Interplanetary positrons near 1 MeV from other than the pion yields muon yields electron process
Satellite detection of interplanetary positrons with higher intensity than cosmic ray meson production
Cline, T. L.↗
SEARCH · Engineering Papers
Results for “yield”
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Rates of reactions ClO + ClO yields Cl2 + O2 and ClO + O yields Cl + O2 at elevated temperatures
Results are reported for an experiment in which a shock tube was employed to measure the reaction rates of chlorine-oxygen systems at high temperatures. In this experiment, a mixture of chlorine and ozone, diluted by oxygen and argon, was heated by two consecutive shock waves (primary and reflected) to produce a temperature in the range from 1000 to 1400 K and a pressure of between 3.5 and 9 atm. The reflected shock thermally decomposed the ozone; the resulting atomic oxygen reacted with molecular chlorine to produce ClO; and the variation in ClO concentration after passage of the reflected shock was observed by monitoring the intensity of 2537-A radiation passing through the test gas. The rates of the two cited reactions are deduced from the variation pattern. The rate coefficient values are found to be compatible with existing data obtained in lower pressure and temperature ranges.
Park, C.↗
Evidence for atom exchange in OH reactions with carbonyl compounds - /O-18/H + CO2 yields /O-18/CO + OH; /O-18/H + CO yields /O-18/C + OH
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Kurylo, M. J.↗
As-built design specification for the Yield Estimation Subsystem (YES) monthly yield data base and supporting programs
There are no author-identified significant results in this report.
Cook, D.↗
Calculated potential surfaces for the reactions: O + N2 yields NO + N and N + O2 yields NO + O
Complete Active Space SCF/Contracted CI (CASSCF/CCI) calculations, using large Gaussian basis sets, are presented for selected portions of the potential surfaces for the reactions in the Zeldovich mechanism for the conversion of N2 to NO. The N + O2 reaction is exoergic by 32 kcal/mole and is computed to have an early barrier of 10.2 kcal/mole for the (sup 2)A(sup prime) surface and 18.0 kcal/mole for the (sup 4)A(sup prime) surface. The O + N2 reaction is endoergic by 75 kcal/mole. The (sup 3)A(sup double prime) surface is calculated to have a late barrier of 0.5 kcal/mole, while the (sup 3)A(sup prime) surface is calculated to have a late barrier of 14.4 kcal/mole.
Walch, Stephen P.↗
Theoretical characterization of the potential energy surface for H + O2 yields HO2(asterisk) yields HO + O. II - The potential for H atom exchange in HO2
The results of CASSCF multireference contracted CI calculations with large ANO basis sets are presented for the exchange region of the HO2 potential-energy surface. The saddle point for H atom exchange is about 13 kcal/mol below the energy of H + O2; therefore, this region of the surface should be accessible during H + O2 recombination and methathesis reactions.
Walch, Stephen P.↗
Theoretical characterization of the reaction CH3 + OH yields CH3OH yields products - The (1)CH2 + H2O, H2 + HCOH, and H2 + H2CO channels
The potential energy surface (PES) for the CH3OH system has been characterized for the (1)CH2 + H2O, H2 + HCOH, and H2 + H2CO product channels using complete-active-space self-consistent-field (CASSCF) gradient calculations to determine the stationary point geometries and frequencies followed by CASSCF/internally contracted configuration-interaction (CCI) calculations to refine the energetics. The (1)CH2 + H2O channel is found to have no barrier. The long range interaction is dominated by the dipole-dipole term, which orients the respective dipole moments parallel to each other but pointing in opposite directions. At shorter separations there is a dative bond structure in which a water lone pair donates into the empty 'a' orbital of CH2. Subsequent insertion of CH2 into an OH bond of water have barriers located at -5.2 kcal/mol and 1.7 kcal/mol, respectively, with respect to CH3 + OH. From comparison of the computed energetics of the reactants and products to known thermochemical data it is estimated that the computed PES is accurate to plus or minus 2 kcal/mol.
Walch, Stephen P.↗
A comparison of CO(J = 1 yields 0) and CO(J = 2 yields 1) emission in the Milky Way molecular ring
We have carried out a CO (J = 2 right arrow 1) survey of the Scutum Arm region of the Milky Way molecular ring. Our goals are to compare CO (J = 2 right arrow 1) maps of individual Galactic clouds with the large-scale CO (J = 2 right arrow 1) emission from the Galactic plane, and to predict the CO (J = 2 right arrow 1) appearance of a Galactic cloud ensemble in an external galaxy. The angular resolution and spatial coverage of our survey are compatible with the existing CO (J = 1 right arrow 0) survey of this region by Sanders et al., which we use for comparison. We identify 34 molecular clouds in our map region; their relationships between size and line width and between virial mass and luminosity are consistent with the relationships seen in CO (J = 1 right arrow 0) emission. However, we note that previous studies have shown considerable variation in these relationships; we attribute much of this variation to differences in their cloud definition algorithm and statistical method. We find that the median ratio of integrated emission in the two lowest CO transitions for the clouds in our sample is I(2 right arrow 1)/ I(1 right arrow 0) = 0.69, implying that the typical emitting region in the line of sight contains cold gas that is not actively star-forming. Our conclusion that the molecular ring emission is not dominated by star-forming regions is consistent with other large-scale studies of the Milky Way. Our distributions of cloud size and temperature also imply that relatively massive molecular clouds that lac k star formation, such as Maddalena's cloud, are not rare in the inner Galaxy.
