How does spatial and temporal resolution of vegetation index impact crop yield estimation?

USDA-ARS?s Scientific Manuscript database

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...

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.

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Effects of urban sprawl on arthropod communities in peri-urban farmed landscape in Shenbei New District, Shenyang, Liaoning Province, China.

PubMed

Bian, Zhen-Xing; Wang, Shuai; Wang, Qiu-Bing; Yu, Miao; Qian, Feng-Kui

2018-01-08

Peri-urban farmland provides a diversity of ecological services. However, it is experiencing increasing pressures from urban sprawl. While the effects of land use associated with farming on arthropod assemblages has received increasing attention, most of this research has been conducted by comparing conventional and organic cropping systems. The present study identifies the effects of urban sprawl and the role of non-cropped habitat in defining arthropod diversity in peri-urban farmed landscapes. Multi-scale arthropod data from 30 sampling plots were used with linear-mixed models to elucidate the effects of distance from urban areas (0-13 km; 13-25 km and >25 km, zones I, II, and III, respectively) on arthropods. Results showed that urban sprawl, disturbed farm landscapes, and disturbance in non-cropped habitats had negative effects on arthropods, the latter requiring arthropods to re-establish annually from surrounding landscapes via dispersal. While arthropod species richness showed no obvious changes, arthropod abundance was lowest in zone II. Generally, patch density (PD), Shannon diversity index (SHDI), and aggregate index (AI) of non-cropped habitat were major drivers of changes in arthropod populations. This study contributes to identifying the effects of urban sprawl on arthropod diversity and documenting the multiple functions of farm landscapes in peri-urban regions.

Forest amount affects soybean productivity in Brazilian agricultural frontier

NASA Astrophysics Data System (ADS)

Rattis, L.; Brando, P. M.; Marques, E. Q.; Queiroz, N.; Silverio, D. V.; Macedo, M.; Coe, M. T.

2017-12-01

Over the past three decades, large tracts of tropical forests have been converted to crop and pasturelands across southern Amazonia, largely to meet the increasing worldwide demand for protein. As the world's population continue to grow and consume more protein per capita, forest conversion to grow more crops could be a potential solution to meet such demand. However, widespread deforestation is expected to negatively affect crop productivity via multiple pathways (e.g., thermal regulation, rainfall, local moisture, pest control, among others). To quantify how deforestation affects crop productivity, we modeled the relationship between forest amount and enhanced vegetation index (EVI—a proxy for crop productivity) during the soybean planting season across southern Amazonia. Our hypothesis that forest amount causes increased crop productivity received strong support. We found that the maximum MODIS-based EVI in soybean fields increased as a function of forest amount across three spatial-scales, 0.5 km, 1 km, 2 km, 5 km, 10 km, 15 km and 20 km. However, the strength of this relationship varied across years and with precipitation, but only at the local scale (e.g., 500 meters and 1 km radius). Our results highlight the importance of considering forests to design sustainable landscapes.

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.

Assessing corn water stress using spectral reflectance

NASA Astrophysics Data System (ADS)

Mefford, Brenna S.

Multiple remote sensing techniques have been developed to identify crop water stress, but some methods may be difficult for farmers to apply. Unlike most techniques, shortwave vegetation indices can be calculated using satellite, aerial, or ground imagery from the green (525-600 nm), red (625-700 nm), and near infrared (750-900 nm) spectral bands. If vegetation indices can be used to monitor crop water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over irrigating. This study occurred in the 2013 growing season near Greeley, CO, where pressurized drip irrigation was used to irrigate twelve corn ( Zea mays L.) treatments of varying water deficit. Multispectral data was collected and four different vegetation indices were evaluated: Normalized Difference Vegetation Index (NDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Green Normalized Difference Vegetation Index (GNDVI), and the Wide Dynamic Range Vegetation Index (WDRVI). The four vegetation indices were compared to corn water stress as indicated by the stress coefficient (Ks) and water deficit in the root zone, calculated by using a water balance that monitors crop evapotranspiration (ET), irrigation events, precipitation events, and deep percolation. ET for the water balance was calculated using two different methods for comparison purposes: (1) calculation of the stress coefficient (Ks) using FAO-56 standard procedures; (2) use of canopy temperature ratio (Tc ratio) of a stressed crop to a non-stressed crop to calculate Ks. It was found that obtaining Ks from Tc ratio is a viable option, and requires less data to obtain than Ks from FAO-56. In order to compare the indices to Ks, vegetation ratios were developed in the process of normalization. Vegetation ratios are defined as the non-stressed vegetation index divided by the stressed vegetation index. Results showed that vegetation ratios were sensitive to water stress as indicated by good R2 values (Nratio = 0.53, G ratio=0.46, Oratio=0.49) and low RMSE values (Nratio = 0.076, Gratio=0.062, Oratio=0.076) when compared to Ks. Therefore it can be concluded that corn spectral reflectance is sensitive to water stress. In order to use spectral reflectance to manage crop water stress an irrigation trigger point of 0.93 for the vegetation ratios was determined. These results were validated using data collected by a MSR5 multispectral sensor in an adjacent field (SWIIM Field). The results from the second field proved better than in the main field giving higher R 2 values (Nratio = 0.66, Gratio = 0.63, Oratio = 0.66), and lower RMSE values (Nratio = 0.043, Gratio = 0.036, Oratio = 0.043) between Ks and the vegetation indices. SWIIM field further validated the results that spectral reflectance can be used to monitor corn water stress.

Corn response to climate stress detected with satellite-based NDVI time series

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

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

Corn response to climate stress detected with satellite-based NDVI time series

DOE PAGES

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

2016-03-23

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

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

PubMed

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

2014-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Canopy cover and leaf area index relationships for wheat, triticale, and corn

USDA-ARS?s Scientific Manuscript database

The AquaCrop model requires canopy cover (CC) measurements to define crop growth and development. Some previously collected data sets that would be useful for calibrating and validating AquaCrop contain only leaf area index (LAI) data, but could be used if relationships were available relating LAI t...

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.

Airborne and ground-based remote sensing for the estimation of evapotranspiration and yield of bean, potato, and sugar beet crops

NASA Astrophysics Data System (ADS)

Jayanthi, Harikishan

The focus of this research was two-fold: (1) extend the reflectance-based crop coefficient approach to non-grain (potato and sugar beet), and vegetable crops (bean), and (2) develop vegetation index (VI)-yield statistical models for potato and sugar beet crops using high-resolution aerial multispectral imagery. Extensive crop biophysical sampling (leaf area index and aboveground dry biomass sampling) and canopy reflectance measurements formed the backbone of developing of canopy reflectance-based crop coefficients for bean, potato, and sugar beet crops in this study. Reflectance-based crop coefficient equations were developed for the study crops cultivated in Kimberly, Idaho, and subsequently used in water availability simulations in the plant root zone during 1998 and 1999 seasons. The simulated soil water profiles were compared with independent measurements of actual soil water profiles in the crop root zone in selected fields. It is concluded that the canopy reflectance-based crop coefficient technique can be successfully extended to non-grain crops as well. While the traditional basal crop coefficients generally expect uniform growth in a region the reflectance-based crop coefficients represent the actual crop growth pattern (in less than ideal water availability conditions) in individual fields. Literature on crop canopy interactions with sunlight states that there is a definite correspondence between leaf area index progression in the season and the final yield. In case of crops like potato and sugar beet, the yield is influenced not only on how early and how quickly the crop establishes its canopy but also on how long the plant stands on the ground in a healthy state. The integrated area under the crop growth curve has shown excellent correlations with hand-dug samples of potato and sugar beet crops in this research. Soil adjusted vegetation index-yield models were developed, and validated using multispectral aerial imagery. Estimated yield images were compared with the actual yields extracted from the ground. The remote sensing-derived yields compared well with the actual yields sampled on the ground. This research has highlighted the importance of the date of spectral emergence, the need to know the duration for which the crops stand on the ground, and the need to identify critical periods of time when multispectral coverages are essential for reliable tuber yield estimation.

Overuse or underuse? An observation of pesticide use in China.

PubMed

Zhang, Chao; Hu, Ruifa; Shi, Guanming; Jin, Yanhong; Robson, Mark G; Huang, Xusheng

2015-12-15

Pesticide use has experienced a dramatic increase worldwide, especially in China, where a wide variety of pesticides are used in large amounts by farmers to control crop pests. While Chinese farmers are often criticized for pesticide overuse, this study shows the coexistence of overuse and underuse of pesticide based on the survey data of pesticide use in rice, cotton, maize, and wheat production in three provinces in China. A novel index amount approach is proposed to convert the amount of multiple pesticides used to control the same pest into an index amount of a referenced pesticide. We compare the summed index amount with the recommended dosage range of the referenced pesticide to classify whether pesticides are overused or underused. Using this new approach, the following main results were obtained. Pesticide overuse and underuse coexist after examining a total of 107 pesticides used to control up to 54 crop pests in rice, cotton, maize, and wheat production. In particular, pesticide overuse in more than half of the total cases for 9 crop pest species is detected. In contrast, pesticide underuse accounts for more than 20% of the total cases for 11 pests. We further indicate that the lack of knowledge and information on pesticide use and pest control among Chinese farmers may cause the coexistence of pesticide overuse and underuse. Our analysis provides indirect evidence that the commercialized agricultural extension system in China probably contributes to the coexistence of overuse and underuse. To improve pesticide use, it is urgent to reestablish the monitoring and forecasting system regarding pest control in China. Copyright © 2015 Elsevier B.V. All rights reserved.

Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Bahrawi, Jarbou A.

2017-03-01

Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

40 CFR Appendix A to Part 161 - Data Requirements for Registration: Use Pattern Index

Code of Federal Regulations, 2013 CFR

2013-07-01

... crops Tropical/subtropical woody crops Drug and medicinal crops Terrestrial nonfood crop Annual...) Janitorial equipment Barber and beauty shop instruments and equipment Morgues, mortuaries, and funeral homes...

40 CFR Appendix A to Part 161 - Data Requirements for Registration: Use Pattern Index

Code of Federal Regulations, 2012 CFR

2012-07-01

... crops Tropical/subtropical woody crops Drug and medicinal crops Terrestrial nonfood crop Annual...) Janitorial equipment Barber and beauty shop instruments and equipment Morgues, mortuaries, and funeral homes...

Pre-sowing Seed Treatments in Direct-seeded Early Rice: Consequences for Emergence, Seedling Growth and Associated Metabolic Events under Chilling Stress

NASA Astrophysics Data System (ADS)

Wang, Weiqin; Chen, Qian; Hussain, Saddam; Mei, Junhao; Dong, Huanglin; Peng, Shaobing; Huang, Jianliang; Cui, Kehui; Nie, Lixiao

2016-01-01

Double direct-seeding for double rice cropping is a simplified, labor saving, and efficient cropping system to improve multiple-crop index and total rice production in central China. However, poor crop establishment of direct-seeded early rice due to chilling stress is the main obstacle to wide spread of this system. A series of experiments were conducted to unravel the effects of pre-sowing seed treatments on emergence, seedling growth and associated metabolic events of direct-seeded early rice under chilling stress. Two seed priming treatments and two seed coating treatments were used in all the experiments. A non-treated control treatment was also maintained for comparison. In both the field and growth chamber studies, seed priming with selenium or salicylic acid significantly enhanced the emergence and seedling growth of rice compared with non-treated control. Nevertheless, such positive effects were not apparent for seed coating treatments. Better emergence and vigorous seedling growth of rice after seed priming was associated with enhanced α-amylase activity, higher soluble sugars contents, and greater respiration rate in primed rice seedlings under chilling stress. Taking together, these findings may provide new avenues for understanding and advancing priming-induced chilling tolerance in direct-seeded early rice in double rice cropping system.

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

NASA Astrophysics Data System (ADS)

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

2014-02-01

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

Changes in reflectance anisotropy of wheat crop during different phenophases

NASA Astrophysics Data System (ADS)

Lunagaria, Manoj M.; Patel, Haridas R.

2017-04-01

The canopy structure of wheat changes significantly with growth stages and leads to changes in reflectance anisotropy. Bidirectional reflectance distribution function characterises the reflectance anisotropy of the targets, which can be approximated. Spectrodirectional reflectance measurements on wheat crop were acquired using a field goniometer system. The bidirectional reflectance spectra were acquired at 54 view angles to cover the hemispheric span up to 60° view zenith. The observations were made during early growth stages till maturity of the crop. The anisotropy was not constant for all wavelengths and anisotropic factors clearly revealed spectral dependence, which was more pronounced in near principal plane. In near infrared, wheat canopy expressed less reflectance anisotropy because of higher multiple scattering. The broad hotspot signature was noticeable in reflectance of canopy whenever view and solar angles were close. Distinct changes in bidirectional reflectance distribution function were observed during booting to flowering stages as the canopy achieves more uniformity, height and head emergence. The function clearly reveals bowl shape during heading to early milking growth stages of the crop. Late growth stages show less prominent gap and shadow effects. Anisotropy index revealed that wheat exhibits changes in reflectance anisotropy with phenological development and with spectral bands.

[Effects of different winter cover crops on soil organic carbon in a double cropping rice paddy field.

PubMed

Tang, Hai Ming; Cheng, Kai Kai; Xiao, Xiao Ping; Tang, Wen Guang; Wang, Ke; Li, Chao; Zhang, Fan; Sun, Yu Tao

2017-02-01

In a double cropping rice field experiment, effects of five winter cover crops on the total organic carbon (TOC), active organic carbon (AOC), carbon pool management index (CPMI) and organic carbon storage were studied in three soil layers (0-5, 5-10 and 10-20 cm).Winter cover crops of ryegrass (Ry), Chinese milk vetch (Mv), potato (Po), and rape (Ra) between two rice crops were compared with fallow as control (CK). The results showed that the TOC and AOC contents under Ry, Mv, Po and Ra treatments were higher than those of CK in all three la-yers. Meanwhile, the TOC and AOC contents in Po treatment were higher than those of other treatments. Compared with CK, the AOC, activity index (AI), carbon pool index (CPI) and CPMI in the soil were improved through the recycling of winter cover crops straw. The AOC, AI, CPI and CPMI in the studied layers increased in order of Po>Mv>Ry>Ra>CK. The results indicated that the recycling of winter cover crops straw promoted the storage of SOC in the 0-20 cm soil profile as compared with CK. The strongest effect of the winter cover crops on the SOC storage occurred in Mv treatment, followed by Mv and Po treatments, and the SOC storage increased with the increasing soil depth.

Detection of meteorological extreme effect on historical crop yield anomaly

NASA Astrophysics Data System (ADS)

Kim, W.; Iizumi, T.; Nishimori, M.

2017-12-01

Meteorological extremes of temperature and precipitation are a critical issue in the global climate change, and some studies investigating how the extreme changes in accordance with the climate change are continuously reported. However, it is rarely understandable that the extremes affect crop yield worldwide as heatwave, coolwave, drought, and flood, albeit some local or national reports are available. Therefore, we globally investigated the extremes effects on the variability of historical yield of maize, rice, soy, and wheat with a standardized index and a historical yield anomaly. For the regression analysis, the standardized index is annually aggregated in the consideration of a crop calendar, and the historical yield is detrended with 5-year moving average. Throughout this investigation, we found that the relationship between the aggregated standardized index and the historical yield anomaly shows not merely positive correlation but also negative correlation in all crops in the globe. Namely, the extremes cause decrease of crop yield as a matter of course, but increase in some regions contrastingly. These results help us to quantify the extremes effect on historical crop yield anomaly.

Effect of water content and organic carbon on remote sensing of crop residue cover

NASA Astrophysics Data System (ADS)

Serbin, G.; Hunt, E. R., Jr.; Daughtry, C. S. T.; McCarty, G. W.; Brown, D. J.; Doraiswamy, P. C.

2009-04-01

Crop residue cover is an important indicator of tillage method. Remote sensing of crop residue cover is an attractive and efficient method when compared with traditional ground-based methods, e.g., the line-point transect or windshield survey. A number of spectral indices have been devised for residue cover estimation. Of these, the most effective are those in the shortwave infrared portion of the spectrum, situated between 1950 and 2500 nm. These indices include the hyperspectral Cellulose Absorption Index (CAI), and advanced multispectral indices, i.e., the Lignin-Cellulose Absorption (LCA) index and the Shortwave Infrared Normalized Difference Residue Index (SINDRI), which were devised for the NASA Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Spectra of numerous soils from U.S. Corn Belt (Indiana and Iowa) were acquired under wetness conditions varying from saturation to oven-dry conditions. The behavior of soil reflectance with water content was also dependent on the soil organic carbon content (SOC) of the soils, and the location of the spectral bands relative to significant water absorptions. High-SOC soils showed the least change in spectral index values with increase in soil water content. Low-SOC soils, on the other hand, showed measurable difference. For CAI, low-SOC soils show an initial decrease in index value followed by an increase, due to the way that water content affects CAI spectral bands. Crop residue CAI values decrease with water content. For LCA, water content increases decrease crop residue index values and increase them for soils, resulting in decreased contrast. SINDRI is also affected by SOC and water content. As such, spatial information on the distribution of surface soil water content and SOC, when used in a geographic information system (GIS), will improve the accuracy of remotely-sensed crop residue cover estimates.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data

PubMed Central

Chang, Sheng; Wu, Bingfang; Yan, Nana; Zhu, Jianjun; Wen, Qi; Xu, Feng

2018-01-01

In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts. PMID:29690639

A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data.

PubMed

Chang, Sheng; Wu, Bingfang; Yan, Nana; Zhu, Jianjun; Wen, Qi; Xu, Feng

2018-04-23

In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts.

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

PubMed

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

2017-03-03

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

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

PubMed Central

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

2017-01-01

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

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.

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

PubMed Central

Lelong, Camille C. D.; Burger, Philippe; Jubelin, Guillaume; Roux, Bruno; Labbé, Sylvain; Baret, Frédéric

2008-01-01

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships. PMID:27879893

Crop identification for the delineation of irrigated regions under scarce data conditions: a new approach based on chaos theory

NASA Astrophysics Data System (ADS)

Mangiarotti, S.; Muddu, S.; Sharma, A. K.; Corgne, S.; Ruiz, L.; Hubert-Moy, L.

2015-12-01

Groundwater is one of the main water reservoirs used for irrigation in regions of scarce water resources. For this reason, crop irrigation is expected to have a direct influence on this reservoir. To understand the time evolution of the groundwater table and its storage changes, it is important to delineate irrigated crops, whose evaporative demand is relatively higher. Such delineation may be performed based on classical classification approaches using optical remote sensing. However, it remains a difficult problem in regions where plots do not exceed a few hectares and exhibit a very heterogeneous pattern with multiple crops. This difficulty is emphasized in South India where two or three months of cloudy conditions during the monsoon period can hide crop growth during the year. An alternative approach is introduced here that takes advantage of such scarce signal. Ten different crops are considered in the present study. A bank of crop models is first established based on the global modeling technique [1]. These models are then tested using original time series (from which models were obtained) in order to evaluate the information that can be deduced from these models in an inverse approach. The approach is then tested on an independent data set and is finally applied to a large ensemble of 10,000 time series of plot data extracted from the Berambadi catchment (AMBHAS site) part of the Kabini River basin CZO, South India. Results show that despite the important two-month gap in satellite observations in the visible band, interpolated vegetation index remains an interesting indicator for identification of crops in South India. [1] S. Mangiarotti, R. Coudret, L. Drapeau, & L. Jarlan, Polynomial search and global modeling: Two algorithms for modeling chaos, Phys. Rev. E, 86(4), 046205 (2012).

Minimizing instrumentation requirement for estimating crop water stress index and transpiration of maize

USDA-ARS?s Scientific Manuscript database

Research was conducted in northern Colorado in 2011 to estimate the Crop Water Stress Index (CWSI) and actual water transpiration (Ta) of maize under a range of irrigation regimes. The main goal was to obtain these parameters with minimum instrumentation and measurements. The results confirmed that ...

An evaluation of time-series MODIS 250-meter vegetation index data for crop mapping in the United States Central Great Plains

NASA Astrophysics Data System (ADS)

Wardlow, Brian Douglas

The objectives of this research were to: (1) investigate time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-meter EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) data for regional-scale crop-related land use/land cover characterization in the U.S. Central Great Plains and (2) develop and test a MODIS-based crop mapping protocol. A pixel-level analysis of the time-series MODIS 250-m VIs for 2,000+ field sites across Kansas found that unique spectral-temporal signatures were detected for the region's major crop types, consistent with the crops' phenology. Intra-class variations were detected in VI data associated with different land use practices, climatic conditions, and planting dates for the crops. The VIs depicted similar seasonal variations and were highly correlated. A pilot study in southwest Kansas found that accurate and detailed cropping patterns could be mapped using the MODIS 250-m VI data. Overall and class-specific accuracies were generally greater than 90% for mapping crop/non-crop, general crops (alfalfa, summer crops, winter wheat, and fallow), summer crops (corn, sorghum, and soybeans), and irrigated/non-irrigated crops using either VI dataset. The classified crop areas also had a high level of agreement (<5% difference) with the USDA reported crop areas. Both VIs produced comparable crop maps with only a 1-2% difference between their classification accuracies and a high level of pixel-level agreement (>90%) between their classified crop patterns. This hierarchical crop mapping protocol was tested for Kansas and produced similar classification results over a larger and more diverse area. Overall and class-specific accuracies were typically between 85% and 95% for the crop maps. At the state level, the maps had a high level of areal agreement (<5% difference) with the USDA crop area figures and their classified patterns were consistent with the state's cropping practices. In general, the protocol's performance was relatively consistent across the state's range of environmental conditions, landscape patterns, and cropping practices. The largest areal differences occurred in eastern Kansas due to the omission of many small cropland areas that were not resolvable at MODIS' 250-m resolution. Notable regional deviations in classified areas also occurred for selected classes due to localized precipitation patterns and specific cropping practices.

Potential health risk assessment of potato (Solanum tuberosum L.) grown on metal contaminated soils in the central zone of Punjab, Pakistan.

PubMed

Khan, Zafar Iqbal; Ahmad, Kafeel; Yasmeen, Sumaira; Akram, Nudrat Aisha; Ashraf, Muhammad; Mehmood, Naunain

2017-01-01

Metal buildup was estimated in potato (Solanum tuberosum L.), grown in central Punjab, Pakistan. This crop was irrigated with multiple water sources like ground, sewage and canal water. Concentrations of different metals like zinc (Zn), arsenic (As), lead (Pb), iron (Fe), nickel (Ni), molybdenum (Mo), copper (Cu), and selenium (Se) were assessed in the potato crop irrigated with different types of waters. Sewage water treated crop and soil had higher metal concentrations than those treated with other two treatments. All metals had positive and significant correlation except for Mo which was non-significantly correlated between the vegetable and soil. Highest daily intake was observed for Fe (0.267), whereas the lowest was seen for Se (0.003). The enrichment factor and health index varied between 0.135-15.08 and 0.285-83.77, respectively. This study concludes that vegetables cultivated on soil treated with sewage water is a potent threat for human health as the metals manifest toxicity after entering the food chain. Copyright © 2016 Elsevier Ltd. All rights reserved.

Harvested area gaps in China between 1981 and 2010: effects of climatic and land management factors

NASA Astrophysics Data System (ADS)

Yu, Qiangyi; van Vliet, Jasper; Verburg, Peter H.; You, Liangzhi; Yang, Peng; Wu, Wenbin

2018-04-01

Previous analyses have shown that cropland in China is intensifying, leading to an increase in crop production. However, these output measures leave the potential for further intensification largely unassessed. This study uses the harvested area gap (HAG), which expresses the amount of harvested area that can be gained if all existing cropland is harvested as frequently as possible, according to their potential limit for multi-cropping. Specifically, we calculate the HAG and changes in the HAG in China between 1981 and 2010. We further assess how climatic and land management factors affect these changes. We find that in China the HAG decreases between the 1980s and the 1990s, and subsequently increases between the 1990s and the 2000s, resulting in a small net increase for the entire study period. The initial decrease in the HAG is the result of an increase in the average multi-cropping index throughout the country, which is larger than the increase in the potential multi-cropping index as a result of the changed climatic factors. The subsequent increase in the HAG is the result of a decrease in average multi-cropping index throughout the country, in combination with a stagnant potential. Despite the overall increase in harvested area in China, many regions, e.g. Northeast and Lower Yangtze, are characterized by an increased HAG, indicating their potential for further increasing the multi-cropping index. The study demonstrates the application of the HAG as a method to identify areas where the harvested area can increase to increase crop production, which is currently underexplored in scientific literature.

NDVI statistical distribution of pasture areas at different times in the Community of Madrid (Spain)

NASA Astrophysics Data System (ADS)

Martín-Sotoca, Juan J.; Saa-Requejo, Antonio; Díaz-Ambrona, Carlos G. H.; Tarquis, Ana M.

2015-04-01

The severity of drought has many implications for society, including its impacts on the water supply, water pollution, reservoir management and ecosystem. However, its impacts on rain-fed agriculture are especially direct. Because of the importance of drought, there have been many attempts to characterize its severity, resulting in the numerous drought indices that have been developed (Niemeyer 2008). 'Biomass index' based on satellite image derived Normalized Difference Vegetation Index (NDVI) has been used in countries like United States of America, Canada and Spain for pasture and forage crops for some years (Rao, 2010). This type of agricultural insurance is named as 'index-based insurance' (IBI). IBI is perceived to be substantially less costly to operate and manage than multiple peril insurance. IBI contracts pay indemnities based not on the actual yield (or revenue) losses experienced by the insurance purchaser but rather based on realized NDVI values (historical data) that is correlated with farm-level losses (Xiaohui Deng et al., 2008). Definition of when drought event occurs is defined on NDVI threshold values mainly based in statistical parameters, average and standard deviation that characterize a normal distribution. In this work a pasture area at the north of Community of Madrid (Spain) has been delimited. Then, NDVI historical data was reconstructed based on remote sensing imaging MODIS, with 500x500m2 resolution. A statistical analysis of the NDVI histograms at consecutives 46 intervals of that area was applied to search for the best statistical distribution based on the maximum likelihood criteria. The results show that the normal distribution is not the optimal representation when IBI is available; the implications in the context of crop insurance are discussed (Martín-Sotoca, 2014). References Kolli N Rao. 2010. Index based Crop Insurance. Agriculture and Agricultural Science Procedia 1, 193-203. Martín-Sotoca, J.J. (2014) Estructura Espacial de la Sequía en Pastos y sus Aplicaciones en el Seguro Agrario. Master Thesis, UPM (In Spanish). Niemeyer, S., 2008: New drought indices. First Int. Conf. on Drought Management: Scientific and Technological Innovations, Zaragoza, Spain, Joint Research Centre of the European Commission. [Available online at http://www.iamz.ciheam.org/medroplan/zaragoza2008/Sequia2008/Session3/S.Niemeyer.pdf.] Xiaohui Deng, Barry J. Barnett, Gerrit Hoogenboom, Yingzhuo Yu and Axel Garcia y Garcia 2008. Alternative Crop Insurance Indexes. Journal of Agricultural and Applied Economics, 40(1), 223-237. Acknowledgements First author acknowledges the Research Grant obtained from CEIGRAM in 2014

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...

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

NASA Astrophysics Data System (ADS)

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

2014-01-01

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

The use of Landsat digital data to detect and monitor vegetation water deficiencies

NASA Technical Reports Server (NTRS)

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

1977-01-01

In the Large Area Crop Inventory Experiment a technique was devised using a vector transformation of Landsat digital data to indicate when vegetation is undergoing moisture stress. A relation was established between the remote-sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index).

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

PubMed

Huang, Wen-jiang; Wang, Jin-di; Mu, Xi-han; Wang, Ji-hua; Liu, Liang-yun; Liu, Qiang; Niu, Zheng

2007-10-01

Investigations have been made on identification of erective and horizontal varieties by bidirectional canopy reflected spectrum and semi-empirical bidirectional reflectance distribution function (BRDF) models. The qualitative effect of leaf area index (LAI) and average leaf angle (ALA) on crop canopy reflected spectrum was studied. The structure parameter sensitive index (SPEI) based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso), was defined in the present study for crop geometry identification. However, the weights associated with the kernels of semi-empirical BRDF model do not have a direct relationship with measurable biophysical parameters. Therefore, efforts have focused on trying to find the relation between these semi-empirical BRDF kernel weights and various vegetation structures. SPEI was proved to be more sensitive to identify crop geometry structures than structural scattering index (SSI) and normalized difference f-index (NDFI), SPEI could be used to distinguish erective and horizontal geometry varieties. So, it is feasible to identify horizontal and erective varieties of wheat by bidirectional canopy reflected spectrum.

Characterisation of caecum and crop microbiota of Indian indigenous chicken targeting multiple hypervariable regions within 16S rRNA gene.

PubMed

Saxena, S; Saxena, V K; Tomar, S; Sapcota, D; Gonmei, G

2016-06-01

A comparative analysis of caecum and crop microbiota of chick, grower and adult stages of Indian indigenous chickens was conducted to investigate the role of the microbiota of the gastrointestinal tract, which play an important role in host performance, health and immunity. High-throughput Illumina sequencing was performed for V3, V4 and V4-V6 hypervariable regions of the 16S rRNA gene. M5RNA and M5NR databases under MG-RAST were used for metagenomic datasets annotation. In the crop, Firmicutes (~78%) and Proteobacteria (~16%) were the predominant phyla whereas in the caecum, Firmicutes (~50%), Bacteroidetes (~29%) and Actinobacteria (~10%) were predominant. The Shannon-Wiener diversity index suggested that sample richness and diversity increased as the chicken aged. For the first time, the presence of Lactobacillus species such as L. frumenti, L. antri, L. mucosae in the chicken crop along with Kineococcus radiotolerans, Desulfohalobium retbaense and L. jensenii in the caecum are reported. Many of these bacterial species have been found to be involved in immune response modulation and disease prevention in pigs and humans. The gut microbiome of the indigenous chicken was enriched with microbes having probiotic potential which might be essential for their adaptability.

[Effects of different multiple cropping systems on paddy field weed community under long term paddy-upland rotation].

PubMed

Yang, Bin-Juan; Huang, Guo-Qin; Xu, Ning; Wang, Shu-Bin

2013-09-01

Based on a long term field experiment, this paper studied the effects of different multiple cropping systems on the weed community composition and species diversity under paddy-upland rotation. The multiple cropping rotation systems could significantly decrease weed density and inhibited weed growth. Among the rotation systems, the milk vetch-early rice-late maize --> milk vetchearly maize intercropped with early soybean-late rice (CCSR) had the lowest weed species dominance, which inhibited the dominant weeds and decreased their damage. Under different multiple cropping systems, the main weed community was all composed of Monochoia vaginalis, Echinochloa crusgalli, and Sagittaria pygmae, and the similarity of weed community was higher, with the highest similarity appeared in milk vetch-early rice-late maize intercropped with late soybean --> milk vetch-early maize-late rice (CSCR) and in CCSR. In sum, the multiple cropping rotations in paddy field could inhibit weeds to a certain extent, but attentions should be paid to the damage of some less important weeds.

Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso state, Brazil.

PubMed

Gusso, Anibal; Arvor, Damien; Ducati, Jorge Ricardo; Veronez, Mauricio Roberto; da Silveira, Luiz Gonzaga

2014-01-01

Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R (2) = 0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state.

Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

USDA-ARS?s Scientific Manuscript database

The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...

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

NASA Technical Reports Server (NTRS)

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

1978-01-01

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

Drought in West Africa: How CHIRPS and Reference Evapotranspiration can be used for Index Insurance in a Non-Stationary Setting

NASA Astrophysics Data System (ADS)

Blakeley, S. L.; Husak, G. J.; Harrison, L.; Funk, C. C.; Osgood, D. E.; Peterson, P.

2017-12-01

Index insurance is increasingly used as a safety net and productivity tool in order to improve the resilience of small-holder farmers in developing countries. In West Africa, there are already index insurance projects in many countries, and various non-governmental organizations are eager to expand implementation of this risk management tool. Often, index insurance payouts rely on rainfall to determine drought years, but designation of years based on precipitation variations is particularly complex in places like West Africa where precipitation is subject to much natural variability across timescales [Giannini 2003, among others]. Furthermore, farmers must also rely on other weather factors for good crop yields, such as the availability of moisture for their plants to absorb and maximum daily temperatures staying within an acceptable range for the crops. In this presentation, the payouts of an index based on rainfall (as measured by the Climate Hazards Group Infrared Precipitation with Stations {CHIRPS} dataset) is compared to the payouts of an index using reference evapotranspiration data (using the ASCE's Penmen-Monteith formula and MERRA-2 drivers). The West African rainfall index exhibits a fair amount of long-term variability, reflective of the Atlantic Multidecadal Oscillation, but the reference evapotranspiration index shows different variability, through changes in radiative forcing and temperatures. Therefore, the use of rainfall for an index is appropriate for capturing rainfall deficits, but reference evapotranspiration may also be an appropriate addition to an index or as a stand-alone index for capturing crop stress. In summary, the results point to farmer input as an invaluable source of knowledge in determining the most appropriate dataset as an index for crop insurance. Alessandra Giannini, R Saravanan, and P Chang. Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science, 302(5647):1027-1030, 2003.

Use of Drought Index and Crop Modelling for Drought Impacts Analysis on Maize (Zea mays L.) Yield Loss in Bandung District

NASA Astrophysics Data System (ADS)

Kurniasih, E.; Impron; Perdinan

2017-03-01

Drought impacts on crop yield loss depend on drought magnitude and duration and on plant genotype at every plant growth stages when droughts occur. This research aims to assess the difference calculation results of 2 drought index methods and to study the maize yield loss variability impacted by drought magnitude and duration during maize growth stages in Bandung district, province of West Java, Indonesia. Droughts were quantified by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at 1- to 3-month lags for the January1986-December 2015 period data. Maize yield responses to droughts were simulated by AquaCrop for the January 1986-May 2016 period of growing season. The analysis showed that the SPI and SPEI methods provided similar results in quantifying drought event. Droughts during maize reproductive stages caused the highest maize yield loss.

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.

AgMIP Training in Multiple Crop Models and Tools

NASA Technical Reports Server (NTRS)

Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

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.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment

USDA-ARS?s Scientific Manuscript database

This study aims to assess the relationship between Leaf Area Index (LAI) and remotely sensed Vegetation Indices (VIs) for major crops, based on a globally explicit dataset of in situ LAI measurements over a significant set of locations. We used a total of 1394 LAI measurements from 29 sites spannin...

Remote sensing of agricultural crops and soils

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator)

1983-01-01

Research in the correlative and noncorrelative approaches to image registration and the spectral estimation of corn canopy phytomass and water content is reported. Scene radiation research results discussed include: corn and soybean LANDSAT MSS classification performance as a function of scene characteristics; estimating crop development stages from MSS data; the interception of photosynthetically active radiation in corn and soybean canopies; costs of measuring leaf area index of corn; LANDSAT spectral inputs to crop models including the use of the greenness index to assess crop stress and the evaluation of MSS data for estimating corn and soybean development stages; field research experiment design data acquisition and preprocessing; and Sun-view angles studies of corn and soybean canopies in support of vegetation canopy reflection modeling.

How Universal Is the Relationship Between Remotely Sensed Vegetation Indices (VI) and Crop Leaf Area Index (LAI)?

NASA Technical Reports Server (NTRS)

Kang, Yanghui; Ozdogan, Mutlu; Zipper, Samuel C.; Roman, Miguel

2016-01-01

Global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. This research enables the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

How can we improve crop genotypes to increase stress resilience and productivity in a future climate? A new crop screening method based on productivity and resistance to abiotic stress

PubMed Central

Thiry, Arnauld A.; Chavez Dulanto, Perla N.; Reynolds, Matthew P.; Davies, William J.

2016-01-01

The need to accelerate the selection of crop genotypes that are both resistant to and productive under abiotic stress is enhanced by global warming and the increase in demand for food by a growing world population. In this paper, we propose a new method for evaluation of wheat genotypes in terms of their resilience to stress and their production capacity. The method quantifies the components of a new index related to yield under abiotic stress based on previously developed stress indices, namely the stress susceptibility index, the stress tolerance index, the mean production index, the geometric mean production index, and the tolerance index, which were created originally to evaluate drought adaptation. The method, based on a scoring scale, offers simple and easy visualization and identification of resilient, productive and/or contrasting genotypes according to grain yield. This new selection method could help breeders and researchers by defining clear and strong criteria to identify genotypes with high resilience and high productivity and provide a clear visualization of contrasts in terms of grain yield production under stress. It is also expected that this methodology will reduce the time required for first selection and the number of first-selected genotypes for further evaluation by breeders and provide a basis for appropriate comparisons of genotypes that would help reveal the biology behind high stress productivity of crops. PMID:27677299

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

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops

NASA Astrophysics Data System (ADS)

Betbeder, Julie; Fieuzal, Remy; Philippets, Yannick; Ferro-Famil, Laurent; Baup, Frederic

2016-04-01

This paper aims to evaluate the contribution of multitemporal polarimetric synthetic aperture radar (SAR) data for winter wheat and rapeseed crops parameters [height, leaf area index, and dry biomass (DB)] estimation, during their whole vegetation cycles in comparison to backscattering coefficients and optical data. Angular sensitivities and dynamics of polarimetric indicators were also analyzed following the growth stages of these two common crop types using, in total, 14 radar images (Radarsat-2), 16 optical images (Formosat-2, Spot-4/5), and numerous ground data. The results of this study show the importance of correcting the angular effect on SAR signals especially for copolarized signals and polarimetric indicators associated to single-bounce scattering mechanisms. The analysis of the temporal dynamic of polarimetric indicators has shown their high potential to detect crop growth changes. Moreover, this study shows the high interest of using SAR parameters (backscattering coefficients and polarimetric indicators) for crop parameters estimation during the whole vegetation cycle instead of optical vegetation index. They particularly revealed their high potential for rapeseed height and DB monitoring [i.e., Shannon entropy polarimetry (r2=0.70) and radar vegetation index (r2=0.80), respectively].

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 ...

Response of Main Maize Varieties to Water Stress and Comprehensive Evaluation in Hebei Province

NASA Astrophysics Data System (ADS)

Yue, Haiwang; Chen, Shuping; Bu, Junzhou; Wei, Jianwei; Peng, Haicheng; Li, Yuan; Li, Chunjie; Xie, Junliang

2018-01-01

Drought is a serious threat to maize production in Hebei province. Planting drought resistant maize varieties is an effective measure to solve drought in arid and less rain areas. Drought resistance in maize is controlled by many genes, and multiple indexes should be used for comprehensive evaluation (Campos H et al.2004). In the arid rain shed, using 34 maize varieties to promote crop production compared to the drought resistance test. The experiment was conducted with two treatments of drought stress (irrigation only at seedling stage) and normal irrigation, and 12 agronomic traits related to drought resistance of maize were determined. The results showed that drought had significant effects on maize yield and main agronomic characters. Under drought stress, plant height, ear length, bare tip, ear row number, row grains, 1000-kernel weight, ASI index can be used as identification index of drought resistance of maize in different period. The results indicated that the variety with strong drought resistance is Zhongdi175, the worst drought resistance is Woyu964.

Development of an unmanned agricultural robotics system for measuring crop conditions for precision aerial application

USDA-ARS?s Scientific Manuscript database

An Unmanned Agricultural Robotics System (UARS) is acquired, rebuilt with desired hardware, and operated in both classrooms and field. The UARS includes crop height sensor, crop canopy analyzer, normalized difference vegetative index (NDVI) sensor, multispectral camera, and hyperspectral radiometer...

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

A hybrid framework for assessing maize drought vulnerability in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Kamali, B.; Abbaspour, K. C.; Wehrli, B.; Yang, H.

2017-12-01

Drought has devastating impacts on crop yields. Quantifying drought vulnerability is the first step to better design of mitigation policies. The vulnerability of crop yield to drought has been assessed with different methods, however they lack a standardized base to measure its components and a procedure that facilitates spatial and temporal comparisons. This study attempts to quantify maize drought vulnerability through linking the Drought Exposure Index (DEI) to the Crop Failure Index (CFI). DEI and CFI were defined by fitting probability distribution functions to precipitation and maize yield respectively. To acquire crop drought vulnerability index (CDVI), DEI and CFI were combined in a hybrid framework which classifies CDVI with the same base as DEI and CFI. The analysis were implemented on Sub-Saharan African countries using maize yield simulated with the Environmental Policy Integrated Climate (EPIC) model at 0.5° resolution. The model was coupled with the Sequential Uncertainty Fitting algorithm for calibration at country level. Our results show that Central Africa and those Western African countries located below the Sahelian strip receive higher amount of precipitation, but experience high crop failure. Therefore, they are identified as more vulnerable regions compared to countries such as South Africa, Tanzania, and Kenya. We concluded that our hybrid approach complements information on crop drought vulnerability quantification and can be applied to different regions and scales.

Salinity modeling by remote sensing in central and southern Iraq

NASA Astrophysics Data System (ADS)

Wu, W.; Mhaimeed, A. S.; Platonov, A.; Al-Shafie, W. M.; Abbas, A. M.; Al-Musawi, H. H.; Khalaf, A.; Salim, K. A.; Chrsiten, E.; De Pauw, E.; Ziadat, F.

2012-12-01

Salinization, leading to a significant loss of cultivated land and crop production, is one of the most active land degradation phenomena in the Mesopotamian region in Iraq. The objectives of this study (under the auspices of ACIAR and Italian Government) are to investigate the possibility to use remote sensing technology to establish salinity-sensitive models which can be further applied to local and regional salinity mapping and assessment. Case studies were conducted in three pilot sites namely Musaib, Dujaila and West Garraf in the central and southern Iraq. Fourteen spring (February - April), seven June and four summer Landsat ETM+ images in the period 2009-2012, RapidEye data (April 2012), and 95 field EM38 measurements undertaken in this spring and summer, 16 relevant soil laboratory analysis result (Dujaila) were employed in this study. The procedure we followed includes: (1) Atmospheric correction using FLAASH model; (2) Multispectral transformation of a set of vegetation and non-vegetation indices such as GDVI (Generalized Difference Vegetation Index), NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), SARVI (Soil Adjusted and Atmospherically Resistant Vegetation Index), NDII (Normalized Difference Infrared Index), Principal Components and surface temperature (T); (3) Derivation of the spring maximum (Musaib) and annual maximum (Dujaila and West Garraf) value in each pixel of each index of the observed period to avoid problems related to crop rotation (e.g. fallow) and the SLC-Off gaps in ETM+ images; (4) Extraction of the values of each vegetation and non-vegetation index corresponding to the field sampling locations (about 3 to 5 controversial samples very close to the roads or located in fallow were excluded); and (5) Coupling remote sensing indices with the available EM38 and soil electrical conductivity (EC) data using multiple linear least-square regression model at the confidence level of 95% in a stepwise (forward) manner. The results reveal that soil salinity and EM38 readings are negatively correlated with the different vegetation indices, especially, GDVI and NDVI, and positively correlated with T. The models obtained for the pilot sites are presented in Table 1. Although we are still waiting for more laboratory analytical result and satellite imagery for more comprehensive analysis, it is clearly possible to build up salinity models by remote sensing, on which further salinity mapping and assessment can be based. It is also noted that among all the vegetation indices, GDVI is the best salinity indicator followed by NDVI and T. RapidEye image shows lower correlation with EM38 measurements and EC because fallow and crop rotation issue cannot be sorted out by one acquisition image.Table 1: Salinity models obtained from the pilot sitesNote: EMV- Vertical reading of EM38, EC - Electrical conductivity in dS/m

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

USDA-ARS?s Scientific Manuscript database

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

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

DOE PAGES

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-04-26

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

NASA Astrophysics Data System (ADS)

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-05-01

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

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

Monier, Erwan; Xu, Liyi; Snyder, Richard

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less

Development of dynamic wheat crop model in ISAM and estimation of impacts of environmental factors on wheat production in India

NASA Astrophysics Data System (ADS)

Gahlot, S.; Lin, T. S.; Jain, A. K.; Baidya Roy, S.; Sehgal, V. K.; Dhakar, R.

2017-12-01

With changing environmental conditions, such as climate and elevated atmospheric CO2 concentrations, questions about food security can be answered by modeling crops based on our understanding of the dynamic crop growth processes and interactions between the crops and their environment in the form of carbon, water and energy fluxes. These interactions and their effect on cropland ecosystems are non-linear because of the feedback mechanisms. Hence, process-based modelling approach can be used to conduct numerical experiments to derive insights into these processes and interactive feedbacks. In this study we have implemented dynamic crop growth processes for wheat into a data-modeling framework, Integrated Science Assessment Model (ISAM), to estimate the impacts of different factors like CO2 fertilization, irrigation, nitrogen limitation and climate change on wheat in India. In specific, we have implemented wheat-specific phenology, C3 photosynthesis mechanism and phenology-specific carbon allocation schemes for assimilated carbon to leaf, stem, root and grain pools. Crop growth limiting stress factors like nutrients, temperature and light have been included. The impact of high temperatures on leaf senescence, anthesis and grain filling has been modeled and found to be causing significant reduction in yield in the recent years. Field data from an experimental wheat site located at the Indian Agricultural Research Institute (IARI), New Delhi, India has been collected for aboveground biomass and leaf area index (LAI) for two growing seasons 2014-15 and 2015-16. This data has been used to study the phenology, growing season length, thermal requirements and growth stages of wheat. Using the field data, the dynamic model for wheat has been evaluated for the site level seasonal variability in leaf area index (LAI) and aboveground biomass. The variations in carbon, water and energy fluxes, plant height and rooting depth have been analyzed on the site level. Model experiments have been performed to calculate the yield for wheat for India for the historical years. In order to identify wheat production regions in India that are prone to one or multiple stresses in years to come, model experiments have been performed based on future climate scenarios RCP 4.5 and 8.5.

Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms

DOE PAGES

Peng, Yi; Nguy-Robertson, Anthony; Arkebauer, Timothy; ...

2017-03-02

Here, canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in threemore » irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.« less

Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms

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

Peng, Yi; Nguy-Robertson, Anthony; Arkebauer, Timothy

Here, canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in threemore » irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.« less

Correlation between Annual Corn Crop per Hectare in Croatia and Drought Indices for Zagreb-Gric Observatory

NASA Astrophysics Data System (ADS)

Pandzic, Kreso; Likso, Tanja

2017-04-01

Correlation coefficients between annual corn crop per hectare in Croatia and 9-month Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) for Zagreb - Gric for August are shown as significant. The results indicate that there is also a significant correlation between those drought indices and drought damages. Thus a forecast of the indices for August could be used for estimation e.g. annual corn crop per hectare in Croatia. Better results could be expected if statistical relationship between annual corn crops per hectare will be considered on county level instead the whole Croatia and indices calculated for weather stations for the same county. Effective way for reduction of drought damages is irrigation which need to be significantly improved in future in Croatia

Methodology for the assessment of the impacts of climate change on land degradation at multiple scales: Use of high resolution satellite imagery, modelling, and ground measurements for the assessment in Ethiopia

NASA Astrophysics Data System (ADS)

Ahmed, Oumer

In this study, a new multi-scalar methodology for assessing land degradation response to climate change is presented by analyzing 22 years of both climatic data and satellite observations, together with future projections from modelling, for Ethiopia. A comprehensive analysis of the impacts of climate change on land degradation was performed as evidenced from the integration of a host of land degradation indicators, namely: normalized difference vegetation Index (NDVI), net primary productivity (NPP), crop yield, biomass, length of growing period (LGP), rainfall use efficiency (RUE), energy use efficiency (EUE) and aridity index (AI). The results from the national level assessment indicate that over the period of 1984-2006, NPP decreased overall. Degrading areas occupy 30% of the country and suffer an average loss of NPP 10.3 kg C ha-1 y-1. The crop yield prediction results indicate a wide range of outcomes is to be expected for the country, due to the heterogeneity of the agro-climatic resources as well as of projected climate change. The results of the sub-national level assessment show that about 29% of the Awash watershed is degrading, and these degrading areas experience an average loss of NPP 4.6 kg C ha-1 y-1. Further, about 33.8% of the degrading area in the watershed is associated with bare land and 25% with agricultural land. Finally, since remotely sensed estimates are frequently used to assess land degradation at multiple scales, scale transfer methods are evaluated in this study to provide a tool to rank both upscaling and downscaling procedures.

