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At least 199 records · Page 11

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DOE OSTI2024

Remarkably High Internal Transcribed Spacer Haplotype Diversity of the Fungal Select Agent Coniothyrium glycines Discovered Throughout Its Range in Sub-Saharan Africa

Red leaf blotch of soybean, caused by the fungus Coniothyrium glycines, is a foliar disease characterized by blotching, necrosis, and defoliation that has only been reported from Africa. The species is listed as a Select Agent by the Federal Select Agent Program due to its potentially devastating impacts to soybean production should it spread to the United States. Despite its potential import, very few isolates are available for study. Herein, we obtained 96 new C. glycines isolates from six soybean-producing countries throughout sub-Saharan Africa. Along with 12 previously collected ones, we sequenced each at the internal transcribed spacer (ITS) region. Between all isolates, we identified a total of 28 single-nucleotide polymorphisms and 23 haplotypes. One hypothesis to explain the tremendous diversity uncovered at the ITS—which is generally conserved within a species—is that our current species concept of C. glycines is too broad and that there may be multiple species that cause red leaf blotch. Zambia contained the highest haplotype diversity, a significant fraction of which remains unsampled. Most haplotypes were specific to a single country, except for two, which were found in Zambia and either neighboring Mozambique or Zimbabwe. This geographic specificity indicates that the ITS region may be useful for identifying source populations or routes of transmission should this pathogen spread beyond Africa. The observed geographic partitioning of this pathogen is likely the result of millions of years of replication on little-studied native hosts, given that soybean has only been cultivated in Africa since the early 1900s.

Plant Sciences↗

NASA NTRS1983

Remote sensing of agricultural crops and soils

Research in the correlative and noncorrelative approaches to image registration and the spectral estimation of corn canopy phytomass and water content is reported. Scene radiation research results discussed include: corn and soybean LANDSAT MSS classification performance as a function of scene characteristics; estimating crop development stages from MSS data; the interception of photosynthetically active radiation in corn and soybean canopies; costs of measuring leaf area index of corn; LANDSAT spectral inputs to crop models including the use of the greenness index to assess crop stress and the evaluation of MSS data for estimating corn and soybean development stages; field research experiment design data acquisition and preprocessing; and Sun-view angles studies of corn and soybean canopies in support of vegetation canopy reflection modeling.

Bauer, M. E.↗

NASA NTRS2000

Response of Two Legumes to Two Ultraviolet-B Radiation Regimes

Depletion of the stratospheric ozone layer has been directly linked to increased levels of UV radiation at the earth's surface. The purpose of this study was to evaluate the responses of soybean (Glycine max) and alfalfa (Medicago sativa) to increased UV-B radiation (280-320 nm). Soybean and alfalfa were grown successively in a growth chamber that provided UV-B intensities 45% above nominal summer field levels. Mylar-D (UVB opaque) and mono-acetate (UV-B transparent) films were used to establish the two UV-B treatments. Soybean grown under increased UV showed 21% smaller internodal lengths and higher concentrations of UV-B absorbing pigments (i.e. flavonoids) compared to plants grown under no UV. Significant results for alfalfa included 22% greater leaf flavonoid concentration under increased UV, 14% greater leaf chlorophyll concentration under no UV, and 32% greater above-ground biomass with no UV. These leguminous species possess mechanisms that protect against UV-B damage as indicated by increases in foliar concentrations of UV-B absorbing compounds. Alfalfa appears to be more sensitive to UV-B damage than soybean. Remote sensing of chlorophyll fluorescence may offer a means of monitoring UV-induced plant stress and damage.