Chiar, Jean E.↗
MP2 Studies of Relativistic Effects on the Linear Stationary Points of the H+Cl2 Yields HCl+Cl and Cl+HCl Yields ClH+Cl Reactions
The influence of relativistic effects on the linear stationary points of the potential energy surfaces of the ClHCl and HCl2 systems is studied. Scalar relativistic effects have little influence on the geometries and the energies of these points. Spin-orbit effects have no influence on the barrier of the forward reaction of H with Cl2, but increase the reaction energy. The total relativistic effect is an increase of the reaction energy by 2.3 kJ /mol. The barrier for the iso-energetic exchange reaction of HCl with Cl is decreased by 0.5 kJ /mol due to the scalar relativistic effects. This small decrease is cancelled by the larger increase of 2.5 kJ /mol due to spin-orbit effects, giving a total relativistic effect of 2.0 kJ /mol.
Visscher, L.↗
Methodology to Assess the Changing Risk of Yield Failure Due to Heat and Drought Stress Under Climate Change
While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.
Climate risk assessment↗
Future Climate Change Significantly Alters Interannual Wheat Yield Variability Over Half of Harvested Areas
Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.
Climate change↗
Spatial and Temporal Uncertainty of Crop Yield Aggregations
The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.
Aggregation uncertainty↗
Climate Change Impact Uncertainties for Maize in Panama: Farm Information, Climate Projections, and Yield Sensitivities
We present results from a pilot project to characterize and bound multi-disciplinary uncertainties around the assessment of maize (Zea mays) production impacts using the CERES-Maize crop model in a climate-sensitive region with a variety of farming systems (Panama). Segunda coa (autumn) maize yield in Panama currently suffers occasionally from high water stress at the end of the growing season, however under future climate conditions warmer temperatures accelerate crop maturation and elevated CO (sub 2) concentrations improve water retention. This combination reduces end-of-season water stresses and eventually leads to small mean yield gains according to median projections, although accelerated maturation reduces yields in seasons with low water stresses. Calibrations of cultivar traits, soil profile, and fertilizer amounts are most important for representing baseline yields, however sensitivity to all management factors is reduced in an assessment of future yield changes (most dramatically for fertilizers), suggesting that yield changes may be more generalizable than absolute yields. Uncertainty around General Circulation Model (GCM)s' projected changes in rainfall gain in importance throughout the century, with yield changes strongly correlated with growing season rainfall totals. Climate changes are expected to be obscured by the large inter-annual variations in Panamanian climate that will continue to be the dominant influence on seasonal maize yield into the coming decades. The relatively high (A2) and low (B1) emissions scenarios show little difference in their impact on future maize yields until the end of the century. Uncertainties related to the sensitivity of CERES-Maize to carbon dioxide concentrations have a substantial influence on projected changes, and remain a significant obstacle to climate change impacts assessment. Finally, an investigation into the potential of simple statistical yield emulators based upon key climate variables characterizes the important uncertainties behind the selection of climate change metrics and their performance against more complex process-based crop model simulations, revealing a danger in relying only on long-term mean quantities for crop impact assessment.
Maize↗
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↗
The Statistical Emulators of GGCMI Phase 2: Responses of Year-to-Year Variation of Crop Yield to CO 2 , Temperature, Water and Nitrogen Perturbations
Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO 2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO 2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.
Climate change↗
Pressure-Dependent Yields and Product Branching Ratios in the Broadband Photolysis of Chlorine Nitrate
The photolysis of chlorine nitrate was studied using broadband flash photolysis coupled with long-path ultraviolet-visible absorption spectroscopy. Branching ratios for the Cl + NO3 and ClO + NO2 product channels were determined from time-dependent measurements of ClO and NO3 concentrations. Yields of the ClO and NO3 products displayed a dependence on the bath gas density and the spectral distribution of the photolysis pulse. Product yields decreased with increasing bath gas density regardless of the spectral distribution of the photolysis pulse; however, the decrease in product yield was much more pronounced when photolysis was limited to longer wavelengths. For photolysis in a quartz cell (lambda > 200 nm) the yield decreased by a factor of 2 over the pressure 10-100 Torr. In a Pyrex cell (lambda > 300 nm), the yield decreased by a factor of 50 over the same pressure range. When photolysis was limited to lambda > 350 nm, the yield decreased by a factor of 250. Branching ratios for the photolysis channels [ClONO2 + h.nu yields ClO + NO2 (1a) and ClONO2 + h.nu yields Cl + NO3 (lb)] were determined from the relative ClO and NO3 product yields at various pressures. Although the absolute product yield displayed a pressure dependence, the branching between the two channels was independent of pressure. The relative branching ratios (assuming negligible contributions from other channels) are 0.61 +/- 0.20 for channel 1a and 0.39 +/- 0.20 for channel lb for photolysis with lambda > 200 nm and 0.44 +/- 0.08 for channel 1a and 0.56 +/- 0.08 for channel 1b for photolysis with lambda > 300 nm. The implications of these results for the chemistry of the lower stratosphere are discussed.
Nickolaisen, Scott L.↗
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↗
Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery
Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.
agriculture; yield; within-field; corn; soybean; r↗