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Future agricultural production is influenced by climate change and changes in policies and behavior, such as the proliferation of battery electric vehicles (BEV). For the United States (U.S.), the influence of the latter is more pronounced due to the size of the U.S. biofuel industry. Using a global agricultural trade model and different climate change pathways until 2050, we show that global commodity price increases triggered by declining yields due to climate change are dampened by an accelerated increase of U.S. BEV sales, which decrease maize ethanol demand. Accelerated BEV sales also reduce cropland requirements compared to baseline electrification scenarios. The accelerated market share of BEVs also (1) lowers the decrease in caloric intake for maize, rice, and wheat which has important food security implications in the presence of climate change and (2) changes trade relationships. The implications of those findings are that policy discussions surrounding policies to promote BEVs should include lower commodity prices and increased food security that dampen some of the negative effects of climate change. Those are additional benefits besides the direct emissions reduction (assuming low-or no-carbon electricity production) from reducing vehicles with internal combustion engines. [ABSTRACT FROM AUTHOR]

Public stocks held by government have emerged as a food security issue as well as an issue in the Doha Round of World Trade Organization talks. Understanding the impact of public stocks requires understanding their crowding out effect on private stocks. A conceptual model of this crowding out effect is developed. It utilises a call option associated with the release of public stocks. The model reveals that the crowding out effect on private stocks decreases as public stocks increase, in contrast to constant marginal crowding out reported by earlier studies. Crowding out of private stocks is also a function of the commodity's demand function, implying crowding out can vary by commodity. It is likely to be highest for commodities with the most inelastic demand. These commodities include wheat, rice, and other food staples often held as public stocks. Empirical analysis confirms these and other insights from the conceptual model. [ABSTRACT FROM AUTHOR]

This study analyzes forecasts of U.S. ending stocks for corn, soybeans, and wheat issued by the USDA. The proposed efficiency tests focus on forecast revisions. Forecast errors are decomposed into monthly unforecastable shocks and idiosyncratic residuals. The error covariance matrix allows for heteroscedasticity and auto-correlations. Results suggest that the USDA forecasts are inefficient, providing strong evidence that the USDA is conservative in forecasting the ending stocks. Unforecastable shocks are heteroscedastic, and idiosyncratic residuals are small. Results are consistent across the three decades analyzed, but soybean forecasts are found to be considerably worse from 2005 to 2015. [ABSTRACT FROM AUTHOR]

Farm-level environmental impact assessments for U.S. agriculture exist, but results are often not comparable because of different methodologies used. The objective of this research was to estimate the greenhouse gas emissions (GHG) levels for multicrop farms in the United States. A partial life cycle assessment (LCA) for 47 representative grain farms was performed, showing heterogeneous emissions across farm size, crops, and regions. Emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were estimated and transformed into CO2equivalents (CO2e) per hectare and kilogram of production. The GHG emission intensity was affected by three characteristics: location, farm size, and cropping system. When crops were grown in different regions, they had different GHG intensities because of differences in soil quality, yields, and production efficiency. Emissions tended to be less per hectare and ton of the crop on larger farms. Crops grown outside their principal production regions were often more GHG-intensive than the same crops in their principal growing regions. The results of this study suggested that there was a correlation between production efficiency and GHG efficiency with higher yielding and/or larger operations having reduced emissions per unit of output. [ABSTRACT FROM AUTHOR]

Highlights: [•] Corn and pea ahead of wheat did not increase wheat WUE in the central Great Plains, USA. [•] Corn ahead of proso millet did not increase proso millet WUE. [•] Dryland wheat yield was not increased with corn included in a rotation. [•] Growing pea ahead of wheat decreased wheat yield. [•] Growing corn ahead of proso millet decreased millet yield. [ABSTRACT FROM AUTHOR]

A limited battle involving the nuclear arsenals of India and Pakistan would have significant climatic impacts upon agricultural crop production in the United States; corn, soybeans, and winter wheat yields would be significantly reduced in the Corn Belt region of the US. The most severe impacts would occur during the second year after the modeled nuclear battle. [ABSTRACT FROM AUTHOR]

Abstract: Saflufenacil is a new herbicide being developed for pre-plant burndown and pre-emergence broadleaf weed control in field crops, including maize, soybean, sorghum and wheat. Field experiments were conducted in 2007 and 2008 in northeast Nebraska, USA to describe dose-response curves of saflufenacil applied in mid (early post-emergence, EPOST) and late May (late post-emergence, LPOST) with several adjuvants for spring emerging broadleaf weed control. Dose-response curves based on log-logistic model were utilized to determine the ED90 values (effective dose that provides 90% weed control efficacy) for Convolvulus arvensis, Lactuca serriola, Lamium amplexicaule, Capsella bursa-pastoris, Taraxacum officinale, Thlaspi arvense and Conyza canadensis. In general, weed control efficacy was influenced by application timing and type of adjuvant. EPOST application of saflufenacil resulted better efficacy of saflufenacil compared to LPOST. Addition of spray adjuvants improved efficacy of saflufenacil. For example, 90% control of T. arvense was obtained at EPOST application with 251, 161, 96 and 59ga.i.ha−1 for saflufenacil applied alone, or tank-mixed with NIS (nonionic surfactant), COC (crop oil concentrate), or MSO (methylated seed oil), respectively. In contrast, the ED90 values with LPOST application were 333, 201, 127 and 79ga.i.ha−1 for saflufenacil applied alone, or with NIS, COC and MSO, respectively. MSO was the best adjuvant, which provided the most enhancement of saflufenacil. COC was the second best, or similar to MSO, on many weed species. NIS provided the least enhancement of saflufenacil. The ED90 values determined for different broadleaf weed species are within the proposed label dose of saflufenacil. [Copyright &y& Elsevier]

