Search NASA⌕ Search

SEARCH · Search NASA

Results for “Crops, Agricultural”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

EcoSpec: Highly Equipped Tower-Based Hyperspectral and Thermal Infrared Automatic Remote Sensing System for Investigating Plant Responses to Environmental Changes

Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.

47 OTHER INSTRUMENTATION↗
NASA NTRS1975

Vegetational analysis with Skylab-3 imagery

The author has identified the following significant results. Color infrared photography from Skylab 3 appeared to be superior to ERTS imagery in a vegetational study of northeastern North Carolina. An accuracy of 87% was achieved in delimiting species composition and zonation patterns of three coastal, vegetation classes. A vegetation map of Perquimans County, North Carolina, seemed to have a high degree of correlation with information provided by high altitude U-2 photography. Random verification sites revealed an overall interpretation accuracy above 84%. Comparison of maps drawn utilizing Skylab photography with North Carolina Dept. of Agriculture estimates of crop acreage revealed some marked discrepancies. The chief difference lies in the nonagricultural category in which there is a 30% discrepancy. This fact raised some questions as to the definition of nonagricultural land uses and methods used by the State Dept. of Agriculture to determine actual percentages of crops grown.

Welby, C. W.↗
DOE OSTI2022

Long‐term yields in annual and perennial bioenergy crops in the Midwestern United States

Abstract Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean ( Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L., Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11 th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.

09 BIOMASS FUELS↗
DOE OSTI2021

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Jagermeyr, Jonas↗
NASA NTRS2021

Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate and Crop Models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO 2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

crop model↗
NASA NTRS2023

Considering Soil Moisture in Models of Climate Impacts on Child Health in Farming-Centric Countries

Soil moisture reflects the amount of water available to crops in the top layer of soil. As such, considering soil moisture provides important insight into water availability and ultimately crop yields in agricultural settings. In studies of climate change, food security, and health, however, soil moisture is rarely empirically considered despite its connection to crop health and yields. In this project, we aim to advance understanding of climate impacts on food security by incorporating soil moisture into quantitative models of child health. Combining spatially referenced health survey data from the Demographic and Health Surveys for 2005 and 2010 in Senegal and 2007, 2011, and 2014 in Bangladesh, with soil moisture data from the Famine Early Warning System Network Land Data Assimilation System, we explore the linkages between sub-annual and sub-seasonal climate conditions and child malnutrition in two rainfed agriculture dependent countries—Bangladesh and Senegal. Results suggest that soil moisture, measured on very short time scales, may be associated with reductions in anthropometric weight-for-height z-scores, but the relationship is highly dependent upon geographic context.

Child health↗
NASA NTRS1983

Development of visible/infrared/microwave agriculture classification and biomass estimation algorithms

This paper describes the results of a study to determine if crop acreage and biomass estimates could be improved by using visible IR and microwave data. The objectives were to (1) develop and test agricultural crop classification models using two or more spectral regions (visible through microwave), and (2) estimate biomass by including microwave with visible and infrared data. Aircraft multispectral data collected during the study included visible and infrared data (multiband data from 0.5 m - 12 m), and active microwave data K band (2 cm), C band (6 cm), L band (20 cm), and P band (75 cm) HH and HV polarizations. Ground truth data from each field consisted of soil moisture and biomass measurements. Results indicated that C, L, and P band active microwave data combined with visible and infrared data improved crop discrimination and biomass estimates compared to results using only visible and infrared data. The active microwave frequencies were sensitive to different biomass levels; K and C being sensitive to differences at low biomass levels, while P band was sensitive to differences at high biomass levels.

Rosenthal, W. D.↗
NASA NTRS1978

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

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.

Thompson, D. R.↗
NASA NTRS1976

Benefits to world agriculture through remote sensing

Remote sensing of agricultural land permits crop classification and mensuration which can lead to improved forecasts of production. This technique is particularly important for nations which do not already have an accurate agricultural reporting system. Better forecasts have important economic effects. International grain traders can make better decisions about when to store, buy, and sell. Farmers can make better planting decisions by taking advantage of production estimates for areas out of phase with their own agricultural calendar. World economic benefits will accrue to both buyers and sellers because of increased food supply and price stabilization. This paper reviews the econometric models used to establish this scenario and estimates the dollar value of benefits for world wheat as 200 million dollars annually for the United States and 300 to 400 million dollars annually for the rest of the world.

Buffalano, A. C.↗
DOE OSTI2025

Fallowed agricultural lands dominate anthropogenic dust sources in California

Air pollution remains a major problem in many parts of California, significantly impacting public health and regional climate. However, the contribution of anthropogenic dust from agricultural sources, among major pollutants in California’s semi-arid Central Valley, remains largely unclear. Here, we used the Cropland Data Layer from the U.S. Department of Agriculture to identify crop types and land use/cover and leveraged satellite-derived estimates of major dust events between 2008 and 2022 over California. We identified fallowed land—an unplanted agricultural land parcel—as a key anthropogenic dust source in California. Specifically, we find that the Central Valley accounts for about 77% of total fallowed land areas in California, where they are associated with about 88% of major anthropogenic dust events. We also find that the geographic coverage of these fallowed lands expanded between 2008 and 2022 with associated increasing anthropogenic dust activities. Additionally, these anthropogenic dust activities are sensitive to the drought severity over the fallowed lands, with potential cumulative effects on downstream dust burden during prolonged multi-year drought conditions. Overall, our results have important implications for public health, including increased risk for Valley fever and for regional climates, such as increases in extreme precipitation and snowmelt over the Sierra Nevada.

