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NASA NTRS2016

Regional Disparities in the Beneficial Effects of Rising CO2 Emissions on Crop Water Productivity

Rising atmospheric carbon dioxide concentrations are expected to enhance photosynthesis and reduce crop water use. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments and global crop models to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated carbon dioxide and associated climate change projected for a high-end greenhouse gas emissions scenario. We find carbon dioxide effects increase global CWP by 10[0;47]%-27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rain fed wheat). If realized in the fields, the effects of elevated carbon dioxide could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4-17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modeling the effects of rising carbon dioxide across crop and hydrological modeling communities.

Deryng, Delphine↗

DOE OSTI2021

Progress and challenges in sorghum biotechnology, a multipurpose feedstock for the bioeconomy

Abstract Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop globally by harvested area and production. Its drought and heat tolerance allow high yields with minimal input. It is a promising biomass crop for the production of biofuels and bioproducts. In addition, as an annual diploid with a relatively small genome compared with other C4 grasses, and excellent germplasm diversity, sorghum is an excellent research species for other C4 crops such as maize. As a result, an increasing number of researchers are looking to test the transferability of findings from other organisms such as Arabidopsis thaliana and Brachypodium distachyon to sorghum, as well as to engineer new biomass sorghum varieties. Here, we provide an overview of sorghum as a multipurpose feedstock crop which can support the growing bioeconomy, and as a monocot research model system. We review what makes sorghum such a successful crop and identify some key traits for future improvement. We assess recent progress in sorghum transformation and highlight how transformation limitations still restrict its widespread adoption. Finally, we summarize available sorghum genetic, genomic, and bioinformatics resources. This review is intended for researchers new to sorghum research, as well as those wishing to include non-food and forage applications in their research.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2016

Tree Canopy Light Interception Estimates in Almond and a Walnut Orchards Using Ground, Low Flying Aircraft, and Satellite Based Methods to Improve Irrigation Scheduling Programs

Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

Satellite↗

DOE OSTI2026

A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions

Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, and economic models to capture two-way feedback among water availability, agricultural production, and market responses under groundwater constraints. The primary goal of this paper is to describe the methodological development of the coupled framework and demonstrate the significance of iterative model interaction. Applied to the western United States, we evaluated adaptive responses to restricting groundwater use beyond recharge levels, represented through changes in irrigation management and expansion or shrinkage of crop markets through land reallocation. Results demonstrate that the iterative coupling converges to stable equilibrium responses within 10 iterations. At equilibrium, deficit irrigation emerges as the dominant adaptation strategy in California, with irrigation levels stabilizing at approximately 70% of full irrigation demand, while Arizona and New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases of up to 10% for fruit and vegetable crops; however, these responses moderate as land allocation and production patterns adjust across regions. Deficit irrigation and spatial reallocation of irrigated land partially offset production losses, with variability observed across different states: California maintains yields primarily via deficit irrigation, whereas Arizona and New Mexico will rely mainly on reducing irrigated area to absorb the shock. By capturing feedback between biophysical and economic processes, this approach highlights how irrigation strategies and land-use decisions evolve under water stress and provides a transferable platform for evaluating water management policies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Machine learning to predict biomass sorghum yields under future climate scenarios

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near-term and long-term pathways for scaling up bio-based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. In this work we used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

09 BIOMASS FUELS↗

NASA NTRS2019

Exposure of the EU-28 Food Imports to Extreme Weather Disasters in Exporting Countries

