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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Spectral behavior of wheat yield variety trials

Little variation between varieties is seen at jointing, but the variability is found to increase during grain filling and decline again at maturity. No relationship is found between spectral response and yield, and when yields are segregated into various classes the spectral response is the same. Spring and winter nurseries are found to separate during the reproductive stage because of differences in dates of heading and maturity, but they exhibit similar spectral responses. The transformed normalized difference is at a minimum after the maximum grain weight occurs and the leaves begin to brown and fall off. These data of 100% ground cover demonstrate that it is not possible to predict grain yield from only spectral data. This, however, may not apply when reduced yields are caused by less-than-full ground cover

Hatfield, J. L.↗
NASA NTRS1982

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

Barnett, T. L.↗
NASA NTRS1977

Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data

Locating areas where soil moisture is limiting to crop growth is important for estimating winter-wheat yields on a regional basis. In the 1975-76 growing season, we evaluated soil-moisture conditions and winter-wheat yields for a five-state region of the Great Plains using Landsat estimates of leaf area index (LAI) and an evapotranspiration (ET) model described by Kanemasu et al (1977). Because LAI was used as an input, the ET model responded to changes in crop growth. Estimated soil-water depletions were high for the Nebraska Panhandle, southwestern Kansas, southeastern Colorado, and the Texas Panhandle. Estimated yields in five-state region ranged from 1.0 to 2.9 metric ton/ha.

Heilman, J. L.↗
NASA NTRS1977

Response of winter and spring wheat grain yields to meteorological variation

Mathematical models which quantify the relation of wheat yield to selected weather-related variables are presented. Other sources of variation (amount of applied nitrogen, improved varieties, cultural practices) have been incorporated in the models to explain yield variation both singly and in combination with weather-related variables. Separate models were developed for fall-planted (winter) and spring-planted (spring) wheats. Meteorological variation is observed, basically, by daily measurements of minimum and maximum temperatures, precipitation, and tabled values of solar radiation at the edge of the atmosphere and daylength. Two different soil moisture budgets are suggested to compute simulated values of evapotranspiration; one uses the above-mentioned inputs, the other uses the measured temperatures and precipitation but replaces the tabled values (solar radiation and daylength) by measured solar radiation and satellite-derived multispectral scanner data to estimate leaf area index. Weather-related variables are defined by phenological stages, rather than calendar periods, to make the models more universally applicable.

Feyerherm, A. M.↗
NASA NTRS1997

Photomorphogenesis, photosynthesis, and seed yield of wheat plants grown under red light-emitting diodes (LEDs) with and without supplemental blue lighting

Red light-emitting diodes (LEDs) are a potential light source for growing plants in spaceflight systems because of their safety, small mass and volume, wavelength specificity, and longevity. Despite these attractive features, red LEDs must satisfy requirements for plant photosynthesis and photomorphogenesis for successful growth and seed yield. To determine the influence of gallium aluminium arsenide (GaAlAs) red LEDs on wheat photomorphogenesis, photosynthesis, and seed yield, wheat (Triticum aestivum L., cv. 'USU-Super Dwarf') plants were grown under red LEDs and compared to plants grown under daylight fluorescent (white) lamps and red LEDs supplemented with either 1% or 10% blue light from blue fluorescent (BF) lamps. Compared to white light-grown plants, wheat grown under red LEDs alone demonstrated less main culm development during vegetative growth through preanthesis, while showing a longer flag leaf at 40 DAP and greater main culm length at final harvest (70 DAP). As supplemental BF light was increased with red LEDs, shoot dry matter and net leaf photosynthesis rate increased. At final harvest, wheat grown under red LEDs alone displayed fewer subtillers and a lower seed yield compared to plants grown under white light. Wheat grown under red LEDs+10% BF light had comparable shoot dry matter accumulation and seed yield relative to wheat grown under white light. These results indicate that wheat can complete its life cycle under red LEDs alone, but larger plants and greater amounts of seed are produced in the presence of red LEDs supplemented with a quantity of blue light.

NASA Discipline Life Support Systems↗
NASA NTRS1976

Crop status evaluations and yield predictions

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.

Haun, J. R.↗
NASA NTRS2016

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

upscaling↗