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.

Detecting crop yield reduction due to irrigation-induced soil salinization in South-West Russia

NASA Astrophysics Data System (ADS)

Argaman, E.; Beets, W.; Croes, J.; Keesstra, S.; Verzandvoort, S.; Zeiliguer, A.

2012-04-01

The South-European part of the Russian Federation has experienced serious land degradation in the form of soil salinization since the 1960s. This land degradation was caused by intensive, large-scale irrigation on reclaimed land in combination with the salt-rich nature of the substrate. Alkaline soil salinity is believed to be an important factor decreasing crop yield in this area. A large research effort has been directed to the effects of soil salinity on crops, there is a need for simple, easily determinable indicators of crop health and soil salinity in irrigated systems, that can help to detect crop water stress in an early stage. The objectives of this research were to study the effects of soil salinity and vegetation water stress on the performance of alfalfa crop yield and physiological crop properties, and to study the possibility to measure soil salinity and alkalinity and the crop water stress index at plot level using a thermal gun and a regular digital camera. The study area was located in Saratov District, in the South-West part of Russia. Variables on the surface energy balance, crop properties, soil properties and visible reflectance were measured on plots with alfalfa cultures in two fields with and without signs of alkaline soil salinity, and with and without irrigation in July 2009. The research showed no clear adverse effects of soil salinity and soil alkalinity on crop yield and physiological crop properties. Soil salinity, as reflected by the electric conductivity, positively affected the root biomass of alfalfa in the range of 0.15 to 1.52 dS/m . This was a result of EC levels being below the documented threshold to negatively affect Alfalfa, as would be the case in truly saline soils. The soil pH also showed a positive correlation with root biomass within the range of pH 6.2 and 8.5 . From the literature these pH values are generally believed to be too high to exhibit a positive relationship with root biomass. No relationship was found between EC and pH on the one hand , and soil moisture content on the other. However, soil moisture content in the topsoil appeared to have a major influence on the crop water stress index, which on its turn affected the leaf area index, the fresh biomass and the mean plant height. The crop leaf color as detected by a regular digital camera appeared to be correlated with pH and EC properties of the soil. The visible light band ratios red/green and blue/green correlated well with the crop water stress index. More research is necessary to prove if this relation is applicable in different environments, and for different crops. A confirmation of these findings would offer scope to increase the spatial support of this technique using satellite images.

Remediation and Safe Production of cd Contaminated Soil Via Multiple Cropping Hyperaccumulator Solanum nigrum L. and Low Accumulation Chinese Cabbage.

PubMed

Niu, Mingfen; Wei, Shuhe; Bai, Jiayi; Wang, Siqi; Ji, Dandan

2015-01-01

Multiple crop experiment of hyperaccumulator Solanum nigrum L. with low accumulation Chinese cabbage Fenyuanxin 3 were conducted in a cadmium (Cd) contaminated vegetable field. In the first round, the average removal rate of S. nigrum to Cd was about 10% without assisted phytoextraction reagent addition for the top soil (0-20 cm) with Cd concentration at 0.53-0.97 mg kg(-1) after its grew 90 days. As for assisted phytoextraction reagent added plots, efficiency of Cd remediation might reach at 20%. However, in the second round, Cd concentration in Chinese cabbage was edible, even in the plots with assisted phytoextraction reagent added. Thus, multiple cropping hyperaccumulator with low accumulation crop could normally remediate contaminated soil and produce crop (obtain economic benefit) in one year, which may be one practical pathway of phytoremediating heavy metal contaminated soil in the future.

Probabilistic assessment of phenophase-wise agricultural drought risk under different sowing windows: a case study with rainfed soybean.

PubMed

Dhakar, Rajkumar; Sarath Chandran, M A; Nagar, Shivani; Visha Kumari, V

2017-11-23

A new methodology for crop-growth stage-specific assessment of agricultural drought risk under a variable sowing window is proposed for the soybean crop. It encompasses three drought indices, which include Crop-Specific Drought Index (CSDI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI). The unique features of crop-growth stage-specific nature and spatial and multi-scalar coverage provide a comprehensive assessment of agricultural drought risk. This study was conducted in 10 major soybean-growing districts of Madhya Pradesh state of India. These areas contribute about 60% of the total soybean production for the country. The phenophase most vulnerable to agricultural drought was identified (germination and flowering in our case) for each district across four sowing windows. The agricultural drought risk was quantified at various severity levels (moderate, severe, and very severe) for each growth stage and sowing window. Validation of the proposed new methodology also yielded results with a high correlation coefficient between percent probability of agricultural drought risk and yield risk (r = 0.92). Assessment by proximity matrix yielded a similar statistic. Expectations for the proposed methodology are better mitigation-oriented management and improved crop contingency plans for planners and decision makers.

Monitoring corn and soybean crop development by remote sensing techniques

NASA Technical Reports Server (NTRS)

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

1978-01-01

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

Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

USDA-ARS?s Scientific Manuscript database

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

Assessment of future crop yield and agricultural sustainable water use in north china plain using multiple crop models

NASA Astrophysics Data System (ADS)

Huang, G.

2016-12-01

Currently, studying crop-water response mechanism has become an important part in the development of new irrigation technology and optimal water allocation in water-scarce regions, which is of great significance to crop growth guidance, sustainable utilization of agricultural water, as well as the sustainable development of regional agriculture. Using multiple crop models(AquaCrop,SWAP,DNDC), this paper presents the results of simulating crop growth and agricultural water consumption of the winter-wheat and maize cropping system in north china plain. These areas are short of water resources, but generates about 23% of grain production for China. By analyzing the crop yields and the water consumption of the traditional flooding irrigation, the paper demonstrates quantitative evaluation of the potential amount of water use that can be reduced by using high-efficient irrigation approaches, such as drip irrigation. To maintain food supply and conserve water resources, the research concludes sustainable irrigation methods for the three provinces for sustainable utilization of agricultural water.

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.

Determining the potential productivity of food crops in controlled environments

NASA Technical Reports Server (NTRS)

Bugbee, Bruce

1992-01-01

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

Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Liu, Xing; Chen, Fei; Barlage, Michael; Zhou, Guangsheng; Niyogi, Dev

2016-12-01

Croplands are important in land-atmosphere interactions and in the modification of local and regional weather and climate; however, they are poorly represented in the current version of the coupled Weather Research and Forecasting/Noah with multiparameterization (Noah-MP) land surface modeling system. This study introduced dynamic corn (Zea mays) and soybean (Glycine max) growth simulations and field management (e.g., planting date) into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at field scales using crop biomass data sets, surface heat fluxes, and soil moisture observations. Compared to the generic dynamic vegetation and prescribed-leaf area index (LAI)-driven methods in Noah-MP, the Noah-MP-Crop showed improved performance in simulating leaf area index (LAI) and crop biomass. This model is able to capture the seasonal and annual variability of LAI and to differentiate corn and soybean in peak values of LAI as well as the length of growing seasons. Improved simulations of crop phenology in Noah-MP-Crop led to better surface heat flux simulations, especially in the early period of growing season where current Noah-MP significantly overestimated LAI. The addition of crop yields as model outputs expand the application of Noah-MP-Crop to regional agriculture studies. There are limitations in the use of current growing degree days (GDD) criteria to predict growth stages, and it is necessary to develop a new method that combines GDD with other environmental factors, to more accurately define crop growth stages. The capability introduced in Noah-MP allows further crop-related studies and development.

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.

Health risks of thallium in contaminated arable soils and food crops irrigated with wastewater from a sulfuric acid plant in western Guangdong province, China.

PubMed

Wang, Chunlin; Chen, Yongheng; Liu, Juan; Wang, Jin; Li, Xiangping; Zhang, Yongbo; Liu, Yimin

2013-04-01

Thallium (Tl) contamination in soils poses a significant threat to human health due to the high toxicity of Tl and its ready assimilation by crops. Consumption of food crops contaminated with Tl is a major food chain route for human exposure. The health risks of Tl in contaminated food crops irrigated with wastewater from a sulfuric acid factory were investigated in this paper. Results indicate that long-term Tl-containing wastewater irrigation resulted in Tl contamination of arable soils and crops. The pollution load index values indicated that the arable soils were moderately enriched with Tl. Tl was highly accumulated in the crops. The content of Tl in the edible plant portions of crops ranged from 1.2 mg/kg to 104.8 mg/kg, exceeding the recommended permissible limits for food crops. The daily intake of metals (DIM) values of Tl for both adults and children via the consumption of the food crops except soya beans were higher than the reference oral dose (RfD) limit recommend by the United States environmental protection agency (US-EPA). Health risk index (HRI) values were generally higher than 1, indicating that health risks associated with Tl exposure are significant and assumed to be dangerous to the health of local villagers. Therefore, much attention should be paid to avoid consumption of these Tl-contaminated crops that can cause great potential risks. Copyright © 2012 Elsevier Inc. All rights reserved.

Farmer's response to changing climate in North East India

NASA Astrophysics Data System (ADS)

De, Utpal Kumar

2015-02-01

Diversification of land use in the cultivation of various crops provides an alternative way to moderate the climate risk. By choosing alternative crops that are resilient to various weather parameters, farmers can reduce the crop damage and achieve optimum output from their limited land resources. Apart from other adaptation measures, crop diversity can reflect farmers' response towards changing climate uncertainty. This paper tries to examine the changing climatic condition through spatio-temporal variation of two important weather variables (precipitation and temperature) in the largest North-East Indian state, Assam, since 1950. It is examined by the variation in crop diversification index. We have used (1) Herfindahl Index for measuring degree of diversification and (2) locational quotient for measuring the changes in the regional crop concentration. The results show that, in almost all the districts, crop specialization has been taking place slowly and that happened mostly in the last phase of our study. The hilly and backward districts recorded more diversification but towards lower value crops. It goes against the normal feature of crop diversification where farmers diversify in favour of high value crops. Employing ordinary least squares method and/or Fixed Effect model, irrigation is found to have significant impact on crop diversification; while the flood plain zones and hill zones are found to have better progress in this regard, which has been due to the survival necessity of poor farmers living the zone. Thus crop diversity does not reflect very significant response from the farmers' side towards changing weather factors (except rainfall) though they have significant impact on the productivity of various crops, and thus profitability. The study thus suggests the necessity for rapid and suitable diversification as alternative climate change mitigation in the long run.

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.

A new nitrogen index for assessment of nitrogen management practices of Andean Mountain cropping systems of Ecuador

USDA-ARS?s Scientific Manuscript database

Corn (Zea mays L.) is the most important crop for food security in several regions of Ecuador. Small farmers are using nitrogen (N) fertilizer without technical advice based on soil, crop and climatological data. The scientific literature lacks studies where tools are validated that can be used to q...

Indexes of Land Use Change to Predict Aggregate Stability in a Mollisol and a Vertisol of Argentina

NASA Astrophysics Data System (ADS)

Novelli, L. E.; Caviglia, O. P.; Wilson, M. G.; Sasal, M. C.

2012-04-01

In several areas of South America, the extensive cropping systems in traditional agricultural lands have increase the area cropped with soybean, mainly as a single annual crop. Also nowadays agriculture has a progressive expansion toward more environmentally fragile areas that were traditionally occupied by livestock or native forests. This change of land use may be characterized through different indexes as the length of the growth period or the frequency of a particular crop in the cropping sequence. On the other hand the consequences of land-use changes on soil physical condition may be monitored through the aggregate stability, which is directly related to soil functionality. However, there are different methods for aggregate stability analysis, which may vary in their potential for prediction. The aim of our work was to assess different quantitative indexes of change in the land use on aggregate stability through two methods in two soils differing in the main agents of aggregation. The study was conducted in a Mollisol and a Vertisol from Argentina. Eleven fields (agricultural and crop-pasture rotation) under no-tillage and one natural grassland were selected in each soil type. The fraction of annual time with vegetal cover (as a measure of the intensification in the land use - ISI) and the frequency of a given crop (soybean - SCF; wheat - WCF; and wheat plus maize - CCF) in the cropping sequence over a 6-year period were calculated. Samples were collected at 0-5 and 5-15 cm depths from each soil. The mean weight diameter (MWD) of the soil aggregates where determined by two methods: Le Bissonnais with three pretreatment (fast wetting, slow wetting and stirring after prewetting) and by wet sieving using an instrument similar to the Yoder apparatus. The MWD by wet-sieving was affected by ISI and SCF, but the impact only was recorded in 0-5cm depth of the Mollisol. The MWD by fast and slow wetting and the means of three pretreatments (MWDm) were directly related to ISI, SCF and WCF in both depth of the Mollisol. Although in the Vertisol, the aggregate stability in natural grassland was higher than under agricultural use, indexes did not show the change in the land use for any pretreatment or depth, except by ISI and SCF in the slow wetting pretreatment at 5-15 cm depth. The method of Le Bissonnais was more sensitive to predict changes in the land use driven by the frequency of a given crop in the cropping sequence that the wet-sieving, mainly in the Mollisol.

Mapping of Biophysical Parameters of Rice Agriculture System from Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Moharana, Shreedevi; Duta, Subashisa

2017-04-01

Chlorophyll, nitrogen and leaf water content are the most essential parameters for paddy crop growth. Ground hyperspectral observations were collected at canopy level during critical growth period of rice by using hand held Spectroradiometer. Chemical analysis was carried out to quantify the total chlorophyll, nitrogen and leaf water content. By exploiting the in-situ hyperspectral measurements, regression models were established between each of the crop growth parameters and the spectral indices specifically designed for chlorophyll, nitrogen and water stress. Narrow band vegetation index models were developed for mapping these parameters from Hyperion imagery in an agriculture system. It was inferred that the modified simple ratio (SR) and leaf nitrogen concentration (LNC) predictive index models, which followed a linear and nonlinear relationship respectively, produced satisfactory results in predicting rice nitrogen content from hyperspectral imagery. The presently developed model was compared with other models proposed by researchers. It was ascertained that, nitrogen content varied widely from 1-4 percentage and only 2-3 percentage for paddy crop using present modified index models and well-known predicted Tian et al. (2011) model respectively. The modified present LNC index model performed better than the established Tian et al. (2011) model as far as the estimated nitrogen content from Hyperion imagery was concerned. Moreover, within the observed chlorophyll range attained from the rice genotypes cultivated in the studied rice agriculture system, the index models (LNC, OASVI, Gitelson, mSR and MTCI) accomplished satisfactory results in the spatial distribution of rice chlorophyll content from Hyperion imagery. Spatial distribution of total chlorophyll content widely varied from 1.77-5.81 mg/g (LNC), 3.0-13 mg/g (OASVI) and 2.90-5.40 mg/g (MTCI). Following the similar guideline, it was found that normalized difference water index (NDWI) and normalized difference infrared index (NDII) predictive models demonstrated the spatial variability of leaf water content from 40 percentage to 90 percentage in the same rice agriculture system which has a good agreement with observed in-situ leaf water measurements. The spatial information of these parameters will be useful for crop nutrient management and yield forecasting, and will serve as inputs to various crop-forecasting models for developing a precision rice agriculture system. Key words: Rice agriculture system, nitrogen, chlorophyll, leaf water content, vegetation index

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

NASA Astrophysics Data System (ADS)

Prabhakara, Kusuma; Hively, W. Dean; McCarty, Gregory W.

2015-07-01

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012-2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

A study on agricultural drought vulnerability at disaggregated level in a highly irrigated and intensely cropped state of India.

PubMed

Murthy, C S; Yadav, Manoj; Mohammed Ahamed, J; Laxman, B; Prawasi, R; Sesha Sai, M V R; Hooda, R S

2015-03-01

Drought is an important global hazard, challenging the sustainable agriculture and food security of nations. Measuring agricultural drought vulnerability is a prerequisite for targeting interventions to improve and sustain the agricultural performance of both irrigated and rain-fed agriculture. In this study, crop-generic agricultural drought vulnerability status is empirically measured through a composite index approach. The study area is Haryana state, India, a prime agriculture state of the country, characterised with low rainfall, high irrigation support and stable cropping pattern. By analysing the multiyear rainfall and crop condition data of kharif crop season (June-October) derived from satellite data and soil water holding capacity and groundwater quality, nine contributing indicators were generated for 120 blocks (sub-district administrative units). Composite indices for exposure, sensitivity and adaptive capacity components were generated after assigning variance-based weightages to the respective input indicators. Agricultural Drought Vulnerability Index (ADVI) was developed through a linear combination of the three component indices. ADVI-based vulnerability categorisation revealed that 51 blocks are with vulnerable to very highly vulnerable status. These blocks are located in the southern and western parts of the state, where groundwater quality is saline and water holding capacity of soils is less. The ADVI map has effectively captured the spatial pattern of agricultural drought vulnerability in the state. Districts with large number of vulnerable blocks showed considerably larger variability of de-trended crop yields. Correlation analysis reveals that crop condition variability, groundwater quality and soil factors are closely associated with ADVI. The vulnerability index is useful to prioritise the blocks for implementation of long-term drought management plans. There is scope for improving the methodology by adding/fine-tuning the indicators and by optimising the weights.

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

USGS Publications Warehouse

Prabhakara, Kusuma; Hively, W. Dean; McCarty, Greg W.

2015-01-01

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

Can Multiple Cropping Help to Avoid the Impacts of Heat Extremes? The Case of Winter Wheat/Soybean Double Cropping in the United States

NASA Astrophysics Data System (ADS)

Seifert, C.; Lobell, D. B.

2014-12-01

In adapting U.S. agriculture to the climate of the 21st century, multiple cropping presents a unique opportunity to help offset projected negative trends in agricultural production while moving critical crop yield formation periods outside of the hottest months of the year. Critical constraints on this practice include moisture availability, and, more importantly, growing season length. We review evidence that this last constraint has decreased in the previous quarter century, allowing for more winter wheat/soybean double cropping in previously phenologically constrained areas. We also carry this pattern forward to 2100, showing a 126% to 211% increase in the area phenologically suitable for double cropping under the RCP45 and RCP85 scenarios respectively. These results suggest that climate change will relieve phenological constraints on wheat-soy double cropping systems over much of the United States, changing production patterns and crop rotations as areas become suitable for the practice.

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

PubMed Central

Din, Mairaj; Zheng, Wen; Rashid, Muhammad; Wang, Shanqin; Shi, Zhihua

2017-01-01

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices. PMID:28588596

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

NASA Technical Reports Server (NTRS)

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

1980-01-01

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

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.

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

PubMed

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

2004-02-01

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

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).

7 CFR 760.814 - Calculation of acreage for crop losses other than prevented planted.

Code of Federal Regulations, 2010 CFR

2010-01-01

... of the crop, as applicable, or actual acreage of the crop planted for harvest. (b) In cases where... good farming practices; and (4) Could reach maturity if each planting was harvested or would have been harvested. (c) In cases where there is multiple-cropped acreage, each crop may be eligible for disaster...

7 CFR 760.814 - Calculation of acreage for crop losses other than prevented planted.

Code of Federal Regulations, 2011 CFR

2011-01-01

... of the crop, as applicable, or actual acreage of the crop planted for harvest. (b) In cases where... good farming practices; and (4) Could reach maturity if each planting was harvested or would have been harvested. (c) In cases where there is multiple-cropped acreage, each crop may be eligible for disaster...

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.

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.

Application of water footprint combined with a unified virtual crop pattern to evaluate crop water productivity in grain production in China.

PubMed

Wang, Y B; Wu, P T; Engel, B A; Sun, S K

2014-11-01

Water shortages are detrimental to China's grain production while food production consumes a great deal of water causing water crises and ecological impacts. Increasing crop water productivity (CWP) is critical, so China is devoting significant resources to develop water-saving agricultural systems based on crop planning and agricultural water conservation planning. A comprehensive CWP index is necessary for such planning. Existing indices such as water use efficiency (WUE) and irrigation efficiency (IE) have limitations and are not suitable for the comprehensive evaluation of CWP. The water footprint (WF) index, calculated using effective precipitation and local water use, has advantages for CWP evaluation. Due to regional differences in crop patterns making the CWP difficult to compare directly across different regions, a unified virtual crop pattern is needed to calculate the WF. This project calculated and compared the WF of each grain crop and the integrated WFs of grain products with actual and virtual crop patterns in different regions of China for 2010. The results showed that there were significant differences for the WF among different crops in the same area or among different areas for the same crop. Rice had the highest WF at 1.39 m(3)/kg, while corn had the lowest at 0.91 m(3)/kg among the main grain crops. The WF of grain products was 1.25 m(3)/kg in China. Crop patterns had an important impact on WF of grain products because significant differences in WF were found between actual and virtual crop patterns in each region. The CWP level can be determined based on the WF of a virtual crop pattern, thereby helping optimize spatial distribution of crops and develop agricultural water savings to increase CWP. Copyright © 2014 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Relationships of Leaf Area Index and NDVI for 12 Brassica Cultivars in Northeastern Montana

NASA Astrophysics Data System (ADS)

Jabro, Jay; Allen, Brett; Long, Dan; Isbell, Terry; Gesch, Russ; Brown, Jack; Hatfield, Jerry; Archer, David; Oblath, Emily; Vigil, Merle; Kiniry, Jim; Hunter, Kimberly; Shonnard, David

2017-04-01

To our knowledge, there is limited information on the relationship of the normalized difference vegetation index (NDVI) and leaf area index (LAI) in spring Brassica oilseed crops. The 2014 results of NDVI and LAI of 12 spring varieties of oilseed crops were measured in a field study conducted in Sidney, Montana, USA under dryland conditions. These 12 varieties were grouped under six species (B. napus, B. rapa, B. juncea, B. carinata, Sinapis alba, and Camelina sativa). The NDVI and LAI were measured weekly throughout the growing season. The NDVI was continually measured at one sample per second across the whole plot using a Crop Circle ACS-470 active crop canopy sensor. The LAI was measured at two locations at 12 samples per plot using an AccuPar model LP-80 Ceptometer. Treatments were replicated four times in a randomized complete block design in plots of 3 m×9 m. Temporal dynamics of NDVI and LAI in various growth stages of 12 varieties were evaluated throughout the growing season. Significant relationships and models between NDVI and LAI were obtained when 12 varieties were grouped under six species.

LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status

USDA-ARS?s Scientific Manuscript database

Optical remote sensing of crop nitrogen (N) status is developing into a powerful diagnostic tool that can improve N management decisions. Crop N status is a function of dry mass per unit area (W) and N concentration (%Na), which can be used to calculate N nutrition index (NNI),where NNI is %Na/%Nc (...

Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images.

PubMed

Liu, Meiling; Wang, Tiejun; Skidmore, Andrew K; Liu, Xiangnan

2018-05-05

Regional-level information on heavy metal pollution in agro-ecosystems is essential for food security because excessive levels of heavy metals in crops may pose risks to humans. However, collecting this information over large areas is inherently costly. This paper investigates the possibility of applying multi-temporal Sentinel-2 satellite images to detect heavy metal-induced stress (i.e., Cd stress) in rice crops in four study areas in Zhuzhou City, Hunan Province, China. For this purpose, we compared seven Sentinel-2 images acquired in 2016 and 2017 with in situ measured hyper-spectral data, chlorophyll content, rice leaf area index, and heavy metal concentrations in soil collected from 2014 to 2017. Vegetation indices (VIs) related to red edge bands were referred to as the sensitive indicators for screening stressed rice from unstressed rice. The coefficients of spatio-temporal variation (CSTV) derived from the VIs allowed us to discriminate crops exposed to pollution from heavy metals as well as environmental stressors. The results indicate that (i) the red edge chlorophyll index, the red edge position index, and the normalized difference red edge 2 index derived from multi-temporal Sentinel-2 images were good indicators for screening stressed rice from unstressed rice; (ii) Rice under Cd stress remained stable with lower CSTV values of VIs overall growth stages in the experimental region, whereas rice under other stressors (i.e., pests and disease) showed abrupt changes at some growth stages and presented "hot spots" with greater CSTV values; and (iii) the proposed spatio-temporal anomaly detection method was successful at detecting rice under Cd stress; and CSTVs of rice VIs stabilized regardless of whether they were applied to consecutive growth stages or to two different crop years. This study suggests that regional heavy metal stress may be accurately detected using multi-temporal Sentinel-2 images, using VIs sensitive to the spatio-temporal characteristics of crops. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Chemistry and microbial functional diversity differences in biofuel crop and grassland soils in multiple geographies

EPA Science Inventory

As crop and non-crop lands are increasingly converted to biofuel feedstock production, it is of interest to identify potential impacts of annual and perennial feedstocks on soil ecosystem services. Soil samples were obtained from diverse regionally distributed biofuel cropping si...

Comparison of Sub-pixel Classification Approaches for Crop-specific Mapping

EPA Science Inventory

The Moderate Resolution Imaging Spectroradiometer (MODIS) data has been increasingly used for crop mapping and other agricultural applications. Phenology-based classification approaches using the NDVI (Normalized Difference Vegetation Index) 16-day composite (250 m) data product...

Effects of Cover Crops on Pratylenchus penetrans and the Nematode Community in Carrot Production

PubMed Central

Grabau, Zane J.; Zar Maung, Zin Thu; Noyes, D. Corey; Baas, Dean G.; Werling, Benjamin P.; Brainard, Daniel C.; Melakeberhan, Haddish

2017-01-01

Cover cropping is a common practice in U.S. Midwest carrot production for soil conservation, and may affect soil ecology and plant-parasitic nematodes—to which carrots are very susceptible. This study assessed the impact of cover crops—oats (Avena sativa), radish (Raphanus sativus) cv. Defender, rape (Brassica napus) cv. Dwarf Essex, and a mixture of oats and radish—on plant-parasitic nematodes and soil ecology based on the nematode community in Michigan carrot production systems. Research was conducted at two field sites where cover crops were grown in Fall 2014 preceding Summer 2015 carrot production. At Site 1, root-lesion (Pratylenchus penetrans) and stunt (Tylenchorhynchus sp.) nematodes were present at low population densities (less than 25 nematodes/100 cm3 soil), but were not significantly affected (P > 0.05) by cover crops. At Site 2, P. penetrans population densities were increased (P ≤ 0.05) by ‘Defender’ radish compared to other cover crops or fallow control during cover crop growth and midseason carrot production. At both sites, there were few short-term impacts of cover cropping on soil ecology based on the nematode community. At Site 1, only at carrot harvest, radish-oats mixture and ‘Dwarf Essex’ rape alone enriched the soil food web based on the enrichment index (P ≤ 0.05) while rape and radish increased structure index values. At Site 2, bacterivore abundance was increased by oats or radish cover crops compared to control, but only during carrot production. In general, cover crops did not affect the nematode community until nearly a year after cover crop growth suggesting that changes in the soil community following cover cropping may be gradual. PMID:28512383

Assessment of water use in the Spanish irrigation district "Río Adaja"

NASA Astrophysics Data System (ADS)

Naroua, Illiassou; Rodriguez-Sinobas, Leonor; Sánchez Calvo, Raúl

2013-04-01

Intensive agricultural practices combined with the increasing pressure of urbanization and the changing lifestyles, have strengthened the problems of competing users over limited water resources in a fragile and already stressed environment. Sustainable irrigated agriculture is prescribed as a policy approach that maximizes economic benefits while maintaining environmental quality. Within this framework a proper management of irrigation systems saving water is required. On the other hand, crops with high tolerance to water stress and deficit irrigation are recommended. However, crop yield, among other factors, is very sensitive to water Thus, studies addressing the relations among crop water requirements, irrigation depth and crop yield are necessary. This type of study has been carried out in the Spanish irrigation District "Río Adaja" in the year 2010-2011 with the crops: wheat, barley, sugarbeet, corn, onion, potato, sunflower, clover and carrot. A soil hydrology balance model was applied taking into account climatic data for the nearby weather station and soil characteristics. Effective precipitation was calculated by the index curve number. Crop water requirements were calculated by the FAO Penman-Monteith with the application of the dual crop coefficient. Likewise, productivity was measured by the following indexes: annual relative irrigation supply (ARIS), relative water supply (RWS), relative rainfall supply (RS) and water productivity (WP). Results show that water applied with the irrigation of clover, sugarbeet, corn and onion was less than their water requirements There was a 35 % difference between the amount of water simulated with the model and the gross amount applied during the irrigation period by the irrigation district. WP values differed among crops depending, mainly, on the crop`s market price and the amount of irrigation water. The highest values corresponded to potato and onion crops.

Assessment of Climate Suitability of Maize in South Korea

NASA Astrophysics Data System (ADS)

Hyun, S.; Choi, D.; Seo, B.

2017-12-01

Assessing suitable areas for crops would be useful to design alternate cropping systems as an adaptation option to climate change adaptation. Although suitable areas could be identified by using a crop growth model, it would require a number of input parameters including cultivar and soil. Instead, a simple climate suitability model, e.g., EcoCrop model, could be used for an assessment of climate suitability for a major grain crop. The objective of this study was to assess of climate suitability for maize using the EcoCrop model under climate change conditions in Korea. A long term climate data from 2000 - 2100 were compiled from weather data source. The EcoCrop model implemented in R was used to determine climate suitability index at each grid cell. Overall, the EcoCrop model tended to identify suitable areas for maize production near the coastal areas whereas the actual major production areas located in inland areas. It is likely that the discrepancy between assessed and actual crop production areas would result from the socioeconomic aspects of maize production. Because the price of maize is considerably low, maize has been grown in an area where moisture and temperature conditions would be less than optimum. In part, a simple algorithm to predict climate suitability for maize would caused a relatively large error in climate suitability assessment under the present climate conditions. In 2050s, the climate suitability for maize increased in a large areas in southern and western part of Korea. In particular, the plain areas near the coastal region had considerably greater suitability index in the future compared with mountainous areas. The expansion of suitable areas for maize would help crop production policy making such as the allocation of rice production area for other crops due to considerably less demand for the rice in Korea.

Evaluating and optimizing horticultural regimes in space plant growth facilities

NASA Astrophysics Data System (ADS)

Berkovich, Y.; Chetirkin, R.; Wheeler, R.; Sager, J.

In designing innovative Space Plant Growth Facilities (SPGF) for long duration space f ightl various limitations must be addressed including onboard resources: volume, energy consumption, heat transfer and crew labor expenditure. The required accuracy in evaluating onboard resources by using the equivalent mass methodology and applying it to the design of such facilities is not precise. This is due to the uncertainty of the structure and not completely understanding of the properties of all associated hardware, including the technology in these systems. We present a simple criteria of optimization for horticultural regimes in SPGF: Qmax = max [M · (EBI) 2 / (V · E · T) ], where M is the crop harvest in terms of total dry biomass in the plant growth system; EBI is the edible biomass index (harvest index), V is a volume occupied by the crop; E is the crop light energy supply during growth; T is the crop growth duration. The criterion reflects directly on the consumption of onboard resources for crop production. We analyzed the efficiency of plant crops and the environmental parameters by examining the criteria for 15 salad and 12 wheat crops from the data in the ALS database at Kennedy Space Center. Some following conclusion have been established: 1. The technology involved in growing salad crops on a cylindrical type surface provides a more meaningful Q-criterion; 2. Wheat crops were less efficient than leafy greens (salad crops) when examining resource utilization; 3. By increasing light intensity of the crop the efficiency of the resource utilization could decrease. Using the existing databases and Q-criteria we have found that the criteria can be used in optimizing design and horticultural regimes in the SPGF.

Acquisition and management of continuous data streams for crop water management

USDA-ARS?s Scientific Manuscript database

Wireless sensor network systems for decision support in crop water management offer many advantages including larger spatial coverage and multiple types of data input. However, collection and management of multiple and continuous data streams for near real-time post analysis can be problematic. Thi...

Evaluating and optimizing horticultural regimes in space plant growth facilities

NASA Technical Reports Server (NTRS)

Berkovich, Y. A.; Chetirkin, P. V.; Wheeler, R. M.; Sager, J. C.

2004-01-01

In designing innovative space plant growth facilities (SPGF) for long duration space flight, various limitations must be addressed including onboard resources: volume, energy consumption, heat transfer and crew labor expenditure. The required accuracy in evaluating on board resources by using the equivalent mass methodology and applying it to the design of such facilities is not precise. This is due to the uncertainty of the structure and not completely understanding the properties of all associated hardware, including the technology in these systems. We present a simple criteria of optimization for horticultural regimes in SPGF: Qmax = max [M x (EBI)2/(V x E x T], where M is the crop harvest in terms of total dry biomass in the plant growth system; EBI is the edible biomass index (harvest index), V is volume occupied by the crop; E is the crop light energy supply during growth; T is the crop growth duration. The criterion reflects directly on the consumption of onboard resources for crop production. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

Daily monitoring of 30 m crop condition over complex agricultural landscapes

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Technical Reports Server (NTRS)

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

1978-01-01

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

Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument

NASA Technical Reports Server (NTRS)

Tan, Bin; Hu, Jiannan; Huang, Dong; Yang, Wenze; Zhang, Ping; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2005-01-01

The first significant processing of Terra MODIS data, called Collection 3, covered the period from November 2000 to December 2002. The Collection 3 leaf area index (LAI) and fraction vegetation absorbed photosynthetically active radiation (FPAR) products for broadleaf crops exhibited three anomalies (a) high LAI values during the peak growing season, (b) differences in LAI seasonality between the radiative transfer-based main algorithm and the vegetation index based back-up algorithm, and (c) too few retrievals from the main algorithm during the summer period when the crops are at full flush. The cause of these anomalies is a mismatch between reflectances modeled by the algorithm and MODIS measurements. Therefore, the Look-Up-Tables accompanying the algorithm were revised and implemented in Collection 4 processing. The main algorithm with the revised Look-Up-Tables generated retrievals for over 80% of the pixels with valid data. Retrievals from the back-up algorithm, although few, should be used with caution as they are generated from surface reflectances with high uncertainties.

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

NASA Astrophysics Data System (ADS)

Hernandez Díaz-Ambrona, Carlos G.

2016-04-01

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

The production diversity of subsistence farms in the Bolivian Andes is associated with the quality of child feeding practices as measured by a validated summary feeding index.

PubMed

Jones, Andrew D

2015-02-01

To determine the validity of a summary infant and child feeding index (ICFI) and the association with the index of factors related to agricultural production. A cross-sectional survey in eight health-post jurisdictions identified as priority nutrition regions. All households with children aged 6-23 months in eligible communities were administered an integrated survey on agricultural production and nutrition-related practices. Quantitative 24 h dietary recall, food frequency data and anthropometric measurements were collected for each child. Ninety-one per cent of eligible families participated. The northern region of the Potosí department in the Bolivian highlands. Two hundred and fifty-one households with children aged 6-23 months. In multiple regression models controlling for potential confounding variables, infant and young child feeding (IYCF) practices as measured by an ICFI showed positive associations with child length-for-age Z-score (mean difference of 0·47 in length-for-age Z-score between children in the high ICFI tertile compared with the low tertile), child energy intake (mean difference of 1500 kJ between tertiles) and the micronutrient adequacy of child diets (mean difference of 7·2 % in mean micronutrient density adequacy between tertiles; P < 0·05). Examining determinants of IYCF practices, mother's education, livestock ownership and the crop diversity of farms were positively associated with the ICFI, while amount of agricultural land cultivated was negatively associated with the ICFI. Crop diversity and IYCF practices were more strongly positively correlated among households at high elevations. Nutrition-sensitive investments in agriculture that aim to diversify subsistence agricultural production could plausibly benefit the adequacy of child diets.

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.

Global Food Security Index Studies and Satellite Information

NASA Astrophysics Data System (ADS)

Medina, T. A.; Ganti-Agrawal, S.; Joshi, D.; Lakhankar, T.

2017-12-01

Food yield is equal to the total crop harvest per unit cultivated area. During the elapsed time of germination and frequent harvesting, both human and climate related effects determine a country's' contribution towards global food security. Each country across the globe's annual income per capita was collected to then determine nine countries for further studies. For a location to be chosen, its income per capita needed to be considered poor, uprising or wealthy. Both physical land cover and regional climate helped categorize potential parameters thought to be studied. Once selected, Normalized Difference Vegetation Index (NDVI) data was collected for Ethiopia, Liberia, Indonesia, United States, Norway, Russia, Kuwait and Saudi Arabia over the recent 16 years for approximately every 16 days starting from early in the year 2000. Software languages such as Geographic Information System (GIS), MatLab and Excel were used to determine how population size, income and deforestation directly determines agricultural yields. Because of high maintenance requirements for large harvests when forest areas are cleared, they often have a reduction in soil quality, requiring fertilizer use to produce sufficient crop yields. Total area and vegetation index of each country is to be studied, to determine crop and deforestation percentages. To determine how deforestation impacts future income and crop yield predictions of each country studied. By using NDVI results a parameter is to be potentially found that will help define an index, to create an equation that will determine a country's annual income and ability to provide for their families and themselves.

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

NASA Astrophysics Data System (ADS)

Chakrabarty, Abhisek

2016-07-01

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

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

NASA Astrophysics Data System (ADS)

Brocks, S.; Bareth, G.

2016-06-01

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

Spectral considerations for modeling yield of canola

USDA-ARS?s Scientific Manuscript database

Conspicuous yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A ...

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

EPA Science Inventory

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

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

NASA Astrophysics Data System (ADS)

Tarnavsky, E.

2016-12-01

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

The scientific grand challenges of the 21st century for the Crop Science Society of America

USDA-ARS?s Scientific Manuscript database

Crop science is a highly integrative science field employing expertise from multiple disciplines to broaden our understanding of agronomic, turf, and forage crops. A major goal of crop science is to ensure an adequate and sustainable production of food, feed, fuel, and fiber for our world’s growing ...

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

USDA-ARS?s Scientific Manuscript database

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

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Quantitative analysis of agricultural land use change in China

NASA Astrophysics Data System (ADS)

Chou, Jieming; Dong, Wenjie; Wang, Shuyu; Fu, Yuqing

This article reviews the potential impacts of climate change on land use change in China. Crop sown area is used as index to quantitatively analyze the temporal-spatial changes and the utilization of the agricultural land. A new concept is defined as potential multiple cropping index to reflect the potential sowing ability. The impacting mechanism, land use status and its surplus capacity are investigated as well. The main conclusions are as following; During 1949-2010, the agricultural land was the greatest in amount in the middle of China, followed by that in the country's eastern and western regions. The most rapid increase and decrease of agricultural land were observed in Xinjiang and North China respectively, Northwest China and South China is also changed rapid. The variation trend before 1980 differed significantly from that after 1980. Agricultural land was affected by both natural and social factors, such as regional climate and environmental changes, population growth, economic development, and implementation of policies. In this paper, the effects of temperature and urbanization on the coverage of agriculture land are evaluated, and the results show that the urbanization can greatly affects the amount of agriculture land in South China, Northeast China, Xinjiang and Southwest China. From 1980 to 2009, the extent of agricultural land use had increased as the surplus capacity had decreased. Still, large remaining potential space is available, but the future utilization of agricultural land should be carried out with scientific planning and management for the sustainable development.

Impact of cover crops on soil nitrate, crop yield and quality

USDA-ARS?s Scientific Manuscript database

There are multiple benefits of incorporating cover crops into current production systems including decreasing erosion, improving water infiltration, increasing soil organic matter and biological activity but in water limited areas caution should be utilized. A field study was established in the fal...

AgMIP: Next Generation Models and Assessments

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2014-12-01

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

Assessment of the potential health risks associated with the aluminium, arsenic, cadmium and lead content in selected fruits and vegetables grown in Jamaica.

PubMed

Antoine, Johann M R; Fung, Leslie A Hoo; Grant, Charles N

2017-01-01

Thirteen Jamaican-grown food crops - ackee ( Blighia sapida ), banana ( Musa acuminate ), cabbage ( Brassica oleracea ), carrot ( Daucus carota ), cassava ( Manihot esculenta ), coco ( Xanthosoma sagittifolium ), dasheen ( Colocasia esculenta ), Irish potato ( Solanum tuberosum ), pumpkin ( Cucurbita pepo ), sweet pepper ( Capsicum annuum ), sweet potato ( Ipomoea batatas ), tomato ( Solanum lycopersicum ) and turnip ( Brassica rapa ) - were analysed for aluminium, arsenic, cadmium and lead by atomic absorption spectrophotometry and instrumental neutron activation analysis. The fresh weight mean concentrations in these food crops (4.25-93.12 mg/kg for aluminium; 0.001-0.104 mg/kg for arsenic; 0.015-0.420 mg/kg for cadmium; 0.003-0.100 mg/kg for lead) were used to calculate the estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI) and target cancer risk (TCR) for arsenic, associated with dietary exposure to these potentially toxic elements. Each food type had a THQ and HI < 1 indicating no undue non-carcinogenic risk from exposure to a single or multiple potentially toxic elements from the same food. The TCR for arsenic in these foods were all below 1 × 10 -4 , the upper limit used for acceptable cancer risk. There is no significant health risk to the consumer associated with the consumption of these Jamaican-grown food crops.

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).

Assessment of the Impacts of Rice Cropping through a Soil Quality Index

NASA Astrophysics Data System (ADS)

Sione, S. M.; Wilson, M. G.; Paz González, A.

2012-04-01

In Entre Ríos (Argentina), rice cultivation is carried out mainly in Vertisols. Several factors, such as the use of sodium bicarbonate waters for irrigation, the excessive tillage required, and the lack of proper planning for land use, mainly regarding the crop sequence, cause serious impacts on the soil and have an effect on sustainable agriculture. Thus, the development of methodologies to detect these impacts has become a priority. The aim of this study was to standardize soil quality indicators (SQI) and integrate them into an index to evaluate the impacts of the rice production system on soil, at the farm scale. The study was conducted in farms of the traditional rice cultivation area of Entre Ríos province, Argentina. We evaluated a minimum data set consisting of six indicators: structural stability and percolation, total organic matter content (TOM), exchangeable sodium content (ESC), electrical conductivity of saturation extract (ECe) and reaction of the soil (pH). From a database from 75 production lots, we determined the reference values, i.e. limits to ensure the maintenance of long-term productivity and the allowable thresholds for each indicator. The indicators were standardized and integrated into a soil quality index. Five ranges of soil quality were established: very low, low, moderate, high and very high, depending on the values assigned to each SQI. This index allowed differentiating the impact of different crop sequences and showed that the increased participation of rice crop in the rotation resulted in a deterioration of the soil structure due to the decrease in the TOM and to the cumulative increase in ESC caused by the sodium bicarbonate water used for irrigation. Soil management strategies should aim to increase TOM values and to reduce the input of sodium to the exchange complex. A rotation with 50% to 60% of pasture and 40 to 50% of agriculture with a participation of rice lower than 20 to 25% would allow the sustainability of the production system. The use of the so called SQI, i.e. soil quality index, for rice crop production will allow generating early warning of degradation and thus adopting recovery measures.

Development and application of a novel crop stress and quality instrument

NASA Astrophysics Data System (ADS)

Huang, Wengjiang; Sun, Gang; Wang, Jihua; Liu, Liangyun; Zheng, Wengang

2005-12-01

In this paper, a portable diagnostic instrument for crop quality analysis was designed and tested, which can measure the normalized difference vegetation index (PRI) and structure insensitive pigment index (NRI) of crop canopy in the field. The instrument have a valid survey area of 1m×1m when the height between instrument and the ground was fixed to 1.3 meter. The crop quality can be assessed based on their PRI and NRI values, so it will be very important for crop management to get these values. The instrument uses sunlight as its light source. There are six special different photoelectrical detectors within red, blue and near infrared bands, which are used for detecting incidence sunlight and reflex light from the canopy of crop. This optical instrument includes photoelectric detector module, signal process and A/D convert module, the data storing and transmission module and human-machine interface module. The detector is the core of the instrument which measures the spectrums at special bands. The microprocessor calculates the NDVI and SIPI value based on the A/D value. And the value can be displayed on the instrument's LCD, stored in the flash memory of instrument and can also be uploaded to PC through the PC's RS232 serial interface. The prototype was tested in the crop field at different view directions. It reveals the on-site and non-sampling mode of crop growth monitoring by fixed on the agricultural machine traveling in the field. Such simple instruments can diagnose the plant growth status by the acquired spectral response.

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

USGS Publications Warehouse

Funk, Chris; Budde, Michael E.

2009-01-01

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

Evaluation of Learning Group Approaches for Fostering Integrated Cropping Systems Management

ERIC Educational Resources Information Center

Blissett, Hana; Simmons, Steve; Jordan, Nicholas; Nelson, Kristen

2004-01-01

Cropping systems management requires integration of multiple forms of knowledge, practice, and learning by farmers, extension educators, and researchers. We evaluated the outcomes of participation in collaborative learning groups organized to address cropping systems and, specifically, challenges of integrated weed management. Groups were…

Crop Damage by Primates: Quantifying the Key Parameters of Crop-Raiding Events

PubMed Central

Wallace, Graham E.; Hill, Catherine M.

2012-01-01

Human-wildlife conflict often arises from crop-raiding, and insights regarding which aspects of raiding events determine crop loss are essential when developing and evaluating deterrents. However, because accounts of crop-raiding behaviour are frequently indirect, these parameters are rarely quantified or explicitly linked to crop damage. Using systematic observations of the behaviour of non-human primates on farms in western Uganda, this research identifies number of individuals raiding and duration of raid as the primary parameters determining crop loss. Secondary factors include distance travelled onto farm, age composition of the raiding group, and whether raids are in series. Regression models accounted for greater proportions of variation in crop loss when increasingly crop and species specific. Parameter values varied across primate species, probably reflecting differences in raiding tactics or perceptions of risk, and thereby providing indices of how comfortable primates are on-farm. Median raiding-group sizes were markedly smaller than the typical sizes of social groups. The research suggests that key parameters of raiding events can be used to measure the behavioural impacts of deterrents to raiding. Furthermore, farmers will benefit most from methods that discourage raiding by multiple individuals, reduce the size of raiding groups, or decrease the amount of time primates are on-farm. This study demonstrates the importance of directly relating crop loss to the parameters of raiding events, using systematic observations of the behaviour of multiple primate species. PMID:23056378

Assessment of water use and its productivity in the Spanish irrigation district "Río Adaja"

NASA Astrophysics Data System (ADS)

Rodriguez-Sinobas, Leonor; Naroua, Iliassou; Sánchez-Calvo, Raúl

2015-04-01

A study of the assessment of the irrigation water use has been carried out in the Spanish irrigation District "Río Adaja" that has analyzed the water use efficiency and the water productivity indicators for the main crops during the first three years of operation (2010/2011, 2011/2012 and 2012/2013). A soil water balance model was applied taking into account climatic data for the nearby weather station and soil properties. Crop water requirements were calculated by the FAO Penman-Monteith with the application of the dual crop coefficient and by considering the readily available soil water content (RAW) concept. Likewise, productivity was measured by the indexes: annual relative irrigation supply (ARIS), annual relative water supply (ARWS), relative rainfall supply (RRS), the water productivity (WP), the evapotranspiration water productivity (ETWP), and the irrigation water productivity (IWP). The results show that the irrigation district applied deficit irrigation in most crops (ARIS<1), and also improved water productivity. This was higher in 2010/2011 which showed the highest effective precipitation Pe. The IWP (€/m3) index varied among crops with the highest values for onion (4.14), potato (2.79), carrot (1.37) and barley (1.21) for the first year and, onion (1.98), potato (1.69), carrot (1.70) and barley (1.16) in the second year. Thus, these crops would be a proper cropping pattern to maximize the gross income in the irrigation district.

Monitoring Agricultural Cropping Patterns across the Laurentian Great Lakes Basin Using MODIS-NDVI Data

EPA Science Inventory

The Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composite data product (MOD12Q) was used to develop annual cropland and crop-specific map products (corn, soybeans, and wheat) for the Laurentian Great Lakes Basin (GLB). Th...

