI time-series data for the two approaches. Annual crop type maps were produced for 8 major crop types in the United States using the generalized classification approach for 2001-2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies greater than 78%, while the generalized classifier had accuracies greater than 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year with overall accuracies > 70% across all independent years. Taken together, these cropland products of extent, cropping intensity, and crop types, are significantly beneficial in agricultural and water use planning and monitoring to formulate policies towards global and North American food security issues.

Satellite Estimation of Fractional Cover in Several California Specialty Crops

NASA Technical Reports Server (NTRS)

Johnson, Lee; Cahn, Michael; Rosevelt, Carolyn; Guzman, Alberto; Farrara, Barry; Melton, Forrest S.

2016-01-01

Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

Satellite Estimation of Fractional Cover in Several California Specialty Crops

NASA Astrophysics Data System (ADS)

Johnson, L.; Cahn, M.; Rosevelt, C.; Guzman, A.; Lockhart, T.; Farrara, B.; Melton, F. S.

2016-12-01

Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

Winter Crop Mapping for Improving Crop Production Estimates in Argentina Using Moderation Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Humber, M. L.; Copati, E.; Sanchez, A.; Sahajpal, R.; Puricelli, E.; Becker-Reshef, I.

2017-12-01

Accurate crop production data is fundamental for reducing uncertainly and volatility in the domestic and international agricultural markets. The Agricultural Estimates Department of the Buenos Aires Grain Exchange has worked since 2000 on the estimation of different crop production data. With this information, the Grain Exchange helps different actors of the agricultural chain, such as producers, traders, seed companies, market analyst, policy makers, into their day to day decision making. Since 2015/16 season, the Grain Exchange has worked on the development of a new earth observations-based method to identify winter crop planted area at a regional scale with the aim of improving crop production estimates. The objective of this new methodology is to create a reliable winter crop mask at moderate spatial resolution using Landsat-8 imagery by exploiting bi-temporal differences in the phenological stages of winter crops as compared to other landcover types. In collaboration with the University of Maryland, the map has been validated by photointerpretation of a stratified statistically random sample of independent ground truth data in the four largest producing provinces of Argentina: Buenos Aires, Cordoba, La Pampa, and Santa Fe. In situ measurements were also used to further investigate conditions in the Buenos Aires province. Preliminary results indicate that while there are some avenues for improvement, overall the classification accuracy of the cropland and non-cropland classes are sufficient to improve downstream production estimates. Continuing research will focus on improving the methodology for winter crop mapping exercises on a yearly basis as well as improving the sampling methodology to optimize collection of validation data in the future.

Investigating Temporal and Spatial Variations in Near Surface Water Content using GPR

NASA Astrophysics Data System (ADS)

Hubbard, S. S.; Grote, K.; Kowalsky, M. B.; Rubin, Y.

2001-12-01

Using only conventional point or well logging measurements, it is difficult to obtain information about water content with sufficient spatial resolution and coverage to be useful for near surface applications such as for input to vadose zone predictive models or for assisting with precision crop management. Prompted by successful results of a controlled ground penetrating radar (GPR) pilot study, we are investigating the applicability of GPR methods to estimate near surface water content at a study site within the Robert Mondavi vineyards in Napa County, California. Detailed information about soil variability and water content within vineyards could assist in estimation of plantable acreage, in the design of vineyard layout and in the design of an efficient irrigation/agrochemical application procedure. Our research at the winery study site involves investigation of optimal GPR acquisition and processing techniques, modeling of GPR attributes, and inversion of the attributes for water content information over space and time. A secondary goal of our project is to compare water content information obtained from the GPR data with information available from other types of measurements that are being used to assist in precision crop management. This talk will focus on point and spatial correlation estimation of water content obtained using GPR groundwave information only, and comparison of those estimates with information obtained from analysis of soils, TDR, neutron probe and remote sensing data sets. This comparison will enable us to 1) understand the potential of GPR for providing water content information in the very shallow subsurface, and to 2) investigate the interrelationships between the different types of measurements (and associated measurement scales) that are being utilized to characterize the shallow subsurface water content over space and time.

Satellite-based mapping of field-scale stress indicators for crop yield forecasting: an application over Mead, NE

NASA Astrophysics Data System (ADS)

Yang, Y.; Anderson, M. C.; Gao, F.; Wardlow, B.; Hain, C.; Otkin, J.; Sun, L.; Dulaney, W.

2017-12-01

In agricultural regions, water is one of the most widely limiting factors of crop performance and production. Evapotranspiration (ET) describes crop water use through transpiration and water lost through direct soil evaporation, which makes it a good indicator of soil moisture availability and vegetation health and thus has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) describes temporal anomalies in a normalized evapotranspiration metric (fRET) as derived from satellite remote sensing and has demonstrated capacity to explain regional yield variability in water limited crop growing regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded. In this study we generated maps of ET, fRET, and ESI at high spatiotemporal resolution (30-m pixels, daily timesteps) using a multi-sensor data fusion method, integrating information from satellite platforms with good temporal coverage and other platforms that provide field-scale spatial detail. The study was conducted over the period 2010-2014, covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of corn yield are investigated at both the field and county level to assess the value of ESI as a yield forecasting tool. To examine the role of phenology in ESI-yield correlations, annual input fRET timeseries were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). Results demonstrate that mapping of fRET and ESI at 30-m has the advantage of being able to resolve different crop types with varying phenology. The study also suggests that incorporating phenological information significantly improves yield-correlations by accounting for effects of phenology such as variable planting date and emergence date. The yield-ESI relationship in this study well captures the inter-annual variability of yields and thus has potential to be used for yield prediction, or for ingestion into a crop simulation model as a crop-specific moisture stress function.

Estimating the agricultural fertilizer NH3 emission in China based on the bi-directional CMAQ model and an agro-ecosystem model

NASA Astrophysics Data System (ADS)

Wang, S.

2014-12-01

Atmospheric ammonia (NH3) plays an important role in fine particle formation. Accurate estimates of ammonia can reduce uncertainties in air quality modeling. China is one of the largest countries emitting ammonia with the majority of NH3 emissions coming from the agricultural practices, such as fertilizer applications and animal operations. The current ammonia emission estimates in China are mainly based on pre-defined emission factors. Thus, there are considerable uncertainties in estimating NH3 emissions, especially in time and space distribution. For example, fertilizer applications vary in the date of application and amount by geographical regions and crop types. In this study, the NH3 emission from the agricultural fertilizer use in China of 2011 was estimated online by an agricultural fertilizer modeling system coupling a regional air-quality model and an agro-ecosystem model, which contains three main components 1) the Environmental Policy Integrated Climate (EPIC) model, 2) the meso-scale meteorology Weather Research and Forecasting (WRF) model and 3) the CMAQ air quality model with bi-directional ammonia fluxes. The EPIC output information about daily fertilizer application and soil characteristics would be the input of the CMAQ model. In order to run EPIC model, much Chinese local information is collected and processed. For example, Crop land data are computed from the MODIS land use data at 500-m resolution and crop categories at Chinese county level; the fertilizer use rate for different fertilizer types, crops and provinces are obtained from Chinese statistic materials. The system takes into consideration many influencing factors on agriculture ammonia emission, including weather, the fertilizer application method, timing, amount, and rate for specific pastures and crops. The simulated fertilizer data is compared with the NH3 emissions and fertilizer application data from other sources. The results of CMAQ modeling are also discussed and analyzed with field measurements. The estimated agricultural fertilizer NH3 emission in this study is about 3Tg in 2011. The regions with the highest emission rates are located in the North China Plain. Monthly, the peak ammonia emissions occur in April to July.

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.

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.

NASA crop calendars: Wheat, barley, oats, rye, sorghum, soybeans, corn

NASA Technical Reports Server (NTRS)

Stuckey, M. R.; Anderson, E. N.

1975-01-01

Crop calenders used to determine when Earth Resources Technology Satellite ERTS data would provide the most accurate wheat acreage information and to minimize the amount of ground verified information needed are presented. Since barley, oats, and rye are considered 'confusion crops, i.e., hard to differentiate from wheat in ERTS imagery, specific dates are estimated for these crops in the following stages of development: (1) seed-bed operation, (2) planting or seeding, (3) intermediate growth, (4) dormancy, (5) development of crop to full ground cover, (6) heading or tasseling, and flowering, (7) harvesting, and (8) posting-harvest operations. Dormancy dates are included for fall-snow crops. A synopsis is given of each states' growing conditions, special cropping practices, and other characteristics which are helpful in identifying crops from ERTS imagery.

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.

Development of a European Ensemble System for Seasonal Prediction: Application to crop yield

NASA Astrophysics Data System (ADS)

Terres, J. M.; Cantelaube, P.

2003-04-01

Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.

Estimating Field Scale Crop Evapotranspiration using Landsat and MODIS Satellite Observations

NASA Astrophysics Data System (ADS)

Wong, A.; Jin, Y.; Snyder, R. L.; Daniele, Z.; Gao, F.

2016-12-01

Irrigation accounts for 80% of human freshwater consumption, and most of it return to the atmosphere through Evapotranspiration (ET). Given the challenges of already-stressed water resources and ground water regulation in California, a cost-effective, timely, and consistent spatial estimate of crop ET, from the farm to watershed level, is becoming increasingly important. The Priestley-Taylor (PT) approach, calibrated with field data and driven by satellite observations, shows great promise for accurate ET estimates across diverse ecosystems. We here aim to improve the robustness of the PT approach in agricultural lands, to enable growers and farm managers to tailor irrigation management based on in-field spatial variability and in-season variation. We optimized the PT coefficients for each crop type with available ET measurements from eddy covariance towers and/or surface renewal stations at six crop fields (Alfalfa, Almond, Citrus, Corn, Pistachio and Rice) in California. Good agreement was found between satellite-based estimates and field measurements of net radiation, with a RMSE of less than 36 W m-2. The crop type specific optimization performed well, with a RMSE of 30 W m-2 and a correlation of 0.81 for predicted daily latent heat flux. The calibrated algorithm was used to estimate ET at 30 m resolution over the Sacramento-San Joaquin Delta region for 2015 water year. It captures well the seasonal dynamics and spatial distribution of ET in Sacramento-San Joaquin Delta. A continuous monitoring of the dynamics and spatial heterogeneity of canopy and consumptive water use at a field scale, will help the growers to be well prepared and informed to adaptively manage water, canopy, and grove density to maximize the yield with the least amount of water.

Field-scale and Regional Variability in Evapotranspiration over Crops in California using Eddy Covariance and Surface Renewal

NASA Astrophysics Data System (ADS)

Kent, E. R.; Clay, J. M.; Leinfelder-Miles, M.; Lambert, J. J.; Little, C.; Monteiro, R. O. C.; Monteiro, P. F. C.; Shapiro, K.; Rice, S.; Snyder, R. L.; Daniele, Z.; Paw U, K. T.

2016-12-01

Evapotranspiration (ET) estimated using a single crop coefficient and a grass reference largely ignores variability due to heterogeneity in microclimate, soils, and crop management. We employ a relatively low cost energy balance residual method using surface renewal and eddy covariance measurements to continuously estimate half-hourly and daily ET across more than 15 fields and orchards spanning four crops and two regions of California. In the Sacramento-San Joaquin River Delta, measurements were taken in corn, pasture, and alfalfa fields, with 4-5 stations in each crop type spread across the region. In the Southern San Joaquin Valley, measurements were taken in three different pistachio orchards, with one orchard having six stations instrumented to examine salinity-induced heterogeneity. We analyze field-scale and regional variability in ET and measured surface energy balance components. Cross comparisons between the eddy covariance and the surface renewal measurements confirm the robustness of the surface renewal method. A hybrid approach in which remotely sensed net radiation is combined with in situ measurements of sensible heat flux is also investigated. This work will provide ground-truth data for satellite and aerial-based ET estimates and will inform water management at the field and regional scales.

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.

An energy balance approach for mapping crop waterstress and yield impacts over the Czech Republic

USDA-ARS?s Scientific Manuscript database

There is a growing demand for timely, spatially distributed information regarding crop condition and water use to inform agricultural decision making and yield forecasting efforts. Remote sensing of land-surface temperature has proven valuable for mapping evapotranspiration (ET) and crop stress from...

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.

Rapid Crop Cover Mapping for the Conterminous United States.

PubMed

Dahal, Devendra; Wylie, Bruce; Howard, Danny

2018-06-05

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.

Sen2-Agri country level demonstration for Ukraine

NASA Astrophysics Data System (ADS)

Kussul, N.; Kolotii, A.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

Due to launch of Sentinel-2 mission European Space Agency (ESA) started Sentinel-2 for Agriculture (Sen2-Agri) project coordinated by Universite catholique de Louvain (UCL). Ukraine is selected as one of 3 country level demonstration sites for benchmarking Sentinel-2 data due to wide range of main crops (both winter and summer), big fields and high enough climate variability over the territory [1-2]. Within this county level demonstration main objectives are following: i) Sentinel's products quality assessment and their suitability estimation for the territory of Ukraine [2]; ii) demonstration in order to convince decision makers and state authorities; iii) assessment of the personnel and facilities required to run the Sen2-Agri system and creation of Sen-2 Agri products (crop type maps and such essential climatic variable as Leaf Area Index - LAI [3]). During this project ground data were collected for crop land mapping and crop type classification along the roads within main agro-climatic zones of Ukraine. For LAI estimation we used indirect non-destructive method which is based on DHP-images and VALERI protocol. Products created with use of Sen2-Agri system deployed during project execution and results of neural-network approach utilization will be compared. References Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508. Kussul, N., Skakun, S., Shelestov, A., Lavreniuk, M., Yailymov, B., & Kussul, O. (2015). Regional scale crop mapping using multi-temporal satellite imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 45-52. Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736

Selection during crop diversification involves correlated evolution of the circadian clock and ecophysiological traits in Brassica rapa.

PubMed

Yarkhunova, Yulia; Edwards, Christine E; Ewers, Brent E; Baker, Robert L; Aston, Timothy Llewellyn; McClung, C Robertson; Lou, Ping; Weinig, Cynthia

2016-04-01

Crop selection often leads to dramatic morphological diversification, in which allocation to the harvestable component increases. Shifts in allocation are predicted to impact (as well as rely on) physiological traits; yet, little is known about the evolution of gas exchange and related anatomical features during crop diversification. In Brassica rapa, we tested for physiological differentiation among three crop morphotypes (leaf, turnip, and oilseed) and for correlated evolution of circadian, gas exchange, and phenological traits. We also examined internal and surficial leaf anatomical features and biochemical limits to photosynthesis. Crop types differed in gas exchange; oilseed varieties had higher net carbon assimilation and stomatal conductance relative to vegetable types. Phylogenetically independent contrasts indicated correlated evolution between circadian traits and both gas exchange and biomass accumulation; shifts to shorter circadian period (closer to 24 h) between phylogenetic nodes are associated with higher stomatal conductance, lower photosynthetic rate (when CO2 supply is factored out), and lower biomass accumulation. Crop type differences in gas exchange are also associated with stomatal density, epidermal thickness, numbers of palisade layers, and biochemical limits to photosynthesis. Brassica crop diversification involves correlated evolution of circadian and physiological traits, which is potentially relevant to understanding mechanistic targets for crop improvement. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Irrigation and Maize Cultivation Erode Plant Diversity Within Crops in Mediterranean Dry Cereal Agro-Ecosystems.

PubMed

Fagúndez, Jaime; Olea, Pedro P; Tejedo, Pablo; Mateo-Tomás, Patricia; Gómez, David

2016-07-01

The intensification of agriculture has increased production at the cost of environment and biodiversity worldwide. To increase crop yield in dry cereal systems, vast farmland areas of high conservation value are being converted into irrigation, especially in Mediterranean countries. We analyze the effect of irrigation-driven changes on the farm biota by comparing species diversity, community composition, and species traits of arable plants within crop fields from two contrasting farming systems (dry and irrigated) in Spain. We sampled plant species within 80 fields of dry wheat, irrigated wheat, and maize (only cultivated under irrigation). Wheat crops held higher landscape and per field species richness, and beta diversity than maize. Within the same type of crop, irrigated wheat hosted lower plant diversity than dry wheat at both field and landscape scales. Floristic composition differed between crop types, with higher frequencies of perennials, cosmopolitan, exotic, wind-pollinated and C4 species in maize. Our results suggest that irrigation projects, that transform large areas of dry cereal agro-ecosystems into irrigated crop systems dominated by maize, erode plant diversity. An adequate planning on the type and proportion of crops used in the irrigated agro-ecosystems is needed in order to balance agriculture production and biodiversity conservation.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins.

PubMed

Belfry, Kimberly D; Trueman, Cheryl; Vyn, Richard J; Loewen, Steven A; Van Eerd, Laura L

2017-01-01

Much of cover crop research to date focuses on key indicators of impact without considering the implications over multiple years, in the absence of a systems-based approach. To evaluate the effect of three years of autumn cover crops on subsequent processing tomato (Solanum lycopersicum L.) production in 2010 and 2011, a field split-split-plot factorial design trial with effects of cover crop type, urea ammonium nitrate fertilizer rate (0 or 140 kg N ha-1 preplant broadcast incorporated) and tomato cultivar (early vs. late) was conducted. The main plot factor, cover crop, included a no cover crop control, oat (Avena sativa L.), winter cereal rye (hereafter referred to as rye) (Secale cereale L.), oilseed radish (OSR) (Raphanus sativus L. var. oleiferus Metzg Stokes), and mix of OSR and rye (OSR + rye) treatments. Cover crop biomass of 0.5 to 2.8 and 1.7 to 3.1 Mg ha-1 was attained in early Oct. and the following early May, respectively. In general, OSR increased soil mineral N during cover crop growth and into the succeeding summer tomato growing season, while the remaining cover crops did not differ from the no cover crop control. The lack of a cover crop by N rate interaction in soil and plant N analyses at harvest suggests that growers may not need to modify N fertilizer rates to tomatoes based on cover crop type. Processing tomato fruit quality at harvest (rots, insect or disease damage, Agtron colour, pH, or natural tomato soluble solids (NTSS)) was not affected by cover crop type. In both years, marketable yield in the no cover crop treatment was lower or not statistically different than all planted cover crops. Partial profit margins over both years were 1320 $ ha-1 higher with OSR and $960 higher with oat compared to the no cover crop control. Thus, results from a systems-based approach suggest that the cover crops tested had no observed negative impact on processing tomato production and have the potential to increase marketable yield and profit margins.

Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins

PubMed Central

Belfry, Kimberly D.; Trueman, Cheryl; Vyn, Richard J.; Loewen, Steven A.; Van Eerd, Laura L.

2017-01-01

Much of cover crop research to date focuses on key indicators of impact without considering the implications over multiple years, in the absence of a systems-based approach. To evaluate the effect of three years of autumn cover crops on subsequent processing tomato (Solanum lycopersicum L.) production in 2010 and 2011, a field split-split-plot factorial design trial with effects of cover crop type, urea ammonium nitrate fertilizer rate (0 or 140 kg N ha-1 preplant broadcast incorporated) and tomato cultivar (early vs. late) was conducted. The main plot factor, cover crop, included a no cover crop control, oat (Avena sativa L.), winter cereal rye (hereafter referred to as rye) (Secale cereale L.), oilseed radish (OSR) (Raphanus sativus L. var. oleiferus Metzg Stokes), and mix of OSR and rye (OSR + rye) treatments. Cover crop biomass of 0.5 to 2.8 and 1.7 to 3.1 Mg ha-1 was attained in early Oct. and the following early May, respectively. In general, OSR increased soil mineral N during cover crop growth and into the succeeding summer tomato growing season, while the remaining cover crops did not differ from the no cover crop control. The lack of a cover crop by N rate interaction in soil and plant N analyses at harvest suggests that growers may not need to modify N fertilizer rates to tomatoes based on cover crop type. Processing tomato fruit quality at harvest (rots, insect or disease damage, Agtron colour, pH, or natural tomato soluble solids (NTSS)) was not affected by cover crop type. In both years, marketable yield in the no cover crop treatment was lower or not statistically different than all planted cover crops. Partial profit margins over both years were 1320 $ ha-1 higher with OSR and $960 higher with oat compared to the no cover crop control. Thus, results from a systems-based approach suggest that the cover crops tested had no observed negative impact on processing tomato production and have the potential to increase marketable yield and profit margins. PMID:28683080

A comprehensive data processing plan for crop calendar MSS signature development from satellite imagery: Crop identification using vegetation phenology

NASA Technical Reports Server (NTRS)

Hlavka, C. A. (Principal Investigator); Carlyle, S. M.; Haralick, R. M.; Yokoyama, R.

1978-01-01

The author has identified the following significant results. The phenological method of crop identification involves the creation of crop signatures which characterize multispectral observations as phenological growth states. The phenological signature models spectral reflectance explicitly as a function of crop maturity rather than as a function of date. A correspondence of time to growth state is established which minimizes the smallest difference between the given multispectral multitemporal vector and a category mean vector. The application of the method to the identification of winter wheat and corn shows (1) the method is capable of discriminating crop type with about the same degree of accuracy as more traditional classifiers; (2) the use of LANDSAT observations on two or more dates yields better results than the use of a single observation; and (3) some potential is demonstrated for labeling the degree of maturity of the crop, as well as the crop type.

Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in western Europe.

PubMed

Hass, Annika L; Kormann, Urs G; Tscharntke, Teja; Clough, Yann; Baillod, Aliette Bosem; Sirami, Clélia; Fahrig, Lenore; Martin, Jean-Louis; Baudry, Jacques; Bertrand, Colette; Bosch, Jordi; Brotons, Lluís; Burel, Françoise; Georges, Romain; Giralt, David; Marcos-García, María Á; Ricarte, Antonio; Siriwardena, Gavin; Batáry, Péter

2018-02-14

Agricultural intensification is one of the main causes for the current biodiversity crisis. While reversing habitat loss on agricultural land is challenging, increasing the farmland configurational heterogeneity (higher field border density) and farmland compositional heterogeneity (higher crop diversity) has been proposed to counteract some habitat loss. Here, we tested whether increased farmland configurational and compositional heterogeneity promote wild pollinators and plant reproduction in 229 landscapes located in four major western European agricultural regions. High-field border density consistently increased wild bee abundance and seed set of radish ( Raphanus sativus ), probably through enhanced connectivity. In particular, we demonstrate the importance of crop-crop borders for pollinator movement as an additional experiment showed higher transfer of a pollen analogue along crop-crop borders than across fields or along semi-natural crop borders. By contrast, high crop diversity reduced bee abundance, probably due to an increase of crop types with particularly intensive management. This highlights the importance of crop identity when higher crop diversity is promoted. Our results show that small-scale agricultural systems can boost pollinators and plant reproduction. Agri-environmental policies should therefore aim to halt and reverse the current trend of increasing field sizes and to reduce the amount of crop types with particularly intensive management. © 2018 The Author(s).

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Marginal cost curves for water footprint reduction in irrigated agriculture: a policy and decision making guide for efficient water use in crop production

NASA Astrophysics Data System (ADS)

Chukalla, Abebe; Krol, Maarten; Hoekstra, Arjen

2016-04-01

Reducing water footprints (WF) in irrigated crop production is an essential element in water management, particularly in water-scarce areas. To achieve this, policy and decision making need to be supported with information on marginal cost curves that rank measures to reduce the WF according to their cost-effectiveness and enable the estimation of the cost associated with a certain WF reduction target, e.g. towards a certain reasonable WF benchmark. This paper aims to develop marginal cost curves (MCC) for WF reduction. The AquaCrop model is used to explore the effect of different measures on evapotranspiration and crop yield and thus WF that is used as input in the MCC. Measures relate to three dimensions of management practices: irrigation techniques (furrow, sprinkler, drip and subsurface drip); irrigation strategies (full and deficit irrigation); and mulching practices (no mulching, organic and synthetic mulching). A WF benchmark per crop is calculated as resulting from the best-available production technology. The marginal cost curve is plotted using the ratios of the marginal cost to WF reduction of the measures as ordinate, ranking with marginal costs rise with the increase of the reduction effort. For each measure, the marginal cost to reduce WF is estimated by comparing the associated WF and net present value (NPV) to the reference case (furrow irrigation, full irrigation, no mulching). The NPV for each measure is based on its capital costs, operation and maintenances costs (O&M) and revenues. A range of cases is considered, including: different crops, soil types and different environments. Key words: marginal cost curve, water footprint benchmark, soil water balance, crop growth, AquaCrop

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

NASA Astrophysics Data System (ADS)

Manfron, Giacinto; Delmotte, Sylvestre; Busetto, Lorenzo; Hossard, Laure; Ranghetti, Luigi; Brivio, Pietro Alessandro; Boschetti, Mirco

2017-05-01

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002-2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.

Mind the Roots: Phenotyping Below-Ground Crop Diversity and Its Influence on Final Yield

NASA Astrophysics Data System (ADS)

Nieters, C.; Guadagno, C. R.; Lemli, S.; Hosseini, A.; Ewers, B. E.

2017-12-01

Changes in global climate patterns and water regimes are having profound impacts on worldwide crop production. An ever-growing population paired with increasing temperatures and unpredictable periods of severe drought call for accurate modeling of future crop yield. Although novel approaches are being developed in high-throughput, above-ground image phenotyping, the below-ground plant system is still poorly phenotyped. Collection of plant root morphology and hydraulics are needed to inform mathematical models to reliably estimate yields of crops grown in sub-optimal conditions. We used Brassica rapa to inform our model as it is a globally cultivated crop with several functionally diverse cultivars. Specifically, we use 7 different accessions from oilseed (R500 and Yellow Sarson), leafy type (Pac choi and Chinese cabbage), a vegetable turnip, and two Wisconsin Fast Plants (Imb211 and Fast Plant self-compatible), which have shorter life cycles and potentially large differences in allocation to roots. Bi-weekly, we harvested above and below-ground biomass to compare the varieties in terms of carbon allocation throughout their life cycle. Using WinRhizo software, we analyzed root system length and surface area to compare and contrast root morphology among cultivars. Our results confirm that root structural characteristics are crucial to explain plant water use and carbon allocation. The root:shoot ratio reveals a significant (p < 0.01) difference among crop accession. To validate the procedure across different varieties and life stages we also compared surface area results from the image-based technology to dry biomass finding a strong linear relationship (R2= 0.85). To assess the influence of a diverse above-ground morphology on the root system we also measured above-ground anatomical and physiological traits such as gas exchange, chlorophyll content, and chlorophyll a fluorescence. A thorough analysis of the root system will clarify carbon dynamics and hydraulics at the whole-plant level, improving final yield predictions.

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.

Drought Information Supported by Citizen Scientists (DISCS)

NASA Astrophysics Data System (ADS)

Molthan, A.; Maskey, M.; Hain, C.; Meyer, P.; Nair, U. S.; Handyside, C. T.; White, K.; Amin, M.

2017-12-01

Each year, drought impacts various regions of the United States on time scales of weeks, months, seasons, or years, which in turn leads to a need to document these impacts and inform key decisions on land management, use of water resources, and disaster response. Mapping impacts allows decision-makers to understand potential damage to agriculture and loss of production, to communicate and document drought impacts on crop yields, and to inform water management decisions. Current efforts to collect this information includes parsing of media reports, collaborations with local extension offices, and partnerships with the National Weather Service cooperative observer network. As part of a NASA Citizen Science for Earth Systems proposal award, a research and applications team from Marshall Space Flight Center, the University of Alabama in Huntsville, and collaborators within the NWS have developed a prototype smartphone application focused on the collection of citizen science observations of crop health and drought impacts, along with development of innovative low-cost soil moisture sensors to supplement subjective assessments of local soil moisture conditions. Observations provided by citizen scientists include crop type and health, phase of growth, soil moisture conditions, irrigation status, along with an optional photo and comment to provide visual confirmation and other details. In exchange for their participation, users of the app also have access to unique land surface modeling data sets produced at MSFC such as the NASA Land Information System soil moisture and climatology/percentile products from the Short-term Prediction Research and Transition (SPoRT) Center, assessments of vegetation health and stress from NASA and NOAA remote sensing platforms (e.g. MODIS/VIIRS), outputs from a crop stress model developed at the University of Alabama in Huntsville, recent rainfall estimates from the NOAA/NWS network of ground-based weather radars, and other observations made by their fellow citizen scientists. This presentation will highlight development of the application, data collected to date, feedback from participants, and opportunities to use the collected information in support of addressing science questions such as verification and validation of modeling and remote sensing data sets.

The value of seasonal forecasting and crop mix adaptation to climate variability for agriculture under climate change

NASA Astrophysics Data System (ADS)

Choi, H. S.; Schneider, U.; Schmid, E.; Held, H.

2012-04-01

Changes to climate variability and frequency of extreme weather events are expected to impose damages to the agricultural sector. Seasonal forecasting and long range prediction skills have received attention as an option to adapt to climate change because seasonal climate and yield predictions could improve farmers' management decisions. The value of seasonal forecasting skill is assessed with a crop mix adaptation option in Spain where drought conditions are prevalent. Yield impacts of climate are simulated for six crops (wheat, barely, cotton, potato, corn and rice) with the EPIC (Environmental Policy Integrated Climate) model. Daily weather data over the period 1961 to 1990 are used and are generated by the regional climate model REMO as reference period for climate projection. Climate information and its consequent yield variability information are given to the stochastic agricultural sector model to calculate the value of climate information in the agricultural market. Expected consumers' market surplus and producers' revenue is compared with and without employing climate forecast information. We find that seasonal forecasting benefits not only consumers but also producers if the latter adopt a strategic crop mix. This mix differs from historical crop mixes by having higher shares of crops which fare relatively well under climate change. The corresponding value of information is highly sensitive to farmers' crop mix choices.

Socio-economic, Biophysical, and Perceptional Factors Associated with Agricultural Adaptation of Smallholder Farmers in Gujarat, Northwest India

NASA Astrophysics Data System (ADS)

Jain, M.; DeFries, R. S.

2012-12-01

Climate change is predicted to negatively impact many agricultural communities across the globe, particularly smallholder farmers who often do not have access to appropriate technologies to reduce their vulnerability. To better predict which farmers will be most impacted by future climate change at a regional scale, we use remote sensing and agricultural census data to examine how cropping intensity and crop type have shifted based on rainfall variability across Gujarat, India from 1990 to 2010. Using household-level interviews, we then identify the socio-economic, biophysical, perceptional, and psychological factors associated with smallholder farmers who are the most impacted and the least able to adapt to contemporaneous rainfall variability. We interviewed 750 farmers in 2011 and 2012 that span a rainfall, irrigation, socio-economic, and caste gradient across central Gujarat. Our results show that farmers shift cropping practices in several ways based on monsoon onset, which farmers state is the main observable rainfall signal influencing cropping decisions during the monsoon season. When monsoon onset is delayed, farmers opt to plant more drought-tolerant crops, push back the date of sowing, and increase the number of irrigations used. Comparing self-reported income and yields, we find that switching crops does not improve agricultural income, shifting planting date does not influence crop yield, yet increasing the number of irrigations significantly increases yield. Future work will identify which social (e.g. social networks), psychological (e.g. risk preference), and knowledge (e.g. information sources) factors are associated with farmers who are best able to adapt to rainfall variability.

Meta-analysis of climate impacts and uncertainty on crop yields in Europe

NASA Astrophysics Data System (ADS)

Knox, Jerry; Daccache, Andre; Hess, Tim; Haro, David

2016-11-01

Future changes in temperature, rainfall and soil moisture could threaten agricultural land use and crop productivity in Europe, with major consequences for food security. We assessed the projected impacts of climate change on the yield of seven major crop types (viz wheat, barley, maize, potato, sugar beet, rice and rye) grown in Europe using a systematic review (SR) and meta-analysis of data reported in 41 original publications from an initial screening of 1748 studies. Our approach adopted an established SR procedure developed by the Centre for Evidence Based Conservation constrained by inclusion criteria and defined methods for literature searches, data extraction, meta-analysis and synthesis. Whilst similar studies exist to assess climate impacts on crop yield in Africa and South Asia, surprisingly, no comparable synthesis has been undertaken for Europe. Based on the reported results (n = 729) we show that the projected change in average yield in Europe for the seven crops by the 2050s is +8%. For wheat and sugar beet, average yield changes of +14% and +15% are projected, respectively. There were strong regional differences with crop impacts in northern Europe being higher (+14%) and more variable compared to central (+6%) and southern (+5) Europe. Maize is projected to suffer the largest negative mean change in southern Europe (-11%). Evidence of climate impacts on yield was extensive for wheat, maize, sugar beet and potato, but very limited for barley, rice and rye. The implications for supporting climate adaptation policy and informing climate impacts crop science research in Europe are discussed.

Determining the probability of pesticide exposures among migrant farmworkers: results from a feasibility study.

PubMed

Ward, M H; Prince, J R; Stewart, P A; Zahm, S H

2001-11-01

Migrant and seasonal farmworkers are exposed to pesticides through their work with crops and livestock. Because workers are usually unaware of the pesticides applied, specific pesticide exposures cannot be determined by interviews. We conducted a study to determine the feasibility of identifying probable pesticide exposures based on work histories. The study included 162 farm workers in seven states. Interviewers obtained a lifetime work history including the crops, tasks, months, and locations worked. We investigated the availability of survey data on pesticide use for crops and livestock in the seven pilot states. Probabilities of use for pesticide types (herbicides, insecticides, fungicides, etc.) and specific chemicals were calculated from the available data for two farm workers. The work histories were chosen to illustrate how the quality of the pesticide use information varied across crops, states, and years. For most vegetable and fruit crops there were regional pesticide use data in the late 1970s, no data in the 1980s, and state-specific data every other year in the 1990s. Annual use surveys for cotton and potatoes began in the late 1980s. For a few crops, including asparagus, broccoli, lettuce, strawberries, plums, and Christmas trees, there were no federal data or data from the seven states before the 1990s. We conclude that identifying probable pesticide exposures is feasible in some locations. However, the lack of pesticide use data before the 1990s for many crops will limit the quality of historic exposure assessment for most workers. Published 2001 Wiley-Liss, Inc.

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

NASA Astrophysics Data System (ADS)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

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

Spatial and temporal patterns of root distribution in developing stands of four woody crop species grown with drip irrigation and fertilization

Treesearch

Mark Coleman

2007-01-01

In forest trees, roots mediate such significant carbon fluxes as primary production and soil C02 efflux. Despite the central role of roots in these critical processes, information on root distribution during stand establishment is limited, yet must be described to accurately predict how various forest types, which are growing with a range of...

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.

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

Climate change and farmers’ cropping patterns in Cemoro watershed area, Central Java, Indonesia

NASA Astrophysics Data System (ADS)

Sugihardjo; Sutrisno, J.; Setyono, P.; Suntoro

2018-03-01

Cropping pattern applied by farmers is usually based on the availability of water. Farmers cultivate rice when water is available. If it is unavailable, farmers will choose to plant crops that need less water. Climate change greatly affects to farmers in determining the cropping pattern as it alters the rainfall pattern and distribution in the region. This condition requires farmers to adjust the cropping pattern so that they can do the farming successfully. This study aims to examine the application of cropping patterns applied by the farmers in the Cemoro Watershed, Central Java, Indonesia. Descriptive analysis approach is employed in this research. The results showed that farmers’ cropping pattern is not based on the availability of water. However, it adopts a habit that has been practiced since long time ago or just adopt others farmer's habit. The cropping pattern applied by irrigated paddy farmers in Cemoro watershed area consists of two types: rice-rice-rice and rice-rice-secondary crops. Among those two types, most farmers apply the rice-rice-rice pattern. Meanwhile, there are three cropping patterns applied in the rain-land, namely rice-rice-rice, rice-rice-secondary crop, and rice-rice-fallow. The majority of farmers apply the second pattern (rice-rice-secondary crops). It was also found that farmers’ cropping pattern was not in accordance with the recommendation of the local government.

Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System

USGS Publications Warehouse

Ward, M.H.; Nuckols, J.R.; Weigel, S. J.; Cantor, K.P.; Miller, Roger S.

2000-01-01

Pesticides used in agriculture may cause adverse health effects among the population living near agricultural areas. However, identifying the populations most likely to be exposed is difficult. We conducted a feasibility study to determine whether satellite imagery could be used to reconstruct historical crop patterns. We used historical Farm Service Agency records as a source of ground reference data to classify a late summer 1984 satellite image into crop species in a three-county area in south central Nebraska. Residences from a population-based epidemiologic study of non-Hodgkin lymphoma were located on the crop maps using a geographic information system (GIS). Corn, soybeans, sorghum, and alfalfa were the major crops grown in the study area. Eighty-five percent of residences could be located, and of these 22% had one of the four major crops within 500 m of the residence, an intermediate distance for the range of drift effects from pesticides applied in agriculture. We determined the proximity of residences to specific crop species and calculated crop-specific probabilities of pesticide use based on available data. This feasibility study demonstrated that remote sensing data and historical records on crop location can be used to create historical crop maps. The crop pesticides that were likely to have been applied can be estimated when information about crop-specific pesticide use is available. Using a GIS, zones of potential exposure to agricultural pesticides and proximity measures can be determined for residences in a study.

