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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Overview of the AgRISTARS research program. I

An account is given of the activities and accomplishments to date of the U.S. Department of Agriculture's Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS) program, which is a cooperative venture with NASA and the Departments of the Interior and of Commerce. AgRISTARS research activities encompass early warning and crop condition assessment, inventory technology development for production forecasting, crop yield model development, soil moisture monitoring, domestic crops and land cover sensing, renewable resources inventory, and conservation and pollution assessment.

Caudill, C. E.↗
NASA NTRS1989

Efficiency of N use by wheat as a function of influx and efflux of NO sub 3

Since N assimilation is one of the most costly functions of a plant, its efflux before assimilation results in a serious energy cost and loss in efficiency which could decrease yields. Efficient crop production is critical to the Controlled Ecological Life-Support System (CELSS). The objective is to determine the extent of efflux of the N species NO3(-), NH4(+), NO2(-), and urea after uptake, and possible means of regulation. It was found that NO3(-) efflux became serious as its substrate level increased. Efflux/Influx (E/I) of 3NO3(-) was greater in darkness (35 pct) than in light (14 pct) and the ratio greatly increased with increased substrate NO3(-), (up to 45 pct at 10 mM). It seems advantageous to use the lowest possible nutrient concentration of NO3(-). The feasibility of using ClO3(-) as a trapping agent (competitive inhibitor of NO3(-) uptake) for effluxed NO3(-) was assessed and its toxicity determined.

Huffaker, R. C.↗
NASA NTRS1990

Efficiency of N use by wheat as a function of influx and efflux of NO3

Since N assimilation is one of the most costly functions of a plant, its efflux before assimilation results in a serious energy cost and loss in efficiency which could decrease yields. Efficient crop production is critical to the Closed Ecology Life Support System (CELSS). The objective is to determine the extent of efflux of the N species NO3(-), NH4(+), NO2(-), and urea after uptake, and possible means of regulation. Researchers found that NO3 efflux became serious as its substrate level increased. Efflux/Influx (E/I) of NO3(-) was greater in darkness (35 percent) than in light (14 percent), and the ratio greatly increased with substrate NO3 (-), (up to 45 percent at 10 mM). It seems advantageous to use the lowest possible nutrient concentration of NO3(-). The feasibility of using ClO3(-) was assessed and its toxicity determined.

Huffaker, R. C.↗
DOE OSTI2022

CO 2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change

Tropospheric ozone (O 3 ) is harmful to plant productivity and negatively impacts crop yields. O3 concentrations are projected to decrease globally in the optimistic Representative Concentration Pathway of 2.6 W m –2 (RCP2.6) but increase globally following the high-emission scenario under the RCP8.5, with substantial implications for global food security. The damaging effect of O 3 on future crop yield is affected by CO 2 fertilization and climate change, and their interactions for RCP scenarios have yet to be quantified. In this study, we used the Joint UK Land Environment Simulator modified to include crops (JULES-crop) to quantify the impacts, and relative importance of present-day and future O3, CO 2 concentration and meteorology on crop production at the regional scale until 2100 following RCP2.6 and RCP8.5 scenarios. We focus on eight major crop-producing regions that cover the production of wheat, soybean, maize, and rice. Our results show that CO 2 alone has the largest effect on regional yields, followed by climate and O 3 . However, the CO 2 fertilization effect is offset by the negative impact of tropospheric O 3 in regions with high O 3 concentrations, such as South Asia and China. Simulated crop yields in 2050 were compared with Food and Agriculture Organisation (FAO) statistics to investigate the differences between a socioeconomic and a biophysical process-based approach. Results showed that FAO estimates are closer to our JULES-crop RCP8.5 scenario. This study demonstrates that air pollution could be the biggest threat to future food production and highlights an urgent policy need to mitigate the threat of climate change and O 3 pollution on food security.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Similar photosynthetic but different yield responses of C 3 and C 4 crops to elevated O 3

The deleterious effects of ozone (O 3 ) pollution on crop physiology, yield, and productivity are widely acknowledged. It has also been assumed that C 4 crops with a carbon concentrating mechanism and greater water use efficiency are less sensitive to O 3 pollution than C 3 crops. This assumption has not been widely tested. Therefore, we compiled 46 journal articles and unpublished datasets that reported leaf photosynthetic and biochemical traits, plant biomass, and yield in five C 3 crops (chickpea, rice, snap bean, soybean, and wheat) and four C 4 crops (sorghum, maize, Miscanthus × giganteus , and switchgrass) grown under ambient and elevated O 3 concentration ([O 3 ]) in the field at free-air O 3 concentration enrichment (O 3 -FACE) facilities over the past 20 y. When normalized by O 3 exposure, C 3 and C 4 crops showed a similar response of leaf photosynthesis, but the reduction in chlorophyll content, fluorescence, and yield was greater in C 3 crops compared with C 4 crops. Additionally, inbred and hybrid lines of rice and maize showed different sensitivities to O 3 exposure. This study quantitatively demonstrates that C 4 crops respond less to elevated [O 3 ] than C 3 crops. This understanding could help maintain cropland productivity in an increasingly polluted atmosphere.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Feasibility of assessing crop condition and yield from LANDSAT data