Deficit irrigation: Arriving at the crop water stress index via gas exchange measurements

USDA-ARS?s Scientific Manuscript database

Plant gas exchange provides a highly sensitive measure of the degree of drought stress. Canopy temperature (Tc) provides a much easier to acquire indication of crop water deficit that has been used in irrigation scheduling systems, but interpretation of this measurement has proven difficult. Our goa...

Using remote sensing and soil physical properties for predicting the spatial distribution of cotton lint yield

USDA-ARS?s Scientific Manuscript database

Timely reflectance data from cotton (Gossypium hirsutum L.) production fields provide a useful tool for crop health assessment and site-specific crop management decisions. This field study investigated the relationships among site-specific normalized difference vegetation index (NDVI), soil physical...

The Growth Periods Responses of Double-season Paddy Rice to Climate Change in Hunan Province, China over the Past Two Decades

NASA Astrophysics Data System (ADS)

Wang, Y.; Li, Y.; Yi, M.; Ye, T.

2015-12-01

The shifts of timing and length of the growing season (TLGS) are important indicators of crop response to climate change. With the help of satellite image data, it becomes feasible to retrieve the TLGS in a spatially continuous manner, which also accommodates local variation of TGSs. In this article, the TGSs of paddy rice in Hunan Province, China since 1995 was retrieved using times-series curves of MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI). The change in TLGS and its connection to regional climate change was discussed. The results showed the advance of TGSs of double-season paddy rice and the reduction of GSL in the past 20 years, which is believed to be linked to the rise in the temperature and precipitation in the growth periods. Understanding the local variation and trend of TLGS influenced by climate change is essential for making agricultural adaptive policies to reduce the risk of crop damaged, also can provide key information for studying how multi-hazards affect crop exposure.

TEMPO Specific Photochemical Reflectance Index for Monitoring Crop Productivity

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Preliminary validation of leaf area index sensor in Huailai

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

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.

Vulnerability of field crops to midcentury temperature changes and yield effects in the Southwestern USA

USDA-ARS?s Scientific Manuscript database

Increased temperatures in the Southwestern United States will impact future crop production via multiple pathways. We used four methods to provide an illustrative analysis of midcentury temperature impacts to eight field crops. By midcentury, cropland area thermally suitable for maize cultivation is...

Detecting and correcting logically inconsistent crop rotations and other land-use sequences

USDA-ARS?s Scientific Manuscript database

Multi-year landuse data of adequate duration and quality has the potential to identify crop rotation history on individual fields. In the diverse landscape of western Oregon where many crops are established perennials whose stands can remain in production for multiple years, our interests included m...

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.

Land use policy and agricultural water management of the previous half of century in Africa

NASA Astrophysics Data System (ADS)

Valipour, Mohammad

2015-12-01

This paper examines land use policy and agricultural water management in Africa from 1962 to 2011. For this purpose, data were gathered from Food and Agriculture Organization of the United Nations (FAO) and the World Bank Group. Using the FAO database, ten indices were selected: permanent crops to cultivated area (%), rural population to total population (%), total economically active population in agriculture to total economically active population (%), human development index, national rainfall index (mm/year), value added to gross domestic product by agriculture (%), irrigation water requirement (mm/year), percentage of total cultivated area drained (%), difference between national rainfall index and irrigation water requirement (mm/year), area equipped for irrigation to cultivated area or land use policy index (%). These indices were analyzed for all 53 countries in the study area and the land use policy index was estimated by two different formulas. The results show that value of relative error is <20 %. In addition, an average index was calculated using various methods to assess countries' conditions for agricultural water management. Ability of irrigation and drainage systems was studied using other eight indices with more limited information. These indices are surface irrigation (%), sprinkler irrigation (%), localized irrigation (%), spate irrigation (%), agricultural water withdrawal (10 km3/year), conservation agriculture area as percentage of cultivated area (%), percentage of area equipped for irrigation salinized (%), and area waterlogged by irrigation (%). Finally, tendency of farmers to use irrigation systems for cultivated crops has been presented. The results show that Africa needs governments' policy to encourage farmers to use irrigation systems and raise cropping intensity for irrigated area.

Remote sensing of water and nitrogen stress in broccoli

NASA Astrophysics Data System (ADS)

Elsheikha, Diael-Deen Mohamed

Remote sensing is being used in agriculture for crop management. Ground based remote sensing data acquisition system was used for collection of high spatial and temporal resolution data for irrigated broccoli crop. The system was composed of a small cart that ran back and forth on a rail system that was mounted on a linear move irrigation system. The cart was equipped with a sensor that had 4 discrete wavelengths; 550 nm, 660 nm, 720 nm, and 810 nm, and an infrared thermometer, all had 10 nm bandwidth. A global positioning system was used to indicate the cart position. The study consisted of two parts; the first was to evaluate remotely sensed reflectance and indices in broccoli during the growing season, and determine whether remotely sensed indices or standard deviation of indices can distinguish between nitrogen and water stress in broccoli, and the second part of the study was to evaluate remotely sensed indices and standard deviation of remotely sensed indices in broccoli during daily changes in solar zenith angle. Results indicated that nitrogen was detected using Ratio Vegetation index, RVI, Normalized Difference Vegetation Index, NDVI, Canopy Chlorophyll Concentration Index, CCCI, and also using the reflectance in the Near-Infrared, NIR, bands. The Red reflectance band capability of showing stress was not as clear as the previous indices and bands reflectance. The Canopy Chlorophyll Concentration Index, CCCI, was the most successful index. The Crop Water Stress Index was able to detect water stress but it was highly affected by the solar zenith angle change along the day.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model.

PubMed

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-04-06

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R²) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R² of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods.

Establishment trial of an oak-pine/soybean-corn-wheat alley-cropping system in the upper coastal plain of North Carolina

Treesearch

H.D. Stevenson; D.J. Robison; F.W. Cubbage; J.P. Mueller; M.G. Burton; M.H. Gocke

2013-01-01

Alley cropping may prove useful in the Southeast United States, providing multiple products and income streams, as well as affording sustainable land use alternatives to conventional farming. An alley-cropping system may be a good alternative in agriculture because of the benefits provided by trees to crops and soils, as well as the income generated from wood products...

Control of Xiphinema index populations by fallow plants under greenhouse and field conditions.

PubMed

Villate, Laure; Morin, Elisa; Demangeat, Gérard; Van Helden, Maarten; Esmenjaud, Daniel

2012-06-01

The dagger nematode Xiphinema index has a high economic impact in vineyards by direct pathogenicity and above all by transmitting the Grapevine fanleaf virus (GFLV). Agrochemicals have been largely employed to restrict the spread of GFLV by reducing X. index populations but are now banned. As an alternative to nematicides, the use of fallow plants between two successive vine crops was assessed. We selected plant species adapted to vineyard soils and exhibiting negative impact on nematodes and we evaluated their antagonistic effect on X. index in greenhouse using artificially infested soil, and in naturally infested vineyard conditions. The screening was conducted with plants belonging to the families Asteraceae (sunflower, marigold, zinnia, and nyjer), Poaceae (sorghum and rye), Fabaceae (white lupin, white melilot, hairy vetch, and alfalfa), Brassicaceae (rapeseed and camelina), and Boraginaceae (phacelia). In the greenhouse controlled assay, white lupin, nyjer, and marigold significantly reduced X. index populations compared with that of bare soil. The vineyard assay, designed to take into account the aggregative pattern of X. index distribution, revealed that marigold and hairy vetch are good candidates as cover crops to reduce X. index populations in vineyard. Moreover, this original experimental design could be applied to manage other soilborne pathogens.

Increasing crop diversity mitigates weather variations and improves yield stability.

PubMed

Gaudin, Amélie C M; Tolhurst, Tor N; Ker, Alan P; Janovicek, Ken; Tortora, Cristina; Martin, Ralph C; Deen, William

2015-01-01

Cropping sequence diversification provides a systems approach to reduce yield variations and improve resilience to multiple environmental stresses. Yield advantages of more diverse crop rotations and their synergistic effects with reduced tillage are well documented, but few studies have quantified the impact of these management practices on yields and their stability when soil moisture is limiting or in excess. Using yield and weather data obtained from a 31-year long term rotation and tillage trial in Ontario, we tested whether crop rotation diversity is associated with greater yield stability when abnormal weather conditions occur. We used parametric and non-parametric approaches to quantify the impact of rotation diversity (monocrop, 2-crops, 3-crops without or with one or two legume cover crops) and tillage (conventional or reduced tillage) on yield probabilities and the benefits of crop diversity under different soil moisture and temperature scenarios. Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations. Introducing small grains into short corn-soybean rotation was enough to provide substantial benefits on long-term soybean yields and their stability while the effects on corn were mostly associated with the temporal niche provided by small grains for underseeded red clover or alfalfa. Crop diversification strategies increased the probability of harnessing favorable growing conditions while decreasing the risk of crop failure. In hot and dry years, diversification of corn-soybean rotations and reduced tillage increased yield by 7% and 22% for corn and soybean respectively. Given the additional advantages associated with cropping system diversification, such a strategy provides a more comprehensive approach to lowering yield variability and improving the resilience of cropping systems to multiple environmental stresses. This could help to sustain future yield levels in challenging production environments.

Exploring the limits of crop productivity: A model to evaluate progress

NASA Technical Reports Server (NTRS)

Bugbee, Bruce

1990-01-01

The goal was to determine the limits of crop productivity when all environmental constraints were removed. Researchers define productivity as food output per unit of input. Researchers evaluated cultivars of wheat with reduced leaf size and number to decrease the leaf area index at high plant densities. These cultivars may also have an improved harvest index. Hydroponic studies indicate that 1 mM nitrate in solution is adequate to support maximum growth in these systems, provided iron nutrition is adequate. Wheat does not accumulate nitrate in leaves even when the solution nitrate concentration is 15 mM. Long-term photosynthetic efficiency (g mol (exp -1) of photons) and harvest index were not altered by photoperiod (16, 20, or 24 hours). Wheat does not need, nor benefit from, a diurnal dark period.

Comparing LAI estimates of corn and soybean from vegetation indices of multi-resolution satellite images

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal ...

Leaf temperature of maize and crop water stress index with variable irrigation and nitrogen supply

USDA-ARS?s Scientific Manuscript database

Water scarcity due to changing climate, population growth, and economic development is a major threat to the sustainability of irrigated agriculture in the Western United States and other regions around the world. Water stress indices based on crop canopy temperature can be useful for assessing plan...

Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

NASA Astrophysics Data System (ADS)

Chen, Ming; Griffis, Tim J.; Baker, John; Wood, Jeffrey D.; Xiao, Ke

2015-02-01

A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water, and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to Earth system models is relatively rare. Here we evaluated two such models (CLM4-Crop and CLM3.5-CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn-soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4-Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM3.5-CornSoy-simulated leaf area index (LAI), energy, and carbon fluxes showed stronger correlations with observations compared to CLM4-Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. This was primarily caused by the positive LAI bias, which led to a positive net long-wave radiation bias. CLM4-Crop underestimated soil water content during midgrowing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the mechanisms that drive early growing season phenology and soil water dynamics is needed to better represent crops including their net radiation balance, energy partitioning, and carbon cycle processes.

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.

Chemistry and microbial functional diversity differences in biofuel crop and grassland soils in multiple geographies

USDA-ARS?s Scientific Manuscript database

As crop and non-crop lands are increasingly becoming converted to biofuel feedstock production, it is of interest to identify potential impacts of annual and perennial feedstocks on soil ecosystem services. Soil samples obtained from 6 regional sets of switchgrass (Panicum virgatum L.) and 3 regiona...

The 4Rs for cover crops and other advances in cover crop management for environmental quality

USDA-ARS?s Scientific Manuscript database

Cover crops (CC) are universal tools that can be used to improve management practices to draw multiple benefits with increased sustainability across different continents (Dabney et al. 2001; Reeves 1994; Woodruff and Siddoway 1965; Frye et al. 1985; Holderbaum et al. 1990; Bilbro 1991; Langdale et a...

Integrating winter camelina into maize and soybean cropping systems

USDA-ARS?s Scientific Manuscript database

Camelina [Camelina sativa (L.) Crantz.] is an industrial oilseed crop in the Brassicaceae family with multiple uses. Currently, camelina is not used as a cover crop, but it has the potential to be used as such in maize (Zea mays L.)-soybean [Glycine max (L.) Merr.] systems. The objectives of this st...

Changes of crop rotation in Iowa determined from the USDA-NASS cropland data layer product

USDA-ARS?s Scientific Manuscript database

Crop rotation is one of the important decisions made independently by numerous farm managers, and is a critical variable in models of crop growth and soil carbon. By combining multiple years (2001-2009) of the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL), it is pos...

An Index of Competition Based on Relative Crown Position and Size

Treesearch

Dwight D. O' Neal; Allan E. Houston; Edward R. Buckner; James S. Meadows

1995-01-01

A new competition index, the Crown Position Index (CPI) was evaluated using a 41-year-old, well stocked, upland hardwood stand in southwestern Tennessee. CPI wss based on relative crown position and crown size as expressed by crown projections and relative heights of crop trees and their competitors. Comparisons were made among CPI, the Hegyl (1974)...

Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery

NASA Astrophysics Data System (ADS)

Moharana, Shreedevi; Dutta, Subashisa

2016-12-01

Chlorophyll and nitrogen are the most essential parameters for paddy crop growth. Spectroradiometric measurements were collected at canopy level during critical growth period of rice. Chemical analysis was performed to quantify the total leaf content. By exploiting the ground based measurements, regression models were established for chlorophyll and nitrogen aimed indices with their corresponding crop growth variables. Vegetation index models were developed for mapping these parameters from Hyperion imagery in an agriculture system. It was inferred that the present Simple Ratio (SR) and Leaf Nitrogen Concentration (LNC) indices, which followed a linear and nonlinear relationship respectively, were completely different from published Tian et al. (2011). The nitrogen content varied widely from 1 to 4% and only 2 to 3% for paddy crop using present modified index models and Tian et al. (2011) respectively. The modified LNC index model performed better than the established Tian et al. (2011) model as far as estimated nitrogen content from Hyperion imagery was concerned. Furthermore, within the observed chlorophyll range obtained from the studied rice varieties grown in the rice agriculture system, the index models (LNC, OASVI, Gitelson, mSR and MTCI) performed well in the spatial distribution of rice chlorophyll content from Hyperion imagery. Spatial distribution of total chlorophyll content varied widely from 1.77 to 5.81 mg/g (LNC), 3.0 to 13 mg/g (OASVI), 0.5 to 10.43 mg/g (Gitelson), 2.18 to 10.61 mg/g (mSR) and 2.90 to 5.40 mg/g (MTCI). The spatial information of these parameters will help in proper nutrient management, yield forecasting, and will serve as inputs for crop growth and forecasting models for a precision rice agriculture system.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

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.

Using Nonmetric Multidimensional Scaling to Analyze Bee Visitation in East Tennessee Crops as an Indicator of Pollination Services Provided by Honey Bees (Apis mellifera L.) and Native Bees.

PubMed

Wilson, Michael E; Skinner, John A; Wszelaki, Annette L; Drummond, Frank

2016-04-01

This study investigated bee visitation on 10 agricultural crops grown on diverse small farms in Tennessee to determine the abundance of native bees and honey bees and the partitioning of visitation among crops. Summaries for each crop are used to generate mean proportions of bee visitation by categories of bees. This shows that native bee visits often occur as frequently, or in greater proportions than non-native honey bee visits. Visitation across multiple crops is then analyzed together with nonmetric multidimensional scaling to show how communities of bees that provide crop pollination change depending on the crop. Within squash and pumpkin plantings, continuous and discrete factors, such as "time of day" and "organic practices," further explain shifts in the community composition of flower visitors. Results from this study show that native bees frequently visit flowers on various crops, indicating that they are likely contributing to pollination services in addition to honey bees. Furthermore, the community of bees visiting flowers changes based on crop type, phenology, and spatial-temporal factors. Results suggest that developing pollinator conservation for farms that grow a wide variety of crops will likely require multiple conservation strategies. Farms that concentrate on a single crop may be able to tailor conservation practices toward the most important bees in their system and geographic locale. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

PubMed Central

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

2015-01-01

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

Nitrogen variability: a constant issue in the field

NASA Astrophysics Data System (ADS)

Tarquis, Ana M.; Castellanos Serrano, Maria Teresa; Cartagena, Maria Carmen; Ribas, Francisco; José Cabello, Maria; Arce, Augusto; Bird, Nigel R.

2015-04-01

In this study we use the relative entropy (E(δ)) to investigate residual effects on wheat and grain, biomass and nitrogen content, of fertigation treatments applied to a previous crop. The wheat crop covered nine subplots from a previous experiment on melon response to fertigation. Each subplot had previously received a different level of applied nitrogen and plants from the previous melon crop had already taken up the applied nitrogen. Many factors affect these variables, causing it to vary at different scales creating a non uniform distribution. E(δ), and their increments between scales, were used to identify the scale at which the variable had a maximum structure and compare with the scaling behavior of the nitrogen applied. The E(δ)is particularly appropriate for this because of does not require any prior assumptions to the structure of the data and it is easy to calculate. References Castellanos, M. T., Cartagena, M. C., Arce, A., Ribas, F., Cabello, M. J., and Tarquis, A. M. 2010. Ef?ciency Indexes for melon crop optimization, Agron. J., 102, 716-722. Lark, R.M., A.E. Milne, T.M. Addiscott, K.W.T. Goulding, C.P. Webster, and S. O'Flaherty. 2004. Scale- and location-dependent correlation of nitrous oxide emissions with soil properties: An analysis using wavelets. Eur. J. Soil Sci. 55:611-627. Milne, A.E., Castellanos, M. T., Cartagena, M. C., Tarquis, A. M. and Lark, R. M. 2010. Investigating the effect of previous treatments on wheat biomass over multiple spatial frequencies. Biogeosciences, 7: 2739-2747. Tarquis, A.M., N.R. Bird, A.P. Whitmore, M.C. Cartagena, and Y. Pachepsky. 2008. Multiscale analysis of soil transect data. Vadose Zone J. 7: 563-569.

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.

Reflectance of vegetation, soil, and water. [effects of measurable plant parameters on multispectral signal variations

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Reflectance of crop residues, that are important in reducing wind and water erosion, was more often different from bare soil in band 4 than in bands 5, 6, or 7. The plant parameters leaf area index, plant population, plant cover, and plant height explained 95.9 percent of the variation in band 7 (reflective infrared) digital counts for cotton and 78.2 percent of the variation in digital counts for the combined crops sorghum and corn; hence, measurable plant parameters explain most of the signal variation recorded for corpland. Leaf area index and plant population are both highly correlated with crop yields; since plant population can be readily measured (or possibly inferred from seeding rates), it is useful measurement for calibrating ERTS-type MSS digital data in terms of yield.

Reconsidering Tree Fruit as Candidate Crops Through the Use of Rapid Cycle Crop Breeding Technologies

NASA Technical Reports Server (NTRS)

Graham, Gary Thomas

2014-01-01

Tree fruit, although desirable from a crew nutrition and menu diversity perspective, have long been dismissed as candidate crops based on their long juvenile phase, large architecture, low short-term harvest index, and dormancy requirements. Recent developments in Rapid Cycle Crop Breeding (RCCB) have overcome these historical limitations, opening the door to a new era in candidate crop research. Researchers at the United States Department of Agriculture (USDA) have developed FT-construct (Flowering Locus T) dwarf plum lines that have a very short juvenile phase, vine-like architecture, and no obligate dormancy period. In a collaborative research effort, NASA and the USDA are evaluating the performance of these FT-lines under controlled environment conditions relevant to spaceflight.

Overview and highlights of Early Warning and Crop Condition Assessment project

NASA Technical Reports Server (NTRS)

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

1985-01-01

Work of the Early Warning and Crop Condition Assessment (EW/CCA) project, one of eight projects in the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS), is reviewed. Its mission, to develop and test remote sensing techniques that enhance operational methodologies for crop condition assessment, was in response to initiatives issued by the Secretary of Agriculture. Meteorologically driven crop stress indicator models have been developed or modified for wheat, maize, grain sorghum, and soybeans. These models provide early warning alerts of potential or actual crop stresses due to water deficits, adverse temperatures, and water excess that could delay planting or harvesting operations. Recommendations are given for future research involving vegetative index numbers and the NOAA and Landsat satellites.

Decision Support Systems To Manage Water Resources At Irrigation District Level In Southern Italy Using Remote Sensing Information. An Integrated Project (AQUATER)

NASA Astrophysics Data System (ADS)

Rinaldi, M.; Castrignanò, A.; Mastrorilli, M.; Rana, G.; Ventrella, D.; Acutis, M.; D'Urso, G.; Mattia, F.

2006-08-01

An efficient management of water resources is crucial point for Italy and in particular for southern areas characterized by Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. A three-year Project (2005-2008) has been funded by the Italian Ministry of Agriculture and Forestry Policies; it involves four Italian research institutions: the Agricultural Research Council (ISA, Bari), the National Research Council (ISSIA, Bari) and two Universities (Federico II-Naples and Milan). It is focused on the remote sensing, the plant and the climate and, for interdisciplinary relationships, the project working group consists of agronomists, engineers and physicists. The aims of the Project are: a) to produce a Decision Support System (DSS) combining remote sensing information, spatial data and simulation models to manage water resources in irrigation districts; b) to simulate irrigation scenarios to evaluate the effects of water stress on crop yield using agro-ecological indicators; c) to identify the most sensitive areas to drought risk in Southern Italy. The tools used in this Project will be: 1. Remote sensing images, topographic maps, soil and land use maps; 2. Geographic Information Systems; 3. Geostatistic methodologies; 4. Ground truth measurements (land use, canopy and soil temperatures, soil and plant water status, Normalized Difference Vegetation Index, Crop Water Stress Index, Leaf Area Index, actual evapotranspiration, crop coefficients, crop yield, agro-ecological indicators); 5. Crop simulation models. The Project is structured in four work packages with specific objectives, high degree of interaction and information exchange: 1) Remote Sensing and Image Analysis; 2) Cropping Systems; 3) Modelling and Softwares Development; 4) Stakeholders. The final product will be a DSS with the purpose of integrating remote sensing images, to estimate crop and soil variables related to drought, to assimilate these variables into a simulation model at district scale and, finally, to estimate evapotranspiration, plant water status and drought indicators. A project Web home page, a technical course about DSS for the employers of irrigation authorities and dissemination of results (meetings, publications, reports), are also planned.

Roots of symptom-free leguminous cover crop and living mulch species harbor diverse Fusarium communities that show highly variable aggressiveness on pea (Pisum sativum)

PubMed Central

Baćanović-Šišić, Jelena; Karlovsky, Petr; Wittwer, Raphaël; Walder, Florian; Campiglia, Enio; Radicetti, Emanuele; Friberg, Hanna; Baresel, Jörg Peter; Finckh, Maria R.

2018-01-01

Leguminous cover crop and living mulch species show not only great potential for providing multiple beneficial services to agro-ecosystems, but may also present pathological risks for other crops in rotations through shared pathogens, especially those of the genus Fusarium. Disease severity on roots of subterranean clover, white clover, winter and summer vetch grown as cover crop and living mulch species across five European sites as well as the frequency, distribution and aggressiveness to pea of Fusarium spp. recovered from the roots were assessed in 2013 and 2014. Disease symptoms were very low at all sites. Nevertheless, out of 1480 asymptomatic roots, 670 isolates of 14 Fusarium spp. were recovered. The most frequently isolated species in both years from all hosts were F. oxysporum and F. avenaceum accounting for 69% of total isolation percentage. They were common at the Swiss, Italian and German sites, whereas at the Swedish site F. oxysporum dominated and F. avenaceum occurred only rarely. The agressiveness and effect on pea biomass were tested in greenhouse assays for 72 isolates of six Fusarium species. Isolates of F. avenaceum caused severe root rot symptoms with mean severity index (DI) of 82 and 74% mean biomass reduction compared to the non-inoculated control. Fusarium oxysporum and F. solani isolates were higly variable in agressiveness and their impact on pea biomass. DI varied between 15 and 50 and biomass changes relative to the non-inoculated control -40% to +10%. Isolates of F. tricinctum, F. acuminatum and F. equiseti were non to weakly agressive often enhancing pea biomass. This study shows that some of the major pea pathogens are characterized by high ecological plasticity and have the ability to endophytically colonize the hosts studied that thus may serve as inoculum reservoir for susceptible main legume grain crops such as pea. PMID:29444142

Comparative assessment of smallholder sustainability using an agricultural sustainability framework and a yield based index insurance: A case study

NASA Astrophysics Data System (ADS)

Moshtaghi, Mehrdad; Adla, Soham; Pande, Saket; Disse, Markus; Savenije, Hubert

2017-04-01

The concept of sustainability is central to smallholder agriculture as subsistence farming is constantly impacted by livelihood insecurity and is constrained by access to capital, water technology and alternative employment opportunities. This study compares two approaches which aim at quantifying smallholder sustainability but differ in their underlying principles, methodologies for assessment and reporting, and applications. The yield index based insurance can protect the smallholder agriculture and help it to more economic sustainability because the income of smallholder depends on selling crops and this insurance scheme is based on crop yields. In this research, the trigger of this insurance sets on the basis of yields in previous years. The crop yields are calculated every year through socio-hydrology modeling and smallholder can get indemnity when crop yields are lower than average of previous five years (a crop failure). The FAO Sustainability Assessment of Food and Agriculture (SAFA) is an inclusive and comprehensive framework for sustainability assessment in the food and agricultural sector. It follows the UN definition of the 4 dimensions of sustainability (good governance, environmental integrity, economic resilience and social well-being) and includes 21 themes and 58 sub-themes with a multi-indicator approach. The direct sustainability corresponding to the FAO SAFA economic resilience dimension is compared with the indirect notion of sustainability derived from the yield based index insurance. A semi-synthetic comparison is conducted to understand the differences in the underlying principles, methodologies and application of the two approaches. Both approaches are applied to data from smallholder regions of Marathwada in Maharashtra (India) which experienced a severe rise in farmer suicides in the 2000s which has been attributed to a combination of socio-hydrological factors.

Estimating riparian and agricultural evapotranspiration by reference crop evapotranspiration and MODIS Enhanced Vegetation Index

USGS Publications Warehouse

Nagler, Pamela L.; Glenn, Edward P.; Nguyen, Uyen; Scott, Russell; Doody, Tania

2013-01-01

Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

The influence of gender and product design on farmers' preferences for weather-indexed crop insurance.

PubMed

Akter, Sonia; Krupnik, Timothy J; Rossi, Frederick; Khanam, Fahmida

2016-05-01

Theoretically, weather-index insurance is an effective risk reduction option for small-scale farmers in low income countries. Renewed policy and donor emphasis on bridging gender gaps in development also emphasizes the potential social safety net benefits that weather-index insurance could bring to women farmers who are disproportionately vulnerable to climate change risk and have low adaptive capacity. To date, no quantitative studies have experimentally explored weather-index insurance preferences through a gender lens, and little information exists regarding gender-specific preferences for (and constraints to) smallholder investment in agricultural weather-index insurance. This study responds to this gap, and advances the understanding of preference heterogeneity for weather-index insurance by analysing data collected from 433 male and female farmers living on a climate change vulnerable coastal island in Bangladesh, where an increasing number of farmers are adopting maize as a potentially remunerative, but high-risk cash crop. We implemented a choice experiment designed to investigate farmers' valuations for, and trade-offs among, the key attributes of a hypothetical maize crop weather-index insurance program that offered different options for bundling insurance with financial saving mechanisms. Our results reveal significant insurance aversion among female farmers, irrespective of the attributes of the insurance scheme. Heterogeneity in insurance choices could however not be explained by differences in men's and women's risk and time preferences, or agency in making agriculturally related decisions. Rather, gendered differences in farmers' level of trust in insurance institutions and financial literacy were the key factors driving the heterogeneous preferences observed between men and women. Efforts to fulfill gender equity mandates in climate-smart agricultural development programs that rely on weather-index insurance as a risk-abatement tool are therefore likely to require a strengthening of institutional credibility, while coupling such interventions with financial literacy programs for female farmers.

The influence of gender and product design on farmers’ preferences for weather-indexed crop insurance

PubMed Central

Akter, Sonia; Krupnik, Timothy J.; Rossi, Frederick; Khanam, Fahmida

2016-01-01

Theoretically, weather-index insurance is an effective risk reduction option for small-scale farmers in low income countries. Renewed policy and donor emphasis on bridging gender gaps in development also emphasizes the potential social safety net benefits that weather-index insurance could bring to women farmers who are disproportionately vulnerable to climate change risk and have low adaptive capacity. To date, no quantitative studies have experimentally explored weather-index insurance preferences through a gender lens, and little information exists regarding gender-specific preferences for (and constraints to) smallholder investment in agricultural weather-index insurance. This study responds to this gap, and advances the understanding of preference heterogeneity for weather-index insurance by analysing data collected from 433 male and female farmers living on a climate change vulnerable coastal island in Bangladesh, where an increasing number of farmers are adopting maize as a potentially remunerative, but high-risk cash crop. We implemented a choice experiment designed to investigate farmers’ valuations for, and trade-offs among, the key attributes of a hypothetical maize crop weather-index insurance program that offered different options for bundling insurance with financial saving mechanisms. Our results reveal significant insurance aversion among female farmers, irrespective of the attributes of the insurance scheme. Heterogeneity in insurance choices could however not be explained by differences in men’s and women’s risk and time preferences, or agency in making agriculturally related decisions. Rather, gendered differences in farmers’ level of trust in insurance institutions and financial literacy were the key factors driving the heterogeneous preferences observed between men and women. Efforts to fulfill gender equity mandates in climate-smart agricultural development programs that rely on weather-index insurance as a risk-abatement tool are therefore likely to require a strengthening of institutional credibility, while coupling such interventions with financial literacy programs for female farmers. PMID:27212804

Allometric method to estimate leaf area index for row crops

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is critical for predicting plant metabolism, biomass production, evapotranspiration, and greenhouse gas sequestration, but direct LAI measurements are difficult and labor intensive. Several methods are available to measure LAI indirectly or calculate LAI using allometric method...

Modelling crop yield in Iberia under drought conditions

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

NASA Astrophysics Data System (ADS)

Johnson, David M.

2016-10-01

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008-2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

Analysis of MODIS 250 m Time Series Product for LULC Classification and Retrieval of Crop Biophysical Parameter

NASA Astrophysics Data System (ADS)

Verma, A. K.; Garg, P. K.; Prasad, K. S. H.; Dadhwal, V. K.

2016-12-01

Agriculture is a backbone of Indian economy, providing livelihood to about 70% of the population. The primary objective of this research is to investigate the general applicability of time-series MODIS 250m Normalized difference vegetation index (NDVI) and Enhanced vegetation index (EVI) data for various Land use/Land cover (LULC) classification. The other objective is the retrieval of crop biophysical parameter using MODIS 250m resolution data. The Uttar Pradesh state of India is selected for this research work. A field study of 38 farms was conducted during entire crop season of the year 2015 to evaluate the applicability of MODIS 8-day, 250m resolution composite images for assessment of crop condition. The spectroradiometer is used for ground reflectance and the AccuPAR LP-80 Ceptometer is used to measure the agricultural crops Leaf Area Index (LAI). The AccuPAR measures Photosynthetically Active Radiation (PAR) and can invert these readings to give LAI for plant canopy. Ground-based canopy reflectance and LAI were used to calibrate a radiative transfer model to create look-up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS-derived LAI was used to find crop parameter by adjusting the LAI simulated from climate-based crop yield model. Cloud free MODIS images of 250m resolution (16 day composite period) were downloaded using LP-DAAC website over a period of 12 months (Jan to Dec 2015). MODIS both the VI products were found to have sufficient spectral, spatial and temporal resolution to detect unique signatures for each class (water, fallow land, urban, dense vegetation, orchard, sugarcane and other crops). Ground truth data were collected using JUNO GPS. Multi-temporal VI signatures for vegetation classes were consistent with its general phenological characteristic and were spectrally separable at some point during the growing season. The MODIS NDVI and EVI multi-temporal images tracked similar seasonal responses for all croplands and were highly correlated across the growing season. The confusion matrix method is used for accuracy assessment and reference data which has been taken during the field visit. Total 520 pixels have been selected for various classes to determine the accuracy. The classification accuracy and kappa coefficient is found to be 79.76% and 0.78 respectively.

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

Paradigm shift of contamination risk of six heavy metals in tea (Camellia sinensis L.) growing soil: A new approach influenced by inorganic and organic amendments.

PubMed

Karak, Tanmoy; Bora, Krishnamoni; Paul, Ranjit Kumar; Das, Sampa; Khare, Puja; Dutta, Amrit Kumar; Boruah, Romesh Kumar

2017-09-15

The present study provides several contamination and ecological risk indices for selected metals (Cd, Cr, Cu, Mn, Ni and Zn) in tea (Camellia sinensis L.; cv. S.3A/3) growing soil influenced by lower to higher doses of inorganic and organic amendments. While ecological risk indices were applied, it was observed that same treatment showed different risk levels but contamination risk status did not vary significantly. All the indices showed significant correlation with heavy metals' concentration in young shoots of tea plants. As the indices characterized experimental soils with different extents of contamination, it would be important to standardize the indices with long term experiments followed by generation of new index. Therefore, we formulated a new contamination index named as Tea Research Association Heavy Metal Contamination Index (TRAHMCI) for tea growing soils. TRAHMCI is based on the probable change of metal status in soil with progress of growth of tea plant. This could be useful to negate discrepancies arised from use of various existing metal contamination indices in tea growing soils amended with different doses of fertilizers. TRAHMCI was formulated based on individual contamination factor using statistical technique and applied to the present dataset which provided a more holistic understanding of overall tea growing soil behavior. The limitation of the developed TRAHMCI index is that, the index had not been validated for other crops in our study not to claim its effective use for crops other than tea. As already mentioned, this new index had been formulated by taking tea as the test crop with above mentioned six heavy metal contents in young shoot and made tea. Copyright © 2017 Elsevier B.V. All rights reserved.

The impact of climate change on hailstorms in southeastern Australia

NASA Astrophysics Data System (ADS)

Niall, Stephanie; Walsh, Kevin

2005-11-01

Data from a number of locations around southeastern Australia were analysed to determine the influence of climate change on the frequency and intensity of hail events in this region. The relationship between Convective Available Potential Energy (CAPE), frequently used as a measure of atmospheric instability, and hailstorms was investigated using both NCEP/NCAR reanalysis data (a data set comprising a blend of observations and model simulations) and also direct sounding data obtained from the Australian National Climate Centre. Two locations were chosen in southeastern Australia, Mount Gambier and Melbourne, over the months August to October for the period 1980-2001. A statistically significant relationship between hail incidence and CAPE values was established for both NCEP/NCAR and sounding data at both study sites. A stronger relationship was found between hail incidence and the CAPE, which was calculated using NCEP/NCAR data, than that between hail and the CAPE from the actual sounding data. A similar analysis was also conducted at both sites using the totals-totals index (TT index), which is an alternative measure of atmospheric instability.The CSIRO Mk3 Climate System Model was used to simulate values of CAPE for Mount Gambier in an environment containing double the pre-industrial concentrations of equivalent CO2. The results showed a significant decrease in CAPE values in the future. From this, assuming the relationship between CAPE and hail remains unchanged under enhanced greenhouse conditions, it is possible that there will be a decrease in the frequency of hail in southeastern Australia if current rates of CO2 emission are sustained. The severity of future hail events was investigated using crop-loss data from insurance companies. Strongest correlations were found between the crop-loss ratio (value of crop lost to hail damage over the total insured value of crop) and the number of days in a crop season with a TT index greater than 55. Results from the CSIRO Mk3 Climate System Model revealed that there was no significant difference between the number of days with a TT index over 55 for the simulation using current CO2 levels and that based on doubled equivalent pre-industrial CO2 concentrations (roughly equivalent to 2050 in the chosen emissions scenario). This implies that, for southeastern Australia, crop losses due to hail damage would not significantly increase under enhanced greenhouse conditions. Copyright

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

Treesearch

Chunwei Liu; Ge Sun; Steve McNulty; Asko Noormets; Yuan Fang

2017-01-01

The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (Kc) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, Kc has been widely used as a parameter to estimate crop water demand by water managers but has...

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

USDA-ARS?s Scientific Manuscript database

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

Crown release increases growth of crop trees

Treesearch

Neil I. Lamson; H. Clay Smith; Arlyn W. Perkey; Samuel M. Brock; Samuel M. Brock

1990-01-01

Two Appalachian hardwood stands in north-central West Virginia were thinned. The principal species were red oak, yellow-poplar, and chestnut oak. For both stands the site index for northern red oak averaged 75 feet. An areawide thinning using "basal-area control" was applied to a 54-yearold stand while specific crop trees were selected and released in a 12-...

Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

NASA Astrophysics Data System (ADS)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Probabilistic Description of the Hydrologic Risk in Agriculture

NASA Astrophysics Data System (ADS)

Vico, G.; Porporato, A. M.

2011-12-01

Supplemental irrigation represents one of the main strategies to mitigate the effects of climatic variability on agroecosystems productivity and profitability, at the expenses of increasing water requirements for irrigation purposes. Optimizing water allocation for crop yield preservation and sustainable development needs to account for hydro-climatic variability, which is by far the main source of uncertainty affecting crop yields and irrigation water requirements. In this contribution, a widely applicable probabilistic framework is proposed to quantitatively define the hydrologic risk of yield reduction for both rainfed and irrigated agriculture. The occurrence of rainfall events and irrigation applications are linked probabilistically to crop development during the growing season. Based on these linkages, long-term and real-time yield reduction risk indices are defined as a function of climate, soil and crop parameters, as well as irrigation strategy. The former risk index is suitable for long-term irrigation strategy assessment and investment planning, while the latter risk index provides a rigorous probabilistic quantification of the emergence of drought conditions during a single growing season. This probabilistic framework allows also assessing the impact of limited water availability on crop yield, thus guiding the optimal allocation of water resources for human and environmental needs. Our approach employs relatively few parameters and is thus easily and broadly applicable to different crops and sites, under current and future climate scenarios, thus facilitating the assessment of the impact of increasingly frequent water shortages on agricultural productivity, profitability, and sustainability.

Water-food-energy nexus index: analysis of water-energy-food nexus of crop's production system applying the indicators approach

NASA Astrophysics Data System (ADS)

El-Gafy, Inas

2017-10-01

Analysis the water-food-energy nexus is the first step to assess the decision maker in developing and evaluating national strategies that take into account the nexus. The main objective of the current research is providing a method for the decision makers to analysis the water-food-energy nexus of the crop production system at the national level and carrying out a quantitative assessment of it. Through the proposed method, indicators considering the water and energy consumption, mass productivity, and economic productivity were suggested. Based on these indicators a water-food-energy nexus index (WFENI) was performed. The study showed that the calculated WFENI of the Egyptian summer crops have scores that range from 0.21 to 0.79. Comparing to onion (the highest scoring WFENI,i.e., the best score), rice has the lowest WFENI among the summer food crops. Analysis of the water-food-energy nexus of forty-two Egyptian crops in year 2010 was caried out (energy consumed for irrigation represent 7.4% of the total energy footprint). WFENI can be applied to developed strategies for the optimal cropping pattern that minimizing the water and energy consumption and maximizing their productivity. It can be applied as a holistic tool to evaluate the progress in the water and agricultural national strategies. Moreover, WFENI could be applied yearly to evaluate the performance of the water-food-energy nexus managmant.

Adaptability and stability of soybean cultivars for grain yield and seed quality.

PubMed

Silva, K B; Bruzi, A T; Zambiazzi, E V; Soares, I O; Pereira, J L A R; Carvalho, M L M

2017-05-10

This study aimed at verifying the adaptability and stability of soybean cultivars, considering the grain yield and quality of seeds, adopting univariate and multivariate approaches. The experiments were conducted in two crops, three environments, in 2013/2014 and 2014/2015 crop seasons, in the county of Inconfidentes, Lavras, and Patos de Minas, in the Minas Gerais State, Brazil. We evaluated 17 commercial soybean cultivars. For adaptability and stability evaluations, the Graphic and GGE biplot methods were employed. Previously, a selection index was estimated based on the sum of the standardized variables (Z index). The data relative to grain yield, mass of one thousand grain, uniformity test (sieve retention), and germination test were standardized (Z ij ) per cultivar. With the sum of Z ij , we obtained the selection index for the four traits evaluated together. In the Graphic method evaluation, cultivars NA 7200 RR and CD 2737 RR presented the highest values for selection index Z. By the GGE biplot method, we verified that cultivar NA 7200 RR presented greater stability in both univariate evaluations, for grain yield, and for selection index Z.

Operational Retrievals of Evapotranspiration: Are we there yet?

NASA Astrophysics Data System (ADS)

Neale, C. M. U.; Anderson, M. C.; Hain, C.; Schull, M.; Isidro, C., Sr.; Goncalves, I. Z.

2017-12-01

Remote sensing based retrievals of evapotranspiration (ET) have progressed significantly over the last two decades with the improvement of methods and algorithms and the availability of multiple satellite sensors with shortwave and thermal infrared bands on polar orbiting platforms. The modeling approaches include simpler vegetation index (VI) based methods such as the reflectance-based crop coefficient approach coupled with surface reference evapotranspiration estimates to derive actual evapotranspiration of crops or, direct inputs to the Penman-Monteith equation through VI relationships with certain input variables. Methods that are more complex include one-layer or two-layer energy balance approaches that make use of both shortwave and longwave spectral band information to estimate different inputs to the energy balance equation. These models mostly differ in the estimation of sensible heat fluxes. For continental and global scale applications, other satellite-based products such as solar radiation, vegetation leaf area and cover are used as inputs, along with gridded re-analysis weather information. This presentation will review the state-of-the-art in satellite-based evapotranspiration estimation, giving examples of existing efforts to obtain operational ET retrievals over continental and global scales and discussing difficulties and challenges.

Short term effect of conventional tillage and cover crops in physical and chemical properties in two olive orchards of southern Spain

NASA Astrophysics Data System (ADS)

Guzmán, Gema; Giráldez, Juan Vicente; Gómez, José Alfonso

2014-05-01

Numerous studies have attempted to assess the differences in soil properties caused by different management systems in olive cropped farms. Nevertheless the influence of the most frequent management systems on the hydraulic properties of these soils has not been evaluated. Contrarily, there are very few studies that have tried to correlate these results with soil losses due to water erosion. There are complementary approaches to traditional degradation indices, as the S index based on the form of the soil retention curve (Dexter 2004a,b,c). The objectives of this study were (i) to evaluate the methods based on the S index to assess the physical quality of soil in olive orchards, (ii) to assess the short-term changes (2 years) in soil physical and chemical properties in two olive orchards under different managements systems, namely conventional tillage and cover crop, and (iii) to formulate strategies for assessing the quality of soil in olive orchards. For the studied soils, degradation processes (associated to conventional tillage) and the improvement of their properties (linked to cover crops) showed a fast response. Chemical changes were quickly observed. However physical changes are slower than chemical changes for both soils. Water retention curves allowed the evaluation of soil porosity based on depth in the profile and the management practices. The S index was computed for every soil using the conventional soil water retention equations fitted to the experimental data. For the olive cropped soils, higher S index values were obtained in the less degradated areas, in most of the cases. Therefore, the S index could be used as a soil quality indicator although further research should be required to study its evolution at a larger temporal scale. References: Dexter, A. R. 2004. a.- Soil physical quality. PartI. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma 120 (2004) 201-214. Dexter, A. R. 2004. b.- Soil physical quality. Part II. Friability, tillage, tilth and hardsetting. Geoderma 120 (2004) 215-225. Dexter, A. R. 2004. c.- Soil physical quality. Part III: Unsaturated hydraulic conductivity and general conclusions about S-theory. Geoderma 120 (2004) 227-239.

Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study

NASA Astrophysics Data System (ADS)

Mishra, Ashok K.; Ines, Amor V. M.; Das, Narendra N.; Prakash Khedun, C.; Singh, Vijay P.; Sivakumar, Bellie; Hansen, James W.

2015-07-01

Drought is of global concern for society but it originates as a local problem. It has a significant impact on water quantity and quality and influences food, water, and energy security. The consequences of drought vary in space and time, from the local scale (e.g. county level) to regional scale (e.g. state or country level) to global scale. Within the regional scale, there are multiple socio-economic impacts (i.e., agriculture, drinking water supply, and stream health) occurring individually or in combination at local scales, either in clusters or scattered. Even though the application of aggregated drought information at the regional level has been useful in drought management, the latter can be further improved by evaluating the structure and evolution of a drought at the local scale. This study addresses a local-scale agricultural drought anatomy in Story County in Iowa, USA. This complex problem was evaluated using assimilated AMSR-E soil moisture and MODIS-LAI data into a crop model to generate surface and sub-surface drought indices to explore the anatomy of an agricultural drought. Quantification of moisture supply in the root zone remains a gray area in research community, this challenge can be partly overcome by incorporating assimilation of soil moisture and leaf area index into crop modeling framework for agricultural drought quantification, as it performs better in simulating crop yield. It was noted that the persistence of subsurface droughts is in general higher than surface droughts, which can potentially improve forecast accuracy. It was found that both surface and subsurface droughts have an impact on crop yields, albeit with different magnitudes, however, the total water available in the soil profile seemed to have a greater impact on the yield. Further, agricultural drought should not be treated equal for all crops, and it should be calculated based on the root zone depth rather than a fixed soil layer depth. We envisaged that the results of this study will enhance our understanding of agricultural droughts in different parts of the world.

Linking Field and Satellite Observations to Reveal Differences in Single vs. Double-Cropped Soybean Yields in Central Brazil

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.

2016-12-01

Soy-corn double cropping (DC) has been widely adopted in Central Brazil alongside single cropped (SC) soybean production. DC involves different cropping calendars, soy varieties, and may be associated with different crop yield patterns and volatility than SC. Study of the performance of the region's agriculture in a changing climate depends on tracking differences in the productivity of SC vs. DC, but has been limited by crop yield data that conflate the two systems. We predicted SC and DC yields across Central Brazil, drawing on field observations and remotely sensed data. We first modeled field yield estimates as a function of remotely sensed DC status and vegetation index (VI) metrics, and other management and biophysical factors. We then used the statistical model estimated to predict SC and DC soybean yields at each 500 m2 grid cell of Central Brazil for harvest years 2001 - 2015. The yield estimation model was constructed using 1) a repeated cross-sectional survey of soybean yields and management factors for years 2007-2015, 2) a custom agricultural land cover classification dataset which assimilates earlier datasets for the region, and 3) 500m 8-day MODIS image composites used to calculate the wide dynamic range vegetation index (WDRVI) and derivative metrics such as area under the curve for WDRVI values in critical crop development periods. A statistical yield estimation model which primarily entails WDRVI metrics, DC status, and spatial fixed effects was developed on a subset of the yield dataset. Model validation was conducted by predicting previously withheld yield records, and then assessing error and goodness-of-fit for predicted values with metrics including root mean squared error (RMSE), mean squared error (MSE), and R2. We found a statistical yield estimation model which incorporates WDRVI and DC status to be way to estimate crop yields over the region. Statistical properties of the resulting gridded yield dataset may be valuable for understanding linkages between crop yields, farm management factors, and climate.

Assessing the impact of climate variability on cropping patterns in Kenya

NASA Astrophysics Data System (ADS)

Wahome, A.; Ndungu, L. W.; Ndubi, A. O.; Ellenburg, W. L.; Flores Cordova, A. I.

