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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Spectral Indices to Monitor Nitrogen-Driven Carbon Uptake in Field Corn

Climate change is heavily impacted by changing vegetation cover and productivity with large scale monitoring of vegetation only possible with remote sensing techniques. The goal of this effort was to evaluate existing reflectance (R) spectroscopic methods for determining vegetation parameters related to photosynthetic function and carbon (C) dynamics in plants. Since nitrogen (N) is a key constituent of photosynthetic pigments and C fixing enzymes, biological C sequestration is regulated in part by N availability. Spectral R information was obtained from field corn grown at four N application rates (0, 70, 140, 280 kg N/ha). A hierarchy of spectral observations were obtained: leaf and canopy with a spectral radiometer; aircraft with the AISA sensor; and satellite with EO-1 Hyperion. A number of spectral R indices were calculated from these hyperspectral observations and compared to geo-located biophysical measures of plant growth and physiological condition. Top performing indices included the R derivative index D730/D705 and the normalized difference of R750 vs. R705 (ND705), both of which differentiated three of the four N fertilization rates at multiple observation levels and yielded high correlations to these carbon parameters: light use efficiency (LUE); C:N ratio; and crop grain yield. These results advocate the use of hyperspectral sensors for remotely monitoring carbon cycle dynamics in managed terrestrial ecosystems.

Corp, Lawrence A.↗
DOE OSTI2024

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Intraspecific Variation for Leaf Physiological and Root Morphological Adaptation to Drought Stress in Alfalfa ( Medicago sativa L.)

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

09 BIOMASS FUELS↗
DOE OSTI2022

Biomass yield improvement in switchgrass through genomic prediction of flowering time

The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late-flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapid selection of late-flowering individuals with reduced time and expense for field evaluations. To evaluate GP, two flowering time traits (heading date and anthesis date) were collected on 1532 genotypes from four breeding populations: Midwest, Gulf, Atlantic, and Hybrid. These were sequenced using genotype-by-sequencing (530,792 single-nucleotide polymorphisms). Predictive ability of single-trait and multi-trait models were evaluated by cross-validation, by prediction of a progeny trial (n = 122), and through prediction of yield performance in a parallel experiment (n = 52). Predictive ability was not improved by sharing information among breeding groups. Overall, multi-trait models provided an advantage during cross-validation, but a smaller advantage during progeny prediction. Within populations, GP resulted in lower per-cycle progress than previously reported field evaluations (3.1 vs. 5.0 day –1 cycle –1 ). However, GP cycles are potentially much faster than field evaluations. When directly predicting biomass yield, the Hybrid training population had a predictive ability of 0.54–0.63. This reinforces the strong linkage between biomass yields in swards and flowering time. Furthermore, these results highlight the value of GP for rapid yield improvement in switchgrass, particularly in a breeding program designed to share information between biomass yield trials and low-cost flowering time evaluations.

09 BIOMASS FUELS↗
NASA NTRS2021

Midwest Food Security & Agriculture II: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest

Agriculture in the Midwest is dominated by monoculture systems that strip the soil of nutrients, decrease yields, and worsen water quality. Crop diversification and cultivating small grains is economically and ecologically advantageous, but limited in practice due to a lack of data about small-grain crop performance as well as monoculture-favoring insurance coverage, economic incentives, and sociocultural traditions. Practical Farmers of Iowa (PFI) strives to create more sustainable agriculture and supports Midwestern farmers in adding small grains into their crop rotations. The DEVELOP team partnered with the USDA Agricultural Research Service and the USA National Phenology Network to assist PFI in applying NASA Earth observation data to track small-grain crop performance. This project developed a Google Earth Engine user interface that enables PFI farmers to compare satellite-assessed vegetation production and climatic conditions of their farm with other PFI farmers. The team assessed the crop performance through Normalized Difference Vegetation Index (NDVI) values derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). Additionally, the team created a regression model to analyze the predictive properties of vegetation indices and crop yield, ultimately establishing a relationship with mean NDVI from the early growing season. PFI farmers can utilize these products to make informed decisions about their crop production as well as advocate for more expansive insurance policies to protect small-grain crops.

