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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Reproductive Ontogeny of Wheat Grown on the MIR Space Station

The reproductive ontogeny of 'Super-Dwarf' wheat grown on the space station Mir is chronicled from the vegetative phase through flower development. Changes in the apical meristem associated with transition From the vegetative phase to floral initiation and development of the reproductive spike were all typical of 'Super Dwarf' wheat up to the point of anthesis. Filament elongation, which characteristically occurs just prior to anthesis and moves the anthers through the stigmatic branches thus facilitating pollination, did no1 xcur in the flowers of spikes grown on Mir. While development of spikes on tillers typically occurs later :han that of spikes on the main stem, all flowers appear to be arrested at the same developmental point.

Bubenheim, David L.↗
NASA NTRS2019

The Use of Landsat 8 and Sentinel-2 Data and Meterological Observations for Winter Wheat Yield Assessment

This study focuses on winter wheat yield assessment from NASA's Harmonized Landsat Sentinel-2 (HLS) product and meteorological observations through phenological fitting. Vegetation indices (VIs), namely difference vegetation index (DVI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2), extracted from satellite optical data, are fitted per pixel against accumulated growing degree days (AGDD) using a quadratic function. Accumulated VIs are correlated against winter wheat yields. Results show a better performance from DVI compared to NDVI and EVI2.

Skakun, S.↗
NASA NTRS2021

FORECASTING WHEAT YIELD USING REMOTE SENSING: THE ARYA FORECASTING SYSTEM

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI)and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied toMODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

B. Franch↗
NASA NTRS2021

Sustainable Use of Groundwater May Dramatically Reduce Irrigated Production of Maize, Soybean, and Wheat

Groundwater extraction in the United States (US) is unsustainable, making it essential to understand the impacts of limited water use on irrigated agriculture. To improve this understanding, we integrated a gridded crop model with satellite observations, recharge estimates, and water survey data to assess the effects of sustainable groundwater withdrawals on US irrigated agricultural production. The gridded crop model agrees with satellite-based estimates of evapotranspiration (R2 = 0.68), as well as survey data from the United States Department of Agriculture (R2 = 0.82–0.94 for county-level production and 0.37–0.54 for county-level yield). Using the optimistic assumption that groundwater extraction equals effective aquifer recharge rate, we find that sustainable groundwater use decreases US irrigated production of maize, soybean, and winter wheat by 20%, 6%, and 25%, respectively. Using a more conservative assumption of groundwater availability, US irrigated production of maize, soybean, and winter wheat decreases by 45%, 37%, and 36%, respectively. The wide range of simulated losses is driven by considerable uncertainty in surface water and groundwater interactions, as well as accounting for the many aspects of sustainability. Our results demonstrate the vulnerability of US irrigated agriculture to unsustainable groundwater pumping, highlighting the difficulty of expanding or even maintaining irrigated food production in the face of climate change, population growth, and shifting dietary demands. These findings are based on reducing pumping by fallowing irrigated farmland; however, alternate pumping reduction strategies or technological advances in crop genetics and irrigation could produce different results.

sustainability↗
NASA NTRS1992

A dopaminergic projection to the rat mammillary nuclei demonstrated by retrograde transport of wheat germ agglutinin-horseradish peroxidase and tyrosine hydroxylase immunohistochemistry

The presence and distribution of dopaminergic neurons and terminals in the hypothalamus of the rat were studied by tyrosine hydroxylase (TH) immunohistochemistry. Strongly labelled TH-immunoreactive neurons were seen in the dorsomedial hypothalamic nucleus, periventricular region, zona incerta, arcuate nucleus, and supramammillary nucleus. A few TH-positive neurons were also identified in the dorsal and ventral premammillary nucleus, as well as the lateral hypothalamic area. TH-immunoreactive fibres and terminals were unevenly distributed in the mammillary nuclei; small, weakly labelled terminals were scattered in the medial mammillary nucleus, while large, strongly labelled, varicose terminals were densely concentrated in the internal part of the lateral mammillary nucleus. A few dorsoventrally oriented TH-positive axon bundles were also identified in the lateral mammillary nucleus. A dopaminergic projection to the mammillary nuclei from the supramammillary nucleus and lateral hypothalamic area was identified by double labelling with retrograde transport of wheat germ agglutinin-horseradish peroxidase and TH-immunohistochemistry. The lateral mammillary nucleus receives a weak dopaminergic projection from the medial, and stronger projections from the lateral, caudal supramammillary nucleus. The double-labelled neurons in the lateral supramammillary nucleus appear to encapsulate the caudal end of the mammillary nuclei. The medial mammillary nucleus receives a very light dopaminergic projection from the caudal lateral hypothalamic area. These results suggest that the supramammillary nucleus is the principal source of the dopaminergic input to the mammillary nuclei, establishing a local TH-pathway in the mammillary complex. The supramammillary cell groups are able to modulate the limbic system through its dopaminergic input to the mammillary nuclei as well as through its extensive dopaminergic projection to the lateral septal nucleus.

