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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

How Can CO2 Help Agriculture in the Face of Climate Change?

Humans are increasing the amount of carbon dioxide (CO2) in the air through CO2 emissions. This is changing the climate, making life harder for many plants in areas that suffer from heat and drought. However, plants need CO2 to grow, and more CO2 can make them grow better. So will plants overall benefit from increased CO2 level or suffer from it? We wanted to test if the positive effect would offset the negative ones. To do so, we used scientific models to calculate future crop production and water use of four important crops all over the world under different scenarios of CO2 emissions and climate change. Our calculations show that although there will be large reductions in crop yield due to climate change over the next century, some crops will still be able to grow well. This is also because crops can grow with less water when CO2 levels are raised.

Delphine, Deryng↗
NASA NTRS1980

AgRISTARS: Yield model development/soil moisture. Interface control document

The interactions and support functions required between the crop Yield Model Development (YMD) Project and Soil Moisture (SM) Project are defined. The requirements for YMD support of SM and vice-versa are outlined. Specific tasks in support of these interfaces are defined for development of support functions.

Source record↗
NASA NTRS1982

The use of large-area spectral data in wheat yield estimation

Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.

Barnett, T. L.↗
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 NTRSDate not supplied

Regional and Model-Specific Response Types in A Global Gridded Crop Model Ensemble

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

crop models↗
NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗
NASA NTRS1977

Wheat yield forecasts using Landsat data

Leaf area index and percentage of vegetative cover, two indices of crop yield developed from Landsat multispectral scanning data, are discussed. Studies demonstrate that the Landsat indicators may be as highly correlated with winter wheat yield as estimates based on traditional field sampling methods; in addition, the Landsat indicators may account for variations in individual field yield which are not explainable by meteorological data. A simple technique employing early-season Landsat data to make wheat yield predictions is also considered.

Colwell, J. E.↗
NASA NTRS2014

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve substantially the climate, crop, and economic simulation tools that are used to characterize the agricultural sector, to assess future world food security under changing climate conditions, and to enhance adaptation capacity both globally and regionally. To understand better and improve the modeled crop responses, AgMIP has conducted detailed crop model intercomparisons at closely observed field sites for wheat (Asseng et al., 2013), rice (Li et al., in review), maize (Bassu et al., 2014), and sugarcane (Singels et al., 2013). A coordinated modeling exercise was one of the original motivations for AgMIP, and C3MP provides rapid estimation of crop responses to CO2, water, and temperature (CTW) changes, adding dimension and insight into the crop model intercomparisons, while facilitating interactions within the global community of modelers. C3MP also contributes a fast-track, multi-model climate sensitivity assessment for the AgMIP climate and crop modeling teams on Research Track 2 (Fig. 1), which seeks to understand the impact of projected climatic changes on crop production and food security (Rosenzweig et al., 2013; Ruane et al., 2014).

agriculture↗
NASA NTRS2022

AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal: Representative Agricultural Pathways, Climate, Crop and Economic Datasets

This paper describes the datasets that were used to implement an AgMIP Regional Integrated Assessment for the Nioro region of Senegal to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin and to assess adaptations of that system to climate change under current as well as future climate and socio-economic conditions. This dataset includes the Representative Agricultural Pathways developed for Nioro from 2000-2050; the climate data that were used to implement crop yield simulations; the data that were used to parameterize the DSSAT and APSIM crop models, including historical climate data and future climate scenarios; and the data that were used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOAMD) economic simulation model, as well as simulated model outputs.

AgMIP↗
NASA NTRS2021

Hybrid phenology matching model for robust crop phenological retrieval

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Phenology↗
NASA NTRS1980

Research in the application of spectral data to crop identification and assessment, volume 2

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

Daughtry, C. S. T.↗
NASA NTRS1988

Exploring the limits of crop productivity. I. Photosynthetic efficiency of wheat in high irradiance environments

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.