Sophie Barrowman↗
DOE OSTI2022

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa (for corresponding manuscript: DOI: 10.1002/bbb.2148) PDF Files: Images of 1H NMR spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum). Suppression of the water peak was achieved using a NOESY-1D with presaturation, a recycle delay of 5 s, and a total of 64 scans. Spectra were acquired at 298 K and processed with automatic phase correction, baseline correction, and chemical shift referencing to TSP-d4. Images show all 1H data from 10 to 1ppm with inset spectra of region of interest (4.0 to 3.1 ppm). Text Files: Spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum) were converted into text files for plotting. Files contain 8192 points of raw spectral data from 12.23 to -2.78 ppm. The text file contains 4 columns of data and includes: Point number, Intensity, Hz, and ppm. Xcel Spreadsheet: HPLC measured monomeric sugar concentrations and bucketed 1H NMR data used to build monomeric sugar composition prediction models. Sugar composition in biomass determined from HPLC analyses are given in mg sugar/mg of biomass. Spectral bucketing was performed using Bruker’s AMIX software. Spectra were divided into 0.005 ppm buckets in the region of 3.10– 4.15 ppm for a total of 210 buckets. Headers for the bucketed data are the chemical shift in ppm of the center of the bucket. Bucketed data was used to build partial least squares models for subsequent predictions in The Unscrambler v. 10.5(CAMO A/S, Trondheim, Norway). The formation of methanol during hydrolysis interferes with the quantitative NMR analysis of sugars, so the methanol peak centered at 3.37 ppm and spanning four buckets (3.2925 – 3.2775 ppm) was set to zero for all spectra.

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↗
DOE OSTI2023

Biomass yield in a genetically diverse Miscanthus sacchariflorus germplasm panel phenotyped at five locations in Asia, North America, and Europe

Abstract Miscanthus is a high‐yielding bioenergy crop that is broadly adapted to temperate and tropical environments. Commercial cultivation of Miscanthus is predominantly limited to a single sterile triploid clone of Miscanthus × giganteus , a hybrid between Miscanthus sacchariflorus and M. sinensis. To expand the genetic base of M. × giganteus , the substantial diversity within its progenitor species should be used for cultivar improvement and diversification. Here, we phenotyped a diversity panel of 605 M. sacchariflorus from six previously described genetic groups and 27 M. × giganteus genotypes for dry biomass yield and 16 yield‐component traits, in field trials grown over 3 years at one subtropical location (Zhuji, China) and four temperate locations (Foulum, Denmark; Sapporo, Japan; Urbana, Illinois; and Chuncheon, South Korea). There was considerable diversity in yield and yield‐component traits among and within genetic groups of M. sacchariflorus , and across the five locations. Biomass yield of M. sacchariflorus ranged from 0.003 to 34.0 Mg ha −1 in year 3. Variation among the genetic groups was typically greater than within, so selection of genetic group should be an important first step for breeding with M. sacchariflorus . The Yangtze 2x genetic group (=ssp. lutarioriparius ) of M. sacchariflorus had the tallest and thickest culms at all locations tested. Notably, the Yangtze 2x genetic group's exceptional culm length and yield potential were driven primarily by a large number of nodes (>29 nodes culm −1 average over all locations), which was consistent with the especially late flowering of this group. The S Japan 4x, the N China/Korea/Russia 4x, and the N China 2x genetic groups were also promising genetic resources for biomass yield, culm length, and culm thickness, especially for temperate environments. Culm length was the best indicator of yield potential in M. sacchariflorus . These results will inform breeders' selection of M. sacchariflorus genotypes for population improvement and adaptation to target production environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A WRKY transcription factor confers broad-spectrum resistance to biotic stresses and yield stability in rice

