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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Operation of agricultural test fields for study of stressed crops by remote sensing

A test site for the study of winter wheat development and collection of ERTS data was established in September of 1973. The test site is a 10 mile square area located 12.5 miles west of Amarillo, Texas on Interstate Hwy. 40, in Randall and Potter counties. The center of the area is the Southwestern Great Plains Research Center at Bushland, Texas. Within the test area all wheat fields were identified by ground truth and designated irrigated or dryland. The fields in the test area other than wheat were identified as to pasture or the crop that was grown. A ground truth area of hard red winter wheat was established west of Hale Center, Texas. Maps showing the location of winter wheat fields in excess of 40 acres in size within a 10 mile radius were supplied NASA. Satellite data was collected for this test site (ERTS-1).

Toler, R. W.↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). LACIE third interim phase 3 accuracy assessment report

The author has identified the following significant results. An accuracy of 90/85 was achieved with the October estimates which had a relative bias of -9.9 percent and a coefficient of variation of 5.2 percent for the total wheat production in the USGP. The probability was 0.9 that the LACIE estimate was within + or - 15 percent of true wheat production for the USGP. The LACIE spring wheat production underestimates in August, September, and October were the results of area underestimates for spring wheat in the USNGP region. The winter wheat blind study showed that the average proportion estimates were significantly different from the average dot-count, ground truth proportions at the USSGP and USGP-7 levels.

Source record↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). LACIE phase 1 and phase 2 accuracy assessment

The author has identified the following significant results. The initial CAS estimates, which were made for each month from April through August, were considerably higher than the USDA/SRS estimates. This was attributed to: (1) the practice of considering bare ground as potential wheat and counting it as wheat; (2) overestimation of the wheat proportions in segments having only a small amount of wheat; and (3) the classification of confusion crops as wheat. At the end of the season most of the segments were reworked using improved methods based on experience gained during the season. In particular, new procedures were developed to solve the three problems listed above. These and other improvements used in the rework experiment resulted in at-harvest estimates that were much closer to the USDA/SRS estimates than those obtained during the regular season.

Source record↗
NASA NTRS1980

Identification of USSR Indicator Regions

Potential indicator regions were determined by comparing the statistics for barley and wheat at the lowest administrative levels for which published statistics were available. Fourteen were selected for review based on their relative abundances of wheat and barely. These potential indicator regions were grouped according to three conditions that could affect labeling and classification accuracies: (1) high-barley content; (2) presence of barley and spring wheat; and (3) presence of barley and winter wheat. Each region was further evaluated based on the availability of crop calendars, LANDSAT acquisitions, and ancillary data. Based on the relative abundance of wheat and barley and the availability of data, three indicator regions were recommended. Within each region, individual oblasts and/or krays were selected according to segment availability and segment acquisition histories for potential barley separation.

Disler, J.↗
NASA NTRS1999

Effects of CO2 on stomatal conductance: do stomata open at very high CO2 concentrations?

Potato and wheat plants were grown for 50 d at 400, 1000 and 10000 micromoles mol-1 carbon dioxide (CO2). and sweetpotato and soybean were grown at 1000 micromoles mol-1 CO2 in controlled environment chambers to study stomatal conductance and plant water use. Lighting was provided with fluorescent lamps as a 12 h photoperiod with 300 micromoles m-2 s-1 PAR. Mid-day stomatal conductances for potato were greatest at 400 and 10000 micromoles mol-1 and least at 1000 micromoles mol-1 CO2. Mid-day conductances for wheat were greatest at 400 micromoles mol-1 and least at 1000 and 10000 micromoles mol-1 CO2. Mid-dark period conductances for potato were significantly greater at 10000 micromoles mol-1 than at 400 or 1000 micromoles mol-1, whereas dark conductance for wheat was similar in all CO2 treatments. Temporarily changing the CO2 concentration from the native 1000 micromoles mol-1 to 400 micromoles mol-1 increased mid-day conductance for all species, while temporarily changing from 1000 to 10000 micromoles mol-1 also increased conductance for potato and sweetpotato. Temporarily changing the dark period CO2 from 1000 to 10000 micromoles mol-1 increased conductance for potato, soybean and sweetpotato. In all cases, the stomatal responses were reversible, i.e. conductances returned to original rates following temporary changes in CO2 concentration. Canopy water use for potato was greatest at 10000, intermediate at 400, and least at 1000 micromoles mol-1 CO2, whereas canopy water use for wheat was greatest at 400 and similar at 1000 and 10000 micromoles mol-1 CO2. Elevated CO2 treatments (i.e. 1000 and 10000 micromoles mol-1) resulted in increased plant biomass for both wheat and potato relative to 400 micromoles mol-1, and no injurious effects were apparent from the 10000 micromoles mol-1 treatment. Results indicate that super-elevated CO2 (i.e. 10000 micromoles mol-1) can increase stomatal conductance in some species, particularly during the dark period, resulting in increased water use and decreased water use efficiency.