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.

Crop yield responses to a hardwood biochar across varied soils and climate conditions

USDA-ARS?s Scientific Manuscript database

Biochars applied to soil for crop yield improvements have produced mixed results. The assorted crop yield responses may be linked to employing biochars with diverse chemical and physical characteristics. To clarify if biochars can improve crop yields, it may be prudent to evaluate one biochar type...

Life history traits and phenotypic selection among sunflower crop–wild hybrids and their wild counterpart: implications for crop allele introgression

PubMed Central

Kost, Matthew A; Alexander, Helen M; Jason Emry, D; Mercer, Kristin L

2015-01-01

Hybridization produces strong evolutionary forces. In hybrid zones, selection can differentially occur on traits and selection intensities may differ among hybrid generations. Understanding these dynamics in crop–wild hybrid zones can clarify crop-like traits likely to introgress into wild populations and the particular hybrid generations through which introgression proceeds. In a field experiment with four crop–wild hybrid Helianthus annuus (sunflower) cross types, we measured growth and life history traits and performed phenotypic selection analysis on early season traits to ascertain the likelihood, and routes, of crop allele introgression into wild sunflower populations. All cross types overwintered, emerged in the spring, and survived until flowering, indicating no early life history barriers to crop allele introgression. While selection indirectly favored earlier seedling emergence and taller early season seedlings, direct selection only favored greater early season leaf length. Further, there was cross type variation in the intensity of selection operating on leaf length. Thus, introgression of multiple early season crop-like traits, due to direct selection for greater early season leaf length, should not be impeded by any cross type and may proceed at different rates among generations. In sum, alleles underlying early season sunflower crop-like traits are likely to introgress into wild sunflower populations. PMID:26029263

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 %.

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.

Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species?

PubMed Central

Archontoulis, S. V.; Yin, X.; Vos, J.; Danalatos, N. G.; Struik, P. C.

2012-01-01

Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C3 leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO2 at the stomatal cavity (An–Ci), the model was parameterized by analysing the photosynthesis response to incident light intensity (An–Iinc). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from An–Ci or from An–Iinc data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored An–Iinc data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model. PMID:22021569

Rapid crop cover mapping for the conterminous United States

USGS Publications Warehouse

Dahal, Devendra; Wylie, Bruce K.; Howard, Daniel

2018-01-01

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

Integrating geospatial data and cropping system simulation within a geographic information system to analyze spatial seed cotton yield, water use, and irrigation requirements

USDA-ARS?s Scientific Manuscript database

The development of sensors that provide geospatial information on crop and soil conditions has been a primary success for precision agriculture. However, further developments are needed to integrate geospatial data into computer algorithms that spatially optimize crop production while considering po...

Fungal Plant Pathogens Associated with Emerging Crops in North America: An Emerging Challenge for Plant Health Professionals

USDA-ARS?s Scientific Manuscript database

'Emerging crops' is a term typically applied to ethnic food plants, or plants used in traditional or ethnic medicine, some of which are becoming viable niche markets in North America. Information on crop protection of these plants is often scarce to lacking. Literature providing information on diagn...

US/Canada wheat and barley crop calender exploratory experiment implementation plan

NASA Technical Reports Server (NTRS)

1980-01-01

A plan is detailed for a supplemental experiment to evaluate several crop growth stage models and crop starter models. The objective of this experiment is to provide timely information to aid in understanding crop calendars and to provide data that will allow a selection between current crop calendar models.

Survey of Crop Losses in Response to Phytoparasitic Nematodes in the United States for 1994

PubMed Central

Koenning, S. R.; Overstreet, C.; Noling, J. W.; Donald, P. A.; Becker, J. O.; Fortnum, B. A.

1999-01-01

Previous reports of crop losses to plant-parasitic nematodes have relied on published results of survey data based on certain commodities, including tobacco, peanuts, cotton, and soybean. Reports on crop-loss assessment by land-grant universities and many commodity groups generally are no longer available, with the exception of the University of Georgia, the Beltwide Cotton Conference, and selected groups concerned with soybean. The Society of Nematologists Extension Committee contacted extension personnel in 49 U.S. states for information on estimated crop losses caused by plant-parasitic nematodes in major crops for the year 1994. Included in this paper are survey results from 35 states on various crops including corn, cotton, soybean, peanut, wheat, rice, sugarcane, sorghum, tobacco, numerous vegetable crops, fruit and nut crops, and golf greens. The data are reported systematically by state and include the estimated loss, hectarage of production, source of information, nematode species or taxon when available, and crop value. The major genera of phytoparasitic nematodes reported to cause crop losses were Heterodera, Hoplolaimus, Meloidogyne, Pratylenchus, Rotylenchulus, and Xiphinema. PMID:19270925

Survey of crop losses in response to phytoparasitic nematodes in the United States for 1994.

PubMed

Koenning, S R; Overstreet, C; Noling, J W; Donald, P A; Becker, J O; Fortnum, B A

1999-12-01

Previous reports of crop losses to plant-parasitic nematodes have relied on published results of survey data based on certain commodities, including tobacco, peanuts, cotton, and soybean. Reports on crop-loss assessment by land-grant universities and many commodity groups generally are no longer available, with the exception of the University of Georgia, the Beltwide Cotton Conference, and selected groups concerned with soybean. The Society of Nematologists Extension Committee contacted extension personnel in 49 U.S. states for information on estimated crop losses caused by plant-parasitic nematodes in major crops for the year 1994. Included in this paper are survey results from 35 states on various crops including corn, cotton, soybean, peanut, wheat, rice, sugarcane, sorghum, tobacco, numerous vegetable crops, fruit and nut crops, and golf greens. The data are reported systematically by state and include the estimated loss, hectarage of production, source of information, nematode species or taxon when available, and crop value. The major genera of phytoparasitic nematodes reported to cause crop losses were Heterodera, Hoplolaimus, Meloidogyne, Pratylenchus, Rotylenchulus, and Xiphinema.

Crop diversification and livelihoods of smallholder farmers in Zimbabwe: adaptive management for environmental change.

PubMed

Makate, Clifton; Wang, Rongchang; Makate, Marshall; Mango, Nelson

2016-01-01

This paper demonstrates how crop diversification impacts on two outcomes of climate smart agriculture; increased productivity (legume and cereal crop productivity) and enhanced resilience (household income, food security, and nutrition) in rural Zimbabwe. Using data from over 500 smallholder farmers, we jointly estimate crop diversification and each of the outcome variables within a conditional (recursive) mixed process framework that corrects for selectivity bias arising due to the voluntary nature of crop diversification. We find that crop diversification depends on the land size, farming experience, asset wealth, location, access to agricultural extension services, information on output prices, low transportation costs and general information access. Our results also indicate that an increase in the rate of adoption improves crop productivity, income, food security and nutrition at household level. Overall, our results are indicative of the importance of crop diversification as a viable climate smart agriculture practice that significantly enhances crop productivity and consequently resilience in rural smallholder farming systems. We, therefore, recommend wider adoption of diversified cropping systems notably those currently less diversified for greater adaptation to the ever-changing climate.

7 CFR 400.137 - Procedures for salary offset; types of collection.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Procedures for salary offset; types of collection. 400.137 Section 400.137 Agriculture Regulations of the Department of Agriculture (Continued) FEDERAL CROP...-Regulations for the 1986 and Succeeding Crop Years § 400.137 Procedures for salary offset; types of collection...

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.

Impact of nowcasting on the production and processing of agricultural crops. [in the US

NASA Technical Reports Server (NTRS)

Dancer, W. S.; Tibbitts, T. W.

1973-01-01

The value was studied of improved weather information and weather forecasting to farmers, growers, and agricultural processing industries in the United States. The study was undertaken to identify the production and processing operations that could be improved with accurate and timely information on changing weather patterns. Estimates were then made of the potential savings that could be realized with accurate information about the prevailing weather and short term forecasts for up to 12 hours. This weather information has been termed nowcasting. The growing, marketing, and processing operations of the twenty most valuable crops in the United States were studied to determine those operations that are sensitive to short-term weather forecasting. Agricultural extension specialists, research scientists, growers, and representatives of processing industries were consulted and interviewed. The value of the crops included in this survey and their production levels are given. The total value for crops surveyed exceeds 24 billion dollars and represents more than 92 percent of total U.S. crop value.

Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers

NASA Astrophysics Data System (ADS)

Niyogi, D.; Andresen, J.

2011-12-01

Corn and soybean production contributes over $100 billion annually to the U.S. economy, most of which comes from the intensely cultivated corn-belt region of the Midwest. Successful crop production in this region is highly dependent on favorable temperatures and appropriate precipitation patters, making this industry vulnerable to changes in climate patterns. Though predictive models are constantly improving, there are gaps in our understanding of how different management practices can be used to help farmers adapt to changes in climate while maintaining economic viability. Furthermore, currently available tools and models are not meeting producers' needs, and little is known about the types of information they would like to access. Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers is an integrated research and extension project that seeks to improve the resilience and profitability of farms in the North Central Region amid variable climate change through the development and dissemination of improved decision support tools, resource materials, and training. The goal is to work closely with producers to help them make better long-term plans on what, when and where to plant, and also how to manage crops for maximum yields and minimum environmental damage. The U2U team is composed of a uniquely qualified group of climatologists, crop modelers, agronomists, economists, and social scientists from 10 partner universities across the Midwest. Over the span of 5 years, collaborators will complete tasks associated with 5 objectives that will enhance the usability of climate information for the agricultural community and lead to more sustainable farming operations. First the team will produce research on the biophysical and economic impacts of different climate scenarios on corn and soybean yields in the North Central Region (objective 1) and conduct complementary research to understand how producers and advisors are likely to use this information (objective 2). Based on these findings, decision support tools (DSTs) and training materials will be developed to effectively deliver climate information to stakeholders (objective 3). Next, DSTs will be piloted in a four-state region (Indiana, Iowa, Nebraska, and Michigan) to help improve tools and evaluate effectiveness (objective 4). After several iterations with stakeholders to ensure the usability and utility of the tools, the program will be extended to all twelve states in the region (objective 5). Decision support tools, along with training products, surveys, feedback mechanisms and collaborative social tools, will be supported using the NSF-funded and Purdue University developed HUBzero web-based technology.

Pumpage data from irrigation wells in eastern Laramie County, Wyoming, and Kimball County, Nebraska

USGS Publications Warehouse

Avery, Charles

1983-01-01

Quantitative information concerning pumpage by irrigation wells is an integral component of the U.S. Geological Survey High Plains Regional Aquifer System Analysis. Thus, operation time, discharge rate, and irrigated acreage were measured at approximately 450 randomly selected irrigation wells within 10 areas of the High Plains during the 1980 irrigation season. The data were used to estimate the seasonal mean application of water to crops and to project total pumpage by irrigation wells in 1980 throughout the High Plains area. As part of the sampling effort, 50 irrigation wells were randomly chosen from the area of eastern Laramie County, Wyoming, and Kimball County, Nebraska. Required information was collected on only 40 of the wells. For these wells, the seasonal mean application of water on the irrigated land was 15.2 inches. For the major crop types, the seasonal mean application, in inches, were as follows: alfalfa, 19.8; corn, 15.4; potatoes, 13.8; beans, 12.8; and small grains 10.2. (USGS)

Timely precipitation drives cover crop outcomes

USDA-ARS?s Scientific Manuscript database

Cover crops can expand ecosystem services, though sound management recommendations for their use within semi-arid cropping systems is currently constrained by a lack of information. This study was conducted to determine agroecosystem responses to late-summer seeded cover crops under no-till managem...

Spatial Optimization of Cropping Pattern in an Agricultural Watershed for Food and Biofuel Production with Minimum Downstream Pollution

NASA Astrophysics Data System (ADS)

Pv, F.; Sudheer, K.; Chaubey, I.; RAJ, C.; Her, Y.

2013-05-01

Biofuel is considered to be a viable alternative to meet the increasing fuel demand, and therefore many countries are promoting agricultural activities that help increase production of raw material for biofuel production. Mostly, the biofuel is produced from grain based crops such as Corn, and it apparently create a shortage in food grains. Consequently, there have been regulations to limit the ethanol production from grains, and to use cellulosic crops as raw material for biofuel production. However, cultivation of such cellulosic crops may have different effects on water quality in the watershed. Corn stover, one of the potential cellulosic materials, when removed from the agricultural field for biofuel production, causes a decrease in the organic nutrients in the field. This results in increased use of pesticides and fertilizers which in turn affect the downstream water quality due to leaching of the chemicals. On the contrary, planting less fertilizer-intensive cellulosic crops, like Switch Grass and Miscanthus, is expected to reduce the pollutant loadings from the watershed. Therefore, an ecologically viable land use scenario would be a mixed cropping of grain crops and cellulosic crops, that meet the demand for food and biofuel without compromising on the downstream water quality. Such cropping pattern can be arrived through a simulation-optimization framework. Mathematical models can be employed to evaluate various management scenarios related to crop production and to assess its impact on water quality. Soil and Water Assessment Tool (SWAT) model is one of the most widely used models in this context. SWAT can simulate the water and nutrient cycles, and also quantify the long-term impacts of land management practices, in a watershed. This model can therefore help take decisions regarding the type of cropping and management practices to be adopted in the watershed such that the water quality in the rivers is maintained at acceptable level. In this study, it is proposed to link SWAT model with an optimization algorithm, whose objective is to identify the optimal cropping pattern that results in maximum biomass production for biofuel generation as well as a minimum guaranteed amount of grain production. The optimal allocation ensures that the downstream water quality in the river is within a desirable limit. The study employed probabilistic information in order to address the uncertainty in model simulations. The residual variance of the model is used to transform the deterministic simulations in to probabilistic information. The proposed framework is illustrated using data pertaining to an agricultural watershed in the USA. The preliminary results of the study are encouraging and suggest that an appropriate combination of Corn, Soyabean, Miscanthus, Switch Grass and Pasture land can be arrived at through the developed framework. The placement of Miscanthus and Switch Grass in the watershed help improve the downstream water quality, while Corn and Soyabean makes it deteriorated. The spatial allocation of these crops therefore certainly plays a major role in the downstream water quality.

Assessment of food-water nexus by water footprint: a case study in Saskatchewan, Canada

NASA Astrophysics Data System (ADS)

Zhao, Y.; Si, B.

2016-12-01

It is important but challengeable to understand the water-food nexus complexity. The water footprint (WF), a relatively new index, is a comprehensive indicator that can be used to evaluate crop water production. This paper aims to 1) determine how water footprint changes at different crop rotational types; 2) investigate what is difference if WF is calculated by yield-based or protein-based; and 3) explore how virtual water flows are responding to regional meteorological, agricultural, and socio-economic factors. The result provided the water footprint and virtual water flow exemplified for Saskatchewan agri-food production industries. By using the water footprint, we determined the best rotation for pulse crops in terms of efficiency of water productivity and water-saving opportunity. While yield is a comprehensive index to assess the productivity (yield-based WF), it underestimated the contribution of some crops, such as pulse crops with relatively low yield but high protein contents (protein-based WF). Consequently, we concluded that water-saving benefits can be achieved by the development and adoption of water efficient technology and better virtual water flows may be achieved by increased area of low water footprint in Saskatchewan. Our finding improves the current concepts of water and food security, informs production and trade decisions, and thus suggests optimal strategies by reduced water footprints in terms of agricultural management.

Data Mining for Forecasting Mississippi Cropland Data Layers

NASA Astrophysics Data System (ADS)

Shore, F. L.; Gregory, T. L.

2011-12-01

In 1999, Mississippi became an early adopter of the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) program. With the support of the NASS Spatial Analysis Research Section (SARS), we have progressed from an annual crop picture to a pixel by pixel history of Mississippi farming. Much of our early work for Mississippi agriculture is now easily provided from the web based application CropScape, released by SARS in 2011. In this study, pixel history data from CDLs has been mined to give forecasts of Mississippi crop acres. Traditionally, such agricultural data mining emphasizes the trends of early adopters driven by factors such as global warming, technology, practices, or the marketplace. These studies provide forecasted CDL products produced using See5° and Imagine°, the same software used in Mississippi CDL production since 2006. Mississippi CDL forecasts were made using historical information available as soon as the CDL for the previous year was completed. For example, the CDL forecast for winter wheat, produced at a date when winter wheat was planted but not most crops, gave results of 104.6 +/- 5.4% of the official NASS estimates for winter wheat for the years 2009-2011. In 2012, all of the states of the contiguous US will have the historical CDL data to do this type of study. A CDL forecast is proposed as a useful addition to CropScape.

Influence of Agricultural Practice on Surface Temperature

NASA Astrophysics Data System (ADS)

Czajkowski, K.; Ault, T.; Hayase, R.; Benko, T.

2006-12-01

Changes in land uses/covers can have a significant effect on the temperature of the Earth's surface. Agricultural fields exhibit a significant change in land cover within a single year and from year to year as different crops are planted. These changes in agricultural practices including tillage practice and crop type influence the energy budget as reflected in differences in surface temperature. In this project, Landsat 5 and 7 imagery were used to investigate the influence of crop type and tillage practice on surface temperature in Iowa and NW Ohio. In particular, the three crop rotation of corn, soybeans and wheat, as well as no-till, conservation tillage and tradition tillage methods, were investigated. Crop type and conservation tillage practices were identified using supervised classification. Student surface temperature observations from the GLOBE program were used to correct for the effects of the atmosphere for some of the satellite thermal observations. Students took surface temperature observations in field sites near there schools using hand- held infrared thermometers.

Improving crop classification through attention to the timing of airborne radar acquisitions

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Protz, R.

1984-01-01

Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

Midwestern US Farmers Perceive Crop Advisers as Conduits of Information on Agricultural Conservation Practices

NASA Astrophysics Data System (ADS)

Eanes, Francis R.; Singh, Ajay S.; Bulla, Brian R.; Ranjan, Pranay; Prokopy, Linda S.; Fales, Mary; Wickerham, Benjamin; Doran, Patrick J.

2017-11-01

Nonpoint source pollution from agricultural land uses continues to pose one of the most significant threats to water quality in the US, with measurable impacts across local, regional, and national scales. The impact and the influence of targeted conservation efforts are directly related to the degree to which farmers are familiar with and trust the entities providing the information and/or outreach. Recent research suggests that farmers consistently rank independent and retail-affiliated crop advisers as among the most trusted and influential sources for agronomic information, but little is understood about whether farmers are willing to receive advice from crop advisers on the use of practices that conserve soil and water, and, if so, whether crop advisers will be perceived as influential. We present survey data from farmers ( n = 1461) in Michigan's Saginaw Bay (Lake Huron) watershed to explore these questions. Results suggest that farmers view crop advisers as trustworthy sources of information about conservation, and influential on management practices that have large conservation implications. We discuss these results, along with perceived barriers and opportunities to crop advisers partnering with traditional conservation agencies to enhance the impact of voluntary conservation programs.

Midwestern US Farmers Perceive Crop Advisers as Conduits of Information on Agricultural Conservation Practices.

PubMed

Eanes, Francis R; Singh, Ajay S; Bulla, Brian R; Ranjan, Pranay; Prokopy, Linda S; Fales, Mary; Wickerham, Benjamin; Doran, Patrick J

2017-11-01

Nonpoint source pollution from agricultural land uses continues to pose one of the most significant threats to water quality in the US, with measurable impacts across local, regional, and national scales. The impact and the influence of targeted conservation efforts are directly related to the degree to which farmers are familiar with and trust the entities providing the information and/or outreach. Recent research suggests that farmers consistently rank independent and retail-affiliated crop advisers as among the most trusted and influential sources for agronomic information, but little is understood about whether farmers are willing to receive advice from crop advisers on the use of practices that conserve soil and water, and, if so, whether crop advisers will be perceived as influential. We present survey data from farmers (n = 1461) in Michigan's Saginaw Bay (Lake Huron) watershed to explore these questions. Results suggest that farmers view crop advisers as trustworthy sources of information about conservation, and influential on management practices that have large conservation implications. We discuss these results, along with perceived barriers and opportunities to crop advisers partnering with traditional conservation agencies to enhance the impact of voluntary conservation programs.

S-World: A high resolution global soil database for simulation modelling (Invited)

NASA Astrophysics Data System (ADS)

Stoorvogel, J. J.

2013-12-01

There is an increasing call for high resolution soil information at the global level. A good example for such a call is the Global Gridded Crop Model Intercomparison carried out within AgMIP. While local studies can make use of surveying techniques to collect additional techniques this is practically impossible at the global level. It is therefore important to rely on legacy data like the Harmonized World Soil Database. Several efforts do exist that aim at the development of global gridded soil property databases. These estimates of the variation of soil properties can be used to assess e.g., global soil carbon stocks. However, they do not allow for simulation runs with e.g., crop growth simulation models as these models require a description of the entire pedon rather than a few soil properties. This study provides the required quantitative description of pedons at a 1 km resolution for simulation modelling. It uses the Harmonized World Soil Database (HWSD) for the spatial distribution of soil types, the ISRIC-WISE soil profile database to derive information on soil properties per soil type, and a range of co-variables on topography, climate, and land cover to further disaggregate the available data. The methodology aims to take stock of these available data. The soil database is developed in five main steps. Step 1: All 148 soil types are ordered on the basis of their expected topographic position using e.g., drainage, salinization, and pedogenesis. Using the topographic ordering and combining the HWSD with a digital elevation model allows for the spatial disaggregation of the composite soil units. This results in a new soil map with homogeneous soil units. Step 2: The ranges of major soil properties for the topsoil and subsoil of each of the 148 soil types are derived from the ISRIC-WISE soil profile database. Step 3: A model of soil formation is developed that focuses on the basic conceptual question where we are within the range of a particular soil property at a particular location given a specific soil type. The soil properties are predicted for each grid cell based on the soil type, the corresponding ranges of soil properties, and the co-variables. Step 4: Standard depth profiles are developed for each of the soil types using the diagnostic criteria of the soil types and soil profile information from the ISRIC-WISE database. The standard soil profiles are combined with the the predicted values for the topsoil and subsoil yielding unique soil profiles at each location. Step 5: In a final step, additional soil properties are added to the database using averages for the soil types and pedo-transfer functions. The methodology, denominated S-World (Soils of the World), results in readily available global maps with quantitative pedon data for modelling purposes. It forms the basis for the Global Gridded Crop Model Intercomparison carried out within AgMIP.

Biotech crop planting resumes high adoption in 2016.

PubMed

Aldemita, Rhodora R; Hautea, Randy A

2018-01-02

The global area of biotech crops in 2016 increased from 179.7 million hectares to 185.1 million hectares, a 3% increase equivalent to 5.4 million hectares. Some 26 countries planted biotech crops, 19 of which were developing countries and seven were industrial. Information and data collected from various credible sources showed variations from the previous year. Fluctuations in biotech crop area (both increases and decreases) are influenced by factors including, among others, acceptance and commercialization of new products, demand for meat and livestock feeds, weather conditions, global market price, disease/pest pressure, and government's enabling policies. Countries which have increased biotech crop area in decreasing order in 2016 were Brazil, United States of America, Canada, South Africa, Australia, Bolivia, Philippines, Spain, Vietnam, Bangladesh, Colombia, Honduras, Chile, Sudan, Slovakia, and Costa Rica. Countries with decreased biotech area in decreasing order were China, India, Argentina, Paraguay, Uruguay, Mexico, Portugal, and Czech Republic, in decreasing incremental decrease in biotech area. Pakistan and Myanmar were the only countries with no change in biotech crop (cotton) planted. Information detailed in the paper including future crops and traits in each country could guide stakeholders in informed crafting of strategies and policies for increased adoption of biotech crops in the country.

African Orphan Crops under Abiotic Stresses: Challenges and Opportunities.

PubMed

Tadele, Zerihun

2018-01-01

A changing climate, a growing world population, and a reduction in arable land devoted to food production are all problems facing the world food security. The development of crops that can yield under uncertain and extreme climatic and soil growing conditions can play a key role in mitigating these problems. Major crops such as maize, rice, and wheat are responsible for a large proportion of global food production but many understudied crops (commonly known as "orphan crops") including millets, cassava, and cowpea feed millions of people in Asia, Africa, and South America and are already adapted to the local environments in which they are grown. The application of modern genetic and genomic tools to the breeding of these crops can provide enormous opportunities for ensuring world food security but is only in its infancy. In this review, the diversity and types of understudied crops will be introduced, and the beneficial traits of these crops as well as their role in the socioeconomics of Africa will be discussed. In addition, the response of orphan crops to diverse types of abiotic stresses is investigated. A review of the current tools and their application to the breeding of enhanced orphan crops will also be described. Finally, few examples of global efforts on tackling major abiotic constraints in Africa are presented.

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.

Marginal cost curves for water footprint reduction in irrigated agriculture: guiding a cost-effective reduction of crop water consumption to a permit or benchmark level

NASA Astrophysics Data System (ADS)

Chukalla, Abebe D.; Krol, Maarten S.; Hoekstra, Arjen Y.

2017-07-01

Reducing the water footprint (WF) of the process of growing irrigated crops is an indispensable element in water management, particularly in water-scarce areas. To achieve this, information on marginal cost curves (MCCs) that rank management packages according to their cost-effectiveness to reduce the WF need to support the decision making. MCCs enable the estimation of the cost associated with a certain WF reduction target, e.g. towards a given WF permit (expressed in m3  ha-1 per season) or to a certain WF benchmark (expressed in m3  t-1 of crop). This paper aims to develop MCCs for WF reduction for a range of selected cases. AquaCrop, a soil-water-balance and crop-growth model, is used to estimate the effect of different management packages on evapotranspiration and crop yield and thus the WF of crop production. A management package is defined as a specific combination of management practices: irrigation technique (furrow, sprinkler, drip or subsurface drip); irrigation strategy (full or deficit irrigation); and mulching practice (no, organic or synthetic mulching). The annual average cost for each management package is estimated as the annualized capital cost plus the annual costs of maintenance and operations (i.e. costs of water, energy and labour). Different cases are considered, including three crops (maize, tomato and potato); four types of environment (humid in UK, sub-humid in Italy, semi-arid in Spain and arid in Israel); three hydrologic years (wet, normal and dry years) and three soil types (loam, silty clay loam and sandy loam). For each crop, alternative WF reduction pathways were developed, after which the most cost-effective pathway was selected to develop the MCC for WF reduction. When aiming at WF reduction one can best improve the irrigation strategy first, next the mulching practice and finally the irrigation technique. Moving from a full to deficit irrigation strategy is found to be a no-regret measure: it reduces the WF by reducing water consumption at negligible yield reduction while reducing the cost for irrigation water and the associated costs for energy and labour. Next, moving from no to organic mulching has a high cost-effectiveness, reducing the WF significantly at low cost. Finally, changing from sprinkler or furrow to drip or subsurface drip irrigation reduces the WF, but at a significant cost.

Farmer response to climatic and agricultural market drivers: characteristic time scales and sensitivities

NASA Astrophysics Data System (ADS)

Wurster, P. M.; Maneta, M. P.; Vicente-Serrano, S. M.; Beguería, S.; Silverman, N. L.; Holden, Z.

2017-12-01

Agriculture in the intermountain western United States is dominated by extensive farming and ranching, mostly reliant on rainfed crops and therefore very exposed to precipitation shortfalls. It is also poorly diversified, dominated by five or six major grain crops, which makes it vulnerable to changes in agricultural markets. The economy of the region is very reliant on this type of agriculture, making the entire economy vulnerable to climatic and market fluctuations. Western agriculture is also of significant importance for national food security. Resource managers in the region are increasingly concerned with the impacts that more frequent and severe droughts, or the collapse of crop prices, may have on producers and food production. Effective resource management requires an understanding not only of the regional impact of adverse climatic and market events, but also of which geographic areas are most vulnerable, and why. Unfortunately, few studies exist that look into how farmers in different geographic areas respond to climate and market drivers. In this study we analyze the influence of precipitation and crop price anomalies on crop production, and map the characteristic time scale of these anomalies that correlate best with production anomalies for the 56 counties of Montana, U.S.A. We conduct this analysis using the standardized precipitation index (SPI), and defining a standardized crop value index (SCVI) and a standardized crop production index (SCPI). We use 38 years of data to calculate precipitation anomalies at monthly time scales and annual data to calculate crop price and production anomalies. The standardization of the indices allows for straightforward comparison of the relative influence of climatic and market fluctuations on production anomalies. We apply our methodology to winter wheat, spring durum wheat, barley, alfalfa, and beets which are the most valuable crops produced in the state. Results from this study show that precipitation anomalies accumulated between 3 and 8 months in spring are most explanatory of production anomalies, but that crop price anomalies interact with climatic factors. This study will provide agricultural producers and water managers with valuable information regarding the resiliency of key crops in the state and of Montana's rural economy.

The impact of soil moisture extremes and their spatiotemporal variability on Zambian maize yields

NASA Astrophysics Data System (ADS)

Zhao, Y.; Estes, L. D.; Vergopolan, N.

2017-12-01

Food security in sub-Saharan Africa is highly sensitive to climate variability. While it is well understood that extreme heat has substantial negative impacts on crop yield, the impacts of precipitation extremes, particularly over large spatial extents, are harder to quantify. There are three primary reasons for this difficulty, which are (1) lack of high quality, high resolution precipitation data, (2) rainfall data provide incomplete information on plant water availability, the variable that most directly affects crop performance, and (3) the type of rainfall extreme that most affects crop yields varies throughout the crop development stage. With respect to the first reason, the spatial and temporal variation of precipitation is much greater than that of temperature, yet the spatial resolution of rainfall data is typically even coarser than it is for temperature, particularly within Africa. Even if there were high-resolution rainfall data, the amount of water available to crops also depends on other physical factors that affect evapotranspiration, which are strongly influenced by heterogeneity in the land surface related to topography, soil properties, and land cover. In this context, soil moisture provides a better measure of crop water availability than rainfall. Furthermore, soil moisture has significantly different influences on crop yield depending on the crop's growth stage. The goal of this study is to understand how the spatiotemporal scales of soil moisture extremes interact with crops, more specifically, the timing and the spatial scales of extreme events like droughts and flooding. In this study, we simulate daily-1km soil moisture using HydroBlocks - a physically based land surface model - and compare it with precipitation and remote sensing derived maize yields between 2000 and 2016 in Zambia. We use a novel combination of the SCYM (scalable satellite-based yield mapper) method with DSSAT crop model, which is a mechanistic model responsive to water stress. Understanding the relationships between soil moisture spatiotemporal variability and yields can help to improve agricultural drought risk assessment and seasonal crop yield forecasting as well as early season warning of potential famines.

Using Geostatistical Data Fusion Techniques and MODIS Data to Upscale Simulated Wheat Yield

NASA Astrophysics Data System (ADS)

Castrignano, A.; Buttafuoco, G.; Matese, A.; Toscano, P.

2014-12-01

Population growth increases food request. Assessing food demand and predicting the actual supply for a given location are critical components of strategic food security planning at regional scale. Crop yield can be simulated using crop models because is site-specific and determined by weather, management, length of growing season and soil properties. Crop models require reliable location-specific data that are not generally available. Obtaining these data at a large number of locations is time-consuming, costly and sometimes simply not feasible. An upscaling method to extend coverage of sparse estimates of crop yield to an appropriate extrapolation domain is required. This work is aimed to investigate the applicability of a geostatistical data fusion approach for merging remote sensing data with the predictions of a simulation model of wheat growth and production using ground-based data. The study area is Capitanata plain (4000 km2) located in Apulia Region, mostly cropped with durum wheat. The MODIS EVI/NDVI data products for Capitanata plain were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC) remote for the whole crop cycle of durum wheat. Phenological development, biomass growth and grain quantity of durum wheat were simulated by the Delphi system, based on a crop simulation model linked to a database including soil properties, agronomical and meteorological data. Multicollocated cokriging was used to integrate secondary exhaustive information (multi-spectral MODIS data) with primary variable (sparsely distributed biomass/yield model predictions of durum wheat). The model estimates looked strongly spatially correlated with the radiance data (red and NIR bands) and the fusion data approach proved to be quite suitable and flexible to integrate data of different type and support.

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

Soybean Physiology Calibration in the Community Land Model

NASA Astrophysics Data System (ADS)

Drewniak, B. A.; Bilionis, I.; Constantinescu, E. M.

2014-12-01

With the large influence of agricultural land use on biophysical and biogeochemical cycles, integrating cultivation into Earth System Models (ESMs) is increasingly important. The Community Land Model (CLM) was augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. However, the strong nonlinearity of ESMs makes parameter fitting a difficult task. In this study, our goal is to calibrate ten of the CLM-Crop parameters for one crop type, soybean, in order to improve model projection of plant development and carbon fluxes. We used measurements of gross primary productivity, net ecosystem exchange, and plant biomass from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). Our scheme can perform model calibration using very few evaluations and, by exploiting parallelism, at a fraction of the time required by plain vanilla Markov Chain Monte Carlo (MCMC). We present the results from a twin experiment (self-validation) and calibration results and validation using real observations from an AmeriFlux tower site in the Midwestern United States, for the soybean crop type. The improved model will help researchers understand how climate affects crop production and resulting carbon fluxes, and additionally, how cultivation impacts climate.

A database for coconut crop improvement.

PubMed

Rajagopal, Velamoor; Manimekalai, Ramaswamy; Devakumar, Krishnamurthy; Rajesh; Karun, Anitha; Niral, Vittal; Gopal, Murali; Aziz, Shamina; Gunasekaran, Marimuthu; Kumar, Mundappurathe Ramesh; Chandrasekar, Arumugam

2005-12-08

Coconut crop improvement requires a number of biotechnology and bioinformatics tools. A database containing information on CG (coconut germplasm), CCI (coconut cultivar identification), CD (coconut disease), MIFSPC (microbial information systems in plantation crops) and VO (vegetable oils) is described. The database was developed using MySQL and PostgreSQL running in Linux operating system. The database interface is developed in PHP, HTML and JAVA. http://www.bioinfcpcri.org.

Farmers' climate information needs for long-term adaptive decisions: A case study of almonds in CA

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Pathak, T. B.; Kerr, A. C.; Doll, D.

2016-12-01

Despite advances in climate modeling and projections, several sources report that current tools and models are not widely used in the agriculture sector. Farmers, depending on their local context, require information on very specific climatic metrics such as start of rains during the planting season, number of low temperature days during the growing season, etc. However, such specific climatic information is either not available, and/or is not synthesized and communicated in a manner that is accessible to these decision-makers. This research aims to bridge the gap between climate information and decision-making needs, by providing an improved understanding of what farmers' consider as relevant climate information, and how these needs compare with current modeling capabilities. Almond is a perennial crop, so any changes in climate within its 25-30 year lifetime can have an adverse impact on crop yield. This makes almond growers vulnerable to medium and long-term climate change. Hence, providing appropriate information on future climate projections can help guide their decisions on crop types & varieties, as well as management practices that are better adapted to future climatic conditions. Semi-structured exploratory interviews have been conducted with almond growers, farm advisors, and other industry stakeholders, with three goals: (1) to understand how growers have used climate information in the past; (2) to identify key climatic variables that are relevant - including appropriate temporal scales and acceptable uncertainty levels; and (3) to understand communication methods that could improve the usability of climate information for farm-level decision-making. The interviews showcased a great diversity amongst growers in terms of how they used weather/climate information. Discussions also indicated that there was a potential for climate information to impact long-term decisions, but only if it is provided within the right context, terminology, and communication channels. The findings offer valuable bottom-up insights into farmers' perspectives on relevance of climate information. These results will also be compared with current modeling capabilities in order to synthesize conclusions for improving the usability of climate science for agricultural decision-makers.

76 FR 60810 - Agency Information Collection Activities: Notice of Intent To Renew Collection, Copies of Crop...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-30

... COMMODITY FUTURES TRADING COMMISSION Agency Information Collection Activities: Notice of Intent To Renew Collection, Copies of Crop and Market Information Reports AGENCY: Commodity Futures Trading Commission. ACTION: Notice. SUMMARY: The Commodity Futures Trading Commission (CFTC) is announcing an...

Tree establishment in floodplain agroforestry practices

Treesearch

Daniel C. Dey; John M. Kabrick; Michael A. Gold

2004-01-01

The benefits of soil mounding, a cover crop, and various nursery stock types were evaluated for establishing pin and swamp white oaks in floodplain crop fields. The two stock types were 1-0 bareroot and large (3- and 5-gallon) container seedlings grown by the RPMTM method.

Growth of tropical legume cover crops as influenced by nitrogen fertilization and Rhizobia

USDA-ARS?s Scientific Manuscript database

Tropical legume cover crops are important components in cropping systems due to their role in improving soil quality. Information is limited on the influence of nitrogen (N) fertilization on growth of tropical legume cover crops grown on Oxisols. A greenhouse experiment was conducted to evaluate the...

Multimodel ensembles of wheat growth: many models are better than one

USDA-ARS?s Scientific Manuscript database

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but suc...

