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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Photosynthetic capacity and dry mass partitioning in dwarf and semi-dwarf wheat (Triticum aestivum L.)

Efficient use of space and high yields are critical for long-term food production aboard the International Space Station. The selection of a full dwarf wheat (less than 30 cm tall) with high photosynthetic and yield potential is a necessary prerequisite for growing wheat in the controlled, volume-limited environments available aboard long-term spaceflight missions. This study evaluated the photosynthetic capacity and carbon partitioning of a full-dwarf wheat cultivar, Super Dwarf, which is routinely used in spaceflight studies aboard U.S. space shuttle and NASA/Mir missions and made comparisons with other dwarf and semi-dwarf wheat cultivars utilized in other ground-based studies in plant space biology. Photosynthetic capacity of the flag leaf in two dwarf (Super Dwarf, BB-19), and three semi-dwarf (Veery-10, Yecora Rojo, IBWSN 199) wheat cultivars (Triticum aestivum L.) was assessed by measuring: net maximum photosynthetic rate, RuBP carboxylation efficiency, chlorophyll concentration and flag leaf area. Dry mass partitioning of carbohydrates to the leaves, sheaths, stems and ear was also assessed. Plants were grown under controlled environmental conditions in three replicate studies: slightly enriched CO2 (370 micromoles mol-1), high photosynthetic photon flux (1000 micromoles m-2 s-1; 58 mol m-2 d-1) for a 16 h photoperiod, 22/15 degrees C day/night temperatures, ample nutrients and water provided by one-half strength Hoagland's nutrient solution (Hoagland and Arnon, 1950). Photosynthetic capacity of the flag leaf was determined at anthesis using net CO2 exchange rate versus internal CO2 concentration curves measured under saturating light (2000 micromoles m-2 s-1) and CO2 (1000 micromoles mol-1). Dwarf wheat cultivars had greater photosynthetic capacities than the taller semi-dwarfs, they averaged 20% higher maximum net photosynthetic rates compared to the taller semi-dwarfs, but these higher rates occurred only at anthesis, had slightly greater carboxylation efficiencies and significantly increased chlorophyll concentrations per unit leaf area. The reduced-height wheat had significantly less dry mass fraction in the stem but greater dry mass partitioned to the ear than the taller semi-dwarfs (Yecora rojo, IBWSN-199). Studies with detached heads confirm that the head is a significant sink in the shorter wheat cultivars.

Non-NASA Center↗
NASA NTRS2022

Global Gridded Crop Models Underestimate Yield Responses to Droughts and Heatwaves

Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models' capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models' capacity to adequately respond to extreme weather events.

extreme events↗
NASA NTRS1977

The Large Area Crop Inventory Experiment

Determination of wheat acreages in the central U.S., as well as the development and testing of yield models, is discussed in the framework of the Large Area Crop Inventory Experiment (LACIE). Particular attention is given to the goal of obtaining a 90% accuracy in yield forecasts in nine out of ten years. Current results of LACIE indicate that Landsat remote sensing data, in conjunction with information from agricultural meteorological surveys, can provide highly accurate early-season and at-harvest yield estimates in the principal wheat-growing regions of the world.

Macdonald, R. B.↗
NASA NTRS1980

Relationship of spectral data to grain yield variation

Two-band hand-held radiometer data from a winter wheat field, collected on 21 dates during the spring growing season, were correlated with within-field final grain yield. Significant linear relationships were found between various combinations of the red and photographic infrared radiance data collected and the grain yield. The spectral data explained about 64 percent of the within-field grain yield variation. This variation in grain yield could not be explained using meteorological data as these were similar for all areas of the wheat field. Most importantly, data collected early in the spring were highly correlated with grain yield, a five-week time window existed from stem elongation through anthesis in which the spectral data were most highly correlated with grain yield, and manifestations of wheat canopy water stress were readily apparent in the spectral data.