Plants are subject to attack by diverse pests and pathogens. Few genes conferring broad-spectrum resistance to both insects and pathogens have been identified. Because of the growth–defense tradeoff, it is often challenging to balance biotic stress resistance and yield for crops. Here, we report thatOsWRKY36suppresses the resistance to insects and pathogens via transcriptional repression ofPhenylalanine Ammonia Lyases(PALs), a key enzyme in phenylpropanoid pathway in rice. Knocking outOsWRKY36causes elevated lignin biosynthesis and increased sclerenchyma thickness of leaf sheath, leading to enhanced resistance to multiple pests and pathogens. Additionally, loss ofOsWRKY36also derepresses the transcription ofIdeal Plant Architecture 1(IPA1) andMONOCULM2(MOC2), resulting in increased spikelet number per panicle and tiller number. These findings provide mechanistic insights into biotic stress tolerance in rice and offer a promising strategy to breed rice cultivars with broad-spectrum resistance to insects and pathogens while maintaining stable yield.

Science & Technology - Other Topics↗
NASA NTRS2015

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

simulation↗
DOE OSTI2021

Long-term excess nitrogen fertilizer increases sensitivity of soil microbial community to seasonal change revealed by ecological network and metagenome analyses

Nitrogen (N) fertilizer has often been generously applied to increase crop biomass yield. Although the influences of inorganic N fertilizer on soil microbial communities have been widely studied, the effect of N fertilizer on microbial co-occurrence networks and its metagenome is largely unknown. Further, seasonal changes in microbial community responses to N addition have rarely been reported. In this study, three N fertilizer rates (0, 56, 196 kg N/ha) were applied annually in switchgrass (Panicum virgatum L.) grown for bioenergy production in the upper Midwest, USA. The soil microbiome was affected by both fertilizer and low pH in the 7th year of fertilization treatments. The microbial community structures were relatively stable during the growing season for each N fertilizer rate. However, the excess N fertilizer (196N) increased the seasonal variation of bacterial and fungal communities. Network analysis showed that the microbial interactions at the 196N treatment were more intense, with decreased bacteria-fungal interactions compared to 56N and 0N. Furthermore, this suggests that the microbial community became more sensitive to environmental change under the influence of long-term excess N fertilizer. Metagenomic analysis showed that the long-term excess N fertilizer promoted many metabolic processes, especially carbohydrate and amino acid related metabolism and Archaea mediated ammonia oxidation. However, N fertilizer also reduced many other traits, especially N 2 fixation and signal transduction, the latter of which may contribute to the decreased interactions between bacteria and fungi.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Impacts of Pasture Conversion to Sugarcane on Water Fluxes and Water Use Efficiency in the Southeastern US

The expansion of sugarcane (cane), a high-yielding perennial crop, will likely reshape the bioenergy landscape in the Southeastern US. However, its ecohydrological implications, particularly following conversion from grazed pastures, a dominant land use in the region, remain highly uncertain. We investigated the impact of cane expansion on evapotranspiration (ET) and its partitioning, and the mechanisms influencing both ET components and water use efficiency (WUE) across multiple scales and growth cycles in subtropical Florida. We combined eddy covariance, biometric measurements, and process-based stomatal conductance (g s ) models. ET was 1.7% lower in cane than in improved pasture (IMP) but exceeded that in semi-native pasture (SN) by 21%. Transpiration (T) followed a similar pattern, consistent with lower g s in cane relative to IMP. Cane had more conservative water use and greater sensitivity of g s to vapor pressure deficit (VPD) compared to IMP pasture, suggesting cane may be more tolerant of increasing atmospheric water demand. In contrast, SN showed lower g s and weaker stomatal sensitivity to VPD compared to cane, resulting in lower T. In cane, stomatal regulation and T varied across growth cycles, with stomata becoming less water conservative as stands matured, highlighting the importance of incorporating stand age-dependent stomatal regulation into hydrological models. Evaporation (E) was higher in cane than pastures (19%–26%), partially offsetting WUE gains. Cane exhibited higher intrinsic WUE (GPP/g s ; Gross Primary Productivity), ecosystem WUE (GPP/ET), and harvest WUE (harvest/ET) than both pasture types. Large-scale pasture-to-cane conversion could produce widely contrasting hydrological outcomes. The net regional impact will depend on the proportion of each pasture type converted and on cane's high g s sensitivity to VPD, which triggers tight stomatal regulation and conservative water use, both of which will become increasingly consequential under intensifying atmospheric water demand.

