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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Heat Stress to Jeopardize Crop Production in the US Corn Belt Based on Downscaled CMIP5 Projections

CONTEXT Global food security faces increasing challenges from the changing climate. Changes of agricultural output from some of the most productive regions such as the US Corn Belt can largely affect the world's food market. Developing predictive understanding of the agricultural risk of climate change and potential mitigation strategies is critical for the global food security. OBJECTIVE The objective of this study is to assess the responses of maize and soybean yield to projected climate changes in the Corn Belt, identify the shifting environment stressors on crop yield, and tackle potential climate adaptation strategies. METHODS We drive a process-based model, the Decision Support System for Agrotechnology Transfer, with high-resolution statistically downscaled and bias-corrected historical and future climates from ten CMIP5 models in the MACA-2 database. RESULTS AND CONCLUSIONS The multi-model ensemble mean suggests a 12% decrease of maize yield by mid-century and 40% by late century, with a high degree of model consensus in the direction of changes; for individual models, the projected decrease of maize yield by late century ranges from <5% to over 80%, with the worst crop outcome corresponding to the most sensitive climate models. Soybean yield is projected to increase by midcentury with a high degree of model consensus, but such consensus is lost by late century as some projections shift to significant decreases. Crop yield in the Corn Belt is currently limited by water stress, but is projected to be increasingly limited by heat stress as well after the midcentury. The mounting heat stress will drive the most productive zone for maize to shift from central to northern part of the Corn Belt, but the projected increase in the northern states cannot fully compensate for the decrease in the south, causing the total production to decrease if agricultural practice stays the same. Earlier planting can alleviate only a small fraction of the heat-induced crop loss in a warmer climate. Climate change will (at least partially) offset the yield boost caused by agricultural technology and intensification. SIGNIFICANCE This study advances our predictive understanding of crop yield responses to climate change, and suggests that a multitude of strategies will be needed to address the climate change challenges for the U.S. agriculture.

Crop yield↗
NASA NTRS2020

Adaptation in U.S. Corn Belt Increases Resistance to Soil Carbon Loss With Climate Change

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

Soil Organic Carbon (SOC)↗
NASA NTRS1972

The 1971 corn blight watch experiment

The successful fulfillment of the objectives for the 1971 corn blight watch experiment is reported. The objectives were: (1) detect the development and spread of corn blight during the growing season across the Corn Belt; (2) assess different levels of infection in the Corn Belt; (3) amplify data acquired by ground observations to better appraise current blight status and the probable impact on crop production; and (4) estimate through extrapolation the applicability of these techniques to similar situations occurring in the future.

Clifton, J. 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 NTRS2017

The Contribution of Solar Brightening to the US Maize Yield Trend

Predictions of crop yield under future climate change are predicated on historical yield trends1,2,3, hence it is important to identify the contributors to historical yield gains and their potential for continued increase. The large gains in maize yield in the US Corn Belt have been attributed to agricultural technologies4, ignoring the potential contribution of solar brightening (decadal-scale increases in incident solar radiation) reported for much of the globe since the mid-1980s. In this study, using a novel biophysical/empirical approach, we show that solar brightening contributed approximately 27% of the US Corn Belt yield trend from 1984 to 2013. Accumulated solar brightening during the post-flowering phase of development of maize increased during the past three decades, causing the yield increase that previously had been attributed to agricultural technology. Several factors are believed to cause solar brightening, but their relative importance and future outlook are unknown, making prediction of continued solar brightening and its future contribution to yield gain uncertain. Consequently, results of this study call into question the implicit use of historical yield trends in predicting yields under future climate change scenarios.

Matthijs Tollenaar↗
NASA NTRS1982

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

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.

Batista, G. T.↗
NASA NTRS2018

Spatially-Explicit Monitoring of Crop Photosynthetic Capacity Through the Use of Space-Based Chlorophyll Fluorescence Data

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V(sub cmax) at a reference temperature of 25 deg C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V(sub cmax), in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V(sub cmax) at the seasonal scale (R(squared)=0.70 for C4 and R(squared)=0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V(sub cmax) with SIF that was used to estimate spatially and temporally varying crop V(sub cmax) for the US Corn Belt region. The resulting V(sub cmax) maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite- based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V(sub cmax) from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V(sub cmax) using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.

Drought↗
NASA NTRS1984

Assessment of technologies for classification of mixed pixels

A new method of directly classifying mixed pixels is described. This method and four frequently used indirect mixed pixel classification techniques are evaluated on Landsat MSS data from the U.S. Corn Belt using an automatic corn and soybean labeling technique. The results indicate that while more sophisticated, physically-based techniques for classsifying mixed pixels may yield a higher Percent Correct Classification (PCC) for those pixels, the net effect on a crop area proportion estimation procedure may be negative.