NASA Discipline Life Support Systems↗
NASA NTRS2015

Assessing the Evolving Fragility of the Global Food System

The world food crisis in 2008 highlighted the susceptibility of the global food system to price shocks. Here we use annual staple food production and trade data from 1992-2009 to analyse the changing properties of the global food system. Over the 18-year study period, we show that the global food system is relatively homogeneous (85 of countries have low or marginal food self-sufficiency) and increases in complexity, with the number of global wheat and rice trade connections doubling and trade flows increasing by 42 and 90, respectively. The increased connectivity and flows within these global trade networks suggest that the global food system is vulnerable to systemic disruptions, especially considering the tendency for exporting countries to switch to non-exporting states during times of food scarcity in the global markets. To test this hypothesis, we superimpose continental-scale disruptions on the wheat and rice trade networks. We find greater absolute reductions in global wheat and rice exports along with larger losses in network connectivity as the networks evolve due to disruptions in European wheat and Asian rice production. Importantly, our findings indicate that least developed countries suffer greater import losses in more connected networks through their increased dependence on imports for staple foods (due to these large-scale disturbances): mean (median) wheat losses as percentages of staple food supply are 8.9 (3.8) for 1992-1996, increasing to 11 (5.7) for 20052009. Over the same intervals, rice losses increase from 8.2 (2.2) to 14 (5.2). Our work indicates that policy efforts should focus on balancing the efficiency of international trade (and its associated specialization) with increased resilience of domestic production and global demand diversity.

complex networks↗
NASA NTRS2020

Simulating the Cascading Effects of an Extreme Agricultural Production Shock: Global Implications of a Contemporary US Dust Bowl Event

Higher temperatures expected by midcentury increase the risk of shocks to crop production, while the interconnected nature of the current global food system functions to spread the impact of localized production shocks throughout the world. In this study, we analyze the global potential impact of a present-day event of equivalent magnitude to the US Dust Bowl, modeling the ways in which a sudden decline in US wheat production could cascade through the global network of agricultural trade. We use observations of country-level production, reserves, and trade data in a Food Shock Cascade model to explore trade adjustments and country-level inventory changes in response to a major, multiyear production decline. We find that a 4-year decline in wheat production of the same proportional magnitude as occurred during the Dust Bowl greatly reduces both wheat supply and reserves in the United States and propagates through the global trade network. By year 4 of the event, US wheat exports fall from 90.5 trillion kcal before the drought to 48 trillion to 52 trillion kcal, and the United States exhausts 94% of its reserves. As a result of reduced US exports, other countries meet their needs by leveraging their own reserves, leading to a 31% decline in wheat reserves globally. These findings demonstrate that an extreme production decline would lead to substantial supply shortfalls in both the United States and in other countries, where impacts outside the United States strongly depend on a country's reserves and on its relative position in the global trade network.