bioenergy↗
DOE OSTI2025

Time‐series multi‐omics analysis of micronutrient stress in Sorghum bicolor reveals iron and zinc crosstalk and regulatory network conservation

Micronutrient stress impacts growth, biomass production, and grain yield in crops. Multi-omics studies are valuable resources in identifying genes for functional studies and trait improvement, such as accumulation of Fe or Zn under deficient or excess conditions for bioenergy or grain agriculture. We conducted transcriptomics and ionomics analyses on Sorghum bicolor BTx623, grown under Fe and Zn limited and excess conditions over a 21-day period. To identify early and late transcriptional response in roots and leaves, 180 RNAseq libraries were sequenced for differential expression and co-expression network analyses. Fe and Zn accumulation was measured using ICP-MS at each time point, and a fluorometer was used to estimate chlorophyll content in leaves. Among the four treatments, Fe limitation and Zn excess resulted in the largest phenotypic effects and transcriptional response in roots and leaves. Several of the reduction (Strategy I) and chelation (Strategy II) strategy genes that improve bioavailability of Fe and Zn in plant roots often used by non-grass and grass species, respectively, were differentially expressed. Gene regulatory network (GRN) analysis of roots revealed enrichment of genes from Fe limiting and Zn excess which strongly connect to homologues of SbFIT, SbPYE, and SbBTS as hub genes. The GRN for leaf responses showed homologues of SbPYE and SbBTS as hubs connecting genes for chloroplast biosynthesis, Fe-S cluster assembly, photosynthesis, and ROS scavenging. Expression analyses suggest sorghum uses Strategy II genes for Fe and Zn uptake, as expected, but can also utilize Strategy I genes, which may be advantageous in variable moisture environments. We found strong overlap between Fe and Zn responsive GRNs, indicative of micronutrient crosstalk. We also found conservation of root and leaf GRNs, and known homologous genes suggest strong constraints on homeostasis networks in plants. These data will provide a resource for functional genetics to enhance micronutrient transport in sorghum, and opportunities to conduct further comparative GRN analysis across diverse crops species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Step Catalysis to Synthesize Fossil-Free Ammonia at Atmospheric Pressure (Final Technical Report)

Affordable fossil-free ammonia synthesis will enable high-yield sustainable crop production for a growing world population while preserving the planet and making the best use of water and land. Simple technology amenable to intermittent operation using renewable energy will be made available through step catalysis activating dinitrogen at atmospheric pressure by nitride formation. Project objectives: (1) At the single particle level, quantify and analyze N2 activation by doped or alloyed manganese macroscopic- and nano particles at atmospheric pressure, (2) Quantify and analyze ammonia synthesis from doped or alloyed metal nitride particles when contacting them with hydrogen at atmospheric pressure, and (3) Integrate experimental data and atomic-level computational models to predict the behavior of metal alloys in nitrogen activation and subsequent ammonia synthesis.

10 SYNTHETIC FUELS↗
DOE OSTI2026

Impacts of Dual-Use Solar (Agrivoltaics) on Crop Productivity and the Agricultural Economy in Massachusetts and Beyond

This project contributes applied research to better understand the impacts of agrivoltaics on farm microclimates, crop productivity, and economics at the farm and sector level. The site trial research is divided into two sets of site trials integrated into commercial farm and agrivoltaic operations – one set on annual vegetable and hay crops and one on perennial cranberry bog operations. The economic research developed methods for recording and evaluating changes in farm operations and costs, and on the public perception of and willingness-to-accept agrivoltaics.

14 SOLAR ENERGY↗