Short-term soil responses to late-seeded cover crops in a semi-arid environment

USDA-ARS?s Scientific Manuscript database

Cover crops can expand ecosystem services, though sound management recommendations for their use within semi-arid cropping systems is currently constrained by a lack of information. This study was conducted to determine agroecosystem responses to late-summer seeded cover crops under no-till managem...

Economic Benefits of Predictive Models for Pest Control in Agricultural Crops

USDA-ARS?s Scientific Manuscript database

Various forms of crop models or decision making tools for managing crops have existed for many years. The potential advantage of all of these decision making tools is that more informed and economically improved crop management or decision making is accomplished. However, examination of some of thes...

Perceptions of Crop Science Instructional Materials.

ERIC Educational Resources Information Center

Elkins, D. M.

1994-01-01

A number of crop science instructors have indicated that there is a shortage of quality, current crop/plant science teaching materials, particularly textbooks. A survey instrument was developed to solicit information from teachers about the use and adequacy of textbooks, laboratory manuals, and videotapes in crop/plant science instruction. (LZ)

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.

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

NASA Astrophysics Data System (ADS)

Ismaeel, A.; Zhou, Q.

2018-04-01

Accurate information of crop rotation in large basin is essential for policy decisions on land, water and nutrient resources around the world. Crop area estimation using low spatial resolution remote sensing data is challenging in a large heterogeneous basin having more than one cropping seasons. This study aims to evaluate the accuracy of two phenological datasets individually and in combined form to map crop rotations in complex irrigated Indus basin without image segmentation. Phenology information derived from Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, having 8-day temporal and 1000 m spatial resolution, was used in the analysis. An unsupervised (temporal space clustering) to supervised (area knowledge and phenology behavior) classification approach was adopted to identify 13 crop rotations. Estimated crop area was compared with reported area collected by field census. Results reveal that combined dataset (NDVI*LAI) performs better in mapping wheat-rice, wheat-cotton and wheat-fodder rotation by attaining root mean square error (RMSE) of 34.55, 16.84, 20.58 and mean absolute percentage error (MAPE) of 24.56 %, 36.82 %, 30.21 % for wheat, rice and cotton crop respectively. For sugarcane crop mapping, LAI produce good results by achieving RMSE of 8.60 and MAPE of 34.58 %, as compared to NDVI (10.08, 40.53 %) and NDVI*LAI (10.83, 39.45 %). The availability of major crop rotation statistics provides insight to develop better strategies for land, water and nutrient accounting frameworks to improve agriculture productivity.

75 FR 25832 - Commodity Credit Corporation Information Collection; Noninsured Crop Disaster Assistance Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-10

... produced for food or fiber. Additionally, NAP provides assistance for losses of floriculture, ornamental... oats and sea grass, and industrial crops. The information collected is necessary to determine whether a...

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.

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

Tuning growth cycles of Brassica crops via natural antisense transcripts of BrFLC.

PubMed

Li, Xiaorong; Zhang, Shaofeng; Bai, Jinjuan; He, Yuke

2016-03-01

Several oilseed and vegetable crops of Brassica are biennials that require a prolonged winter cold for flowering, a process called vernalization. FLOWERING LOCUS C (FLC) is a central repressor of flowering. Here, we report that the overexpression of natural antisense transcripts (NATs) of Brassica rapa FLC (BrFLC) greatly shortens plant growth cycles. In rapid-, medium- and slow-cycling crop types, there are four copies of the BrFLC genes, which show extensive variation in sequences and expression levels. In Bre, a biennial crop type that requires vernalization, five NATs derived from the BrFLC2 locus are rapidly induced under cold conditions, while all four BrFLC genes are gradually down-regulated. The transgenic Bre lines overexpressing a long NAT of BrFLC2 do not require vernalization, resulting in a gradient of shortened growth cycles. Among them, a subset of lines both flower and set seeds as early as Yellow sarson, an annual crop type in which all four BrFLC genes have non-sense mutations and are nonfunctional in flowering repression. Our results demonstrate that the growth cycles of biennial crops of Brassica can be altered by changing the expression levels of BrFLC2 NATs. Thus, BrFLC2 NATs and their transgenic lines are useful for the genetic manipulation of crop growth cycles. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Brachypodium as an experimental system for the study of stem parenchyma biology in grasses

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

Jensen, Jacob Kruger; Wilkerson, Curtis Gene; Ma, Wujun

Stem parenchyma is a major cell type that serves key metabolic functions for the plant especially in large grasses, such as sugarcane and sweet sorghum, where it serves to store sucrose or other products of photosynthesis. It is therefore desirable to understand the metabolism of this cell type as well as the mechanisms by which it provides its function for the rest of the plant. Ultimately, this information can be used to selectively manipulate this cell type in a controlled manner to achieve crop improvement. In this study, we show that Brachypodium distachyon is a useful model system for stemmore » pith parenchyma biology. Brachypodium can be grown under condition where it resembles the growth patterns of important crops in that it produces large amounts of stem material with the lower leaves senescing and with significant stores of photosynthate located in the stem parenchyma cell types. We further characterize stem plastid morphology as a function of tissue types, as this organelle is central for a number of metabolic pathways, and quantify gene expression for the four main classes of starch biosynthetic genes. Notably, we find several of these genes differentially regulated between stem and leaf. Furthermore, these studies show, consistent with other grasses, that the stem functions as a specialized storage compartment in Brachypodium.« less

Brachypodium as an experimental system for the study of stem parenchyma biology in grasses

DOE PAGES

Jensen, Jacob Kruger; Wilkerson, Curtis Gene; Ma, Wujun

2017-03-01

Stem parenchyma is a major cell type that serves key metabolic functions for the plant especially in large grasses, such as sugarcane and sweet sorghum, where it serves to store sucrose or other products of photosynthesis. It is therefore desirable to understand the metabolism of this cell type as well as the mechanisms by which it provides its function for the rest of the plant. Ultimately, this information can be used to selectively manipulate this cell type in a controlled manner to achieve crop improvement. In this study, we show that Brachypodium distachyon is a useful model system for stemmore » pith parenchyma biology. Brachypodium can be grown under condition where it resembles the growth patterns of important crops in that it produces large amounts of stem material with the lower leaves senescing and with significant stores of photosynthate located in the stem parenchyma cell types. We further characterize stem plastid morphology as a function of tissue types, as this organelle is central for a number of metabolic pathways, and quantify gene expression for the four main classes of starch biosynthetic genes. Notably, we find several of these genes differentially regulated between stem and leaf. Furthermore, these studies show, consistent with other grasses, that the stem functions as a specialized storage compartment in Brachypodium.« less

Large scale maps of cropping intensity in Asia from MODIS

NASA Astrophysics Data System (ADS)

Gray, J. M.; Friedl, M. A.; Frolking, S. E.; Ramankutty, N.; Nelson, A.

2013-12-01

Agricultural systems are geographically extensive, have profound significance to society, and also affect regional energy, carbon, and water cycles. Since most suitable lands worldwide have been cultivated, there is growing pressure to increase yields on existing agricultural lands. In tropical and sub-tropical regions, multi-cropping is widely used to increase food production, but regional-to-global information related to multi-cropping practices is poor. Such information is of critical importance to ensure sustainable food production while mitigating against negative environmental impacts associated with agriculture such as contamination and depletion of freshwater resources. Unfortunately, currently available large-area inventory statistics are inadequate because they do not capture important spatial patterns in multi-cropping, and are generally not available in a timeframe that can be used to help manage cropping systems. High temporal resolution sensors such as MODIS provide an excellent source of information for addressing this need. However, relative to studies that document agricultural extensification, systematic assessment of agricultural intensification via multi-cropping has received relatively little attention. The goal of this work is to help close this methodological and information gap by developing methods that use multi-temporal remote sensing to map multi-cropping systems in Asia. Image time series analysis is especially challenging in Asia because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low quality remote sensing observations, especially during the Asian Monsoon. The methodology that we use for this work builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but employs refined methods to segment, smooth, and gap-fill 8-day EVI time series calculated from MODIS BRDF corrected surface reflectances. Crop cycle segments are identified based on changes in slope for linear regressions estimated for local windows, and constrained by the EVI amplitude and length of crop cycles that are identified. The procedure can be used to map seasonal or long-term average cropping strategies, and to characterize changes in cropping intensity over longer time periods. The datasets produced using this method therefore provide information related to global cropping systems, and more broadly, provide important information that is required to ensure sustainable management of Earth's resources and ensure food security. To test our algorithm, we applied it to time series of MODIS EVI images over Asia from 2000-2012. Our results demonstrate the utility of multi-temporal remote sensing for characterizing multi-cropping practices in some of the most important and intensely agricultural regions in the world. To evaluate our approach, we compared results from MODIS to field-scale survey data at the pixel scale, and agricultural inventory statistics at sub-national scales. We then mapped changes in multi-cropped area in Asia from the early MODIS period (2001-2004) to present (2009-2012), and characterizes the magnitude and location of changes in cropping intensity over the last 12 years. We conclude with a discussion of the challenges, future improvements, and broader impacts of this work.

African Orphan Crops under Abiotic Stresses: Challenges and Opportunities

PubMed Central

2018-01-01

A changing climate, a growing world population, and a reduction in arable land devoted to food production are all problems facing the world food security. The development of crops that can yield under uncertain and extreme climatic and soil growing conditions can play a key role in mitigating these problems. Major crops such as maize, rice, and wheat are responsible for a large proportion of global food production but many understudied crops (commonly known as “orphan crops”) including millets, cassava, and cowpea feed millions of people in Asia, Africa, and South America and are already adapted to the local environments in which they are grown. The application of modern genetic and genomic tools to the breeding of these crops can provide enormous opportunities for ensuring world food security but is only in its infancy. In this review, the diversity and types of understudied crops will be introduced, and the beneficial traits of these crops as well as their role in the socioeconomics of Africa will be discussed. In addition, the response of orphan crops to diverse types of abiotic stresses is investigated. A review of the current tools and their application to the breeding of enhanced orphan crops will also be described. Finally, few examples of global efforts on tackling major abiotic constraints in Africa are presented. PMID:29623231

Multiple transgene traits may create un-intended fitness effects in Brassica napus

EPA Science Inventory

Increasingly, genetically modified crops are being developed to express multiple “stacked” traits for different types of transgenes, for example, herbicide resistance, insect resistance, crop quality and resistance to environmental factors. The release of crops that express mult...

A database for coconut crop improvement

PubMed Central

Rajagopal, Velamoor; Manimekalai, Ramaswamy; Devakumar, Krishnamurthy; Rajesh; Karun, Anitha; Niral, Vittal; Gopal, Murali; Aziz, Shamina; Gunasekaran, Marimuthu; Kumar, Mundappurathe Ramesh; Chandrasekar, Arumugam

2005-01-01

Coconut crop improvement requires a number of biotechnology and bioinformatics tools. A database containing information on CG (coconut germplasm), CCI (coconut cultivar identification), CD (coconut disease), MIFSPC (microbial information systems in plantation crops) and VO (vegetable oils) is described. The database was developed using MySQL and PostgreSQL running in Linux operating system. The database interface is developed in PHP, HTML and JAVA. Availability http://www.bioinfcpcri.org PMID:17597858

SPATIAL AND TEMPORAL PATTERNS OF THE MOVEMENT OF SEASONAL AGRICULTURAL MIGRANT CHILDREN INTO WISCONSIN, EDUCATIONAL PROGRAMS FOR CHILDREN OF MIGRATORY AGRICULTURAL WORKERS IN WISCONSIN, REPORT 2.

ERIC Educational Resources Information Center

LINDSEY, HERBERT H.; AND OTHERS

USEFUL MEANS OF ANTICIPATING THE MOVEMENTS OF MIGRANT CHILDREN INCLUDE ANALYSIS OF CROPS, THE HARVESTING OF WHICH REQUIRES OUT-OF-STATE WORKERS, DISTRIBUTIONAL MAPS OF CROP ACREAGE, NORMAL TIME SCHEDULES FOR CROPS, AND INFORMATION ON AGRICULTURAL DEVELOPMENTS. SUCH INFORMATION ASSISTS IN THE PLANNING OF SCHOOL PROGRAMS. IN WISCONSIN, MOST MIGRANT…

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.

Increasing public understanding of transgenic crops through the World Wide Web.

PubMed

Byrne, Patrick F; Namuth, Deana M; Harrington, Judy; Ward, Sarah M; Lee, Donald J; Hain, Patricia

2002-07-01

Transgenic crops among the most controversial "science and society" issues of recent years. Because of the complex techniques involved in creating these crops and the polarized debate over their risks and beliefs, a critical need has arisen for accessible and balanced information on this technology. World Wide Web sites offer several advantages for disseminating information on a fast-changing technical topic, including their global accessibility; and their ability to update information frequently, incorporate multimedia formats, and link to networks of other sites. An alliance between two complementary web sites at Colorado State University and the University of Nebraska-Lincoln takes advantage of the web environment to help fill the need for public information on crop genetic engineering. This article describes the objectives and features of each site. Viewership data and other feedback have shown these web sites to be effective means of reaching public audiences on a complex scientific topic.

A Decade of Carbon Flux Measurements with Annual and Perennial Crop Rotations on the Canadian Prairies

NASA Astrophysics Data System (ADS)

Amiro, B. D.; Tenuta, M.; Gao, X.; Gervais, M.

2016-12-01

The Fluxnet database has over 100 cropland sites, some of which have long-term (over a decade) measurements. Carbon neutrality is one goal of sustainable agriculture, although measurements over many annual cropping systems have indicated that soil carbon is often lost. Croplands are complex systems because the CO2 exchange depends on the type of crop, soil, weather, and management decisions such as planting date, nutrient fertilization and pest management strategy. Crop rotations are often used to decrease pest pressure, and can range from a simple 2-crop system, to have 4 or more crops in series. Carbon dioxide exchange has been measured using the flux-gradient technique since 2006 in agricultural systems in Manitoba, Canada. Two cropping systems are being followed: one that is a rotation of annual crops (corn, faba bean, spring wheat, rapeseed, barley, spring wheat, corn, soybean, spring wheat, soybean); and the other with a perennial phase of alfalfa/grass in years 3 to 6. Net ecosystem production ranged from a gain of 330 g C m-2 y-1 in corn to a loss of 75 g C m-2 y-1 in a poor spring-wheat crop. Over a decade, net ecosystem production for the annual cropping system was not significantly different from zero (carbon neutral), but the addition of the perennial phase increased the sink to 130 g C m-2 y-1. Once harvest removals were included, there was a net loss of carbon ranging from 77 g C m-2 y-1 in the annual system to 52 g C m-2 y-1 in the annual-perennial system; but neither of these were significantly different from zero. Termination of the perennial phase of the rotation only caused short-term increases in respiration. We conclude that both these systems were close to carbon-neutral over a decade even though they were tilled with a short growing season (90 to 130 days). We discuss the need for more datasets on agricultural systems to inform management options to increase the soil carbon sink.

Food safety assessment of planting patterns of four vegetable-type crops grown in soil contaminated by electronic waste activities.

PubMed

Liu, Ling; Hu, Liangliang; Tang, Jianjun; Li, Yuefang; Zhang, Qian; Chen, Xin

2012-01-01

A field experiment was conducted to assess the effect of crop and planting pattern on levels of cadmium (Cd), lead (Pb), and copper (Cu) in crops grown in soil contaminated by electronic waste. The crops were maize (Zea mays L. var. Shentian-1), tomato (Solanum lycopersicum L. var. Zhongshu-4), cabbage (Brassica oleracea L. var. Jingfeng-1), and pakchoi (Brassica chinensis (L.) Makino. var. Youdonger-Hangzhou). The planting patterns were crop monoculture, crop co-planted with a legume, and crop co-planted with another crop. Metal concentrations in the edible parts of the crops varied with types of metals and crops. Pb concentration was higher in leafy vegetables (cabbage and pakchoi) than in maize or tomato, Cd concentration was higher in tomato and pakchoi than in maize or cabbage, and Cu concentration was higher in maize and pakchoi than in tomato or cabbage. Metal concentrations in the edible part were also influenced by planting pattern. Relative to monoculture, co-planting and especially co-planting with Japanese clover tended to decrease Pb accumulation and increase Cd accumulation. According to the maximum permissible concentration (MPC) standard of the National Standard Agency in China, only maize (under all planting patterns) could be safely consumed. Because co-planting tended to increase Cd accumulation even in maize, however, the results suggest that maize monoculture is the optimal crop and planting pattern for this kind of contaminated soil. Copyright © 2011 Elsevier Ltd. All rights reserved.

A crops and soils data base for scene radiation research

NASA Technical Reports Server (NTRS)

Biehl, L. L.; Bauer, M. E.; Robinson, B. F.; Daughtry, C. S. T.; Silva, L. F.; Pitts, D. E.

1982-01-01

Management and planning activities with respect to food production require accurate and timely information on crops and soils on a global basis. The needed information can be obtained with the aid of satellite-borne sensors, if the relations between the spectral properties and the important biological-physical parameters of crops and soils are known. In order to obtain this knowledge, the development of a crops and soils scene radiation research data base was initiated. Work related to the development of this data base is discussed, taking into account details regarding the conducted experiments, the performed measurements, the calibration of spectral data, questions of data base access, and the expansion of the crops and soils scene radiation data base for 1982.

Agricultural Capacity to Increase the Production of Select Fruits and Vegetables in the US: A Geospatial Modeling Analysis.

PubMed

Conrad, Zach; Peters, Christian J; Chui, Kenneth; Jahns, Lisa; Griffin, Timothy S

2017-09-23

The capacity of US agriculture to increase the output of specific foods to accommodate increased demand is not well documented. This research uses geospatial modeling to examine the capacity of the US agricultural landbase to increase the per capita availability of an example set of nutrient-dense fruits and vegetables. These fruits and vegetables were selected based on nutrient content and an increasing trend of domestic production and consumption. Geographic information system models were parameterized to identify agricultural land areas meeting crop-specific growing requirements for monthly precipitation and temperature; soil depth and type; cropland availability; and proximity to existing production centers. The results of these analyses demonstrate that crop production can be expanded by nearly 144,000 ha within existing national production centers, generating an additional 0.05 cup-equivalents of fruits and vegetables per capita per day, representing a 1.7% increase above current total F&V availability. Expanding the size of national crop production centers can further increase the availability of all F&V by 2.5%-5.4%, which is still less than the recommended amount. Challenges to increasing F&V production in the US include lack of labor availability, barriers to adoption among producers, and threats to crop yields from environmental concerns.

Agricultural Capacity to Increase the Production of Select Fruits and Vegetables in the US: A Geospatial Modeling Analysis

PubMed Central

Peters, Christian J.; Chui, Kenneth; Jahns, Lisa; Griffin, Timothy S.

2017-01-01

The capacity of US agriculture to increase the output of specific foods to accommodate increased demand is not well documented. This research uses geospatial modeling to examine the capacity of the US agricultural landbase to increase the per capita availability of an example set of nutrient-dense fruits and vegetables. These fruits and vegetables were selected based on nutrient content and an increasing trend of domestic production and consumption. Geographic information system models were parameterized to identify agricultural land areas meeting crop-specific growing requirements for monthly precipitation and temperature; soil depth and type; cropland availability; and proximity to existing production centers. The results of these analyses demonstrate that crop production can be expanded by nearly 144,000 ha within existing national production centers, generating an additional 0.05 cup-equivalents of fruits and vegetables per capita per day, representing a 1.7% increase above current total F&V availability. Expanding the size of national crop production centers can further increase the availability of all F&V by 2.5%–5.4%, which is still less than the recommended amount. Challenges to increasing F&V production in the US include lack of labor availability, barriers to adoption among producers, and threats to crop yields from environmental concerns. PMID:28946618

Deriving crop calendar using NDVI time-series

NASA Astrophysics Data System (ADS)

Patel, J. H.; Oza, M. P.

2014-11-01

Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting - peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.

Climate change impacts on risks of groundwater pollution by herbicides: a regional scale assessment

NASA Astrophysics Data System (ADS)

Steffens, Karin; Moeys, Julien; Lindström, Bodil; Kreuger, Jenny; Lewan, Elisabet; Jarvis, Nick

2014-05-01

Groundwater contributes nearly half of the Swedish drinking water supply, which therefore needs to be protected both under present and future climate conditions. Pesticides are sometimes found in Swedish groundwater in concentrations exceeding the EU-drinking water limit and thus constitute a threat. The aim of this study was to assess the present and future risks of groundwater pollution at the regional scale by currently approved herbicides. We identified representative combinations of major crop types and their specific herbicide usage (product, dose and application timing) based on long-term monitoring data from two agricultural catchments in the South-West of Sweden. All these combinations were simulated with the regional version of the pesticide fate model MACRO (called MACRO-SE) for the periods 1970-1999 and 2070-2099 for a major crop production region in South West Sweden. To represent the uncertainty in future climate data, we applied a five-member ensemble based on different climate model projections downscaled with the RCA3-model (Swedish Meteorological and Hydrological Institute). In addition to the direct impacts of changes in the climate, the risks of herbicide leaching in the future will also be affected by likely changes in weed pressure and land use and management practices (e.g. changes in crop rotations and application timings). To assess the relative importance of such factors we performed a preliminary sensitivity analysis which provided us with a hierarchical structure for constructing future herbicide use scenarios for the regional scale model runs. The regional scale analysis gave average concentrations of herbicides leaching to groundwater for a large number of combinations of soils, crops and compounds. The results showed that future scenarios for herbicide use (more autumn-sown crops, more frequent multiple applications on one crop, and a shift from grassland to arable crops such as maize) imply significantly greater risks of herbicide leaching to groundwater in a changing climate, and that these indirect effects outweigh the direct effects of changes in climate driving variables. Due to the large uncertainties in climate change impact assessments, drawing firm conclusions is not possible, but this type of analysis provides indications of likely future concerns and can be used as an early-warning tool to inform the general public, responsible public authorities and decision makers.

Cover Crop Chart: An Intuitive Educational Resource for Extension Professionals

ERIC Educational Resources Information Center

Liebig, Mark A.; Johnson, Holly; Archer, David; Hendrickson, John; Nichols, Kristine; Schmer, Marty; Tanaka, Don

2013-01-01

Interest in cover crops by agricultural producers has increased the need for information regarding the suitability of crops for addressing different production and natural resource goals. To help address this need, staff at the USDA-ARS Northern Great Plains Research Laboratory developed a decision aid called the Cover Crop Chart (CCC). Visually…

Comparison of Sub-Pixel Classification Approaches for Crop-Specific Mapping

EPA Science Inventory

This paper examined two non-linear models, Multilayer Perceptron (MLP) regression and Regression Tree (RT), for estimating sub-pixel crop proportions using time-series MODIS-NDVI data. The sub-pixel proportions were estimated for three major crop types including corn, soybean, a...

National-scale crop type mapping and area estimation using multi-resolution remote sensing and field survey

NASA Astrophysics Data System (ADS)

Song, X. P.; Potapov, P.; Adusei, B.; King, L.; Khan, A.; Krylov, A.; Di Bella, C. M.; Pickens, A. H.; Stehman, S. V.; Hansen, M.

2016-12-01

Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is first illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be implemented in an operational mode. The approach has also been applied for other crops in other regions, such as winter wheat in Pakistan, soybean in Argentina and soybean in the entire South America. Similar levels of accuracy and timeliness were achieved as in the US.

Airborne Hyperspectral Imagery for the Detection of Agricultural Crop Stress

NASA Technical Reports Server (NTRS)

Cassady, Philip E.; Perry, Eileen M.; Gardner, Margaret E.; Roberts, Dar A.

2001-01-01

Multispectral digital imagery from aircraft or satellite is presently being used to derive basic assessments of crop health for growers and others involved in the agricultural industry. Research indicates that narrow band stress indices derived from hyperspectral imagery should have improved sensitivity to provide more specific information on the type and cause of crop stress, Under funding from the NASA Earth Observation Commercial Applications Program (EOCAP) we are identifying and evaluating scientific and commercial applications of hyperspectral imagery for the remote characterization of agricultural crop stress. During the summer of 1999 a field experiment was conducted with varying nitrogen treatments on a production corn-field in eastern Nebraska. The AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) hyperspectral imager was flown at two critical dates during crop development, at two different altitudes, providing images with approximately 18m pixels and 3m pixels. Simultaneous supporting soil and crop characterization included spectral reflectance measurements above the canopy, biomass characterization, soil sampling, and aerial photography. In this paper we describe the experiment and results, and examine the following three issues relative to the utility of hyperspectral imagery for scientific study and commercial crop stress products: (1) Accuracy of reflectance derived stress indices relative to conventional measures of stress. We compare reflectance-derived indices (both field radiometer and AVIRIS) with applied nitrogen and with leaf level measurement of nitrogen availability and chlorophyll concentrations over the experimental plots (4 replications of 5 different nitrogen levels); (2) Ability of the hyperspectral sensors to detect sub-pixel areas under crop stress. We applied the stress indices to both the 3m and 18m AVIRIS imagery for the entire production corn field using several sub-pixel areas within the field to compare the relative sensitivity of each stress index; and (3) Comparative sensitivity of stress indices to realistic measurement uncertainties. We compare the stress indices calculated with several levels of spectral uncertainty (by shifting the wavelengths) and reflectance uncertainty (by systematically varying the reflectance retrieval code initialization).

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

USGS Publications Warehouse

Maxwell, S.K.; Craig, M.E.

2008-01-01

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (−0.8%–1.6% using the 2002 segment model to −5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.

Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Franch, Belen; Vermote, Eric; Roger, Jean-Claude; Becker-Reshef, Inbal; Justice, Christopher; Kussul, Nataliia

2017-01-01

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

Seed crops of forest trees in the pine region of California

Treesearch

H.A Fowells; G.H. Schubert

1956-01-01

To provide a better basis for silvicultural practices in the pine region of California, we are reporting the results of 28 years of study of seed crops. The study covered the development of cones, periodicity of cone crops, types of trees bearing cones, climatic and biotic factors affecting cone crops, and the dispersal of seed. The findings reported here should help...

Soybean crop-water production functions in a humid region across years and soils determined with APEX model

Treesearch

Bangbang Zhang; Gary Feng; Lajpat R. Ahuja; Xiangbin Kong; Ying Ouyang; Ardeshir Adeli; Johnie N. Jenkins

2018-01-01

Crop production as a function of water use or water applied, called the crop water production function (CWPF), is a useful tool for irrigation planning, design and management. However, these functions are not only crop and variety specific they also vary with soil types and climatic conditions (locations). Derivation of multi-year average CWPFs through field...

Stem-quality changes on young, mixed upland hardwoods after crop-tree release

Treesearch

David L. Sonderman; David L. Sonderman

1987-01-01

Relative change of several types of stem defects was studied over an 8-year period to determine the effects of crop-tree thinning on the development of tree quality. Special interest was given to changes in relative quality associated with defect indicators of crop trees compared to trees in unthinned plots. The relative quality classes of the crop trees went from...

The development, evaluation, and application of O3 flux and flux-response models for additional agricultural crops

Treesearch

L. D. Emberson; W. J. Massman; P. Buker; G. Soja; I. Van De Sand; G. Mills; C. Jacobs

2006-01-01

Currently, stomatal O3 flux and flux-response models only exist for wheat and potato (LRTAP Convention, 2004), as such there is a need to extend these models to include additional crop types. The possibility of establishing robust stomatal flux models for five agricultural crops (tomato, grapevine, sugar beet, maize and sunflower) was investigated. These crops were...

Passive exposure to agricultural pesticides and risk of childhood leukemia in an Italian community

PubMed Central

Malagoli, Carlotta; Costanzini, Sofia; Heck, Julia E.; Malavolti, Marcella; De Girolamo, Gianfranco; Oleari, Paola; Palazzi, Giovanni; Teggi, Sergio; Vinceti, Marco

2016-01-01

Background Exposure to pesticides has been suggested as a risk factor for childhood leukemia, but definitive evidence on this relation and the specific pesticides involved is still not clear. Objective We carried out a population-based case-control study in a Northern Italy community to assess the possible relation between passive exposure to agricultural pesticides and risk of acute childhood leukemia. Methods We assessed passive pesticide exposure of 111 childhood leukemia cases and 444 matched controls by determining density and type of agricultural land use within a 100-m radius buffer around children’s homes. We focused on four common crop types, arable, orchard, vineyard and vegetable, characterized by the use of specific pesticides that are potentially involved in childhood induced leukemia. The use of these pesticides was validated within the present study. We computed the odds ratios (OR) of the disease and their 95% confidence intervals (CI) according to type and density of crops around the children’s homes, also taking into account traffic pollution and high-voltage power line magnetic field exposure. Results Childhood leukemia risk did not increase in relation with any of the crop types with the exception of arable crops, characterized by the use of 2.4-D, MCPA, glyphosate, dicamba, triazine and cypermethrin. The very few children (n=11) residing close to arable crops had an OR for childhood leukemia of 2.04 (95% CI 0.50–8.35), and such excess risk was further enhanced among children aged < 5 years. Conclusions Despite the null association with most crop types and the statistical imprecision of the estimates, the increased leukemia risk among children residing close to arable crops indicates the need to further investigate the involvement in disease etiology of passive exposure to herbicides and pyrethroids, though such exposure is unlikely to play a role in the vast majority of cases. PMID:27693118

Passive exposure to agricultural pesticides and risk of childhood leukemia in an Italian community.

PubMed

Malagoli, Carlotta; Costanzini, Sofia; Heck, Julia E; Malavolti, Marcella; De Girolamo, Gianfranco; Oleari, Paola; Palazzi, Giovanni; Teggi, Sergio; Vinceti, Marco

2016-11-01

Exposure to pesticides has been suggested as a risk factor for childhood leukemia, but definitive evidence on this relation and the specific pesticides involved is still not clear. We carried out a population-based case-control study in a Northern Italy community to assess the possible relation between passive exposure to agricultural pesticides and risk of acute childhood leukemia. We assessed passive pesticide exposure of 111 childhood leukemia cases and 444 matched controls by determining density and type of agricultural land use within a 100-m radius buffer around children's homes. We focused on four common crop types, arable, orchard, vineyard and vegetable, characterized by the use of specific pesticides that are potentially involved in childhood induced leukemia. The use of these pesticides was validated within the present study. We computed the odds ratios (OR) of the disease and their 95% confidence intervals (CI) according to type and density of crops around the children's homes, also taking into account traffic pollution and high-voltage power line magnetic field exposure. Childhood leukemia risk did not increase in relation with any of the crop types with the exception of arable crops, characterized by the use of 2.4-D, MCPA, glyphosate, dicamba, triazine and cypermethrin. The very few children (n=11) residing close to arable crops had an OR for childhood leukemia of 2.04 (95% CI 0.50-8.35), and such excess risk was further enhanced among children aged <5 years. Despite the null association with most crop types and the statistical imprecision of the estimates, the increased leukemia risk among children residing close to arable crops indicates the need to further investigate the involvement in disease etiology of passive exposure to herbicides and pyrethroids, though such exposure is unlikely to play a role in the vast majority of cases. Copyright © 2016 Elsevier GmbH. All rights reserved.

Estimating Crop Growth Stage by Combining Meteorological and Remote Sensing Based Techniques

NASA Astrophysics Data System (ADS)

Champagne, C.; Alavi-Shoushtari, N.; Davidson, A. M.; Chipanshi, A.; Zhang, Y.; Shang, J.

2016-12-01

Estimations of seeding, harvest and phenological growth stage of crops are important sources of information for monitoring crop progress and crop yield forecasting. Growth stage has been traditionally estimated at the regional level through surveys, which rely on field staff to collect the information. Automated techniques to estimate growth stage have included agrometeorological approaches that use temperature and day length information to estimate accumulated heat and photoperiod, with thresholds used to determine when these stages are most likely. These approaches however, are crop and hybrid dependent, and can give widely varying results depending on the method used, particularly if the seeding date is unknown. Methods to estimate growth stage from remote sensing have progressed greatly in the past decade, with time series information from the Normalized Difference Vegetation Index (NDVI) the most common approach. Time series NDVI provide information on growth stage through a variety of techniques, including fitting functions to a series of measured NDVI values or smoothing these values and using thresholds to detect changes in slope that are indicative of rapidly increasing or decreasing `greeness' in the vegetation cover. The key limitations of these techniques for agriculture are frequent cloud cover in optical data that lead to errors in estimating local features in the time series function, and the incongruity between changes in greenness and traditional agricultural growth stages. There is great potential to combine both meteorological approaches and remote sensing to overcome the limitations of each technique. This research will examine the accuracy of both meteorological and remote sensing approaches over several agricultural sites in Canada, and look at the potential to integrate these techniques to provide improved estimates of crop growth stage for common field crops.

Environmental risk assessments for transgenic crops producing output trait enzymes

PubMed Central

Tuttle, Ann; Shore, Scott; Stone, Terry

2009-01-01

The environmental risks from cultivating crops producing output trait enzymes can be rigorously assessed by testing conservative risk hypotheses of no harm to endpoints such as the abundance of wildlife, crop yield and the rate of degradation of crop residues in soil. These hypotheses can be tested with data from many sources, including evaluations of the agronomic performance and nutritional quality of the crop made during product development, and information from the scientific literature on the mode-of-action, taxonomic distribution and environmental fate of the enzyme. Few, if any, specific ecotoxicology or environmental fate studies are needed. The effective use of existing data means that regulatory decision-making, to which an environmental risk assessment provides essential information, is not unnecessarily complicated by evaluation of large amounts of new data that provide negligible improvement in the characterization of risk, and that may delay environmental benefits offered by transgenic crops containing output trait enzymes. PMID:19924556

A Site-sPecific Agricultural water Requirement and footprint Estimator (SPARE:WATER 1.0)

NASA Astrophysics Data System (ADS)

Multsch, S.; Al-Rumaikhani, Y. A.; Frede, H.-G.; Breuer, L.

2013-07-01

The agricultural water footprint addresses the quantification of water consumption in agriculture, whereby three types of water to grow crops are considered, namely green water (consumed rainfall), blue water (irrigation from surface or groundwater) and grey water (water needed to dilute pollutants). By considering site-specific properties when calculating the crop water footprint, this methodology can be used to support decision making in the agricultural sector on local to regional scale. We therefore developed the spatial decision support system SPARE:WATER that allows us to quantify green, blue and grey water footprints on regional scale. SPARE:WATER is programmed in VB.NET, with geographic information system functionality implemented by the MapWinGIS library. Water requirements and water footprints are assessed on a grid basis and can then be aggregated for spatial entities such as political boundaries, catchments or irrigation districts. We assume inefficient irrigation methods rather than optimal conditions to account for irrigation methods with efficiencies other than 100%. Furthermore, grey water is defined as the water needed to leach out salt from the rooting zone in order to maintain soil quality, an important management task in irrigation agriculture. Apart from a thorough representation of the modelling concept, we provide a proof of concept where we assess the agricultural water footprint of Saudi Arabia. The entire water footprint is 17.0 km3 yr-1 for 2008, with a blue water dominance of 86%. Using SPARE:WATER we are able to delineate regional hot spots as well as crop types with large water footprints, e.g. sesame or dates. Results differ from previous studies of national-scale resolution, underlining the need for regional estimation of crop water footprints.

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.

Natural cycles and agricultural inputs: a farm gate Ecological Footprint analysis

NASA Astrophysics Data System (ADS)

Passeri, Nicolo; Blasi, Emanuele; Borucke, Michael; Galli, Alessandro; Franco, Silvio

2014-05-01

Land suitability for different crops depends on soil, water and climate conditions, as well as farmers' cultivation choices. Moreover, the use of agricultural inputs affects the natural cycles of crops and impacts their production. By assessing the ecological performance of farms as influenced by crop types, cultivation choices and land suitability one can therefore evaluate the effectiveness of agricultural practices and governance's options. Ecological Footprint accounts can be used to measure such ecological performance. These accounts track human demand for natural resources and ecological services and compare this demand with nature ability to regenerate these resource and services. This regenerative capacity is called biocapacity. Both demand (Footprint) and supply (biocapacity) are expressed in global hectares. Farming different from most other human activities, not only uses natural resources, but also enhances or erodes ecological supply. It therefore affects all factors that determine both Footprint and biocapacity. Climate, farmers' skills and choices (fertilizers, pesticides, machines) determine crop productivity, and to what extent crops preserve or compromise soils. The aim of this work is to evaluate how farmer's choices affect resources overexploitation. The study analysed how the use of inputs influences natural cycles within farm boundaries. This result from a pilot case study will show how particular farming practices affect both the farm's biocapacity and Ecological Footprint. Such analysis is relevant for informing involved stakeholders, namely the farmers on more sustainable agricultural practices and the policy makers on more suitable agricultural policies.

Crop yield response to increasing biochar rates

USDA-ARS?s Scientific Manuscript database

The benefit or detriment to crop yield from biochar application varies with biochar type/rate, soil, crop, or climate. The objective of this research was to identify yield response of cotton (Gossypium hirsutum L.), corn (Zea mayes L.), and peanut (Arachis hypogaea L.) to hardwood biochar applied at...