EU-28 relies on a diversified foreign market, even for crops for which it has a high self-sufficiency.This study contributes to the discussion on the vulnerability of agri-food supply to the impacts of extreme weather disasters (EWD). We focus on the largest import commodities of the EU-28 and we aim to (1) map external dependencies of EU-28 agri-food sector, (2) estimate the impact of EWD on crop production in countries from which the EU-28 receives their imports, and (3) assess the exposure of EU-28 agri-food imports to such impacts. Crop and trade data areacquired through EUROSTAT and FAOSTAT, EWD records from EM-DAT, all between 1961 and 2016. A superposed epoch analysis is used to estimate the impact of EWD on the average national production, yield and harvested area of selected crops in exporting countries. The EU-28 imports between 35-100% of its consumption of soybeans, banana, tropical fruits, coffee and cocoa. Our study reveals a substantial impact of EWD, especially due to droughts andheat waves, on the production of soybeans, tropical fruits, and cocoa, with import weighted impacts of 3, 8, and 7%, respectively. Floods cause weighted impacts of 7% (soybeans) and 8% (tropical fruits). Coffee production shows gains during cold waves, but the inter-annual variability offsets these effects.

Teresa Armada Bras↗

NASA NTRS2021

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗

NASA NTRS2024

Northern Brazil Agriculture: Measuring Soybean Yields in Northern Brazil During El Niño-Southern Oscillation Conditions to Evaluate Trends in Agricultural Production and Support Crop Forecasting, 1984 – 2023

As one of the largest agricultural exporters in the world, Brazil’s crop productivity is highly influential to the world’s food supply. Crop productivity across Brazil is heavily influenced by climatic factors, such as the El Niño-Southern Oscillation (ENSO), which drives spatially heterogenous effects on growing conditions. To understand the relationship between ENSO conditions and staple crop productivity, the team used 1984 – 2023 climatic data and vegetation indices for four Brazilian states: Bahia, Mato Grosso, Pará, and Tocantins. The team focused on soybean growing areas in each state derived from annual land use/land cover classifications. Monthly mean Normalized Difference Vegetation Index (NDVI) per state were calculated using multispectral imagery from the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imagery (OLI) sensors. In addition, the team used crop production indices, ENSO anomalies, temperature, and precipitation data in this study. The team found that although monthly ENSO anomaly did have a positive relationship between temperature and a slight negative relationship with precipitation, ENSO and monthly soy NDVI were not significantly correlated. Results find no association between ENSO conditions and NDVI in the study area at the state level spatial resolution during the study period. Furthermore, the team found that there was no correlation between cumulative growing season soy NDVI and detrended soy production yield for this study region at the spatial and temporal scale of the analysis. Findings from this project will be used by the United States Department of Agriculture’s Foreign Agriculture Service to inform crop productivity forecasting.

remote sensing↗

DOE OSTI2026

Data for Immediate Impacts of Soybean Cover Crop on Bacterial Community Composition and Diversity in Soil Under Long-Term Saccharum Monoculture

Saccharum yield decline results from long-term monoculture practices. Changes in cropping management can improve soil health and productivity. Below-ground bacterial community diversity and composition across soybean (Glycine max (L.) Merr) cover crop, Saccharum monoculture (30+ year) and fallowed soil were determined. Near full length (~1,400 base pairs) of 16S rRNA gene sequences were extracted from the rhizospheres of sugarcane and soybean and fallowed soil were compared. Higher soil bacterial diversity was observed in the soybean cover crop than sugarcane monoculture across all measured indices (observed operationational taxonomic units, Chao1, Shannon, reciprocal Simpson and Jackknife). Acidocateria, Proteobacteria, Bacteroidetes and Planctomycetes were the most abundant bacterial phyla across the treatments. Indicator species analysis identified nine indicator phyla. Planctomycetes, Armatimonadetes and candidate phylum FBP were associated with soybean; Proteobacteria and Firmicutes were linked with sugarcane and Gemmatimonadetes, Nitrospirae, Rokubacteria and unclassified bacteria were associated with fallowed soil. Non-metric multidimensional scaling analysis showed distinct groupings of bacterial operational taxonomic units (97% identity) according to management system (soybean, sugarcane or fallow) indicating compositional differences among treatments. This is confirmed by the results of the multi-response permutation procedures (A = 0.541, p = 0.00045716). No correlation between soil parameters and bacterial community structure was observed according to Mantel test (r = 211865, p = 0.14). Use of soybean cover-crop fostered bacterial diversity and altered community structure. This indicates cover crops could have a restorative effect and potentially promote sustainability in long-term Saccharum production systems.