Levy, Daniel L.↗

NASA NTRS1996

Response of Two Plant Species to Two Ultraviolet-B Radiation Regimes

The depleted stratospheric ozone layer has been directly linked to increased levels of ultraviolet radiation at the earth's surface. It is important to understand what effect this will have on plants. We tested the hypothesis that in response to increased UV-B radiation (280-320 man), soybean (Glycine max Merrill) and alfalfa (Mercado Saliva L.) would produce higher concentrations of flavonoids than plants screened from UV-B. Soybean and alfalfa plants were grown successively in a growth chamber that provided UV-B radiation intensities 45% above summer field levels. A wooden frame was used to suspend mylar-D film over one group of plants and mono-acetate film over another group. Mylar is opaque in the 280-316 nm range, and acetate absorbs most radiation from 280-290 nm and then reduces intensities in the 290-320 nm range by roughly 15%. Leaf chlorophyll concentration was determined with a Minolta SPAD-502 chlorophyll meter; the BRAD meter was calibrated with N,N- extractions. Flavonoids were extracted with an acidified methanol/water solution. Soybean grown under the acetate treatment showed 26% smaller internodal lengths and higher concentrations of flavonoids compared to plants grown under mylar. Significant results for alfalfa included 22% greater leaf flavonoid concentration under acetate, 14% greater leaf chlorophyll concentration under mylar, and 32% greater above-ground biomass under mylar. We found that increased UV-B radiation leads to increased production of UV-B absorbing compounds (i.e. flavonoids) in soybean and alfalfa leaves. This suggests that a protective mechanism in these plants is triggered by UV-B. In response, flavonoids are produced that absorb UV-B, and consequently decrease potentially damaging effects to the plants. In addition, we hypothesize that this flavonoid protection mechanism saturates at certain UV-B intensities.

Levy, Daniel L.↗

NASA NTRS2017

Integrating Growth Stage Deficit Irrigation into a Process Based Crop Model

Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in water restricted scenarios. For example, the mean WP across 38 years of maize production was 1.1 kg/cu m for non-optimized irrigation schedules with 50 mm of seasonal available water and 2.1 kg/cu m optimized ion schedules, a 91% improvement in WP with optimized irrigation schedules. The framework described in this work could be used to estimate WP for regional to global assessments, as well as derive location specific irrigation guidance.

crop model↗

DOE OSTI2024

Impacts of feeding three strains of microalgae alone or in combination on growth performance, protein metabolism, and meat quality of broiler chickens

Variations in nutrient compositions, especially amino acid (AA) profiles, among microalgal species may enable a superior feeding outcome from a combined than singular supplementation in poultry diets. Therefore, a feeding trial was conducted to compare the effects of three strains of microalgal biomass supplemented alone or in combination to replace 5 % (starter) and 10 % (grower) soybean meal (on weight-to-weight basis) on growth performance, protein metabolism, and meat quality of broiler chickens. Day-old Cornish Cross male chicks (total = 180) were divided into 5 groups (6 cages/treatment, 6 birds/cage) and fed a corn-soybean meal basal diet (BD), BD + H117 (Chlorella sp., H117), BD + C985 (Tetraselmis sp., C985), BD + Nannochloropsis oceanica (NO), and BD + H117 + C985 + NO (Combination). Feeding any of the microalgae diets did not alter growth performance nor meat quality including texture, pH, color, and water holding capacity of breast and thigh meats. However, the breast weight percentages were decreased (P < 0.05) by feeding the C985, NO, and Combination diets. Compared with the BD, the 4 microalgal diets led to higher (P < 0.05) plasma uric acid and protein concentrations at weeks 3 and (or) 6. The mRNA levels of MAFbx, MURF1, FOXO1, and calpastatin in the breast and thigh muscles were altered by the microalgal diets but not those of genes associated with other quality traits. In conclusion, replacing 5 % or 10 % soybean meal with three sources of microalgae in broiler diets decreased breast weights percentage but not absolute weight. Furthermore, feeding chickens with the combination of three microalgae did not restore the breast loss and induced different expressions of genes related to muscle hypertrophy or atrophy.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1996