2017-12-01

Climate variability coupled with over-reliance on rain-fed agricultural production on already strained land that is facing degradation and declining soil fertility; highly impacts food security in Africa. In Kenya, dependence on the approximately 20% of land viable for agricultural production under climate stressors such as variations in amount and frequency of rainfall within the main growing season in March-April-May(MAM) and changing temperatures influence production. With time, cropping zones have changed with the changing climatic conditions. In response, the needs of decision makers to effectively assess the current cropped areas and the changes in cropping patterns, SERVIR East and Southern Africa developed updated crop maps and change maps. Specifically, the change maps depict the change in cropping patterns between 2000 and 2015 with a further assessment done on important food crops such as maize. Between 2001 and 2015 a total of 5394km2 of land was converted to cropland with 3370km2 being conversion to maize production. However, 318 sq km were converted from maize to other crops or conversion to other land use types. To assess the changes in climatic conditions, climate parameters such as precipitation trends, variation and averages over time were derived from CHIRPs (Climate Hazards Infra-red Precipitation with stations) which is a quasi-global blended precipitation dataset available at a resolution of approximately 5km. Water Requirements Satisfaction Index (WRSI) water balance model was used to assess long term trends in crop performance as a proxy for maize yields. From the results, areas experiencing declining and varying precipitation with a declining WRSI index during the long rains displayed agricultural expansion with new areas being converted to cropland. In response to climate variability, farmers have converted more land to cropland instead of adopting better farming methods such as adopting drought resistant cultivars and using better farm inputs.

VegScape: U.S. Crop Condition Monitoring Service

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model

PubMed Central

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-01-01

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R2) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R2 of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods. PMID:29642395

Normalized difference vegetation index (NDVI) variation among cultivars and environments

USDA-ARS?s Scientific Manuscript database

Although Nitrogen (N) is an essential nutrient for crop production, large preplant applications of fertilizer N can result in off-field loss that causes environmental concerns. Canopy reflectance is being investigated for use in variable rate (VR) N management. Normalized difference vegetation index...

Actively learning human gaze shifting paths for semantics-aware photo cropping.

PubMed

Zhang, Luming; Gao, Yue; Ji, Rongrong; Xia, Yingjie; Dai, Qionghai; Li, Xuelong

2014-05-01

Photo cropping is a widely used tool in printing industry, photography, and cinematography. Conventional cropping models suffer from the following three challenges. First, the deemphasized role of semantic contents that are many times more important than low-level features in photo aesthetics. Second, the absence of a sequential ordering in the existing models. In contrast, humans look at semantically important regions sequentially when viewing a photo. Third, the difficulty of leveraging inputs from multiple users. Experience from multiple users is particularly critical in cropping as photo assessment is quite a subjective task. To address these challenges, this paper proposes semantics-aware photo cropping, which crops a photo by simulating the process of humans sequentially perceiving semantically important regions of a photo. We first project the local features (graphlets in this paper) onto the semantic space, which is constructed based on the category information of the training photos. An efficient learning algorithm is then derived to sequentially select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path, which simulates humans actively perceiving semantics in a photo. Furthermore, we learn a prior distribution of such active graphlet paths from training photos that are marked as aesthetically pleasing by multiple users. The learned priors enforce the corresponding active graphlet path of a test photo to be maximally similar to those from the training photos. Experimental results show that: 1) the active graphlet path accurately predicts human gaze shifting, and thus is more indicative for photo aesthetics than conventional saliency maps and 2) the cropped photos produced by our approach outperform its competitors in both qualitative and quantitative comparisons.

Food Crops Response to Climate Change

NASA Astrophysics Data System (ADS)

Butler, E.; Huybers, P.

2009-12-01

Projections of future climate show a warming world and heterogeneous changes in precipitation. Generally, warming temperatures indicate a decrease in crop yields where they are currently grown. However, warmer climate will also open up new areas at high latitudes for crop production. Thus, there is a question whether the warmer climate with decreased yields but potentially increased growing area will produce a net increase or decrease of overall food crop production. We explore this question through a multiple linear regression model linking temperature and precipitation to crop yield. Prior studies have emphasised temporal regression which indicate uniformly decreased yields, but neglect the potentially increased area opened up for crop production. This study provides a compliment to the prior work by exploring this spatial variation. We explore this subject with a multiple linear regression model from temperature, precipitation and crop yield data over the United States. The United States was chosen as the training region for the model because there are good crop data available over the same time frame as climate data and presumably the yield from crops in the United States is optimized with respect to potential yield. We study corn, soybeans, sorghum, hard red winter wheat and soft red winter wheat using monthly averages of temperature and precipitation from NCEP reanalysis and yearly yield data from the National Agriculture Statistics Service for 1948-2008. The use of monthly averaged temperature and precipitation, which neglect extreme events that can have a significant impact on crops limits this study as does the exclusive use of United States agricultural data. The GFDL 2.1 model under a 720ppm CO2 scenario provides temperature and precipitation fields for 2040-2100 which are used to explore how the spatial regions available for crop production will change under these new conditions.

Evaluation of the Performance of Multiple Drought Indices for Tunisia

NASA Astrophysics Data System (ADS)

Geli, H. M. E.; Jedd, T.; Svoboda, M.; Wardlow, B.; Hayes, M. J.; Neale, C. M. U.; Hain, C.; Anderson, M. C.

2016-12-01

The recent and frequent drought events in the Middle East and Northern Africa (MENA) create an urgent need for scientists, stakeholders, and decision makers to improve the understanding of drought in order to mitigate its effects. It is well documented that drought is not caused by meteorological or hydrological conditions alone; social, economic, and political governance factors play a large part in whether the components in a water supply system are balanced. In the MENA region, for example, agricultural production can place a significant burden on water supply systems. Understanding the connection between drought and agricultural production is an important first step in developing a sound drought monitoring and mitigation system that links physical indicators with on-the-ground impacts. Drought affect crop yield, livestock health, and water resources availability, among others. A clear depiction of drought onset, duration and severity is essential to provide valuable information to adapt and mitigate drought impact. Therefore, it is important that to be able to connect and evaluate scientific drought data and informational products with societal impact data to more effectively initiate mitigation actions. This approach will further the development of drought maps that are tailored and responsive to immediate and specific societal needs for a region or country. Within the context of developing and evaluating drought impacts maps for the MENA region, this analysis investigates the use of different drought indices and indicators including the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) anomaly, land surface temperature (LST), and Evaporative Stress Index (ESI) for their ability to characterize historic drought events in Tunisia. Evaluation of a "drought map" product is conducted using data at the county level including crop yield, precipitation, in-country interviews with drought monitoring experts and agricultural producers, and a questionnaire follow-up written survey to evaluate stakeholder perceptions of its effectiveness. This case study results indicate an urgent need to contextualize the meteorological, hydrological, and phenological indicators of drought within the larger socio-political context of the MENA region.

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

PubMed Central

Liu, Xiaojun; Ferguson, Richard B.; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status. PMID:28338637

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).

PubMed

Liu, Xiaojun; Ferguson, Richard B; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-03-24

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = ( 1 + e - 15.2829 × ( R A G D D i - 0.1944 ) ) - 1 - ( 1 + e - 11.6517 × ( R A G D D i - 1.0267 ) ) - 1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

Considerations in miniaturizing simplified agro-ecosystems for advanced life support

NASA Technical Reports Server (NTRS)

Volk, T.

1996-01-01

Miniaturizing the Earth's biogeochemical cycles to support human life during future space missions is the goal of the NASA research and engineering program in advanced life support. Mission requirements to reduce mass, volume, and power have focused efforts on (1) a maximally simplified agro-ecosystem of humans, food crops, and microbes; and, (2) a design for optimized productivity of food crops with high light levels over long days, with hydroponics, with elevated carbon dioxide and other controlled environmental factors, as well as with genetic selection for desirable crop properties. Mathematical modeling contributes to the goals by establishing trade-offs, by analyzing the growth and development of experimental crops, and by pointing to the possibilities of directed phasic control using modified field crop models to increase the harvest index.

Considerations in miniaturizing simplified agro-ecosystems for advanced life support.

PubMed

Volk, T

1996-01-01

Miniaturizing the Earth's biogeochemical cycles to support human life during future space missions is the goal of the NASA research and engineering program in advanced life support. Mission requirements to reduce mass, volume, and power have focused efforts on (1) a maximally simplified agro-ecosystem of humans, food crops, and microbes; and, (2) a design for optimized productivity of food crops with high light levels over long days, with hydroponics, with elevated carbon dioxide and other controlled environmental factors, as well as with genetic selection for desirable crop properties. Mathematical modeling contributes to the goals by establishing trade-offs, by analyzing the growth and development of experimental crops, and by pointing to the possibilities of directed phasic control using modified field crop models to increase the harvest index.

Detecting crop growth stages of maize and soybeans by using time-series MODIS data

NASA Astrophysics Data System (ADS)

Sakamoto, T.; Wardlow, B. D.; Gitelson, A. A.; Verma, S. B.; Suyker, A. E.; Arkebauer, T. J.

2009-12-01

The crop phenological stages are one of essential parameters for evaluating crop productivity based on a crop simulation model. In this study, we improved a method named the Wavelet-based Filter for detecting Crop Phenology (WFCP) for detecting the specific phenological dates of maize and soybeans. The improved method was applied to MODIS-derived Wide Dynamic Range Vegetation Index (WDRVI) over a 6-year period (2003 to 2008) for three experimental fields planted to either maize or soybeans as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln (UNL). Using the ground-based crop growth stage observations collected by the CSP, it was confirmed that the improved method can estimate the specific phenological dates of maize (V2.5, R1, R5 and R6) and soybeans (V1, R5, R6 and R7) with reasonable accuracy.

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.

Modeling the long-term effect of winter cover crops on nitrate transport in artificially drained fields across the Midwest U.S.

USDA-ARS?s Scientific Manuscript database

A fall-planted cover crop is a management practice with multiple benefits including reducing nitrate losses from artificially drained fields. We used the Root Zone Water Quality Model (RZWQM) to simulate the impact of a cereal rye cover crop on reducing nitrate losses from drained fields across five...

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

Development and assessment of Transpirative Deficit Index (D-TDI) for agricultural drought monitoring

NASA Astrophysics Data System (ADS)

Borghi, Anna; Rienzner, Michele; Gandolfi, Claudio; Facchi, Arianna

2017-04-01

Drought is a major cause of crop yield loss, both in rainfed and irrigated agroecosystems. In past decades, many approaches have been developed to assess agricultural drought, usually based on the monitoring or modelling of the soil water content condition. All these indices show weaknesses when applied for a real time drought monitoring and management at the local scale, since they do not consider explicitly crops and soil properties at an adequate spatial resolution. This work describes a newly developed agricultural drought index, called Transpirative Deficit Index (D-TDI), and assesses the results of its application over a study area of about 210 km2 within the Po River Plain (northern Italy). The index is based on transforming the interannual distribution of the transpirative deficit (potential crop transpiration minus actual transpiration), calculated daily by means of a spatially distributed conceptual hydrological model and cumulated over user-selected time-steps, to a standard normal distribution (following the approach proposed by the meteorological index SPI - Standard Precipitation Index). For the application to the study area a uniform maize crop cover (maize is the most widespread crop in the area) and 22-year (1993-2014) meteorological data series were considered. Simulation results consist in maps of the index cumulated over 10-day time steps over a mesh with cells of 250 m. A correlation analysis was carried out (1) to study the characteristics and the memory of D-TDI and to assess its intra- and inter-annual variability, (2) to assess the response of the agricultural drought (i.e., the information provided by D-TDI) to the meteorological drought computed through the SPI over different temporal steps. The D-TDI is positively auto-correlated with a persistence of 30 days, and positively cross-correlated to the SPI with a persistence of 40 days, demonstrating that D-TDI responds to meteorological forcing. Correlation analyses demonstrate that soils characterized by high available water content (AWC) can more easily compensate for a short-term variability in the precipitation pattern, while soils with low AWC are more strictly linked to the SPI variability. Since D-TDI relies both on climate and fine-resolution soil and land cover data, it provides a reliable measure of the evolution of agricultural drought over the territory with respect to that achieved through meteorological drought indices. The accumulation of the index over a 10-day period considering a mesh with cells of 250 m allows to capture the response of the territory to drought at time and spatial scales of interest for stakeholders. Modelling efforts utilizing the D-TDI have potential to shed light on the vulnerability of agricultural areas to drought; future work using the D-TDI as a tool to map drought prone areas could therefore improve the ability of farmers and irrigation district managers to cope with agricultural droughts and set up adaptation actions. Despite D-TDI was used in this study on historical data series, the index has the potential to be applied for real-time or provisional monitoring by incorporating real time or provisional meteorological data, giving the opportunity to stakeholders to promptly cope with droughts.

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

2017-02-01

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

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

PubMed

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

2018-02-01

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

An indirect approach to assess the pests on sorghum by remote sensing

NASA Astrophysics Data System (ADS)

Singh, D.; Sao, R.

In today's world of advanced technology various techniques are being used to study ecological parameter and gathering data for agricultural benefits. The major aspects of remote sensing are timely estimates of agriculture crop yield, prediction of pest etc. The damage caused by the pest to crop is well known. Therefore, in this paper, an attempt has to be made to estimate the number of pests on sorghum by remote sensing technique. The studies were made on crop Sorghum (Meethi Sudan) that is a forage variety and the pest observed is a species of grasshopper. The beds of crop sorghum were specially prepared for pests as well as microwave scattering measurements. In first phase of study, dependence of number of pests on sorghum plant parameters (i.e., crop covered moist soil (SM), plant height (PH), leaf area index (LAI), percentage Biomass (BIO), Total chlorophyll (TC)) have been observed by the regression analyses and it was found that pests were more dependent on sorghum chlorophyll than other plant parameters, while climatic conditions were taken as constant. A linear relationship has been obtained between number of pests and TC with quite significant values of coefficient of determination (r^2=0.86). These crop parameters are easily assessable through microwave remote sensing so they can form the basis for prediction of pest remotely. In second phase of study, several observations were carried out for various growth stages of sorghum using bistatic scatterometer for both like polarizations (i.e., HH- and VV-) and different incidence angles at X-band (9.5 GHz). Linear, and multiple regression analysis were carried out to check dependence of scattering coefficient on these crop parameters and it was noticed that scattering coefficient was more dependent on sorghum TC than other plant parameters at X-band. A negative correlation has been obtained between TC and scattering coefficient with quite good values of r^2 (0.82). VV-pol gives better results than HH-pol and incidence angle should be more than 40 degree for both like pols for assessing the sorghum TC at X-band. The TC assessed by the microwave measurements was helpful to estimate the number of pests on sorghum. Combining both phase of study, number of pests was estimated and a quite good agreement (r^2=0.76) was found between observed and estimated pests.

Cloning crops in a CELSS via tissue culture: Prospects and problems

NASA Technical Reports Server (NTRS)

Carman, John G.; Hess, J. Richard

1990-01-01

Micropropagation is currently used to clone fruits, nuts, and vegetables and involves controlling the outgrowth in vitro of basal, axillary, or adventitious buds. Following clonal multiplication, shoots are divided and rooted. This process has greatly reduced space and energy requirements in greenhouses and field nurseries and has increased multiplication rates by greater than 20 fold for some vegetatively propagated crops and breeding lines. Cereal and legume crops can also be cloned by tissue culture through somatic embryogenesis. Somatic embryos can be used to produce 'synthetic seed', which can tolerate desiccation and germinate upon rehydration. Synthetic seed of hybrid wheat, rice, soybean and other crops could be produced in a controlled ecological life support system. Thus, yield advantages of hybreds over inbreds (10 to 20 percent) could be exploited without having to provide additional facilities and energy for parental-line and hybrid seed nurseries.

Human health risk from heavy metal via food crops consumption with wastewater irrigation practices in Pakistan.

PubMed

Khan, Muhammad Usman; Malik, Riffat Naseem; Muhammad, Said

2013-11-01

The current study was designed to investigate the potential human health risks associated with consumption of food crops contaminated with toxic heavy metals. Cadmium (Cd) concentration in surface soils; Cd, lead (Pb) and chromium (Cr) in the irrigation water and food crops were above permissible limits. The accumulation factor (AF) was >1 for manganese (Mn) and Pb in different food crops. The Health Risk Index (HRI) was >1 for Pb in all food crops irrigated with wastewater and tube well water. HRI >1 was also recorded for Cd in all selected vegetables; and for Mn in Spinacia oleracea irrigated with wastewater. All wastewater irrigated samples (soil and food crops) exhibited high relative contamination level as compared to samples irrigated with tube well water. Our results emphasized the need for pretreatment of wastewater and routine monitoring in order to avoid contamination of food crops from the wastewater irrigation system. Copyright © 2013 Elsevier Ltd. All rights reserved.

Early warning and crop condition assessment research

NASA Technical Reports Server (NTRS)

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

1986-01-01

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

Use of the Nitrogen Index to assess nitrate leaching and water drainage from plastic-mulched horticultural cropping systems of Florida

USDA-ARS?s Scientific Manuscript database

Water quality in Florida is significantly impacted by nitrogen (N) losses from agriculture in a large part of the state, where there is a close interaction between surface water and groundwater that has a high water table. Horticultural crops are planted across large areas of Florida, including area...

Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

USDA-ARS?s Scientific Manuscript database

Precision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress ind...

Sequence-indexed mutant library for fast discovery of casual gene mutations for drought tolerance in sorghum

USDA-ARS?s Scientific Manuscript database

As the filth largest grain crop in the world, sorghum is well adapted to high temperature, drought, and low fertilizer input conditions. It can also be used as a fodder and bioenergy crop. Given the trend of global warming, depletion of refresh water resources, reduction in arable land due to soil d...

Increasing Crop Diversity Mitigates Weather Variations and Improves Yield Stability

PubMed Central

Gaudin, Amélie C. M.; Tolhurst, Tor N.; Ker, Alan P.; Janovicek, Ken; Tortora, Cristina; Martin, Ralph C.; Deen, William

2015-01-01

Cropping sequence diversification provides a systems approach to reduce yield variations and improve resilience to multiple environmental stresses. Yield advantages of more diverse crop rotations and their synergistic effects with reduced tillage are well documented, but few studies have quantified the impact of these management practices on yields and their stability when soil moisture is limiting or in excess. Using yield and weather data obtained from a 31-year long term rotation and tillage trial in Ontario, we tested whether crop rotation diversity is associated with greater yield stability when abnormal weather conditions occur. We used parametric and non-parametric approaches to quantify the impact of rotation diversity (monocrop, 2-crops, 3-crops without or with one or two legume cover crops) and tillage (conventional or reduced tillage) on yield probabilities and the benefits of crop diversity under different soil moisture and temperature scenarios. Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations. Introducing small grains into short corn-soybean rotation was enough to provide substantial benefits on long-term soybean yields and their stability while the effects on corn were mostly associated with the temporal niche provided by small grains for underseeded red clover or alfalfa. Crop diversification strategies increased the probability of harnessing favorable growing conditions while decreasing the risk of crop failure. In hot and dry years, diversification of corn-soybean rotations and reduced tillage increased yield by 7% and 22% for corn and soybean respectively. Given the additional advantages associated with cropping system diversification, such a strategy provides a more comprehensive approach to lowering yield variability and improving the resilience of cropping systems to multiple environmental stresses. This could help to sustain future yield levels in challenging production environments. PMID:25658914

Light, plants, and power for life support on Mars

NASA Technical Reports Server (NTRS)

Salisbury, F. B.; Dempster, W. F.; Allen, J. P.; Alling, A.; Bubenheim, D.; Nelson, M.; Silverstone, S.

2002-01-01

Regardless of how well other growing conditions are optimized, crop yields will be limited by the available light up to saturation irradiances. Considering the various factors of clouds on Earth, dust storms on Mars, thickness of atmosphere, and relative orbits, there is roughly 2/3 as much light averaged annually on Mars as on Earth. On Mars, however, crops must be grown under controlled conditions (greenhouse or growth rooms). Because there presently exists no material that can safely be pressurized, insulated, and resist hazards of puncture and deterioration to create life support systems on Mars while allowing for sufficient natural light penetration as well, artificial light will have to be supplied. If high irradiance is provided for long daily photoperiods, the growing area can be reduced by a factor of 3-4 relative to the most efficient irradiance for cereal crops such as wheat and rice, and perhaps for some other crops. Only a small penalty in required energy will be incurred by such optimization. To obtain maximum yields, crops must be chosen that can utilize high irradiances. Factors that increase ability to convert high light into increased productivity include canopy architecture, high-yield index (harvest index), and long-day or day-neutral flowering and tuberization responses. Prototype life support systems such as Bios-3 in Siberia or the Mars on Earth Project need to be undertaken to test and further refine systems and parameters.

A framework for standardized calculation of weather indices in Germany

NASA Astrophysics Data System (ADS)

Möller, Markus; Doms, Juliane; Gerstmann, Henning; Feike, Til

2018-05-01

Climate change has been recognized as a main driver in the increasing occurrence of extreme weather. Weather indices (WIs) are used to assess extreme weather conditions regarding its impact on crop yields. Designing WIs is challenging, since complex and dynamic crop-climate relationships have to be considered. As a consequence, geodata for WI calculations have to represent both the spatio-temporal dynamic of crop development and corresponding weather conditions. In this study, we introduce a WI design framework for Germany, which is based on public and open raster data of long-term spatio-temporal availability. The operational process chain enables the dynamic and automatic definition of relevant phenological phases for the main cultivated crops in Germany. Within the temporal bounds, WIs can be calculated for any year and test site in Germany in a reproducible and transparent manner. The workflow is demonstrated on the example of a simple cumulative rainfall index for the phenological phase shooting of winter wheat using 16 test sites and the period between 1994 and 2014. Compared to station-based approaches, the major advantage of our approach is the possibility to design spatial WIs based on raster data characterized by accuracy metrics. Raster data and WIs, which fulfill data quality standards, can contribute to an increased acceptance and farmers' trust in WI products for crop yield modeling or weather index-based insurances (WIIs).

Light, plants, and power for life support on Mars.

PubMed

Salisbury, F B; Dempster, W F; Allen, J P; Alling, A; Bubenheim, D; Nelson, M; Silverstone, S

2002-01-01

Regardless of how well other growing conditions are optimized, crop yields will be limited by the available light up to saturation irradiances. Considering the various factors of clouds on Earth, dust storms on Mars, thickness of atmosphere, and relative orbits, there is roughly 2/3 as much light averaged annually on Mars as on Earth. On Mars, however, crops must be grown under controlled conditions (greenhouse or growth rooms). Because there presently exists no material that can safely be pressurized, insulated, and resist hazards of puncture and deterioration to create life support systems on Mars while allowing for sufficient natural light penetration as well, artificial light will have to be supplied. If high irradiance is provided for long daily photoperiods, the growing area can be reduced by a factor of 3-4 relative to the most efficient irradiance for cereal crops such as wheat and rice, and perhaps for some other crops. Only a small penalty in required energy will be incurred by such optimization. To obtain maximum yields, crops must be chosen that can utilize high irradiances. Factors that increase ability to convert high light into increased productivity include canopy architecture, high-yield index (harvest index), and long-day or day-neutral flowering and tuberization responses. Prototype life support systems such as Bios-3 in Siberia or the Mars on Earth Project need to be undertaken to test and further refine systems and parameters.

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.

Potato Production as Affected by Crop Parameters and Meteoro Logical Elements

NASA Astrophysics Data System (ADS)

Pereira, André B.; Villa Nova, Nilson A.; Pereira, Antonio R.

Meteorological elements directly influence crop potential productivity, regulating its transpiration, photosynthesis, and respiration processes in such a way as to control the growth and development of the plants throughout their physiological mechanisms at a given site. The interaction of the meteorological factors with crop responses is complex and has been the target of attention of many researchers from all over the world. There is currently a great deal of interest in estimating crop productivity as a function of climate by means of different crop weather models in order to help growers choose planting locations and timing to produce high yields with good tuber quality under site-specific atmospheric conditions. In this manuscript an agrometeorological model based on maximum carbon dioxide assimilation rates for C3 plants, fraction of photosynthetically active radiation, air temperature, photoperiod duration, and crop parameters is assessed as to its performance under tropical conditions. Crop parameters include leaf areaand harvest indexes, dry matter content of potato tubers, and crop cycles to estimate potato potential yields. Productivity obtained with the cultivar Itararé, grown with adequate soil water supply conditions at four different sites in the State of São Paulo (Itararé, Piracicaba, TatuÍ, and São Manuel), Brazil, were used to test the model. The results showed thatthe agrometeorological model tested under the climatic conditions of the State of São Paulo in general underestimated irrigated potato yield by less than 10%.This justifies the recommendation to test the performance of the model in study in other climaticregions for different crops and genotypes under optimal irrigationconditions in further scientific investigations. We reached the conclusion that the agrometeorological model taking into account information on leaf area index, photoperiod duration, photosynthetically active radiation and air temperature is feasible to estimate potential tuber yield at a commercial scale. The performance test shows that it can then be used to forecast harvest time, and also as an effective tool to predict the suitability of potential regions to the cultivation of potato crop, cultivar Itararé, at the State of São Paulo, Brazil.

What is the Optimal Water Productivity Index for Irrigated Grapevines? Case of 'Godello' and 'Albariño' cultivars

NASA Astrophysics Data System (ADS)

Fandiño, María; Martínez, Emma M.; Rey, Benjamín J.; Cancela, Javier J.

2015-04-01

Different studies have tackled the conceptual and terminological study of crop water use indicators, mainly water use efficiency (WUE) and water productivity (WP) (Pereira et al., 2012; Scheierling et al., 2014). The high number of stakeholders, working about agricultural water use (hydrology and hydrogeology, civil and irrigation engineering, agronomy and crop physiology, economics), has hindered the real improvement thereof, from a multidisciplinary perspective. For example, Flexas et al. (2010) reviewed the future improvements in water use efficiency in grapevines, from a physiological approach. In this study, two grapevine cultivars, priority in Galicia (Spain): 'Godello' (DO Valdeorras) and 'Albariño' (DO Rías Baixas, two locations), was assessed in relation to four water productivity index, focus on irrigation systems, agronomy and crop physiology aspects, during a wet year (2012). All WP index was referred to farm yield level (kg ha-1); where the denominator applied to WPTWU, include all components of soil water balance; to WPTWUfarm, introduced rainfall and irrigation depth; to WPIrrig, only irrigation depth applied; and to WPT, crop transpiration was used. In the last index, SIMDualKc model was used to partitioning crop evapotranspiration and cover crop transpiration. Different ranges of values was obtained for both cultivars, WPTWUfarm was higher in cv 'Godello' than in cv 'Albariño', 3.8 and 0.9 kg m-3 respectively. Average value to WPIrrig has showed: 17.6 kg m-3 for cv 'Albariño' and 15.5 kg m-3 for cv 'Godello', due to a reduction of 60% of irrigation depth in DO Rías Baixas. However, for both locations, higher WPIrrig was obtained to drip irrigation system versus subsurface drip irrigation. WPT showed a different tendency, rain-fed 'Godello' and surface drip irrigation 'Albariño' treatments obtained higher values (6.8 and 3.6 kg m-3), with higher WPT to cv 'Godello' for all treatments versus 'Albariño'. Results had showed that water productivity indexes are cultivar depending, similar values was achieved in near locations (data not showed). Special care must be taken when analysing water productivity indexes at the farm level, considering identical irrigation depth, density, canopy management system, age of the plantation, management practices, among other factors, which may affect of water consumed or supplied to the vineyard. Agronomical economic aspects should be studied, taken into account irrigation systems cost and benefit crop yield, at basin scale. Temperate viticulture should pursue greater WUE and WP, identifying the most productive cultivars adapted to near-future climate conditions. References: Flexas J, Galmés J, Gallé A, Gulías J, Pou A, Ribas-Carbo M, Tomàs M, Medrano H (2010). Improving water use efficiency in grapevines: potential physiological targets for biotechnological improvement. Australian Journal of Grape and Wine Research, 16(s1):106-121 Pereira LS, Cordery I, Iacovides I (2012). Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agricultural Water Management, 108:39-51 Scheierling SM, Treguer DO, Booker JF, Decker E (2014). How to assess agricultural water productivity? looking for water in the agricultural productivity and efficiency literature. Looking for Water in the Agricultural Productivity and Efficiency Literature (July 1, 2014). World Bank Policy Research Working Paper, (6982)

The photochemical reflectance index from directional cornfield reflectances: Observations and simulations

USDA-ARS?s Scientific Manuscript database

The two-layer Markov chain Analytical Canopy Reflectance Model (ACRM) was linked with in situ hyperspectral leaf optical properties to simulate the Photochemical Reflectance Index (PRI) for a corn crop canopy at three different growth stages. This is an extended study after a successful demonstratio...

The use of LANDSAT digital data to detect and monitor vegetation water deficiencies. [South Dakota

NASA Technical Reports Server (NTRS)

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

1977-01-01

A technique devised using a vector transformation of LANDSAT digital data to indicate when vegetation is undergoing moisture stress is described. A relation established between the remote sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index) is discussed.

Estimating the relative water content of leaves in a cotton canopy.

USDA-ARS?s Scientific Manuscript database

Remotely sensing plant canopy water status remains a long term goal of remote sensing research. Established approaches to estimating canopy water status — the Crop Water Stress Index, the Water Deficit Index, the Equivalent Water Thickness and the many other indices — involve measurements in the the...

Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...

Assessing Nitrogen Status of Dryland Wheat Using the Canopy Chlorophyll Content Index

USDA-ARS?s Scientific Manuscript database

Ground-based, active light sensing relies upon the Normalized Difference Vegetation Index (NDVI) for assessing crop nitrogen (N) response and applying N fertilizer. However, NDVI may not work well in semiarid environments where biomass and yields depend upon plant water. This study evaluated the C...

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).

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.

Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes

PubMed Central

Werling, Ben P.; Dickson, Timothy L.; Isaacs, Rufus; Gaines, Hannah; Gratton, Claudio; Gross, Katherine L.; Liere, Heidi; Malmstrom, Carolyn M.; Meehan, Timothy D.; Ruan, Leilei; Robertson, Bruce A.; Robertson, G. Philip; Schmidt, Thomas M.; Schrotenboer, Abbie C.; Teal, Tracy K.; Wilson, Julianna K.; Landis, Douglas A.

2014-01-01

Agriculture is being challenged to provide food, and increasingly fuel, for an expanding global population. Producing bioenergy crops on marginal lands—farmland suboptimal for food crops—could help meet energy goals while minimizing competition with food production. However, the ecological costs and benefits of growing bioenergy feedstocks—primarily annual grain crops—on marginal lands have been questioned. Here we show that perennial bioenergy crops provide an alternative to annual grains that increases biodiversity of multiple taxa and sustain a variety of ecosystem functions, promoting the creation of multifunctional agricultural landscapes. We found that switchgrass and prairie plantings harbored significantly greater plant, methanotrophic bacteria, arthropod, and bird diversity than maize. Although biomass production was greater in maize, all other ecosystem services, including methane consumption, pest suppression, pollination, and conservation of grassland birds, were higher in perennial grasslands. Moreover, we found that the linkage between biodiversity and ecosystem services is dependent not only on the choice of bioenergy crop but also on its location relative to other habitats, with local landscape context as important as crop choice in determining provision of some services. Our study suggests that bioenergy policy that supports coordinated land use can diversify agricultural landscapes and sustain multiple critical ecosystem services. PMID:24474791

Improving Snow Process Modeling with Satellite-Based Estimation of Near-Surface-Air-Temperature Lapse Rate

NASA Astrophysics Data System (ADS)

Wahome, A.; Ndungu, L. W.; Ndubi, A. O.; Ellenburg, W. L.; Flores Cordova, A. I.

2016-12-01

Climate variability coupled with over-reliance on rain-fed agricultural production on already strained land that is facing degradation and declining soil fertility; highly impacts food security in Africa. In Kenya, dependence on the approximately 20% of land viable for agricultural production under climate stressors such as variations in amount and frequency of rainfall within the main growing season in March-April-May(MAM) and changing temperatures influence production. With time, cropping zones have changed with the changing climatic conditions. In response, the needs of decision makers to effectively assess the current cropped areas and the changes in cropping patterns, SERVIR East and Southern Africa developed updated crop maps and change maps. Specifically, the change maps depict the change in cropping patterns between 2000 and 2015 with a further assessment done on important food crops such as maize. Between 2001 and 2015 a total of 5394km2 of land was converted to cropland with 3370km2 being conversion to maize production. However, 318 sq km were converted from maize to other crops or conversion to other land use types. To assess the changes in climatic conditions, climate parameters such as precipitation trends, variation and averages over time were derived from CHIRPs (Climate Hazards Infra-red Precipitation with stations) which is a quasi-global blended precipitation dataset available at a resolution of approximately 5km. Water Requirements Satisfaction Index (WRSI) water balance model was used to assess long term trends in crop performance as a proxy for maize yields. From the results, areas experiencing declining and varying precipitation with a declining WRSI index during the long rains displayed agricultural expansion with new areas being converted to cropland. In response to climate variability, farmers have converted more land to cropland instead of adopting better farming methods such as adopting drought resistant cultivars and using better farm inputs.

A model of plant canopy polarization

NASA Technical Reports Server (NTRS)

Vanderbilt, V. C.

1980-01-01

A model for the amount of linearly polarized light reflected by the shiny leaves of grain crops is based on the morphological and phenological characteristics of the plant canopy and upon the Fresnel equations which describe the light reflection process at the smooth boundary separating two dielectrics. The theory used demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops such as wheat and barley, two crops which are so spectrally similar that they are misclassified with unacceptable frequency. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The information content of these measurements is needed to evaluate the proposed polarization sensor for the satellite-borne multispectral resource sampler.

Multisite Evaluation of APEX for Water Quality: II. Regional Parameterization.

PubMed

Nelson, Nathan O; Baffaut, Claire; Lory, John A; Anomaa Senaviratne, G M M M; Bhandari, Ammar B; Udawatta, Ranjith P; Sweeney, Daniel W; Helmers, Matt J; Van Liew, Mike W; Mallarino, Antonio P; Wortmann, Charles S

2017-11-01

Phosphorus (P) Index assessment requires independent estimates of long-term average annual P loss from fields, representing multiple climatic scenarios, management practices, and landscape positions. Because currently available measured data are insufficient to evaluate P Index performance, calibrated and validated process-based models have been proposed as tools to generate the required data. The objectives of this research were to develop a regional parameterization for the Agricultural Policy Environmental eXtender (APEX) model to estimate edge-of-field runoff, sediment, and P losses in restricted-layer soils of Missouri and Kansas and to assess the performance of this parameterization using monitoring data from multiple sites in this region. Five site-specific calibrated models (SSCM) from within the region were used to develop a regionally calibrated model (RCM), which was further calibrated and validated with measured data. Performance of the RCM was similar to that of the SSCMs for runoff simulation and had Nash-Sutcliffe efficiency (NSE) > 0.72 and absolute percent bias (|PBIAS|) < 18% for both calibration and validation. The RCM could not simulate sediment loss (NSE < 0, |PBIAS| > 90%) and was particularly ineffective at simulating sediment loss from locations with small sediment loads. The RCM had acceptable performance for simulation of total P loss (NSE > 0.74, |PBIAS| < 30%) but underperformed the SSCMs. Total P-loss estimates should be used with caution due to poor simulation of sediment loss. Although we did not attain our goal of a robust regional parameterization of APEX for estimating sediment and total P losses, runoff estimates with the RCM were acceptable for P Index evaluation. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Quantitative genetic studies relating to the domestication of meadowfoam (Limnanthes spp. ) as a new oil crop

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

Abuelgasim, E.H.

1982-01-01

Meadowfoam (Limnanthes spp.) has recently aroused interest as a promising new source of industrial oil that is a good substitute for sperm whale oil. Fourteen natural populations of Limnanthes, involving two species and five taxonomic varieties, were studied for 12 quantitative characters, during two seasons. The objectives were: to evaluate the potential use of the populations for domestication purposes, study the interrelationships among characters, and to obtain heritability estimates for some of the important characters. A great deal of variability among the populations was observed for all the characters studied. Although no single taxon or population had all the desiredmore » characters combined, nevertheless, three populations of L. douglasii var. nivea and one population of L. douglasii var. sulphurea, gave consistently higher values for seed yield, seed number, plant dry weight, harvest index and fertility index, than L. alba or L. douglasii var. douglasii populations. The nivea group was also the earliest to flower, however, this group together with var. sulphurea population, suffer from severe seed shattering at maturity. In both seasons, seed yield was positively and highly significantly correlated with all of the other characters with the exception of days to the first flower and days to flower, which showed a highly significant negative correlation with seed yield. Stepwise multiple regression and path-coefficient analyses showed that seed number was the most important character in contribution to variation in seed yield; it accounted for over 85% of the variation in seed yield. The four characters: seed number, 100-seed weight, plant dry weight and harvest index, in that order, were the most important characters to be included in a multiple regression equation for determination of seed yield.« less

Crop effect to soil moisture retrieval at different microwave frequencies

NASA Astrophysics Data System (ADS)

Zhang, Zhongjun; Luan, Jinzhe

2006-12-01

In soil moisture retrieval by microwave remote sensing technology, vegetation effect is important, due to its emission upward as well as masking the soil surface contribution. Because of good penetration characteristics through crop at low frequencies, L-band is often used, where crop is treated as a uniform layer, and 0 th-order Brightness Temperature model is used. Higher frequencies upper than L-band, the frequencies both on NASA AQUA AMSR-E and FY-3 to be launched next year in CHINA, may be more informative in SM retrieval. The multiple-scattering effects inside crop and that between crop layer and soil surface will be increasing when frequencies go higher from L-band. In this paper, a Matrix-Doubling model that account for multiple-scattering based on ray tracing technique is used to simulate the microwave emission of vegetated-surface at C- and X-band. The orientation and size of crop element such as leaves and cylinders are accounted for in crop layer, and AIEM is used for calculation of ground surface scattering. Simulation results from this model for corn and SGP99 experiment data are in good agreement. Since complicated theoretical model as used in this paper involves too many parameters, to make SM retrieval more directly, corresponding terms from the developed model are matched with 0 th-order,so as to derive effective single scattering albedo and vegetation opacity at C- and X-band.

Validation of Leaf Area Index measurements based on the Wireless Sensor Network platform

NASA Astrophysics Data System (ADS)

Song, Q.; Li, X.; Liu, Q.

2017-12-01

The leaf area index (LAI) is one of the important parameters for estimating plant canopy function, which has significance for agricultural analysis such as crop yield estimation and disease evaluation. The quick and accurate access to acquire crop LAI is particularly vital. In the study, LAI measurement of corn crops is mainly through three kinds of methods: the leaf length and width method (LAILLW), the instruments indirect measurement method (LAII) and the leaf area index sensor method(LAIS). Among them, LAI value obtained from LAILLW can be regarded as approximate true value. LAI-2200,the current widespread LAI canopy analyzer,is used in LAII. LAIS based on wireless sensor network can realize the automatic acquisition of crop images,simplifying the data collection work,while the other two methods need person to carry out field measurements.Through the comparison of LAIS and other two methods, the validity and reliability of LAIS observation system is verified. It is found that LAI trend changes are similar in three methods, and the rate of change of LAI has an increase with time in the first two months of corn growth when LAIS costs less manpower, energy and time. LAI derived from LAIS is more accurate than LAII in the early growth stage,due to the small blade especially under the strong light. Besides, LAI processed from a false color image with near infrared information is much closer to the true value than true color picture after the corn growth period up to one and half months.

Relay cropping as a sustainable approach: problems and opportunities for sustainable crop production.

PubMed

Tanveer, Mohsin; Anjum, Shakeel Ahmad; Hussain, Saddam; Cerdà, Artemi; Ashraf, Umair

2017-03-01

Climate change, soil degradation, and depletion of natural resources are becoming the most prominent challenges for crop productivity and environmental sustainability in modern agriculture. In the scenario of conventional farming system, limited chances are available to cope with these issues. Relay cropping is a method of multiple cropping where one crop is seeded into standing second crop well before harvesting of second crop. Relay cropping may solve a number of conflicts such as inefficient use of available resources, controversies in sowing time, fertilizer application, and soil degradation. Relay cropping is a complex suite of different resource-efficient technologies, which possesses the capability to improve soil quality, to increase net return, to increase land equivalent ratio, and to control the weeds and pest infestation. The current review emphasized relay cropping as a tool for crop diversification and environmental sustainability with special focus on soil. Briefly, benefits, constraints, and opportunities of relay cropping keeping the goals of higher crop productivity and sustainability have also been discussed in this review. The research and knowledge gap in relay cropping was also highlighted in order to guide the further studies in future.

Precommercial crop-tree release increases diameter growth of Appalachian hardwood saplings

Treesearch

H. Clay Smith; Neil I. Lamson

1983-01-01

Codominant seedling-origin crop trees 25 to 39 feet tall in even-aged, precommercial-size hardwood stands were released in West Virginia. Trees were located on two sites: good oak site index 75 and fair oak site 63. Species studied were black cherry, sweet birch, and yellow-poplar. Three-year results indicated that the trees generally responded to release; the 3-year...

Increased Night Temperature Negatively Affects Grain Yield, Biomass and Grain Number in Chilean Quinoa

PubMed Central

Lesjak, Jurka; Calderini, Daniel F.

2017-01-01

Quinoa high nutritive value increases interest worldwide, especially as a crop that could potentially feature in different cropping systems, however, climate change, particularly rising temperatures, challenges this and other crop species. Currently, only limited knowledge exists regarding the grain yield and other key traits response to higher temperatures of this crop, especially to increased night temperatures. In this context, the main objective of this study was to evaluate the effect of increased night temperature on quinoa yield, grain number, individual grain weight and processes involved in crop growth under the environmental conditions (control treatment) and night thermal increase at two phases: flowering (T1) and grain filling (T2) in southern Chile. A commercial genotype, Regalona, and a quinoa accession (Cod. BO5, N°191, grain bank from Semillas Baer, hereby referred to as Accession) were used, due to their adaptability to Southern Chilean conditions and contrasting grain yield potential, grain weight and size of plants. Temperature was increased ≈4°C above the ambient from 8 pm until 9 am the next morning. Control treatments reached a high grain yield (600 and 397 g m-2, i.e., Regalona and Accession). Temperature increase reduced grain yield by 31% under T1 treatment and 12% when under T2 in Regalona and 23 and 26% in Accession, respectively. Aboveground biomass was negatively affected by the thermal treatments and a positive linear association was found between grain yield and aboveground biomass across treatments. By contrast, the harvest index was unaffected either by genotype, or by thermal treatments. Grain number was significantly affected between treatments and this key trait was linearly associated with grain yield. On the other hand, grain weight showed a narrow range of variation across treatments. Additionally, leaf area index was not affected, but significant differences were found in SPAD values at the end of T1 treatment, compared to control. Little change was found in the harvest index, individual grain weight, grain protein content or water soluble carbohydrates in response to the increased night temperature in this crop. PMID:28386266

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat.

PubMed

Yabe, Shiori; Hara, Takashi; Ueno, Mariko; Enoki, Hiroyuki; Kimura, Tatsuro; Nishimura, Satoru; Yasui, Yasuo; Ohsawa, Ryo; Iwata, Hiroyoshi

2018-01-01

To evaluate the potential of genomic selection (GS), a selection experiment with GS and phenotypic selection (PS) was performed in an allogamous crop, common buckwheat ( Fagopyrum esculentum Moench). To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598-50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding.

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat

PubMed Central

Yabe, Shiori; Hara, Takashi; Ueno, Mariko; Enoki, Hiroyuki; Kimura, Tatsuro; Nishimura, Satoru; Yasui, Yasuo; Ohsawa, Ryo; Iwata, Hiroyoshi

2018-01-01

To evaluate the potential of genomic selection (GS), a selection experiment with GS and phenotypic selection (PS) was performed in an allogamous crop, common buckwheat (Fagopyrum esculentum Moench). To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598–50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding. PMID:29619035

Use of Satellite-based Remote Sensing to inform Evapotranspiration parameters in Cropping System Models

NASA Astrophysics Data System (ADS)

Dhungel, S.; Barber, M. E.

2016-12-01

The objectives of this paper are to use an automated satellite-based remote sensing evapotranspiration (ET) model to assist in parameterization of a cropping system model (CropSyst) and to examine the variability of consumptive water use of various crops across the watershed. The remote sensing model is a modified version of the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC™) energy balance model. We present the application of an automated python-based implementation of METRIC to estimate ET as consumptive water use for agricultural areas in three watersheds in Eastern Washington - Walla Walla, Lower Yakima and Okanogan. We used these ET maps with USDA crop data to identify the variability of crop growth and water use for the major crops in these three watersheds. Some crops, such as grapes and alfalfa, showed high variability in water use in the watershed while others, such as corn, had comparatively less variability. The results helped us to estimate the range and variability of various crop parameters that are used in CropSyst. The paper also presents a systematic approach to estimate parameters of CropSyst for a crop in a watershed using METRIC results. Our initial application of this approach was used to estimate irrigation application rate for CropSyst for a selected farm in Walla Walla and was validated by comparing crop growth (as Leaf Area Index - LAI) and consumptive water use (ET) from METRIC and CropSyst. This coupling of METRIC with CropSyst will allow for more robust parameters in CropSyst and will enable accurate predictions of changes in irrigation practices and crop rotation, which are a challenge in many cropping system models.

From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

NASA Astrophysics Data System (ADS)

JÉGO, G.; Pattey, E.; Liu, J.

2013-12-01

The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%

Assessments of Future Maize Yield Potential Changes in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Lim, C. H.; Kim, J.; Lee, W. K.; Kafatos, M.

2016-12-01

The Korean Peninsula has unique agricultural environment due to the differences of political and socio-economical system between Republic of Korea (SK, hereafter) and Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering lack of food supplies caused by natural disasters, land degradation and political failure. The neighboring developed country SK has better agricultural system but very low food self-sufficiency rate. Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore, evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we utilized multiple process-based crop models, having ability of regional scale assessment, to evaluate maize Yp and assess the model uncertainties -EPIC, GEPIC, DSSAT, and APSIM model that has capability of regional scale expansion (apsimRegions). First we evaluated each crop model for 3 years from 2012 to 2014 using reanalysis data (RDAPS; Regional Data Assimilation and Prediction System produced by Korea Meteorological Agency) and observed yield data. Each model performances were compared over the different regions in the Korean Peninsula having different local climate characteristics. To quantify of the major influence of at each climate variables, we also conducted sensitivity test using 20 years of climatology in historical period from 1981 to 2000. Lastly, the multi-crop model ensemble analysis was performed for future period from 2031 to 2050. The required weather variables projected for mid-century were employed from COordinated Regional climate Downscaling EXperiment (CORDEX) East Asia. The high-resolution climate data were obtained from multiple regional climate models (RCM) driven by multiple climate scenarios projected from multiple global climate models (GCMs) in conjunction with multiple greenhouse gas concentration pathways. The results indicate that the projected Yp in the Korean peninsula is significantly changed comparing to the historical period and proper adaptation strategies such as optimized planting dates can considerably alleviate Yp decrease.

Performance of Vegetation Indices for Wheat Yield Forecasting for Punjab, Pakistan

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Becker-Reshef, I.; Adusei, B.; Barker, B.