Field Data↗

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↗

DOE OSTI2022

The nitrogen gap in soil health concepts and fertility measurements

Soil nitrogen (N) often limits productivity in agroecosystems, prompting fertilizer applications that increase crop yields but can degrade the environment. Nitrogen's dual role in both productivity and environmental quality should center it in soil health frameworks. We use recent evidence to argue that N availability is an emergent property of the integrated soil biogeochemical system and is strongly influenced by plant traits and their interactions with microbes and minerals. Building upon this, we theorize that the sources of plant and microbial N shift across soil health gradients, from inorganic N dependence in ecologically simple systems with poor soil health to a highly networked supply of organic N in healthy soils; ergo, investments in soil health should increase ecological complexity and the pathways by which plants can access N, leading to more resilient nutrient supplies and yields in a variable climate. However, current N assessment methods derive from a historical emphasis on inorganic N pool sizes and are unable to capture the shifting drivers of N availability across soil health gradients. We highlight the need to better understand the plant-microbial-mineral interactions that regulate bioavailable N as a first step to improving our ability to measure it. We conclude it will be necessary to harness agroecosystem complexity, account for plant and microbial drivers, and carefully integrate external N inputs into soils' internal N network to expand the routes by which N from organic pools can be made bioavailable. Finally, by emphasizing N in soil health concepts, we argue that researchers can accelerate advances in N use efficiency and resiliency.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1979

Predicting crop production from satellite data

Crop area and yield for winter wheat is found by analysis of LANDSAT pictures. Correlation between measure and yield is established from measurements on known wheatfields with known yields.

Colwell, J. E.↗

NASA NTRS2014

Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

fluorescence↗

DOE OSTI2024

Optimizing tradeoffs implicit during bioenergy crop improvement: Understanding the effect of altered cell wall and sugar content on sorghum-associated pathogenic bacteria

Plant-derived production of renewable fuels and chemicals has the potential to reduce fossil fuel dependency, increase domestic energy security, enhance US farming and agricultural economic opportunities, and reduce greenhouse gas emissions. Realizing the potential of these alternate energy sources necessitates the development of high biomass-yielding crops. To limit competition with the production of food and feed, these specialized crop varieties, many of which will harbor modifications to cell wall structures and display altered carbon partitioning, will be cultivated on marginal land that does not provide ideal growing conditions. Consequently, the plants are anticipated to be more vulnerable to diseases.

09 BIOMASS FUELS↗

NASA NTRS2024

Quantifying Food Security and Mitigation Risks Consequential to Climate Change Impacts on Crop Yields

Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

crop yields↗

NASA NTRS1995

Accelerating Commercial Remote Sensing

Through the Visiting Investigator Program (VIP) at Stennis Space Center, Community Coffee was able to use satellites to forecast coffee crops in Guatemala. Using satellite imagery, the company can produce detailed maps that separate coffee cropland from wild vegetation and show information on the health of specific crops. The data can control coffee prices and eventually may be used to optimize application of fertilizers, pesticides and irrigation. This would result in maximal crop yields, minimal pollution and lower production costs. VIP is a mechanism involving NASA funding designed to accelerate the growth of commercial remote sensing by promoting general awareness and basic training in the technology.

Source record↗

NASA NTRS2005

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Teng, William↗

DOE OSTI2023

Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change

Abstract Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N 2 O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N 2 O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N 2 O emission among 40 years. Both corn yield and soil N 2 O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N 2 O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N 2 O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N 2 O emission than those in the normal and wet years, while increases in precipitation increased more soil N 2 O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N 2 O emission to precipitation and highlights that mitigation strategy for soil N 2 O emission reduction should consider different background climate conditions.

54 ENVIRONMENTAL SCIENCES↗