Heslin, Alison↗
NASA NTRS2017

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

wheat↗
NASA NTRS2023

Climate Variability and Simultaneous Breadbasket Yield Shocks as Observed in Long-Term Yield Records

That climate variability and change can potentially force multiple simultaneous breadbasket crop yield shocks has been established. But research quantifying the mechanisms behind such simultaneous shocks has been constrained by short records of crop yields. Here we compile a dataset of subnational crop yields in 25 countries dating back to 1900 to study the frequency and trends in multiple breadbasket yield shocks and how large-scale climate anomalies on interannual timescales have affected multiple breadbasket yield shocks over the last century. We find that major simultaneous breadbasket yield shocks have occurred in at least three, four, or five of nine breadbaskets 10.3%, 2.3% and 1.1% of the time for maize and 18.4%, 4.6% and 2.3% of the time for wheat. Furthermore, we find that multiple breadbasket yield shocks decreased in frequency even as those breadbaskets experience increasingly frequent climate-related shocks. For both maize and wheat breadbaskets, there were fewer simultaneous yield shocks during the 1975–2017 time period as compared to 1931–1975. Finally, we find that interannual modes of climate variability - such as the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO) - have all affected the relative probability of simultaneous yield shocks in pairs of breadbaskets by up to 20–40% in both maize and wheat breadbaskets. While past literature has focused on the effects of ENSO, we find that at the global scale the NAO affects the overall number of wheat yield shocks most strongly despite only affecting northern hemisphere breadbaskets.

agriculture↗
NASA NTRS1974

Kansas environmental and resource study: A Great Plains model. Extraction of agricultural statistics from ERTS-1 data of Kansas

The author has identified the following significant results. Wheat area, yield, and production statistics as derived from satellite image analysis, combined with a weather model, are presented for a ten county area in southwest Kansas. The data (representing the 1972-73 crop year) are compared for accuracy against both the USDA August estimate and its final (official) tabulation. The area estimates from imagery for both dryland and irrigated winter wheat were within 5% of the official figures for the same area, and predated them by almost one year. Yield on dryland wheat was estimated by the Thompson weather model to within 0.1% of the observed yield. A combined irrigated and dryland wheat production estimate for the ten county area was completed in July, 1973 and was within 1% of the production reported by USDA in February, 1974.

Morain, S. A.↗
NASA NTRS1974

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 3: Intensive use of living resources, agriculture. Part 2: Distribution effects

The results of an investigation of the value of improving information for forecasting future crop harvests are described. A theoretical model is developed to calculate the value of increased speed of availablitiy of that information. The analysis of U.S. domestic wheat consumption was implemented. New estimates of a demand function for wheat and of a cost of storage function were involved, along with a Monte Carlo simulation for the wheat spot and future markets and a model of market determinations of wheat inventories. Results are shown to depend critically on the accuracy of current and proposed measurement techniques.

Bradford, D. F.↗
NASA NTRS1978

Dryland pasture and crop conditions as seen by HCMM

The author has identified the following significant results. The soil moisture difference between the flight lines was partly due to water-holding capacity differences of the two soil types. Fields along the east flight line were in clay; while along the west flight line, the soil was sandy loam which holds less moisture. Due to differences in the amount of green material, the pastures were wetter than the wheat fields. Most of the pastures average from 40-80% green material, while wheat averages from 90-100% green material. A large amount of green material transpired more water and depleted the soil water content faster than dead vegetation. Visicorder data found temperature differences between the rangeland and winter wheat fields. Pasture had a larger percentage of dead material with different thermal properties than live vegetation, and surface temperature was primarily dependent on insolation. Dead material transpired less, but warms up faster than wheat fields.

Rosenthal, W. D.↗
NASA NTRS1979

Large Area Crop Inventory Experiment (LACIE). An overview of the Large Area Crop Inventory Experiment and the outlook for a satellite crop inventory

The author has identified the following significant results. The most important LACIE finding was that the technology worked very well in estimating wheat production in important geographic locations. Based on working through the many successes and shortcomings of LACIE, it can be stated with confidence that: (1) the current technology can successfully monitor what production in regions having similar characteristics to those of the U.S.S.R. wheat areas and the U.S. hard red winter wheat areas; (2) with additional applied research, significant improvements in capabilities to monitor wheat in these and other important production regions can be expected in the near future; (3) the remote sensing and weather effects modeling technology approached used by LACIE is generally applicable to other major crops and crop-producing regions of the world; and (4) with suitable effort, this technology can now advance rapidly and could be widespread use in the late 1980's.