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.

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

NASA Astrophysics Data System (ADS)

Gunda, T.; Bazuin, J. T.; Nay, J.; Yeung, K. L.

2017-03-01

Access to seasonal climate forecasts can benefit farmers by allowing them to make more informed decisions about their farming practices. However, it is unclear whether farmers realize these benefits when crop choices available to farmers have different and variable costs and returns; multiple countries have programs that incentivize production of certain crops while other crops are subject to market fluctuations. We hypothesize that the benefits of forecasts on farmer livelihoods will be moderated by the combined impact of differing crop economics and changing climate. Drawing upon methods and insights from both physical and social sciences, we develop a model of farmer decision-making to evaluate this hypothesis. The model dynamics are explored using empirical data from Sri Lanka; primary sources include survey and interview information as well as game-based experiments conducted with farmers in the field. Our simulations show that a farmer using seasonal forecasts has more diversified crop selections, which drive increases in average agricultural income. Increases in income are particularly notable under a drier climate scenario, when a farmer using seasonal forecasts is more likely to plant onions, a crop with higher possible returns. Our results indicate that, when water resources are scarce (i.e. drier climate scenario), farmer incomes could become stratified, potentially compounding existing disparities in farmers’ financial and technical abilities to use forecasts to inform their crop selections. This analysis highlights that while programs that promote production of certain crops may ensure food security in the short-term, the long-term implications of these dynamics need careful evaluation.

Efficient crop type mapping based on remote sensing in the Central Valley, California

NASA Astrophysics Data System (ADS)

Zhong, Liheng

Most agricultural systems in California's Central Valley are purposely flexible and intentionally designed to meet the demands of dynamic markets. Agricultural land use is also impacted by climate change and urban development. As a result, crops change annually and semiannually, which makes estimating agricultural water use difficult, especially given the existing method by which agricultural land use is identified and mapped. A minor portion of agricultural land is surveyed annually for land-use type, and every 5 to 8 years the entire valley is completely evaluated. So far no effort has been made to effectively and efficiently identify specific crop types on an annual basis in this area. The potential of satellite imagery to map agricultural land cover and estimate water usage in the Central Valley is explored. Efforts are made to minimize the cost and reduce the time of production during the mapping process. The land use change analysis shows that a remote sensing based mapping method is the only means to map the frequent change of major crop types. The traditional maximum likelihood classification approach is first utilized to map crop types to test the classification capacity of existing algorithms. High accuracy is achieved with sufficient ground truth data for training, and crop maps of moderate quality can be timely produced to facilitate a near-real-time water use estimate. However, the large set of ground truth data required by this method results in high costs in data collection. It is difficult to reduce the cost because a trained classification algorithm is not transferable between different years or different regions. A phenology based classification (PBC) approach is developed which extracts phenological metrics from annual vegetation index profiles and identifies crop types based on these metrics using decision trees. According to the comparison with traditional maximum likelihood classification, this phenology-based approach shows great advantages when the size of the training set is limited by ground truth availability. Once developed, the classifier is able to be applied to different years and a vast area with only a few adjustments according to local agricultural and annual weather conditions. 250 m MODIS imagery is utilized as the main input to the PBC algorithm and displays promising capacity in crop identification in several counties in the Central Valley. A time series of Landsat TM/ETM+ images at a 30 m resolution is necessary in the crop mapping of counties with smaller land parcels, although the processing time is longer. Spectral characteristics are also employed to identify crops in PBC. Spectral signatures are associated with phenological stages instead of imaging dates, which highly increases the stability of the classifier performance and overcomes the problem of over-fitting. Moderate accuracies are achieved by PBC, with confusions mostly within the same crop categories. Based on a quantitative analysis, misclassification in PBC has very trivial impacts on the accuracy of agricultural water use estimate. The cost of the entire PBC procedure is controlled to a very low level, which will enable its usage in routine annual crop mapping in the Central Valley.

Molecular and morphological differentiation between the crop and weedy types in velvetleaf (Abutilon theophrasti Medik.) using a chloroplast DNA marker: seed source of the present invasive velvetleaf in Japan.

PubMed

Kurokawa, S; Shibaike, H; Akiyama, H; Yoshimura, Y

2004-12-01

A comparison of chloroplast DNA (cpDNA) sequences was carried out between the crop and weed types of Abutilon theophrasti to clarify the seed source of the present weedy velvetleaf in Japan. A sequencing analysis of approx. 6% of the chloroplast genome (ca 10 kbp) detected three nucleotide substitutions, one six-base-pair insertion/deletion (indel) and one 30-base pair inversion, which distinguish two haplotypes of cpDNA. A PCR-based survey of the indel and the inversion revealed that the 93 accessions of velvetleaf collected from the world could be divided into two groups. A morphological marker (capsule color) could be used to discriminate the crop type and the weed type, and hence, along with cpDNA haplotype, to distinguish three genotypes (Type I, II, and III). All Japanese cultivars and crop accessions from other countries were Type I. Weed types were divided into Type II and III. All of the samples from the USA, and the samples taken from grain imports to Japan were Type III. Since most of the weedy types distributed in Japan were of Type III, it is argued that they were introduced as seeds in the imported grain. We also found that the Type II plants sporadically occurred in Japan. It is suggested that they originated as hybrids, with indigenous cultivars as the maternal ancestor. Such hybrids must have survived since the cessation of velvetleaf cultivation about a century ago.

Identification of bacteria in total mixed ration silage produced with and without crop silage as an ingredient.

PubMed

Nishino, Naoki; Ogata, Yu; Han, Hongyan; Yamamoto, Yasunari

2015-01-01

As a forage source for total mixed ration (TMR) silage production, locally produced crop silage is now used in addition to imported hay. This type of TMR ensiling is regarded as a two-step fermentation process; hence, a survey was carried out to determine whether the bacteria in crop silage affect the subsequent TMR ensiling. Fermentation product contents and bacterial community were determined for TMR silage and its ingredient silages collected in August, October and November. August product contained corn, sorghum and Italian ryegrass silages, October product had wheat silage exclusively and November product did not include any crop silages. Acetic acid, lactic acid, 2,3-butanediol and ethanol were predominant fermentation products in corn, sorghum, Italian ryegrass and wheat silages, respectively. Robust lactic acid fermentation was seen in TMR silage, even if acetate-type and alcohol-type silages were mixed as ingredients. The finding that bacterial community of the TMR silage appeared unrelated to those of ingredient silage supported this. Silages of various fermentation types can therefore be formulated without interfering with lactate-type fermentation in TMR silage. © 2014 Japanese Society of Animal Science.

The effects of different types of crop straw on the transformation of pentachlorophenol in flooded paddy soil.

PubMed

Lin, Jiajiang; Meng, Jun; He, Yan; Xu, Jianming; Chen, Zuliang; Brookes, Philip C

2018-02-01

The incorporation of various types of crop straw to agricultural soils has long been practiced to improve soil fertility. However, the effects of crop straw on the fate of organo-chlorine pesticides in flooded paddy soils are not well understood. The dechlorination of pentachlorophenol (PCP) in four vertical profiles (0-10, 10-20, 20-30, 30-50 mm depth) of two flooded paddy soils, a Plinthudult (Soil 1) and a Tropudult (Soil 2) was investigated following the application of four crop straws (rice, wheat, rape and Chinese milk vetch) to them. In all treatments, PCP dechlorination decreased with increasing soil depth. In the crop straw treatments, PCP was almost completely dechlorinated within 60 days, and rapidly transformed to 2,3,4,5-tetrachlorophenol, and further to 3,4,5-trichlorophenol. Further dechlorination of 3,4,5-trichlorophenol also occurred in all treatments except for the rape straw. It is possible that the NH 4 + and NO 3 - derived from the straw are responsible for the inhibition of the 3,4,5-trichlorophenol dechlorination. The reduction of Fe (III) and SO 4 2- increased following application of the crop straws. The RDA analysis indicated that the Fe (III) reducing bacteria might be involved in the ortho-dechlorination, while SO 4 2- reducing bacteria were involved in para- and meta-dechlorination of PCP. The complete detoxification of PCP depended upon both the crop straw type and soil properties. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Nitrogen in Streams: A Comparison of Two Estimates of Fertilizer Application

NASA Astrophysics Data System (ADS)

Mehaffey, M.; Neale, A.

2011-12-01

Decision makers frequently rely on water and air quality models to develop nutrient management strategies. Obviously, the results of these models (e.g., SWAT, SPARROW, CMAQ) are only as good as the nutrient source input data and recently the Nutrient Innovations Task Group has called for a better accounting of nonpoint nutrient sources. Currently, modelers frequently rely on county level fertilizer sales records combined with acreage of crops to estimate nitrogen sources from fertilizer for counties or watersheds. However, since fertilizer sales data are based on reported amounts they do not necessarily reflect actual use on the fields. In addition the reported sales data quality varies by state resulting in differing accuracy between states. In this study we examine an alternative method potentially providing a more uniform, spatially explicit, estimate of fertilizer use. Our nitrogen application data is estimated at a 30m pixel resolution which allows for scalable inputs for use in water and air quality models. To develop this dataset we combined raster data from the National Cropland data layer (CDL) data with the National Land Cover Data (NLCD). This process expanded the NLCD's 'cultivated crops' classes to included major grains, cover crops, and vegetable and fruits. The Agriculture Resource Management Survey chemical fertilizer application rate data were summarized by crop type and year for each state, encompassing the corn, soybean, spring wheat, and winter wheat crop types (ARMS, 2002-2005). The chemical fertilizer application rate data were then used to estimate annual application parameters for nitrogen, phosphate, potash, herbicide, pesticide, and total pesticide, all expressed on a mass-per-unit-crop-area basis for each state for each crop type. By linking crop types to nitrogen application rates, we can better estimate where applied fertilizer would likely be in excess of the amounts used by crops or where conservation practices may improve retention and uptake helping offset the impacts to water. To test the accuracy of our finer resolution nitrogen application data, we compare its ability to predict nitrogen concentrations in streams with the ability of the county sales data to do the same.

An investigation of spectral change as influenced by irrigation and evapotranspiration volume estimation in western Nebraska

USGS Publications Warehouse

Seevers, P.M.; Sadowski, F.C.; Lauer, D.T.

1990-01-01

Retrospective satellite image data were evaluated for their ability to demonstrate the influence of center-pivot irrigation development in western Nebraska on spectral change and climate-related factors for the region. Periodic images of an albedo index and a normalized difference vegetation index (NDVI) were generated from calibrated Landsat multispectral scanner (MSS) data and used to monitor spectral changes associated with irrigation development from 1972 through 1986. The albedo index was not useful for monitoring irrigation development. For the NDVI, it was found that proportions of counties in irrigated agriculture, as discriminated by a threshold, were more highly correlated with reported ground estimates of irrigated agriculture than were county mean greenness values. A similar result was achieved when using coarse resolution Advanced Very High Resolution Radiometer (AVHRR) image data for estimating irrigated agriculture. The NDVI images were used to evaluate a procedure for making areal estimates of actual evapotranspiration (ET) volumes. Estimates of ET volumes for test counties, using reported ground acreages and corresponding standard crop coefficients, were correlated with the estimates of ET volume using crop coefficients scaled to NDVI values and pixel counts of crop areas. These county estimates were made under the assumption that soil water availability was unlimited. For nonirrigated vegetation, this may result in over-estimation of ET volumes. Ground information regarding crop types and acreages are required to derive the NDVI scaling factor. Potential ET, estimated with the Jensen-Haise model, is common to both methods. These results, achieved with both MSS and AVHRR data, show promise for providing climatologically important land surface information for regional and global climate models. ?? 1990 Kluwer Academic Publishers.

The GRIN-Taxonomy Crop Wild Relative Inventory. Pp 453-457 in Maxted, N., Mulloo, M.E., Ford-Lloyd, B.V. Enhancing crop genepool use: capturing wild relative and landrace diversity for crop improvement

USDA-ARS?s Scientific Manuscript database

In order to provide an informational tool for assessing and prioritizing germplasm needs for ex situ conservation in the U.S. National Plant Germplasm System (NPGS), the USDA Agricultural Research Service in 2008 initiated a project to identify crop wild relatives (CWR) of major and minor crops. Eac...

An overview of CERES-Sorghum as implemented in the cropping systems model version 4.5

USDA-ARS?s Scientific Manuscript database

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important grain crop globally. It stands out for its diversity of plant types, end-uses, and roles in cropping systems. This diversity presents opportunities but also complicates evaluation of production options, especially under climate uncert...

40 CFR 158.100 - Pesticide use patterns.

Code of Federal Regulations, 2010 CFR

2010-07-01

... use patterns. There are six broad use categories used in the data tables. The six broad categories... outdoor uses, and indoor uses of all types. The 6 broad use categories are further subdivided into 12... part are: (1) Terrestrial food crop use. (2) Terrestrial feed crop use. (3) Terrestrial nonfood crop...

Correlations between the modelled potato crop yield and the general atmospheric circulation

NASA Astrophysics Data System (ADS)

Sepp, Mait; Saue, Triin

2012-07-01

Biology-related indicators do not usually depend on just one meteorological element but on a combination of several weather indicators. One way to establish such integral indicators is to classify the general atmospheric circulation into a small number of circulation types. The aim of present study is to analyse connections between general atmospheric circulation and potato crop yield in Estonia. Meteorologically possible yield (MPY), calculated by the model POMOD, is used to characterise potato crop yield. Data of three meteorological stations and the biological parameters of two potato sorts were applied to the model, and 73 different classifications of atmospheric circulation from catalogue 1.2 of COST 733, domain 05 are used to qualify circulation conditions. Correlation analysis showed that there is at least one circulation type in each of the classifications with at least one statistically significant (99%) correlation with potato crop yield, whether in Kuressaare, Tallinn or Tartu. However, no classifications with circulation types correlating with MPY in all three stations at the same time were revealed. Circulation types inducing a decrease in the potato crop yield are more clearly represented. Clear differences occurred between the observed geographical locations as well as between the seasons: derived from the number of significant circulation types, summer and Kuressaare stand out. Of potato varieties, late 'Anti' is more influenced by circulation. Analysis of MSLP maps of circulation types revealed that the seaside stations (Tallinn, Kuressaare) suffer from negative effects of anti-cyclonic conditions (drought), while Tartu suffers from the cyclonic activity (excessive water).

Economic Benefits of Improved Information on Worldwide Crop Production: An Optimal Decision Model of Production and Distribution with Application to Wheat, Corn, and Soybeans

NASA Technical Reports Server (NTRS)

Andrews, J.

1977-01-01

An optimal decision model of crop production, trade, and storage was developed for use in estimating the economic consequences of improved forecasts and estimates of worldwide crop production. The model extends earlier distribution benefits models to include production effects as well. Application to improved information systems meeting the goals set in the large area crop inventory experiment (LACIE) indicates annual benefits to the United States of $200 to $250 million for wheat, $50 to $100 million for corn, and $6 to $11 million for soybeans, using conservative assumptions on expected LANDSAT system performance.

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.

Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information

NASA Astrophysics Data System (ADS)

Peng, Hong-Gang; Wang, Jian-Qiang

2017-11-01

In recent years, sustainable energy crop has become an important energy development strategy topic in many countries. Selecting the most sustainable energy crop is a significant problem that must be addressed during any biofuel production process. The focus of this study is the development of an innovative multi-criteria decision-making (MCDM) method to handle sustainable energy crop selection problems. Given that various uncertain data are encountered in the evaluation of sustainable energy crops, linguistic intuitionistic fuzzy numbers (LIFNs) are introduced to present the information necessary to the evaluation process. Processing qualitative concepts requires the effective support of reliable tools; then, a cloud model can be used to deal with linguistic intuitionistic information. First, LIFNs are converted and a novel concept of linguistic intuitionistic cloud (LIC) is proposed. The operations, score function and similarity measurement of the LICs are defined. Subsequently, the linguistic intuitionistic cloud density-prioritised weighted Heronian mean operator is developed, which served as the basis for the construction of an applicable MCDM model for sustainable energy crop selection. Finally, an illustrative example is provided to demonstrate the proposed method, and its feasibility and validity are further verified by comparing it with other existing methods.

20 CFR 220.144 - Evaluation guides for a self-employed claimant.

Code of Federal Regulations, 2012 CFR

2012-04-01

... farm. (B) The claimant will have presented strong evidence that he or she is materially participating... land. (iii) Production. The term “production” refers to the physical work performed and the expenses... on matters, such as rotation of crops, the type of crops to be grown, the type of livestock to be...

Conjecture regarding posttranslational modifications to the arabidopsis type I proton-pumping pyrophosphatase (AVP1)

USDA-ARS?s Scientific Manuscript database

Agbiotechnology uses genetic engineering to improve the output and value of crops. Altering the expression of the plant Type I Proton-pumping Pyrophosphatase (H+-PPase) has already proven to be a useful tool to enhance crop productivity. Despite the effective use of this gene in translational resear...

Crop physiology calibration in the CLM

DOE PAGES

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

2015-04-15

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Crop physiology calibration in the CLM

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

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Assessment of Food Chain Pathway Parameters in Biosphere Models: Annual Progress Report for Fiscal Year 2004

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

Napier, Bruce A.; Krupka, Kenneth M.; Fellows, Robert J.

2004-12-02

This Annual Progress Report describes the work performed and summarizes some of the key observations to date on the U.S. Nuclear Regulatory Commission’s project Assessment of Food Chain Pathway Parameters in Biosphere Models, which was established to assess and evaluate a number of key parameters used in the food-chain models used in performance assessments of radioactive waste disposal facilities. Section 2 of this report describes activities undertaken to collect samples of soils from three regions of the United States, the Southeast, Northwest, and Southwest, and perform analyses to characterize their physical and chemical properties. Section 3 summarizes information gathered regardingmore » agricultural practices and common and unusual crops grown in each of these three areas. Section 4 describes progress in studying radionuclide uptake in several representative crops from the three soil types in controlled laboratory conditions. Section 5 describes a range of international coordination activities undertaken by Project staff in order to support the underlying data needs of the Project. Section 6 provides a very brief summary of the status of the GENII Version 2 computer program, which is a “client” of the types of data being generated by the Project, and for which the Project will be providing training to the US NRC staff in the coming Fiscal Year. Several appendices provide additional supporting information.« less

Risk Assessment and Stewardship of Bt Crops

EPA Science Inventory

Registration of Bt crops as part of the FIFRA requirements involves the assessment of environmental risk associated with the new crop variety. The assessment analysis stipulates that the seed producer provide clear and unambiguous information relating to certain risk categories a...

Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; Verdery, Ashton M.; McDaniel, Phillip M.; Rindfuss, Ronald R.

2012-01-01

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops. PMID:24187378

Estimation of Rice Crop Yields Using Random Forests in Taiwan

NASA Astrophysics Data System (ADS)

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

2017-12-01

Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2.9%, respectively. Although there are several uncertainties attributed to the data quality of input layers, our study demonstrates the promising application of random forests for estimating rice crop yields at the national level in Taiwan. This approach could be transferable to other regions of the world for improving large-scale estimation of rice crop yields.

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.

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.

A plan for application system verification tests: The value of improved meteorological information, volume 1. [economic consequences of improved meteorological information

NASA Technical Reports Server (NTRS)

1976-01-01

The framework within which the Applications Systems Verification Tests (ASVTs) are performed and the economic consequences of improved meteorological information demonstrated is described. This framework considers the impact of improved information on decision processes, the data needs to demonstrate the economic impact of the improved information, the data availability, the methodology for determining and analyzing the collected data and demonstrating the economic impact of the improved information, and the possible methods of data collection. Three ASVTs are considered and program outlines and plans are developed for performing experiments to demonstrate the economic consequences of improved meteorological information. The ASVTs are concerned with the citrus crop in Florida, the cotton crop in Mississippi and a group of diverse crops in Oregon. The program outlines and plans include schedules, manpower estimates and funding requirements.

A future scenario of the global regulatory landscape regarding genome-edited crops

PubMed Central

Araki, Motoko

2017-01-01

ABSTRACT The global agricultural landscape regarding the commercial cultivation of genetically modified (GM) crops is mosaic. Meanwhile, a new plant breeding technique, genome editing is expected to make genetic engineering-mediated crop breeding more socially acceptable because it can be used to develop crop varieties without introducing transgenes, which have hampered the regulatory review and public acceptance of GM crops. The present study revealed that product- and process-based concepts have been implemented to regulate GM crops in 30 countries. Moreover, this study analyzed the regulatory responses to genome-edited crops in the USA, Argentina, Sweden and New Zealand. The findings suggested that countries will likely be divided in their policies on genome-edited crops: Some will deregulate transgene-free crops, while others will regulate all types of crops that have been modified by genome editing. These implications are discussed from the viewpoint of public acceptance. PMID:27960622

Soil total carbon and nitrogen and crop yields after eight years of tillage, crop rotation, and cultural practice

USDA-ARS?s Scientific Manuscript database

Information on the long-term effect of management practices on soil C and N stocks is lacking. An experiment was conducted from 2004 to 2011 in the northern Great Plains, USA to examine the effects of tillage, crop rotation, and cultural practice on annualized crop biomass (stems + leaves) residue r...

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

Crop Management to Cope with Global Change: A Systems Perspective Aided by Information Technologies

USDA-ARS?s Scientific Manuscript database

Optimizing crop management must consider the dynamic interaction of abiotic and biotic factors within the context of economic, environmental, sociological, and policy constraints. A wide array of information technologies exists to assist producers, consultants, scientists, agribusiness, action agenc...

Hyperspectral imagery for mapping crop yield for precision agriculture

USDA-ARS?s Scientific Manuscript database

Crop yield is perhaps the most important piece of information for crop management in precision agriculture. It integrates the effects of various spatial variables such as soil properties, topographic attributes, tillage, plant population, fertilization, irrigation, and pest infestations. A yield map...

Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

USGS Publications Warehouse

Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

2016-01-01

Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.

Alternative scenarios of bioenergy crop production in an agricultural landscape and implications for bird communities.

PubMed

Blank, Peter J; Williams, Carol L; Sample, David W; Meehan, Timothy D; Turner, Monica G

2016-01-01

Increased demand and government mandates for bioenergy crops in the United States could require a large allocation of agricultural land to bioenergy feedstock production and substantially alter current landscape patterns. Incorporating bioenergy landscape design into land-use decision making could help maximize benefits and minimize trade-offs among alternative land uses. We developed spatially explicit landscape scenarios of increased bioenergy crop production in an 80-km radius agricultural landscape centered on a potential biomass-processing energy facility and evaluated the consequences of each scenario for bird communities. Our scenarios included conversion of existing annual row crops to perennial bioenergy grasslands and conversion of existing grasslands to annual bioenergy row crops. The scenarios explored combinations of four biomass crop types (three potential grassland crops along a gradient of plant diversity and one annual row crop [corn]), three land conversion percentages to bioenergy crops (10%, 20%, or 30% of row crops or grasslands), and three spatial configurations of biomass crop fields (random, clustered near similar field types, or centered on the processing plant), yielding 36 scenarios. For each scenario, we predicted the impact on four bird community metrics: species richness, total bird density, species of greatest conservation need (SGCN) density, and SGCN hotspots (SGCN birds/ha ≥ 2). Bird community metrics consistently increased with conversion of row crops to bioenergy grasslands and consistently decreased with conversion of grasslands to bioenergy row crops. Spatial arrangement of bioenergy fields had strong effects on the bird community and in some cases was more influential than the amount converted to bioenergy crops. Clustering grasslands had a stronger positive influence on the bird community than locating grasslands near the central plant or at random. Expansion of bioenergy grasslands onto marginal agricultural lands will likely benefit grassland bird populations, and bioenergy landscapes could be designed to maximize biodiversity benefits while meeting targets for biomass production.

Chili peppers: Challenges and advances in transitioning harvesting of New Mexico's signature crop

USDA-ARS?s Scientific Manuscript database

New Mexico-type chile (Capsicum annuum L.), often referred to as ‘Anaheim’, is the signature crop of New Mexico. Both the red and green (fully sized, but physiologically immature) crops are celebrated in local cuisine, culture and art, and production and processing of chile is an integral contributo...

Carbon budgets of thirteen years at the FLUXNET cropland site Oensingen, Switzerland

NASA Astrophysics Data System (ADS)

Emmel, Carmen; Revill, Andrew; Hörtnagl, Lukas; Eugster, Werner

2017-04-01

The FLUXNET cropland site at Oensingen, Switzerland (CH-Oe2) is located on the Swiss Plateau, which is representative for the average domain of agricultural crop production in Switzerland. The site is managed under the low pesticide integrated production (IP) farming protocol and features a crop rotation focusing on winter wheat, but also includes winter barley, rapeseed, peas and potatoes as well as intermediate cover crops. Thirteen years of eddy covariance and meteorological measurements are available for the site. The carbon imports through manure applications and sowing, along with the exports through harvests, were quantified. In this study, we analyze the carbon budgets of all crop types and measurement years. These results will be compared to changes in soil carbon content. We will answer the questions: (1) Has the crop rotation and field management resulted in a net carbon source or sink? (2) To what extent are the different crop types linked to net carbon exchanges? (3) What are the climatic potential drivers for the interannual cropland carbon budget? (4) Is the carbon budget reflected in the changes in soil carbon content?

Cropland Capture: A Game to Improve Global Cropland through Crowdsourcing

NASA Astrophysics Data System (ADS)

Fritz, Steffen; Sturn, Tobias; See, Linda; Perger, Christoph; Schill, Christian; McCallum, Ian; Schepaschenko, Dmitry; Karner, Mathias; Dueruer, Martina; Kraxner, Florian; Obersteiner, Michael

2014-05-01

Accurate and reliable global cropland extent maps are essential for estimating and forecasting crop yield, in particular losses due to drought and production anomalies. Major questions surrounding energy futures and environmental change (EU and US biofuel target setting, determination of greenhouse gas emissions, REDD initiatives, and implications of climate change on crop production and productivity patterns) also require reliable information on the spatial distribution of cropland as well as crop types. Although global land cover maps identify cropland (which exist as one or more land cover categories), this information is currently not accurate enough for many applications. There are several ways of improving current cropland extent though hybrid approaches and by integrating information collected though Geo-Wiki (a global crowdsourcing platform) from very high resolution imagery such as that found on Google Earth. Another way of getting improved cropland extent maps would be to classify all very high resolution images found on Google Earth and to create a wall-to-wall map of cropland. This is a very ambitious task that would require a large number of individuals, like that found in massive multiplayer online games. For this reason we have developed a game called 'Cropland Capture'. The game can be played on a desktop, on a tablet (iPad or Android) or mobile phone (iPhone or Android) where the game mechanics are very simple. The player is provided with a satellite image or in-situ photo and they must determine if the image contains cropland or not. The game was launched in the middle of November 2013 and will run for 6 months, after which the weekly winners will be entered into a draw to win large prizes. To date we have collected more than 2.5 million areas, where we will continue to expand the sample to more locations around the world. Eventually the data will be used to calibrate and validate a new version of our global cropland map, where the latest version is available from http://beta-hybrid.geo-wiki.org. If we find, however, that a large number of people participate in the game, we will aim to make wall-to-wall cropland maps for those countries where no national maps exist. This paper will present an overview of the game and a summary of the crowdsourced data from the game, including information about quality and user performance. If successful, this gaming approach could be used to gather information about other land cover types in the future in order to improve global land cover information more generally.

78 FR 53370 - Common Crop Insurance Regulations; Forage Seed Crop Provisions

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-29

... process of good farming practices, as applicable, must be exhausted before any action against FCIC may be... for all producers regardless of the size of their farming operation. For instance, all producers are... guarantee per acre for each type and practice in the unit by the insured acreage of that type and practice...

77 FR 3227 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-23

... year experience, weather occurrences and projections, market demand, new farming techniques and... elements, as required: crop planted, planting date, crop's intended use, type or variety, practice...

Radar response to vegetation. [soil moisture mapping via microwave backscattering

NASA Technical Reports Server (NTRS)

Ulaby, F. T.

1975-01-01

Active microwave measurements of vegetation backscatter were conducted to determine the utility of radar in mapping soil moisture through vegetation and mapping crop types. Using a truck-mounted boom, spectral response data were obtained for four crop types (corn, milo, soybeans, and alfalfa) over the 4-8 GHz frequency band, at incidence angles of 0 to 70 degrees in 10-degree steps, and for all four linear polarization combinations. Based on a total of 125 data sets covering a wide range of soil moisture, content, system design criteria are proposed for each of the aforementioned objectives. Quantitative soil moisture determination was best achieved at the lower frequency end of the 4-8 GHz band using HH polarized waves in the 5- to 15-degree incidence angle range. A combination of low and high frequency measurements are suggested for classifying crop types. For crop discrimination, a dual-frequency dual-polarization (VV and cross) system operating at incidence angles above 40 degrees is suggested.

[The public perception of information about the potential risks of genetically modified crops in the food chain].

PubMed

Furnival, Ariadne Chloë; Pinheiro, Sônia Maria

2008-01-01

At a time when genetically modified (GM) crops are entering the Brazilian food chain, we present the findings of a study that makes use of a qualitative technique involving focal groups to look into the public's interpretation of the information available about this biotechnological innovation. This methodology produced results that revealed the interconnections drawn by the research subjects between this form of biotechnology, changes to the environment, and food production in general. The mistrust expressed about GM crops was particularly attributed by the participants to the non-availability of comprehensible information in the mass media or on product labels.

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.

Modeling global annual N2O and NO emissions from fertilized fields

NASA Astrophysics Data System (ADS)

Bouwman, A. F.; Boumans, L. J. M.; Batjes, N. H.

2002-12-01

Information from 846 N2O emission measurements in agricultural fields and 99 measurements for NO emissions was used to describe the influence of various factors regulating emissions from mineral soils in models for calculating global N2O and NO emissions. Only those factors having a significant influence on N2O and NO emissions were included in the models. For N2O these were (1) environmental factors (climate, soil organic C content, soil texture, drainage and soil pH); (2) management-related factors (N application rate per fertilizer type, type of crop, with major differences between grass, legumes and other annual crops); and (3) factors related to the measurements (length of measurement period and frequency of measurements). The most important controls on NO emission include the N application rate per fertilizer type, soil organic-C content and soil drainage. Calculated global annual N2O-N and NO-N emissions from fertilized agricultural fields amount to 2.8 and 1.6 Mtonne, respectively. The global mean fertilizer-induced emissions for N2O and NO amount to 0.9% and 0.7%, respectively, of the N applied. These overall results account for the spatial variability of the main N2O and NO emission controls on the landscape scale.

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

USGS Publications Warehouse

Mariotto, Isabella; Thenkabail, Prasad S.; Huete, Alfredo; Slonecker, E. Terrence; Platonov, Alexander

2013-01-01

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ1 (400–2500 nm) versus λ2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm).Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%).Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

GIS and crop simulation modelling applications in climate change research

USDA-ARS?s Scientific Manuscript database

The challenges that climate change presents humanity require an unprecedented ability to predict the responses of crops to environment and management. Geographic information systems (GIS) and crop simulation models are two powerful and highly complementary tools that are increasingly used for such p...

Cover Crop Chart: An intuitive educational resource for extension professionals

USDA-ARS?s Scientific Manuscript database

Interest in cover crops by agricultural producers has increased the need for information regarding the suitability of crops for addressing different production and natural resource goals. To help address this need, staff at the USDA Agricultural Research Service Northern Great Plains Research Labor...

Plant available nitrogen from anaerobically digested sludge and septic tank sludge applied to crops grown in the tropics.

PubMed

Sripanomtanakorn, S; Polprasert, C

2002-04-01

Agricultural land is an attractive alternative for the disposal of biosolids since it utilises the recyclable nutrients in the production of crops. In Thailand and other tropical regions, limited field-study information exists on the effect of biosolids management strategies on crop N utilisation and plant available N (PAN) of biosolids. A field study was conducted to quantify the PAN of the applied biosolids, and to evaluate the N uptake rates of some tropical crops. Sunflower (Helianthus annuus) and tomato (Lycopersicon esculentum) were chosen in this study. Two types of biosolids used were: anaerobically digested sludge and septic tank sludge. The soil is acid sulfate and is classified as Sulfic Tropaquepts with heavy clay in texture. The anaerobically digested sludge applied rates were: 0, 156 and 312 kg N ha(-1) for the sunflower plots, and 0, 586, and 1172 kg N ha(-1) for the tomato plots. The septic tank sludge applied rates were: 0, 95 and 190 kg N ha(-1) for the sunflower plots, and 0, 354 and 708 kg N ha(-1) for the tomato plots, respectively. The results indicated the feasibility of applying biosolids to grow tropical crops. The applications of the anaerobically digested sludge and the septic tank sludge resulted in the yields of sunflower seeds and tomato fruits and the plant N uptakes comparable or better than that applied with only the chemical fertiliser. The estimated PAN of the anaerobically digested sludge was about 27-42% of the sludge organic N during the growing season. For the septic tank sludge, the PAN was about 15-58% of the sludge organic N. It is interesting to observe that an increase of the rate of septic tank sludge incorporated into this heavy clay soil under the cropping system resulted in the decrease of N mineralisation rate. This situation could cause the reduction of yield and N uptake of crops.

Weather based risks and insurances for agricultural production

NASA Astrophysics Data System (ADS)

Gobin, Anne

2015-04-01

Extreme weather events such as frost, drought, heat waves and rain storms can have devastating effects on cropping systems. According to both the agriculture and finance sectors, a risk assessment of extreme weather events and their impact on cropping systems is needed. The principle of return periods or frequencies of natural hazards is adopted in many countries as the basis of eligibility for the compensation of associated losses. For adequate risk management and eligibility, hazard maps for events with a 20-year return period are often used. Damages due to extreme events are strongly dependent on crop type, crop stage, soil type and soil conditions. The impact of extreme weather events particularly during the sensitive periods of the farming calendar therefore requires a modelling approach to capture the mixture of non-linear interactions between the crop, its environment and the occurrence of the meteorological event in the farming calendar. Physically based crop models such as REGCROP (Gobin, 2010) assist in understanding the links between different factors causing crop damage. Subsequent examination of the frequency, magnitude and impacts of frost, drought, heat stress and soil moisture stress in relation to the cropping season and crop sensitive stages allows for risk profiles to be confronted with yields, yield losses and insurance claims. The methodology is demonstrated for arable food crops, bio-energy crops and fruit. The perspective of rising risk-exposure is exacerbated further by limited aid received for agricultural damage, an overall reduction of direct income support to farmers and projected intensification of weather extremes with climate change. Though average yields have risen continuously due to technological advances, there is no evidence that relative tolerance to adverse weather events has improved. The research is funded by the Belgian Science Policy Organisation (Belspo) under contract nr SD/RI/03A.

Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming.

PubMed

Hartman, Kyle; van der Heijden, Marcel G A; Wittwer, Raphaël A; Banerjee, Samiran; Walser, Jean-Claude; Schlaeppi, Klaus

2018-01-16

Harnessing beneficial microbes presents a promising strategy to optimize plant growth and agricultural sustainability. Little is known to which extent and how specifically soil and plant microbiomes can be manipulated through different cropping practices. Here, we investigated soil and wheat root microbial communities in a cropping system experiment consisting of conventional and organic managements, both with different tillage intensities. While microbial richness was marginally affected, we found pronounced cropping effects on community composition, which were specific for the respective microbiomes. Soil bacterial communities were primarily structured by tillage, whereas soil fungal communities responded mainly to management type with additional effects by tillage. In roots, management type was also the driving factor for bacteria but not for fungi, which were generally determined by changes in tillage intensity. To quantify an "effect size" for microbiota manipulation, we found that about 10% of variation in microbial communities was explained by the tested cropping practices. Cropping sensitive microbes were taxonomically diverse, and they responded in guilds of taxa to the specific practices. These microbes also included frequent community members or members co-occurring with many other microbes in the community, suggesting that cropping practices may allow manipulation of influential community members. Understanding the abundance patterns of cropping sensitive microbes presents the basis towards developing microbiota management strategies for smart farming. For future targeted microbiota management-e.g., to foster certain microbes with specific agricultural practices-a next step will be to identify the functional traits of the cropping sensitive microbes.