Tucker, C. J.↗
NASA NTRS2002

Sensitivity of wheat and rice to low levels of atmospheric ethylene

Ethylene (C2H4) gas is produced throughout the life cycle of plants and can accumulate in closed growth chambers to levels 100 times higher than in outside environments. Elevated atmospheric C2H4 can cause a variety of abnormal responses, but the sensitivity to elevated C2H4 is not well characterized. We evaluated the C2H4 sensitivity of wheat (Triticum aestivum L.) and rice (Oryza sativa L.) in five studies. The first three studies compared the effects of continuous C2H4 levels ranging from 0 to 1000 nmol mol-1 (ppb) in a growth chamber throughout the life cycle of the plants. A short-term 1000 nmol mol-1 treatment was included in which exposure was stopped at anthesis. Yield was reduced by 36% in wheat and 63% in rice at 50 nmol mol-1 and both species were virtually sterile when continuously exposed to 1000 nmol mol-1. However, the yield reductions were much less with exposure that stopped at anthesis, suggesting the detrimental effect of C2H4 on yield was greatest around the time of seed set. Two additional studies evaluated the differential sensitivity of two wheat cultivars (Super Dwarf and USU-Apogee) to 50 nmol mol-1 C2H4 at three CO2 levels [350, 1200, 5000 micromoles mol-1 (ppm)] in a greenhouse. Yield of USU-Apogee was not significantly reduced by C2H4 but the yield of Super Dwarf was reduced by 60%. Elevated CO2 did not influence the sensitivity to C2H4. A difference in the C2H4 sensitivity of USU-Apogee between greenhouse and growth chamber trials suggests that C2H4 sensitivity is dependent on the environment. Collectively, the data suggest that relatively low levels of C2H4 could induce anomalous plant responses by accumulation in greenhouses and growth chambers with inadequate ventilation. The data also suggest that C2H4 sensitivity can be reduced by both genetic and environmental manipulations. 2002 Crop Science Society of America.

NASA Discipline Life Sciences Technologies↗
NASA NTRS1998

Adaptation to high CO2 concentration in an optimal environment: radiation capture, canopy quantum yield and carbon use efficiency

The effect of elevated [CO2] on wheat (Triticum aestivum L. Veery 10) productivity was examined by analysing radiation capture, canopy quantum yield, canopy carbon use efficiency, harvest index and daily C gain. Canopies were grown at either 330 or 1200 micromoles mol-1 [CO2] in controlled environments, where root and shoot C fluxes were monitored continuously from emergence to harvest. A rapidly circulating hydroponic solution supplied nutrients, water and root zone oxygen. At harvest, dry mass predicted from gas exchange data was 102.8 +/- 4.7% of the observed dry mass in six trials. Neither radiation capture efficiency nor carbon use efficiency were affected by elevated [CO2], but yield increased by 13% due to a sustained increase in canopy quantum yield. CO2 enrichment increased root mass, tiller number and seed mass. Harvest index and chlorophyll concentration were unchanged, but CO2 enrichment increased average life cycle net photosynthesis (13%, P < 0.05) and root respiration (24%, P < 0.05). These data indicate that plant communities adapt to CO2 enrichment through changes in C allocation. Elevated [CO2] increases sink strength in optimal environments, resulting in sustained increases in photosynthetic capacity, canopy quantum yield and daily C gain throughout the life cycle.

Non-NASA Center↗
NASA NTRS1980

Spatial variability of surface temperature as related to cropping practice with implications for irrigation management

Crop stress measured using thermal infrared emission is evaluated with the stress-degree-day (SDD) concept. Throughout the season, the accumulation of SDD during the reproductive stage of growth is inversely related to yield. This relationship is shown for durum wheat, hard red winter wheat, barley, grain sorghum and soybeans. It is noted that SDD can be used to schedule irrigations for maximizing yields and for applying remotely sensed data to management of water resources. An airborne flight with a thermal-IR scanner was used to examine the variability in temperature that may exist from one field to another and to determine realistic within-field temperature variations. It was found that the airborne and the ground-based data agreed very well and that there was less variability in the fields that were completely covered with crops than those of bare soil.

Hatfield, J. L.↗
NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗
NASA NTRS2021

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

crop yield projections↗
NASA NTRS1987

Wheat production in controlled environments

The present optimization study for maximum yield and quality conditions in the lunar or Martian Controlled Environment Life Support System (CELSS)-based growth of wheat has determined that, for 23-57 g/sq m per day of edible biomass, minimum CELSS size must be of the order of 12-30 sq m/person. About 600 W/sq m of electricity would be consumed by the artificial lighting required; temperature, irradiance, photoperiod, CO2 levels, humidity, and wind velocity are all controlled. A rock wool plant support allows direct seeding, and densities of up to 10,000 plants/sq m. Densities of up to 2000 plants/sq m appear to increase seed yields.