Climate sciences↗
DOE OSTI2022

Asymmetric daytime and nighttime surface temperature feedback induced by crop greening across Northeast China

Mid-high latitude Northeast China witnessed significant crop greening from 2001 to 2020, as evidenced by satellite records and field observations. The land surface temperature of croplands during the growing season showed a decreasing trend, suggesting negative surface temperature feedback to crop greening of agricultural ecosystems in mid-high latitude Northeast China. Here, using time-series remote sensing products and long-term scenario simulations, the present study highlights that crop greening can slow climate warming. Our study noted a stronger surface cooling effect induced by crop greening during the growing season in the day than at the night, which contributed to asymmetric diurnal temperature cycle changes in Northeast China. In addition, our biophysical mechanism analysis revealed aerodynamic and surface resistances as the major driving factors for the daytime land surface temperature (LST) cooling effect induced by crop greening, while the ground heat flux and ambient temperature feedback as the major attributes of the nighttime LST cooling impact due to crop greening.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

2005 AG20/20 Annual Review

Topics covered include: Implementation and Validation of Sensor-Based Site-Specific Crop Management; Enhanced Management of Agricultural Perennial Systems (EMAPS) Using GIS and Remote Sensing; Validation and Application of Geospatial Information for Early Identification of Stress in Wheat; Adapting and Validating Precision Technologies for Cotton Production in the Mid-Southern United States - 2004 Progress Report; Development of a System to Automatically Geo-Rectify Images; Economics of Precision Agriculture Technologies in Cotton Production-AG 2020 Prescription Farming Automation Algorithms; Field Testing a Sensor-Based Applicator for Nitrogen and Phosphorus Application; Early Detection of Citrus Diseases Using Machine Vision and DGPS; Remote Sensing of Citrus Tree Stress Levels and Factors; Spectral-based Nitrogen Sensing for Citrus; Characterization of Tree Canopies; In-field Sensing of Shallow Water Tables and Hydromorphic Soils with an Electromagnetic Induction Profiler; Maintaining the Competitiveness of Tree Fruit Production Through Precision Agriculture; Modeling and Visualizing Terrain and Remote Sensing Data for Research and Education in Precision Agriculture; Thematic Soil Mapping and Crop-Based Strategies for Site-Specific Management; and Crop-Based Strategies for Site-Specific Management.

Ross, Kenton W.↗
NASA NTRS2024

How Can Future Climate Change Affect the Corn Production System in Nebraska, USA?

The increasing temperatures and changing precipitation patterns have affected both crop yields and water requirements challenging agricultural systems in sustaining high crop yields and ensuring water and food security. In the present study, we evaluated the impact of climate change on the corn production under irrigated and rainfed cropping systems in Nebraska considering four climate zones until 2100 as a case study for the US Corn Belt. Additionally, suggests ways to reduce the impact of climate change on corn yield and water resources. Five global circulation model datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) interconnected to Shared Socioeconomic Pathways (SSPs), were selected. Three future periods: near (2020–2039), mid (2040–2069), and far future (2070–2100) under SSP226, SSP370, and SSP585 scenarios were used to predict the climate changes, crop cycle length, crop yield, and crop water requirement. Additionally, we input annual CO2 concentration scenarios for each SSP scenario to model changes from 2015 to 2100 using the CERES-Maize model. Temperatures are forecasted to rise, precipitation to decline, and crop cycle length to decrease, particularly for SSP370 (up to 49% yield decrease) and SSP585 (up to 67% yield decrease) by 2070–2100. Yields are expected to decline across all scenarios and periods for irrigated and rainfed fields due to the reduction in crop cycle length. Crop water demand is projected to increase, with rainfed systems facing heightened impact due to reduced precipitation. Addressing climate change in Nebraska requires actions like developing new corn varieties to longer the crop season, transitioning to efficient irrigation systems, improving irrigation management for water and food security, and reducing the risk of conflicts over its use. These results open possibilities for discussion about the actions that can be taken to cope with climate change and guarantee water and food security in the US Corn Belt.

CMIP6↗
NASA NTRS2019

Parameterization-Induced Uncertainties and Impacts of Crop Management Harmonization in a Global Gridded Crop Model Ensemble

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Folberth, Christian↗
NASA NTRS1974

Operation of agricultural test fields for study of stressed crops by remote sensing

A test site for the study of winter wheat development and collection of ERTS data was established in September of 1973. The test site is a 10 mile square area located 12.5 miles west of Amarillo, Texas on Interstate Hwy. 40, in Randall and Potter counties. The center of the area is the Southwestern Great Plains Research Center at Bushland, Texas. Within the test area all wheat fields were identified by ground truth and designated irrigated or dryland. The fields in the test area other than wheat were identified as to pasture or the crop that was grown. A ground truth area of hard red winter wheat was established west of Hale Center, Texas. Maps showing the location of winter wheat fields in excess of 40 acres in size within a 10 mile radius were supplied NASA. Satellite data was collected for this test site (ERTS-1).

Toler, R. W.↗