Can Climate Information be relevant to decision making for Agriculture on the 1-10 year timescale? Case studies from southern Africa

NASA Astrophysics Data System (ADS)

Fujisawa, Mariko

2016-04-01

Climate forecasts have been developed to assist decision making in sectors averse to, and affected by, climate risks, and agriculture is one of those. In agriculture and food security, climate information is now used on a range of timescales, from days (weather), months (seasonal outlooks) to decades (climate change scenarios). Former researchers have shown that when seasonal climate forecast information was provided to farmers prior to decision making, farmers adapted by changing their choice of planting seeds and timing or area planted. However, it is not always clear that the end-users' needs for climate information are met and there might be a large gap between information supplied and needed. It has been pointed out that even when forecasts were available, they were often not utilized by farmers and extension services because of lack of trust in the forecast or the forecasts did not reach the targeted farmers. Many studies have focused on the use of either seasonal forecasts or longer term climate change prediction, but little research has been done on the medium term, that is, 1 to 10 year future climate information. The agriculture and food system sector is one potential user of medium term information, as land use policy and cropping systems selection may fall into this time scale and may affect farmers' decision making process. Assuming that reliable information is provided and it is utilized by farmers for decision making, it might contribute to resilient farming and indeed to longer term food security. To this end, we try to determine the effect of medium term climate information on farmers' strategic decision making process. We explored the end-users' needs for climate information and especially the possible role of medium term information in agricultural system, by conducting interview surveys with farmers and agricultural experts. In this study, the cases of apple production in South Africa, maize production in Malawi and rice production in Tanzania will be presented. With case studies of various crops, we also aim to identify what climatic factors and timescale of prediction may be critical to what crop types of farmers, which may be of value to climate prediction community to further develop climate prediction useful for agricultural system.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

78 FR 17627 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-22

... variables such as previous year experience, weather occurrences and projections, market demand, new farming..., as required: crop planted, planting date, crop's intended use, type or variety, practice (irrigated...

Final Environmental Assessment- Air Traffic Control Tower and Fire Station Pope AFB, NC

DTIC Science & Technology

2004-06-01

collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE JUN 2004 2. REPORT TYPE 3. DATES COVERED 00-00...of undeveloped land are located east of the base. Pope AFB covers approximately 2,140 acres, of which 151 acres are owned by the Air Force. The...protection against decreased visibility and damage to animals, crops , vegetation, and buildings. 4 PM2.s = Particulate matter less than 2.5 microns in

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

Herbicide and cover crop residue integration in conservation tillage tomato

USDA-ARS?s Scientific Manuscript database

The increased adoption of conservation tillage in vegetable production requires more information on the role of various cover crops in weed control, tomato quality, and yield. Three conservation-tillage systems utilizing crimson clover, turnip, and cereal rye as winter cover crops were compared to a...

Cover Crop Chart: An Outreach Tool for Agricultural Producers

USDA-ARS?s Scientific Manuscript database

Interest in cover crops by farmers and ranchers throughout the Northern Great Plains has increased the need for information on the suitability of a diverse portfolio of crops for different production and management resource goals. To help address this need, Northern Great Plains Research Laboratory...

Agricultural Issues of Significance to Iowa Crop Producers and Their Educational Implications

ERIC Educational Resources Information Center

Licht, Melea A. R.; Martin, Robert A.

2007-01-01

The purpose of this study was to determine the agricultural information preferences of crop producers in Iowa and the implications for agricultural extension education. The objective was to identify agricultural information issues producers perceive as significant to their businesses. The results will help agricultural extension educators and…

A method for fast selecting feature wavelengths from the spectral information of crop nitrogen

USDA-ARS?s Scientific Manuscript database

Research on a method for fast selecting feature wavelengths from the nitrogen spectral information is necessary, which can determine the nitrogen content of crops. Based on the uniformity of uniform design, this paper proposed an improved particle swarm optimization (PSO) method. The method can ch...

Integrated Universal Soil Loss Equation (USLE) and Geographical Information System (GIS) for Soil Erosion Measurement in basin of Asap river, Central Vietnam

NASA Astrophysics Data System (ADS)

Pham Gia, Tung; Degener, Jan; Kappas, Martin

2017-04-01

The study was conducted in Asap river basin, A Luoi district, Thua Thien Hue Province, Vietnam, using the Universal Soil Loss Equation (USLE) and Geographical Information System (GIS) to determine the soil erosion status. The results show strong effect of the heavy rainfall and high slope on the erosion level in the research area. More than 40% of land area lost over 10 tons/ha/year. The natural forest land lost the most by averagely is 38.4 tons/ha/year, while the agricultural land showed less with 2.79 tons for paddy rice land use type and 7.58 tons for upland crops yearly. Comparison between some places of Vietnam and the Southeast Asia showed that soil erosion in watersheds of Asap is more serious. We have been proposed a recommendation on changing the classification system of land use type in Vietnam for more accurate in soil erosion measurement. Keywords: Land use type, Soil erosion, USLE, Central Vietnam.

Combining optical remote sensing, agricultural statistics and field observations for culture recognition over a peri-urban region

NASA Astrophysics Data System (ADS)

Delbart, Nicolas; Emmanuelle, Vaudour; Fabienne, Maignan; Catherine, Ottlé; Jean-Marc, Gilliot

2017-04-01

This study explores the potential of multi-temporal optical remote sensing, with high revisit frequency, to derive missing information on agricultural calendar and crop types over the agricultural lands in the Versailles plain in the western Paris suburbs. This study comes besides past and ongoing studies on the use of radar and high spatial resolution optical remote sensing to monitor agricultural practices in this study area (e.g. Vaudour et al. 2014). Agricultural statistics, such as the Land Parcel Identification System (LPIS) for France, permit to know the nature of annual crops for each digitized declared field of this land parcel registry. However, within each declared field several cropped plots and a diversity of practices may exist, being marked by agricultural rotations which vary both spatially and temporally within it and differ from one year to the other. Even though the new LPIS to be released in 2016 is expected to describe individual plots within declared fields, its attributes may not enable to discriminate between winter and spring crops. Here we evaluate the potential of high observation frequency remote sensing to differentiate seasonal crops based essentially on the seasonality of the spectral properties. In particular, we use the Landsat data to spatially disaggregate the LPIS statistical data, on the basis of the analysis of the remote sensing spectral seasonality measured on a number of selected ground-observed fields. This work is carried out in the framework of the CNES TOSCA-PLEIADES-CO of the French Space Agency.

Changes in water budgets and sediment yields from a hypothetical agricultural field as a function of landscape and management characteristics--A unit field modeling approach

USGS Publications Warehouse

Roth, Jason L.; Capel, Paul D.

2012-01-01

Crop agriculture occupies 13 percent of the conterminous United States. Agricultural management practices, such as crop and tillage types, affect the hydrologic flow paths through the landscape. Some agricultural practices, such as drainage and irrigation, create entirely new hydrologic flow paths upon the landscapes where they are implemented. These hydrologic changes can affect the magnitude and partitioning of water budgets and sediment erosion. Given the wide degree of variability amongst agricultural settings, changes in the magnitudes of hydrologic flow paths and sediment erosion induced by agricultural management practices commonly are difficult to characterize, quantify, and compare using only field observations. The Water Erosion Prediction Project (WEPP) model was used to simulate two landscape characteristics (slope and soil texture) and three agricultural management practices (land cover/crop type, tillage type, and selected agricultural land management practices) to evaluate their effects on the water budgets of and sediment yield from agricultural lands. An array of sixty-eight 60-year simulations were run, each representing a distinct natural or agricultural scenario with various slopes, soil textures, crop or land cover types, tillage types, and select agricultural management practices on an isolated 16.2-hectare field. Simulations were made to represent two common agricultural climate regimes: arid with sprinkler irrigation and humid. These climate regimes were constructed with actual climate and irrigation data. The results of these simulations demonstrate the magnitudes of potential changes in water budgets and sediment yields from lands as a result of landscape characteristics and agricultural practices adopted on them. These simulations showed that variations in landscape characteristics, such as slope and soil type, had appreciable effects on water budgets and sediment yields. As slopes increased, sediment yields increased in both the arid and humid environments. However, runoff did not increase with slope in the arid environment as was observed in the humid environment. In both environments, clayey soils exhibited the greatest amount of runoff and sediment yields while sandy soils had greater recharge and lessor runoff and sediment yield. Scenarios simulating the effects of the timing and type of tillage practice showed that no-till, conservation, and contouring tillages reduced sediment yields and, with the exception of no-till, runoff in both environments. Changes in land cover and crop type simulated the changes between the evapotransporative potential and surface roughness imparted by specific vegetations. Substantial differences in water budgets and sediment yields were observed between most agricultural crops and the natural covers selected for each environment: scrub and prairie grass for the arid environment and forest and prairie grass for the humid environment. Finally, a group of simulations was performed to model selected agricultural management practices. Among the selected practices subsurface drainage and strip cropping exhibited the largest shifts in water budgets and sediment yields. The practice of crop rotation (corn/soybean) and cover cropping (corn/rye) were predicted to increase sediment yields from a field planted as conventional corn.

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...

Mechanizing chile peppers: Challenges and advances in transitioning harvest of New Mexico's signature crop

USDA-ARS?s Scientific Manuscript database

New Mexican-type chile (Capsicum annuum L.), often referred to as 'Anaheim', is the signature crop of New Mexico. Both the red and green (fully sized, but physiologically immature) crops are integral to the state's culture and economy. Lack of a predictable labor supply and higher input costs have p...

Whole genome comparison of Aspergillus flavus L-morphotype strain NRRL 3357 (type) and S-morphotype strain AF70

USDA-ARS?s Scientific Manuscript database

Aspergillus flavus is a saprophytic fungus that infects corn, peanuts, tree nuts and other agriculturally important crops. Once the crop is infected the fungus has the potential to secrete one or more mycotoxins, the most carcinogenic of which is aflatoxin. Aflatoxin contaminated crops are deemed un...

Developing Land Surface Type Map with Biome Classification Scheme Using Suomi NPP/JPSS VIIRS Data

NASA Astrophysics Data System (ADS)

Zhang, Rui; Huang, Chengquan; Zhan, Xiwu; Jin, Huiran

2016-08-01

Accurate representation of actual terrestrial surface types at regional to global scales is an important element for a wide range of applications, such as land surface parameterization, modeling of biogeochemical cycles, and carbon cycle studies. In this study, in order to meet the requirement of the retrieval of global leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the vegetation (fPAR) and other studies, a global map generated from Suomi National Polar- orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance data in six major biome classes based on their canopy structures, which include: Grass/Cereal Crops, Shrubs, Broadleaf Crops, Savannas, Broadleaf Forests, and Needleleaf Forests, was created. The primary biome classes were converted from an International Geosphere-Biosphere Program (IGBP) legend global surface type data that was created in previous study, and the separation of two crop types are based on a secondary classification.

Targeting the right input data to improve crop modeling at global level

NASA Astrophysics Data System (ADS)

Adam, M.; Robertson, R.; Gbegbelegbe, S.; Jones, J. W.; Boote, K. J.; Asseng, S.

2012-12-01

Designed for location-specific simulations, the use of crop models at a global level raises important questions. Crop models are originally premised on small unit areas where environmental conditions and management practices are considered homogeneous. Specific information describing soils, climate, management, and crop characteristics are used in the calibration process. However, when scaling up for global application, we rely on information derived from geographical information systems and weather generators. To run crop models at broad, we use a modeling platform that assumes a uniformly generated grid cell as a unit area. Specific weather, specific soil and specific management practices for each crop are represented for each of the cell grids. Studies on the impacts of the uncertainties of weather information and climate change on crop yield at a global level have been carried out (Osborne et al, 2007, Nelson et al., 2010, van Bussel et al, 2011). Detailed information on soils and management practices at global level are very scarce but recognized to be of critical importance (Reidsma et al., 2009). Few attempts to assess the impact of their uncertainties on cropping systems performances can be found. The objectives of this study are (i) to determine sensitivities of a crop model to soil and management practices, inputs most relevant to low input rainfed cropping systems, and (ii) to define hotspots of sensitivity according to the input data. We ran DSSAT v4.5 globally (CERES-CROPSIM) to simulate wheat yields at 45arc-minute resolution. Cultivar parameters were calibrated and validated for different mega-environments (results not shown). The model was run for nitrogen-limited production systems. This setting was chosen as the most representative to simulate actual yield (especially for low-input rainfed agricultural systems) and assumes crop growth to be free of any pest and diseases damages. We conducted a sensitivity analysis on contrasting management practices, initial soil conditions, and soil characteristics information. Management practices were represented by planting date and the amount of fertilizer, initial conditions estimates for initial nitrogen, soil water, and stable soil carbon, and soil information is based on a simplified version of the WISE database, characterized by soil organic matter, texture and soil depth. We considered these factors as the most important determinants of nutrient supply to crops during their growing season. Our first global results demonstrate that the model is most sensitive to the initial conditions in terms of soil carbon and nitrogen (CN): wheat yields decreased by 45% when soil CN is null and increase by 15% when twice the soil CN content of the reference run is used. The yields did not appear to be very sensitive to initial soil water conditions, varying from 0% yield increase when initial soil water is set to wilting point to 6% yield increase when it was set to field capacity. They are slightly sensitive to nitrogen application: 8% yield decrease when no N is applied to 9% yield increase when 150 kg.ha-1 is applied. However, with closer examination of results, the model is more sensitive to nitrogen application than to initial soil CN content in Vietnam, Thailand and Japan compared to the rest of the world. More analyses per region and results on the planting dates and soil properties will be presented.

High yielding tropical energy crops for bioenergy production: Effects of plant components, harvest years and locations on biomass composition.

PubMed

Surendra, K C; Ogoshi, Richard; Zaleski, Halina M; Hashimoto, Andrew G; Khanal, Samir Kumar

2018-03-01

The composition of lignocellulosic feedstock, which depends on crop type, crop management, locations and plant parts, significantly affects the conversion efficiency of biomass into biofuels and biobased products. Thus, this study examined the composition of different parts of two high yielding tropical energy crops, Energycane and Napier grass, collected across three locations and years. Significantly higher fiber content was found in the leaves of Energycane than stems, while fiber content was significantly higher in the stems than the leaves of Napier grass. Similarly, fiber content was higher in Napier grass than Energycane. Due to significant differences in biomass composition between the plant parts within a crop type, neither biological conversion, including anaerobic digestion, nor thermochemical pretreatment alone is likely to efficiently convert biomass components into biofuels and biobased products. However, combination of anaerobic digestion with thermochemical conversion technologies could efficiently utilize biomass components in generating biofuels and biobased products. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantifiers more or less quantify online: ERP evidence for partial incremental interpretation

PubMed Central

Urbach, Thomas P.; Kutas, Marta

2010-01-01

Event-related brain potentials were recorded during RSVP reading to test the hypothesis that quantifier expressions are incrementally interpreted fully and immediately. In sentences tapping general knowledge (Farmers grow crops/worms as their primary source of income), Experiment 1 found larger N400s for atypical (worms) than typical objects (crops). Experiment 2 crossed object typicality with non-logical subject-noun phrase quantifiers (most, few). Off-line plausibility ratings exhibited the crossover interaction predicted by full quantifier interpretation: Most farmers grow crops and Few farmers grow worms were rated more plausible than Most farmers grow worms and Few farmers grow crops. Object N400s, although modulated in the expected direction, did not reverse. Experiment 3 replicated these findings with adverbial quantifiers (Farmers often/rarely grow crops/worms). Interpretation of quantifier expressions thus is neither fully immediate nor fully delayed. Furthermore, object atypicality was associated with a frontal slow positivity in few-type/rarely quantifier contexts, suggesting systematic processing differences among quantifier types. PMID:20640044

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.

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

NASA Astrophysics Data System (ADS)

Pasqualotto, Nieves; Delegido, Jesús; Van Wittenberghe, Shari; Verrelst, Jochem; Rivera, Juan Pablo; Moreno, José

2018-05-01

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (<30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.

Models that predict standing crop of stream fish from habitat variables: 1950-85.

Treesearch

K.D. Fausch; C.L. Hawkes; M.G. Parsons

1988-01-01

We reviewed mathematical models that predict standing crop of stream fish (number or biomass per unit area or length of stream) from measurable habitat variables and classified them by the types of independent habitat variables found significant, by mathematical structure, and by model quality. Habitat variables were of three types and were measured on different scales...

Winter wheat production forecast in United States of America using AVHRR historical data and NCAR Growing Degree Day

NASA Astrophysics Data System (ADS)

Claverie, M.; Franch, B.; Vermote, E.; Becker-Reshef, I.; Justice, C. O.

2015-12-01

Wheat is one of the key cereals crop grown worldwide. Thus, accurate and timely forecasts of its production are critical for informing agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) used an empirical generalized model for forecasting winter wheat production using combined BRDF-corrected daily surface reflectance from the Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel, and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. Recently, Franch et al. (2015) included Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts between a month to a month and a half prior to the peak NDVI (i.e. 1-2.5 months prior to harvest), while conserving the accuracy of the original model. In this study, we apply these methods to historical data from the Advanced Very High Resolution Radiometer (AVHRR). We apply both the original and the modified model to United States of America from 1990 to 2014 and inter-compare the AVHRR results to MODIS from 2000 to 2014.

Improving Timeliness of Winter Wheat Production Forecast in United States of America, Ukraine and China Using MODIS Data and NCAR Growing Degree Day

NASA Astrophysics Data System (ADS)

Vermote, E.; Franch, B.; Becker-Reshef, I.; Claverie, M.; Huang, J.; Zhang, J.; Sobrino, J. A.

2014-12-01

Wheat is the most important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely forecasts of its production are critical for informing agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) used an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel, and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: United States of America, Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest) while conserving an accuracy of 10% in the production forecast.

Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

NASA Astrophysics Data System (ADS)

Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

2017-12-01

Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.

Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data

USGS Publications Warehouse

Thenkabail, P.S.; Mariotto, I.; Gumma, M.K.; Middleton, E.M.; Landis, D.R.; Huemmrich, K.F.

2013-01-01

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysi- al characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

NASA Technical Reports Server (NTRS)

Thenkabail, Prasad S.; Mariotto, Isabella; Gumma, Murali Krishna; Middleton, Elizabeth M.; Landis, David R.; Huemmrich, K. Fred

2013-01-01

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

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.

Characterization of particulate-bound PAHs in rural households using different types of domestic energy in Henan Province, China.

PubMed

Wu, Fuyong; Liu, Xueping; Wang, Wei; Man, Yu Bon; Chan, Chuen Yu; Liu, Wenxin; Tao, Shu; Wong, Ming Hung

2015-12-01

The concentrations and composition of sixteen PAHs adsorbed to respirable particulate matter (PM10≤10 μm) and inhalable particulate matter (PM2.5≤2.5 μm) were determined during autumn and winter in rural households of Henan Province, China, which used four types of domestic energy [crop residues, coal, liquid petroleum gas (LPG) and electricity] for cooking and heating. The present results show that there were significantly (p<0.05) seasonal variations of particulate-bound PAHs in the rural households. The daily mean concentrations of particulate-bound PAHs in the kitchens, sitting rooms and outdoors were apparently higher in winter than those in autumn, except those in the kitchens using coal. The present study also shows that there were obvious variations of particulate-bound PAHs among the four types of domestic energy used in the rural households. The households using LPG for cooking can, at least in some circumstances, have higher concentrations of PAHs in the kitchens than using crop residues or electricity. In addition, using coal in the sitting rooms seemed to result in apparently higher concentrations of particulate-bound PAHs than using the other three types of domestic energy during winter. The most severe contamination occurred in the kitchens using LPG in winter, where the daily mean concentrations of PM2.5-bound PAHs were up to 762.5±931.2 ng m(-3), indicating that there was serious health risk of inhalation exposure to PAHs in the rural households of Henan Province. Rural residents' exposure to PM2.5-bound PAHs in kitchens would be roughly reduced by 69.8% and 85.5% via replacing coal or crop residues with electricity in autumn. The pilot research would provide important supplementary information to the indoor air pollution studies in rural area. Copyright © 2015 Elsevier B.V. All rights reserved.

Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL).

PubMed

Hooper, Cornelia M; Castleden, Ian R; Aryamanesh, Nader; Jacoby, Richard P; Millar, A Harvey

2016-01-01

Barley, wheat, rice and maize provide the bulk of human nutrition and have extensive industrial use as agricultural products. The genomes of these crops each contains >40,000 genes encoding proteins; however, the major genome databases for these species lack annotation information of protein subcellular location for >80% of these gene products. We address this gap, by constructing the compendium of crop protein subcellular locations called crop Proteins with Annotated Locations (cropPAL). Subcellular location is most commonly determined by fluorescent protein tagging of live cells or mass spectrometry detection in subcellular purifications, but can also be predicted from amino acid sequence or protein expression patterns. The cropPAL database collates 556 published studies, from >300 research institutes in >30 countries that have been previously published, as well as compiling eight pre-computed subcellular predictions for all Hordeum vulgare, Triticum aestivum, Oryza sativa and Zea mays protein sequences. The data collection including metadata for proteins and published studies can be accessed through a search portal http://crop-PAL.org. The subcellular localization information housed in cropPAL helps to depict plant cells as compartmentalized protein networks that can be investigated for improving crop yield and quality, and developing new biotechnological solutions to agricultural challenges. © The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Research and the planned Space Experiment Research and Processing Laboratory

NASA Technical Reports Server (NTRS)

2000-01-01

Original photo and caption dated June 22, 1988: 'A dwarf wheat variety known as Yecoro Rojo flourishes in KSC's Biomass Production Chamber. Researchers are gathering information on the crop's ability to produce food, water and oxygen, and then remove carbon dioxide. The confined quarters associated with space travel require researchers to focus on smaller plants that yield proportionately large amounts of biomass. This wheat crop takes about 85 days to grow before harvest.' Plant experiments such as this are the type of life sciences research that will be conducted at the Space Experiment Research Procession Laboratory (SERPL). The SERPL is a planned 100,000-square-foot laboratory that will provide expanded and upgraded facilities for hosting International Space Station experiment processing. In addition, it will provide better support for other biological and life sciences payload processing at KSC. It will serve as a magnet facility for a planned 400-acre Space Station Commerce Park.

Redescription of Campoletis sonorensis (Cameron, 1886) (Hymenoptera, Ichneumonidae, Campopleginae), parasitoid of Spodoptera frugiperda (J. E. Smith, 1797) (Lepidoptera, Noctuidae) in Brazil.

PubMed

Camargo, L F; Brito, R A; Penteado-Dias, A M

2015-11-01

The fall armyworm Spodoptera frugiperda (Lepidoptera; Noctuidae) is a voracious pest of numerous crops of economic importance throughout the New World. In Brazil, its larvae are attacked by several species of parasitoid wasps, making them potential candidate as biological control agents against this pest. A survey of the parasitoid fauna on S. frugiperda in maize crops throughout Brazil reveals two species of Campoletis, which are morphologicaly very similar species. In this paper we combine these data with pictures from the type material of C. sonorensis and C. flavicincta, as well as their descriptions to provide a redescription to Campoletis sonorensis (Cameron, 1886) using for this both morphological characters and DNA Barcoding (Hebert et al., 2003) information, in an attempt to help with the correct identification of the taxa to improve biological control studies.

The Effect of Five Biomass Cropping Systems on Soil-Saturated Hydraulic Conductivity Across a Topographic Gradient

Treesearch

Usman Anwar; Lisa A. Schulte; Matthew Helmers; Randall K. Kolka

2017-01-01

Understanding the environmental impact of bioenergy crops is needed to inform bioenergy policy development. We determined the effects of five biomass cropping systems—continuous maize (Zea mays), soybean (Glycine max)-triticale (Triticosecale ×)/soybean-maize, maize-switchgrass (Panicum virgatum...

Center pivot mounted infrared sensors: Retrieval of ET and interface with satellite systems

USDA-ARS?s Scientific Manuscript database

Infrared sensors mounted aboard cener pivot irrigation systems can remotely sense the surface temperatures of the crops and soils, which provides important information on crop water status. This can be used for irrigation management and irrigation automation, which can increase crop water productivi...

Crop identification using Landsat temporal-spectral profiles

NASA Technical Reports Server (NTRS)

Odenweller, J. B.; Johnson, K. I.

1982-01-01

The temporal-spectral profile is a detailed indicator of the physical state of a field through time. Characteristic profiles have been observed for a variety of crops and other cover classes from Landsat data in the United States Corn Belt. These profiles contain information to support crop identification at various levels.

Effect of pyrasulfotole carryover to peanut and tobacco

USDA-ARS?s Scientific Manuscript database

In the southeastern United States, growers often double-crop soft red winter wheat with peanut. In some areas, tobacco is also grown as a rotational crop. Pyrasulfotole is a residual post-emergence applied herbicide used in winter wheat, but information about its effects on rotational crops is limi...

Soil phosphatase and urease activities impacted by cropping systems and water management

USDA-ARS?s Scientific Manuscript database

Soil enzymes can play an important role in nutrient availability to plants. Consequently, soil enzyme measurements can provide useful information on soil fertility for crop production. We examined the impact of cropping system and water management on phosphatase, urease, and microbial biomass C in s...

Agroforestry Systems in Zimbabwe: Promoting Trees in Agriculture.

ERIC Educational Resources Information Center

Vukasin, Helen L., Ed.

Agroforestry has been defined as a sustainable crop management system which combines the production of forest crops with field crops. In June, 1987, an agroforestry workshop took place in Nyanga, Manicaland, Zimbabwe. This document was prepared to share the information presented at this workshop with other non-government organizations around the…

A comparison of drill and broadcast methods for establishing cover crops on beds

USDA-ARS?s Scientific Manuscript database

Cover crops stands that are sufficiently dense soon after planting are more likely to suppress weeds, scavenge nutrients, and reduce erosion. Small-scale organic vegetable farmers often use broadcasting methods to establish cover crops but lack information on the most effective tool to incorporate ...

Life-cycle analysis of dryland greenhouse gases affected by cropping sequence and nitrogen fertilization

USDA-ARS?s Scientific Manuscript database

Little information is available about management practices effect on net global warming potential (GWP) and greenhouse gas intensity (GHGI) under dryland cropping systems. We evaluated the effects of cropping sequences (conventional till malt barley-fallow [CTB-F], no-till malt barley-pea [NTB-P], a...

Conservation priorities for tree crop wild relatives in the United States

USDA-ARS?s Scientific Manuscript database

Our native crop wild relatives have proved useful as genetic resources in breeding more productive, nutritious, and resilient crops. Their utilization is expected only to increase with better information on the species and improving breeding tools, but may well be constrained by their limited repres...

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

USDA-ARS?s Scientific Manuscript database

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

Ecogeography and utility to plant breeding of the crop wild relatives of sunflower (Helianthus annuus L.)

USDA-ARS?s Scientific Manuscript database

Crop wild relatives (CWR) are a rich source of genetic diversity for crop improvement. Combining ecogeographic and phylogenetic techniques can inform both conservation and breeding. Geographic occurrence, bioclimatic, and biophysical data were used to predict species distributions, range overlap and...

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

PubMed Central

2012-01-01

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

Convolutional Neural Network for Multi-Source Deep Learning Crop Classification in Ukraine

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.

2016-12-01

Land cover and crop type maps are one of the most essential inputs when dealing with environmental and agriculture monitoring tasks [1]. During long time neural network (NN) approach was one of the most efficient and popular approach for most applications, including crop classification using remote sensing data, with high an overall accuracy (OA) [2]. In the last years the most popular and efficient method for multi-sensor and multi-temporal land cover classification is convolution neural networks (CNNs). Taking into account presence clouds in optical data, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of optical imagery from Landsat-8 satellite. After missing data restoration, optical data from Landsat-8 was merged with Sentinel-1A radar data for better crop types discrimination [3]. An ensemble of CNNs is proposed for multi-temporal satellite images supervised classification. Each CNN in the corresponding ensemble is a 1-d CNN with 4 layers implemented using the Google's library TensorFlow. The efficiency of the proposed approach was tested on a time-series of Landsat-8 and Sentinel-1A images over the JECAM test site (Kyiv region) in Ukraine in 2015. Overall classification accuracy for ensemble of CNNs was 93.5% that outperformed an ensemble of multi-layer perceptrons (MLPs) by +0.8% and allowed us to better discriminate summer crops, in particular maize and soybeans. For 2016 we would like to validate this method using Sentinel-1 and Sentinel-2 data for Ukraine territory within ESA project on country level demonstration Sen2Agri. 1. A. Kolotii et al., "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine," The Int. Arch. of Photogram., Rem. Sens. and Spatial Inform. Scie., vol. 40, no. 7, pp. 39-44, 2015. 2. F. Waldner et al., "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity," Int. Journal of Rem. Sens. vol. 37, no. 14, pp 3196-3231, 2016. 3. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297.

Meteorological and hydrological extremes derived from taxation records: case study for south-western Moravia (Czech Republic)

NASA Astrophysics Data System (ADS)

Chromá, Kateřina; Brázdil, Rudolf; Valášek, Hubert; Zahradníček, Pavel

2013-04-01

Meteorological and hydrological extremes (MHEs) cause great material damage or even loss of human lives in the present time, similarly as it was in the past. In the Czech Lands (recently the Czech Republic), systematic meteorological and hydrological observations started generally in the latter half of the 19th century. Therefore, in order to create long-term series of such extremes, it is necessary to search for other sources of information. Different types of documentary evidence are used in historical climatology and hydrology to find such information. Some of them are related to records connected with taxation system. The taxation system in Moravia allowed farmers to request tax relief if their crops have been damaged by MHEs. The corresponding documents contain information about the type of extreme event and the date of its occurrence; often also impacts on crops or land may be derived. The nature of events leading to damage include particularly hailstorms, torrential rain, flash floods, floods (in regions along larger rivers), less frequently windstorms, late frosts and in some cases also information about droughts or extreme snow depths. However, the results obtained are influenced by uncertainties related to taxation records - their temporal and spatial incompleteness, limitation of the MHEs occurrence in the period of main agricultural work (May-August) and the purpose for which they were originally collected (primarily tax alleviation, i.e. information about MHEs was of secondary importance). All these aspects related to the study of MHEs from taxation records are demonstrated for five estates (Bítov, Budkov, Jemnice with Staré Hobzí, Nové Syrovice and Uherčice) in the south-western part of Moravia for the 18th-19th centuries. The analysis shows importance of taxation records for the study of past MHEs as well as great potential for their use.

Building a Digital Library from the Ground Up: an Examination of Emergent Information Resources in the Machine Learning Community

NASA Astrophysics Data System (ADS)

Cunningham, Sally Jo

The current crop of digital libraries for the computing community are strongly grounded in the conventional library paradigm: they provide indexes to support searching of collections of research papers. As such, these digital libraries are relatively impoverished; the present computing digital libraries omit many of the documents and resources that are currently available to computing researchers, and offer few browsing structures. These computing digital libraries were built 'top down': the resources and collection contents are forced to fit an existing digital library architecture. A 'bottom up' approach to digital library development would begin with an investigation of a community's information needs and available documents, and then design a library to organize those documents in such a way as to fulfill the community's needs. The 'home grown', informal information resources developed by and for the machine learning community are examined as a case study, to determine the types of information and document organizations 'native' to this group of researchers. The insights gained in this type of case study can be used to inform construction of a digital library tailored to this community.

Relevance of Crop Biology for Environmental Risk Assessment of Genetically Modified Crops in Africa.

PubMed

Akinbo, Olalekan; Hancock, James F; Makinde, Diran

2015-01-01

Knowledge about the crop biology of economic crops in Africa is needed for regulators to accurately review dossiers and conduct comprehensive environmental risk assessments (ERAs). This information allows regulators to decide whether biotech crops present a risk to biodiversity, since crossing between domesticated crops and their wild relatives could affect the adaptations of the wild species. The criteria that should be used in the evaluation of African crops for ERA include growth habit, center of origin, center of genetic diversity, proximity of wild relatives, inter-fertility, mode of pollen dispersal, length of pollen viability, mating system, invasiveness, weediness, mode of propagation, mode of seed dispersal, and length of seed dormancy. In this paper, we discuss the crops being genetic engineered in Africa and describe the crop biology of those with native relatives.

Analysis of scanner data for crop inventories

NASA Technical Reports Server (NTRS)

Horvath, R. (Principal Investigator); Cicone, R. C.; Kauth, R. J.; Malila, W. A.; Pont, W.; Thelen, B.; Sellman, A.

1981-01-01

Accomplishments for a machine-oriented small grains labeler T&E, and for Argentina ground data collection are reported. Features of the small grains labeler include temporal-spectral profiles, which characterize continuous patterns of crop spectral development, and crop calendar shift estimation, which adjusts for planting date differences of fields within a crop type. Corn and soybean classification technology development for area estimation for foreign commodity production forecasting is reported. Presentations supporting quarterly project management reviews and a quarterly technical interchange meeting are also included.

Benchmark study on glyphosate-resistant cropping systems in the United States. Part 3: Grower awareness, information sources, experiences and management practices regarding glyphosate-resistant weeds.

PubMed

Givens, Wade A; Shaw, David R; Newman, Michael E; Weller, Stephen C; Young, Bryan G; Wilson, Robert G; Owen, Micheal D K; Jordan, David L

2011-07-01

A survey was conducted with nearly 1200 growers in US states (Illinois, Indiana, Iowa, Mississippi, Nebraska and North Carolina) in 2005 with the objective in part of determining the awareness of the potential for development of glyphosate resistance, the experience with glyphosate-resistant (GR) weeds and the sources of information that growers had utilized for information on glyphosate resistance. Growers were asked a series of questions to determine the level of glyphosate resistance awareness and to list the sources of information used to learn about glyphosate resistance issues. The majority of the growers (88%) were aware of a weed's potential to evolve resistance to herbicide, while 44% were aware of state-specific documented cases of GR weeds, and 15% reported having had personal experience with GR weeds. Among sources of information concerning glyphosate resistance issues, farm publications, dealers/retailers and university/extension were the most frequent responses (41, 17 and 14% respectively). Based on a 1-10 effectiveness scale, growers ranked tillage the least effective practice (5.5) and using the correct label rates of herbicides at the proper timing for the size and type of weeds present the most effective practice (8.6) with respect to how effectively the practices mitigated the evolution of GR weeds. Results from this survey can be used by researchers, extension specialists and crop advisors further to bridge the information gap between growers and themselves and better to disseminate information concerning glyphosate resistance and glyphosate resistance management practices through more targeted information and information delivery methods. Copyright © 2011 Society of Chemical Industry.

The Wheat NAC Transcription Factor TaNAC2L Is Regulated at the Transcriptional and Post-Translational Levels and Promotes Heat Stress Tolerance in Transgenic Arabidopsis.

PubMed

Guo, Weiwei; Zhang, Jinxia; Zhang, Ning; Xin, Mingming; Peng, Huiru; Hu, Zhaorong; Ni, Zhongfu; Du, Jinkun

2015-01-01

Heat stress poses a serious threat to global crop production. In efforts that aim to mitigate the adverse effects of heat stress on crops, a variety of genetic tools are being used to develop plants with improved thermotolerance. The characterization of important regulators of heat stress tolerance provides essential information for this aim. In this study, we examine the wheat (Triticum aestivum) NAC transcription factor gene TaNAC2L. High temperature induced TaNAC2L expression in wheat and overexpression of TaNAC2L in Arabidopsis thaliana enhanced acquired heat tolerance without causing obvious alterations in phenotype compared with wild type under normal conditions. TaNAC2L overexpression also activated the expression of heat-related genes in the transgenic Arabidopsis plants, suggesting that TaNAC2L may improve heat tolerance by regulating the expression of stress-responsive genes. Notably, TaNAC2L is also regulated at the post-translational level and might be degraded via a proteasome-mediated pathway. Thus, this wheat transcription factor may have potential uses in enhancing thermotolerance in crops.

A system of regional agricultural land use mapping tested against small scale Apollo 9 color infrared photography of the Imperial Valley (California)

USGS Publications Warehouse

Johnson, Claude W.; Browden, Leonard W.; Pease, Robert W.

1969-01-01

Interpretation results of the small scale ClR photography of the Imperial Valley (California) taken on March 12, 1969 by the Apollo 9 earth orbiting satellite have shown that world wide agricultural land use mapping can be accomplished from satellite ClR imagery if sufficient a priori information is available for the region being mapped. Correlation of results with actual data is encouraging although the accuracy of identification of specific crops from the single image is poor. The poor results can be partly attributed to only one image taken during mid-season when the three major crops were reflecting approximately the same and their ClR image appears to indicate the same crop type. However, some incapacity can be attributed to lack of understanding of the subtle variations of visual and infrared color reflectance of vegetation and surrounding environment. Analysis of integrated color variations of the vegetation and background environment recorded on ClR imagery is discussed. Problems associated with the color variations may be overcome by development of a semi-automatic processing system which considers individual field units or cells. Design criteria for semi-automatic processing system are outlined.

Maize diversity and ethnolinguistic diversity in Chiapas, Mexico

PubMed Central

Perales, Hugo R.; Benz, Bruce F.; Brush, Stephen B.

2005-01-01

The objective of this study is to investigate whether ethnolinguistic diversity influences crop diversity. Factors suggest a correlation between biological diversity of crops and cultural diversity. Although this correlation has been noted, little systematic research has focused on the role of culture in shaping crop diversity. This paper reports on research in the Maya highlands (altitude > 1,800 m) of central Chiapas in southern Mexico that examined the distribution of maize (Zea mays) types among communities of two groups, the Tzeltal and Tzotzil. The findings suggest that maize populations are distinct according to ethnolinguistic group. However, a study of isozymes indicates no clear separation of the region's maize into two distinct populations based on ethnolin-guistic origin. A reciprocal garden experiment shows that there is adaptation of maize to its environment but that Tzeltal maize sometimes out-yields Tzotzil maize in Tzotzil environments. Because of the proximity of the two groups and selection for yield, we would expect that the superior maize would dominate both groups' maize populations, but we find that such domination is not the case. The role of ethnolinguistic identity in shaping social networks and information exchange is discussed in relation to landrace differentiation. PMID:15640353

Maize diversity and ethnolinguistic diversity in Chiapas, Mexico.