2013-12-01

Forecasting wheat yield in major producer countries early in the growing season allows better planning for harvest deficits and surplus with implications for food security, world market transactions, sustaining adequate grain stocks, policy making and other matters. Remote sensing imagery is well suited for yield forecasting over large areas. The Normalized Difference Vegetation Index (NDVI) has been the most-used spectral index derived from remote sensing imagery for assessing crop condition of major crops and forecasting crop yield. Many authors have found that the highest correlation between NDVI and yield of wheat crops occurs at the height of the growing season when NDVI values and photosynthetic activity of the wheat plants are at their relative maximum. At the same time NDVI saturates in very dense and vigorous (healthy, green) canopies such as wheat fields during the seasonal peak and shows significantly reduced sensitivity to further increases in photosynthetic activity. In this study we compare the performance of different vegetation indices derived from space-borne red and near-infrared spectral reflectance measurements for wheat yield forecasting in the Punjab Province, Pakistan. Areas covered by wheat crop each year were determined using a time series of MODIS 8-day composites at 250 m resolution converted to temporal metrics and classified using a bagged decision tree approach, driven by classified multi-temporal Landsat scenes. Within the wheat areas we analyze and compare wheat yield forecasts derived from three different satellite-based vegetation indices at the peak of the growing season. We regressed in turn NDVI, Wide Dynamic Range Vegetation Index (WDRVI) and the Vegetation Condition Index (VCI) from the four years preceding the wheat growing season 2011/12 against reported yield values and applied the regression equations to forecast wheat yield for the 2011/12 season per district for each of 36 Punjab districts. Yield forecasts overall corresponded well with reported values. NDVI-based forecasts showed high correlations of r squared = 0.881 and RMSE 11%. The VCI performed similarly well with r squared = 0.886 and RMSE 11%. WDRVI performed better than either of the other indices with r squared = 0.909 and RMSE 10%, probably due to the increased sensitivity of the index at high values. Wheat yields in Pakistan show on average a slow but steady annual increase but overall are comparatively stable due to the fact that the majority of fields are irrigated. The next steps in this study will be to compare NDVI- with WDRVI-based yield forecasts in other environments dominated by rain-fed agriculture, such as Ukraine, Australia and the United States.

Towards Developing a Regional Drought Information System for Lower Mekong

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Carbon exchange by establishing biofuel crops in Central Illinois

USDA-ARS?s Scientific Manuscript database

Perennial grass biofuels may contribute to long-term carbon sequestration in soils, thereby providing a broad range of environmental benefits at multiple scales. To quantify those benefits, the carbon balance was investigated over three perennial grass biofuel crops miscanthus (Miscanthus giganteus)...

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.

Growth and reflectance characteristics of winter wheat canopies

NASA Technical Reports Server (NTRS)

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

1984-01-01

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

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.

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.

A 5-year analysis of crop phenologies from the United States Heartland (Invited)

NASA Astrophysics Data System (ADS)

Johnson, D. M.

2010-12-01

Time series imagery data from the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) was intersected with annually updated field-level crop data from the United States Department of Agriculture (USDA) Farm Service Agency (FSA). Phenological metrics were derived for major crop types found in the United States (US) Heartland region. The specific MODIS data consisted of the 16-day composited Normalized Difference Vegetation Index (NDVI) 250 meter spatial resolution imagery from the Terra satellite. Crops evaluated included corn, soybeans, wheat, cotton, sorghum, rice, and other small grains. Charts showing the annual average state-level NDVI phenologies by crop were constructed for the five years between 2006 and 2010. The states of interest covered the intensively cultivated regions in the US Great Plains, Corn Belt, and Mississippi River Alluvial Plain. Results demonstrated the recent biophysical growth cycles of prevalent and widespread US crops and how they varied by geography and year. Linkages between the time series data and planting practices, weather impacts, crop progress reports, and yields were also investigated.

RGB picture vegetation indexes for High-Throughput Phenotyping Platforms (HTPPs)

NASA Astrophysics Data System (ADS)

Kefauver, Shawn C.; El-Haddad, George; Vergara-Diaz, Omar; Araus, José Luis

2015-10-01

Extreme and abnormal weather events, as well as the more gradual meteorological changes associated with climate change, often coincide with not only increased abiotic risks (such as increases in temperature and decreases in precipitation), but also increased biotic risks due to environmental conditions that favor the rapid spread of crop pests and diseases. Durum wheat is by extension the most cultivated cereal in the south and east margins of the Mediterranean Basin. It is of strategic importance for Mediterranean agriculture to develop new varieties of durum wheat with greater production potential, better adaptation to increasingly adverse environmental conditions (drought) and better grain quality. Similarly, maize is the top staple crop for low-income populations in Sub-Saharan Africa and is currently suffering from the appearance of new diseases, which, together with increased abiotic stresses from climate change, are challenging the very sustainability of African societies. Current constraints in field phenotyping remain a major bottleneck for future breeding advances, but RGB-based High-Throughput Phenotyping Platforms (HTPPs) have shown promise for rapidly developing both disease-resistant and weather-resilient crops. RGB cameras have proven costeffective in studies assessing the effect of abiotic stresses, but have yet to be fully exploited to phenotype disease resistance. Recent analyses of durum wheat in Spain have shown RGB vegetation indexes to outperform multispectral indexes such as NDVI consistently in disease and yield prediction. Towards HTTP development for breeding maize disease resistance, some of the same RGB picture vegetation indexes outperformed NDVI (Normalized Difference Vegetation Index), with R2 values up to 0.65, compared to 0.56 for NDVI. . Specifically, hue, a*, u*, and Green Area (GA), as produced by FIJI and BreedPix open source software, performed similar to or better than NDVI in predicting yield and disease severity conditions for wheat and maize. Results using UAVs (Unmanned Aerial Vehicles) have produced similar results demonstrating the robust strengths, and limitations, of the more cost-effective RGB picture indexes.

Corn and soybean Landsat MSS classification performance as a function of scene characteristics

NASA Technical Reports Server (NTRS)

Batista, G. T.; Hixson, M. M.; Bauer, M. E.

1982-01-01

In order to fully utilize remote sensing to inventory crop production, it is important to identify the factors that affect the accuracy of Landsat classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil, and weather variables on the accuracy of Landsat classifications of corn and soybeans. Segments sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier on multitemporally registered data from two key acquisition periods. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil drainage, slope, soil order, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather, and crop development stage.

An experimental test of plant canopy reflectance models on cotton

NASA Technical Reports Server (NTRS)

Lemaster, E. W.

1974-01-01

Extensive data on the plant parameters necessary to evaluate any model are presented for a cotton crop. The variation of the bidirectional reflectance function with observer altitude, observer azimuth, and sun altitude angle is presented for a high density cotton crop having leaf index of 19. A comparison with the quantitative behavior obtained from the Suits model is accomplished in the wavelength region from 400 nm to 1050 nm.

Assessment of actual transpiration rate in olive tree field combining sap-flow, leaf area index and scintillometer measurements

NASA Astrophysics Data System (ADS)

Agnese, C.; Cammalleri, C.; Ciraolo, G.; Minacapilli, M.; Provenzano, G.; Rallo, G.; de Bruin, H. A. R.

2009-09-01

Models to estimate the actual evapotranspiration (ET) in sparse vegetation area can be fundamental for agricultural water managements, especially when water availability is a limiting factor. Models validation must be carried out by considering in situ measurements referred to the field scale, which is the relevant scale of the modelled variables. Moreover, a particular relevance assumes to consider separately the components of plant transpiration (T) and soil evaporation (E), because only the first is actually related to the crop stress conditions. Objective of the paper was to assess a procedure aimed to estimate olive trees actual transpiration by combining sap flow measurements with the scintillometer technique at field scale. The study area, located in Western Sicily (Italy), is mainly cultivated with olive crop and is characterized by typical Mediterranean semi-arid climate. Measurements of sap flow and crop actual evapotranspiration rate were carried out during 2008 irrigation season. Crop transpiration fluxes, measured on some plants by means of sap flow sensors, were upscaled considering the leaf area index (LAI). The comparison between evapotranspiration values, derived by displaced-beam small-aperture scintillometer (DBSAS-SLS20, Scintec AG), with the transpiration fluxes obtained by the sap flow sensors, also allowed to evaluate the contribute of soil evaporation in an area characterized by low vegetation coverage.

A Pretest for Introductory Crops Students.

ERIC Educational Resources Information Center

Elkins, Donald M.

1987-01-01

Discusses the advantages of using a pretest in introductory agronomy courses. Provides a pretest that has been developed for use in an introductory crops course taught at Southern Illinois University. Includes 25 definitions, 17 true-false and multiple choice questions, and 6 short answer questions. (TW)

Incorporating pest management into the design of multiple goal-oriented cropping systems

USDA-ARS?s Scientific Manuscript database

Suggestions are offered to facilitate efforts to incorporate pest management goals into the design of crop production systems. The scope of research programs should be expanded to ensure broad multidisciplinary cooperation. Inclusion of farmers, production specialists and researchers from discipli...

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Comparison of SPI and SPEI applicability for drought impact assessment on crop production in the Danubian Lowland and the East Slovakian Lowland

NASA Astrophysics Data System (ADS)

Labudová, L.; Labuda, M.; Takáč, J.

2017-04-01

Drought belongs among the main impact factors considering crop yields. Therefore, this paper is focused on the assessment of drought occurrence and intensity as well as on its impact on crop yields on the Danubian and the East Slovakian lowlands with the spatial resolution at district level. Yield data were the main limitation of the study, which resulted in the limited length of the assessed period (1996-2013). The standardized yields of ten crops (winter wheat, spring wheat, winter barley, spring barley, rye, maize, potatoes, oilseed rape, sunflower, and sugar beet) were correlated with monthly, 2-, and 3-monthly standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI). For this purpose, the common significance level of alpha = 0.05 was used. The temporal evolution of both indices and drought occurrence during the period 1961-2013 were assessed for each district. Most crops show a higher correlation with the SPEI than with the SPI in contrast to potatoes, which reached a higher significant correlation using the SPI. The correlation also increases with increasing number of months within a time step. The highest correlation can be seen between maize and the 3-monthly SPEI in August representing summer precipitation and potential evapotranspiration conditions. Furthermore, a very high correlation was recorded considering sugar beet, which is influenced mainly by summer precipitation, because the correlation coefficient between the sugar beet and the 3-monthly SPI is as high as using the 3-monthly SPEI. Crop yields in the East Slovakian Lowland do not seem to be influenced by wet/dry periods identified using the SPI and the SPEI as their correlation with both indices is quite low and insignificant.

Macronutrients and trace metals in soil and food crops of Isfahan Province, Iran.

PubMed

Keshavarzi, Behnam; Moore, Farid; Ansari, Maryam; Rastegari Mehr, Meisam; Kaabi, Helena; Kermani, Maryam

2015-01-01

The distribution of 10 macronutrients and trace metals in the arable soils of Isfahan Province, their phytoavailability, and associated health risks were investigated; 134 plant and 114 soil samples (from 114 crop fields) were collected and analyzed at harvesting time. Calculation of the soil pollution index (SPI) revealed that arable soil polluted by metals was more severe in the north and southwest of the study area. The results of cluster analysis indicated that Pb, Zn, and Cu share a similar origin from industries and traffic. The concentrations of macronutrients and trace metals in the sampled crops were found in the order of K > Ca > S > Mg > P and Fe > Mn > Zn > Cu > Pb, respectively, whereas calculation of the bioconcentration factor (BCF) indicated that the accumulation of the investigated elements in crops was generally in the order of S ≈ K > P > Mg > Ca and Zn > Cu > Mn > Pb > Fe, respectively. Thus, various parameters including crop species and the physical, chemical, and biological properties of soil also affected the bioavailability of the elements besides the total element contents in soil. Daily intake (DI) values of elements were lower than the recommended daily intake (RDI) levels in rice grains except for Fe and Mn, but for wheat grains, all elements displayed DI values higher than the RDI. Moreover, based on the hazard index (HI) values, inhabitants are experiencing a significant potential health risk solely due to the consumption of wheat and rice grains (particularly wheat grains). Mn health quotient (HQ) also indicated a high risk of Mn absorption for crop consumer inhabitants.

Functional homogeneous zones (fHZs) in viticultural zoning procedure: an Italian case study on Aglianico vine

NASA Astrophysics Data System (ADS)

Bonfante, A.; Agrillo, A.; Albrizio, R.; Basile, A.; Buonomo, R.; De Mascellis, R.; Gambuti, A.; Giorio, P.; Guida, G.; Langella, G.; Manna, P.; Minieri, L.; Moio, L.; Siani, T.; Terribile, F.

2015-06-01

This paper aims to test a new physically oriented approach to viticulture zoning at farm scale that is strongly rooted in hydropedology and aims to achieve a better use of environmental features with respect to plant requirements and wine production. The physics of our approach are defined by the use of soil-plant-atmosphere simulation models, applying physically based equations to describe the soil hydrological processes and solve soil-plant water status. This study (part of the ZOVISA project) was conducted on a farm devoted to production of high-quality wines (Aglianico DOC), located in southern Italy (Campania region, Mirabella Eclano, AV). The soil spatial distribution was obtained after standard soil survey informed by geophysical survey. Two homogeneous zones (HZs) were identified; in each one a physically based model was applied to solve the soil water balance and estimate the soil functional behaviour (crop water stress index, CWSI) defining the functional homogeneous zones (fHZs). For the second process, experimental plots were established and monitored for investigating soil-plant water status, crop development (biometric and physiological parameters) and daily climate variables (temperature, solar radiation, rainfall, wind). The effects of crop water status on crop response over must and wine quality were then evaluated in the fHZs. This was performed by comparing crop water stress with (i) crop physiological measurement (leaf gas exchange, chlorophyll a fluorescence, leaf water potential, chlorophyll content, leaf area index (LAI) measurement), (ii) grape bunches measurements (berry weight, sugar content, titratable acidity, etc.) and (iii) wine quality (aromatic response). This experiment proved the usefulness of the physically based approach, also in the case of mapping viticulture microzoning.

Crop-specific seasonal estimates of irrigation-water demand in South Asia

NASA Astrophysics Data System (ADS)

Biemans, Hester; Siderius, Christian; Mishra, Ashok; Ahmad, Bashir

2016-05-01

Especially in the Himalayan headwaters of the main rivers in South Asia, shifts in runoff are expected as a result of a rapidly changing climate. In recent years, our insight into these shifts and their impact on water availability has increased. However, a similar detailed understanding of the seasonal pattern in water demand is surprisingly absent. This hampers a proper assessment of water stress and ways to cope and adapt. In this study, the seasonal pattern of irrigation-water demand resulting from the typical practice of multiple cropping in South Asia was accounted for by introducing double cropping with monsoon-dependent planting dates in a hydrology and vegetation model. Crop yields were calibrated to the latest state-level statistics of India, Pakistan, Bangladesh and Nepal. The improvements in seasonal land use and cropping periods lead to lower estimates of irrigation-water demand compared to previous model-based studies, despite the net irrigated area being higher. Crop irrigation-water demand differs sharply between seasons and regions; in Pakistan, winter (rabi) and monsoon summer (kharif) irrigation demands are almost equal, whereas in Bangladesh the rabi demand is ~ 100 times higher. Moreover, the relative importance of irrigation supply versus rain decreases sharply from west to east. Given the size and importance of South Asia improved regional estimates of food production and its irrigation-water demand will also affect global estimates. In models used for global water resources and food-security assessments, processes like multiple cropping and monsoon-dependent planting dates should not be ignored.

Designing a new cropping system for high productivity and sustainable water usage under climate change

NASA Astrophysics Data System (ADS)

Meng, Qingfeng; Wang, Hongfei; Yan, Peng; Pan, Junxiao; Lu, Dianjun; Cui, Zhenling; Zhang, Fusuo; Chen, Xinping

2017-02-01

The food supply is being increasingly challenged by climate change and water scarcity. However, incremental changes in traditional cropping systems have achieved only limited success in meeting these multiple challenges. In this study, we applied a systematic approach, using model simulation and data from two groups of field studies conducted in the North China Plain, to develop a new cropping system that improves yield and uses water in a sustainable manner. Due to significant warming, we identified a double-maize (M-M; Zea mays L.) cropping system that replaced the traditional winter wheat (Triticum aestivum L.) -summer maize system. The M-M system improved yield by 14-31% compared with the conventionally managed wheat-maize system, and achieved similar yield compared with the incrementally adapted wheat-maize system with the optimized cultivars, planting dates, planting density and water management. More importantly, water usage was lower in the M-M system than in the wheat-maize system, and the rate of water usage was sustainable (net groundwater usage was ≤150 mm yr-1). Our study indicated that systematic assessment of adaptation and cropping system scale have great potential to address the multiple food supply challenges under changing climatic conditions.

Prioritizing agricultural pesticides used in South Africa based on their environmental mobility and potential human health effects.

PubMed

Dabrowski, James Michael; Shadung, Justinus Madimetja; Wepener, Victor

2014-01-01

South Africa is the largest user of pesticides in sub-Saharan Africa and many studies have highlighted the occurrence of pesticides in water resources. Poor management of water treatment facilities in combination with a relatively high dependency on untreated water from boreholes and rivers creates the potential for exposure of human communities to pesticides and their associated health effects. Pesticide use, physicochemical and toxicity data was therefore used to prioritize pesticides in terms of their potential risk to human health. After eliminating pesticides used in very low quantities, four indices were used to prioritize active ingredients applied in excess of 1000 kg per annum; the quantity index (QI) which ranked pesticides in terms of the quantity of their use; the toxicity potential index (TP) which ranked pesticides according to scores derived for their potential to cause five health effects (endocrine disruption, carcinogenicity, teratogenicity, mutagenicity and neurotoxicity); hazard potential index (HP) which multiplied the TP by an exposure potential score determined by the GUS index for each pesticide (to provide an indication of environmental hazard); and weighted hazard potential (WHP), which multiplied the HP for a pesticide by the ratio of its use to the total use of all pesticides in the country. The top 25 pesticides occurring in each of these indices were identified as priority pesticides, resulting in a combined total of 69 priority pesticides. A principal component analysis identified the indices that were most important in determining why a specific pesticide was included in the final priority list. As crop specific application pesticide use data was available it was possible to identify crops to which priority pesticides were applied to. Furthermore it was possible to prioritize crops in terms of the specific pesticide applied to the crop (by expressing the WHP as a ratio of the total amount of pesticide applied to the crop to the total use of all pesticides applied in the country). This allows for an improved spatial assessment of the use of priority pesticides. The methodology applied here provides a first level of basic, important information that can be used to develop monitoring programmes, identify priority areas for management interventions and to investigate optimal mitigation strategies. © 2013.

An Ultrasonic System for Weed Detection in Cereal Crops

PubMed Central

Andújar, Dionisio; Weis, Martin; Gerhards, Roland

2012-01-01

Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as well as the management practice requires additional efforts. A new sensor-based weed detection method is presented in this paper and its applicability to cereal crops is evaluated. An ultrasonic distance sensor for the determination of plant heights was used for weed detection. It was hypothesised that the weed infested zones have a higher amount of biomass than non-infested areas and that this can be determined by plant height measurements. Ultrasonic distance measurements were taken in a winter wheat field infested by grass weeds and broad-leaved weeds. A total of 80 and 40 circular-shaped samples of different weed densities and compositions were assessed at two different dates. The sensor was pointed directly to the ground for height determination. In the following, weeds were counted and then removed from the sample locations. Grass weeds and broad-leaved weeds were separately removed. Differences between weed infested and weed-free measurements were determined. Dry-matter of weeds and crop was assessed and evaluated together with the sensor measurements. RGB images were taken prior and after weed removal to determine the coverage percentages of weeds and crop per sampling point. Image processing steps included EGI (excess green index) computation and thresholding to separate plants and background. The relationship between ultrasonic readings and the corresponding coverage of the crop and weeds were assessed using multiple regression analysis. Results revealed a height difference between infested and non-infested sample locations. Density and biomass of weeds present in the sample influenced the ultrasonic readings. The possibilities of weed group discrimination were assessed by discriminant analysis. The ultrasonic readings permitted the separation between weed infested zones and non-infested areas with up to 92.8% of success. This system will potentially reduce the cost of weed detection and offers an opportunity to its use in non-selective methods for weed control. PMID:23443401

An ultrasonic system for weed detection in cereal crops.

PubMed

Andújar, Dionisio; Weis, Martin; Gerhards, Roland

2012-12-13

Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as well as the management practice requires additional efforts. A new sensor-based weed detection method is presented in this paper and its applicability to cereal crops is evaluated. An ultrasonic distance sensor for the determination of plant heights was used for weed detection. It was hypothesised that the weed infested zones have a higher amount of biomass than non-infested areas and that this can be determined by plant height measurements. Ultrasonic distance measurements were taken in a winter wheat field infested by grass weeds and broad-leaved weeds. A total of 80 and 40 circular-shaped samples of different weed densities and compositions were assessed at two different dates. The sensor was pointed directly to the ground for height determination. In the following, weeds were counted and then removed from the sample locations. Grass weeds and broad-leaved weeds were separately removed. Differences between weed infested and weed-free measurements were determined. Dry-matter of weeds and crop was assessed and evaluated together with the sensor measurements. RGB images were taken prior and after weed removal to determine the coverage percentages of weeds and crop per sampling point. Image processing steps included EGI (excess green index) computation and thresholding to separate plants and background. The relationship between ultrasonic readings and the corresponding coverage of the crop and weeds were assessed using multiple regression analysis. Results revealed a height difference between infested and non-infested sample locations. Density and biomass of weeds present in the sample influenced the ultrasonic readings. The possibilities of weed group discrimination were assessed by discriminant analysis. The ultrasonic readings permitted the separation between weed infested zones and non-infested areas with up to 92.8% of success. This system will potentially reduce the cost of weed detection and offers an opportunity to its use in non-selective methods for weed control.

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

NASA Astrophysics Data System (ADS)

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

2005-10-01

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

A black color morph of adult Nezara viridula (L.)

USDA-ARS?s Scientific Manuscript database

The southern green stink bug is a worldwide pest of cotton and other row crops, affecting crop yield and transmitting diseases. Adult coloration is sometimes used to identify southern green stink bugs and to determine their physiological condition. Multiple colors occur in southern green stink bug. ...

Fall cover crops boost soil arbuscular mycorrhizal fungi which can lead to reduced inputs

USDA-ARS?s Scientific Manuscript database

Fall cover crops provide multiple benefits to producers. These benefits include pathogen and pest protection, drought protection, weed control, reduced soil erosion, nutrient acquisition and retention, increased soil organic matter, and conservation of soil water by improvement of soil structure th...

A global sensitivity analysis of crop virtual water content

NASA Astrophysics Data System (ADS)

Tamea, S.; Tuninetti, M.; D'Odorico, P.; Laio, F.; Ridolfi, L.

2015-12-01

The concepts of virtual water and water footprint are becoming widely used in the scientific literature and they are proving their usefulness in a number of multidisciplinary contexts. With such growing interest a measure of data reliability (and uncertainty) is becoming pressing but, as of today, assessments of data sensitivity to model parameters, performed at the global scale, are not known. This contribution aims at filling this gap. Starting point of this study is the evaluation of the green and blue virtual water content (VWC) of four staple crops (i.e. wheat, rice, maize, and soybean) at a global high resolution scale. In each grid cell, the crop VWC is given by the ratio between the total crop evapotranspiration over the growing season and the crop actual yield, where evapotranspiration is determined with a detailed daily soil water balance and actual yield is estimated using country-based data, adjusted to account for spatial variability. The model provides estimates of the VWC at a 5x5 arc minutes and it improves on previous works by using the newest available data and including multi-cropping practices in the evaluation. The model is then used as the basis for a sensitivity analysis, in order to evaluate the role of model parameters in affecting the VWC and to understand how uncertainties in input data propagate and impact the VWC accounting. In each cell, small changes are exerted to one parameter at a time, and a sensitivity index is determined as the ratio between the relative change of VWC and the relative change of the input parameter with respect to its reference value. At the global scale, VWC is found to be most sensitive to the planting date, with a positive (direct) or negative (inverse) sensitivity index depending on the typical season of crop planting date. VWC is also markedly dependent on the length of the growing period, with an increase in length always producing an increase of VWC, but with higher spatial variability for rice than for other crops. The sensitivity to the reference evapotranspiration is highly variable with the considered crop and ranges from positive values (for soybean), to negative values (for rice and maize) and near-zero values for wheat. This variability reflects the different yield response factors of crops, which expresses their tolerance to water stress.

Effect of brewery wastewater obtained from different phases of treatment plant on seed germination of chickpea (Cicer arietinum), maize (Zea mays), and pigeon pea (Cajanus cajan).

PubMed

Salian, Rupa; Wani, Suhas; Reddy, Ramamohan; Patil, Mukund

2018-03-01

Brewing industry releases large quantities of wastewater after product generation. Brewery wastewater contains organic compounds which are biodegradable in nature. These biodegradable wastes can be recycled and reused and hence considered as suitable products for agriculture. But before using wastewater for agriculture, it is better to evaluate the phytotoxic effects of wastewater on crops. Hence, the main objective of this study is to evaluate the effects of brewery effluent on seed germination and growth parameters of selected crop species like chickpea (Cicer arietinum), maize (Zea mays), and pigeon pea (Cajanus cajan). Study comprised seven types of water treatments-tap water as control, diluted UASBR effluent (50% effluent + 50% distilled water): UASBR50, undiluted UASBR effluent: UASBR100, diluted TC effluent (50% effluent + 50% distilled water): ETP50,TC effluent without dilution: ETP100, 10% diluted reverse osmosis (RO10) reject (10% RO reject + 90% distilled water), and 25% diluted reverse osmosis(RO25) reject (25% RO reject + 75% distilled water) with three replications in completely randomized design. Germination test was performed in petri plates for 5 days. Parameters like germination percentage, germination rate index, seedling length, phytotoxicity index, seed vigor index, and biomass were calculated. All parameters decreased with increase in respective effluent concentration. Among all treatments, RO25 showed highest inhibitory effect on all three crops. Even though undiluted effluent of UASBR and ETP effluent showed positive effect on germination, seedling growth of three crops was promoted to the maximum by UASBR50 and ETP50. Hence, from the study, it was concluded that dilution of brewery effluent can be recommended before using it for irrigational purpose.

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.

Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

USDA-ARS?s Scientific Manuscript database

We develop a robust understanding of the effects of assimilating remote sensing observations of leaf area index and soil moisture (in the top 5 cm) on DSSAT-CSM CropSim-Ceres wheat yield estimates. Synthetic observing system simulation experiments compare the abilities of the Ensemble Kalman Filter...

Comparison of Soil Quality Index Using Three Methods

PubMed Central

Mukherjee, Atanu; Lal, Rattan

2014-01-01

Assessment of management-induced changes in soil quality is important to sustaining high crop yield. A large diversity of cultivated soils necessitate identification development of an appropriate soil quality index (SQI) based on relative soil properties and crop yield. Whereas numerous attempts have been made to estimate SQI for major soils across the World, there is no standard method established and thus, a strong need exists for developing a user-friendly and credible SQI through comparison of various available methods. Therefore, the objective of this article is to compare three widely used methods to estimate SQI using the data collected from 72 soil samples from three on-farm study sites in Ohio. Additionally, challenge lies in establishing a correlation between crop yield versus SQI calculated either depth wise or in combination of soil layers as standard methodology is not yet available and was not given much attention to date. Predominant soils of the study included one organic (Mc), and two mineral (CrB, Ko) soils. Three methods used to estimate SQI were: (i) simple additive SQI (SQI-1), (ii) weighted additive SQI (SQI-2), and (iii) statistically modeled SQI (SQI-3) based on principal component analysis (PCA). The SQI varied between treatments and soil types and ranged between 0–0.9 (1 being the maximum SQI). In general, SQIs did not significantly differ at depths under any method suggesting that soil quality did not significantly differ for different depths at the studied sites. Additionally, data indicate that SQI-3 was most strongly correlated with crop yield, the correlation coefficient ranged between 0.74–0.78. All three SQIs were significantly correlated (r = 0.92–0.97) to each other and with crop yield (r = 0.65–0.79). Separate analyses by crop variety revealed that correlation was low indicating that some key aspects of soil quality related to crop response are important requirements for estimating SQI. PMID:25148036

Indexing, screening, coding and cataloging of earth resources aircraft mission data

NASA Technical Reports Server (NTRS)

1977-01-01

Tasks completed are as follows: (1) preparation of large Area Crop Inventory experiment for data base entry;(2) preparation of Earth Observations Aircraft Flight summary reports for publication; (3) updating of the aircraft mission index coverage map and Ames aircraft flight map; (4) Prepared of Earth Observation Helicopter Flight reports for publication; and (5) indexing of LANDSAT imagery. (6) formulation of phase 3 biowindows 1, 2, 3, and 4 listings by country, footprint, and acqusition dates; (7) preparation of flight summary reports; and (8) preparation of an Alaska state index coverage map.

Long-term and widespread changes in agricultural practices influence ring-necked pheasant abundance in California

USGS Publications Warehouse

Coates, Peter S.; Brussee, Brianne E.; Howe, Kristy B.; Fleskes, Joseph P.; Dwight, Ian; Connelly, Daniel P.; Meshriy, Matt G.; Gardner, Scott C.

2017-01-01

Declines in bird populations in agricultural regions of North America and Europe have been attributed to agricultural industrialization, increases in use of agrochemical application, and increased predation related to habitat modification. Based on count data compiled from Breeding Bird Survey (BBS) from 1974 to 2012, Christmas Bird Count (CBC) collected from 1914 to 2013, and hunter data from Annual Game Take Survey (AGTS) for years 1948–2010, ring-necked pheasants (Phasianus colchicus) in California have experienced substantial declines in agricultural environments. Using a modeling approach that integrates all three forms of survey data into a joint response abundance index, we found pheasant abundance was related to the amount of harvested and unharvested crop land, types of crops produced, amount of total pesticide applied, minimum temperature, precipitation, and numbers of avian competitors and predators. Specifically, major changes in agricultural practices over the last three decades were associated with declines in pheasant numbers and likely reflected widespread loss of habitat. For example, increases in cropland were associated with increased pheasant abundance during early years of study but this effect decreased through time, such that no association in recent years was evidenced. A post hoc analysis revealed that crops beneficial to pheasant abundance (e.g., barley) have declined substantially in recent decades and were replaced by less advantageous crops (e.g., nut trees). An additional analysis using a restricted data set (1990–2013) indicated recent negative impacts on pheasant numbers associated with land use practices were also associated with relatively high levels of pesticide application. Our results may provide valuable information for management policies aimed at reducing widespread declines in pheasant populations in California and may be applicable to other avian species within agricultural settings. Furthermore, this general analytical approach is not limited to pheasants and could be applied to other taxa for which multiple survey data sources exist.

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

PubMed Central

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

2017-01-01

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

Value of Available Global Soil Moisture Products for Agricultural Monitoring

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

PubMed

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

2017-06-18

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

Effect of different crops on soil organic matter and biological activity in Oxisols under three different crops

NASA Astrophysics Data System (ADS)

Toledo, Diana Marcela; Arzuaga, Silvia; Dalurzo, Humberto; Zornoza, Raúl; Vazquez, Sara

2015-04-01

The objective of this work was to evaluate changes in soil organic matter in Oxisols under different crops compared to native rainforest, and to assess if acid phosphatase activity (APA) could be a good indicator for SOC changes and soil quality. The experimental design consisted of four completely randomized blocks with four treatments: subtropical rainforest (F); yerba mate crop (I) (Ilex paraguariensis SH.); citrus crop (C) (Citrus unshiu Marc); and tobacco crop (T) (Nicotiana tabacum L.). Soil samples were taken at 0-10; 10-20 and 20-30 cm depths. The variables measured were soil organic carbon (SOC), APA, clay content, pH, total nitrogen (Nt), available phosphorus (P) and CO2 emissions. All data were analyzed by ANOVA to assess the effects of land-use changes. The treatment means were compared through Duncan's multiple range tests (p<0.05). The relationship between variables was determined with a simple correlation analysis and with a multiple linear regression analysis through the stepwise method. These soils showed an acid reaction and their clay content was over 650 g kg-1 for the three depths. SOC and N contents were higher in native soils, intermediate for the citrus crop, and lower under both tobacco and yerba mate crops. CO2 emissions were higher in the rainforest (47.32 kg ha-1 of CO2) than in cultivated soils, which indicates that biological activity is enhanced in rainforest soils where substrates for soil biota and fauna are more readily available. The variability of 76% in APA was explained by total nitrogen, which is closely related to soil organic matter, and by available P. Conversion of subtropical rainforests into agricultural lands reduced SOC content and acid phosphatase activity, thereby lowering soil quality. In this study, acid phosphatase activity proved to be a sensitive indicator to detect changes from pristine to cropped soils, but it failed to distinguish differences among crop systems.

Economic selection indexes for Hereford and Braford cattle raised in southern Brazil.

PubMed

Costa, R F; Teixeira, B B M; Yokoo, M J; Cardoso, F F

2017-07-01

Economic selection indexes (EI) are considered the best way to select the most profitable animals for specific production systems. Nevertheless, in Brazil, few genetic evaluation programs deliver such indexes to their breeders. The aims of this study were to determine the breeding goals (BG) and economic values (EV, in US$) for typical beef cattle production systems in southern Brazil, to propose EI aimed to maximize profitability, and to compare the proposed EI with the currently used empirical index. Bioeconomic models were developed to characterize 3 typical production systems, identifying traits of economic impact and their respective EV. The first was called the calf-crop system and included the birth rate (BR), direct weaning weight (WWd), and mature cow weight (MCW) as selection goals. The second system was called the full-cycle system, and its breeding goals were BR, WWd, MCW, and carcass weight (CW). Finally, the third was called the stocking and finishing system, which had WWd and CW as breeding goals. To generate the EI, we adopted the selection criteria currently measured and used in the empirical index of PampaPlus, which is the genetic evaluation program of the Brazilian Hereford and Braford Association. The comparison between the EI and the current PampaPlus index was made by the aggregated genetic-economic gain per generation (Δ). Therefore, for each production system an index was developed using the derived economic weights, and it was compared with the current empirical index. The relative importance (RI) for BR, WWd, and MCW for the calf-crop system was 68.03%, 19.35%, and 12.62%, respectively. For the full-cycle system, the RI for BR, WWd, MCW, and CW were 69.63%, 7.31%, 5.01%, and 18.06%, respectively. For the stocking and finishing production system, the RI for WWd and CW was 34.20% and 65.80%, respectively. The Δ for the calf-crop system were US$6.12 and US$4.36, using the proposed economic and empirical indexes, respectively. Respective values were US$19.87 and US$18.22 for the full-cycle system and US$20.52 and US$18.52 in the stocking and finishing system. The efficiency of the proposed EI had low sensitivity to changes in the values of the economic and genetic parameters. The 3 EI generated higher Δ when using the proposed economic weight compared to the Δ provided by a PampaPlus index, suggesting the use of proposed EI to obtain greater economic profitability in relation to the current empirical PampaPlus index.

Agroforestry: Conifers. (Latest citations from the Cab Abstracts database). NewSearch

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

Not Available

The bibliography contains citations concerning the use of lands forested with conifers for crop and livestock production. Citations cover the grazing of livestock and the production of crops, including tomatoes, soybeans, lespedeza, wheat, rape, taro, cotton, cabbages, ginger, watermelons, and strawberries. Livestock discussed include cattle, sheep, geese, and horses. Economic analyses and economic models are presented. (Contains a minimum of 147 citations and includes a subject term index and title list.)

A multiple chamber, semicontinuous, crop carbon dioxide exchange system: design, calibration, and data interpretation

NASA Technical Reports Server (NTRS)

van Iersel, M. W.; Bugbee, B.

2000-01-01

Long-term, whole crop CO2 exchange measurements can be used to study factors affecting crop growth. These factors include daily carbon gain, cumulative carbon gain, and carbon use efficiency, which cannot be determined from short-term measurements. We describe a system that measures semicontinuously crop CO2 exchange in 10 chambers over a period of weeks or months. Exchange of CO2 in every chamber can be measured at 5 min intervals. The system was designed to be placed inside a growth chamber, with additional environmental control provided by the individual gas exchange chambers. The system was calibrated by generating CO2 from NaHCO3 inside the chambers, which indicated that accuracy of the measurements was good (102% and 98% recovery for two separate photosynthesis systems). Since the systems measure net photosynthesis (P-net, positive) and dark respiration(R-dark, negative), the data can be used to estimate gross photosynthesis, daily carbon gain, cumulative carbon gain, and carbon use efficiency. Continuous whole-crop measurements are a valuable tool that complements leaf photosynthesis measurements. Multiple chambers allow for replication and comparison among several environmental or cultural treatments that may affect crop growth. Example data from a 2 week study with petunia (Petunia x hybrida Hort. Vilm.-Andr.) are presented to illustrate some of the capabilities of this system.

Effect of new auxin herbicide formulations on control of herbicide resistant weeds and on microbial activities in the rhizosphere

USDA-ARS?s Scientific Manuscript database

Widespread distribution of glyphosate-resistant weeds in soybean-growing areas across Mississippi has economically affected soybean planting and follow-up crop management operations. New multiple herbicide-resistant crop (including soybean) technologies with associated formulations will soon be comm...

Multiple microbial activity-based measures reflect effects of cover cropping and tillage on soils

USDA-ARS?s Scientific Manuscript database

Agricultural producers, conservation professionals, and policy makers are eager to learn of soil analytical techniques and data that document improvement in soil health by agricultural practices such as no-till and incorporation of cover crops. However, there is considerable uncertainty within the r...

Optimization based trade-off analysis of biodiesel crop production for managing a German agricultural catchment

USDA-ARS?s Scientific Manuscript database

In agricultural production, the existence of multiple trade-offs among several conflicting objectives, such as food production, water quantity, water quality, biodiversity and ecosystem services, is well known. However, quantification of the trade-offs among objectives in bioenergy crop production i...

Fruit metabolite networks in engineered and non-engineered tomato genotypes reveal fluidity in a hormone and agroecosystem specific manner

USDA-ARS?s Scientific Manuscript database

Multiple strategies have been explored throughout the world to meet food security. These include molecular breeding, transgenic genotype development, reduced-tillage crop production, modification of the soil environment with cover crops or polyethylene mulches and tunnels, and organic farming. Unde...

Soil health, crop productivity, microbial transport, and mine spoil response to biochars

USDA-ARS?s Scientific Manuscript database

Biochar is being evaluated by scientists from the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) for its potential to sequester soil C, to improve soil health, and to increase crop yields. ARS scientists from multiple locations such as Florence, SC, Kimberly, ID,...

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

Chaubey, Indrajeet; Cibin, Raj; Bowling, Laura

The overall goal of this project was to conduct a watershed-scale sustainability assessment of multiple species of energy crops and removal of crop residues within two watersheds (Wildcat Creek, and St. Joseph River) representative of conditions in the Upper Midwest. The sustainability assessment included bioenergy feedstock production impacts on environmental quality, economic costs of production, and ecosystem services.

Native grass ground covers in California vineyards provide multiple ecosystem services

USDA-ARS?s Scientific Manuscript database

The mechanisms responsible for the success or failure of agricultural diversification are often unknown. Most studies in this area have focused on enhancing the effectiveness of natural enemies, but non-crop plants can also improve pest suppression by changing the host quality of crop plants through...

Characterization of some useful traits in sweet sorghum for bioenergy production

USDA-ARS?s Scientific Manuscript database

Multiple yearly harvests can increase crop productivity but the crop may encounter different environmental challenges (such as early-spring cold or late-fall frost) depending on cultivation zones. Sweet sorghum as a feedstock may be planted early to get a double harvest or be rotated with sugarcane ...

Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

NASA Astrophysics Data System (ADS)

Xie, Qiaoyun; Huang, Wenjiang; Dash, Jadunandan; Song, Xiaoyu; Huang, Linsheng; Zhao, Jinling; Wang, Renhong

2015-12-01

Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices.

Modeling and control for closed environment plant production systems

NASA Technical Reports Server (NTRS)

Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)

2002-01-01

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

Geologic map of the Skykomish River 30- by 60-minute quadrangle, Washington

USGS Publications Warehouse

Tabor, R.W.; Frizzell, D.A.; Booth, D.B.; Waitt, R.B.; Whetten, J.T.; Zartman, R.E.

1993-01-01

From the eastern-most edges of suburban Seattle, the Skykomish River quadrangle stretches east across the low rolling hills and broad river valleys of the Puget Lowland, across the forested foothills of the North Cascades, and across high meadowlands to the bare rock peaks of the Cascade crest. The Straight Creek Fault, a major Pacific Northwest structure which almost bisects the quadrangle, mostly separates unmetamorphosed and low-grade metamorphic Paleozoic and Mesozoic oceanic rocks on the west from medium- to high-grade metamorphic rocks on the east. Within the quadrangle the lower grade rocks are mostly Mesozoic melange units. To the east, the higher-grade terrane is mostly the Chiwaukum Schist and related gneisses of the Nason terrane and invading mid-Cretaceous stitching plutons. The Early Cretaceous Easton Metamorphic Suite crops out on both sides of the Straight Creek fault and records it's dextral displacement. On the south margin of the quadrangle, the fault separates the lower Eocene Swauk Formation on the east from the upper Eocene and Oligocene(?) Naches Formation and, farther north, it's correlative Barlow Pass Volcanics the west. Stratigraphically equivalent rocks ot the Puget Group crop out farther to the west. Rocks of the Cascade magmatic arc are mostly represented by Miocene and Oligocene plutons, including the Grotto, Snoqualmie, and Index batholiths. Alpine river valleys in the quadrangle record multiple advances and retreats of alpine glaciers. Multiple advances of the Cordilleran ice sheet, originating in the mountains of British Columbia, Canada, have left an even more complex sequence of outwash and till along the western mountain front, up these same alpine river valleys, and over the Puget Lowland.

Effect of crop residues on soil properties, plant growth, and crop yield. Agronomy Farm, Lincoln, Nebraska

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

Power, J.F.

1981-01-01

Progress is reported in a study designed to evaluate the effects of quantity of crop residues left on soil surface on soil properties, plant growth, and crop yield and to determine the effects of quantity of surface residues upon soil, fertilizer, and residue N transformations, availability, and efficiency of use. In a dryland corn-sorghum-soybean rotation produced on a clay loam, residues remaining after harvest of the previous crop were removed and respread on plots at rates of 0, 0.5, 1.0, and 1.5 times the quantity of residues originally present. The above crops were planted in four replications the following springmore » without tillage, after broadcasting 50 kg N/ha as ammonium nitrate. In 1980, /sup 15/N-depleted NH/sub 4/NO/sub 3/ was applied to half of each plot. After harvest, crop residues produced on the half-plot receiving the N-isotope were transferred to the half-plot receiving regular fertilizer, and visa versa. In 1981, /sup 15/N-depleted NH/sub 4/NO/sub 3/ was applied to half of each plot again, except at right angles to the fertilizer applied in 1980. After planting each year, thermocouples were installed in each plot and soil temperatures were recorded. Also access tubes were installed in all plots and soil water content was measured to the 150 cm soil depth periodically during the growing season. Dry matter production and N uptake by the plant tissue was measured periodically during the growing season and at maturity. Additional measurements taken included leaf area index, xylem water potentials, and soil microbial populations. Data are presented on corn and soybean production characteristics as affected by rate of crop residue on soil surface. Results are also given on leaf area index (LAI) and dry matter production of corn and soybeans as affected by surface residue rate. Total N content of corn and soybean plant materials and surface residues, and total and inorganic soil N (1980) are reported.« less

Low-cost multispectral imaging for remote sensing of lettuce health

NASA Astrophysics Data System (ADS)

Ren, David D. W.; Tripathi, Siddhant; Li, Larry K. B.

2017-01-01

In agricultural remote sensing, unmanned aerial vehicle (UAV) platforms offer many advantages over conventional satellite and full-scale airborne platforms. One of the most important advantages is their ability to capture high spatial resolution images (1-10 cm) on-demand and at different viewing angles. However, UAV platforms typically rely on the use of multiple cameras, which can be costly and difficult to operate. We present the development of a simple low-cost imaging system for remote sensing of crop health and demonstrate it on lettuce (Lactuca sativa) grown in Hong Kong. To identify the optimal vegetation index, we recorded images of both healthy and unhealthy lettuce, and used them as input in an expectation maximization cluster analysis with a Gaussian mixture model. Results from unsupervised and supervised clustering show that, among four widely used vegetation indices, the blue wide-dynamic range vegetation index is the most accurate. This study shows that it is readily possible to design and build a remote sensing system capable of determining the health status of lettuce at a reasonably low cost (

EnviroAtlas - Cultivated biological nitrogen fixation in agricultural lands by 12-digit HUC in the Conterminous United States, 2006

EPA Pesticide Factsheets

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not

Simple Assessment of Nitrogen Nutrition Index in Summer Maize by Using Chlorophyll Meter Readings.

PubMed

Zhao, Ben; Ata-Ul-Karim, Syed Tahir; Liu, Zhandong; Zhang, Jiyang; Xiao, Junfu; Liu, Zugui; Qin, Anzhen; Ning, Dongfeng; Yang, Qiuxia; Zhang, Yonghui; Duan, Aiwang

2018-01-01

Rapid and non-destructive diagnostic tools to accurately assess crop nitrogen nutrition index (NNI) are imperative for improving crop nitrogen (N) diagnosis and sustaining crop production. This study was aimed to develop the relationships among NNI, leaf N gradient, chlorophyll meter (CM) readings gradient, and positional differences chlorophyll meter index [PDCMI, the ratio of CM readings between different leaf layers (LLs) of crop canopy] and to validate the accuracy and stability of these relationships across the different LLs, years, sites, and cultivars. Six multi-N rates (0-320 kg ha -1 ) field experiments were conducted with four summer maize cultivars (Zhengdan958, Denghai605, Xundan20, and Denghai661) at two different sites located in China. Six summer maize plants per plot were harvested at each sampling stage to assess NNI, leaf N concentration and CM readings of different LLs during the vegetative growth period. The results showed that the leaf N gradient, CM readings gradient and PDCMI of different LLs decreased, while the NNI values increased with increasing N supply. The leaf N gradient and CM readings gradient increased gradually from top to bottom of the canopy and CM readings of the bottom LL were more sensitive to changes in plant N concentration. The significantly positive relationship between NNI and CM readings of different LLs ( LL 1 to LL 3) was observed, yet these relationships varied across the years. In contrast, the relationships between NNI and PDCMI of different LLs ( LL 1 to LL 3) were significantly negative. The strongest relationship between PDCMI and NNI which was stable across the cultivars and years was observed for PDCMI1-3 (NNI = -5.74 × PDCMI1-3+1.5, R 2 = 0.76 ** ). Additionally, the models developed in this study were validated with the data acquired from two independent experiments to assess their accuracy of prediction. The root mean square error value of 0.1 indicated that the most accurate and robust relationship was observed between PDCMI1-3 and NNI. The projected results would help to develop a simple, non-destructive and reliable approach to accurately assess the crop N status for precisely managing N application during the growth period of summer maize crop.

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China.

PubMed

Hu, Bifeng; Jia, Xiaolin; Hu, Jie; Xu, Dongyun; Xia, Fang; Li, Yan

2017-09-10

Heavy metal (HM) contamination and accumulation is a serious problem around the world due to the toxicity, abundant sources, non-biodegradable properties, and accumulative behaviour of HMs. The degree of soil HM contamination in China, especially in the Yangtze River Delta, is prominent. In this study, 1822 pairs of soil and crop samples at corresponding locations were collected from the southern Yangtze River Delta of China, and the contents of Ni, Cr, Zn, Cd, As, Cu, Hg, and Pb were measured. The single pollution index in soil (SPI) and Nemerow composite pollution index (NCPI) were used to assess the degree of HM pollution in soil, and the crop pollution index (CPI) was used to explore the degree of HM accumulation in crops. The bioaccumulation factor (BAF) was used to investigate the translocation of heavy metals in the soil-crop system. The health risks caused by HMs were calculated based on the model released by the U.S. Environmental Protection Agency. The SPIs of all elements were at the unpolluted level. The mean NCPI was at the alert level. The mean CPIs were in the following decreasing order: Ni (1.007) > Cr (0.483) > Zn (0.335) > Cd (0.314) > As (0.232) > Cu (0.187) > Hg (0.118) > Pb (0.105). Only the mean content of Ni in the crops exceeded the national standard value. The standard exceeding rates were used to represent the percentage of samples whose heavy metal content is higher than the corresponding national standard values. The standard exceeding rates of Cu, Hg, and Cd in soil were significantly higher than corresponding values in crops. Meanwhile, the standard exceeding rates of Ni, As, and Cr in crops were significantly higher than corresponding values in soil. The chronic daily intake (CDI) of children (13.8 × 10 -3 ) was the largest among three age groups, followed by adults (6.998 × 10 -4 ) and seniors (5.488 × 10 -4 ). The bioaccumulation factors (BAFs) of all crops followed the order Cd (0.249) > Zn (0.133) > As (0.076) > Cu (0.064) > Ni (0.018) > Hg (0.011) > Cr (0.010) > Pb (0.001). Therefore, Cd was most easily absorbed by crops, and different crops had different capacities to absorb HMs. The hazard quotient (HQ) represents the potential non-carcinogenic risk for an individual HM and it is an estimation of daily exposure to the human population that is not likely to represent an appreciable risk of deleterious effects during a lifetime. All the HQs of the HMs for the different age groups were significantly less than the alert value of 1.0 and were at a safe level. This indicated that citizens in the study area face low potential non-carcinogenic risk caused by HMs. The total carcinogens risks (TCRs) for children, adults, and seniors were 5.24 × 10 -5 , 2.65 × 10 -5 , and 2.08 × 10 -5 , respectively, all of which were less than the guideline value but at the alert level. Ingestion was the main pathway of carcinogen risk to human health.

Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras

NASA Astrophysics Data System (ADS)

Naito, Hiroki; Ogawa, Satoshi; Valencia, Milton Orlando; Mohri, Hiroki; Urano, Yutaka; Hosoi, Fumiki; Shimizu, Yo; Chavez, Alba Lucia; Ishitani, Manabu; Selvaraj, Michael Gomez; Omasa, Kenji

2017-03-01

Application of field based high-throughput phenotyping (FB-HTP) methods for monitoring plant performance in real field conditions has a high potential to accelerate the breeding process. In this paper, we discuss the use of a simple tower based remote sensing platform using modified single-lens reflex cameras for phenotyping yield traits in rice under different nitrogen (N) treatments over three years. This tower based phenotyping platform has the advantages of simplicity, ease and stability in terms of introduction, maintenance and continual operation under field conditions. Out of six phenological stages of rice analyzed, the flowering stage was the most useful in the estimation of yield performance under field conditions. We found a high correlation between several vegetation indices (simple ratio (SR), normalized difference vegetation index (NDVI), transformed vegetation index (TVI), corrected transformed vegetation index (CTVI), soil-adjusted vegetation index (SAVI) and modified soil-adjusted vegetation index (MSAVI)) and multiple yield traits (panicle number, grain weight and shoot biomass) across a three trials. Among all of the indices studied, SR exhibited the best performance in regards to the estimation of grain weight (R2 = 0.80). Under our tower-based field phenotyping system (TBFPS), we identified quantitative trait loci (QTL) for yield related traits using a mapping population of chromosome segment substitution lines (CSSLs) and a single nucleotide polymorphism data set. Our findings suggest the TBFPS can be useful for the estimation of yield performance during early crop development. This can be a major opportunity for rice breeders whom desire high throughput phenotypic selection for yield performance traits.

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

An integrated soil-crop system model for water and nitrogen management in North China

PubMed Central

Liang, Hao; Hu, Kelin; Batchelor, William D.; Qi, Zhiming; Li, Baoguo

2016-01-01

An integrated model WHCNS (soil Water Heat Carbon Nitrogen Simulator) was developed to assess water and nitrogen (N) management in North China. It included five main modules: soil water, soil temperature, soil carbon (C), soil N, and crop growth. The model integrated some features of several widely used crop and soil models, and some modifications were made in order to apply the WHCNS model under the complex conditions of intensive cropping systems in North China. The WHCNS model was evaluated using an open access dataset from the European International Conference on Modeling Soil Water and N Dynamics. WHCNS gave better estimations of soil water and N dynamics, dry matter accumulation and N uptake than 14 other models. The model was tested against data from four experimental sites in North China under various soil, crop, climate, and management practices. Simulated soil water content, soil nitrate concentrations, crop dry matter, leaf area index and grain yields all agreed well with measured values. This study indicates that the WHCNS model can be used to analyze and evaluate the effects of various field management practices on crop yield, fate of N, and water and N use efficiencies in North China. PMID:27181364

Wine grape cultivar influence on the performance of models that predict the lower threshold canopy temperature of a water stress index

USDA-ARS?s Scientific Manuscript database

The calculation of a thermal based Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions. The objective of this study was to assess the influence of different wine grape cultivars on the performance of models that predict canopy temperature non...

Development of an Optical Device to Investigatechlorophyll Content of Tomato Leaves

NASA Astrophysics Data System (ADS)

Cui, Di; Li, Minzan; Li, Xiuhua

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

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

PubMed

Collier, Marcus J; Mullins, Ewen

2010-01-01

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

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 .

Integrated crop management practices for maximizing grain yield of double-season rice crop.

PubMed

Wang, Depeng; Huang, Jianliang; Nie, Lixiao; Wang, Fei; Ling, Xiaoxia; Cui, Kehui; Li, Yong; Peng, Shaobing

2017-01-12

Information on maximum grain yield and its attributes are limited for double-season rice crop grown under the subtropical environment. This study was conducted to examine key characteristics associated with high yielding double-season rice crop through a comparison between an integrated crop management (ICM) and farmers' practice (FP). Field experiments were conducted in the early and late seasons in the subtropical environment of Wuxue County, Hubei Province, China in 2013 and 2014. On average, grain yield in ICM was 13.5% higher than that in FP. A maximum grain yield of 9.40 and 10.53 t ha -1 was achieved under ICM in the early- and late-season rice, respectively. Yield improvement of double-season rice with ICM was achieved with the combined effects of increased plant density and optimized nutrient management. Yield gain of ICM resulted from a combination of increases in sink size due to more panicle number per unit area and biomass production, further supported by the increased leaf area index, leaf area duration, radiation use efficiency, crop growth rate, and total nitrogen uptake compared with FP. Further enhancement in the yield potential of double-season rice should focus on increasing crop growth rate and biomass production through improved and integrated crop management practices.

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

NASA Astrophysics Data System (ADS)

Huang, Qing; Zhou, Qing-bo; Zhang, Li

2009-07-01

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

Response to Crop-Tree Release: Sugar Maple, Red Oak, Black Cherry, and Yellow-Poplar Saplings in a 9-Year-Old Stand

Treesearch

Neil I. Lamson; H. Clay Smith

1978-01-01

Crop trees were released in an Appalachian hardwood stand (site index 70 for northern red oak) that had been clearcut 9 years earlier. We released 134 yellow-poplar, red oak, black cherry, and sugar maple stems of seedling origin to a 5-foot radius around the bole of each study tree; 140 comparable stems were not released. These trees were dominant, codominant, or...

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.

Shading effect on microclimate and thermal comfort indexes in integrated crop-livestock-forest systems in the Brazilian Midwest

NASA Astrophysics Data System (ADS)

Karvatte, Nivaldo; Klosowski, Elcio Silvério; de Almeida, Roberto Giolo; Mesquita, Eduardo Eustáquio; de Oliveira, Caroline Carvalho; Alves, Fabiana Villa

2016-12-01

The objective of this paper was to perform a microclimate evaluation and determine the indexes of thermal comfort indexes, in sun and shade, in integrated crop-livestock-forest systems with different arrangements of eucalyptus and native trees, in the Brazilian Midwest. The experiment was conducted at Embrapa Beef Cattle in Campo Grande, state of Mato Grosso do Sul, Brazil, from July to September 2013. The evaluations were conducted on four consecutive days, from 8:00 a.m. to 5:00 p.m., local time (GMT -4:00), with 1 hour intervals, recording the microclimate parameters: air temperature (°C), black globe temperature (°C), wet bulb temperature (°C), relative humidity (%), and wind speed (m.s-1), for the subsequent calculation of the Temperature and Humidity Index, the Black Globe Temperature and Humidity Index, and the Radiant Thermal Load. The largest changes in microclimate parameters were found in the full sun, between 12:00 p.m. and 1:00 p.m., in less dense eucalyptus system, followed by the scattered native trees system, resulting in a maximum Temperature and Humidity Index of 81, Black Globe Temperature and Humidity Index of 88 and Radiant Thermal Load of 794 W m-2. Therefore, it is observed that with the presence of trees in pastures were possible reductions of up to 3.7 % in Temperature and Humidity Index, 10.2 % in the Black Globe Temperature and Humidity Index, and 28.3 % of the Radiant Thermal Load in the shade. Thus, one can conclude that the presence of trees and their arrangement in the systems provide better microclimate conditions and animal thermal comfort in pastures.

Shading effect on microclimate and thermal comfort indexes in integrated crop-livestock-forest systems in the Brazilian Midwest.

PubMed

Karvatte, Nivaldo; Klosowski, Elcio Silvério; de Almeida, Roberto Giolo; Mesquita, Eduardo Eustáquio; de Oliveira, Caroline Carvalho; Alves, Fabiana Villa

2016-12-01

The objective of this paper was to perform a microclimate evaluation and determine the indexes of thermal comfort indexes, in sun and shade, in integrated crop-livestock-forest systems with different arrangements of eucalyptus and native trees, in the Brazilian Midwest. The experiment was conducted at Embrapa Beef Cattle in Campo Grande, state of Mato Grosso do Sul, Brazil, from July to September 2013. The evaluations were conducted on four consecutive days, from 8:00 a.m. to 5:00 p.m., local time (GMT -4:00), with 1 hour intervals, recording the microclimate parameters: air temperature (°C), black globe temperature (°C), wet bulb temperature (°C), relative humidity (%), and wind speed (m.s -1 ), for the subsequent calculation of the Temperature and Humidity Index, the Black Globe Temperature and Humidity Index, and the Radiant Thermal Load. The largest changes in microclimate parameters were found in the full sun, between 12:00 p.m. and 1:00 p.m., in less dense eucalyptus system, followed by the scattered native trees system, resulting in a maximum Temperature and Humidity Index of 81, Black Globe Temperature and Humidity Index of 88 and Radiant Thermal Load of 794 W m -2 . Therefore, it is observed that with the presence of trees in pastures were possible reductions of up to 3.7 % in Temperature and Humidity Index, 10.2 % in the Black Globe Temperature and Humidity Index, and 28.3 % of the Radiant Thermal Load in the shade. Thus, one can conclude that the presence of trees and their arrangement in the systems provide better microclimate conditions and animal thermal comfort in pastures.

The effectiveness of habitat modification schemes for enhancing beneficial insects: Assessing the importance of trap cropping management approach

NASA Astrophysics Data System (ADS)

Trisnawati, Indah; Azis, Abdul

2017-06-01

Many farms in regions of intensive crop production lack the habitats that historically provided resources to beneficial insects, and this lack has compromised the ability of farmers to rely on natural enemies for pest control. One of the strategies to boost populations of existing or naturally occurring beneficial insects is to supply them with appropriate habitat and alternative food sources, such as diversifying trap crop systems and plant populations in or around fields include perennials and flowering plants. Trap cropping using insectary plant that attracts beneficial insects as natural enemies, especially flowering plants, made for provision of habitat for predators or parasitoids that are useful for biological control. Perimeter trap cropping (PTC) is a method of integrated pest management in which the main crop is surrounded with a perimeter trap crop that is more attractive to pests. We observed PTC habitat modification and conventionaly-managed tobacco farms in Purwosari Village, Pasuruan (East Java) to evaluate the effectiveness of habitat modification management prescription (perimeter trap crop using flowering plant Crotalaria juncea) on agroecosystem natural enemies. Field tests were conducted in natural enemies (predator and parasitoid) abundance dynamic and diversity on tobacco field in Purwoasri, Pasuruan. Yellow pan trap, sweep net and hand collecting methods were applied in each 10 days during tobacco growth stage (vegetative, generative until reproductive/harvesting. The results showed that application perimeter trap crop with C. juncea in tobacco fields able to help arthropod conservation of natural enemies on all tobacco growth stages. These results were evidenced the increase in abundance of predators and parasitoids and the increased value of the Diversity Index (H') and Evenness Index (EH) in all tobacco growth phases. Composition of predator and parasitoid in the habitat modification field were more diverse than in the conventional field. Three specific predator species were found on habitat modification field, i.e.: Crocothemis servilia, Orthetrum sabina and Paratrechina sp., as well as specific parasitoid species, i.e.: Polistes sp. (vegetative stage), Chloromyia sp., Theronia sp., Sarcophaga sp. and Cletus sp (generative stage), Condylodtylus sp., Trichogramma sp. (reproductive stage). Trends in predator abundance toward parasitoid insects were indicated a positive linear trend, with the abundance of predator on habitat modification field has an influence on the level of 67.1% parasitoid.

Soil Moisture as an Estimator for Crop Yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan

2015-04-01

Annual crop yield depends on various factors such as soil properties, management decisions, and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields allows to mitigate negative effects of weather extremes which are assumed to occur more often in the future due to climate change. A standard approach in economics is to predict the impact of climate change on agriculture as a function of temperature and precipitation. This approach has been developed further using concepts like growing degree days. Other econometric models use nonlinear functions of heat or vapor pressure deficit. However, none of these approaches uses soil moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to explain stress on plant growth than estimations based on precipitation and temperature. This is the case because the latter variables do not explicitly account for the available water content in the root zone, which is the primary source of water supply for plant growth. In this study, a reduced form panel approach is applied to estimate a multivariate econometric production function for the years 1999 to 2010. Annual crop yield data of various crops on the administrative district level serve as depending variables. The explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4 km2 grid resolution covering entire Germany. A reduced model approach is suitable because the SMI is the result of a stochastic weather process and therefore can be considered exogenous. For the ease of interpretation a linear functionality is preferred. Meteorological, phenological, geological, agronomic, and socio-economic variables are also considered to extend the model in order to reveal the proper causal relation. First results show that dry as well as wet extremes of SMI have a negative impact on crop yield for winter wheat. This indicates that soil moisture has at least a limiting affect on crop production.

Cover crops in the upper midwestern United States: Simulated effect on nitrate leaching with artificial drainage

USDA-ARS?s Scientific Manuscript database

Fall-planted winter cover crops are an agricultural management practice with multiple benefits that includes reducing nitrate losses from artificially drained fields. While the practice is commonly used in the southern and eastern U.S., little is known about its efficacy in Midwestern states where a...

Trace gas emissions from a sun and shade grown ornamental crop

USDA-ARS?s Scientific Manuscript database

Previous work has begun to establish baseline approximations for greenhouse gas (GHG) (CO2, CH4, and N2O) emissions of several horticultural crops, though much work is still needed to expand contingencies for multiple best management practices. In this study, GHG emissions from one shade-grown speci...

Using FACE systems to screen wheat cultivars for yield increases at elevated CO2

USDA-ARS?s Scientific Manuscript database

Because of continuing increases in atmospheric CO2, identifying cultivars of crops with larger yield increases at elevated CO2 may provide an avenue to increase crop yield potential in future climates. Free-air CO2 enrichment (FACE) systems have most often been used with multiple replications of ea...

Genome-wide association mapping of resistance to a Brazilian isolate of Sclerotinia sclerotiorum in soybean genotypes mostly from Brazil

USDA-ARS?s Scientific Manuscript database

Sclerotinia Stem Rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is ubiquitous in cooler climates where soybean crops are grown. Breeding for resistance to SSR remains challenging in crops like soybean, where no single gene provides strong resistance, but instead, multiple genes w...

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

EPA Science Inventory

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 I...

Multiple rolling/crimping effects on termination of two summer cover crops in a conservation system

USDA-ARS?s Scientific Manuscript database

A field experiment was initiated in the 2015 growing season at the USDA-NSDL to determine the effectiveness of a prototype two-stage roller/crimper in mechanical termination of two summer cover crops intended for organic systems. The experiment was a randomized complete block design with four replic...

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.

Assessment of spatially distributed values of Kc using vegetation indices derived from medium resolution satellite data

NASA Astrophysics Data System (ADS)

Greco, M.; Simoniello, T.; Lanfredi, M.; Russo, A. L.

2010-09-01

In the last years, the theme of suitable assessment of irrigation water supply has been raised relevant interest for both general principles of sustainable development and optimization of water resources techniques and management. About 99% of the water used in agriculture is lost by crops as evapotranspiration (ET). Thus, it becomes crucial to drive direct or indirect measurement in order to perform a suitable evaluation of water loss by evapotranspiration (i.e. actual evapotranspiration) as well as crop water status and its effect on the production. The main methods used to measure evapotranspiration are available only at field scale (Bowen ratio, eddy correlation system, soil water balance) confined to a small pilot area, generally due to expense and logistical constraints. This led over the last 50 years to the development of a large number of empirical methods to estimate evapotranspiration through different climatic and meteorological variables as well as combining models, based on aerodynamic theory and energy balance, taking into account both canopy properties and meteorological conditions. Among these, the Penman-Monteith equation seems to give the best results providing a robust and consistent method world wide accepted. Such conventional methods only provide accurate evapotranspiration assessment for a homogeneous region nearby the meteorological gauge station and cannot be extrapolated to other different sites; whereas remote sensing techniques allow for filling up such a gap. Some of these satellite techniques are based on the use of thermal band signals as inputs for energy balance equations. Another common approach is mainly based on the FAO method for estimating crop evapotranspiration, in which evapotranspiration data are multiplied by crop coefficients, Kc, derived from satellite multispectral vegetation indices obtained. The rationale behind such a link considers that Kc and vegetation indices are sensitive to both leaf area index and fractional ground cover. Thermal-based energy balance models are more suitable than the FAO-Kc model for estimating crop ET, especially under moisture stress conditions, but they require many inputs and detailed theoretical background knowledge; so they can be only used in regions where high quality, hourly agricultural weather data are readily available providing instantaneous values of heat fluxes corresponding to the time of the satellite overpass. Thus, FAO-Kc approach is widely used in research activities and real-time irrigation scheduling for several water applications since it does not require temporal upscaling for obtaining daily values and satellite imagery in the reflective bands used for vegetation index computation are more readily available at higher spatial resolution than thermal band data. There is no simple way to compute crop coefficients because they depend on climate, soil type, crop and its varieties, irrigation method, soil water, nutrient content and plant phenology. Consequently, specific calibrations of crop coefficient are required in various climatic regions. Many authors suggested a linear relationship between Kc and vegetation indices, but non-linear relationships have been proposed too. However, according to the radiative transfer theory, the nature of such relationships depends on the crop architecture and the definition of the adopted vegetation index, but the linear assumption can be adopted as first. Such studies, mainly investigated the possibility to use high resolution satellite data, such as Quickbird, Ikonos, TM, which are not suitable for operational purposes since in spite of the high spatial sampling they have an inadequate revisiting time over a given area. To obtain adequate temporal sampling, some authors proposed the use of a virtual constellation made by all currently available high-resolution satellites (e.g., DEMETER project). However the joint use of data from different satellites requires a carefully inter-satellite cross-calibration and co-registration. In order to avoid such problems and to generate spatially distributed values of Kc capturing field-specific crop development, the employment of vegetation indices derived from medium resolution MODIS data having a higher temporal sampling has been investigated. The spatial and temporal correlation between NDVI (Normalized Difference Vegetation Index) and crop coefficients for different herbaceous and arboreal cultivations has been investigated to define their relationships. Through this approach site-specific crop coefficients were derived taking into account the effective ground coverage and status. The analysis has been applied on the 2005-2008 time series for the Basilicata region, Southern Italy.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

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

2010-10-01

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

Designing a new cropping system for high productivity and sustainable water usage under climate change

PubMed Central

Meng, Qingfeng; Wang, Hongfei; Yan, Peng; Pan, Junxiao; Lu, Dianjun; Cui, Zhenling; Zhang, Fusuo; Chen, Xinping

2017-01-01

The food supply is being increasingly challenged by climate change and water scarcity. However, incremental changes in traditional cropping systems have achieved only limited success in meeting these multiple challenges. In this study, we applied a systematic approach, using model simulation and data from two groups of field studies conducted in the North China Plain, to develop a new cropping system that improves yield and uses water in a sustainable manner. Due to significant warming, we identified a double-maize (M-M; Zea mays L.) cropping system that replaced the traditional winter wheat (Triticum aestivum L.) –summer maize system. The M-M system improved yield by 14–31% compared with the conventionally managed wheat-maize system, and achieved similar yield compared with the incrementally adapted wheat-maize system with the optimized cultivars, planting dates, planting density and water management. More importantly, water usage was lower in the M-M system than in the wheat-maize system, and the rate of water usage was sustainable (net groundwater usage was ≤150 mm yr−1). Our study indicated that systematic assessment of adaptation and cropping system scale have great potential to address the multiple food supply challenges under changing climatic conditions. PMID:28155860

Estimation of Crop Gross Primary Production (GPP). 2; Do Scaled (MODIS) Vegetation Indices Improve Performance?

NASA Technical Reports Server (NTRS)

Zhang, Qingyuan; Cheng, Yen-Ben; Lyapustin, Alexei I.; Wang, Yujie; Zhang, Xiaoyang; Suyker, Andrew; Verma, Shashi; Shuai, Yanmin; Middleton, Elizabeth M.

2015-01-01

Satellite remote sensing estimates of Gross Primary Production (GPP) have routinely been made using spectral Vegetation Indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs, and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions.

Contrasting effects of landscape composition on crop yield mediated by specialist herbivores.

PubMed

Perez-Alvarez, Ricardo; Nault, Brian A; Poveda, Katja

2018-04-01

Landscape composition not only affects a variety of arthropod-mediated ecosystem services, but also disservices, such as herbivory by insect pests that may have negative effects on crop yield. Yet, little is known about how different habitats influence the dynamics of multiple herbivore species, and ultimately their collective impact on crop production. Using cabbage as a model system, we examined how landscape composition influenced the incidence of three specialist cruciferous pests (aphids, flea beetles, and leaf-feeding Lepidoptera), lepidopteran parasitoids, and crop yield across a gradient of landscape composition in New York, USA. We expected that landscapes with a higher proportion of cropland and lower habitat diversity would lead to an increase in pest pressure of the specialist herbivores and a reduction in crop yield. However, results indicated that neither greater cropland area nor lower landscape diversity influenced pest pressure or yield. Rather, pest pressure and yield were best explained by the presence of non-crop habitats (i.e., meadows) in the landscape. Specifically, cabbage was infested with fewer Lepidoptera in landscapes with a higher proportion of meadows likely resulting from increased parasitism. Conversely, cabbage was infested with more flea beetles and aphids as the proportion of meadows in the landscape increased, suggesting that these pests benefit from non-crop habitats. Furthermore, path analysis confirmed that these landscape-mediated effects on pest populations can have either positive or negative cascading effects on crop yield. Our findings illustrate how different pest species within the same cropping system show contrasting responses to landscape composition with respect to both the direction and spatial scale of the relationship. Such tradeoffs resulting from the complex interaction between multiple-pests, natural enemies, and landscape composition must be considered, if we are to manage landscapes for pest suppression benefits. © 2018 by the Ecological Society of America.

Multiple pathways of commodity crop expansion in tropical forest landscapes

NASA Astrophysics Data System (ADS)

Meyfroidt, Patrick; Carlson, Kimberly M.; Fagan, Matthew E.; Gutiérrez-Vélez, Victor H.; Macedo, Marcia N.; Curran, Lisa M.; DeFries, Ruth S.; Dyer, George A.; Gibbs, Holly K.; Lambin, Eric F.; Morton, Douglas C.; Robiglio, Valentina

2014-07-01

Commodity crop expansion, for both global and domestic urban markets, follows multiple land change pathways entailing direct and indirect deforestation, and results in various social and environmental impacts. Here we compare six published case studies of rapid commodity crop expansion within forested tropical regions. Across cases, between 1.7% and 89.5% of new commodity cropland was sourced from forestlands. Four main factors controlled pathways of commodity crop expansion: (i) the availability of suitable forestland, which is determined by forest area, agroecological or accessibility constraints, and land use policies, (ii) economic and technical characteristics of agricultural systems, (iii) differences in constraints and strategies between small-scale and large-scale actors, and (iv) variable costs and benefits of forest clearing. When remaining forests were unsuitable for agriculture and/or policies restricted forest encroachment, a larger share of commodity crop expansion occurred by conversion of existing agricultural lands, and land use displacement was smaller. Expansion strategies of large-scale actors emerge from context-specific balances between the search for suitable lands; transaction costs or conflicts associated with expanding into forests or other state-owned lands versus smallholder lands; net benefits of forest clearing; and greater access to infrastructure in already-cleared lands. We propose five hypotheses to be tested in further studies: (i) land availability mediates expansion pathways and the likelihood that land use is displaced to distant, rather than to local places; (ii) use of already-cleared lands is favored when commodity crops require access to infrastructure; (iii) in proportion to total agricultural expansion, large-scale actors generate more clearing of mature forests than smallholders; (iv) property rights and land tenure security influence the actors participating in commodity crop expansion, the form of land use displacement, and livelihood outcomes; (v) intensive commodity crops may fail to spare land when inducing displacement. We conclude that understanding pathways of commodity crop expansion is essential to improve land use governance.

7 CFR 1437.13 - Multiple benefits.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 10 2012-01-01 2012-01-01 false Multiple benefits. 1437.13 Section 1437.13... General Provisions § 1437.13 Multiple benefits. (a) If a producer is eligible to receive payments under this part and benefits under any other program administered by the Secretary for the same crop loss...

7 CFR 1437.13 - Multiple benefits.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 10 2014-01-01 2014-01-01 false Multiple benefits. 1437.13 Section 1437.13... General Provisions § 1437.13 Multiple benefits. (a) If a producer is eligible to receive payments under this part and benefits under any other program administered by the Secretary for the same crop loss...

7 CFR 1437.13 - Multiple benefits.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false Multiple benefits. 1437.13 Section 1437.13... General Provisions § 1437.13 Multiple benefits. (a) If a producer is eligible to receive payments under this part and benefits under any other program administered by the Secretary for the same crop loss...

7 CFR 1437.13 - Multiple benefits.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 10 2011-01-01 2011-01-01 false Multiple benefits. 1437.13 Section 1437.13... General Provisions § 1437.13 Multiple benefits. (a) If a producer is eligible to receive payments under this part and benefits under any other program administered by the Secretary for the same crop loss...

7 CFR 1437.13 - Multiple benefits.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 10 2013-01-01 2013-01-01 false Multiple benefits. 1437.13 Section 1437.13... General Provisions § 1437.13 Multiple benefits. (a) If a producer is eligible to receive payments under this part and benefits under any other program administered by the Secretary for the same crop loss...

Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison.

PubMed

Osmond, Deanna; Bolster, Carl; Sharpley, Andrew; Cabrera, Miguel; Feagley, Sam; Forsberg, Adam; Mitchell, Charles; Mylavarapu, Rao; Oldham, J Larry; Radcliffe, David E; Ramirez-Avila, John J; Storm, Dan E; Walker, Forbes; Zhang, Hailin

2017-11-01

Phosphorus (P) Indices in the southern United States frequently produce different recommendations for similar conditions. We compared risk ratings from 12 southern states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Texas) using data collected from benchmark sites in the South (Arkansas, Georgia, Mississippi, North Carolina, Oklahoma, and Texas). Phosphorus Index ratings were developed using both measured erosion losses from each benchmark site and Revised Universal Soil Loss Equation 2 predictions; mostly, there was no difference in P Index outcome. The derived loss ratings were then compared with measured P loads at the benchmark sites by using equivalent USDA-NRCS P Index ratings and three water quality models (Annual P Loss Estimator [APLE], Agricultural Policy Environmental eXtender [APEX], and Texas Best Management Practice Evaluation Tool [TBET]). Phosphorus indices were finally compared against each other using USDA-NRCS loss ratings model estimate correspondence with USDA-NRCS loss ratings. Correspondence was 61% for APEX, 48% for APLE, and 52% for TBET, with overall P index correspondence at 55%. Additive P Indices (Alabama and Texas) had the lowest USDA-NRCS loss rating correspondence (31%), while the multiplicative (Arkansas, Florida, Louisiana, Mississippi, South Carolina, and Tennessee) and component (Georgia, Kentucky, and North Carolina) indices had similar USDA-NRCS loss rating correspondence-60 and 64%, respectively. Analysis using Kendall's modified Tau suggested that correlations between measured and calculated P-loss ratings were similar or better for most P Indices than the models. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Improvements in agricultural water decision support using remote sensing

NASA Astrophysics Data System (ADS)

Marshall, M. T.

2012-12-01

Population driven water scarcity, aggravated by climate-driven evaporative demand in dry regions of the world, has the potential of transforming ecological and social systems to the point of armed conflict. Water shortages will be most severe in agricultural areas, as the priority shifts to urban and industrial use. In order to design, evaluate, and monitor appropriate mitigation strategies, predictive models must be developed that quantify exposure to water shortage. Remote sensing data has been used for more than three decades now to parametrize these models, because field measurements are costly and difficult in remote regions of the world. In the past decade, decision-makers for the first time can make accurate and near real-time evaluations of field conditions with the advent of hyper- spatial and spectral and coarse resolution continuous remote sensing data. Here, we summarize two projects representing diverse applications of remote sensing to improve agricultural water decision support. The first project employs MODIS (coarse resolution continuous data) to drive an evapotranspiration index, which is combined with the Standardized Precipitation Index driven by meteorological satellite data to improve famine early warning in Africa. The combined index is evaluated using district-level crop yield data from Kenya and Malawi and national-level crop yield data from the United Nations Food and Agriculture Organization. The second project utilizes hyper- spatial (GeoEye 1, Quickbird, IKONOS, and RapidEye) and spectral (Hyperion/ALI), as well as multi-spectral (Landsat ETM+, SPOT, and MODIS) data to develop biomass estimates for key crops (alfalfa, corn, cotton, and rice) in the Central Valley of California. Crop biomass is an important indicator of crop water productivity. The remote sensing data is combined using various data fusion techniques and evaluated with field data collected in the summer of 2012. We conclude with a brief discussion on implementation of these tools into two new decision support systems: FEWSNET Early Warning Explorer (http://earlywarning.usgs.gov/fews/ewxindex.php) and the NASA Terrestrial Observation and Prediction System (http://ecocast.arc.nasa.gov/) for the first and second project respectively.

As-Built documentation of programs to implement the Robertson and Doraiswamy/Thompson models

NASA Technical Reports Server (NTRS)

Valenziano, D. J. (Principal Investigator)

1981-01-01

The software which implements two spring wheat phenology models is described. The main program routines for the Doraiswamy/Thompson crop phenology model and the basic Robertson crop phenology model are DTMAIN and BRMAIN. These routines read meteorological data files and coefficient files, accept the planting date information and other information from the user, and initiate processing. Daily processing for the basic Robertson program consists only of calculation of the basic Robertson increment of crop development. Additional processing in the Doraiswamy/Thompson program includes the calculation of a moisture stress index and correction of the basic increment of development. Output for both consists of listings of the daily results.

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Recent Advances in Utilizing Transcription Factors to Improve Plant Abiotic Stress Tolerance by Transgenic Technology

PubMed Central

Wang, Hongyan; Wang, Honglei; Shao, Hongbo; Tang, Xiaoli

2016-01-01

Agricultural production and quality are adversely affected by various abiotic stresses worldwide and this will be exacerbated by the deterioration of global climate. To feed a growing world population, it is very urgent to breed stress-tolerant crops with higher yields and improved qualities against multiple environmental stresses. Since conventional breeding approaches had marginal success due to the complexity of stress tolerance traits, the transgenic approach is now being popularly used to breed stress-tolerant crops. So identifying and characterizing the critical genes involved in plant stress responses is an essential prerequisite for engineering stress-tolerant crops. Far beyond the manipulation of single functional gene, engineering certain regulatory genes has emerged as an effective strategy now for controlling the expression of many stress-responsive genes. Transcription factors (TFs) are good candidates for genetic engineering to breed stress-tolerant crop because of their role as master regulators of many stress-responsive genes. Many TFs belonging to families AP2/EREBP, MYB, WRKY, NAC, bZIP have been found to be involved in various abiotic stresses and some TF genes have also been engineered to improve stress tolerance in model and crop plants. In this review, we take five large families of TFs as examples and review the recent progress of TFs involved in plant abiotic stress responses and their potential utilization to improve multiple stress tolerance of crops in the field conditions. PMID:26904044

Direct radiative effect of carbonaceous aerosols from crop residue burning during the summer harvest season in East China

NASA Astrophysics Data System (ADS)

Yao, Huan; Song, Yu; Liu, Mingxu; Archer-Nicholls, Scott; Lowe, Douglas; McFiggans, Gordon; Xu, Tingting; Du, Pin; Li, Jianfeng; Wu, Yusheng; Hu, Min; Zhao, Chun; Zhu, Tong

2017-04-01

East China experiences extensive crop residue burnings in fields during harvest season. The direct radiative effect (DRE) of carbonaceous aerosols from crop residue burning in June 2013 in East China was investigated using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). Absorption of organic aerosol (OA) in the presence of brown carbon was considered using the parameterization of Saleh et al. (2014), in which the imaginary part of the OA refractive index is a function of wavelength and the ratio of black carbon (BC) and OA. The carbonaceous emissions from crop fires were estimated using the Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power (FRP) product with a localized crop-burning-sourced BC-to-organic carbon (OC) ratio emission ratio of 0.27. Evaluation of the model results with in situ measurements of particulate matter with aerodynamic diameter less than 2.5 µm (PM2. 5) chemical composition, MODIS aerosol optical depth (AOD) detections and meteorological observations showed that this model was able to reproduce the magnitude, spatial variation and optical characteristics of carbonaceous aerosol pollution. The observed BC and OC peak concentrations at the site in Suixi, Anhui province, during the 2013 wheat burning season reached 55.3 µg m-3 and 157.9 µg m-3. WRF-Chem simulations reproduced these trends with a correlation coefficient of 0.74, estimating that crop residue burning contributed 86 and 90 % of peak BC and OC, respectively. The simulated hourly DRE from crop residue burning at the top of atmosphere (TOA) reached a maximum of +22.66 W m-2 at the Suixi site. On average, the simulations showed that the crop residue burning introduced a net positive DRE of +0.14 W m-2 at TOA throughout East China, with BC from this source as the main heating contributor (+0.79 W m-2). The OA DRE from crop burning (-0.22 W m-2) was a combined effect of the positive DRE of absorption (+0.21 W m-2) and a stronger negative DRE of scattering (-0.43 W m-2). Sensitivity tests showed that the DRE of OA absorption strongly depended on the imaginary part of the OA refractive index, the BC-to-OA emission ratio from crop residue burning and the assumed mixing state of the aerosol, whereby the volume mixing treatment resulted in a higher positive DRE compared to the core-shell treatment. The BC mixing state and associated absorption enhancement during BC aging processes will be investigated in detail in future research.

Mapping Collective Identity: Territories and Boundaries of Human Terrain

DTIC Science & Technology

2011-06-10

Line MAP-HT Mapping the Human Terrain NDVI Normalized Difference Vegetation Index NGA National Geospatial-Intelligence Agency xi OBIA Object-Based...The Normalized Difference Vegetation Index ( NDVI ) uses the red band to represent the low reflectance from vegetation and the expanded near infrared...spectrum to provide greater delineation of agricultural areas. This layer highlights different fields, crops, and their boundaries. NDVI layers are

Evaluation of a native vegetation masking technique

NASA Technical Reports Server (NTRS)

Kinsler, M. C.

1984-01-01

A crop masking technique based on Ashburn's vegetative index (AVI) was used to evaluate native vegetation as an indicator of crop moisture condition. A mask of the range areas (native vegetation) was generated for each of thirteen Great Plains LANDSAT MSS sample segments. These masks were compared to the digitized ground truth and accuracies were computed. An analysis of the types of errors indicates a consistency in errors among the segments. The mask represents a simple quick-look technique for evaluating vegetative cover.

Comments on "Bioprocessing in space"

PubMed

Volk, T

1993-10-01

An analysis developed by Westgate et al. for the digestible energy of edible and inedible biomass, including hydrolysis and fermentation, is reexamined with state-of-the-art values for the harvest index of hydroponic crops.

Photochemical Reflectance Index (PRI) as a proxy of Light Use Efficiency (LUE) and transpiration in Mediterranean crop sites

NASA Astrophysics Data System (ADS)

LE Dantec, V.; Chebbi, W.; Boulet, G.; Merlin, O.; Lili-Chabaane, Z.; Er Raki, S.; Ceschia, E.; Khabba, S.; Fanise, P.; Zawilski, B.; Simonneaux, V.; Jarlan, L.

2016-12-01

The Photochemical Reflectance Index (PRI) is based on the short term reversible xanthophyll pigment changes accompanying plant stress and therefore of the associated photosynthetic activities. Strong relationships between PRI and Light Use Efficiency (LUE) were shown at leaf and canopy scales and over a wide range of species (Garbulsky et al., 2011). But very few previous works have explored the potential link with plant water status. In this study, we have first analyzed the link between PRI and LUE at canopy scale on two different crops in terms of canopy structure and crop management: olive grove (Tunisia) and wheat grown under different water regimes (irrigated or rainfed) and climate zones (France, Morocco). We have investigated the daily and seasonal dynamics of PRI; linking its variations to meteorological factors (global radiation and sun angle effects, soil water content, relative air humidity …) and plant processes. The highest correlations were mainly observed in clear skies conditions. We have found, whatever site, linear negative relationships between PRI and LUE using data acquired in midday (i.e. in solar zenithal angle condition). Linear link between PRI and sapflow measurements was also revealed. This correlation was obtained over periods characterized by a moderate soil water deficit, i.e. by when transpiration rate was mainly control by Vapor Pressure Deficit. We will then briefly presented alternative and complementary approaches to this index, to detect different level of water stress using thermal infrared emissions.

Crop growth and production responses to commercial humic products in U.S. Midwestern rainfed conditions

USDA-ARS?s Scientific Manuscript database

Humic products (humic and/or fulvic acids) have been in use for over 100 years, yet published research is scant on crop responses to humics under differing soil and weather conditions. We initiated field research experiments on corn (Zea mays L.) in Iowa in 2009 and have since expanded to multiple U...

Accelerating Silphium domestication: an opportunity to develop new crop ideotypes and breeding strategies informed by multiple disciplines

USDA-ARS?s Scientific Manuscript database

Silphium perfoliatum L. (cup plant, silphie) and S. integrifolium Michx. (rosinweed, silflower) are in the same sub-family and tribe as sunflower (Helianthus annuus L.). S. perfoliatum has been grown in many countries a forage or bioenergy crop with forage quality approaching that of alfalfa and bio...

Evidence of multiple/cross resistance to Bt and Organophosphate insecticides in Puerto Rico population of the fall armyworm, Spodoptera frugiperda

USDA-ARS?s Scientific Manuscript database

BACKGROUND: Fall armyworm (FAW) is a damaging pest of many economic crops. Long-term use of chemical control prompted resistance development to many insecticide classes. Many populations were found to be significantly less susceptible to major Bt toxins expressed in transgenic crops. In this study, ...

Identifying Hail Signatures in Satellite Imagery from the 9-10 August 2011 Severe Weather Event

NASA Technical Reports Server (NTRS)

Dryden, Rachel L.; Molthan, Andrew L.; Cole, Tony A.; Bell, Jordan

2014-01-01

Severe thunderstorms can produce large hail that causes property damage, livestock fatalities, and crop failure. However, detailed storm surveys of hail damage conducted by the National Weather Service (NWS) are not required. Current gaps also exist between Storm Prediction Center (SPC) hail damage estimates and crop-insurance payouts. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites can be used to support NWS damage assessments, particularly to crops during the growing season. The two-day severe weather event across western Nebraska and central Kansas during 9-10 August 2011 offers a case study for investigating hail damage signatures by examining changes in Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery. By analyzing hail damage swaths in satellite imagery, potential economic losses due to crop damage can be quantified and further improve the estimation of weather impacts on agriculture without significantly increasing manpower requirements.

OCO-2 Solar-induced Fluorescence Data Portal and Applications to Crop Yield Estimation

NASA Astrophysics Data System (ADS)

Zhai, A. J.; Jiang, J. H.; Frankenberg, C.; Yung, Y. L.; Choi, Y. S.

2016-12-01

Solar-induced fluorescence (SIF) is a direct byproduct of photosynthesis and is an index that can represent overall plant productivity level of any region around the globe. Recently, in 2014, NASA launched the Orbiting Carbon Observatory 2 (OCO-2) satellite, which collects SIF measurements at a higher spatial resolution than any previous instrument has. We have first assembled a web-based data portal, which can be easily utilized by both farmers and researchers, to allow convenient access to the SIF data from OCO-2. One possible use of SIF is to estimate agricultural status of crop fields anywhere in the world. We are using OCO-2 level 2 measurements in conjunction with the USDA's Cropland Data Layer and reported crop yield data to study how effectively SIF can estimate agricultural yield on various types of landscape and various species of crops. Results, methods, and future implications will be presented.

The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons.

PubMed

Vitale, Luca; Di Tommasi, Paul; D'Urso, Guido; Magliulo, Vincenzo

2016-03-01

The eddy correlation technique was used to investigate the influence of biophysical variables and crop phenological phases on the behaviour of ecosystem carbon fluxes of a maize crop, in two contrasting growing seasons. In 2009, the reduced water supply during the early growing stage limited leaf area expansion, thus negatively affecting canopy photosynthesis. The variability of gross primary production (GPP) and ecosystem respiration (R eco) was mainly explained by seasonal variation of leaf area index (LAI). The seasonal variation of R eco was positively influenced by soil temperatures (T soil) in 2008 but not in 2009. In 2008, a contribution of both autotrophic and heterotrophic components to total R eco could be hypothesized, while during 2009, autotrophic respiration is supposed to be the most important component. Crop phenological phases affected the response of ecosystem fluxes to biophysical drivers.

Sunn Hemp Cover Cropping and Organic Fertilizer Effects on the Nematode Community Under Temperate Growing Conditions

PubMed Central

Hinds, Jermaine; Wang, Koon-Hui; Marahatta, Sharadchandra P.; Meyer, Susan L. F.; Hooks, Cerruti R. R.

2013-01-01

Field experiments were conducted in Maryland to investigate the influence of sunn hemp cover cropping in conjunction with organic and synthetic fertilizers on the nematode community in a zucchini cropping system. Two field treatments, zucchini planted into a sunn hemp living and surface mulch (SH) and zucchini planted into bare-ground (BG) were established during three field seasons from 2009 to 2011. In 2009, although SH slightly increased nematode richness compared with BG by the first harvest (P < 0.10), it reduced nematode diversity and enrichment indices (P < 0.01 and P < 0.10, respectively) and increased the channel index (P < 0.01) compared to BG at the final harvest. This suggests a negative impact of SH on nematode community structure. The experiment was modified in 2010 and 2011 where the SH and BG main plots were further split into two subplots to investigate the added influence of an organic vs. synthetic fertilizer. In 2010, when used as a living and surface mulch in a no-till system, SH increased bacterivorous, fungivorous, and total nematodes (P < 0.05) by the final zucchini harvest, but fertilizer type did not influence nematode community structure. In 2011, when incorporated into the soil before zucchini planting, SH increased the abundance of bacterivorous and fungivorous nematodes early in the cropping season. SH increased species richness also at the end of the season (P < 0.05). Fertilizer application did not appear to influence nematodes early in the season. However, in late season, organic fertilizers increased enrichment and structure indices and decreased channel index by the end of the zucchini cropping cycle. PMID:24379485

Future crop production threatened by extreme heat

NASA Astrophysics Data System (ADS)

Siebert, Stefan; Ewert, Frank

2014-04-01

Heat is considered to be a major stress limiting crop growth and yields. While important findings on the impact of heat on crop yield have been made based on experiments in controlled environments, little is known about the effects under field conditions at larger scales. The study of Deryng et al (2014 Global crop yield response to extreme heat stress under multiple climate change futures Environ. Res. Lett. 9 034011), analysing the impact of heat stress on maize, spring wheat and soya bean under climate change, represents an important contribution to this emerging research field. Uncertainties in the occurrence of heat stress under field conditions, plant responses to heat and appropriate adaptation measures still need further investigation.

Putting mechanisms into crop production models.

PubMed

Boote, Kenneth J; Jones, James W; White, Jeffrey W; Asseng, Senthold; Lizaso, Jon I

2013-09-01

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects. © 2013 John Wiley & Sons Ltd.

Modeling ecohydrological dynamics of smallholder strategies for food production in dryland agricultural systems

NASA Astrophysics Data System (ADS)

Gower, Drew B.; Dell'Angelo, Jampel; McCord, Paul F.; Caylor, Kelly K.; Evans, Tom P.

2016-11-01

In dryland environments, characterized by low and frequently variable rainfall, smallholder farmers must take crop water sensitivity into account along with other characteristics like seed availability and market price when deciding what to plant. In this paper we use the results of surveys conducted among smallholders located near Mount Kenya to identify clusters of farmers devoting different fractions of their land to subsistence and market crops. Additionally, we explore the tradeoffs between water-insensitive but low-value subsistence crops and a water-sensitive but high-value market crop using a numerical model that simulates soil moisture dynamics and crop production over multiple growing seasons. The cluster analysis shows that most farmers prefer to plant either only subsistence crops or only market crops, with a minority choosing to plant substantial fractions of both. The model output suggests that the value a farmer places on a successful growing season, a measure of risk aversion, plays a large role in whether the farmer chooses a subsistence or market crop strategy. Furthermore, access to irrigation, makes market crops more appealing, even to very risk-averse farmers. We then conclude that the observed clustering may result from different levels of risk aversion and access to irrigation.

Increased occurrence of pesticide residues on crops grown in protected environments compared to crops grown in open field conditions.

PubMed

Allen, Gina; Halsall, Crispin J; Ukpebor, Justina; Paul, Nigel D; Ridall, Gareth; Wargent, Jason J

2015-01-01

Crops grown under plastic-clad structures or in greenhouses may be prone to an increased frequency of pesticide residue detections and higher concentrations of pesticides relative to equivalent crops grown in the open field. To test this we examined pesticide data for crops selected from the quarterly reports (2004-2009) of the UK's Pesticide Residue Committee. Five comparison crop pairs were identified whereby one crop of each pair was assumed to have been grown primarily under some form of physical protection ('protected') and the other grown primarily in open field conditions ('open'). For each pair, the number of detectable pesticide residues and the proportion of crop samples containing pesticides were statistically compared (n=100 s samples for each crop). The mean concentrations of selected photolabile pesticides were also compared. For the crop pairings of cabbage ('open') vs. lettuce ('protected') and 'berries' ('open') vs. strawberries ('protected') there was a significantly higher number of pesticides and proportion of samples with multiple residues for the protected crops. Statistically higher concentrations of pesticides, including cypermethrin, cyprodinil, fenhexamid, boscalid and iprodione were also found in the protected crops compared to the open crops. The evidence here demonstrates that, in general, the protected crops possess a higher number of detectable pesticides compared to analogous crops grown in the open. This may be due to different pesticide-use regimes, but also due to slower rates of pesticide removal in protected systems. The findings of this study raise implications for pesticide management in protected-crop systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of new land boundary conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the climatology of land surface variables

NASA Astrophysics Data System (ADS)

Tian, Y.; Dickinson, R. E.; Zhou, L.; Shaikh, M.

2004-10-01

This paper uses the Community Land Model (CLM2) to investigate the improvements of a new land surface data set, created from multiple high-quality collection 4 Moderate Resolution Imaging Spectroradiometer data of leaf area index (LAI), plant functional type, and vegetation continuous fields, for modeled land surface variables. The previous land surface data in CLM2 underestimate LAI and overestimate the percent cover of grass/crop over most of the global area. For snow-covered regions with abundant solar energy the increased LAI and percent cover of tree/shrub in the new data set decreases the percent cover of surface snow and increases net radiation and thus increases ground and surface (2-m) air temperature, which reduces most of the model cold bias. For snow-free regions the increased LAI and changes in the percent cover from grass/crop to tree or shrub decrease ground and surface air temperature by converting most of the increased net radiation to latent heat flux, which decreases the model warm bias. Furthermore, the new data set greatly decreases ground evaporation and increases canopy evapotranspiration over tropical forests, especially during the wet season, owing to the higher LAI and more trees in the new data set. It makes the simulated ground evaporation and canopy evapotranspiration closer to reality and also reduces the warm biases over tropical regions.