Non-NASA Center↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

wheat↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

model intercomparison↗
NASA NTRS2019

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.

phenological fitting↗
NASA NTRS1973

Wheat classification exercise, using 11 June 1973, ERTS MSS data for Fayette County, Illinois (for CITARS task)

The prime emphasis was on classification of pixels in field centers, away from boundary effects. Results were encouraging in both training and test field centers for wheat and other major types of vegetation present. However, the location of fields was found to be a serious problem and it was even more difficult to select field-center pixels for fields of sizes less than 20 acres (or even larger, depending upon field shape) for use in the field-center analysis. The majority of fields in the segment are less than 20 acres in size. ERTS-1 data were received on 12 September 1973. Ground truth information and aerial photography were received on 9 and 15 September. The data were analyzed and processed digitally using the ERIM multispectral software system.

Malila, W. A.↗
NASA NTRS1974

The action of ultrasounds on Bezostaia 1 winter wheat grown in sand pots treated with Knop solution

The results of 3 years of research are presented concerning the increase in length and weight of the aerial biomass as compared with the underground biomass, as well as the productivity indexes of Bezostaia 1 winter wheat. The experiment involved two varieties, each of them with nine repetitions. Sterile sifted sand treated several times with a Knop solution during the vegetative period was used as a seed bed. The seeds were kept in water for 2 hours before treatment. Biometric and statistical measurements showed significant responses in the treated varieties.

Albu, N.↗
NASA NTRS1974

Predicting soil moisture and wheat vegetative growth from ERTS-1 imagery

The author has identified the following significant results. Study results indicate: (1) Vegetative growth was best predicted by a linear relationship between leaf area index and the radio of band 4 to band 5. All significant soil moisture effects were cancelled by the ratio. (2) Soil moisture at a depth of 0 to 15 cm, with specific soil factors, was predicted by band 4 and leaf area index with a high regression coefficient. (3) Vegetative growth, measured by leaf area index, was one of the necessary inputs in evaluating the winter wheat crop coefficient from March to maturity.

Krupp, J. W.↗
NASA NTRS1974

Seasonal canopy reflectance patterns of wheat, sorghum, and soybean

An investigation was conducted of canopy-reflectance patterns as a basis for the determination of surface conditions. Two fields each of wheat, sorghum, and soybeans were selected in a bottom land area. One field contained a dark-colored, silty clay loam and the other a light-colored, silt loam. The study suggests that the reflectance ratio of the 545- to 655-nm-wavelengths may be used as an indicator of crop growth.

Kanemasu, E. T.↗
NASA NTRS1975

Wheat productivity estimates using LANDSAT data

The author has identified the following significant results. The electro-optical leaf area meter was the most accurate of the approaches tested on harvested wheat samples, but it is very time consuming. It was decided to infer leaf area from dry weight biomass after establishing a relationship between dry weight biomass and area as measured by the leaf area meter. There is a good correlation between leaf area as measured by the meter and dry leaf biomass. There is a less consistent relationship between stem area and stem biomass.

Nalepka, R. F.↗
NASA NTRS1975

Wheat Productivity Estimates Using LANDSAT Data

The author has identified the following significant results. The biological leaf area index data show that there can be large variations in field vegetative condition from point to point. This is especially true in flood-irrigated fields, in which plant density (and development) varies drastically between rows that are in channels vs. those that are in raised areas. Considerable care must be used in interpreting the significance of isolated leaf area index measurements made from a single wheat row.

Nalepka, R. F.↗