PubMed

Perales, Hugo R; Benz, Bruce F; Brush, Stephen B

2005-01-18

The objective of this study is to investigate whether ethnolinguistic diversity influences crop diversity. Factors suggest a correlation between biological diversity of crops and cultural diversity. Although this correlation has been noted, little systematic research has focused on the role of culture in shaping crop diversity. This paper reports on research in the Maya highlands (altitude >1,800 m) of central Chiapas in southern Mexico that examined the distribution of maize (Zea mays) types among communities of two groups, the Tzeltal and Tzotzil. The findings suggest that maize populations are distinct according to ethnolinguistic group. However, a study of isozymes indicates no clear separation of the region's maize into two distinct populations based on ethnolinguistic origin. A reciprocal garden experiment shows that there is adaptation of maize to its environment but that Tzeltal maize sometimes out-yields Tzotzil maize in Tzotzil environments. Because of the proximity of the two groups and selection for yield, we would expect that the superior maize would dominate both groups' maize populations, but we find that such domination is not the case. The role of ethnolinguistic identity in shaping social networks and information exchange is discussed in relation to landrace differentiation.

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

NASA Technical Reports Server (NTRS)

Elliott, Joshua; Muller, Christoff

2015-01-01

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

Transport and fate of nitrate within soil units of glacial origin

NASA Astrophysics Data System (ADS)

Moore, Suzanna L.; Peterson, Eric W.

2007-08-01

Questions concerning the influence of soil type and crop cover on the fate and transport of nitrate (NO{3/-}) were examined. During a growing season, soils derived from glacial material underlying either corn or soybeans were sampled for levels of NO{3/-} within the pore water. Measured levels of NO{3/-} ranged from below detection limit to 14.9 g NO{3/-} per kilogram of soil (g/kg). In fields with the same crop cover, the silty-clayey soil exhibited a greater decrease in NO{3/-} levels with depth than the sandier soil. Crop uptake of NO{3/-} occurs within the root zone; however, the type of crop cover did not have a direct impact on the fate or transport during the growing season. The soils underlying soybeans had an increase in NO{3/-} levels following harvest, suggesting that the decomposition of the soybean roots contributed to the net gain of NO{3/-} in the shallow soil. For all of the soil types, conditions below 100 cm are conducive for microbial denitrification, with both a high water saturation level (>60%) and moderate organic carbon content (1-2%). At depths below 100 cm, temporal differences in NO{3/-} levels of over a magnitude, up to a 95% reduction, were recorded in the soil units as the growing season progressed. Physical properties that control the transport of NO{3/-} or denitrification have a larger influence on NO{3/-} levels than crop type.

Biofuels on the landscape: Is "land sharing" preferable to "land sparing"?

NASA Astrophysics Data System (ADS)

DeLucia, E. H.; Anderson-Teixeira, K. J.; Duval, B. D.; Long, S. P.

2012-12-01

Widespread land use changes—and ensuing effects on biodiversity and ecosystem services—are expected as a result of expanding bioenergy production. Although almost all US production of ethanol today is from corn, it is envisaged that future ethanol production will also draw from cellulosic sources such as perennial grasses. In selecting optimal bioenergy crops, there is debate as to whether it is preferable from an environmental standpoint to cultivate bioenergy crops with high ecosystem services (a "land sharing" strategy) or to grow crops with lower ecosystem services but higher yield, thereby requiring less land to meet bioenergy demand (a "land sparing" strategy). Here, we develop a simple model to address this question. Assuming that bioenergy crops are competing with uncultivated land, our model calculates land requirements to meet a given bioenergy demand intensity based upon the yields of bioenergy crops and combines fractional land cover of each ecosystem type with its associated ecosystem services to determine whether land sharing or land sparing strategies maximize ecosystem services at the landscape level. We apply this model to a case in which climate protection through GHG regulation—an ecosystem's greenhouse gas value (GHGV)—is the ecosystem service of interest. We consider five bioenergy crops competing for land area with five unfarmed ecosystem types in the central and eastern US. Our results show that the relative advantages of land sparing and land sharing depend upon the type of ecosystem with which the bioenergy crop is competing for land; as the GHGV value of the unfarmed land increases, the preferable strategy shifts from land sharing to land sparing. This implies that, while it may be preferable to replace ecologically degraded land with high-GHGV, lower yielding bioenergy crops, average landscape GHGV will most often be maximized through high yielding bioenergy crops that leave more land for uncultivated, high-GHGV ecosystems. While our case study focuses on GHGV, the same principles will be generally applicable to any ecosystem service whose value does not depend upon the spatial configuration of the landscape. Whenever bioenergy crops have substantially lower ecosystem services than the ecosystems with which they are competing for land, the most effective strategy for meeting bioenergy demand while maximizing ecosystem services on a landscape level is one of land sparing—that is, focusing simultaneously on maximizing the yield of bioenergy crops while preserving or restoring natural ecosystems.

The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

NASA Astrophysics Data System (ADS)

Ines, A. V. M.; Han, E.; Baethgen, W.

2017-12-01

Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

Meteorological risks and impacts on crop production systems in Belgium

NASA Astrophysics Data System (ADS)

Gobin, Anne

2013-04-01

Extreme weather events such as droughts, heat stress, rain storms and floods can have devastating effects on cropping systems. The perspective of rising risk-exposure is exacerbated further by projected increases of extreme events with climate change. More limits to aid received for agricultural damage and an overall reduction of direct income support to farmers further impacts farmers' resilience. Based on insurance claims, potatoes and rapeseed are the most vulnerable crops, followed by cereals and sugar beets. Damages due to adverse meteorological events are strongly dependent on crop type, crop stage and soil type. Current knowledge gaps exist in the response of arable crops to the occurrence of extreme events. The degree of temporal overlap between extreme weather events and the sensitive periods of the farming calendar requires a modelling approach to capture the mixture of non-linear interactions between the crop and its environment. The regional crop model REGCROP (Gobin, 2010) enabled to examine the likely frequency and magnitude of drought, heat stress and waterlogging in relation to the cropping season and crop sensitive stages of six arable crops: winter wheat, winter barley, winter rapeseed, potato, sugar beet and maize. Since crop development is driven by thermal time, crops matured earlier during the warmer 1988-2008 period than during the 1947-1987 period. Drought and heat stress, in particular during the sensitive crop stages, occur at different times in the cropping season and significantly differ between two climatic periods, 1947-1987 and 1988-2008. Soil moisture deficit increases towards harvesting, such that earlier maturing winter crops may avoid drought stress that occurs in late spring and summer. This is reflected in a decrease both in magnitude and frequency of soil moisture deficit around the sensitive stages during the 1988-2008 period when atmospheric drought may be compensated for with soil moisture. The risk of drought spells during the sensitive stages of summer crops increases and may be further aggravated by atmospheric moisture deficits and heat stress. Summer crops may therefore benefit from earlier planting dates and beneficial moisture conditions during early canopy development, but will suffer from increased drought and heat stress during crop maturity. During the harvesting stages, the number of waterlogged days increases in particular for tuber crops. Physically based crop models assist in understanding the links between different factors causing crop damage. The approach allows for assessing the meteorological impacts on crop growth due to the sensitive stages occurring earlier during the growing season and due to extreme weather events. Though average yields have risen continuously between 1947 and 2008 mainly due to technological advances, there is no evidence that relative tolerance to adverse weather conditions such as atmospheric moisture deficit and temperature extremes has changed.

Sources and Uses of Weather Information for Agricultural Decision Makers.

NASA Astrophysics Data System (ADS)

McNew, Kevin P.; Mapp, Harry P.; Duchon, Claude E.; Merritt, Earl S.

1991-04-01

Numerous studies have examined the importance of weather information to farmers and ranchers across the U.S. This study is focused on the kinds of weather information received by farmers and ranchers, the sources of that information, and its use in production and marketing decisions. Our results are based on a survey of 292 producers from the principal agricultural areas of Oklahoma. Producers were classified into five categories related to their source of income from crop and livestock sales.Among temperature, precipitation, relative humility, and wind speed, temperature information was most widely received. Forecast lengths of highest interest were 24-h and 5-day forecasts. Precipitation information was used by many respondents for planting and harvesting decisions. Weather data and forecasts seem to be of greater value to diversified crop and livestock operators than specialized crop and livestock, perhaps due to more frequent timing decisions. Relative humility and wind information appear to be important especially during specific times of the growing season, for example, at harvest time and time of pesticide application. Television is the primary source of weather information for more than 60% of the producers.It appears that there may be a role for both public and private entities in transforming weather data and forecasts into recommendations to crop and livestock producers. Further research is needed to determine the potential value of weather information for alternative production, marketing and livestock decisions, different categories of producers, and different geographic regions.

Soil quality differences in a mature alley cropping system in temperate North America

USDA-ARS?s Scientific Manuscript database

Alley cropping in agroforestry practices has been shown to improve soil quality, however information on long-term effects (>10 years) of alley cropping on soils in the temperate zone is very limited. The objective of this study was to examine effects of management, landscape, and soil depth on soil...

Crop Production Handbook for Peace Corps Volunteers. Appropriate Technologies for Development. Reprint R-6.

ERIC Educational Resources Information Center

1982

This manual, prepared for use by Peace Corps volunteers, provides background information and practical knowledge about crop production. The manual is designed to convey insights into basic crop production, principles, and practices. Primary emphasis is given to providing explanations and illustrations of soil, plant, and water relationships as…

Weeds and their effect on the performance of maize and fingermillet in the mid-hills of Nepal

USDA-ARS?s Scientific Manuscript database

Relay cropping of maize with fingermillet (maize/fingermillet) is the predominant cropping system for sustaining food security in the hilly regions of Nepal. In this region weed pressure severely reduces crop yields, yet basic information on weed species composition, biomass production and their eff...

Long-term conventional and no-tillage effects on field hydrology and yields of a dryland crop rotation

USDA-ARS?s Scientific Manuscript database

Semiarid dryland crop yields with no-till, NT, residue management are often greater than stubble-mulch, SM, tillage as a result of improved soil conditions and water conservation, but information on long-term tillage effects on field hydrology and sustained crop production are needed. Our objective ...

New insights to lateral rooting: Differential responses to heterogeneous nitrogen availability among maize root types

PubMed Central

Yu, Peng; White, Philip J; Li, Chunjian

2015-01-01

Historical domestication and the "Green revolution" have both contributed to the evolution of modern, high-performance crops. Together with increased irrigation and application of chemical fertilizers, these efforts have generated sufficient food for the growing global population. Root architecture, and in particular root branching, plays an important role in the acquisition of water and nutrients, plant performance, and crop yield. Better understanding of root growth and responses to the belowground environment could contribute to overcoming the challenges faced by agriculture today. Manipulating the abilities of crop root systems to explore and exploit the soil environment could enable plants to make the most of soil resources, increase stress tolerance and improve grain yields, while simultaneously reducing environmental degradation. In this article it is noted that the control of root branching, and the responses of root architecture to nitrate availability, differ between root types and between plant species. Since the control of root branching depends upon both plant species and root type, further work is urgently required to determine the appropriate genes to manipulate to improve resource acquisition by specific crops. PMID:26443081

New insights to lateral rooting: Differential responses to heterogeneous nitrogen availability among maize root types.

PubMed

Yu, Peng; White, Philip J; Li, Chunjian

2015-01-01

Historical domestication and the "Green revolution" have both contributed to the evolution of modern, high-performance crops. Together with increased irrigation and application of chemical fertilizers, these efforts have generated sufficient food for the growing global population. Root architecture, and in particular root branching, plays an important role in the acquisition of water and nutrients, plant performance, and crop yield. Better understanding of root growth and responses to the belowground environment could contribute to overcoming the challenges faced by agriculture today. Manipulating the abilities of crop root systems to explore and exploit the soil environment could enable plants to make the most of soil resources, increase stress tolerance and improve grain yields, while simultaneously reducing environmental degradation. In this article it is noted that the control of root branching, and the responses of root architecture to nitrate availability, differ between root types and between plant species. Since the control of root branching depends upon both plant species and root type, further work is urgently required to determine the appropriate genes to manipulate to improve resource acquisition by specific crops.

Ecoclimatic indicators to study climate suitability of areas for the cultivation of specific crops

NASA Astrophysics Data System (ADS)

Caubel, J.; Garcia de Cortazar Atauri, I.; Cufi, J.; Huard, F.; Launay, M.; Ripoche, D.; Graux, A.; deNoblet, N.

2013-12-01

Climatic conditions play a fundamental role in the suitability of geographical areas for cropping. In the context of climate change, we could expect changes in overall climatic conditions and so, on the suitability for cropping. Therefore, assessing the future climate suitability of areas for cropping is decisive for anticipating agriculture in a given area. Moreover, it is crucial to have access to the split up information concerning the effect of climate on the achievement of the main ecophysiological processes and cultural practices taking place during the crop cycle. In this way, stakeholders can envisage land use adaptations under climate change conditions, such as changes in cultural practices or development of new varieties for example. We proposed an aggregation tool of ecoclimatic indicators to design evaluation trees of climate suitability of areas for cropping, GETARI (Generic Evaluation Tool of Ecoclimatic Indicators). It calculates an overall climate suitability index at the annual scale, from a designed evaluation tree. This aggregation tool allows to characterize climate suitability according to crop ecophysiology, grain/fruit quality or crop management. GETARI proposes the major ecophysiological processes and cultural practices taking place during phenological periods, together with the climatic effects that are known to affect their achievement. The climatic effects on the ecophysiological processes (or cultural practices) during phenological periods are captured by the ecoclimatic indicators, which are agroclimatic indicators calculated over phenological periods. They give information about crop response to climate through ecophysiological or agronomic thresholds. Those indices of suitability are normalized and aggregated according to aggregation rules in order to compute an overall climate index. In order to illustrate how GETARI can be used, we designed evaluation trees in order to study the climate suitability for maize cropping regarding ecophysiology, for wheat cropping regarding its management and for grape cropping regarding its quality. The designed evaluation trees were developed in accordance with expert assessment and were applied in some past climatic conditions in France to verify their consistence. To conclude, the use of indicators does not replace models but represent additional tools for understanding and spatializing some results obtained by models. Their use can provide information about suitability of geographical areas for cropping in future climatic conditions and can enable to minimize the risk of crop failure. This work is carried out under the research program ORACLE (Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France (and Europe).

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys

PubMed Central

Yao, Rongjiang; Yang, Jingsong; Wu, Danhua; Xie, Wenping; Gao, Peng; Jin, Wenhui

2016-01-01

Reliable and real-time information on soil and crop properties is important for the development of management practices in accordance with the requirements of a specific soil and crop within individual field units. This is particularly the case in salt-affected agricultural landscape where managing the spatial variability of soil salinity is essential to minimize salinization and maximize crop output. The primary objectives were to use linear mixed-effects model for soil salinity and crop yield calibration with horizontal and vertical electromagnetic induction (EMI) measurements as ancillary data, to characterize the spatial distribution of soil salinity and crop yield and to verify the accuracy of spatial estimation. Horizontal and vertical EMI (type EM38) measurements at 252 locations were made during each survey, and root zone soil samples and crop samples at 64 sampling sites were collected. This work was periodically conducted on eight dates from June 2012 to May 2013 in a coastal salt-affected mud farmland. Multiple linear regression (MLR) and restricted maximum likelihood (REML) were applied to calibrate root zone soil salinity (ECe) and crop annual output (CAO) using ancillary data, and spatial distribution of soil ECe and CAO was generated using digital soil mapping (DSM) and the precision of spatial estimation was examined using the collected meteorological and groundwater data. Results indicated that a reduced model with EMh as a predictor was satisfactory for root zone ECe calibration, whereas a full model with both EMh and EMv as predictors met the requirement of CAO calibration. The obtained distribution maps of ECe showed consistency with those of EMI measurements at the corresponding time, and the spatial distribution of CAO generated from ancillary data showed agreement with that derived from raw crop data. Statistics of jackknifing procedure confirmed that the spatial estimation of ECe and CAO exhibited reliability and high accuracy. A general increasing trend of ECe was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone ECe coincided with those of daily rainfall, water table and groundwater data. Long-range EMI surveys and data collection are needed to capture the spatial and temporal variability of soil and crop parameters. Such results allowed us to conclude that, cost-effective and efficient EMI surveys, as one part of multi-source data for DSM, could be successfully used to characterize the spatial variability of soil salinity, to monitor the spatial and temporal dynamics of soil salinity, and to spatially estimate potential crop yield. PMID:27203697

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys.

PubMed

Yao, Rongjiang; Yang, Jingsong; Wu, Danhua; Xie, Wenping; Gao, Peng; Jin, Wenhui

2016-01-01

Reliable and real-time information on soil and crop properties is important for the development of management practices in accordance with the requirements of a specific soil and crop within individual field units. This is particularly the case in salt-affected agricultural landscape where managing the spatial variability of soil salinity is essential to minimize salinization and maximize crop output. The primary objectives were to use linear mixed-effects model for soil salinity and crop yield calibration with horizontal and vertical electromagnetic induction (EMI) measurements as ancillary data, to characterize the spatial distribution of soil salinity and crop yield and to verify the accuracy of spatial estimation. Horizontal and vertical EMI (type EM38) measurements at 252 locations were made during each survey, and root zone soil samples and crop samples at 64 sampling sites were collected. This work was periodically conducted on eight dates from June 2012 to May 2013 in a coastal salt-affected mud farmland. Multiple linear regression (MLR) and restricted maximum likelihood (REML) were applied to calibrate root zone soil salinity (ECe) and crop annual output (CAO) using ancillary data, and spatial distribution of soil ECe and CAO was generated using digital soil mapping (DSM) and the precision of spatial estimation was examined using the collected meteorological and groundwater data. Results indicated that a reduced model with EMh as a predictor was satisfactory for root zone ECe calibration, whereas a full model with both EMh and EMv as predictors met the requirement of CAO calibration. The obtained distribution maps of ECe showed consistency with those of EMI measurements at the corresponding time, and the spatial distribution of CAO generated from ancillary data showed agreement with that derived from raw crop data. Statistics of jackknifing procedure confirmed that the spatial estimation of ECe and CAO exhibited reliability and high accuracy. A general increasing trend of ECe was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone ECe coincided with those of daily rainfall, water table and groundwater data. Long-range EMI surveys and data collection are needed to capture the spatial and temporal variability of soil and crop parameters. Such results allowed us to conclude that, cost-effective and efficient EMI surveys, as one part of multi-source data for DSM, could be successfully used to characterize the spatial variability of soil salinity, to monitor the spatial and temporal dynamics of soil salinity, and to spatially estimate potential crop yield.

An automatic approach for rice mapping in temperate region using time series of MODIS imagery: first results for Mediterranean environment

NASA Astrophysics Data System (ADS)

Boschetti, M.; Nelson, A.; Manfrom, G.; Brivio, P. A.

2012-04-01

Timely and accurate information on crop typology and status are required to support suitable action to better manage agriculture production and reduce food insecurity. More specifically, regional crop masking and phenological information are important inputs for spatialized crop growth models for yield forecasting systems. Digital cartographic data available at global/regional scale, such as GLC2000, GLOBCOVER or MODIS land cover products (MOD12), are often not adequate for this crop modeling application. For this reason, there is a need to develop and test methods that can provide such information for specific cropsusing automated classification techniques.. In this framework we focused our analysis on the rice cultivation area detection due to the importance of this crop. Rice is a staple food for half of the world's population (FAO 2004). Over 90% of the world's rice is produced and consumed in Asia and the region is home to 70% of the world's poor, most of whom depend on rice for their livelihoods andor food security. Several initiatives are being promoted at the international level to provide maps of rice cultivated areas in South and South East Asia using different approaches available in literature for rice mapping in tropical regions. We contribute to these efforts by proposing an automatic method to detect rice cultivated areas in temperate regions exploiting MODIS 8-Day composite of Surface Reflectance at 500m spatial resolution (MOD09A1product). Temperate rice is cultivated worldwide in more than 20 countries covering around 16M ha for a total production of about 65M tons of paddy per year. The proposed method is based on a common approach available in literature that first identifies flood condition that can be related to rice agronomic practice and then checks for vegetation growth. The method presents innovative aspects related both to the flood detection, exploiting Short Wave Infrared spectral information, and to the crop grow monitoring analyzing vegetation index seasonal trend. Tests conducted in European Mediterranean environment demonstrated that our approach is able to provide accurate rice map (User Accuracy > 80%) when compared to available Corine Land Cover land use map (1:100.000 scale, MMU 25 ha). Map accuracy in term of omission and commission error has been analyzed in north of Italy where about 60 % of total European riceis produced. For this study area thematic cartography at 1:10.000scale allowed to analyze the type of commission errors and evaluate the entity of omission errors in relation to low resolution bias and/or algorithm performance. Pareto boundary method has been used to assess the level of accuracy of the method respect a maximum achievable accuracy with medium resolution MODIS data. Results demonstrate that the proposed approach outperform the method developed for tropical and sub-tropical environment.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Innovative combination of spectroscopic techniques to reveal nanoparticle fate in a crop plant

NASA Astrophysics Data System (ADS)

Larue, Camille; Castillo-Michel, Hiram; Stein, Ricardo J.; Fayard, Barbara; Pouyet, Emeline; Villanova, Julie; Magnin, Valérie; Pradas del Real, Ana-Elena; Trcera, Nicolas; Legros, Samuel; Sorieul, Stéphanie; Sarret, Géraldine

2016-05-01

Nanotechnology is the new industrial revolution of our century. Its development leads to an increasing use of nanoparticles and thus to their dissemination. Their fate in the environment is of great concern and especially their possible transfer in trophic chains might be an issue for food safety. However, so far our knowledge on this topic has been restricted by the lack of appropriate techniques to characterize their behavior in complex matrices. Here, we present in detail the use of cutting-edge beam-based techniques for nanoparticle in situ localization, quantification and speciation in a crop plant species (Lactuca sativa). Lettuce seedlings have been exposed to TiO2 and Ag nanoparticles and analyzed by inductively coupled plasma spectrometry, micro-particle induced X-ray emission coupled to Rutherford backscattering spectroscopy on nuclear microprobe, micro-X-ray fluorescence spectroscopy and X-ray absorption near edge structure spectroscopy. The benefits and drawbacks of each technique are discussed, and the types of information that can be drawn, for example on the translocation to edible parts, change of speciation within the plant, detoxification mechanisms, or impact on the plant ionome, are highlighted. Such type of coupled approach would be an asset for nanoparticle risk assessment.

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.

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.

Crop Identification Technolgy Assessment for Remote Sensing (CITARS). Volume 1: Task design plan

NASA Technical Reports Server (NTRS)

Hall, F. G.; Bizzell, R. M.

1975-01-01

A plan for quantifying the crop identification performances resulting from the remote identification of corn, soybeans, and wheat is described. Steps for the conversion of multispectral data tapes to classification results are specified. The crop identification performances resulting from the use of several basic types of automatic data processing techniques are compared and examined for significant differences. The techniques are evaluated also for changes in geographic location, time of the year, management practices, and other physical factors. The results of the Crop Identification Technology Assessment for Remote Sensing task will be applied extensively in the Large Area Crop Inventory Experiment.

Effects of Nitrogen Fixing Pre-Crops and Fertilizers on Physical and Chemical Properties Down the Soil Profile

NASA Astrophysics Data System (ADS)

Hobley, E.; Honermeier, B.; Don, A.; Gocke, M. I.; Amelung, W.; Kogel-Knabner, I.

2016-12-01

We investigated the effects of pre-crops with and without biological nitrogen fixation capacity (fava beans, clover mulch, fodder maize) and fertilization (no fertilizer, NPK fertilizer, PK fertilizer) on soil physico-chemical properties (bulk density, electrical conductivity, soil organic carbon (SOC) concentration and stocks, N concentration and stocks) and their depth distribution (down to 1 m) at a long-term field experiment set up in 1982 in Gießen, Germany. Fertilization had significant but small impacts on the soil chemical environment, most particularly the salt content of the soil, with PK fertilization increasing electrical conductivity throughout the soil profile. Similarly, fertilization resulted in a small reduction of soil pH throughout the entire soil profile. The soil was physically and chemically affected by the type of pre-crop. Plots with fava beans and maize had lower bulk densities in the subsoil than those with clover. Pre-crop type also significantly affected the depth distribution of both N and SOC. Specifically, clover pre-cropping led to an enrichment of N at the surface compared with fava beans and maize. SOC enrichment at the surface was also observed under clover, with the effect most pronounced under PK fertilization. Combined with the bulk density effects, this shift in N distribution resulted in significantly higher N stocks under clover than under fava beans. However, the total stocks of SOC were not affected by pre-crop or fertilizer regime. Our results indicate that humans influence C and N cycling and distribution in soils through the selection of pre-crops and that the influence of crop type is greater than that of fertilization regimes. Pre-cropping with clover, which is used as a mulch, leads to N enrichment in the topsoil, reducing the need for N fertilizer for the subsequent cereal crop. In contrast, the use of fava beans as a pre-crop does not lead to N enrichment. We believe this is due to the greater rooting depth of fava beans compared with clover, resulting in lower bulk density in the subsoil and associated lower stocks. Additionally, the harvest of fava beans removes N-rich biomass from the soil, lowering N-input. Lastly, the uptake of water at depth may facilitate subsoil N uptake, so that fava bean N is utilized by the cereal crop but does not lead to its enrichment in the subsoil.

Soil management, fertilization and plant nutrition in organic systems in Spain: A review of the research in last 20 years

NASA Astrophysics Data System (ADS)

Gonzalvez, Victor; Raigon Jiménez, M.° Dolores

2016-04-01

The Spanish Society for Agroecology/Organic Farming (SEAE) is a private charity association, founded in 1992, with the purpose to support organic farming practitioners. The principal aim is to join the efforts farmers, technicians and scientifics and others organizations and persons, related to develop sustainable agriculture systems, based on ecological and socioeconomic principles promoted by the international organic farming movement, with the purpose to obtain foods and first resources with high quality, considering the vulnerability of the environment and preserving the soil fertility, with the optimal and adequate use of the local resources, taking in account the rural culture and the ethical value of the social development and the life quality. One of the most relevant and know activity of SEAE is the celebration of one (scientific) Congress every two years. This is the most important event on this issue in Spain. In the last 20 year, eleven events of this kind have been organised in 11 different places (Toledo, Pamplona, Valencia, Córdoba, Gijón-Asturias, Almeria, Zaragoza, Bullas-Murcia, Lleida, Albacete, Vitoria-Gasteiz). The average participation in the Congress was growing up from 100 to 350 persons), from all over Spain. During this events, researchers, advisors, trainers, politicians and operators (farmers, processors, certifiers, marketers, consumers, etc.) shared and update the scientific results, projects in force, political measures, statistics and proposals to develop the organic farming sector Research in organic farming is still low in Spain and the majority of the results in this matter are being presented as papers in this Congresses. Over 1500 papers from over 100 spanish research groups giving information about the research results have been presented in this events, One of the most relevant topic of this research is done on soil conservation, soil fertility and organic crop fertilization and organic matter management in the soil, after organic plant health and plant protection. In total 12 % of the papers presented in these events were devoted to soil conservation, soil fertility and plant nutrition management. We have analyzed this papers contributions dividing in five categories: a) organic and mineral fertilization; b) general evaluation of soil fertility under organic management; c) compost making and compost types; d) soil conservation and fertilization; e) crop fertilization and food quality The results shows that over 20 % of the total papers presented were related to general aspects of crop fertilization in 16% types of vegetables crops, 14% on arable crops and pastures and 8% on perennial crops (almonds, citrus, vineyards, olive trees, and banana) have been presented. Most studies were done on vegetables and very few on nutrient balance have been published. Some papers deal with cover crops. The soil fertility impact of organic farming compared with conventional is focused is included in nearly 30 % of all the scientific papers presented. Compost from different crop residues and the effects on soil and on different crops, including waste sludge (not allowed in organic farming) have been researched. Also some studies deal with how to use the residues of the olive oil mills or residues of vineyards as organic fertilizer. Some of the most recent studies are focused on how compost can control pest and diseases in crop cultivation. Another type of study has analyzed the soil disinfection potential of manure with high exposition to the sun (high temperature) to be used in greenhouses. Few studies are concentrated in the application of mycorrhizae to enhance the capacity of the plants to absorber nutrients from soil. We found some few studies on biofertilisers, but there are many different inputs being offered to organic farmers as natural fertilizer. Soil conservation and organic fertilisation studies are scarce and not sufficiently detailed. Finally we found a five category of very few studies on the relation between fertilization of different crops and the final quality of the crops and fresh foods. The paper presents the most relevant results of research about organic farming fertilization in several crops conducted in Spain, which could be useful for Mediterranean countries with similar soil and clima Key words: compost, , mediterranean, nutrients balance, soil fertility,

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.

Detecting crop population growth using chlorophyll fluorescence imaging.

PubMed

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

2017-12-10

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

The agro-ecological suitability of Atriplex nummularia and A. halimus for biomass production in Argentine saline drylands.

PubMed

Falasca, Silvia Liliana; Pizarro, María José; Mezher, Romina Nahir

2014-09-01

The choice of the best species to cultivate in semi-arid and arid climates is of fundamental importance, and is determined by many factors, including temperature and rainfall, soil type, water availability for irrigation and crop purposes. Soil or water salinity represents one of the major causes of crop stress. Species of the genus Atriplex are characterized by high biomass productivity, high tolerance to drought and salinity, and high efficiency in use of solar radiation and water. Based on a search of the international literature, the authors outline an agro-climatic zoning model to determine potential production areas in Argentina for Atriplex halimus and Atriplex numularia. Using the agroclimatic limits presented in this work, this model may be applied to any part of the world. When superimposed on the saline areas map, the agroclimatic map shows the suitability of agro-ecological zoning for both species for energy purposes on land unsuitable for food production. This innovative study was based on the implementation of a geographic information system that can be updated by further incorporation of complementary information, with consequent improvement of the original database.

The agro-ecological suitability of Atriplex nummularia and A. halimus for biomass production in Argentine saline drylands

NASA Astrophysics Data System (ADS)

Falasca, Silvia Liliana; Pizarro, María José; Mezher, Romina Nahir

2014-09-01

The choice of the best species to cultivate in semi-arid and arid climates is of fundamental importance, and is determined by many factors, including temperature and rainfall, soil type, water availability for irrigation and crop purposes. Soil or water salinity represents one of the major causes of crop stress. Species of the genus Atriplex are characterized by high biomass productivity, high tolerance to drought and salinity, and high efficiency in use of solar radiation and water. Based on a search of the international literature, the authors outline an agro-climatic zoning model to determine potential production areas in Argentina for Atriplex halimus and Atriplex numularia. Using the agroclimatic limits presented in this work, this model may be applied to any part of the world. When superimposed on the saline areas map, the agroclimatic map shows the suitability of agro-ecological zoning for both species for energy purposes on land unsuitable for food production. This innovative study was based on the implementation of a geographic information system that can be updated by further incorporation of complementary information, with consequent improvement of the original database.

On estimating the economic value of insectivorous bats: Prospects and priorities for biologists

USGS Publications Warehouse

Boyles, Justin G.; Sole, Catherine L.; Cryan, Paul M.; McCracken, Gary F.

2013-01-01

Bats are among the most economically important nondomesticated mammals in the world. They are well-known pollinators and seed dispersers, but crop pest suppression is probably the most valuable ecosystem service provided by bats. Scientific literature and popular media often include reports of crop pests in the diet of bats and anecdotal or extrapolated estimates of how many insects are eaten by bats. However, quantitative estimates of the ecosystem services provided by bats in agricultural systems are rare, and the few estimates that are available are limited to a single cotton-dominated system in Texas. Despite the tremendous value for conservation and economic security of such information, surprisingly few scientific efforts have been dedicated to quantifying the economic value of bats. Here, we outline the types of information needed to better quantify the value of bats in agricultural ecosystems. Because of the complexity of the ecosystems involved, creative experimental design and innovative new methods will help advance our knowledge in this area. Experiments involving bats in agricultural systems may be needed sooner than later, before population declines associated with white-nose syndrome and wind turbines potentially render them impossible.

Variation in Biomass Composition Components among Forage, Biomass, Sorghum-Sudangrass, and Sweet Sorghum Types

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

Stefaniak, T. R.; Dahlberg, J. A.; Bean, B. W.

2012-07-01

Alternative biomass sources must be developed if the United States is to meet the goal in the U.S. Energy Security Act of 2007 to derive 30% of its petroleum from renewable sources, and several different biomass crops are currently in development. Sorghum [Sorghum bicolor (L.) Moench] is one such crop that will be an important feedstock source for biofuel production. As composition influences productivity, there exists a need to understand the range in composition observed within the crop. The goal of this research was to assess the range in dietary fiber composition observed within different types of biomass sorghums. Amore » total of 152 sorghum samples were divided into the four end-use types of sorghum: biomass, forage, sorghum-sudangrass, and sweet. These samples were analyzed chemically using dietary fiber analysis performed at the National Renewable Energy Laboratory using published protocols. Significant variation among the groups was detected for glucan and ash. Positive and highly significant correlations were detected between structural carbohydrates in the biomass and sweet sorghums while many of these correlations were negative or not significant in the forage and sorghum-sudangrass types. In addition, a wide range of variation was present within each group indicating that there is potential to manipulate the composition of the crop.« less

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq.

PubMed

Qader, Sarchil Hama; Dash, Jadunandan; Atkinson, Peter M

2018-02-01

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R 2 =0.70 compared to the date of MODIS EVI (Avg R 2 =0.68) and a NPP (Avg R 2 =0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach. Copyright © 2017 Elsevier B.V. All rights reserved.

Disaggregating and mapping crop statistics using hypertemporal remote sensing

NASA Astrophysics Data System (ADS)

Khan, M. R.; de Bie, C. A. J. M.; van Keulen, H.; Smaling, E. M. A.; Real, R.

2010-02-01

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998-2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered "small scale maps". These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.

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...

Dryland pea production and water use in responses to tillage, crop rotation, and weed management practice

USDA-ARS?s Scientific Manuscript database

Pea has been used to replace fallow and sustain dryland crop yields in arid and semiarid regions, but information to optimize its management is required. We evaluated pea growth, yield, and water use in response to tillage, crop rotation, and weed management practice from 2005 to 2010 in the norther...

Tillage and cropping sequence impacts on nitrogen cycling in dryland farming in eastern Montana, USA

USDA-ARS?s Scientific Manuscript database

Information on N cycling in dryland crops and soils as influenced by long-term tillage and cropping sequence is needed to quantify soil N sequestration, mineralization, and N balance to reduce N fertilization rate and N losses through soil processes. We evaluated the 21-yr effects of combinations of...

7 CFR 457.131 - Macadamia nut crop insurance provisions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... the orchard for the purpose of picking all or a portion of the crop. Graft. The uniting of a macadamia.... Picking of mature macadamia nuts from the ground. Interplanted. Acreage on which two or more crops are... information that we request in order to establish your approved yield. We will reduce the yield used to...

Conservation priorities for tree crop wild relatives in the United States

Treesearch

Colin K. Khoury; Stephanie L. Greene; Karen A. Williams; Chrystian C. Sosa; Chris Richards

2017-01-01

Crop wild relatives native to the United States have proved useful as genetic resources in breeding more productive, nutritious, and resilient crops. Their utilization is expected to increase with better information about the species and improving breeding tools. But this utilization may be constrained by their limited representation in genebanks and the ongoing loss...

Dryland soil chemical properties and crop yields affected by long-term tillage and cropping sequence

USDA-ARS?s Scientific Manuscript database

Information on the effect of long-term management on soil nutrients and chemical properties is scanty. We examined the 30-yr effect of tillage frequency and cropping sequence combination on dryland soil Olsen-P, K, Ca, Mg, Na, SO4-S, and Zn concentrations, pH, electrical conductivity (EC), and catio...

