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NASA NTRS2018

Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2>0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.

GSOD↗
NASA NTRS2019

Evaluation of an Alternate Method for Determining Yield Strength Offset Values for Selective Laser Sintered Polymeric Materials

Due to the unique characteristics of Additively Manufactured (AM) polymeric materials, typical mechanical strength characterization methods such as those commonly used for traditionally-processed polymers or composite materials can produce results that do not accurately represent material capabilities. In order to characterize mechanical properties of these materials, new test and analysis methods are required. As part of the National Aeronautics and Space Administration (NASA) Advanced Composites Project (ACP), Boeing has evaluated true yield testing as an alternative or complimentary test to 0.2% offset yield testing for determining appropriate yield strength values of polymer materials. Previous testing has shown high strain, low modulus polymer materials such as selective laser sintered (SLS) Nylon 11 at elevated temperatures produce large variations in yield strength. The true yield test method was successful in finding the applied strain level when yield commences and appears to offer an increase in data robustness.

Henry, Chelsey↗
NASA NTRS2024

Investigating the Strength and Variability of El Niño Southern Oscillation Teleconnections to Hydroclimate and Maize Yields in Southern and East Africa

The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.

El Niño Southern Oscillation↗
NASA NTRS2016

Monitoring Interannual Variation in Global Crop Yield Using Long-Term AVHRR and MODIS Observations

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05deg (approx.5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Crop phenology↗
NASA NTRS2016

Yield and Failure Behavior Investigated for Cross-Linked Phenolic Resins Using Molecular Dynamics

Molecular dynamics simulations were conducted to fundamentally evaluate the yield and failure behavior of cross-linked phenolic resins at temperatures below the glass transition. Yield stress was investigated at various temperatures, strain rates, and degrees of cross-linking. The onset of non-linear behavior in the cross-linked phenolic structures was caused by localized irreversible molecular rearrangements through the rotation of methylene linkers followed by the formation or annihilation of neighboring hydrogen bonds. The yield stress results, with respect to temperature and strain rate, could be fit by existing models used to describe yield behavior of amorphous glasses. The degree of cross-linking only indirectly influences the maximum yield stress through its influence on glass transition temperature (Tg), however there is a strong relationship between the degree of cross-linking and the failure mechanism. Low cross-linked samples were able to separate through void formation, whereas the highly cross-linked structures exhibited bond scission.

Molecular dynamics↗
NASA NTRS2016

Yield and Failure Behavior Investigated for Cross-linked Phenolic Resins Using Molecular Dynamics

Molecular dynamics simulations were conducted to fundamentally evaluate the yield and failure behavior of cross-linked phenolic resins at temperatures below the glass transition. Yield stress was investigated at various temperatures, strain rates, and degrees of cross-linking. The onset of non-linear behavior in the cross-linked phenolic structures was caused by localized irreversible molecular rearrangements through the rotation of methylene linkers followed by the formation or annihilation of neighboring hydrogen bonds. The yield stress results, with respect to temperature and strain rate, could be fit by existing models used to describe yield behavior of amorphous glasses. The degree of cross-linking only indirectly influences the maximum yield stress through its influence on the glass transition temperature (Tg), however there is a strong relationship between the degree of cross-linking and the failure mechanism. Low cross-linked samples were able to separate through void formation, whereas the highly cross-linked structures exhibited bond scission.

Stress-strain curves↗
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100 percent of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (tonnes per hectare) (15-18 percent). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7 percent) while for Ukraine it is 0.27 t/ha (8.4 percent).

DVI↗
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).

remote sensing↗
NASA NTRS2024

Quantifying Food Security and Mitigation Risks Consequential to Climate Change Impacts on Crop Yields

Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

crop yields↗
NASA NTRS1974

Remote sensing techniques for mapping range sites and estimating range yield

Image interpretation procedures for determining range yield and for extrapolating range information were investigated for an area of the Pine Ridge Indian Reservation in southwestern South Dakota. Soil and vegetative data collected in the field utilizing a grid sampling design and digital film data from color infrared film and black and white films were analyzed statistically using correlation and regression techniques. The pattern recognition techniques used were K-class, mode seeking, and thresholding. The herbage yield equation derived for the detailed test site was used to predict yield for an adjacent similar field. The herbage yield estimate for the adjacent field was 1744 lbs. of dry matter per acre and was favorably compared to the mean yield of 1830 lbs. of dry matter per acre based upon ground observations. Also an inverse relationship was observed between vegetative cover and the ratio of MSS 5 to MSS 7 of ERTS-1 imagery.

Benson, L. A.↗
NASA NTRS1982

Evaluation of the CEAS trend and monthly weather data models for soybean yields in Iowa, Illinois, and Indiana

The CEAS models evaluated use historic trend and meteorological and agroclimatic variables to forecast soybean yields in Iowa, Illinois, and Indiana. Indicators of yield reliability and current measures of modeled yield reliability were obtained from bootstrap tests on the end of season models. Indicators of yield reliability show that the state models are consistently better than the crop reporting district (CRD) models. One CRD model is especially poor. At the state level, the bias of each model is less than one half quintal/hectare. The standard deviation is between one and two quintals/hectare. The models are adequate in terms of coverage and are to a certain extent consistent with scientific knowledge. Timely yield estimates can be made during the growing season using truncated models. The models are easy to understand and use and are not costly to operate. Other than the specification of values used to determine evapotranspiration, the models are objective. Because the method of variable selection used in the model development is adequately documented, no evaluation can be made of the objectivity and cost of redevelopment of the model.