The author has identified the following significant results. Yield modelling for crop production estimation derived a means of predicting the within-a-year yield and the year-to-year variability of yield over some fixed or randomly located unit of area. Preliminary studies indicated that the requirements for interpreting LANDSAT data for yield may be sufficiently similar to those of signature extension that it is feasible to investigate the automated estimation of production. The concept of an advanced yield model consisting of both spectral and meteorological components was endorsed. Rationale for using meteorological parameters originated from known between season and near harvest dynamics in crop environmental-condition-yield relationships.

Source record↗
NASA NTRS1992

The limits of crop productivity

The component processes that govern yield limits in food crops are reviewed and how each process can be individually measured is described. The processes considered include absorption of photosynthetic radiation by green tissue, carbon-fixation efficiency in photosynthesis, carbon use efficiency in respiration, biomass allocation to edible products, and efficiency of photosynthesis and respiration. The factors limiting yields in optimal environments are considered.

Bugbee, Bruce↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Responsiveness of miscanthus and switchgrass yields to stand age and nitrogen fertilization: A meta‐regression analysis

Abstract Optimal management of the perennial bioenergy crops, miscanthus and switchgrass, requires an understanding of their responsiveness to nitrogen (N) fertilizer at different maturity stages across locations and growing conditions. Earlier studies that have examined the yield response of these crops to N and stand age using field experiments or meta‐analysis techniques provide mixed evidence. We extend earlier studies by applying a multi‐level mixed‐effects (MLME) meta‐regression model to conduct a more extensive multivariate regression of yield response of these crops to N and stand age, while controlling for climate and location conditions and unobserved factors related to study design. Our findings are based on 1403 and 2811 yield observations for miscanthus and switchgrass, respectively, from experiments conducted between 2002 and 2019 across the rainfed region in the United States. We find statistically significant evidence that an additional year of maturity increases miscanthus and switchgrass yields but at a decreasing rate; yields peak at the 7th and 6th year respectively, for the observed range of applied N rates and stands. We also find that an increase in N application increases yield by a statistically significant level, but at a declining rate; the magnitude of the yield response to N is, however, small and varies with the age of the crop. The impact of N is larger on older compared to younger and middle‐aged stands of miscanthus. In contrast, the impact of N on switchgrass is larger on middle‐aged compared to younger and older stands of switchgrass. We do not find a statistically significant effect of soil productivity on yield for either crop. This analysis provides a basis for developing N application recommendations and optimal rotation age for miscanthus and switchgrass and shows that these energy crops can grow just as productively on low productivity land as on high productivity land.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluating the regional risks to food availability and access from land-based climate policies in an Integrated Assessment Model

Mitigating greenhouse gas (GHG) emissions is necessary to reduce the overall negative climate change impacts on crop yields and agricultural production. However, certain mitigation measures may generate unintended consequences to food availability and food access due to both land use competition and economic burden of mitigation. Integrated assessment models (IAM) are generally used to evaluate these policies; however, currently these models may not capture the importance of income and food prices for hunger and overall economic wellbeing. Here, we implement a measure of food security that captures the nutritional and economic aspects as the total expenditures on staple foods divided by income and weighted by total caloric consumption in an IAM, the Global Change Analysis Model (GCAM4.0). We then project consumer prices and our measure of food security along the Shared Socioeconomic Pathways (SSPs). Sustained economic growth underpins increases in caloric consumption and lowering expenditures on staple foods. Strict conservation policies affect food accessibility in a larger number of developing countries, whereas the negative effects of pricing terrestrial emissions is more concentrated on the poor in Sub-Saharan Africa, by substantially replacing their cropland with forests and affecting the production of key staples.