Effect of non-crop vegetation types on conservation biological control of pests in olive groves

PubMed Central

Cayuela, Luis; Gurr, Geoff M.; Campos, Mercedes

2013-01-01

Conservation biological control (CBC) is an environmentally sound potential alternative to the use of chemical insecticides. It involves modifications of the environment to promote natural enemy activity on pests. Despite many CBC studies increasing abundance of natural enemies, there are far fewer demonstrations of reduced pest density and very little work has been conducted in olive crops. In this study we investigated the effects of four forms of non-crop vegetation on the abundance of two important pests: the olive psyllid (Euphyllura olivina) and the olive moth (Prays oleae). Areas of herbaceous vegetation and areas of woody vegetation near olive crops, and smaller patches of woody vegetation within olive groves, decreased pest abundance in the crop. Inter-row ground covers that are known to increase the abundance of some predators and parasitoids had no effect on the pests, possibly as a result of lack of synchrony between pests and natural enemies, lack of specificity or intra-guild predation. This study identifies examples of the right types of diversity for use in conservation biological control in olive production systems. PMID:23904994

Summer Crop Classification by Multi-Temporal COSMO-SkyMed® Data

NASA Astrophysics Data System (ADS)

Guarini, Rocchina; Bruzzone, Lorenzo; Santoni, Massimo; Vuolo, Francesco; Luigi, Dini

2016-08-01

In this study, we propose a multi-temporal and multi- polarization approach to discriminate different crop types in the Marchefel region, Austria. The sensitivity of X-band COSMO-SkyMed® (CSK®) data with respect to five crop classes, namely carrot, corn, potato, soybean and sugarbeet is investigated. In particular, the capabilities of dual-polarization (StripMap PingPong) HH/HV, and single-polarization (StripMap Himage), HH and VH, in distinguishing among the five crop types are evaluated. A total of twenty-one Himage and ten PingPong images were acquired in a seven-months period, from April to October 2014. Therefore, the backscattering coefficient was extracted for each dataset and the classification was performed using a pixel-based support vector machine (SVM) approach. The accuracy of the obtained crop classifications was assessed by comparing them with ground truth. The dual-polarization results are contrasted between the HH and HV polarization, and with single-polarization ones (HH and VH polarizations). The best accuracy is obtained by using time-series of StripMap Himage data, at VH polarization, covering the whole season period.

78 FR 38483 - Area Risk Protection Insurance Regulations and Area Risk Protection Insurance Crop Provisions

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-26

...The Federal Crop Insurance Corporation (FCIC) finalizes the Area Risk Protection Insurance (ARPI) Basic Provisions, ARPI Barley Crop Insurance Provisions, ARPI Corn Crop Insurance Provisions, ARPI Cotton Crop Insurance Provisions, ARPI Forage Crop Insurance Provisions, ARPI Grain Sorghum Crop Insurance Provisions, ARPI Peanut Crop Insurance Provisions, ARPI Soybean Crop Insurance Provisions, and ARPI Wheat Crop Insurance Provisions to provide area yield protection and area revenue protection. These provisions will replace the Group Risk Plan (GRP) provisions in 7 CFR part 407, which includes the: GRP Basic Provisions, GRP Barley Crop Provisions, GRP Corn Crop Provisions, GRP Cotton Crop Provisions, GRP Forage Crop Provisions, GRP Peanut Crop Provisions, GRP Sorghum Crop Provisions, GRP Soybean Crop Provisions, and GRP Wheat Crop Provisions. The ARPI provisions will also replace the Group Risk Income Protection (GRIP) Basic Provisions, the GRIP Crop Provisions, and the GRIP-Harvest Revenue Option (GRIP-HRO). The GRP and GRIP plans of insurance will no longer be available. The intended effect of this action is to offer producers a choice of Area Revenue Protection, Area Revenue Protection with the Harvest Price Exclusion, or Area Yield Protection, all within one Basic Provision and the applicable Crop Provisions. This will reduce the amount of information producers must read to determine the best risk management tool for their operation and will improve the provisions to better meet the needs of insureds. The changes will apply for the 2014 and succeeding crop years.

Nitrous Oxide Emission and Denitrifier Abundance in Two Agricultural Soils Amended with Crop Residues and Urea in the North China Plain.

PubMed

Gao, Jianmin; Xie, Yingxin; Jin, Haiyang; Liu, Yuan; Bai, Xueying; Ma, Dongyun; Zhu, Yunji; Wang, Chenyang; Guo, Tiancai

2016-01-01

The application of crop residues combined with Nitrogen (N) fertilizer has been broadly adopted in China. Crop residue amendments can provide readily available C and N, as well as other nutrients to agricultural soils, but also intensify the N fixation, further affecting N2O emissions. N2O pulses are obviously driven by rainfall, irrigation and fertilization. Fertilization before rainfall or followed by flooding irrigation is a general management practice for a wheat-maize rotation in the North China Plain. Yet, little is known on the impacts of crop residues combined with N fertilizer application on N2O emission under high soil moisture content. A laboratory incubation experiment was conducted to investigate the effects of two crop residue amendments (maize and wheat), individually or in combination with N fertilizer, on N2O emissions and denitrifier abundance in two main agricultural soils (one is an alluvial soil, pH 8.55, belongs to Ochri-Aquic Cambosols, OAC, the other is a lime concretion black soil, pH 6.61, belongs to Hapli-Aquic Vertosols, HAV) under 80% WFPS (the water filled pore space) in the North China Plain. Each type soil contains seven treatments: a control with no N fertilizer application (CK, N0), 200 kg N ha-1 (N200), 250 kg N ha-1 (N250), maize residue plus N200 (MN200), maize residue plus N250 (MN250), wheat residue plus N200 (WN200) and wheat residue plus N250 (WN250). Results showed that, in the HAV soil, MN250 and WN250 increased the cumulative N2O emissions by 60% and 30% compared with N250 treatment, respectively, but MN200 and WN200 decreased the cumulative N2O emissions by 20% and 50% compared with N200. In the OAC soil, compared with N200 or N250, WN200 and WN250 increased the cumulative N2O emission by 40%-50%, but MN200 and MN250 decreased the cumulative N2O emission by 10%-20%. Compared with CK, addition of crop residue or N fertilizer resulted in significant increases in N2O emissions in both soils. The cumulative N2O emissions from the treatments of 250 kg N ha-1 were 1.1-3.3 times higher than those of treatments with 200 kg N ha-1 in both soils with adding equal amounts of the same type of crop residue. Abundance of the 16S rRNA gene did not significantly change in all treatments in two soils, but the nosZ and nirS genes were more abundant in soils amended with crop residues compared with CK or N-only treatments. N2O emission, however, were not related to the abundance of denitrifier containing nirS or nosZ. The research provided some information regarding the effect of crop residues with N fertilizer on N2O emissions and denitrifier abundances in two soils. Our results imply the property of crop residue and rate of N fertilizer are important influencing factors of N2O emission when crop residues combined with N fertilizer are applied to different agricultural soils.

Soil organic carbon sequestration and tillage systems in Mediterranean environments

NASA Astrophysics Data System (ADS)

Francaviglia, Rosa; Di Bene, Claudia; Marchetti, Alessandro; Farina, Roberta

2016-04-01

Soil carbon sequestration is of special interest in Mediterranean areas, where rainfed cropping systems are prevalent, inputs of organic matter to soils are low and mostly rely on crop residues, while losses are high due to climatic and anthropic factors such as intensive and non-conservative farming practices. The adoption of reduced or no tillage systems, characterized by a lower soil disturbance in comparison with conventional tillage, has proved to be positively effective on soil organic carbon (SOC) conservation and other physical and chemical processes, parameters or functions, e.g. erosion, compaction, ion retention and exchange, buffering capacity, water retention and aggregate stability. Moreover, soil biological and biochemical processes are usually improved by the reduction of tillage intensity. The work deals with some results available in the scientific literature, and related to field experiment on arable crops performed in Italy, Greece, Morocco and Spain. Data were organized in a dataset containing the main environmental parameters (altitude, temperature, rainfall), soil tillage system information (conventional, minimum and no-tillage), soil parameters (bulk density, pH, particle size distribution and texture), crop type, rotation, management and length of the experiment in years, initial SOCi and final SOCf stocks. Sampling sites are located between 33° 00' and 43° 32' latitude N, 2-860 m a.s.l., with mean annual temperature and rainfall in the range 10.9-19.6° C and 355-900 mm. SOC data, expressed in t C ha-1, have been evaluated both in terms of Carbon Sequestration Rate, given by [(SOCf-SOCi)/length in years], and as percentage change in comparison with the initial value [(SOCf-SOCi)/SOCi*100]. Data variability due to the different environmental, soil and crop management conditions that influence SOC sequestration and losses will be examined.

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

NASA Astrophysics Data System (ADS)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

Community and household socioeconomic factors associated with pesticide-using, small farm household members' health: a multi-level, longitudinal analysis

PubMed Central

2011-01-01

Background Longitudinal studies using multi-level models to examine health inequalities in lower and middle income countries (LMICs) are rare. We explored socio-economic gradients in health among small farm members participating in a pesticide-related health and agriculture program in highland Ecuador. Methods We profiled 24 communities through key informant interviews, secondary data (percent of population with unsatisfied basic needs), and intervention implementation indicators. Pre (2005) and post (2007) surveys of the primary household and crop managers included common questions (education, age, and the health outcome - digit span scaled 0-10)) and pesticide-related practice questions specific to each. Household assets and pesticide use variables were shared across managers. We constructed multi-level models predicting 2007 digit span for each manager type, with staged introduction of predictor variables. Results 376 household managers (79% of 2005 participants) and 380 crop managers (76% of 2005 participants) had complete data for analysis. The most important predictor of 2007 digit span was 2005 digit span: β (Standard Error) of 0.31(0.05) per unit for household and 0.17(0.04) for crop managers. Household asset score was next most important: 0.14(0.06) per unit for household and 0.14(0.05) for crop managers. Community percent with unsatisfied basic needs was associated with reductions in 2007 digit span: -0.04(0.01) per percent for household and -0.03(0.01) for crop managers. Conclusions The important roles of life endowments and/or persistent neurotoxicity were exemplified by limited change in the health outcome. Gradients by household assets and community deprivation were indicative of ongoing, structural inequities within this LMIC. PMID:22094171

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.

Sustainable harvest: managing plasticity for resilient crops

PubMed Central

Bloomfield, Justin A; Rose, Terry J; King, Graham J

2014-01-01

Maintaining crop production to feed a growing world population is a major challenge for this period of rapid global climate change. No consistent conceptual or experimental framework for crop plants integrates information at the levels of genome regulation, metabolism, physiology and response to growing environment. An important role for plasticity in plants is assisting in homeostasis in response to variable environmental conditions. Here, we outline how plant plasticity is facilitated by epigenetic processes that modulate chromatin through dynamic changes in DNA methylation, histone variants, small RNAs and transposable elements. We present examples of plant plasticity in the context of epigenetic regulation of developmental phases and transitions and map these onto the key stages of crop establishment, growth, floral initiation, pollination, seed set and maturation of harvestable product. In particular, we consider how feedback loops of environmental signals and plant nutrition affect plant ontogeny. Recent advances in understanding epigenetic processes enable us to take a fresh look at the crosstalk between regulatory systems that confer plasticity in the context of crop development. We propose that these insights into genotype × environment (G × E) interaction should underpin development of new crop management strategies, both in terms of information-led agronomy and in recognizing the role of epigenetic variation in crop breeding. PMID:24891039

Neuro-classification of multi-type Landsat Thematic Mapper data

NASA Technical Reports Server (NTRS)

Zhuang, Xin; Engel, Bernard A.; Fernandez, R. N.; Johannsen, Chris J.

1991-01-01

Neural networks have been successful in image classification and have shown potential for classifying remotely sensed data. This paper presents classifications of multitype Landsat Thematic Mapper (TM) data using neural networks. The Landsat TM Image for March 23, 1987 with accompanying ground observation data for a study area In Miami County, Indiana, U.S.A. was utilized to assess recognition of crop residues. Principal components and spectral ratio transformations were performed on the TM data. In addition, a layer of the geographic information system (GIS) for the study site was incorporated to generate GIS-enhanced TM data. This paper discusses (1) the performance of neuro-classification on each type of data, (2) how neural networks recognized each type of data as a new image and (3) comparisons of the results for each type of data obtained using neural networks, maximum likelihood, and minimum distance classifiers.

Weather based risks and insurances for crop production in Belgium

NASA Astrophysics Data System (ADS)

Gobin, Anne

2014-05-01

Extreme weather events such as late frosts, droughts, heat waves and rain storms can have devastating effects on cropping systems. Damages due to extreme events are strongly dependent on crop type, crop stage, soil type and soil conditions. The perspective of rising risk-exposure is exacerbated further by limited aid received for agricultural damage, an overall reduction of direct income support to farmers and projected intensification of weather extremes with climate change. According to both the agriculture and finance sectors, a risk assessment of extreme weather events and their impact on cropping systems is needed. The impact of extreme weather events particularly during the sensitive periods of the farming calendar requires a modelling approach to capture the mixture of non-linear interactions between the crop, its environment and the occurrence of the meteorological event. The risk of soil moisture deficit increases towards harvesting, such that drought stress occurs in spring and summer. Conversely, waterlogging occurs mostly during early spring and autumn. Risks of temperature stress appear during winter and spring for chilling and during summer for heat. Since crop development is driven by thermal time and photoperiod, the regional crop model REGCROP (Gobin, 2010) enabled to examine the likely frequency, magnitude and impacts of frost, drought, heat stress and waterlogging in relation to the cropping season and crop sensitive stages. The risk profiles were subsequently confronted with yields, yield losses and insurance claims for different crops. Physically based crop models such as REGCROP assist in understanding the links between different factors causing crop damage as demonstrated for cropping systems in Belgium. Extreme weather events have already precipitated contraction of insurance coverage in some markets (e.g. hail insurance), and the process can be expected to continue if the losses or damages from such events increase in the future. Climate change will stress this further and impacts on crop growth are expected to be twofold, owing to the sensitive stages occurring earlier during the growing season and to the changes in return period of extreme weather events. Though average yields have risen continuously due to technological advances, there is no evidence that relative tolerance to adverse weather events has improved. The research is funded by the Belgian Science Policy Organisation (Belspo) under contract nr SD/RI/03A.

Genotype x environment interaction and its implication in identification of common bean populations with high calcium content.

PubMed

Fernandes, S B; Abreu, A F B; Ramalho, M A P

2016-06-24

The common bean is a food with high mineral content. Of the various types of beans cultivated in Brazil, carioca type beans are the most consumed. The aim of this study was to identify promising common bean populations with an emphasis toward the selection of carioca type bean lines with high calcium content. We also aimed to verify whether and how the crop season and the genotype (parental line and hybrid populations) x crop season interaction affect calcium content. A group of 3 lines of good agronomic characteristics were crossed with a group of 4 lines with high calcium content in a 3 x 4 partial diallel design. Great variability was identified among both the parental lines and the hybrid populations derived from the diallel crosses among the parental lines. We found significant interactions between crop season and both parental line and hybrid population. In the diallel analysis, only the general combining ability was significant, explaining 89.4% of the sum of squares. The RP-1, CNF05, and Safira lines exhibited the greatest calcium contents and a positive GCA. RP-1 is a line that presents high calcium content, in addition to having carioca type beans and an upright plant with high yield. To further increase the calcium content of the RP-1 line, we suggest crossing it with the CNF05 and Safira lines. Although there was a hybrid population x crop season interaction, it was possible to identify populations that performed best in terms of calcium content in both crop seasons.

The impact of cotton growing practices on soil microbiology and its relation to plant and soil health

NASA Astrophysics Data System (ADS)

Pereg, Lily

2013-04-01

Crop production and agricultural practices heavily impact the soil microbial communities, which differ among varying types of soils and environmental conditions. Soil-borne microbial communities in cotton production systems, as in every other cropping system, consist of microbial populations that may either be pathogenic, beneficial or neutral with respect to the cotton crop. Crop production practices have major roles in determining the composition of microbial communities and function of microbial populations in soils. The structure and function of any given microbial community is determined by various factors, including those that are influenced by farming and those not controlled by farming activities. Examples of the latter are environmental conditions such as soil type, temperature, daylight length and UV radiation, air humidity, atmospheric pressure and some abiotic features of the soil. On the other hand, crop production practices may determine other abiotic soil properties, such as water content, density, oxygen levels, mineral and elemental nutrient levels and the load of other crop-related soil amendments. Moreover, crop production highly influences the biotic properties of the soil and has a major role in determining the fate of soil-borne microbial communities associated with the crop plant. Various microbial strains react differently to the presence of certain plants and plant exudates. Therefore, the type of plant and crop rotations are important factors determining microbial communities. In addition, practice management, e.g. soil cultivation versus crop stubble retention, have a major effect on the soil conditions and, thus, on microbial community structure and function. All of the above-mentioned factors can lead to preferential selection of certain microbial population over others. It may affect not only the composition of microbial communities (diversity and abundance of microbial members) but also the function of the community (the ability of different microbes to perform certain activities). Therefore, agricultural practices may determine the ability of beneficial microbes to realise their plant growth promoting potential or the pathogenic expression of others. This presentation will review the current knowledge about the impact of cotton growing practices on microbial communities and soil health in different environments as well as endeavour to identify gaps worthwhile exploring in future research for promoting plant growth in healthy soils.

Disaggregated N2O emission factors in China based on cropping parameters create a robust approach to the IPCC Tier 2 methodology

PubMed Central

Shepherd, Anita; Yan, Xiaoyuan; Nayak, Dali; Newbold, Jamie; Moran, Dominic; Dhanoa, Mewa Singh; Goulding, Keith; Smith, Pete; Cardenas, Laura M.

2015-01-01

China accounts for a third of global nitrogen fertilizer consumption. Under an International Panel on Climate Change (IPCC) Tier 2 assessment, emission factors (EFs) are developed for the major crop types using country-specific data. IPCC advises a separate calculation for the direct nitrous oxide (N2O) emissions of rice cultivation from that of cropland and the consideration of the water regime used for irrigation. In this paper we combine these requirements in two independent analyses, using different data quality acceptance thresholds, to determine the influential parameters on emissions with which to disaggregate and create N2O EFs. Across China, the N2O EF for lowland horticulture was slightly higher (between 0.74% and 1.26% of fertilizer applied) than that for upland crops (values ranging between 0.40% and 1.54%), and significantly higher than for rice (values ranging between 0.29% and 0.66% on temporarily drained soils, and between 0.15% and 0.37% on un-drained soils). Higher EFs for rice were associated with longer periods of drained soil and the use of compound fertilizer; lower emissions were associated with the use of urea or acid soils. Higher EFs for upland crops were associated with clay soil, compound fertilizer or maize crops; lower EFs were associated with sandy soil and the use of urea. Variation in emissions for lowland vegetable crops was closely associated with crop type. The two independent analyses in this study produced consistent disaggregated N2O EFs for rice and mixed crops, showing that the use of influential cropping parameters can produce robust EFs for China. PMID:26865831

Disaggregated N2O emission factors in China based on cropping parameters create a robust approach to the IPCC Tier 2 methodology

NASA Astrophysics Data System (ADS)

Shepherd, Anita; Yan, Xiaoyuan; Nayak, Dali; Newbold, Jamie; Moran, Dominic; Dhanoa, Mewa Singh; Goulding, Keith; Smith, Pete; Cardenas, Laura M.

2015-12-01

China accounts for a third of global nitrogen fertilizer consumption. Under an International Panel on Climate Change (IPCC) Tier 2 assessment, emission factors (EFs) are developed for the major crop types using country-specific data. IPCC advises a separate calculation for the direct nitrous oxide (N2O) emissions of rice cultivation from that of cropland and the consideration of the water regime used for irrigation. In this paper we combine these requirements in two independent analyses, using different data quality acceptance thresholds, to determine the influential parameters on emissions with which to disaggregate and create N2O EFs. Across China, the N2O EF for lowland horticulture was slightly higher (between 0.74% and 1.26% of fertilizer applied) than that for upland crops (values ranging between 0.40% and 1.54%), and significantly higher than for rice (values ranging between 0.29% and 0.66% on temporarily drained soils, and between 0.15% and 0.37% on un-drained soils). Higher EFs for rice were associated with longer periods of drained soil and the use of compound fertilizer; lower emissions were associated with the use of urea or acid soils. Higher EFs for upland crops were associated with clay soil, compound fertilizer or maize crops; lower EFs were associated with sandy soil and the use of urea. Variation in emissions for lowland vegetable crops was closely associated with crop type. The two independent analyses in this study produced consistent disaggregated N2O EFs for rice and mixed crops, showing that the use of influential cropping parameters can produce robust EFs for China.

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.

EnviroAtlas - Manure application to agricultural lands from confined animal feeding operations by 12-digit HUC for the Conterminous United States, 2006

EPA Pesticide Factsheets

This EnviroAtlas dataset contains data on the mean livestock manure application to cultivated crop and hay/pasture lands by 12-digit Hydrologic Unit (HUC) in 2006. Livestock manure inputs to cultivated crop and hay/pasture lands were estimated using county-level estimates of recoverable animal manure from confined feeding operations compiled for 2007. Recoverable manure is defined as manure that is collected, stored, and available for land application from confined feeding operations. County-scale data on livestock populations -- needed to calculate manure inputs -- were only available for the year 2007 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We acquired county-level data describing total farm-level inputs (kg N/yr) of recoverable manure to individual counties in 2007 from the International Plant Nutrition Institute (IPNI) Nutrient Geographic Information System (NuGIS; http://www.ipni.net/nugis). These data were converted to per area rates (kg N/ha/yr) of manure N inputs by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture (agricultural) lands within a county as determined from county-level summarization of the 2006 NLCD. We distributed county-specific, per area N inputs rates to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county. Manure data described here represent an average input to a typical agricultural land type within a county, i.e., the

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.

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.

Spillover from adjacent crop and forest habitats shapes carabid beetle assemblages in fragmented semi-natural grasslands.

PubMed

Schneider, Gudrun; Krauss, Jochen; Boetzl, Fabian A; Fritze, Michael-Andreas; Steffan-Dewenter, Ingolf

2016-12-01

Semi-natural grasslands in Europe are insect biodiversity hotspots and important source habitats delivering ecosystem services to adjacent agricultural land by species spillover. However, this spillover might also occur in the opposite direction, affecting the diversity of semi-natural grasslands. This opposite spillover has got little attention in scientific literature even though generalist species penetrating into the grasslands can affect local biotic interactions, community composition and the conservation value of grassland habitats. In this study, we examined spillover effects from two different adjacent habitat types on carabid beetle assemblages in 20 semi-natural calcareous grasslands. The grasslands were either adjacent to a cereal crop field or to a coniferous forest. We found distinct differences in carabid beetle assemblages in calcareous grasslands depending on adjacent habitat type. Species richness and activity density were higher, but the evenness was lower in calcareous grasslands adjacent to crop fields compared with calcareous grasslands adjacent to coniferous forests. Further, we found a strong spillover of carabid beetles from adjacent crop fields after crop harvest, which may result in transiently increased predation pressure and resource competition in calcareous grasslands. Our results highlight that species composition, diversity and presumably ecosystem functions within semi-natural habitats are affected by the type and management of surrounding habitats. This needs to be considered by nature conservation measures, which aim to protect the unique insect communities of semi-natural European grasslands.

Application of crowdsourced hail data and damage information for hail risk assessment in the province of Styria, Austria

NASA Astrophysics Data System (ADS)

Tani, Satyanarayana; Rechberger, Andreas; Süsser Rechberger, Barbara; Teschl, Reinhard; Paulitsch, Helmut

2017-04-01

Hail storm damage is a major concern to the farmers in the province of Styria, Austria. Each year severe hail storms are causing damages to crops, resulting in losses of millions of euros. High spatial and timely ground truth information of the hail event and crop damage measurements are essential for better hail risk assessment. Usually, hail pad networks and visual damage surveys are used to collect the hail data and corresponding damage information. However, these hail pad networks are expensive and need laborious maintenance. The traditional crop damage assessment approaches are very labour-intensive and time-consuming. The advancements in information and communication technology (ICT) and the power of citizen based crowdsourcing data, will help to overcome these problems and ultimately provide a platform for data collection. A user-friendly and bilingual web interface was developed to collect hail data and crop damage information in the province of Styria, Austria. The dynamic web interface was developed using HTML5, JavaScript, and PHP7 combined with a MySQL database back-end. OpenStreetMap was integrated into the web interface and tile server optimised for an easy identification of geolocation information. The user needs an internet connection to transfer the data through smartphone or computer. Crowdsourced data will be quality tested and evaluated with 3D single polarisation C-band weather radar data to remove potential false reports. Further, the relationship between the reported hail events and radar-based hail detection algorithms (Waldvogel and Auer) and derived hail signature information intended for crop hail risk assessment will be investigated. The details about the web interface tool, application and verification methods to collect, analyse, and integrate different data sets are given. Further, the high spatial risk assessment information is communicated to support risk management policy.

Seasonal forecasts for the agricultural sector in Peru through user-tailored indices

NASA Astrophysics Data System (ADS)

Sedlmeier, Katrin; Gubler, Stefanie; Spierig, Christoph; Quevedo, Karim; Escajadillo, Yury; Avalos, Griña; Liniger, Mark A.; Schwierz, Cornelia

2017-04-01

In the agricultural sector, the demand for seasonal forecast information is high since agriculture depends strongly on climatic conditions during the growing season. Unfavorable weather and climate events, such as droughts or frost events, can lead to crop losses and thereby to large economic damages or life-threatening conditions in case of subsistence farming. The generally used presentation form of tercile probabilities of seasonally averaged meteorological quantities are not specific enough for end users. More user-tailored seasonal information is necessary. For example, warmer than average temperatures might be favorable for a crop as long as they remain below a plant-specific critical threshold. If, on the other hand, too many days show temperatures above this critical threshold, a mitigation action such as e.g. changing the crop type would be required. In the framework of the CLIMANDES project (a pilot project of the Global Framework for Climate Services led by WMO [http://www.wmo.int/gfcs/climandes]), user-tailored seasonal forecast products are developed for the agricultural sector in the Peruvian Andes. Such products include indices such as e.g. the frost risk, the occurrence of long dry periods, or the start of the rainy season which is crucial to schedule sowing. Furthermore, more specific indices derived from crop requirement studies are elaborated such as the number of days exceeding or falling below plant specific temperature thresholds for given phenological stages. The applicability of these products highly depends on forecast skill. In this study, the potential predictability and the skill of selected indicators are presented using seasonal hindcast data of the ECMWF system 4 for Peru during the time period 1981-2010. Furthermore, the influence of ENSO on the prediction skill is investigated. In this study, reanalysis data, ground measurements, and a gridded precipitation dataset are used for verification. The results indicate that temperature-based indicators show sizeable skill in the Peruvian highlands while precipitation-based forecasts are much more challenging.

Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan.

PubMed

Abid, Muhammad; Schilling, Janpeter; Scheffran, Jürgen; Zulfiqar, Farhad

2016-03-15

Pakistan is among the countries highly exposed and vulnerable to climate change. The country has experienced many severe floods, droughts and storms over the last decades. However, little research has focused on the investigation of vulnerability and adaptation to climate-related risks in Pakistan. Against this backdrop, this article investigates the farm level risk perceptions and different aspects of vulnerability to climate change including sensitivity and adaptive capacity at farm level in Pakistan. We interviewed a total of 450 farming households through structured questionnaires in three districts of Punjab province of Pakistan. This study identified a number of climate-related risks perceived by farm households such as extreme temperature events, insect attacks, animal diseases and crop pests. Limited water availability, high levels of poverty and a weak role of local government in providing proper infrastructure were the factors that make farmers more sensitive to climate-related risks. Uncertainty or reduction in crop and livestock yields; changed cropping calendars and water shortage were the major adverse impacts of climate-related risks reported by farmers in the study districts. Better crop production was reported as the only positive effect. Further, this study identified a number of farm level adaptation methods employed by farm households that include changes in crop variety, crop types, planting dates and input mix, depending upon the nature of the climate-related risks. Lack of resources, limited information, lack of finances and institutional support were some constraints that limit the adaptive capacity of farm households. This study also reveals a positive role of cooperation and negative role of conflict in the adaptation process. The study suggests to address the constraints to adaptation and to improve farm level cooperation through extended outreach and distribution of institutional services, particularly climate-specific farm advisory services. Copyright © 2015 Elsevier B.V. All rights reserved.

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

Tracing crop-specific sediment sources in agricultural catchments

NASA Astrophysics Data System (ADS)

Blake, William H.; Ficken, Katherine J.; Taylor, Philip; Russell, Mark A.; Walling, Desmond E.

2012-02-01

A Compound Specific Stable Isotope (CSSI) sediment tracing approach is evaluated for the first time in an agricultural catchment setting against established geochemical fingerprinting techniques. The work demonstrates that novel CSSI techniques have the potential to provide important support for soil resource management policies and inform sediment risk assessment for the protection of aquatic habitats and water resources. Analysis of soil material from a range of crop covers in a mixed land-use agricultural catchment shows that the carbon CSSI signatures of particle-reactive fatty acids label surface agricultural soil with distinct crop-specific signatures, thus permitting sediment eroded from each land-cover to be tracked downstream. High resolution sediment sampling during a storm event and analysis for CSSI and conventional geochemical fingerprints elucidated temporal patterns of sediment mobilisation under different crop regimes and the specific contribution that each crop type makes to downstream sediment load. Pasture sources (65% of the catchment area) dominated the sediment load but areal yield (0.13 ± 0.02 t ha - 1 ) was considerably less than that for winter wheat (0.44 ± 0.15 t ha - 1 ). While temporal patterns in crop response matched runoff and erosion response predictions based on plot-scale rainfall simulation experiments, comparison of biomarker and geochemical fingerprinting data indicated that the latter overestimated cultivated land inputs to catchment sediment yield due to inability to discriminate temporary pasture (in rotation) from cultivated land. This discrepancy, however, presents an opportunity since combination of the two datasets revealed the extremely localised nature of erosion from permanent pasture fields in this system (estimated at up to 0.5 t ha - 1 ). The novel use of CSSI and geochemical tracers in tandem provided unique insights into sediment source dynamics that could not have been derived from each method alone. Research into CSSI signature development (plant and soil processes) and the influence of cultivation regimes are required to support future development of this new tool.

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.

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 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.

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

The socioeconomics of genetically modified biofortified crops: a systematic review and meta-analysis.

PubMed

De Steur, Hans; Wesana, Joshua; Blancquaert, Dieter; Van Der Straeten, Dominique; Gellynck, Xavier

2017-02-01

Building upon the growing interest and research on genetically modified (GM) biofortification, its socioeconomic potential has been increasingly examined. We conducted two systematic reviews and meta-analyses to provide comprehensive evidence of consumers' willingness to pay (11 economic valuation studies, 64 estimates) and cost-effectiveness/benefits (five economic evaluation studies, 30 estimates). Worldwide, consumers were willing to pay 23.9% more for GM biofortified food crops. Aside from crop and design-related differences, information provision was deemed crucial. Positive information (nutrition and GM benefits) is associated with the highest consumer willingness to pay, compared with negative, objective, and conflicting GM information, especially when negative information was mentioned last. This health intervention would reduce the aggregated micronutrient deficiency burden in Asia (15.6 million disability-adjusted life years (DALYs)) by 12.5-51.4%, at a low cost of USD 7.9-27.8 per DALY in a pessimistic and optimistic scenario, respectively. Given that GM biofortified crops could tackle hidden hunger in a cost-effective and well-accepted way, its implementation is worth pursuing. A case study on folate biofortification further elaborates on the importance of socioeconomic research and the determinants of their market potential. © 2016 New York Academy of Sciences.

Preliminary evaluation of spectral, normal and meteorological crop stage estimation approaches

NASA Technical Reports Server (NTRS)

Cate, R. B.; Artley, J. A.; Doraiswamy, P. C.; Hodges, T.; Kinsler, M. C.; Phinney, D. E.; Sestak, M. L. (Principal Investigator)

1980-01-01

Several of the projects in the AgRISTARS program require crop phenology information, including classification, acreage and yield estimation, and detection of episodal events. This study evaluates several crop calendar estimation techniques for their potential use in the program. The techniques, although generic in approach, were developed and tested on spring wheat data collected in 1978. There are three basic approaches to crop stage estimation: historical averages for an area (normal crop calendars), agrometeorological modeling of known crop-weather relationships agrometeorological (agromet) crop calendars, and interpretation of spectral signatures (spectral crop calendars). In all, 10 combinations of planting and biostage estimation models were evaluated. Dates of stage occurrence are estimated with biases between -4 and +4 days while root mean square errors range from 10 to 15 days. Results are inconclusive as to the superiority of any of the models and further evaluation of the models with the 1979 data set is recommended.

Discrimination of crop types with TerraSAR-X-derived information

NASA Astrophysics Data System (ADS)

Sonobe, Rei; Tani, Hiroshi; Wang, Xiufeng; Kobayashi, Nobuyuki; Shimamura, Hideki

Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J-M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5-96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications.

A Novel Approach for Forecasting Crop Production and Yield Using Remotely Sensed Satellite Images

NASA Astrophysics Data System (ADS)

Singh, R. K.; Budde, M. E.; Senay, G. B.; Rowland, J.

2017-12-01

Forecasting crop production in advance of crop harvest plays a significant role in drought impact management, improved food security, stabilizing food grain market prices, and poverty reduction. This becomes essential, particularly in Sub-Saharan Africa, where agriculture is a critical source of livelihoods, but lacks good quality agricultural statistical data. With increasing availability of low cost satellite data, faster computing power, and development of modeling algorithms, remotely sensed images are becoming a common source for deriving information for agricultural, drought, and water management. Many researchers have shown that the Normalized Difference Vegetation Index (NDVI), based on red and near-infrared reflectance, can be effectively used for estimating crop production and yield. Similarly, crop production and yield have been closely related to evapotranspiration (ET) also as there are strong linkages between production/yield and transpiration based on plant physiology. Thus, we combined NDVI and ET information from remotely sensed images for estimating total production and crop yield prior to crop harvest for Niger and Burkina Faso in West Africa. We identified the optimum time (dekads 23-29) for cumulating NDVI and ET and developed a new algorithm for estimating crop production and yield. We used the crop data from 2003 to 2008 to calibrate our model and the data from 2009 to 2013 for validation. Our results showed that total crop production can be estimated within 5% of actual production (R2 = 0.98) about 30-45 days before end of the harvest season. This novel approach can be operationalized to provide a valuable tool to decision makers for better drought impact management in drought-prone regions of the world.

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Agricultural irrigated land-use inventory for Polk County, Florida, 2016

USGS Publications Warehouse

Marella, Richard L.; Berry, Darbi; Dixon, Joann F.

2017-08-16

An accurate inventory of irrigated crop acreage is not available at the level of resolution needed to better estimate agricultural water use or to project future water demands in many Florida counties. A detailed digital map and summary of irrigated acreage was developed for Polk County, Florida, during the 2016 growing season. This cooperative project between the U.S. Geological Survey and the Office of Agricultural Water Policy of the Florida Department of Agriculture and Consumer Services is part of an effort to improve estimates of water use and projections of future demands across all counties in the State. The irrigated areas were delineated by using land-use data provided by the Florida Department of Agriculture and Consumer Services, along with information obtained from the South and Southwest Florida Water Management Districts consumptive water-use permits. Delineations were field verified between April and December 2016. Attribute data such as crop type, primary water source, and type of irrigation system were assigned to the irrigated areas.The results of this inventory and field verification indicate that during the 2016 growing seasons (spring, summer, fall, and winter), an estimated 88,652 acres were irrigated within Polk County. Of the total field-verified crops, 83,995 acres were in citrus; 2,893 acres were in other non-citrus fruit crops (blueberries, grapes, peaches, and strawberries); 621 acres were in row crops (primarily beans and watermelons); 1,117 acres were in nursery (container and tree farms) and sod production; and 26 acres were in field crops including hay and pasture. Of the total inventoried irrigated acreage within Polk County, 98 percent (86,566 acres) was in the Southwest Florida Water Management District, and the remaining 2 percent (2,086 acres) was in the South Florida Water Management District.About 85,788 acres (96.8 percent of the acreage inventoried) were irrigated by a microirrigation system, including drip, bubblers, and spray emitters. The remaining 3.2 percent of the irrigated acreage was irrigated by a sprinkler system (2,360 acres) or subsurface flood systems (504 acres). Groundwater was the primary source of water used on irrigated acreage (88 percent, or 78,050 acres); the remaining 10,602 acres (12 percent) used groundwater combined with surface water as the irrigation source.The irrigated acreage estimated by the U.S. Geological Survey (USGS) for this 2016 inventory (88,652 acres) is about 11 percent higher than the 79,869 acres estimated by the U.S. Department of Agriculture (USDA) for 2012. Citrus and pasture in Polk County show the biggest difference in irrigated acreage between the USGS and USDA totals. Irrigated citrus acreage inventoried in 2016 by the USGS totaled 83,996 acres, whereas the USDA reported 78,305 acres of citrus in 2012. The USGS identified 6 acres of irrigated pasture and 20 acres of hay, whereas the USDA reported 6,631 acres of irrigated pasture and 1,349 acres of hay for 2012. In general, differences between the 2016 USGS field-verified acreage totals and acreage published by the USDA for 2012 could be due to (1) irrigated acreage for some specific crops increased or decreased substantially during the 4-year interval between 2012 and 2016 because of production or economic changes, (2) the assumption that if an irrigation system was present, it was used in 2016, when in fact some landowners may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreage published by the USDA for selected crops may be underestimated as a result of how information is obtained and formulated by the agency during census compilations.