French, V.↗
NASA NTRS1982

Evaluation of Thompson-type trend and monthly weather data models for corn yields in Iowa, Illinois, and Indiana

An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.

French, V.↗
NASA NTRS1982

The use of large-area spectral data in wheat yield estimation

Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.

Barnett, T. L.↗
NASA NTRS1983

The dissociative recombination of O2(+) - The quantum yield of O(1S) and O(1D)

Data from the visible airglow experiment on the Atmosphere Explorer-E satellite have been used to determine the quantum yield of O(1S) and O(1D) from the dissociative recombination of O2(+). A range of values between 0.09 and 0.23 has been obtained for the quantum yield of O(1S). It is shown that the quantum yield of O(1S) depends on the ratio of electron density to atomic oxygen density. This suggests that the quantum yield of O(1S) may depend on the degree of vibrational excitation of the recombining O2(+). The quantum yield of O(1D) has been measured to be 1.23 + or - 0.42, with no dependence on the electron-oxygen ratio.

Abreu, V. J.↗
NASA NTRS1983

Large-area relation of Landsat MSS and NOAA-6 AVHRR spectral data to wheat yields

Landsat MSS data transformed into Kauth-Thomas greenness were averaged over 5 n.mi x 6 n.mi. sample segments from the U.S. Great Plains winter and spring wheat (Triticum aestivum) regions, and related by regression analysis to yields reported by county, crop reporting district (CRD) and state levels. Evidence of a linear relation between winter- and spring-wheat yields and Landsat spectral data at a broad scale is shown for 1978 and 1979. A common slope of about 1.6 (Bu/A)/unit greenness is discerned for the relation between yield and spectral greenness. Tests at both a smaller scale on sets of field-level spectal data and yield and at a large scale on 25 mi. x 25 mi. gridded spectral data from the NOAA-6 AVHRR sensor support the relation. The implications of these results to yield estimation from satellite spectral data are discussed.

Barnett, T. L.↗
NASA NTRS1984

Carbon and energy yields in prebiotic syntheses using atmospheres containing CH4, CO and CO2

Yields based on carbon are usually reported in prebiotic experiments, while energy yields (moles/cal) are more useful in estimating the yields of products that would have been obtained from the primitive atmosphere of the earth. Energy yields for the synthesis of HCN and H2CO from a spark discharge were determined for various mixtures of CH4, CO, CO2, H2, H2O, N2 an NH3. The maximum yields of HCN and H2CO from CH4, CO, and CO2 as carbon sources are about 4 x 10 to the -8th moles/cal.

Miller, S. L.↗
NASA NTRS1984

Arguments for fundamental emission by the parametric process L yields T + S in interplanetary type III bursts

Observations of low frequency ion acoustic-like waves associated with Langmuir waves present during interplanetary Type 3 bursts are used to study plasma emission mechanisms and wave processes involving ion acoustic waves. It is shown that the observed wave frequency characteristics are consistent with the processes L yields T + S (where L = Langmuir waves, T = electromagnetic waves, S = ion acoustic waves) and L yields L' + S proceeding. The usual incoherent (random phase) version of the process L yields T + S cannot explain the observed wave production time scale. The clumpy nature of the observed Langmuir waves is vital to the theory of IP Type 3 bursts. The incoherent process L yields T + S may encounter difficulties explaining the observed Type 3 brightness temperatures when Langmuir wave clumps are incorporated into the theory. The parametric process L yields T + S may be the important emission process for the fundamental radiation of interplanetary Type 3 bursts.

Cairns, I. H.↗
NASA NTRS1986

Energy yields in the prebiotic synthesis of hydrogen cyanide and formaldehyde

Prebiotic experiments are usually reported in terms of carbon yields, i.e., the yield of product based on the total carbon in the system. These experiments usually involve a large input of energy and are designed to maximize the yields of product. However, large inputs of energy result in multiple activation of the reactants and products. A more realistic prebiotic experiment is to remove the products of the activation step so they are not exposed a second time to the energy source. This is equivalent to transporting the products synthesized in the primitive atmosphere to the ocean, and thereby protecting them from destruction by atmospheric energy sources. Experiments of this type, using lower inputs of energy, give energy yields (moles of products/joule) which can be used to estimate the relative importance of the different energy sources on the primitive earth. Simulated prebiotic atmospheres containing either CH4, CO or CO2 with N2, H2O and variable amounts of H2 were subjected to a high frequency Tesla coil. Samples of the aqueous phase were taken at various time intervals from 1 hr to 7 days, and the energy yields were obtained by extrapolation to zero time. The samples were analyzed for HCN with the cyanide electrode and for H2CO by chromotropic acid. The spark energy was estimated by calorimetry. The temperature rise in an insulated discharge flask was compared with the temperature rise from a resistance heater in the same flask. These results will be compared with calculated production rates of HCN and H2CO from lightning and a number of photochemical processes on the primitive Earth.

Stribling, R.↗