Cui, Ryna↗
DOE OSTI2024

Small-Scale Irrigation: Improving Food Security under Changing Climate and Water Resource Conditions in Ethiopia

We develop a new systems modeling tool that integrates knowledge from hydrology, agriculture, and economics to understand the effect of small-scale irrigation on food security and groundwater sustainability in Ethiopia. Irrigation is an effective tool to mitigate climate impacts and improve agricultural yields. Small-scale irrigation, such as decentralized groundwater irrigation, is well suited for developing countries where smallholder farming communities are widely dispersed and can only afford small infrastructure investment. We study the underlying interdependencies between food and water systems in Ethiopia, where small-holder agriculture is the foundation of the nation’s economy and climate variability has led to great challenges to its food security. Our coupled market and crop model with groundwater module captures the interdependencies of climate, water availability (including irrigation), crop yield, farmland allocation, crop production, transport and consumption based on a system approach across multiple spatial scales. We study the implication of small-scale irrigation to Ethiopia’s food security and water resource conditions as a “what-if” question by comparing an irrigation scenario to the calibrated baseline in 2015, a year of significant drought and crop failure over a large portion of Ethiopia. Our model offers fresh insights into geographic disparities in outcomes that are driven by baseline climate variability, soil fertility, and market conditions. In general, we find that small-scale irrigation can potentially improve food security through increases in food consumption, but it requires policy support to direct the increases of production to domestic consumption while maintaining a sustainable groundwater condition. By using Ethiopia as an example, we show the strength of our model to study how water infrastructure resources support critical functions and service in water and food systems.

Zhang, Ying↗
NASA NTRS1996

Consideration in selecting crops for the human-rated life support system: a Linear Programming model

A Linear Programming model has been constructed which aids in selecting appropriate crops for CELSS (Controlled Environment Life Support System) food production. A team of Controlled Environment Agriculture (CEA) faculty, staff, graduate students and invited experts representing more than a dozen disciplines, provided a wide range of expertise in developing the model and the crop production program. The model incorporates nutritional content and controlled-environment based production yields of carefully chosen crops into a framework where a crop mix can be constructed to suit the astronauts' needs. The crew's nutritional requirements can be adequately satisfied with only a few crops (assuming vitamin mineral supplements are provided) but this will not be satisfactory from a culinary standpoint. This model is flexible enough that taste and variety driven food choices can be built into the model.

NASA Center JSC↗
NASA NTRSDate not supplied

A21I-1875: Leveraging TEMPO Formaldehyde Observations to Assess Surface Air Quality

Surface ozone (O3) pollution is detrimental to human health, crop yields, and ecosystem productivity. Ground-based ozone observations are sparse and surface ozone retrievals from space are challenging. The process of producing ozone from the oxidation of volatile organic compounds (VOCs) in the presence of nitrogen oxides (NOx) results in formaldehyde (HCHO), which can be observed from satellite and by surface-based remote sensing. From limited field campaign observations, column formaldehyde has been shown to exhibit a strong relationship with surface ozone and, therefore, could be an useful proxy for detecting unmonitored regions of elevated ozone pollution. The spatiotemporal characteristics of this relationship are explored using ground-based Pandora spectrometers and airborne remote sensing data with surface ozone monitors across the United States during recent field campaigns over major cities (TRACER-AQ and STAQS). We extend our analysis to take advantage of HCHO column observations from recently launched Tropospheric Emissions: Monitoring of Pollution (TEMPO), a geostationary satellite over North America. TEMPO observations show preliminary promise in mapping regions of elevated surface ozone from space using column HCHO. We demonstrate the use of column HCHO in assessing the strength of the surface monitoring network, and in detecting the extent of regional ozone exceedances.

Prajjwal Rawat↗
NASA NTRS2015

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Guan, Kaiyu↗
DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗
NASA NTRS2010

An Investigation of Widespread Ozone Damage to the Soybean Crop in the Upper Midwest Determined From Ground-Based and Satellite Measurements

Elevated concentrations of ground-level ozone (O3) are frequently measured over farmland regions in many parts of the world. While numerous experimental studies show that O3 can significantly decrease crop productivity, independent verifications of yield losses at current ambient O3 concentrations in rural locations are sparse. In this study, soybean crop yield data during a 5-year period over the Midwest of the United States were combined with ground and satellite O3 measurements to provide evidence that yield losses on the order of 10% could be estimated through the use of a multiple linear regression model. Yield loss trends based on both conventional ground-based instrumentation and satellite-derived tropospheric O3 measurements were statistically significant and were consistent with results obtained from open-top chamber experiments and an open-air experimental facility (SoyFACE, Soybean Free Air Concentration Enrichment) in central Illinois. Our analysis suggests that such losses are a relatively new phenomenon due to the increase in background tropospheric O3 levels over recent decades. Extrapolation of these findings supports previous studies that estimate the global economic loss to the farming community of more than $10 billion annually.

Fishman, Jack↗