NASA Discipline Number 61-10↗
NASA NTRS2024

The Economics of Farming Expansion in the Brazilian Cerrado Under Possible Effects of Climate Change

This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to predict potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis.

agricultural expansion↗
NASA NTRS1994

Yield and seed oil content response of dwarf, rapid-cycling Brassica to nitrogen treatments, planting density, and carbon dioxide enrichment

Effects of N level (15 to 30 mM), time of N increase (14 to 28 days after planting), and planting density (1163 to 2093 plants/m2) were determined for crop yield responses of dwarf, rapid-cycling brassica (Brassica napus L., CrGC 5-2, Genome: ACaacc). Crops were grown in solid-matrix hydroponic systems and under controlled-environment conditions, including nonsupplemented (ambient) or elevated CO2 concentrations (998 +/- 12 micromoles mol-1). The highest seed yield rate obtained (4.4 g m-2 day-1) occurred with the lowest N level (15 mM) applied at the latest treatment time (day 28). In all trials, CO2 enrichment reduced seed yield rate and harvest index by delaying the onset of flowering and senescence and stimulating vegetative shoot growth. The highest shoot biomass accumulation rate (55.5 g m-2 day-1) occurred with the highest N level (30 mM) applied at the earliest time (day 14). Seed oil content was not significantly affected by CO2 enrichment. Maximum seed oil content (30% to 34%, dry weight basis) was obtained using the lowest N level (15 mM) initiated at the latest treatment time (day 28). In general, an increase in seed oil content was accompanied by a decrease in seed protein. Seed carbohydrate, moisture, and ash contents did not vary significantly in response to experimental treatments. Effects of N level and time of N increase were consistently significant for most crop responses. Planting density was significant only under elevated CO2 conditions.

Non-NASA Center↗
NASA NTRS2024

Georgia’s Potentials for Sustainable Intensification, Increasing Food Security and Rural Incomes

Increasing global demand for agricultural commodities spurs conversions of natural ecosystems. Sustainable intensification in areas of high yield gaps can contribute to reducing the impact of commodity production, while also supporting development, food security, and livelihoods. Following the dissolution of the Union of Soviet Socialist Republics (USSR), Georgia experienced one the highest losses of agricultural productivity among all former USSR countries and is now highly dependent on food imports. Closing yield gaps in Georgia through sustainable intensification has the potential to increase food self-sufficiency, support rural livelihoods, and strengthen food security and sovereignty. We estimated it’s potential for sustainable intensification on current agricultural areas to achieve self-sufficiency for wheat, maize, and barley. We found that crop yields can be doubled to tripled under high input production systems, using high-yielding varieties, optimized inputs, fertilizers, and pest control. Yet, self-sufficiency in wheat can only be reached if at least 60-80% of the potentially attainable yields are achieved and if land is optimally allocated between crops. To achieve such increases, farmers need access to and training for using different crop varieties, fertilizers, and pest and disease control practices and products. Intensification increases the risks to ecosystem services health and livelihoods, particularly raising equity concern. Yet, intensifying very low input systems is often found to be more sustainable, with high yield increases compared to limited impacts on the environment. The high employment in the agricultural sector in Georgia particularly provides opportunities to reduce poverty and increase livelihoods through increasing incomes and food security.

Florian Gollnow↗
NASA NTRS2005

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Kocurek, Michael J.↗
NASA NTRS2008

Potential Carbon Negative Commercial Aviation through Land Management

Brazilian terra preta soil and char-enhanced soil agricultural systems have demonstrated both enhanced plant biomass and crop yield and functions as a carbon sink. Similar carbon sinking has been demonstrated for both glycophyte and halophyte plants and plant roots. Within the assumption of 3.7 t-C/ha/yr soils and plant root carbon sinking, it is possible to provide carbon neutral U.S. commercial aviation using about 8.5% of U.S. arable lands. The total airline CO2 release would be offset by carbon credits for properly managed soils and plant rooting, becoming carbon neutral for carbon sequestered synjet processing. If these lands were also used to produce biomass fuel crops such as soybeans at an increased yield of 60 bu/acre (225gal/ha), they would provide over 3.15 10(exp 9) gallons biodiesel fuel. If all this fuel were refined into biojet it would provide a 16% biojet-84% synjet blend. This allows the U.S. aviation industry to become carbon negative (carbon negative commercial aviation through carbon credits). Arid land recovery could yield even greater benefits.

Hendricks, Robert C.↗
NASA NTRS1982

Spectral estimates of solar radiation intercepted by corn canopies

Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types. Agronomic data collected included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable, greenness, was associated with 78 percent of the variation in LAI over all treatments. Single observations of LAI or greenness have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both SRI estimates, when accumulated over the growing season, accounted for approximately 65 percent of the variation in yields. Models which simulated the daily effects of weather and intercepted solar radiation on growth had the highest correlations to grain yields. This concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models.

Bauer, M. E.↗