Effects of vegetation types on soil moisture estimation from the normalized land surface temperature versus vegetation index space

NASA Astrophysics Data System (ADS)

Zhang, Dianjun; Zhou, Guoqing

2015-12-01

Soil moisture (SM) is a key variable that has been widely used in many environmental studies. Land surface temperature versus vegetation index (LST-VI) space becomes a common way to estimate SM in optical remote sensing applications. Normalized LST-VI space is established by the normalized LST and VI to obtain the comparable SM in Zhang et al. (Validation of a practical normalized soil moisture model with in situ measurements in humid and semiarid regions [J]. International Journal of Remote Sensing, DOI: 10.1080/01431161.2015.1055610). The boundary conditions in the study were set to limit the point A (the driest bare soil) and B (the wettest bare soil) for surface energy closure. However, no limitation was installed for point D (the full vegetation cover). In this paper, many vegetation types are simulated by the land surface model - Noah LSM 3.2 to analyze the effects on soil moisture estimation, such as crop, grass and mixed forest. The locations of point D are changed with vegetation types. The normalized LST of point D for forest is much lower than crop and grass. The location of point D is basically unchanged for crop and grass.

Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information

NASA Astrophysics Data System (ADS)

Zhu, Xiaohua; Li, Chuanrong; Tang, Lingli

2018-03-01

Leaf area index (LAI) is a key structural characteristic of vegetation and plays a significant role in global change research. Several methods and remotely sensed data have been evaluated for LAI estimation. This study aimed to evaluate the suitability of the look-up-table (LUT) approach for crop LAI retrieval from Satellite Pour l'Observation de la Terre (SPOT)-5 data and establish an LUT approach for LAI inversion based on scale information. The LAI inversion result was validated by in situ LAI measurements, indicating that the LUT generated based on the PROSAIL (PROSPECT+SAIL: properties spectra + scattering by arbitrarily inclined leaves) model was suitable for crop LAI estimation, with a root mean square error (RMSE) of ˜0.31m2 / m2 and determination coefficient (R2) of 0.65. The scale effect of crop LAI was analyzed based on Taylor expansion theory, indicating that when the SPOT data aggregated by 200 × 200 pixel, the relative error is significant with 13.7%. Finally, an LUT method integrated with scale information was proposed in this article, improving the inversion accuracy with RMSE of 0.20 m2 / m2 and R2 of 0.83.

Variable-rate nitrogen application algorithm based on canopy reflected spectrum and its influence on wheat

NASA Astrophysics Data System (ADS)

Liang, Hongxia; Zhao, Chunjiang; Huang, Wenjiang; Liu, Liangyun; Wang, Jihua; Ma, Youhua

2005-01-01

This study was to develop the time-specific and time-critical method to overcome the limitations of traditional field sampling methods for variable rate fertilization. Farmers, agricultural managers and grain processing enterprises are interested in measuring and assessing soil and crop status in order to apply adequate fertilizer quantities to crop growth. This paper focused on studying the relationship between vegetation index (OSAVI) and nitrogen content to determine the amount of nitrogen fertilizer recommended for variable rate management in precision agriculture. The traditional even rate fertilizer management was chosen as the CK. The grain yield, ear numbers, 1000-grain weight and grain protein content were measured among the CK, uniform treatments and variable rate fertilizer treatments. It indicated that variable rate fertilization reduced the variability of wheat yield, ear numbers and dry biomass, but it didn't increased crop yield and grain protein content significantly and did not decrease the variety of 1000-grain weight, compared to traditional rate application. The nitrogen fertilizer use efficiency was improved, for this purpose, the variable rate technology based on vegetation index could be used to prevent under ground water pollution and environmental deterioration.

Indexes and efficiencies of N optimum dose reviewed as water- and Nitrogen- footprint

NASA Astrophysics Data System (ADS)

Castellanos, Maria Teresa; Cartagena, Maria Carmen; Cabello, Maria Jesus; Rivas, Francisco; Tarquis, Ana Maria; Arce, Augusto

2013-04-01

In order to establish rational nitrogen (N) fertilization and reduce groundwater contamination, a clearer understanding of the N distribution through the growing season and its balance is crucial. In three successive years, a melon crop (Cucumis melo L. cv. Sancho) was grown under field conditions to determine the uptake of N fertilizer, applied by means of fertigation at different stages of plant growth. In addition, Strategies are being sought to increase water use in cropping systems and to reduce drainage. The estimation of N mineralized from soil organic matter is an essential tool to determine the amount necessary to optimize crop yield and minimize the environmental impact of excess N. In this study we propose a methodology that allows us to study fertigated management integrating several aspects: economic and environmental. Even the complexity of the system, we have reduced the number of indexes and efficiencies need to establish the framework of N management and its economical and environmental consequences. At the same time, we have translated all them into a water- and Nitrogen- footprint in each year. ACKNOWLEDGEMENTS This work has been partially supported by INIA under Project INIA-RTA 2010-00110-C03-02

Gene flow from single and stacked herbicide-resistant rice (Oryza sativa): modeling occurrence of multiple herbicide-resistant weedy rice.

PubMed

Dauer, Joseph; Hulting, Andrew; Carlson, Dale; Mankin, Luke; Harden, John; Mallory-Smith, Carol

2018-02-01

Provisia™ rice (PV), a non-genetically engineered (GE) quizalofop-resistant rice, will provide growers with an additional option for weed management to use in conjunction with Clearfield ® rice (CL) production. Modeling compared the impact of stacking resistance traits versus single traits in rice on introgression of the resistance trait to weedy rice (also called red rice). Common weed management practices were applied to 2-, 3- and 4-year crop rotations, and resistant and multiple-resistant weedy rice seeds, seedlings and mature plants were tracked for 15 years. Two-year crop rotations resulted in resistant weedy rice after 2 years with abundant populations (exceeding 0.4 weedy rice plants m -2 ) occurring after 7 years. When stacked trait rice was rotated with soybeans in a 3-year rotation and with soybeans and CL in a 4-year rotation, multiple-resistance occurred after 2-5 years with abundant populations present in 4-9 years. When CL rice, PV rice, and soybeans were used in 3- and 4-year rotations, the median time of first appearance of multiple-resistance was 7-11 years and reached abundant levels in 10-15 years. Maintaining separate CL and PV rice systems, in rotation with other crops and herbicides, minimized the evolution of multiple herbicide-resistant weedy rice through gene flow compared to stacking herbicide resistance traits. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Designing hydrological and financial instruments for small scale farmers in Sub-Saharan Africa: A socio-hydrological analysis

NASA Astrophysics Data System (ADS)

Moshtaghi, M.; Pande, S.; Savenije, H. H. G.; den Besten, N. I.

2016-12-01

Eighty percent of the farmland in Sub-Saharan Africa is managed by smallholders and they are often economically stressed; low income as a result of poor crop yields. Indeed, smallholders' well-being is naturally important, which often suffers due to hydro-climatic variability and fluctuations in prices of inputs (seeds, fertilizer) and outputs (crops). Appropriate designed insurances can guarantee their wellbeing and food security in whole continent, if they focus on specified requirement of smallholders in each region. In this research, we apply recently developed socio-hydrologic modelling, which interprets a small scale farm system as a coupled system of 6 variables: soil moisture, solid fertility, capital, livestock, fodder and labor availability. By using datasets of potential evaporation, rainfall, land cover and etc, we want to make a comparison between application of yield index insurance, weather index insurance and biomass Index Insurance to highlight the importance of considering the interplay between fertilizer and water availability in food security and also determine type of regional insurance which works better in a certain land.

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

PubMed

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

2007-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition, landscape configuration, and bioenergy crop area

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

Graham, John B.; Nassauer, Joan I.; Currie, William S.

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees. Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations. Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7%, 23.5% and 28.8% of agricultural landmore » converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape. Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale. Moreover, strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.« less

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition, landscape configuration, and bioenergy crop area

DOE PAGES

Graham, John B.; Nassauer, Joan I.; Currie, William S.; ...

2017-03-25

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees. Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations. Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7%, 23.5% and 28.8% of agricultural landmore » converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape. Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale. Moreover, strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.« less

An Analysis of the Impact of Heat Waves in Labor and Crop Productivity in the Agricultural Sector in California

NASA Astrophysics Data System (ADS)

Castillo, F.; Wehner, M. F.; Gilless, J. K.

2017-12-01

California agriculture is an important economic activity for the state. California leads the nation in farms sales since 1950. In addition, agricultural employment in California reached approximately 410,000. Production of many fruits and vegetables is labor intensive and labor costs represent anywhere from 20% to 40% of total production costs. In additon, agricutlural production growth has been the highest for labor intensive crops such as berries (all types) and nuts. Given the importance of the agricultural sector and the labor component whithin it, the analysis of the impact of climate change on the agricultural sector of California becomes imperative. Heat waves are a weather related extreme that impact labor productivity, specially outdoor labor producitivity. We use crop production function analysis that incorporates socio economic variables such as crop prices, total acreage, production levels and harvest timiline with climate related variables such as an estimated Heat Index (HI) to analize the impact of heat waves on crop production via an impact on labor productivity for selected crops in the Central and Imperial Valleys in California. The analysis finds that the impact of heat waves varies by the degree of labor intensity of the crop and the relative intensity of the heat wave.

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China

PubMed Central

Hu, Bifeng; Jia, Xiaolin; Hu, Jie; Xu, Dongyun; Xia, Fang; Li, Yan

2017-01-01

Heavy metal (HM) contamination and accumulation is a serious problem around the world due to the toxicity, abundant sources, non-biodegradable properties, and accumulative behaviour of HMs. The degree of soil HM contamination in China, especially in the Yangtze River Delta, is prominent. In this study, 1822 pairs of soil and crop samples at corresponding locations were collected from the southern Yangtze River Delta of China, and the contents of Ni, Cr, Zn, Cd, As, Cu, Hg, and Pb were measured. The single pollution index in soil (SPI) and Nemerow composite pollution index (NCPI) were used to assess the degree of HM pollution in soil, and the crop pollution index (CPI) was used to explore the degree of HM accumulation in crops. The bioaccumulation factor (BAF) was used to investigate the translocation of heavy metals in the soil-crop system. The health risks caused by HMs were calculated based on the model released by the U.S. Environmental Protection Agency. The SPIs of all elements were at the unpolluted level. The mean NCPI was at the alert level. The mean CPIs were in the following decreasing order: Ni (1.007) > Cr (0.483) > Zn (0.335) > Cd (0.314) > As (0.232) > Cu (0.187) > Hg (0.118) > Pb (0.105). Only the mean content of Ni in the crops exceeded the national standard value. The standard exceeding rates were used to represent the percentage of samples whose heavy metal content is higher than the corresponding national standard values. The standard exceeding rates of Cu, Hg, and Cd in soil were significantly higher than corresponding values in crops. Meanwhile, the standard exceeding rates of Ni, As, and Cr in crops were significantly higher than corresponding values in soil. The chronic daily intake (CDI) of children (13.8 × 10−3) was the largest among three age groups, followed by adults (6.998 × 10−4) and seniors (5.488 × 10−4). The bioaccumulation factors (BAFs) of all crops followed the order Cd (0.249) > Zn (0.133) > As (0.076) > Cu (0.064) > Ni (0.018) > Hg (0.011) > Cr (0.010) > Pb (0.001). Therefore, Cd was most easily absorbed by crops, and different crops had different capacities to absorb HMs. The hazard quotient (HQ) represents the potential non-carcinogenic risk for an individual HM and it is an estimation of daily exposure to the human population that is not likely to represent an appreciable risk of deleterious effects during a lifetime. All the HQs of the HMs for the different age groups were significantly less than the alert value of 1.0 and were at a safe level. This indicated that citizens in the study area face low potential non-carcinogenic risk caused by HMs. The total carcinogens risks (TCRs) for children, adults, and seniors were 5.24 × 10−5, 2.65 × 10−5, and 2.08 × 10−5, respectively, all of which were less than the guideline value but at the alert level. Ingestion was the main pathway of carcinogen risk to human health. PMID:28891954

Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands

NASA Astrophysics Data System (ADS)

Wang, Cuizhen; Fan, Qian; Li, Qingting; SooHoo, William M.; Lu, Linlin

2017-02-01

Since the mid-2000s, agricultural lands in the United States have been undergoing rapid change to meet the increasing bioenergy demand. In 2009 the USDA Biomass Crop Assistance Program (BCAP) was established. In its Project Area 1, land owners are financially supported to grow perennial prairie grasses (switchgrass) in their row-crop lands. To promote the program, this study tested the feasibility of biomass crop mapping based on unique timings of crop development. With a previously published data fusion algorithm - the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), a 10-day normalized difference vegetation index (NDVI) time series in 2007 was established by fusing MODIS reflectance into TM image series. Two critical dates - peak growing (PG) and peak drying (PD) - were extracted and a unique "PG-0-PD" timing sequence was defined for each crop. With a knowledge-based decision tree approach, the classification of enhanced TM/MODIS time series reached an overall accuracy of 76% against the USDA Crop Data layer (CDL). Especially, our results showed that winter wheat single cropping and wheat-soybean double cropping were much better classified, which may provide additional information for the CDL product. More importantly, this study extracted the first spatial layer of warm-season prairie grasses that have not been published in any national land cover products, which could serve as a base map for decision making of bioenergy land use in BCAP land.

Integrated weed management systems with herbicide-tolerant crops in the European Union: lessons learnt from home and abroad.

PubMed

Lamichhane, Jay Ram; Devos, Yann; Beckie, Hugh J; Owen, Micheal D K; Tillie, Pascal; Messéan, Antoine; Kudsk, Per

2017-06-01

Conventionally bred (CHT) and genetically modified herbicide-tolerant (GMHT) crops have changed weed management practices and made an important contribution to the global production of some commodity crops. However, a concern is that farm management practices associated with the cultivation of herbicide-tolerant (HT) crops further deplete farmland biodiversity and accelerate the evolution of herbicide-resistant (HR) weeds. Diversification in crop systems and weed management practices can enhance farmland biodiversity, and reduce the risk of weeds evolving herbicide resistance. Therefore, HT crops are most effective and sustainable as a component of an integrated weed management (IWM) system. IWM advocates the use of multiple effective strategies or tactics to manage weed populations in a manner that is economically and environmentally sound. In practice, however, the potential benefits of IWM with HT crops are seldom realized because a wide range of technical and socio-economic factors hamper the transition to IWM. Here, we discuss the major factors that limit the integration of HT crops and their associated farm management practices in IWM systems. Based on the experience gained in countries where CHT or GMHT crops are widely grown and the increased familiarity with their management, we propose five actions to facilitate the integration of HT crops in IWM systems within the European Union.

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.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Snapshots of biodiversity in Georgia agroecosystems

USDA-ARS?s Scientific Manuscript database

Georgia agricultural landscapes are composed of a diversity of commodities. Here we present biodiversity and biotic interaction data from multiple agricultural systems including: cotton, corn, peanut, blueberry and non-cropping wildflower areas over multiple years. Our goal is to better understand t...

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

USGS Publications Warehouse

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

UAS imaging for automated crop lodging detection: a case study over an experimental maize field

NASA Astrophysics Data System (ADS)

Chu, Tianxing; Starek, Michael J.; Brewer, Michael J.; Masiane, Tiisetso; Murray, Seth C.

2017-05-01

Lodging has been recognized as one of the major destructive factors for crop quality and yield, particularly in corn. A variety of contributing causes, e.g. disease and/or pest, weather conditions, excessive nitrogen, and high plant density, may lead to lodging before harvesting season. Traditional lodging detection strategies mainly rely on ground data collection, which is insufficient in efficiency and accuracy. To address this problem, this research focuses on the use of unmanned aircraft systems (UAS) for automated detection of crop lodging. The study was conducted over an experimental corn field at the Texas A and M AgriLife Research and Extension Center at Corpus Christi, Texas, during the growing season of 2016. Nadir-view images of the corn field were taken by small UAS platforms equipped with consumer grade RGB and NIR cameras on a per week basis, enabling a timely observation of the plant growth. 3D structural information of the plants was reconstructed using structure-from-motion photogrammetry. The structural information was then applied to calculate crop height, and rates of growth. A lodging index for detecting corn lodging was proposed afterwards. Ground truth data of lodging was collected on a per row basis and used for fair assessment and tuning of the detection algorithm. Results show the UAS-measured height correlates well with the ground-measured height. More importantly, the lodging index can effectively reflect severity of corn lodging and yield after harvesting.

Different continuous cropping spans significantly affect microbial community membership and structure in a vanilla-grown soil as revealed by deep pyrosequencing.

PubMed

Xiong, Wu; Zhao, Qingyun; Zhao, Jun; Xun, Weibing; Li, Rong; Zhang, Ruifu; Wu, Huasong; Shen, Qirong

2015-07-01

In the present study, soil bacterial and fungal communities across vanilla continuous cropping time-series fields were assessed through deep pyrosequencing of 16S ribosomal RNA (rRNA) genes and internal transcribed spacer (ITS) regions. The results demonstrated that the long-term monoculture of vanilla significantly altered soil microbial communities. Soil fungal diversity index increased with consecutive cropping years, whereas soil bacterial diversity was relatively stable. Bray-Curtis dissimilarity cluster and UniFrac-weighted principal coordinate analysis (PCoA) revealed that monoculture time was the major determinant for fungal community structure, but not for bacterial community structure. The relative abundances (RAs) of the Firmicutes, Actinobacteria, Bacteroidetes, and Basidiomycota phyla were depleted along the years of vanilla monoculture. Pearson correlations at the phyla level demonstrated that Actinobacteria, Armatimonadetes, Bacteroidetes, Verrucomicrobia, and Firmicutes had significant negative correlations with vanilla disease index (DI), while no significant correlation for fungal phyla was observed. In addition, the amount of the pathogen Fusarium oxysporum accumulated with increasing years and was significantly positively correlated with vanilla DI. By contrast, the abundance of beneficial bacteria, including Bradyrhizobium and Bacillus, significantly decreased over time. In sum, soil weakness and vanilla stem wilt disease after long-term continuous cropping can be attributed to the alteration of the soil microbial community membership and structure, i.e., the reduction of the beneficial microbes and the accumulation of the fungal pathogen.

A one-layer satellite surface energy balance for estimating evapotranspiration rates and crop water stress indexes.

PubMed

Barbagallo, Salvatore; Consoli, Simona; Russo, Alfonso

2009-01-01

Daily evapotranspiration fluxes over the semi-arid Catania Plain area (Eastern Sicily, Italy) were evaluated using remotely sensed data from Landsat Thematic Mapper TM5 images. A one-source parameterization of the surface sensible heat flux exchange using satellite surface temperature has been used. The transfer of sensible and latent heat is described by aerodynamic resistance and surface resistance. Required model inputs are brightness, temperature, fractional vegetation cover or leaf area index, albedo, crop height, roughness lengths, net radiation, air temperature, air humidity and wind speed. The aerodynamic resistance (r(ah)) is formulated on the basis of the Monin-Obukhov surface layer similarity theory and the surface resistance (r(s)) is evaluated from the energy balance equation. The instantaneous surface flux values were converted into evaporative fraction (EF) over the heterogeneous land surface to derive daily evapotranspiration values. Remote sensing-based assessments of crop water stress (CWSI) were also made in order to identify local irrigation requirements. Evapotranspiration data and crop coefficient values obtained from the approach were compared with: (i) data from the semi-empirical approach "K(c) reflectance-based", which integrates satellite data in the visible and NIR regions of the electromagnetic spectrum with ground-based measurements and (ii) surface energy flux measurements collected from a micrometeorological tower located in the experiment area. The expected variability associated with ET flux measurements suggests that the approach-derived surface fluxes were in acceptable agreement with the observations.

Selection index based on the relative importance of traits and possibilities in breeding popcorn.

PubMed

Vieira, R A; Rocha, R; Scapim, C A; Amaral Júnior, A T; Vivas, M

2016-04-26

One of the major difficulties faced by popcorn breeders is the negative correlation between popping expansion (PE) and grain yield (GY). It is necessary to overcome this difficulty to obtain promising genotypes. One helpful tool in this process is a selection index because it allows multiple features of interest to be selected. Thus, the present study proposes a new and comprehensive selection index applied in 169 half-sib families in UEM-Co1 and UEM-Co2 composites during two cycles of recurrent selection. An experiment was conducted in a 13 x 13 lattice square in the 2004/2005 and 2006/2007 crop years in Maringá, Paraná State, and PE and GY were evaluated. To calculate Fi statistics, the following relative importance (RI) assignments were used: 0.5 for both PE and GY, and 0.70 and 0.30 for PE and GY, respectively. Families were classified according to Fi values such that Fi = 0 indicated that genotypes met the average of those selected by direct selection, Fi < 0 indicated that genotypes fell below the average of those selected, and Fi > 0 indicated that genotypes exceeded the average of those selected. Thus, desirable values of Fi were positive, indicating that the selected families were higher than those families that would be selected by direct selection for both traits. Therefore, we concluded that the novel Fi statistic was satisfactory for family selection because simultaneous and higher gains for both traits in both composites were obtained.

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

USDA-ARS?s Scientific Manuscript database

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

Variation in fumonisin and ochratoxin production associated with differences in biosynthetic gene content in Aspergillus niger and A. welwitschiae isolates from multiple crop and geographic origins

USDA-ARS?s Scientific Manuscript database

The fungi Aspergillus niger and A. welwitschiae are morphologically indistinguishable species used for industrial fermentation and for food and beverage production. The fungi also occur widely on food crops. Concerns about their safety have arisen with the discovery that some isolates of both specie...

Droughts in India from 1981 to 2013 and Implications to Wheat Production

NASA Astrophysics Data System (ADS)

Zhang, Xiang; Obringer, Renee; Wei, Chehan; Chen, Nengcheng; Niyogi, Dev

2017-03-01

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.

Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

NASA Technical Reports Server (NTRS)

Beaudoing, Hiroko Kato; Rodell, Matthew; Ozdogan, Mutlu

2010-01-01

Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Zhang, Qingyuan

2016-04-01

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05° (∼5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Interactive effects of pests increase seed yield.

PubMed

Gagic, Vesna; Riggi, Laura Ga; Ekbom, Barbara; Malsher, Gerard; Rusch, Adrien; Bommarco, Riccardo

2016-04-01

Loss in seed yield and therefore decrease in plant fitness due to simultaneous attacks by multiple herbivores is not necessarily additive, as demonstrated in evolutionary studies on wild plants. However, it is not clear how this transfers to crop plants that grow in very different conditions compared to wild plants. Nevertheless, loss in crop seed yield caused by any single pest is most often studied in isolation although crop plants are attacked by many pests that can cause substantial yield losses. This is especially important for crops able to compensate and even overcompensate for the damage. We investigated the interactive impacts on crop yield of four insect pests attacking different plant parts at different times during the cropping season. In 15 oilseed rape fields in Sweden, we estimated the damage caused by seed and stem weevils, pollen beetles, and pod midges. Pest pressure varied drastically among fields with very low correlation among pests, allowing us to explore interactive impacts on yield from attacks by multiple species. The plant damage caused by each pest species individually had, as expected, either no, or a negative impact on seed yield and the strongest negative effect was caused by pollen beetles. However, seed yield increased when plant damage caused by both seed and stem weevils was high, presumably due to the joint plant compensatory reaction to insect attack leading to overcompensation. Hence, attacks by several pests can change the impact on yield of individual pest species. Economic thresholds based on single species, on which pest management decisions currently rely, may therefore result in economically suboptimal choices being made and unnecessary excessive use of insecticides.

Spatial diversification of agroecosystems to enhance biological control and other regulating services: An agroecological perspective.

PubMed

Hatt, Séverin; Boeraeve, Fanny; Artru, Sidonie; Dufrêne, Marc; Francis, Frédéric

2018-04-15

Spatial diversification of crop and non-crop habitats in farming systems is promising for enhancing natural regulation of insect pests. Nevertheless, results from recent syntheses show variable effects. One explanation is that the abundance and diversity of pests and natural enemies are affected by the composition, design and management of crop and non-crop habitats. Moreover, interactions between both local and landscape elements and practices carried out at different spatial scales may affect the regulation of insect pests. Hence, research is being conducted to understand these interdependencies. However, insects are not the only pests and pests are not the only elements to regulate in agroecosystems. Broadening the scope could allow addressing multiple issues simultaneously, but also solving them together by enhancing synergies. Indeed, spatial diversification of crop and non-crop habitats can allow addressing the issues of weeds and pathogens, along with being beneficial to several other regulating services like pollination, soil conservation and nutrient cycling. Although calls rise to develop multifunctional landscapes that optimize the delivery of multiple ecosystem services, it still represents a scientific challenge today. Enhancing interdisciplinarity in research institutions and building interrelations between scientists and stakeholders may help reach this goal. Despite obstacles, positive results from research based on such innovative approaches are encouraging for engaging science in this path. Hence, the aim of the present paper is to offer an update on these issues by exploring the most recent findings and discussing these results to highlight needs for future research. Copyright © 2017 Elsevier B.V. All rights reserved.

Remote sensing technologies applied to the irrigation water management on a golf course

NASA Astrophysics Data System (ADS)

Pedras, Celestina; Lança, Rui; Martins, Fernando; Soares, Cristina; Guerrero, Carlos; Paixão, Helena

2015-04-01

An adequate irrigation water management in a golf course is a complex task that depends upon climate (multiple microclimates) and land cover (where crops differ in morphology, physiology, plant density, sensitivity to water stress, etc.). These factors change both in time and space on a landscape. A direct measurement provides localized values of the evapotranspiration and climate conditions. Therefore this is not a practical or economical methodology for large-scale use due to spatial and temporal variability of vegetation, soils, and irrigation management strategies. Remote sensing technology combines large scale with ground measurement of vegetation indexes. These indexes are mathematical combinations of different spectral bands mostly in the visible and near infrared regions of the electromagnetic spectrum. They represent the measures of vegetation activity that vary not only with the seasonal variability of green foliage, but also across space, thus they are suitable for detecting spatial landscape variability. The spectral vegetation indexes may enhance irrigation management through the information contained in spectral reflectance data. This study was carried out on the 18th fairway of the Royal Golf Course, Vale do Lobo, Portugal, and it aims to establish the relationship between direct measurements and vegetation indexes. For that it is required (1) to characterize the soil and climatic conditions, (2) to assessment of the irrigation system, (3) to estimate the evapotranspiration (4) and to calculate the vegetation indices. The vegetation indices were determined with basis on spectral bands red, green and blue, RGB, and near Infrared, NIR, obtained from the analysis of images acquired from a unpiloted aerial vehicle, UAV, platform. The measurements of reference evapotranspiration (ETo) were obtained from two meteorological stations located in the study area. The landscape evapotranspiration, ETL, was determined in the fairway with multiple microclimates and managed stress. The ETL was obtained thru the use of mobile reference ET stations and also by the development of the surface renewal (SR) measurement technique. The sprinkler irrigation system installed was evaluated according to the methodology described by ASAE. The Normalized Difference Vegetation Index, NDVI, and Visible atmospherically Resistant Index, VARI, are confronted with the direct localized measurements. The NDVI is the most used indicator to assess the vigor status of the vegetation. However, this index depends of the use of NIR bands which demands quite expensive sensors. The use vegetation indexes obtained by sensors that collect data in the visible wavelength, such as VARI is less expensive and allow the vegetative vigor evaluation with a similar rigor. The information of vegetation indices is crossed with edafoclimatic data obtained in situ, in order to improve the irrigation water management based on aerial imagery.

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

NASA Astrophysics Data System (ADS)

Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

2017-04-01

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

Phenological Metrics Extraction for Agricultural Land-use Types Using RapidEye and MODIS

NASA Astrophysics Data System (ADS)

Xu, Xingmei; Doktor, Daniel; Conrad, Christopher

2016-04-01

Crop phenology involves the various agricultural events, such as planting, emergence, flowering, development of fruit and harvest. These phenological stages of a crop contain essential information for practical agricultural management, crop productivity estimation, investigations of crop-weather relationships, and also play an important role in improving agricultural land-use classification. In this study, we used MODIS and RapidEye images to extract phenological metrics in central Germany between 2010 and 2014. The Best Index Slope Extraction algorithm was used to remove undesirable data noise from Normalized Difference Vegetation Index (NDVI) time series of both satellite data before fast Fourier transformation was applied. Metrics optimization for phenology of major crops in the study area (winter wheat, winter barley, winter oilseed rape and sugar beet) and validation were performed with intensive ground observations from the German Weather Service (2010-2014) and our own measurements of BBCH code (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) (in 2014). We found that the dates with maximum NDVI have a close link to the heading stage of cereals (RMSE = 9.48 days for MODIS and RMSE = 13.55 days for RapidEye), and the dates of local half maximum during senescence period of winter crops was strongly related to ripeness stage (BBCH: 87) (RMSE = 8.87 days for MODIS and RMSE = 9.62 days for RapidEye). The root-mean-square errors (RMSE) of derived green up dates for both winter and summer crops were larger than 2 weeks, which was caused by limited number of good quality images during the winter season. Comparison between RapidEye and homogeneous MODIS pixels indicated that phenological metrics derived from both satellites were similar to the crop calendar in this region. We also investigated the influence of spatial aggregation of RapidEye-scale phenology to MODIS scale as well as the effect of decreasing the temporal resolution of MODIS to RapidEye scale. Our method to smooth and construct NDVI time-series works well in monitoring agricultural phenology and can be applied to other areas with daily MODIS data coverage. High spatial resolution data provides us with a unique opportunity to explore within-field phenology variation, and reduce effects of spatial heterogeneity. We suggest that further studies might not have to consider daily or composite-daily observations as first criteria for selection of remote sensing product in terms of phenology extraction, if the crop calendar is reliable.

Quinoa: An emerging new crop with potential for CELSS

NASA Technical Reports Server (NTRS)

Schlick, Greg; Bubenheim, David L.

1993-01-01

Chenopodium quinoa is being considered as a new crop for the Controlled Ecological Life Support System (CELSS) because of its high protein values (12 - 18%) and unique amino acid composition. Lysine, and essential amino acid that is deficient in many grain crops, is found in quinoa approaching Food and Agriculture Organization of the United Nations (FAO) standards set for humans. This 'new' crop, rich in protein and with desirable proportions of important amino acids, may provide greater versatility in meeting the needs of humans on long-term space missions. Initially, the cultivars CO407 x ISLUGA, CO407 Heat Tolerant Population 1, and Real' (a Bolivian variety) were examined. The first cultivar showed the most promise in greenhouse studies. When grown hydroponically in the greenhouse, with no attempt to maximize productivity, this cultivar produced 202 g m(exp -2) with a harvest index of 37%. None of the cultivars were greater than 70 cm in height. Initial results indicate that quinoa could be an excellent crop for CELSS because of the high concentration of protein, ease of use, versatility in preparation, and potential for greatly increased yields in controlled environments.

Adaptation to climate change: changes in farmland use and stocking rate in the U.S.

USGS Publications Warehouse

Mu, Jianhong E.; McCarl, Bruce A.; Wein, Anne M.

2013-01-01

This paper examines possible adaptations to climate change in terms of pasture and crop land use and stocking rate in the United States (U.S.). Using Agricultural Census and climate data in a statistical model, we find that as temperature and precipitation increases agricultural commodity producers respond by reducing crop land and increasing pasture land. In addition, cattle stocking rate decreases as the summer Temperature-humidity Index (THI) increases and summer precipitation decreases. Using the statistical model with climate data from four General Circulation Models (GCMs), we project that land use shifts from cropping to grazing and the stocking rate declines, and these adaptations are more pronounced in the central and the southeast regions of the U.S. Controlling for other farm production variables, crop land decreases by 6 % and pasture land increases by 33 % from the baseline. Correspondingly, the associated economic impact due to adaptation is around -14 and 29 million dollars to crop producers and pasture producers by the end of this century, respectively. The national and regional results have implications for farm programs and subsidy policies.

Monitoring crop gross primary productivity using Landsat data (Invited)

NASA Astrophysics Data System (ADS)

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

2009-12-01

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

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

NASA Astrophysics Data System (ADS)

Domiri, D. D.

2017-01-01

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

Meeting the challenge of food and energy security.

PubMed

Karp, Angela; Richter, Goetz M

2011-06-01

Growing crops for bioenergy or biofuels is increasingly viewed as conflicting with food production. However, energy use continues to rise and food production requires fuel inputs, which have increased with intensification. Focussing on the question of food or fuel is thus not helpful. The bigger, more pertinent, challenge is how the increasing demands for food and energy can be met in the future, particularly when water and land availability will be limited. Energy crop production systems differ greatly in environmental impact. The use of high-input food crops for liquid transport fuels (first-generation biofuels) needs to be phased out and replaced by the use of crop residues and low-input perennial crops (second/advanced-generation biofuels) with multiple environmental benefits. More research effort is needed to improve yields of biomass crops grown on lower grade land, and maximum value should be extracted through the exploitation of co-products and integrated biorefinery systems. Policy must continually emphasize the changes needed and tie incentives to improved greenhous gas reduction and environmental performance of biofuels.

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

NASA Astrophysics Data System (ADS)

Liu, Chunwei; Sun, Ge; McNulty, Steven G.; Noormets, Asko; Fang, Yuan

2017-01-01

The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (Kc) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, Kc has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly Kc for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly Kc data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), Kc values had large seasonal variation across all land covers. The spatial variability of Kc was well explained by latitude, suggesting site factors are a major control on Kc. Seasonally, Kc increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly Kc in all land covers, except in EBF. During the peak growing season, forests had the highest Kc values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for Kc by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. The Kc models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared.

PubMed

Poss, J A; Russell, W B; Grieve, C M

2006-01-01

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

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

Liu, Chunwei; Sun, Ge; McNulty, Steven G.

The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient ( K c) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, K c has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. Thismore » study aimed at deriving monthly K c for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly K c data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), K c values had large seasonal variation across all land covers. The spatial variability of K c was well explained by latitude, suggesting site factors are a major control on K c. Seasonally, K c increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly K c in all land covers, except in EBF. During the peak growing season, forests had the highest K c values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for K c by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. Here, the K c models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.« less

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

DOE PAGES

Liu, Chunwei; Sun, Ge; McNulty, Steven G.; ...

2017-01-18

The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient ( K c) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, K c has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. Thismore » study aimed at deriving monthly K c for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly K c data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), K c values had large seasonal variation across all land covers. The spatial variability of K c was well explained by latitude, suggesting site factors are a major control on K c. Seasonally, K c increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly K c in all land covers, except in EBF. During the peak growing season, forests had the highest K c values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for K c by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. Here, the K c models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.« less

Excessive Heat Events and National Security: Building Resilience based on Early Warning Systems

NASA Astrophysics Data System (ADS)

Vintzileos, A.

2017-12-01

Excessive heat events (EHE) affect security of Nations in multiple direct and indirect ways. EHE are the top cause for morbidity/mortality associated to any atmospheric extremes. Higher energy consumption used for cooling can lead to black-outs and social disorder. EHE affect the food supply chain reducing crop yield and increasing the probability of food contamination during delivery and storage. Distribution of goods during EHE can be severely disrupted due to mechanical failure of transportation equipment. EHE during athletic events e.g., marathons, may result to a high number of casualties. Finally, EHE may also affect military planning by e.g. reducing hours of exercise and by altering combat gear. Early warning systems for EHE allow for building resilience. In this paper we first define EHE as at least two consecutive heat days; a heat day is defined as a day with a maximum heat index with probability of occurrence that exceeds a certain threshold. We then use retrospective forecasts performed with a multitude of operational models and show that it is feasible to forecast EHE at forecast lead of week-2 and week-3 over the contiguous United States. We finally introduce an improved definition of EHE based on an intensity index and investigate forecast skill of the predictive system in the tropics and subtropics.

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

USGS Publications Warehouse

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

2011-01-01

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

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.

An evaluation of MODIS 250-m data for green LAI estimation in crops

NASA Astrophysics Data System (ADS)

Gitelson, Anatoly A.; Wardlow, Brian D.; Keydan, Galina P.; Leavitt, Bryan

2007-10-01

Green leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. Remotely sensed data provide considerable potential for estimating LAI at local, regional, and global scales. The goal of this study was to retrieve green LAI from MODIS 250-m vegetation index (VI) data for irrigated and rainfed maize and soybeans. The performance of both MODIS-derived NDVI and Wide Dynamic Range Vegetation Index (WDRVI) were evaluated across three growing seasons (2002 through 2004) over a wide range of LAI and also compared to the performance of NDVI and WDRVI derived from reflectance data collected at close-range across the same field locations. The NDVI vs. LAI relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the NDVI to LAI exceeding 2 m2/m2 for both crops. WDRVI vs. LAI relation was linear across the entire range of LAI variation with determination coefficients above 0.93. Importantly, the coefficients of the close-range WDRVI vs. LAI equation and the MODIS-retrieved WDRVI vs. LAI equation were very close. The WDRVI was found to be capable of accurately estimating LAI across a much greater LAI range than the NDVI and can be used for assessing even slight variations in LAI, which are indicative of the early stages of plant stress. These results demonstrate the new possibilities for analyzing the spatio-temporal variation of the LAI of crops using multi-temporal MODIS 250-m imagery.

Exploring the limits of crop productivity. I. Photosynthetic efficiency of wheat in high irradiance environments

NASA Technical Reports Server (NTRS)

Bugbee, B. G.; Salisbury, F. B.

1988-01-01

The long-term vegetative and reproductive growth rates of a wheat crop (Triticum aestivum L.) were determined in three separate studies (24, 45, and 79 days) in response to a wide range of photosynthetic photon fluxes (PPF, 400-2080 micromoles per square meter per second; 22-150 moles per square meter per day; 16-20 hour photoperiod) in a near-optimum, controlled-environment. The CO2 concentration was elevated to 1200 micromoles per mole, and water and nutrients were supplied by liquid hydroponic culture. An unusually high plant density (2000 plants per square meter) was used to obtain high yields. Crop growth rate and grain yield reached 138 and 60 grams per square meter per day, respectively; both continued to increase up to the highest integrated daily PPF level, which was three times greater than a typical daily flux in the field. The conversion efficiency of photosynthesis (energy in biomass/energy in photosynthetic photons) was over 10% at low PPF but decreased to 7% as PPF increased. Harvest index increased from 41 to 44% as PPF increased. Yield components for primary, secondary, and tertiary culms were analyzed separately. Tillering produced up to 7000 heads per square meter at the highest PPF level. Primary and secondary culms were 10% more efficient (higher harvest index) than tertiary culms; hence cultural, environmental, or genetic changes that increase the percentage of primary and secondary culms might increase harvest index and thus grain yield. Wheat is physiologically and genetically capable of much higher productivity and photosynthetic efficiency than has been recorded in a field environment.

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

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2011-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2009-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

Development and Analysis of Global, High-Resolution Diagnostic Metrics for Vegetation Monitoring, Yield Estimation and Famine Mitigation

NASA Astrophysics Data System (ADS)

Anderson, B. T.; Zhang, P.; Myneni, R.

2008-12-01

Drought, through its impact on food scarcity and crop prices, can have significant economic, social, and environmental impacts - presently, up to 36 countries and 73 million people are facing food crises around the globe. Because of these adverse affects, there has been a drive to develop drought and vegetation- monitoring metrics that can quantify and predict human vulnerability/susceptibility to drought at high- resolution spatial scales over the entire globe. Here we introduce a new vegetation-monitoring index utilizing data derived from satellite-based instruments (the Moderate Resolution Imaging Spectroradiometer - MODIS) designed to identify the vulnerability of vegetation in a particular region to climate variability during the growing season. In addition, the index can quantify the percentage of annual grid-point vegetation production either gained or lost due to climatic variability in a given month. When integrated over the growing season, this index is shown to be better correlated with end-of-season crop yields than traditional remotely-sensed or meteorological indices. In addition, in-season estimates of the index, which are available in near real-time, provide yield forecasts comparable to concurrent in situ objective yield surveys, which are only available in limited regions of the world. Overall, the cost effectiveness and repetitive, near-global view of earth's surface provided by this satellite-based vegetation monitoring index can potentially improve our ability to mitigate human vulnerability/susceptibility to drought and its impacts upon vegetation and agriculture.

Mapping Crop Yield and Sow Date Using High Resolution Imagery

NASA Astrophysics Data System (ADS)

Royal, K.

2015-12-01

Keitasha Royal, Meha Jain, Ph.D., David Lobell, Ph.D Mapping Crop Yield and Sow Date Using High Resolution ImageryThe use of satellite imagery in agriculture is becoming increasingly more significant and valuable. Due to the emergence of new satellites, such as Skybox, these satellites provide higher resolution imagery (e.g 1m) therefore improving the ability to map smallholder agriculture. For the smallholder farm dominated area of northern India, Skybox high-resolution satellite imagery can aid in understanding how to improve farm yields. In particular, we are interested in mapping winter wheat in India, as this region produces approximately 80% of the country's wheat crop, which is important given that wheat is a staple crop that provides approximately 20% of household calories. In northeast India, the combination of increased heat stress, limited irrigation access, and the difficulty for farmers to access advanced farming technologies results in farmers only producing about 50% of their potential crop yield. The use of satellite imagery can aid in understanding wheat yields through time and help identify ways to increase crop yields in the wheat belt of India. To translate Skybox satellite data into meaningful information about wheat fields, we examine vegetation indices, such as the normalized difference vegetation index (NDVI), to measure the "greenness" of plants to help determine the health of the crops. We test our ability to predict crop characteristics, like sow date and yield, using vegetation indices of 59 fields for which we have field data in Bihar, India.

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.

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

NASA Astrophysics Data System (ADS)

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

2007-10-01

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

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.

Investigating hydrometeorological impacts of perennial bioenergy crops under realistic scenario expansions

NASA Astrophysics Data System (ADS)

Wagner, M.; Wang, M.; Miguez-Macho, G.; Miller, J. N.; Bagley, J. E.; Bernacchi, C.; Georgescu, M.

2016-12-01

Perennial bioenergy crops, such as switchgrass and miscanthus, have been posed as a more sustainable energy pathway relative to annual bioenergy crops due to their reduced carbon footprint and ability to grow on abandoned and degraded land, thereby, avoiding competition with food crops. Previous studies that replaced annual bioenergy crops with perennial crops noted regional cooling associated with enhanced ET due to their deeper rooting systems extracting deeper soil moisture. This study provides a more realistic assessment by (1) analyzing perennial bioenergy expansion only in suitable abandoned and degraded farmlands, and (2) using field scale measurements of albedo in conjunction with known vegetation fraction and leaf area index (LAI) values. High-resolution (2 km grid spacing) simulations were performed using a state-of-the-art atmospheric model (Weather Research and Forecasting system) dynamically coupled to a land surface model system over the Southern Plains of the U.S., during a normal precipitation year (2007) and a drought year (2011). Our results show that perennial bioenergy crop expansion leads to regional cooling (1-2 oC), that is driven primarily by enhanced reflection of shortwave radiation, and secondarily, by enhanced ET. Perennial bioenergy crop expansion was also shown to mitigate drought impacts through moistening and cooling of the near-surface environment. These impacts, however, were reduced during the drought year as a result of differential environmental conditions, when compared to those of the normal cimate year. This study serves as a major step towards assessing the sustainability of perennial bioenergy crop expansion under diverse hydrometeorological conditions by highlighting the driving mechanisms and processes associated with this energy pathway.

Mapping rice extent map with crop intensity in south China through integration of optical and microwave images based on google earth engine

NASA Astrophysics Data System (ADS)

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

2017-12-01

Rice is one of the main staple foods in East Asia and Southeast Asia, which has occupied more than half of the world's population with 11% of cultivated land. Study on rice can provide direct or indirect information on food security and water source management. Remote sensing has proven to be the most effective method to monitoring the cropland in large scale by using temporary and spectral information. There are two main kinds of satellite have been used to mapping rice including microwave and optical. Rice, as the main crop of paddy fields, the main feature different from other crops is flooding phenomenon at planning stage (Figure 1). Microwave satellites can penetrate through clouds and efficiency on monitoring flooding phenomenon. Meanwhile, the vegetation index based on optical satellite can well distinguish rice from other vegetation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. Google has collected large number of remote sensing satellite data around the world, which providing researchers with the possibility of doing application by using multi-source remote sensing data in a large area. In this work, we map rice planting area in south China through integration of Landsat-8 OLI, Sentienl-2, and Sentinel-1 Synthetic Aperture Radar (SAR) images. The flowchart is shown in figure 2. First, a threshold method the VH polarized backscatter from SAR sensor and vegetation index including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from optical sensor were used the classify the rice extent map. The forest and water surface extent map provided by earth engine were used to mask forest and water. To overcome the problem of the "salt and pepper effect" by Pixel-based classification when the spatial resolution increased, we segment the optical image and use the pixel- based classification results to merge the object-oriented segmentation data, and finally get the rice extent map. At last, by using the time series analysis, the peak count was obtained for each rice area to ensure the crop intensity. In this work, the rice ground point from a GVG crowdsourcing smartphone and rice area statistical results from National Bureau of Statistics were used to validate and evaluate our result.

Evaluating Evapotranspiration of Pine Forest, Switchgrass, and Pine- Switchgrass Intercroppings using Remote Sensing and Ground-based Methods

NASA Astrophysics Data System (ADS)

Amatya, D. M.; Panda, S.; Chescheir, G. M.; Nettles, J. E.; Appelboom, T.; Skaggs, R. W.

2011-12-01

Vast areas of the land in the Southeastern United States are under pine forests managed primarily for timber and related byproducts. Evapotranspiration (ET) is the major loss in the water balance of this forest ecosystem. A long-term (1988-2008) study to evaluate hydrologic and nutrient balance during a life cycle of a pine stand was just completed. The study used both monitoring and modeling approaches to evaluate hydrologic and water quality effects of silvicultural and water management treatments on three 25 ha experimental watersheds in eastern North Carolina (NC). The research was extended in 2009 to include a dedicated energy crop, switchgrass (Panicum virgatum), by adding an adjacent 25 ha watershed. These multiple watersheds are being used to evaluate the hydrologic and water quality effects of switchgrass alone, young pine with natural understory, and young pine with switchgrass intercropping compared to the control (pine stand with a natural understory). The biofuels study has been further expanded to two other southern states, Alabama (AL) and Mississippi (MS). Each has five small watersheds (< 25 ha size) consisting of the above treatments and an additional woody biomass removal treatment. In this presentation we provide methods for estimating ET for these treatment watersheds in all three states (NC, AL, and MS) using remote sensing based spatial high resolution multispectral satellite imagery data with ground truthing, where possible, together with sensor technology. This technology is making ET parameter estimation a reality for various crops and vegetation surfaces. Slope-based vegetation indices like Normalized Difference Vegetation Index (NDVI) and Green Vegetation Index (GVI) and distance-based vegetation indices like Soil Adjusted Vegetation Index (SAVI) and Perpendicular Vegetation Index (PVI) will be developed using the R and NIR bands, vegetation density, and background soil reflectance as necessary. Landsat and high resolution aerial imageries of vegetation and soils will be used. IDRISI Taiga software will be used for the indices development. The forested vegetation health will be correlated to the leaf chlorophyll content for determining the vegetation health with a subsequent derivation of available plant water for radiation. Models will be developed to correlate the plant and soil available water to different vegetation indices. Correlation models will also be developed to obtain information on climatic parameters like surface air temperature, net radiation, albedo, soil moisture content, and stomatal water availability from Landsat imageries. On-site weather parameters used for the PET estimates will be combined with other vegetation parameters like leaf area index (LAI) obtained using LIDAR data and NAIP orthophotos of different seasons. That will also help detect the upper and understory vegetation. The LIDAR data will be processed to obtain the volume of vegetation to correctly estimate the total ET for each treatment.

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.

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.

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

NASA Astrophysics Data System (ADS)

Yu, B.; Shang, S.

2016-12-01

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

PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions.