Metzler, M. D.↗
NASA NTRS1981

Analysis of scanner data for crop inventories

Progress and technical issues are reported in the development of corn/soybeans area estimation procedures for use on data from South America, with particular emphasis on Argentina. Aspects related to the supporting research section of the AgRISTARS Project discussed include: (1) multisegment corn/soybean estimation; (2) through the season separability of corn and soybeans within the U.S. corn belt; (3) TTS estimation; (4) insights derived from the baseline corn and soybean procedure; (5) small fields research; and (6) simulating the spectral appearance of wheat as a function of its growth and development. To assist the foreign commodity production forecasting, the performance of the baseline corn/soybean procedure was analyzed and the procedure modified. Fundamental limitations were found in the existing guidelines for discriminating these two crops. The temporal and spectral characteristics of corn and soybeans must be determined because other crops grow with them in Argentina. The state of software technology is assessed and the use of profile techniques for estimation is considered.

Horvath, R.↗
NASA NTRS1984

Research in satellite-aided crop forecasting

Evaluations of remote sensing procedures developed specifically to estimate non-U.S. spring small grains area show accuracies of less than 10 percent relative difference to reference statistics for North Dakota in 1978 and good comparison with 9000 square miles of observations over four states and Saskatchewan, Canada during the years 1976-79. Processing a 5 x 6-nautical-mile sample site requires a few minutes manual time and a few minutes central processing unit time on an AS-3000 computer. Evaluations of summer crop, corn, and soybeans area estimates show unbiased summer crops estimates in the U.S. central corn belt but significant bias in one of two years for area estimates of corn and soybeans. Based on results to date, a highly automated corn/sorghum/soybean area estimation procedure should be achieved that is applicable to Argentina.

Erickson, J. D.↗
NASA NTRS1981

AgRISTARS: Foreign commodity production forecasting. The 1980 US corn and soybeans exploratory experiment

The U.S. corn and soybeans exploratory experiment is described which consisted of evaluations of two technology components of a production forecasting system: classification procedures (crop labeling and proportion estimation at the level of a sampling unit) and sampling and aggregation procedures. The results from the labeling evaluations indicate that the corn and soybeans labeling procedure works very well in the U.S. corn belt with full season (after tasseling) LANDSAT data. The procedure should be readily adaptable to corn and soybeans labeling required for subsequent exploratory experiments or pilot tests. The machine classification procedures evaluated in this experiment were not effective in improving the proportion estimates. The corn proportions produced by the machine procedures had a large bias when the bias correction was not performed. This bias was caused by the manner in which the machine procedures handled spectrally impure pixels. The simulation test indicated that the weighted aggregation procedure performed quite well. Although further work can be done to improve both the simulation tests and the aggregation procedure, the results of this test show that the procedure should serve as a useful baseline procedure in future exploratory experiments and pilot tests.

Malin, J. T.↗
NASA NTRS1975

The CITARS effort by the environmental research institute of Michigan

The objectives of the research task for crop identification technology assessment for remote sensing are outlined. Data gathered by the Landsat 1 multispectral scanner over the U.S. Corn Belt during 1973 is described, and procedures for recognition processing of the data is discussed in detail. The major crops of prime interest were corn and soybeans; they were recognized with different levels of accuracy throughout the growing season, but particularly during late August. Wheat was the major crop of interest in early June.

Malila, W. A.↗
NASA NTRS1987

Landsat classification of Argentina summer crops

A Landsat MSS and TM classification approach based on three features derived from the greenness profile has proved very effective in separating and identifying corn, soybeans, and other ground cover classes in the U.S. The objective of this study is to investigate the separation of summer crops in Argentina, one of the most important commodity exporters, using the same greenness profile features that have proved effective in the U.S. Corn Belt. The area chosen for study is a more complex cropping practice area located in the north-west corner of Buenos Aires province in Pampa Humeda, where corn, soybean, sorghum, sunflower, and pastures are cultivated. It is shown that the profile features can provide very effective separation, except in the case of corn from sorghum. Separation between corn and soybeans was found to be greater than in the U.S. This study suggests that the automatic, unsupervised classification approach developed in the U.S., with relatively minor modification, can be used for summer crop area estimation in Argentina.

Badhwar, G. D.↗
NASA NTRS1973

Results of the 1971 Corn Blight Watch experiment.

The objective of the experiment was to evaluate the use of advanced remote sensing techniques to detect the development and spread of southern corn leaf blight during the growing season across the Corn Belt region. The sampling plan involved the selection of the study area, the determination of the flightline, and the determination of a field sample design. Aspects of data acquisition are discussed, giving attention to ground data collection and aerial data collection. Details of data flow are considered along with data analysis procedures and corn blight records. The experiment results are examined, taking into account photointerpretation results, the machine analysis of multispectral scanner data results, the influence of blight on yields, and questions of crop identification.

Macdonald, R. B.↗
NASA NTRS1972

Corn blight watch experiment results

Results pertaining to the detection and assessment of the severity and extent of southern corn leaf blight in the Corn Belt area are discussed. Ground observations, interpretation of color infrared photography, and machine analysis of multispectral scanner data were used to identify the blight.

Johannsen, C. J.↗