Proximate composition of CELSS crops grown in NASA's Biomass Production Chamber

Edible biomass from four crops of wheat (Triticum aestivum L.), four crops of lettuce (Lactuca sativa L.), four crops of potato (Solanum tuberosum L.), and three crops of soybean (Glycine max (L.) Merr.) grown in NASA's CELSS Biomass Production Chamber were analyzed for proximate composition. All plants were grown using recirculating nutrient (hydroponic) film culture with pH and electrical conductivity automatically controlled. Temperature and humidity were controlled to near optimal levels for each species and atmospheric carbon dioxide partial pressures were maintained near 100 Pa during the light cycles. Soybean seed contained the highest percentage of protein and fat, potato tubers and wheat seed contained the highest levels of carbohydrate, and lettuce leaves contained the highest level of ash. Analyses showed values close to data published for field-grown plants with several exceptions: In comparison with field-grown plants, wheat seed had higher protein levels; soybean seed had higher ash and crude fiber levels; and potato tubers and lettuce leaves had higher protein and ash levels. The higher ash and protein levels may have been a result of the continuous supply of nutrients (e.g., potassium and nitrogen) to the plants by the recirculating hydroponic culture.

NASA Discipline Life Support Systems↗

NASA NTRS2021

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

crop yield↗

DOE OSTI2025

Cultivating clarity: understanding the impact of land cost assumptions on biofuel viability

The transition to sustainable energy has increased interest in biofuel production to reduce greenhouse gas emissions, decrease reliance on imported oil, and ensure energy resilience. Here, this study examines the often-overlooked impact of land cost assumptions on the economic viability of biofuel production. Using a discounted cash flow techno-economic framework, we evaluated three land cost scenarios—no land costs, land rental costs, and land purchase costs—across six bioenergy feedstocks (corn, soybeans, switchgrass, miscanthus, poplar, and microalgae) and three biofuel products (corn ethanol, soybean biodiesel, and sustainable aviation fuel) at the county level for the contiguous United States. The analysis reveals substantial variation in minimum fuel selling prices due to these scenarios. High-yield crops like algae showed low sensitivity to land costs, while low-yield crops such as soybeans were highly sensitive. Geographical differences were significant, with minimum fuel selling price increases most pronounced in high-value land regions like the Corn Belt. Case studies further illustrate the influence of local productivity and land costs on economic outcomes across the United States. These findings emphasize the importance of maintaining consistent land cost assumptions in biofuel economic assessments. By quantifying the interplay between land value, crop productivity, and economic feasibility, this study provides essential insights for policymakers and stakeholders to advance sustainable energy solutions.

09 BIOMASS FUELS↗

DOE OSTI2026

Economic Incentives for Agrivoltaics Systems with Commodity Crops in the Midwestern United States

Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.

14 SOLAR ENERGY↗

DOE OSTI2025

Impact of drought on global food security by 2050

Drought is a major driver of crop production loss, threatening global food security as the population rises toward 9 billion by 2050. Using a process-based crop model within an Earth system model, we assess drought-related impacts on maize, soybean, rice, and wheat production at global and country levels. We then develop a food insecurity index combining drought impacts with socio-economic factors. Our results indicate that by 2050, globally averaged drought losses for combined maize, soybean, rice, and wheat production are <2%, though soybean losses reach 3.6%. Despite these small average changes at the global scale, 62 countries experience maximum production losses over 10%, and 24 countries over 20%. Our index identifies regions at greatest risk, including large parts of South America, Africa, Eastern Europe, and Southeast Asia. These results illustrate the need for drought adaptation to mitigate future drought impacts on crop production.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

Crop identification from radar imagery of the Huntington County, Indiana test site

The author has identified the following significant results. Like polarization was successful in discriminating corn and soybeans; however, pasture and woods were consistently confused as soybeans and corn, respectively. The probability of correct classification was about 65%. The cross polarization component (highest for woods and lowest for pasture) helped in separating the woods from corn, and pasture from soybeans, and when used with the like polarization component, the probability of correct classification increased to 74%.