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

NASA Technical Reports Server (NTRS)

Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P.; Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.;

AgRISTARS: Foreign Commodity production forecasting. Project procedures designation and description document, volume 1

NASA Technical Reports Server (NTRS)

Waggoner, J. T.; Phinney, D. E. (Principal Investigator)

1981-01-01

The crop estimation analysis procedures documentation of the AgRISTARS - Foreign Commodity Production Forecasting Project (FCPF) is presented. Specifically it includes the technical/management documentation of the remote sensing data analysis procedures prepared in accordance with the guidelines provided in the FCPF communication/documentation standards manual. Standard documentation sets are given arranged by procedural type and level then by crop types or other technically differentiating categories.

Carbon budget over 12 years in a production crop under temperate climate

NASA Astrophysics Data System (ADS)

Buysse, Pauline; Bodson, Bernard; Debacq, Alain; De Ligne, Anne; Heinesch, Bernard; Manise, Tanguy; Moureaux, Christine; Aubinet, Marc

2017-04-01

Carbon dioxide (CO2) exchanges between crops and the atmosphere are influenced by both climatic and crop management drivers. The investigated crop, situated at the Lonzée Terrestrial Observatory (LTO, candidate ICOS site) in Belgium and managed for more than 70 years using conventional farming practices, was monitored over three complete sugar beet (or maize)/winter wheat/potato/winter wheat rotation cycles from 2004 to 2016. Continuous eddy-covariance measurements and regular biomass samplings were performed in order to obtain the daily and seasonal Net Ecosystem Exchange (NEE), Gross Primary Productivity, Total Ecosystem Respiration, Net Primary Productivity, and Net Biome Production (NBP). Meteorological data and crop management practices were also recorded. The main objectives were to analyze the CO2 flux responses to climatic drivers and to establish the C budget of the cropland. Crop type significantly influenced the measured CO2 fluxes. In addition to crop season duration, which had an obvious impact on cumulated NEE values for each crop type, the CO2 flux response to photosynthetic photon flux density, vapor pressure deficit and temperature differed between crop types, while no significant response to soil water content was observed in any of them. Besides, a significant positive relationship between crop residue amount and ecosystem respiration was observed. Over the 12 years, NEE was negative (-4.34 ± 0.21 kg C m-2) but NBP was positive (1.05 ± 0.30 kg C m-2), i.e. as soon as all lateral carbon fluxes - dominated by carbon exportation - are included in the budget, the site behaves as a carbon source. Intercrops were seen to play a major role in the carbon budget, being mostly due to the long time period it represented (59 % of the 12 year time period). An in-depth analysis of intercrop periods and, more specifically, growing cover crops (mustard in the case of our study), is developed in a companion poster (ref. abstract EGU2017-12216, session SSS9.14/BG9.46/CL3.13). Although in line with preceding studies, the large C loss rate observed at LTO (NBP = + 87 ± 25 kg C m-2 yr-1) raises several questions as it corresponds to 1.8 % of the C stock in the top soil: is it realistic? Wouldn't it be affected by an undetected systematic error? If correct, could soil properties be preserved on the long term? This result at least calls for extensive C stock inventory for (in)validation.

Evaporation from irrigated crops: Its measurement, modeling and estimation from remotely sensed data

NASA Astrophysics Data System (ADS)

Garatuza-Payan, Jaime

The research described in this dissertation is predicated on the hypothesis that remotely sensed information from climatological satellites can be used to estimate the actual evapotranspiration from agricultural crops to improve irrigation scheduling and water use efficiency. The goal of the enabling research program described here was to facilitate and demonstrate the potential use of satellite data for the rapid and routine estimation of water use by irrigated crops in the Yaqui Valley irrigation scheme, an extensive irrigated area in Sonora, Mexico. The approach taken was first, to measure and model the evapotranspiration and crop factors for wheat and cotton, the most common irrigated crops in the Yaqui Valley scheme. Second, to develop and test a high-resolution (4 km x 4 km) method for determining cloud cover and solar radiation from GOES satellite data. Then third, to demonstrate the application of satellite data to calculate the actual evaporation for sample crops in the Yaqui Valley scheme by combining estimates of potential rate with relevant crop factors and information on crop management. Results show that it is feasible to provide routine estimates of evaporation for the most common crops in the Yaqui Valley irrigation scheme from satellite data. Accordingly, a system to provide such estimates has been established and the Water Users Association, the entity responsible for water distribution in Yaqui Valley, can now use them to decide whether specific fields need irrigation. A Web site (teka-pucem.itson.mx) is also being created which will allow individual farmers to have direct access to the evaporation estimates via the Internet.

DNA-informed breeding of rosaceous crops: promises, progress and prospects

PubMed Central

Peace, Cameron P

2017-01-01

Crops of the Rosaceae family provide valuable contributions to rural economies and human health and enjoyment. Sustained solutions to production challenges and market demands can be met with genetically improved new cultivars. Traditional rosaceous crop breeding is expensive and time-consuming and would benefit from improvements in efficiency and accuracy. Use of DNA information is becoming conventional in rosaceous crop breeding, contributing to many decisions and operations, but only after past decades of solved challenges and generation of sufficient resources. Successes in deployment of DNA-based knowledge and tools have arisen when the ‘chasm’ between genomics discoveries and practical application is bridged systematically. Key steps are establishing breeder desire for use of DNA information, adapting tools to local breeding utility, identifying efficient application schemes, accessing effective services in DNA-based diagnostics and gaining experience in integrating DNA information into breeding operations and decisions. DNA-informed germplasm characterization for revealing identity and relatedness has benefitted many programs and provides a compelling entry point to reaping benefits of genomics research. DNA-informed germplasm evaluation for predicting trait performance has enabled effective reallocation of breeding resources when applied in pioneering programs. DNA-based diagnostics is now expanding from specific loci to genome-wide considerations. Realizing the full potential of this expansion will require improved accuracy of predictions, multi-trait DNA profiling capabilities, streamlined breeding information management systems, strategies that overcome plant-based features that limit breeding progress and widespread training of current and future breeding personnel and allied scientists. PMID:28326185

Organic weed conrol and cover crop residue integration impacts on weed control, quality, and yield and economics in conservation tillage tomato - A case study

USDA-ARS?s Scientific Manuscript database

The increased use of conservation tillage in vegetable production requires more information be developed on the role of cover crops in weed control, tomato quality and yield. Three conservation-tillage systems utilizing crimson clover, brassica and cereal rye as winter cover crops were compared to ...

Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information

NASA Astrophysics Data System (ADS)

Mishra, Varun Narayan; Prasad, Rajendra; Kumar, Pradeep; Srivastava, Prashant K.; Rai, Praveen Kumar

2017-10-01

Updated and accurate information of rice-growing areas is vital for food security and investigating the environmental impact of rice ecosystems. The intent of this work is to explore the feasibility of dual-polarimetric C-band Radar Imaging Satellite-1 (RISAT-1) data in delineating rice crop fields from other land cover features. A two polarization combination of RISAT-1 backscatter, namely ratio (HH/HV) and difference (HH-HV), significantly enhanced the backscatter difference between rice and nonrice categories. With these inputs, a QUEST decision tree (DT) classifier is successfully employed to extract the spatial distribution of rice crop areas. The results showed the optimal polarization combination to be HH along with HH/HV and HH-HV for rice crop mapping with an accuracy of 88.57%. Results were further compared with a Landsat-8 operational land imager (OLI) optical sensor-derived rice crop map. Spatial agreement of almost 90% was achieved between outputs produced from Landsat-8 OLI and RISAT-1 data. The simplicity of the approach used in this work may serve as an effective tool for rice crop mapping.

Observed Variation in Carbon and Water Exchange Across Crop Types, Seasons, and Years in Un-irrigated Land of the Southern Great Plains

NASA Astrophysics Data System (ADS)

Fischer, M. L.; Billesbach, D. P.; Riley, W. J.; Berry, J. A.; Torn, M. S.

2004-12-01

Accurate prediction of the regional responses of carbon and water fluxes to changing climate, land use, and management requires models that are parameterized and tested against measurements made in multiple land cover types and over seasonal and inter-annual time scales. In particular, modelers predicting fluxes for un-irrigated agriculture are posed with the additional challenge of characterizing the onset and severity of water stress. We report results from three years of an ongoing series of measurement campaigns that quantify the spatial heterogeneity of land surface-atmosphere exchanges of carbon dioxide, water, and energy. Eddy covariance flux measurements were made in pastures and dominant crop types surrounding the US-DOE Atmospheric Radiation Measurement Program central facility near Lamont, Oklahoma (36.605 N, 97.485 W). Ancillary measurements included radiation budget, meteorology, soil moisture and temperature, leaf area index, plant biomass, and plant and soil carbon and nitrogen content. Within a given year, the dominant spatial variation in fluxes of carbon, water, and energy are caused by variations of land cover due to the distinct phenology of winter-spring (winter wheat) versus summer crops (e.g., pasture, sorghum, soybeans). Within crop and yearly variations were smaller. In 2002, variations in net ecosystem carbon exchange (NEE), for three closely spaced winter wheat fields was 10-20%. Variations between years for the same crop types were also large. Net primary production (NPP) of winter wheat in the spring of 2003 versus 2002 increased by a factor of two, while NEE increased by 35%. The large increase in production and NEE are positively correlated with precipitation, integrated over the previous summer-fall periods. We discuss the implications of these results by extracting and comparing factors relevant for parameterization of land surface models and by comparing crop yield with historic variations in yield at the landscape scale.

Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: an example at corn fields in western Mexico.

PubMed

Chen, Pei-Yu; Fedosejevs, Gunar; Tiscareño-López, Mario; Arnold, Jeffrey G

2006-08-01

Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

A methodological approach for deriving regional crop rotations as basis for the assessment of the impact of agricultural strategies using soil erosion as example.

PubMed

Lorenz, Marco; Fürst, Christine; Thiel, Enrico

2013-09-01

Regarding increasing pressures by global societal and climate change, the assessment of the impact of land use and land management practices on land degradation and the related decrease in sustainable provision of ecosystem services gains increasing interest. Existing approaches to assess agricultural practices focus on the assessment of single crops or statistical data because spatially explicit information on practically applied crop rotations is mostly not available. This provokes considerable uncertainties in crop production models as regional specifics have to be neglected or cannot be considered in an appropriate way. In a case study in Saxony, we developed an approach to (i) derive representative regional crop rotations by combining different data sources and expert knowledge. This includes the integration of innovative crop sequences related to bio-energy production or organic farming and different soil tillage, soil management and soil protection techniques. Furthermore, (ii) we developed a regionalization approach for transferring crop rotations and related soil management strategies on the basis of statistical data and spatially explicit data taken from so called field blocks. These field blocks are the smallest spatial entity for which agricultural practices must be reported to apply for agricultural funding within the frame of the European Agricultural Fund for Rural Development (EAFRD) program. The information was finally integrated into the spatial decision support tool GISCAME to assess and visualize in spatially explicit manner the impact of alternative agricultural land use strategies on soil erosion risk and ecosystem services provision. Objective of this paper is to present the approach how to create spatially explicit information on agricultural management practices for a study area around Dresden, the capital of the German Federal State Saxony. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cereal Crop Proteomics: Systemic Analysis of Crop Drought Stress Responses Towards Marker-Assisted Selection Breeding

PubMed Central

Ghatak, Arindam; Chaturvedi, Palak; Weckwerth, Wolfram

2017-01-01

Sustainable crop production is the major challenge in the current global climate change scenario. Drought stress is one of the most critical abiotic factors which negatively impact crop productivity. In recent years, knowledge about molecular regulation has been generated to understand drought stress responses. For example, information obtained by transcriptome analysis has enhanced our knowledge and facilitated the identification of candidate genes which can be utilized for plant breeding. On the other hand, it becomes more and more evident that the translational and post-translational machinery plays a major role in stress adaptation, especially for immediate molecular processes during stress adaptation. Therefore, it is essential to measure protein levels and post-translational protein modifications to reveal information about stress inducible signal perception and transduction, translational activity and induced protein levels. This information cannot be revealed by genomic or transcriptomic analysis. Eventually, these processes will provide more direct insight into stress perception then genetic markers and might build a complementary basis for future marker-assisted selection of drought resistance. In this review, we survey the role of proteomic studies to illustrate their applications in crop stress adaptation analysis with respect to productivity. Cereal crops such as wheat, rice, maize, barley, sorghum and pearl millet are discussed in detail. We provide a comprehensive and comparative overview of all detected protein changes involved in drought stress in these crops and have summarized existing knowledge into a proposed scheme of drought response. Based on a recent proteome study of pearl millet under drought stress we compare our findings with wheat proteomes and another recent study which defined genetic marker in pearl millet. PMID:28626463

Characteristics of AVIRIS Band Measurements in Desert Agroecosystems in the Area of Blythe, California. 1; Studies of Cotton Spectra

NASA Technical Reports Server (NTRS)

Hanna, Safwat H. Shakir

2001-01-01

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from Blythe, California, were acquired in June 1997 to study agricultural spectra from different crops and to identify crops in other areas with similar environmental factors and similar spectral properties. The main objectives of this study are: (1) to compare the spectral and radiometric characteristics of AVIRIS data from agriculture crops with ground spectra measured by a FieldSpec ASD spectrometer; (2) to explore the use of AVIRIS spectral images for identifying agricultural crops; (3) to study the spectral expression of environmental factors on selected crops; and (4) to build a spectral library for the crops that were studied. A long-term goal is to extend the spectral library for different vegetation or crops in different stages of growth. To support our study, on July 18 and 19, 2000, we collected spectra using the FieldSpec spectrometer from selected fields with different crops in the Blythe area of California (longitude 114 deg 33.28 W and latitude 33 deg 25.42 N to longitude 1140 44.53 W and latitude 33 deg 39.77 N). These crops were cotton in different stages of growth, varieties of grass pure or mixed, Sudan grass, Bermuda grass, Teff grass, and alfalfa. Some of the fields were treated with different types of irrigation (i.e., wet to dry conditions). Additional parameters were studied such as the soil water content (WC), pH, and organic matter (OM). The results of this study showed that for crops known to be similar, there is a significant correlation between the spectra that were collected by AVIRIS in 1997 and spectra measured by the FieldSpec (registered) spectrometer in 2000. This correlation allowed development of a spectral library to be used in ENVI-IDL analysis software. This library was used successfully to identify different crops. Furthermore, using IDL algorithms of Spectral Angle Mapper classification (SAM), spectral feature fitting (SFF) and spectral binary encoding (SPE) showed that there is excellent agreement between the predicted and the actual crop type (i.e., the correlation is between 85-90% match). Further use of the AVIRIS images can be of a value to crop identification or crop yield for commercial use.

Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity

PubMed Central

Yin, Xinyou

2013-01-01

Background Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental research on crop growth response to [CO2]. Analysis A review of the literature indicates that the quantitative relationships for a number of traits, once expressed as a function of internal plant nitrogen status, are altered little by the elevated [CO2]. A model incorporating these nitrogen-based functional relationships and mechanisms simulated photosynthetic acclimation to elevated [CO2], thereby reducing the chance of over-estimating crop response to [CO2]. Robust crop models to have small parameterization requirements and yet generate phenotypic plasticity under changing environmental conditions need to capture the carbon–nitrogen interactions during crop growth. Conclusions The performance of the improved models depends little on the type of the experimental facilities used to obtain data for parameterization, and allows accurate projections of the impact of elevated [CO2] and other climatic variables on crop productivity. PMID:23388883

PpTFDB: A pigeonpea transcription factor database for exploring functional genomics in legumes

PubMed Central

Singh, Akshay; Sharma, Ajay Kumar; Singh, Nagendra Kumar

2017-01-01

Pigeonpea (Cajanus cajan L.), a diploid legume crop, is a member of the tribe Phaseoleae. This tribe is descended from the millettioid (tropical) clade of the subfamily Papilionoideae, which includes many important legume crop species such as soybean (Glycine max), mung bean (Vigna radiata), cowpea (Vigna ungiculata), and common bean (Phaseolus vulgaris). It plays major role in food and nutritional security, being rich source of proteins, minerals and vitamins. We have developed a comprehensive Pigeonpea Transcription Factors Database (PpTFDB) that encompasses information about 1829 putative transcription factors (TFs) and their 55 TF families. PpTFDB provides a comprehensive information about each of the identified TFs that includes chromosomal location, protein physicochemical properties, sequence data, protein functional annotation, simple sequence repeats (SSRs) with primers derived from their motifs, orthology with related legume crops, and gene ontology (GO) assignment to respective TFs. (PpTFDB: http://14.139.229.199/PpTFDB/Home.aspx) is a freely available and user friendly web resource that facilitates users to retrieve the information of individual members of a TF family through a set of query interfaces including TF ID or protein functional annotation. In addition, users can also get the information by browsing interfaces, which include browsing by TF Categories and by, GO Categories. This PpTFDB will serve as a promising central resource for researchers as well as breeders who are working towards crop improvement of legume crops. PMID:28651001

Can orchards help connect Mediterranean ecosystems? Animal movement data alter conservation priorities

USGS Publications Warehouse

Nogeire, Theresa M.; Davis, Frank W.; Crooks, Kevin R.; McRae, Brad H.; Lyren, Lisa M.; Boydston, Erin E.

2015-01-01

As natural habitats become fragmented by human activities, animals must increasingly move through human-dominated systems, particularly agricultural landscapes. Mapping areas important for animal movement has therefore become a key part of conservation planning. Models of landscape connectivity are often parameterized using expert opinion and seldom distinguish between the risks and barriers presented by different crop types. Recent research, however, suggests different crop types, such as row crops and orchards, differ in the degree to which they facilitate or impede species movements. Like many mammalian carnivores, bobcats (Lynx rufus) are sensitive to fragmentation and loss of connectivity between habitat patches. We investigated how distinguishing between different agricultural land covers might change conclusions about the relative conservation importance of different land uses in a Mediterranean ecosystem. Bobcats moved relatively quickly in row crops but relatively slowly in orchards, at rates similar to those in natural habitats of woodlands and scrub. We found that parameterizing a connectivity model using empirical data on bobcat movements in agricultural lands and other land covers, instead of parameterizing the model using habitat suitability indices based on expert opinion, altered locations of predicted animal movement routes. These results emphasize that differentiating between types of agriculture can alter conservation planning outcomes.

Soil thresholds and a decision tool to manage food safety of crops grown in chlordecone polluted soil in the French West Indies.

PubMed

Clostre, Florence; Letourmy, Philippe; Lesueur-Jannoyer, Magalie

2017-04-01

Due to the persistent pollution of soils by an organochlorine, chlordecone (CLD also known as Kepone © ) in the French West Indies, some crops may be contaminated beyond the European regulatory threshold, the maximum residue limit (MRL). Farmers need to be able to foresee the risk of not complying with the regulatory threshold in each field and for each crop, if not, farmers whose fields are contaminated would have to stop cultivating certain crops in the fields concerned. To help farmers make the right choices, we studied the relationship between contamination of the soil and contamination of crops. We showed that contamination of a crop by CLD depended on the crop concerned, the soil CLD content and the type of soil. We grouped crop products in three categories: (i) non-uptakers and low-uptakers, (ii) medium-uptakers, and (iii) high-uptakers, according to their level of contamination and the resulting risk of exceeding MRL. Using a simulation model, we computed the soil threshold required to ensure the risk of not complying with MRL was sufficiently low for each crop product and soil type. Threshold values ranged from 0.02 μgkg -1 for dasheen grown in nitisol to 1.7 μgkg -1 for yam grown in andosol in the high-uptake category, and from 1 μgkg -1 for lettuce grown in nitisol to 45 μgkg -1 for the leaves of spring onions grown in andosol in the medium-uptake category. Contamination of non-uptakers and low-uptakers did not depend on soil contamination. With these results, we built an easy-to-use decision support tool based on two soil thresholds (0.1 and 1 μgkg -1 ) to enable growers to adapt their cropping system and hence to be able to continue farming. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transgenes sustain epigeal insect biodiversity in diversified vegetable farm systems.

PubMed

Leslie, T W; Hoheisel, G A; Biddinger, D J; Rohr, J R; Fleischer, S J

2007-02-01

Many ecological studies have focused on the effects of transgenes in field crops, but few have considered multiple transgenes in diversified vegetable systems. We compared the epigeal, or soil surface-dwelling, communities of Coleoptera and Formicidae between transgenic and isoline vegetable systems consisting of sweet corn, potato, and acorn squash, with transgenic cultivars expressing Cry1(A)b, Cry3, or viral coat proteins. Vegetables were grown in replicated split plots over 2 yr with integrated pest management (IPM) standards defining insecticide use patterns. More than 77.6% of 11,925 insects from 1,512 pitfall traps were identified to species, and activity density was used to compare dominance distribution, species richness, and community composition. Measures of epigeal biodiversity were always equal in transgenic vegetables, which required fewer insecticide applications than their near isolines. There were no differences in species richness between transgenic and isoline treatments at the farm system and individual crop level. Dominance distributions were also similar between transgenic and isoline farming systems. Crop type, and not genotype, had a significant influence on Carabidae and Staphylinidae community composition in the first year, but there were no treatment effects in the second year, possibly because of homogenizing effects of crop rotations. Communities were more influenced by crop type, and possibly crop rotation, than by genotype. The heterogeneity of crops and rotations in diversified vegetable farms seems to aid in preserving epigeal biodiversity, which may be supplemented by reductions in insecticide use associated with transgenic cultivars.

An integrated framework to assess adaptation options to climate change impacts in an irrigated basin in Central North Chile

NASA Astrophysics Data System (ADS)

Vicuna, S.; Melo, O.; Meza, F. J.; Alvarez, P.; Maureira, F.; Sanchez, A.; Tapia, A.; Cortes, M.; Dale, L. L.

2013-12-01

Future climate conditions could potentially affect water supply and demand on water basins throughout the world but especially on snowmelt-driven agriculture oriented basins that can be found throughout central Chile. Increasing temperature and reducing precipitation will affect both the magnitude and timing of water supply this part of the world. Different adaptation strategies could be implemented to reduce the impacts of such scenarios. Some could be incorporated as planned policies decided at the basin or Water Use Organization levels. Examples include changing large scale irrigation infrastructure (reservoirs and main channels) either physically or its operation. Complementing these strategies it is reasonable to think that at a disaggregated level, farmers would also react (adapt) to these new conditions using a mix of options to either modify their patterns of consumption (irrigation efficiency, crop mix, crop area reduction), increase their ability to access new sources of water (groundwater, water markets) or finally compensate their expected losses (insurance). We present a modeling framework developed to represent these issues using as a case study the Limarí basin located in Central Chile. This basin is a renowned example of how the development of reservoirs and irrigation infrastructure can reduce climate vulnerabilities allowing the economic development of a basin. Farmers in this basin tackle climate variability by adopting different strategies that depend first on the reservoir water volume allocation rule, on the type and size of investment they have at their farms and finally their potential access to water markets and other water supplies options. The framework developed can be used to study these strategies under current and future climate scenarios. The cornerstone of the framework is an hydrology and water resources model developed on the WEAP platform. This model is able to reproduce the large scale hydrologic features of the basin such as snowmelt hydrology, reservoir operation and groundwater dynamics. Crop yield under different water irrigation patterns have been inferred using a calibrated Cropsyst model. These crop yields together with user association irrigation constraints are used in a GAMS optimization model embedded dynamically in WEAP in order to obtain every year decisions on crop mix (including fallow land), irrigation patterns and participation in the spot water market. The GAMS optimization model has been calibrated using annual crop mix time series derived using a combination of sources of information ranging from different type of census plus satellite images. The resulting modeling platform is able to simulate under historic and future climate scenarios water availability in different locations of the basin with associated crop yield and economic consequences. The platform also allows the implementation of autonomous and planned adaptation strategies that could reduce the impacts of climate variability and climate change.

Mapping Multi-Cropped Land Use to Estimate Water Demand Using the California Pesticide Reporting Database

NASA Astrophysics Data System (ADS)

Henson, W.; Baillie, M. N.; Martin, D.

2017-12-01

Detailed and dynamic land-use data is one of the biggest data deficiencies facing food and water security issues. Better land-use data results in improved integrated hydrologic models that are needed to look at the feedback between land and water use, specifically for adequately representing changes and dynamics in rainfall-runoff, urban and agricultural water demands, and surface fluxes of water (e.g., evapotranspiration, runoff, and infiltration). Currently, land-use data typically are compiled from annual (e.g., Crop Scape) or multi-year composites if mapped at all. While this approach provides information about interannual land-use practices, it does not capture the dynamic changes in highly developed agricultural lands prevalent in California agriculture such as (1) dynamic land-use changes from high frequency multi-crop rotations and (2) uncertainty in sub-annual crop distribution, planting times, and cropped areas. California has collected spatially distributed data for agricultural pesticide use since 1974 through the California Pesticide Information Portal (CalPIP). A method leveraging the CalPIP database has been developed to provide vital information about dynamic agricultural land use (e.g., crop distribution and planting times) and water demand issues in Salinas Valley, California, along the central coast. This 7 billion dollar/year agricultural area produces up to 50% of U.S. lettuce and broccoli. Therefore, effective and sustainable water resource development in the area must balance the needs of this essential industry, other beneficial uses, and the environment. This new tool provides a way to provide more dynamic crop data in hydrologic models. While the current application focuses on the Salinas Valley, the methods are extensible to all of California and other states with similar pesticide reporting. The improvements in representing variability in crop patterns and associated water demands increase our understanding of land-use change and precision of hydrologic decision models. Ultimately, further refinement to the parcel level will completely capture the changing topology of agricultural land use.

Controlled Ecological Life Support System (CELSS) modeling

NASA Technical Reports Server (NTRS)

Drysdale, Alan; Thomas, Mark; Fresa, Mark; Wheeler, Ray

1992-01-01

Attention is given to CELSS, a critical technology for the Space Exploration Initiative. OCAM (object-oriented CELSS analysis and modeling) models carbon, hydrogen, and oxygen recycling. Multiple crops and plant types can be simulated. Resource recovery options from inedible biomass include leaching, enzyme treatment, aerobic digestion, and mushroom and fish growth. The benefit of using many small crops overlapping in time, instead of a single large crop, is demonstrated. Unanticipated results include startup transients which reduce the benefit of multiple small crops. The relative contributions of mass, energy, and manpower to system cost are analyzed in order to determine appropriate research directions.

Sustainable Agriculture: Cover Cropping

ERIC Educational Resources Information Center

Webster, Megan

2018-01-01

Sustainable agriculture practices are increasingly being used by farmers to maintain soil quality, increase biodiversity, and promote production of food that is environmentally safe. There are several types of sustainable agriculture practices such as organic farming, crop rotation, and aquaculture. This lesson plan focuses on the sustainable…

Development of an agricultural job-exposure matrix for British Columbia, Canada.

PubMed

Wood, David; Astrakianakis, George; Lang, Barbara; Le, Nhu; Bert, Joel

2002-09-01

Farmers in British Columbia (BC), Canada have been shown to have unexplained elevated proportional mortality rates for several cancers. Because agricultural exposures have never been documented systematically in BC, a quantitative agricultural Job-exposure matrix (JEM) was developed containing exposure assessments from 1950 to 1998. This JEM was developed to document historical exposures and to facilitate future epidemiological studies. Available information regarding BC farming practices was compiled and checklists of potential exposures were produced for each crop. Exposures identified included chemical, biological, and physical agents. Interviews with farmers and agricultural experts were conducted using the checklists as a starting point. This allowed the creation of an initial or 'potential' JEM based on three axes: exposure agent, 'type of work' and time. The 'type of work' axis was determined by combining several variables: region, crop, job title and task. This allowed for a complete description of exposures. Exposure assessments were made quantitatively, where data allowed, or by a dichotomous variable (exposed/unexposed). Quantitative calculations were divided into re-entry and application scenarios. 'Re-entry' exposures were quantified using a standard exposure model with some modification while application exposure estimates were derived using data from the North American Pesticide Handlers Exposure Database (PHED). As expected, exposures differed between crops and job titles both quantitatively and qualitatively. Of the 290 agents included in the exposure axis; 180 were pesticides. Over 3000 estimates of exposure were conducted; 50% of these were quantitative. Each quantitative estimate was at the daily absorbed dose level. Exposure estimates were then rated as high, medium, or low based on comparing them with their respective oral chemical reference dose (RfD) or Acceptable Daily Intake (ADI). This data was mainly obtained from the US Environmental Protection Agency (EPA) Integrated Risk Information System database. Of the quantitative estimates, 74% were rated as low (< 100%) and only 10% were rated as high (>500%). The JEM resulting from this study fills a void concerning exposures for BC farmers and farm workers. While only limited validation of assessments were possible, this JEM can serve as a benchmark for future studies. Preliminary analysis at the BC Cancer Agency (BCCA) using the JEM with prostate cancer records from a large cancer and occupation study/survey has already shown promising results. Development of this JEM provides a useful model for developing historical quantitative exposure estimates where is very little documented information available.

Linking field observations, Landsat and MODIS data to estimate agricultural change in European Russia.

NASA Astrophysics Data System (ADS)

de Beurs, K. M.; Ioffe, G.

2011-12-01

Agricultural reform has been one of the most important anthropogenic change processes in European Russia that has been unfolding since the formal collapse of the Soviet Union at the end of 1991. Widespread land abandonment is perhaps the most vivid side effect of the reform, even visible in synoptic imagery. Currently, Russia is transitioning into a country with an internal "archipelago" of islands of productive agriculture around cities embedded in a matrix of unproductive, abandoned lands. This heterogeneous spatial pattern is mainly driven by depopulation of the least favorable parts of the countryside, where "least favorable" is a function of fertility, remoteness, and their interaction. In this work we provide a satellite, GIS and field based overview of the current agricultural developments in Russia and look beyond the unstable period immediately following the collapse of the Soviet Union. We apply Landsat images in one of Russia's oblasts to create a detailed land cover map. We then use a logistic model to link the Landsat land cover map with the inter-annual variability in key phenological parameters calculated from MODIS to derive the percent of cropland per 500m MODIS pixel. By evaluating the phenological characteristics of the MODIS curves for each year we determine whether a pixel was actually cropped or left fallow. A comparison of satellite-estimated cropped areas with regional statistics (by rayon) revealed that the satellite estimates are highly correlated with the regional statistics for both arable lands and successfully cropped areas. We use the crop maps to determine the number of times a particular area was cropped between 2002 and 2009 by summing all the years with crops per pixel. This variable provides a good indication about the intensification and de-intensification of the Russian croplands over the last decade. We have visited several rural areas in Russia and we link the satellite data with information acquired through field interviews and photographs. Russian farmers employ a variety of crop-rotation schemes. In the Russian grain belt, the farmers used to be on a seven-year rotation, which typically included only one year of fallow and a variety of grain crops in the remaining six years. Through field interviews and satellite observations we learned that the crop rotation schedules are changing from a seven year crop cycle focused on grain production to a three year crop cycle focused on the production of sunflower which is currently most profitable. In addition, a switch is underway from the dominant growth of spring wheat to stronger reliance on winter wheat which has better growth potential in the area. The number of cropped years, or the complementary number of fallow years, gives an indication of the type of crop cycle that is applied. In addition, drier areas are predicted to reveal more fallow years due to decisions by farm administrators. We will discuss how the ongoing changes represent adaptations to changing climatological and social circumstances.

Statistical theory and methodology for remote sensing data analysis

NASA Technical Reports Server (NTRS)

Odell, P. L.

1974-01-01

A model is developed for the evaluation of acreages (proportions) of different crop-types over a geographical area using a classification approach and methods for estimating the crop acreages are given. In estimating the acreages of a specific croptype such as wheat, it is suggested to treat the problem as a two-crop problem: wheat vs. nonwheat, since this simplifies the estimation problem considerably. The error analysis and the sample size problem is investigated for the two-crop approach. Certain numerical results for sample sizes are given for a JSC-ERTS-1 data example on wheat identification performance in Hill County, Montana and Burke County, North Dakota. Lastly, for a large area crop acreages inventory a sampling scheme is suggested for acquiring sample data and the problem of crop acreage estimation and the error analysis is discussed.

Fusarium graminearum: pathogen or endophyte of North American grasses?

PubMed

Lofgren, Lotus A; LeBlanc, Nicholas R; Certano, Amanda K; Nachtigall, Jonny; LaBine, Kathryn M; Riddle, Jakob; Broz, Karen; Dong, Yanhong; Bethan, Bianca; Kafer, Christopher W; Kistler, H Corby

2018-02-01

Mycotoxin-producing Fusarium graminearum and related species cause Fusarium head blight on cultivated grasses, such as wheat and barley. However, these Fusarium species may have had a longer evolutionary history with North American grasses than with cultivated crops and may interact with the ancestral hosts in ways which are biochemically distinct. We assayed 25 species of asymptomatic native grasses for the presence of Fusarium species and confirmed infected grasses as hosts using re-inoculation tests. We examined seed from native grasses for the presence of mycotoxin-producing Fusarium species and evaluated the ability of these fungi to produce mycotoxins in both native grass and wheat hosts using biochemical analysis. Mycotoxin-producing Fusarium species were shown to be prevalent in phylogenetically diverse native grasses, colonizing multiple tissue types, including seeds, leaves and inflorescence structures. Artificially inoculated grasses accumulated trichothecenes to a much lesser extent than wheat, and naturally infected grasses showed little to no accumulation. Native North American grasses are commonly inhabited by Fusarium species, but appear to accommodate these toxigenic fungi differently from cultivated crops. This finding highlights how host identity and evolutionary history may influence the outcome of plant-fungal interactions and may inform future efforts in crop improvement. No claim to original US Government works. New Phytologist © 2017 New Phytologist Trust.

Crop changes from the XVI century to the present in a hill/mountain area of eastern Liguria (Italy)

PubMed Central

Gentili, Rodolfo; Gentili, Elio; Sgorbati, Sergio

2009-01-01

Background Chronological information on the composition and structure of agrocenoses and detailed features of land cover referring to specific areas are uncommon in ethnobotanical studies, especially for periods before the XIX century. The aim of this study was to analyse the type of crop or the characteristics of soil cover from the XVI century to the present. Methods This diachronic analysis was accomplished through archival research on the inventories of the Parish of St. Mary and those of the Municipality of Pignone and from recent surveys conducted in an area of eastern Liguria (Italy). Results Archival data revealed that in study area the primary means of subsistence during the last five centuries, until the first half of the XX century, was chestnuts. In the XVIII and XIX centuries, crop diversification strongly increased in comparison with previous and subsequent periods. In more recent times, the abandonment of agricultural practices has favoured the re-colonisation of mixed woodland or cluster-pine woodland. Conclusion Ancient documents in the ecclesiastic or municipal inventories can be a very useful tool for enhancing the knowledge of agricultural practice, as well as of subsistence methods favoured by local populations during a particular time and for reconstructing land use change over time. PMID:19361339

Fixating picture boundaries does not eliminate boundary extension: Implications for scene representation

PubMed Central

Gagnier, Kristin Michod; Dickinson, Christopher A.; Intraub, Helene

2015-01-01

Observers frequently remember seeing more of a scene than was shown (boundary extension). Does this reflect a lack of eye fixations to the boundary region? Single-object photographs were presented for 14–15 s each. Main objects were either whole or slightly cropped by one boundary, creating a salient marker of boundary placement. All participants expected a memory test, but only half were informed that boundary memory would be tested. Participants in both conditions made multiple fixations to the boundary region and the cropped region during study. Demonstrating the importance of these regions, test-informed participants fixated them sooner, longer, and more frequently. Boundary ratings (Experiment 1) and border adjustment tasks (Experiments 2–4) revealed boundary extension in both conditions. The error was reduced, but not eliminated, in the test-informed condition. Surprisingly, test knowledge and multiple fixations to the salient cropped region, during study and at test, were insufficient to overcome boundary extension on the cropped side. Results are discussed within a traditional visual-centric framework versus a multisource model of scene perception. PMID:23547787

Fixating picture boundaries does not eliminate boundary extension: implications for scene representation.

PubMed

Michod Gagnier, Kristin; Dickinson, Christopher A; Intraub, Helene

2013-01-01

Observers frequently remember seeing more of a scene than was shown (boundary extension). Does this reflect a lack of eye fixations to the boundary region? Single-object photographs were presented for 14-15 s each. Main objects were either whole or slightly cropped by one boundary, creating a salient marker of boundary placement. All participants expected a memory test, but only half were informed that boundary memory would be tested. Participants in both conditions made multiple fixations to the boundary region and the cropped region during study. Demonstrating the importance of these regions, test-informed participants fixated them sooner, longer, and more frequently. Boundary ratings (Experiment 1) and border adjustment tasks (Experiments 2-4) revealed boundary extension in both conditions. The error was reduced, but not eliminated, in the test-informed condition. Surprisingly, test knowledge and multiple fixations to the salient cropped region, during study and at test, were insufficient to overcome boundary extension on the cropped side. Results are discussed within a traditional visual-centric framework versus a multisource model of scene perception.