Salisbury, Frank B.↗
NASA NTRS1983

Rain-induced spring wheat harvest losses

When rain or a combination of rain and high humidity delay wheat harvest, losses can occur in grain yield and/or grain quality. Yield losses can result from shattering, from reduction in test weight, and in the case of windrowed grain, from rooting of sprouting grain at the soil: windrow contact. Losses in grain quality can result from reduction in test weight and from sprouting. Sprouting causes a degradation of grain proteins and starches, hence flour quality is reduced, and the grain price deteriorates to the value of feed grain. Although losses in grain yield and quality are rain-induced, these losses do not necessarily occur because a standing or windrowed crop is wetted by rain. Spike water concentration in hard red spring wheat must be increased to about 45-49% before sprouting is initiated in grain that has overcome dormancy. The time required to overcome this dormancy after the cultivar has dried to 12 to 14% water concentration differs with hard red spring cultivars. The effect of rain on threshing-ready standing and windrowed hard red spring wheat grain yeild and quality was evaluated. A goal was to develop the capability to forecast the extent of expected loss of grain yield and quality from specific climatic events that delay threshing.

Bauer, A.↗
NASA NTRS2024

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

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

Florian Gollnow↗
NASA NTRS1984

Growth and reflectance characteristics of winter wheat canopies

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. The effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat (Triticum aestivum L.) were determined through field experiments. Spectral reflectance was measured during the 1979 and 1980 growing seasons with a spectroradiometer. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index (LAI), plant moisture, and fresh and dry phytomass. Nitrogen deficiency caused increased visible, reduced near infrared, and increased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the N-deficient plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. The potential of remote sensing in distinguishing stressed from healthy crops is demonstrated. Evidence suggests multispectral imagery may be useful for monitoring crop condition.

Hinzman, L. D.↗
NASA NTRS2017

The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for is greater than 50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 C to 33 C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Wang, Enli↗
NASA NTRSDate not supplied

An Assessment of Irrigated Agriculture and Drought-related Hazard Risks for the Aral Sea Region

This was part of a larger UNDP/Uzbekistan project on the Aral Sea Region, which is a prominent ecological disaster caused by water diversion and unsustainable land use management. Columbia University CCSR and the Climate Impacts Group are working with the UNDP to explore climate change impacts on irrigated agriculture in the region, particularly with regards to yield changes and drought risk for wheat and cotton systems in the Amu Darya River Basin. My talk described our project plan and described the types of data that we would need from project partners in the country in order to connect our global satellites and models to local conditions.

Irrigated agriculture↗
NASA NTRS1995

Interactions of CO2, temperature and management practices: simulations with a modified version of CERES-Wheat

A new growth subroutine was developed for CERES-Wheat, a computer model of wheat (Triticum aestivum) growth and development. The new subroutine simulates canopy photosynthetic response to CO2 concentrations and light levels, and includes the effects of temperature on canopy light-use efficiency. Its performance was compared to the original CERES-Wheat V-2 10 in 30 different cases. Biomass and yield predictions of the two models were well correlated (correlation coefficient r > 0.95). As an application, summer growth of spring wheat was simulated at one site. Modeled crop responses to higher mean temperatures, different amounts of minimum and maximum warming, and doubled CO2 concentrations were compared to observations. The importance of irrigation and nitrogen fertilization in modulating the wheat crop climatic responses were also analyzed. Specifically, in agreement with observations, rainfed crops were found to be more sensitive to CO2 increases than irrigated ones. On the other hand, low nitrogen applications depressed the ability of the wheat crop to respond positively to CO2 increases. In general, the positive effects of high CO2 on grain yield were found to be almost completely counterbalanced by the negative effects of high temperatures. Depending on how temperature minima and maxima were increased, yield changes averaged across management practices ranged from -4% to 8%.

NASA Discipline Life Support Systems↗
NASA NTRS2015

Benchmark Data Set for Wheat Growth Models: Field Experiments and AgMIP Multi-Model Simulations.

The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

climate change impact↗
NASA NTRS1976

LACIE: Yield-weather regression models for the Canadian prairies

Most of the variability in wheat production is due to weather fluctuations. Climatic differences within the region account for a large portion of the variability in yields for different parts of the region. Separate regression models were developed for each of the areas indicated.

Source record↗