PubMed

Yue, Junyang; Zhang, Danfeng; Ban, Rongjun; Ma, Xiaojing; Chen, Danyang; Li, Guangwei; Liu, Jia; Wisniewski, Michael; Droby, Samir; Liu, Yongsheng

2017-01-01

Penicillium expansum , the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium -crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein-protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium -Crop Protein-Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen-plant systems and available domain-domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. : http://bdg.hfut.edu.cn/pcppi/index.html. © The Author(s) 2017. Published by Oxford University Press.

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.

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.

[Impacts of climate change on food production in Gansu: A review].

PubMed

Yang, Feng-ke; He, Bao-lin; Gao, Shi-ming

2015-03-01

The climate of Gansu turned to be overall warming-drying and partly warming-wetting since 1986. In contrast to that of 1960, the average annual temperature had raised by 1.1°C with the average annual precipitation decreased by 28 mm correspondingly, which made the arid region expanded southward by 50 km in 2010. Climate warming increased the growth period effective accumulated temperature of main food grain crops and lengthened the crop growth period. It changed crop maturity, crop disposition, cropping system and generally increased the cultivatable area and planting altitude above the sea level of major crops and expanded northward the multiple cropping system, which further resulted in expansion of autumn grain crop sown area, shrink of summer grain crop sown area, and replacement of strong winter early maturing varieties by weak winter middle late maturing varieties. It benefited the crop yield by increasing the use efficiency of photo-thermal resources. Warming-wetting climate increased the climate productivity of oasis crop while warming-drying weather decreased the climate productivity of rainfed crops, which were mostly determined by the precipitation regimes and water conditions. Any advanced technique that can increase precipitation use ratio and water use efficiency as well as improve and promote soil quality and fertility should be regarded as an effective countermeasure to increase food grain production under climate change in Gsansu. So, selecting and breeding new crop varieties with the characteristics of strong resistance, weak winter, middle-late mature and high water use efficiency, establishing new planting structure and cropping system that suitable to the precipitation and temperature features of changed climate, are the development direction of food grain production in Gansu to cope with the climate change.

A national research & development strategy for biomass crop feedstocks

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

Wright, L.L.; Cushman, J.H.

Planning was initiated in 1996 with the objective of reevaluating current biomass feedstock research and development strategies to: (1) assure that by 2005, one or more commercial lignocellulosic to ethanol projects will be able to acquire a dependable supply of biomass crop feedstocks; (2) assure that recently initiated demonstrations of crops to electricity will be successful and; (3) assure that the research base needed to support future biomass industry expansion is being developed. Multiple trends and analyses indicate that biomass energy research and development strategies must take into account the fact that competition for land will define the upper limitsmore » of available biomass energy crop supplies and will largely dictate the price of those supplies. Only crop production and utilization strategies which contribute profit to the farmer or landowner and to energy producers will be used commercially for biomass energy production. Strategies for developing biomass {open_quotes}energy{close_quotes} crop supplies must take into consideration all of the methods by which biomass crops will enter biomass energy markets. The lignocellulosic materials derived from crops can be available as primary residues or crop by-products; secondary residues or processing by-products; co-products (at both the crop production and processing stages); or, as dedicated energy crops. Basic research and development (R&D) leading to yield improvement continues to be recommended as a major long-term focus for dedicated energy crops. Many additional near term topics need attention, some of which are also applicable to by-products and co-products. Switchgrass R&D should be expanded and developed with greater collaboration of USDA and state extension groups. Woody crop research should continue with significant cost-share from industries developing the crops for other commercial products. Co-product options need more investigation.« less

Semi-automatic digital image impact assessments of Maize Lethal Necrosis (MLN) at the leaf, whole plant and plot levels

NASA Astrophysics Data System (ADS)

Kefauver, S. C.; Vergara-Diaz, O.; El-Haddad, G.; Das, B.; Suresh, L. M.; Cairns, J.; Araus, J. L.

2016-12-01

Maize is the top staple crop for low-income populations in Sub-Saharan Africa and is currently suffering from the appearance of new diseases, which, together with increased abiotic stresses from climate change, are challenging the very sustainability of African societies. Current constraints in field phenotyping remain a major bottleneck for future breeding advances, but RGB-based High-Throughput Phenotyping Platforms (HTPPs) have demonstrated promise for rapidly developing both disease-resistant and weather-resilient crops. RGB HTTPs have proven cost-effective in studies assessing the effect of abiotic stresses, but have yet to be fully exploited to phenotype disease resistance. RGB image quantification using different alternate color space transforms, including BreedPix indices, were produced as part of a FIJI plug-in (http://fiji.sc/Fiji; http://github.com/george-haddad/CIMMYT). For validation, Maize Lethal Necrosis (MLN) visual scale impact assessments from 1 to 5 were scored by the resident CIMMYT plant pathologist, with 1 being MLN resistant (healthy plants with no visual symptoms) and 5 being totally susceptible (entirely necrotic with no green tissue). Individual RGB vegetation indexes outperformed NDVI (Normalized Difference Vegetation Index), with correlation values up to 0.72, compared to 0.56 for NDVI. Specifically, Hue, Green Area (GA), and the Normalized Green Red Difference Index (NGRDI) consistently outperformed NDVI in estimating MLN disease severity. In multivariate linear and various decision tree models, Necrosis Area (NA) and Chlorosis Area (CA), calculated similar to GA and GGA from Breedpix, also contributed significantly to estimating MLN impact scores. Results using UAS (Unmanned Aerial Systems), proximal field photography of plants and plots and flatbed scanners of individual leaves have produced similar results, demonstrating the robustness of these cost-effective RGB indexes. Furthermore, the application of the indices using classification and regression trees and conditional inference trees allows for their immediate implementation within the same open-source plugin for providing real time tools to crop breeders.

Temporal dynamics of direct N2O fluxes from agro-ecosystems in cold climates: importance of year-round measurements in multiple cropping systems

NASA Astrophysics Data System (ADS)

Wagner-Riddle, C.; Tenuta, M.

2014-12-01

Soil N2O fluxes (direct emissions) are highly variable in time and space due to soil, weather and management drivers. In cold climates, freeze/thaw cycles and short growing seasons can enhance soil N2O production contributing to the temporal variability of fluxes. Year-round measurements of N2O fluxes in multiple cropping systems are needed to decrease the uncertainty of annual emission estimates and to devise mitigation practices for emission reduction in cold climates. We have deployed a micrometeorological flux-gradient approach coupled to a tunable diode laser absorption spectroscopy system at two long-term sites in Canada: Elora, Ontario (2000-2014) and Glenlea, Manitoba (2006-2014). Quasi-simultaneous half-hourly flux measurements on four 4-ha fields within a level and aerodynamically homogeneous landscape were obtained allowing for comparison of crop type and/or management practices within and between years. Annual crops such as corn, soybeans, wheat, and barley received typical inorganic fertilizer and/or manure applications, and best management practices such as timing of application and reduced tillage were studied. Perennial grass-alfalfa hayfields were compared to annual crops during selected time periods. Here we synthesize the long-term datasets from these two sites, and quantify the overall contribution of non-growing season (mainly freeze/thaw cycles) and growing season (mainly nitrogen application) to annual emission totals. Uncertainties of regional estimates for cold-climates will be assessed using these long-term datasets.

Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Remote sensing based water-use efficiency evaluation in sub-surface irrigated wine grape vines

NASA Astrophysics Data System (ADS)

Zúñiga, Carlos Espinoza; Khot, Lav R.; Jacoby, Pete; Sankaran, Sindhuja

2016-05-01

Increased water demands have forced agriculture industry to investigate better irrigation management strategies in crop production. Efficient irrigation systems, improved irrigation scheduling, and selection of crop varieties with better water-use efficiencies can aid towards conserving water. In an ongoing experiment carried on in Red Mountain American Viticulture area near Benton City, Washington, subsurface drip irrigation treatments at 30, 60 and 90 cm depth, and 15, 30 and 60% irrigation were applied to satisfy evapotranspiration demand using pulse and continuous irrigation. These treatments were compared to continuous surface irrigation applied at 100% evapotranspiration demand. Thermal infrared and multispectral images were acquired using unmanned aerial vehicle during the growing season. Obtained results indicated no difference in yield among treatments (p<0.05), however there was statistical difference in leaf temperature comparing surface and subsurface irrigation (p<0.05). Normalized vegetation index obtained from the analysis of multispectral images showed statistical difference among treatments when surface and subsurface irrigation methods were compared. Similar differences in vegetation index values were observed, when irrigation rates were compared. Obtained results show the applicability of aerial thermal infrared and multispectral images to characterize plant responses to different irrigation treatments and use of such information in irrigation scheduling or high-throughput selection of water-use efficient crop varieties in plant breeding.

Analysis on the distribution characteristics and sources of soil heavy metals in suburban farmland in Xiangtan City

NASA Astrophysics Data System (ADS)

Zhang, Yong; Sun, Xinxin

2018-01-01

The rapid development of the economy will inevitably have an impact on the farmland soil environment. The content of heavy metal is increasing day by day, and the heavy metal can enter people's body through different channels and endanger people's health. Based on agricultural land and crop types in accordance with the regional land use classification, using the method of the Single Factor Index and Comprehensive Pollution Index, the pollution status of heavy metals in farmland soil in the suburbs of Xiangtan city was studied and evaluated. At the same time, we use SPSS software to analyze the four heavy metal elements (Cu, Zn, As and Pb) and analyze their possible sources. The results showed that the farmland soils in Erhuan Road and Zhubu Port were polluted, and the farmland soil in Shuangma (an old industrial district) was not polluted; for different crop lands, orchards and vegetable lands were not contaminated, but rape and rice lands were contaminated. Pearson correlation analysis showed that Cu, As and Pb might come from the same pollution source, while Zn might come from other sources. Waste water from a chemical plant, crop types, automobile exhaust and other human factors may be important sources of soil pollution in agricultural fields.

Comparison of fluoride effects on germination and growth of Zea mays, Glycine max and Sorghum vulgare.

PubMed

Fina, Brenda L; Lupo, Maela; Dri, Nicolas; Lombarte, Mercedes; Rigalli, Alfredo

2016-08-01

Fluorosis is a disease caused by over-exposure to fluoride (F). Argentina's rural lands have higher fluorine content than urban lands. Evidence confirms that plants grown in fluoridated areas could have higher F content. We compared F uptake and growth of crops grown in different F concentrations. The effect of 0-8 ppm F concentrations on maize, soybeans and sorghum germination and growth was compared. After 6 days seeding, the germination was determined, the roots and aerial parts lengths were measured, and vigor index was calculated. F content was measured in each part of the plants. Controls with equal concentrations of NaCl were carried out. Significant decrease in roots and aerial parts lengths, and in vigor index of maize and soybeans plants was observed with F concentrations greater than 2 ppm. This was not observed in sorghum seedlings. Also, the amount of F in all crops augmented as F increases, being higher in roots and ungerminated seeds. Sorghum was the crop with the highest F content. Fluoride decreased the germination and growth of maize and soybeans and therefore could influence on their production. Conversely, sorghum seems to be resistant to the action of F. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh

NASA Astrophysics Data System (ADS)

Akhand, Kawsar; Nizamuddin, Mohammad; Roytman, Leonid; Kogan, Felix

2016-09-01

Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world's top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture's contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.

A novel way to establish fertilization recommendations based on agronomic efficiency and a sustainable yield index for rice crops.

PubMed

Liu, Chuang; Liu, Yi; Li, Zhiguo; Zhang, Guoshi; Chen, Fang

2017-04-24

A simpler approach for establishing fertilizer recommendations for major crops is urgently required to improve the application efficiency of commercial fertilizers in China. To address this need, we developed a method based on field data drawn from the China Program of the International Plant Nutrition Institute (IPNI) rice experiments and investigations carried out in southeastern China during 2001 to 2012. Our results show that, using agronomic efficiencies and a sustainable yield index (SYI), this new method for establishing fertilizer recommendations robustly estimated the mean rice yield (7.6 t/ha) and mean nutrient supply capacities (186, 60, and 96 kg/ha of N, P 2 O 5 , and K 2 O, respectively) of fertilizers in the study region. In addition, there were significant differences in rice yield response, economic cost/benefit ratio, and nutrient-use efficiencies associated with agronomic efficiencies ranked as high, medium and low. Thus, ranking agronomic efficiency could strengthen linear models relating rice yields and SYI. Our results also indicate that the new method provides better recommendations in terms of rice yield, SYI, and profitability than previous methods. Hence, we believe it is an effective approach for improving recommended applications of commercial fertilizers to rice (and potentially other crops).

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

A triangular climate-based decision model to forecast crop anomalies in Kenya

NASA Astrophysics Data System (ADS)

Guimarães Nobre, G.; Davenport, F.; Veldkamp, T.; Jongman, B.; Funk, C. C.; Husak, G. J.; Ward, P.; Aerts, J.

2017-12-01

By the end of 2017, the world is expected to experience unprecedented demands for food assistance where, across 45 countries, some 81 million people will face a food security crisis. Prolonged droughts in Eastern Africa are playing a major role in these crises. To mitigate famine risk and save lives, government bodies and international donor organisations are increasingly building up efforts to resolve conflicts and secure humanitarian relief. Disaster-relief and financing organizations traditionally focus on emergency response, providing aid after an extreme drought event, instead of taking actions in advance based on early warning. One of the reasons for this approach is that the seasonal risk information provided by early warning systems is often considered highly uncertain. Overcoming the reluctance to act based on early warnings greatly relies on understanding the risk of acting in vain, and assessing the cost-effectiveness of early actions. This research develops a triangular climate-based decision model for multiple seasonal time-scales to forecast strong anomalies in crop yield shortages in Kenya using Casual Discovery Algorithms and Fast and Frugal Decision Trees. This Triangular decision model (1) estimates the causality and strength of the relationship between crop yields and hydro climatological predictors (extracted from the Famine Early Warning Systems Network's data archive) during the crop growing season; (2) provides probabilistic forecasts of crop yield shortages in multiple time scales before the harvesting season; and (3) evaluates the cost-effectiveness of different financial mechanisms to respond to early warning indicators of crop yield shortages obtained from the model. Furthermore, we reflect on how such a model complements and advances the current state-of-art FEWS Net system, and examine its potential application to improve the management of agricultural risks in Kenya.

Sensitivity of simulated maize crop yields to regional climate in the Southwestern United States

NASA Astrophysics Data System (ADS)

Kim, S.; Myoung, B.; Stack, D.; Kim, J.; Hatzopoulos, N.; Kafatos, M.

2013-12-01

The sensitivity of maize yield to the regional climate in the Southwestern United States (SW US) has been investigated by using a crop-yield simulation model (APSIM) in conjunction with meteorological forcings (daily minimum and maximum temperature, precipitation, and radiation) from the North American Regional Reanalysis (NARR) dataset. The primary focus of this study is to look at the effects of interannual variations of atmospheric components on the crop productivity in the SW US over the 21-year period (1991 to 2011). First of all, characteristics and performance of APSIM was examined by comparing simulated maize yields with observed yields from United States Department of Agriculture (USDA) and the leaf-area index (LAI) from MODIS satellite data. Comparisons of the simulated maize yield with the available observations show that the crop model can reasonably reproduce observed maize yields. Sensitivity tests were performed to assess the relative contribution of each climate driver to regional crop yield. Sensitivity experiments show that potential crop production responds nonlinearly to climate drivers and the yield sensitivity varied among geographical locations depending on their mean climates. Lastly, a detailed analysis of both the spatial and temporal variations of each climate driver in the regions where maize is actually grown in three states (CA, AZ, and NV) in the SW US was performed.

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

Influence of ecohydrologic feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios

NASA Astrophysics Data System (ADS)

van Walsum, P. E. V.; Supit, I.

2012-06-01

Hydrologic climate change modelling is hampered by climate-dependent model parameterizations. To reduce this dependency, we extended the regional hydrologic modelling framework SIMGRO to host a two-way coupling between the soil moisture model MetaSWAP and the crop growth simulation model WOFOST, accounting for ecohydrologic feedbacks in terms of radiation fraction that reaches the soil, crop coefficient, interception fraction of rainfall, interception storage capacity, and root zone depth. Except for the last, these feedbacks are dependent on the leaf area index (LAI). The influence of regional groundwater on crop growth is included via a coupling to MODFLOW. Two versions of the MetaSWAP-WOFOST coupling were set up: one with exogenous vegetation parameters, the "static" model, and one with endogenous crop growth simulation, the "dynamic" model. Parameterization of the static and dynamic models ensured that for the current climate the simulated long-term averages of actual evapotranspiration are the same for both models. Simulations were made for two climate scenarios and two crops: grass and potato. In the dynamic model, higher temperatures in a warm year under the current climate resulted in accelerated crop development, and in the case of potato a shorter growing season, thus partly avoiding the late summer heat. The static model has a higher potential transpiration; depending on the available soil moisture, this translates to a higher actual transpiration. This difference between static and dynamic models is enlarged by climate change in combination with higher CO2 concentrations. Including the dynamic crop simulation gives for potato (and other annual arable land crops) systematically higher effects on the predicted recharge change due to climate change. Crop yields from soils with poor water retention capacities strongly depend on capillary rise if moisture supply from other sources is limited. Thus, including a crop simulation model in an integrated hydrologic simulation provides a valuable addition for hydrologic modelling as well as for crop modelling.

Functional invertebrate prey groups reflect dietary responses to phenology and farming activity and pest control services in three sympatric species of aerially foraging insectivorous birds.

PubMed

Orłowski, Grzegorz; Karg, Jerzy; Karg, Grzegorz

2014-01-01

Farming activity severely impacts the invertebrate food resources of farmland birds, with direct mortality to populations of above-ground arthropods thorough mechanical damage during crop harvests. In this study we assessed the effects of phenological periods, including the timing of harvest, on the composition and biomass of prey consumed by three species of aerial insectivorous birds. Common Swifts Apus apus, Barn Swallows Hirundo rustica and House Martins Delichon urbica breed sympatrically and most of their diet is obtained from agricultural sources of invertebrate prey, especially from oil-seed rape crops. We categorized invertebrate prey into six functional groups, including oil-seed rape pests; pests of other arable crops; other crop-provisioned taxa; coprophilous taxa; and taxa living in non-crop and mixed crop/non-crop habitats. Seasonality impacted functional groups differently, but the general direction of change (increase/decrease) of all groups was consistent as indexed by prey composition of the three aerial insectivores studied here. After the oil-seed rape crop harvest (mid July), all three species exhibited a dietary shift from oil-seed rape insect pests to other aerial invertebrate prey groups. However, Common Switfts also consumed a relative large quantity of oil-seed rape insect pests in the late summer (August), suggesting that they could reduce pest insect emigration beyond the host plant/crop. Since these aerially foraging insectivorous birds operate in specific conditions and feed on specific pest resources unavailable to foliage/ground foraging avian predators, our results suggest that in some crops like oil-seed rape cultivations, the potential integration of the insectivory of aerial foraging birds into pest management schemes might provide economic benefits. We advise further research into the origin of airborne insects and the role of aerial insectivores as agents of the biological control of crop insect pests, especially the determination of depredation rates and the cascading effects of insectivory on crop damage and yield.

New insights into phosphorus management in agriculture--A crop rotation approach.

PubMed

Łukowiak, Remigiusz; Grzebisz, Witold; Sassenrath, Gretchen F

2016-01-15

This manuscript presents research results examining phosphorus (P) management in a soil–plant system for three variables: i) internal resources of soil available phosphorus, ii) cropping sequence, and iii) external input of phosphorus (manure, fertilizers). The research was conducted in long-term cropping sequences with oilseed rape (10 rotations) and maize (six rotations) over three consecutive growing seasons (2004/2005, 2005/2006, and 2006/2007) in a production farm on soils originated from Albic Luvisols in Poland. The soil available phosphorus pool, measured as calcium chloride extractable P (CCE-P), constituted 28% to 67% of the total phosphorus input (PTI) to the soil–plant system in the spring. Oilseed rape and maize dominant cropping sequences showed a significant potential to utilize the CCE-P pool within the soil profile. Cropping sequences containing oilseed rape significantly affected the CCE-P pool, and in turn contributed to the P(TI). The P(TI) uptake use efficiency was 50% on average. Therefore, the CCE-P pool should be taken into account as an important component of a sound and reliable phosphorus balance. The instability of the yield prediction, based on the P(TI), was mainly due to an imbalanced management of both farmyard manure and phosphorus fertilizer. Oilseed rape plants provide a significant positive impact on the CCE-P pool after harvest, improving the productive stability of the entire cropping sequence. This phenomenon was documented by the P(TI) increase during wheat cultivation following oilseed rape. The Unit Phosphorus Uptake index also showed a higher stability in oilseed rape cropping systems compared to rotations based on maize. Cropping sequences are a primary factor impacting phosphorus management. Judicious implementation of crop rotations can improve soil P resources, efficiency of crop P use, and crop yield and yield stability. Use of cropping sequences can reduce the need for external P sources such as farmyard manure and chemical fertilizers.

A UAS-based remote sensing platform for crop water stress detection

NASA Astrophysics Data System (ADS)

Zhang, H.; Wang, D.; Ayars, J. E.

2014-12-01

The remote detection of water stress in a biofuel crop field was investigated using canopy temperature measurements. An experimental trial was set up in the central valley of Maui, Hawaii, comprising different sugarcane varieties and irrigation regimes. An unmanned aerial system (UAS) was equipped with a FLIR A615 thermal camera to acquire canopy temperature imagery. Images were mosaicked and processed to show spatial temperature difference of entire field. A weather station was installed in a full irrigation plot to collect meteorological parameters. The sensitivity of canopy to air temperature difference and crop water stress index were investigated on detecting cop water stress levels. The results showed that low irrigation level treatment plots resulted in higher canopy temperatures compared to the high irrigation level treatment plots. Canopy temperatures also showed differences in water stress in different sugarcane varieties. The study demonstrated the feasibility of UAS-based thermal method to quantify plant water status of sugar canes used for biofuel crops.

[New method and instrument to diagnose crop growth status in greenhouse based on spectroscopy].

PubMed

Zhang, Xi-Jie; Li, Min-Zan; Cui, Di; Zhao, Peng; Sun, Jian-Ying; Tang, Ning

2006-05-01

Spectral reflectance of cucumber leaves in greenhouse was measured using an ASD FieldSpec Pro VNIR spectrometer with natural illumination. Two sensitive wavelengths, 527 nm and 762 nm, were selected to evaluate the nitrogen content of the cucumber leaves. A model was established and validated using normal difference color index(NDCI) with the correlation coefficient of 0.881. Based on the above efforts, a handheld spectral instrument was developed to diagnose the growth status of the crop in greenhouse using fiber optics. The instrument was mainly composed of four parts: reflected light acquisition system, light intensity measurement unit, signal conditioning unit, and data acquisition system. The sunlight reflected by the crop was transmitted by the fiber, and passed through the light filter to obtain light at the sensitive wavelengths. Finally it was transformed into electronic signal by the photoelectric transistor, and was used to diagnose the growth status of the crop according to the evaluation model. The result showed that the developed instrument was practical.

Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.

Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice.

PubMed

Jones, Susan; Baizan-Edge, Amanda; MacFarlane, Stuart; Torrance, Lesley

2017-01-01

Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question "how many different viruses are present in this crop plant?" without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing.

High Resolution Melt (HRM) analysis is an efficient tool to genotype EMS mutants in complex crop genomes.

PubMed

Lochlainn, Seosamh Ó; Amoah, Stephen; Graham, Neil S; Alamer, Khalid; Rios, Juan J; Kurup, Smita; Stoute, Andrew; Hammond, John P; Østergaard, Lars; King, Graham J; White, Phillip J; Broadley, Martin R

2011-12-08

Targeted Induced Loci Lesions IN Genomes (TILLING) is increasingly being used to generate and identify mutations in target genes of crop genomes. TILLING populations of several thousand lines have been generated in a number of crop species including Brassica rapa. Genetic analysis of mutants identified by TILLING requires an efficient, high-throughput and cost effective genotyping method to track the mutations through numerous generations. High resolution melt (HRM) analysis has been used in a number of systems to identify single nucleotide polymorphisms (SNPs) and insertion/deletions (IN/DELs) enabling the genotyping of different types of samples. HRM is ideally suited to high-throughput genotyping of multiple TILLING mutants in complex crop genomes. To date it has been used to identify mutants and genotype single mutations. The aim of this study was to determine if HRM can facilitate downstream analysis of multiple mutant lines identified by TILLING in order to characterise allelic series of EMS induced mutations in target genes across a number of generations in complex crop genomes. We demonstrate that HRM can be used to genotype allelic series of mutations in two genes, BraA.CAX1a and BraA.MET1.a in Brassica rapa. We analysed 12 mutations in BraA.CAX1.a and five in BraA.MET1.a over two generations including a back-cross to the wild-type. Using a commercially available HRM kit and the Lightscanner™ system we were able to detect mutations in heterozygous and homozygous states for both genes. Using HRM genotyping on TILLING derived mutants, it is possible to generate an allelic series of mutations within multiple target genes rapidly. Lines suitable for phenotypic analysis can be isolated approximately 8-9 months (3 generations) from receiving M3 seed of Brassica rapa from the RevGenUK TILLING service.

High Resolution Melt (HRM) analysis is an efficient tool to genotype EMS mutants in complex crop genomes

PubMed Central

2011-01-01

Background Targeted Induced Loci Lesions IN Genomes (TILLING) is increasingly being used to generate and identify mutations in target genes of crop genomes. TILLING populations of several thousand lines have been generated in a number of crop species including Brassica rapa. Genetic analysis of mutants identified by TILLING requires an efficient, high-throughput and cost effective genotyping method to track the mutations through numerous generations. High resolution melt (HRM) analysis has been used in a number of systems to identify single nucleotide polymorphisms (SNPs) and insertion/deletions (IN/DELs) enabling the genotyping of different types of samples. HRM is ideally suited to high-throughput genotyping of multiple TILLING mutants in complex crop genomes. To date it has been used to identify mutants and genotype single mutations. The aim of this study was to determine if HRM can facilitate downstream analysis of multiple mutant lines identified by TILLING in order to characterise allelic series of EMS induced mutations in target genes across a number of generations in complex crop genomes. Results We demonstrate that HRM can be used to genotype allelic series of mutations in two genes, BraA.CAX1a and BraA.MET1.a in Brassica rapa. We analysed 12 mutations in BraA.CAX1.a and five in BraA.MET1.a over two generations including a back-cross to the wild-type. Using a commercially available HRM kit and the Lightscanner™ system we were able to detect mutations in heterozygous and homozygous states for both genes. Conclusions Using HRM genotyping on TILLING derived mutants, it is possible to generate an allelic series of mutations within multiple target genes rapidly. Lines suitable for phenotypic analysis can be isolated approximately 8-9 months (3 generations) from receiving M3 seed of Brassica rapa from the RevGenUK TILLING service. PMID:22152063

Aerial thermal images to assess irrigation efficiency in 'Vitis vinifera' cv. Albariño

NASA Astrophysics Data System (ADS)

Gonzalez, Xesús Pablo; Fandiño, María; Rey, Benjamín J.; José Cancela, Javier

2017-04-01

Canopy temperature was defined as key data to irrigation management and to detect crop water stress (Jackson, 1982). Recently, temperature camera was installed on board in a Unmanned Aerial Vehicle (UAV), thus heterogeneity within field could be determined. Pereira et al. (2012) have defined the conceptual and terminological study of crop water use indicators, mainly water use efficiency (WUE) and water productivity (WP). Actually, it is crucial achieve higher WP and WUE, where crop yield variability between years must be reduced with the smallest irrigation water, but with a correct management of crop water stress during the season. In this study, Albariño cultivar grapevine, priority in Galicia (Spain) in Designation of Origen 'Rías Baixas', was assessed in relation to water productivity index, focus on irrigation treatments aspects, during 2016. Albariño vineyard was planted in 1996 on 110-Richter at a spacing of 3 × 2 m (1667 vines ha-1) (41°57 6 N, 8°49 26 W, elevation 101 m). Vines were trained to a vertical trellis system on a Guyot oriented in the East-West direction. Three irrigation treatments were applied: irrigation from budburst to maturation (T1), from flowering to maturation (T2), and from veraison to maturation (T3), moreover a rain-fed treatment was implemented. All WP index was referred to farm yield level (kg ha-1); where the denominator applied to WP TWUfarm, introduced rainfall and irrigation depth; to WP Irrig, only irrigation depth applied; was used. Moreover, crop water stress index (CWSI) was used to determine homogenize areas within experimental plot, using an UAV with a thermal camera (ThermoMAP, senseFly, SW) to achieve a final map with 14 cm per pixel resolution. During August 11th, at the end of veraison, camera was installed in an 'eBee Ag' UAV (senseFly, SW) with a median flight altitude of 75 m over ground level. Yield per hectare were recorded and total irrigation depth per treatment during the growing season from March to harvest. Preliminary results have showed that CWSI is useful to determine heterogeneity areas within field, concretely areas with identic irrigation treatments were grouped in a similar range, a good correlation was achieved with steam water potential measured in verasion during the flight. This aspect permit establishes a tool to manage irrigation with efficiency, during the growing season, using thermal data and CWSI. Finally, WP were higher in rain-fed than irrigated treatments, where T3 treatment showed higher WP Irrig, than T1 and T2 treatments. A new step Economic aspects should be studied, taken into account benefit crop yield, and cost of pumping irrigation water. References: Jackson, RD (1982). Canopy temperature and crop water stress. Advances in irrigation, 1:43-85 Pereira LS, Cordery I, Iacovides I (2012). Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agricultural Water Management, 108:39-51

Developing an operational rangeland water requirement satisfaction index

USGS Publications Warehouse

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

2011-01-01

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

An Analysis of the Joint Modular Intermodal Distribution System

DTIC Science & Technology

2007-06-01

the differing airframes. “Two methods are available to move a CROP-load of ammunition: 1. Reconfigure the load from the CROP onto multiple 463L...used among the services lack: • Transportability across different modes without re-handling/packaging • Quick reconfiguration for onward movement...numerous linkages among different channels of distribution. In the world of integrated logistics, that means that ground, rail, air, and sea modes of

Droughts in India from 1981 to 2013 and Implications to Wheat Production

PubMed Central

Zhang, Xiang; Obringer, Renee; Wei, Chehan; Chen, Nengcheng; Niyogi, Dev

2017-01-01

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation. PMID:28294189

Parsing multiple processes of high temperature impacts on corn/soybean yield using a newly developed CLM-APSIM modeling framework

NASA Astrophysics Data System (ADS)

Peng, B.; Guan, K.; Chen, M.

2016-12-01

Future agricultural production faces a grand challenge of higher temperature under climate change. There are multiple physiological or metabolic processes of how high temperature affects crop yield. Specifically, we consider the following major processes: (1) direct temperature effects on photosynthesis and respiration; (2) speed-up growth rate and the shortening of growing season; (3) heat stress during reproductive stage (flowering and grain-filling); (4) high-temperature induced increase of atmospheric water demands. In this work, we use a newly developed modeling framework (CLM-APSIM) to simulate the corn and soybean growth and explicitly parse the above four processes. By combining the strength of CLM in modeling surface biophysical (e.g., hydrology and energy balance) and biogeochemical (e.g., photosynthesis and carbon-nitrogen interactions), as well as that of APSIM in modeling crop phenology and reproductive stress, the newly developed CLM-APSIM modeling framework enables us to diagnose the impacts of high temperature stress through different processes at various crop phenology stages. Ground measurements from the advanced SoyFACE facility at University of Illinois is used here to calibrate, validate, and improve the CLM-APSIM modeling framework at the site level. We finally use the CLM-APSIM modeling framework to project crop yield for the whole US Corn Belt under different climate scenarios.

Heterogeneity and topsoil depletion due to tillage erosion and soil co-extraction with root vegetables: a serious threat to sustainable agricultural land use in the UK

NASA Astrophysics Data System (ADS)

Quine, Timothy; van Oost, Kristof

2010-05-01

The term soil erosion has become almost synonymous with water erosion and yet tillage erosion and soil loss with root crop harvest, although less visible, may be responsible for the majority of the on-site costs of soil erosion in many arable areas of the UK. The study reported here is a first attempt to model soil erosion associated with these processes in England and Wales, at the National scale. A GIS-based modelling approach in the Arc/Info environment is employed in order to meet the requirement for large-scale evaluation of erosion severity. Existing models that have been subject to independent test are used or adapted and widely available data is employed in model parameterisation. Tillage erosion is simulated using a diffusion-type model and a slope curvature index derived from coarse-scale topographic data. The curvature index is calibrated by statistical comparison to curvature values derived from a high resolution digital terrain model. Soil loss with root crop harvest is simulated using information concerning patterns of sugar beet and potato cultivation and estimation of soil moisture during the crop harvest season. Soil loss associated with root crop harvest may be as high as 1 t ha-1 year-1 if land is permanently used for root crops in a 3 year rotation. However, when the arable area of the UK is considered as a whole root crop harvest is responsible for a mean rate of soil loss of approximately 0.1 t ha-1 year-1. Tillage erosion is found to be the dominant process of soil redistribution and onsite erosion on arable land, in comparison with both soil loss through root crop harvest and with long-term water erosion rates. Mean gross rates of tillage erosion were found to be 3.7 t ha-1 year-1, representing approximately 7.4 t ha-1 year-1 erosion and the same rate of deposition. Soil redistribution at these rates is generating an heterogeneous soilscape in which continued functioning for food and fibre production may be jeopardized. These problems may be exacerbated by increased water stress in eroded soils if climate change does, as predicted, result in hotter and drier summers.

Development of remote sensing techniques for assessment of salinity induced plant stresses

NASA Astrophysics Data System (ADS)

Stong, Matthew Harold

Salinity has been shown to reduce vegetative growth, crop quality, and yield in agricultural crops. Remote sensing is capable of providing data about large areas. This project was designed to induce salinity stress in a crop, pak choi, and thereafter monitor the response of the crop as expressed by its spectral reflectances. The project was conducted in the National Taiwan University Phytotron, and spectral data was collected using a GER 2600. Yield and soil salinity (ECe) were also measured. After three seasons of data were collected, wavelengths sensitive to salinity were selected. These wavelengths, which are within the spectral response of biochemicals produced by plants as a response to soil salinity, were used to create two indices, the Salinity Stress Index (SSI) and the Normalized Salinity Stress Index (NSSI). After creating the indices tests were conducted to determine the efficacy of these indices in detecting salinity and drought stresses as compared to existing indices (SRVI and NDVI). This project induced salinity and drought stress in a crop, pak choi, and thereafter monitored the response of the crop as expressed by its spectral reflectances. The SSI and NSSI correlated well to both ECe and marketable yield. Additionally the SSI and NSSI were found to provide statistical differences between salinity stressed treatments and the control treatment. Drought stress was not detected well by any of the indices reviewed although the SSI and NSSI indices tended to increase with drought stress and decrease with salinity stress. As a final test, specific ion toxicities of sodium and chloride were tested against the developed indices (SSI and NSSI) and existing indices (NDVI, SRVI, and NDWI). There were no differences in SSI and NSSI responses to specific ion concentration in the high salinity treatments. These results indicated that the SSI and NSSI are not sensitive to the specific ion concentration in irrigation water. However, the SSI and NSSI were higher for the sodium water than the choride water in the low salinity treatments. It is likely that this difference was caused by the fact that the high SAR water decreased infiltration and caused water stress.

Response of Pennsylvania native plant species, corn and soybean to tank mixes of dicamba and glyphosate

EPA Science Inventory

Crops such as soybean are being genetically modified to be tolerant to multiple herbicides, such as dicamba and glyphosate, in order to allow treatment with several herbicides to control the development of herbicide resistance in weeds. However, with increased use of multiple-he...

A maize caffeoyl-CoA O-methyltransferase gene confers quantitative resistance to multiple pathogens

USDA-ARS?s Scientific Manuscript database

Alleles that confer multiple disease resistance (MDR) are valuable in crop improvement though molecular mechanisms underlying their functions remain largely unknown. A QTL, qMdr9.02, associated with resistance to three important foliar maize diseases, southern leaf blight (SLB), gray leaf spot (GLS)...

The benefits of herbicide-resistant crops.

PubMed

Green, Jerry M

2012-10-01

Since 1996, genetically modified herbicide-resistant crops, primarily glyphosate-resistant soybean, corn, cotton and canola, have helped to revolutionize weed management and have become an important tool in crop production practices. Glyphosate-resistant crops have enabled the implementation of weed management practices that have improved yield and profitability while better protecting the environment. Growers have recognized their benefits and have made glyphosate-resistant crops the most rapidly adopted technology in the history of agriculture. Weed management systems with glyphosate-resistant crops have often relied on glyphosate alone, have been easy to use and have been effective, economical and more environmentally friendly than the systems they have replaced. Glyphosate has worked extremely well in controlling weeds in glyphosate-resistant crops for more than a decade, but some key weeds have evolved resistance, and using glyphosate alone has proved unsustainable. Now, growers need to renew their weed management practices and use glyphosate with other cultural, mechanical and herbicide options in integrated systems. New multiple-herbicide-resistant crops with resistance to glyphosate and other herbicides will expand the utility of existing herbicide technologies and will be an important component of future weed management systems that help to sustain the current benefits of high-efficiency and high-production agriculture. Copyright © 2012 Society of Chemical Industry.

Increased food production and reduced water use through optimized crop distribution

NASA Astrophysics Data System (ADS)

Davis, Kyle Frankel; Rulli, Maria Cristina; Seveso, Antonio; D'Odorico, Paolo

2017-12-01

Growing demand for agricultural commodities for food, fuel and other uses is expected to be met through an intensification of production on lands that are currently under cultivation. Intensification typically entails investments in modern technology — such as irrigation or fertilizers — and increases in cropping frequency in regions suitable for multiple growing seasons. Here we combine a process-based crop water model with maps of spatially interpolated yields for 14 major food crops to identify potential differences in food production and water use between current and optimized crop distributions. We find that the current distribution of crops around the world neither attains maximum production nor minimum water use. We identify possible alternative configurations of the agricultural landscape that, by reshaping the global distribution of crops within current rainfed and irrigated croplands based on total water consumption, would feed an additional 825 million people while reducing the consumptive use of rainwater and irrigation water by 14% and 12%, respectively. Such an optimization process does not entail a loss of crop diversity, cropland expansion or impacts on nutrient and feed availability. It also does not necessarily invoke massive investments in modern technology that in many regions would require a switch from smallholder farming to large-scale commercial agriculture with important impacts on rural livelihoods.

Summer Flowering Cover Crops Support Wild Bees in Vineyards.

PubMed

Wilson, Houston; Wong, Jessica S; Thorp, Robbin W; Miles, Albie F; Daane, Kent M; Altieri, Miguel A

2018-02-08

Agricultural expansion and intensification negatively affect pollinator populations and has led to reductions in pollination services across multiple cropping systems. As a result, growers and researchers have utilized the restoration of local and landscape habitat diversity to support pollinators, and wild bees in particular. Although a majority of studies to date have focussed on effects in pollinator-dependent crops such as almond, tomato, sunflower, and watermelon, supporting wild bees in self-pollinated crops, such as grapes, can contribute to broader conservation goals as well as provide other indirect benefits to growers. This study evaluates the influence of summer flowering cover crops and landscape diversity on the abundance and diversity of vineyard bee populations. We showed that diversity and abundance of wild bees were increased on the flowering cover crop, but were unaffected by changes in landscape diversity. These findings indicate that summer flowering cover crops can be used to support wild bees and this could be a useful strategy for grape growers interested in pollinator conservation as part of a broader farmscape sustainability agenda. © The Author(s) 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Quantifying the effect of Tmax extreme events on local adaptation to climate change of maize crop in Andalusia for the 21st century

NASA Astrophysics Data System (ADS)

Gabaldon, Clara; Lorite, Ignacio J.; Ines Minguez, M.; Lizaso, Jon; Dosio, Alessandro; Sanchez, Enrique; Ruiz-Ramos, Margarita

2015-04-01

Extreme events of Tmax can threaten maize production on Andalusia (Ruiz-Ramos et al., 2011). The objective of this work is to attempt a quantification of the effects of Tmax extreme events on the previously identified (Gabaldón et al., 2013) local adaptation strategies to climate change of irrigated maize crop in Andalusia for the first half of the 21st century. This study is focused on five Andalusia locations. Local adaptation strategies identified consisted on combinations of changes on sowing dates and choice of cultivar (Gabaldón et al., 2013). Modified cultivar features were the duration of phenological phases and the grain filling rate. The phenological and yield simulations with the adaptative changes were obtained from a modelling chain: current simulated climate and future climate scenarios (2013-2050) were taken from a group of regional climate models at high resolution (25 km) from the European Project ENSEMBLES (http://www.ensembles-eu.org/). After bias correcting these data for temperature and precipitation (Dosio and Paruolo, 2011; Dosio et al., 2012) crop simulations were generated by the CERES-maize model (Jones and Kiniry, 1986) under DSSAT platform, previously calibrated and validated. Quantification of the effects of extreme Tmax on maize yield was computed for different phenological stages following Teixeira et al. (2013). A heat stress index was computed; this index assumes that yield-damage intensity due to heat stress increases linearly from 0.0 at a critical temperature to a maximum of 1.0 at a limit temperature. The decrease of crop yield is then computed by a normalized production damage index which combines attainable yield and heat stress index for each location. Selection of the most suitable adaptation strategy will be reviewed and discussed in light of the quantified effect on crop yield of the projected change of Tmax extreme events. This study will contribute to MACSUR knowledge Hub within the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE - JPI) of EU and is financed by MULCLIVAR project (CGL2012-38923-C02-02) and IFAPA project AGR6126 from Junta de Andalucía, Spain. References Dosio A. and Paruolo P., 2011. Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. Journal of Geophysical Research, VOL. 116, D16106, doi:10.1029/2011JD015934 Dosio A., Paruolo P. and Rojas R., 2012. Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal. Journal of Geophysical Research, Volume 117, D17, doi: 0.1029/2012JD017968 Gabaldón C, Lorite IJ, Mínguez MI, Dosio A, Sánchez-Sánchez E and Ruiz-Ramos M, 2013. Evaluation of local adaptation strategies to climate change of maize crop in Andalusia for the first half of 21st century. Geophysical Research Abstracts. Vol. 15, EGU2013-13625, 2013. EGU General Assembly 2013, April 2013, Vienna, Austria. Jones C.A. and J.R. Kiniry. 1986. CERES-Maize: A simulation model of maize growth and development. Texas A&M Univ. Press, College Station. Ruiz-Ramos M., E. Sanchez, C. Galllardo, and M.I. Minguez. 2011. Impacts of projected maximum temperature extremes for C21 by an ensemble of regional climate models on cereal cropping systems in the Iberian Peninsula. Natural Hazards and Earth System Science 11: 3275-3291. Teixeira EI, Fischer G, van Velthuizen H, Walter C, Ewert F. Global hotspots of heat stress on agricultural crops due to climate change. Agric For Meteorol. 2013;170(15):206-215.

Enhancement of crop photosynthesis by diffuse light: quantifying the contributing factors

PubMed Central

Li, T.; Heuvelink, E.; Dueck, T. A.; Janse, J.; Gort, G.; Marcelis, L. F. M.

2014-01-01

Background and Aims Plants use diffuse light more efficiently than direct light. However, experimental comparisons between diffuse and direct light have been obscured by co-occurring differences in environmental conditions (e.g. light intensity). This study aims to analyse the factors that contribute to an increase in crop photosynthesis in diffuse light and to quantify their relative contribution under different levels of diffuseness at similar light intensities. The hypothesis is that the enhancement of crop photosynthesis in diffuse light results not only from the direct effects of more uniform vertical and horizontal light distribution in the crop canopy, but also from crop physiological and morphological acclimation. Methods Tomato (Solanum lycopersicum) crops were grown in three greenhouse compartments that were covered by glass with different degrees of light diffuseness (0, 45 and 71 % of the direct light being converted into diffuse light) while maintaining similar light transmission. Measurements of horizontal and vertical photosynthetic photon flux density (PPFD) distribution in the crop, leaf photosynthesis light response curves and leaf area index (LAI) were used to quantify each factor's contribution to an increase in crop photosynthesis in diffuse light. In addition, leaf temperature, photoinhibition, and leaf biochemical and anatomical properties were studied. Key Results The highest degree of light diffuseness (71 %) increased the calculated crop photosynthesis by 7·2 %. This effect was mainly attributed to a more uniform horizontal (33 % of the total effect) and vertical PPFD distribution (21 %) in the crop. In addition, plants acclimated to the high level of diffuseness by gaining a higher photosynthetic capacity of leaves in the middle of the crop and a higher LAI, which contributed 23 and 13 %, respectively, to the total increase in crop photosynthesis in diffuse light. Moreover, diffuse light resulted in lower leaf temperatures and less photoinhibition at the top of the canopy when global irradiance was high. Conclusions Diffuse light enhanced crop photosynthesis. A more uniform horizontal PPFD distribution played the most important role in this enhancement, and a more uniform vertical PPFD distribution and higher leaf photosynthetic capacity contributed more to the enhancement of crop photosynthesis than did higher values of LAI. PMID:24782436

Crop candidates for the bioregenerative life support systems in China

NASA Astrophysics Data System (ADS)

Chunxiao, Xu; Hong, Liu

The use of plants for life support applications in space is appealing because of the multiple life support functions by the plants. Research on crops that were grown in the life support system to provide food and oxygen, remove carbon dioxide was begun from 1960. To select possible crops for research on the bioregenerative life support systems in China, criteria for the selection of potential crops were made, and selection of crops was carried out based on these criteria. The results showed that 14 crops including 4 food crops (wheat, rice, soybean and peanut) and 7 vegetables (Chinese cabbage, lettuce, radish, carrot, tomato, squash and pepper) won higher scores. Wheat ( Triticum aestivum L.), rice ( Oryza sativa L.), soybean ( Glycine max L.) and peanut ( Arachis hypogaea L.) are main food crops in China. Chinese cabbage ( Brassica campestris L. ssp. chinensis var. communis), lettuce ( Lactuca sativa L. var. longifolia Lam.), radish ( Raphanus sativus L.), carrot ( Daucus carota L. var. sativa DC.), tomato ( Lycopersicon escalentum L.), squash ( Cucurbita moschata Duch.) and pepper ( Capsicum frutescens L. var. longum Bailey) are 7 vegetables preferred by Chinese. Furthermore, coriander ( Coriandum sativum L.), welsh onion ( Allium fistulosum L. var. giganteum Makino) and garlic ( Allium sativum L.) were selected as condiments to improve the taste of space crew. To each crop species, several cultivars were selected for further research according to their agronomic characteristics.

Ozone risk assessment for agricultural crops in Europe: Further development of stomatal flux and flux-response relationships for European wheat and potato

NASA Astrophysics Data System (ADS)

Pleijel, H.; Danielsson, H.; Emberson, L.; Ashmore, M. R.; Mills, G.

Applications of a parameterised Jarvis-type multiplicative stomatal conductance model with data collated from open-top chamber experiments on field grown wheat and potato were used to derive relationships between relative yield and stomatal ozone uptake. The relationships were based on thirteen experiments from four European countries for wheat and seven experiments from four European countries for potato. The parameterisation of the conductance model was based both on an extensive literature review and primary data. Application of the stomatal conductance models to the open-top chamber experiments resulted in improved linear regressions between relative yield and ozone uptake compared to earlier stomatal conductance models, both for wheat ( r2=0.83) and potato ( r2=0.76). The improvement was largest for potato. The relationships with the highest correlation were obtained using a stomatal ozone flux threshold. For both wheat and potato the best performing exposure index was AF st6 (accumulated stomatal flux of ozone above a flux rate threshold of 6 nmol ozone m -2 projected sunlit leaf area, based on hourly values of ozone flux). The results demonstrate that flux-based models are now sufficiently well calibrated to be used with confidence to predict the effects of ozone on yield loss of major arable crops across Europe. Further studies, using innovations in stomatal conductance modelling and plant exposure experimentation, are needed if these models are to be further improved.