Batlivala, P. P.↗

NASA NTRS1981

Analysis of scanner data for crop inventories

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

Horvath, R.↗

NASA NTRS1982

Spectral characterization of the LANDSAT-D multispectral scanner subsystems

Relative spectral response data for the multispectral scanner subsystems (MSS) to be flown on LANDSAT-D and LANDSAT-D backup, the protoflight and flight models, respectively, are presented and compared to similar data for the Landsat 1,2, and 3 subsystems. Channel-bychannel (six channels per band) outputs for soil and soybean targets were simulated and compared within each band and between scanners. The two LANDSAT-D scanners proved to be nearly identical in mean spectral response, but they exhibited some differences from the previous MSS's. Principal differences between the spectral responses of the D-scanners and previous scanners were: (1) a mean upper-band edge in the green band of 606 nm compared to previous means of 593 to 598 nm; (2) an average upper-band edge of 697 nm in the red band compared to previous averages of 701 to 710 nm; and (3) an average bandpass for the first near-IR band of 702-814 nm compared to a range of 693-793 to 697-802 nm for previous scanners. These differences caused the simulated D-scanner outputs to be 3 to 10 percent lower in the red band and 3 to 11 percent higher in the first near-IR band than previous scanners for the soybeans target. Otherwise, outputs from soil and soybean targets were only slightly affected. The D-scanners were generally more uniform from channel to channel within bands than previous scanners.

Markham, B. L.↗

NASA NTRS1984

Spectral Characterization of the LANDSAT-4 MSS Sensors

Relative spectral response data for the LANDSAT-4 and LANDSAT-4 backup multispectral scanner subsystems (MSS), the protoflight and flight models are presented and compared to similar data for the LANDSAT 1, 2 and 3 scanners. Channel (six channels per band) outputs for soil and soybean targets were simulated and compared within each band and between scanners. The principal differences between the spectral responses of the LANDSAT-4 scanners and previous scanners are discussed. The simulated LANDSAT-4 scanner outputs were 3 to 10 percent lower in the red band and 3 to 11 percent higher in the first near-IR band than previous scanners for the soybeans targets. The LANDSAT-4 scanners were generally more uniform from channel to channel within bands than previous scanners. In the upper-band edge of the red band of the protoflight scanner, one channel was markedly different (12 nm) from the rest. For a soybeans target, this nonuniformity resulted in a within-band difference of 6.2 percent in simulated outputs between channels.

Markham, B. L.↗

NASA NTRS1984

Cultural and environmental effects on crop spectral development patterns as viewed by Landsat

The typical patterns of spectral development (profiles) for corn and soybeans are presented, based on field-collected reflectance data transformed to correspond to Landsat-MSS Tasseled Cap coordinates. Reasonable variations in field conditions and cultural practices are shown to significantly influence profile features. The separability of the two crops is determined to be primarily related to the maximum value of the reflectance equivalent of Greenness, and to the plateau effect seen in corn Greenness profiles. The impact of changes in conditions on separability is described. In addition, association is made between profile features and stages of development for corn and soybeans. Corn is shown to peak at a stage well before tasseling or maximum LAI, while the characteristics of the soybean profile are shown to be unrelated to any particular stage of development.

Crist, E. P.↗

NASA NTRS1985

Spectral characterization of the Landsat Thematic Mapper sensors

Relative spectral response data for the Landsat-4 and Landsat-4 backup multispectral scanner subsystems (MSS), the protoflight and flight models, are presented and compared to similar data for the Landsat 1, 2 and 3 scanners. Channel (six channels per band) outputs for soil and soybean targets were simulated and compared within each band and between scanners.The principal differences between the spectral responses of the Landsat-4 scanners and previous scanners are discussed. The simulated Landsat-4 scanner outputs were 3 to 10 percent lower in the red band and 3 to 11 percent higher in the first near-IR band than previous scanners for the soybeans targets. The Landsat-4 scanners were generally more uniform from channel to channel within bands than previous scanners. In the upper-band edge of the red band of the protoflight scanner, one channel was markedly different (12 nm) from the rest. For a soybeans target, this nonuniformity resulted in a within-band difference of 6.2 percent in simulated outputs between channels.

Markham, B. L.↗

NASA NTRS1987

Landsat classification of Argentina summer crops

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

Badhwar, G. D.↗