Erb, R. B.↗
NASA NTRS1979

Large Area Crop Inventory Experiment (LACIE). Evaluation of the LACIE transition year crop calendar model

The author has identified the following significant results. The estimates of developmental stage dates from the LACIE adjustable crop calendar (ACC) winter wheat model was somewhat more accurate than the historical crop calendar after jointing. The ACC winter wheat model was not so accurate for the Texas Panhandle as it was for the other areas of the USPG-7 because dry soil conditions delayed fall planting in the Panhandle. Since the LACIE ACC winter wheat model does not contain a moisture term and it was started with historical planting dates, lengthy delays in planting mean that the ACC model will probably be started early and will estimate the developmental growth stages to occur too early in the season. The LACIE ACC spring wheat model was also started early in most areas because of late planting due to fields wet from melting snow and rain. The starter model used to estimate spring planting dates was not accurate under these wet soil conditions and tended to predict the developmental stages to occur earlier than the dates observed in the fields.

Cheffin, R. E.↗
NASA NTRS1979

Growth stage estimation

Of the three candidate approaches to adjustment of the crop calendar to account for year-to-year weather differences, the Robertson triquadratic unit, a function of a nonlinear function of maximum and minimum temperature and day length, best described the rate of phenological development of wheat. The adjustable crop calendar (ACC) as implemented for LACIE is used to calculate the daily increment of development through six physiological stages of growth. Topics covered include dormancy modeling, the spring restart model, spring wheat starter model, winter starter model, winter wheat starter model, inclusion of the moisture variable, and display of crop stage estimation results. Assessment of the ACC accuracy over the period of LACIE operation indicates that the adjustable crop calendars used provided more accurate information than would have been available using historical norms. The models performed best under the conditions from which they were derived (Canadian spring wheat) and most poorly for the dwarf varieties and Southern Hemisphere applications.

Whitehead, V. S.↗
NASA NTRS1979

LACIE area, yield, and production estimate characteristics: U.S.S.R.

No estimates were generated for the U.S.S.R. during LACIE phase 1. Phase 2 effort was limited to two indicator regions: winter wheat areas where 385 segments were allocated, and spring wheat areas with 362 allocated segments. The level of activity for phase 3 was extended to the entire country which automatically increased the segment workload from 747 to 1947 segments. Production, area, and yield estimates, and their accuracy are discussed for phases 2 and 3 with emphasis on scope, sampling strategy, data base, LANDSAT data, yield analysis for winter and spring wheat, area and production analysis for winter and spring wheat, and technical issues and problems.

Hickman, J. R.↗
NASA NTRS1981

Selection of the Australian indicator region

Each Australian state was examined for the availability of LANDSAT data, area, yield, and production characteristics, statistics, crop calendars, and other ancillary data. Agrophysical conditions that could influence labeling and classification accuracies were identified in connection with the highest producing states as determined from available Australian crop statistics. Based primarily on these production statistics, Western Australia and New South Wales were selected as the wheat indicator region for Australia. The general characteristics of wheat in the indicator region, with potential problems anticipated for proportion estimation are considered. The varieties of wheat, the diseases and pests common to New South Wales, and the wheat growing regions of both states are examined.

Reed, C. R.↗
NASA NTRS1984

Modeling the backscattering and transmission properties of vegetation canopies

Experimental measurements of canopy attenuation at 10.2 GHz (X-band) for canopies of wheat and soybeans, experimental observations of the effect upon the microwave backscattering coefficient (sigma) of free water in a vegetation canopy, and experimental measurements of sigma (10.2 GHz, 50 deg, VV and VH polarization) of 30 agricultural fields over the growing season of each crop are discussed. The measurements of the canopy attenuation through wheat independently determined the attenuation resulting from the wheat heads and that from the stalks. An experiment conducted to simulate the effects of rain or dew on sigma showed that sigma increases by about 3 dB as a result of spraying a vegetation canopy with water. The temporal observations of sigma for the 30 agricultural fields (10 each of wheat, corn, and soybeans) indicated fields of the same crop type exhibits similar temporal patterns. Models previously reported were tested using these multitemporal sigma data, and a new model for each crop type was developed and tested. The new models proved to be superior to the previous ones.

Allen, C. T.↗