Contamination of maize grain, Zea mays L., with aflatoxin, a toxin produced by the fungus Aspergillus flavus, reduces its value and marketability. Growing hybrids with resistance is generally considered a highly desirable way to reduce A. flavus infection and aflatoxin accumulation. Identifying maize germplasm with resistance is critical to the development and production of such hybrids. USDA-ARS scientists at Mississippi State, Mississippi; Tifton, Georgia; and Raleigh, North Carolina; have engaged in a multilocation approach to germplasm screening. A major component of this has been the evaluation of accessions obtained from the Germplasm Enhancement of Maize (GEM) project at both Mississippi State and Tifton. Selections from GEM accessions 250_01_XL370A_S11_F2S4_9214_Blk21/00-# and 2250_02_XL370A_S11_F2S4_3363_Blk03/00-# exhibited the highest levels of resistance both as lines per se and in testcrosses. Lines developed at the International Maize and Wheat Improvement Center (CIMMYT) and North Carolina State University also exhibited reduced levels of aflatoxin contamination. CML348, NC388, NC400, NC408, and NC458 were among those with low levels of aflatoxin contamination. The lines that displayed low levels of contamination should be useful in maize breeding programs for developing parental inbred lines and aflatoxin-resistant maize hybrids. [ABSTRACT FROM AUTHOR]

Recent studies into price transmission have recognized the important role played by transport and transaction costs. Threshold models are one approach to accommodate such costs. We develop a generalized Threshold Error Correction Model to test for the presence and form of threshold behavior in price transmission that is symmetric around equilibrium. We use monthly wheat, maize, and soya prices from the United States, Argentina, and Brazil to demonstrate this model. Classical estimation of these generalized models can present challenges but Bayesian techniques avoid many of these problems. Evidence for thresholds is found in three of the five commodity price pairs investigated. [ABSTRACT FROM AUTHOR]

Abstract Remote sensing of net primary production (NPP) is a critical tool for assessing spatial and temporal patterns of carbon exchange between the atmosphere and biosphere. However, satellite estimates suffer from a lack of large-scale field data needed for validation, as well as the need to parameterize plant light-use efficiencies (LUEs). In this study, we estimated cropland NPP with the Carnegie-Ames-Stanford-Approach (CASA), a biogeochemical model driven by satellite observations, and then compared these results with field estimates based on harvest data from United States Department of Agriculture National Agriculture Statistics Service (NASS) county statistics. Observed interannual variations in NPP over a 17-year period were well modelled by CASA, with exceptions mainly due to occasional difficulties in estimating NPP from harvest yields. The role of environmental stressors in agriculture was investigated by running CASA with and without temperature and moisture down-regulators, which are used in the model to simulate climate impacts on plant LUE. In most cases, correlations with NASS data were highest with modelled stresses, while the opposite was true for irrigated and temperature resistant crops. Analysis of the spatial variability in computed LUE revealed significantly higher values for corn than for other crops, suggesting a simple parameterization of LUE for future studies based on the fraction of area with corn. Absolute values of LUE were much lower than those reported in field trials, due to uncommonly high yields in most field trials, as well as overestimates of absorbed radiation in CASA attributed to bias from temporal compositing of satellite data. Total NPP for US croplands, excluding Alaska and Hawaii, was estimated as 0.62 Pg C year-1 , representing ∼20% of total US NPP, and exhibited a positive trend of 3.7 Tg C year-2 . These results have several implications for large-scale carbon cycle research that are... [ABSTRACT FROM AUTHOR]

• We determined frequency of environment types (ENVT) for US maize, soybean, and wheat. • Each ENVT corresponded to a combination of drought and heat stress during critical period. • Inter-annual variation in drought and heat stress was higher than spatial variation. • The ENVT scheme explained observed variation in average actual yield for the three crops. • ENVTs can be used to link field-year experiments with target production environments. The impact of agricultural technologies on crop yield is influenced by the environment type (ENVT) as determined by weather and soil. Understanding the correlation between the ENVT of the testing site in relation to the ENVT of the target production region is important for the evaluation and scaling out of agricultural technologies. Here we propose and apply the first explicit method to characterize ENVTs for major rainfed maize, soybean, and wheat producing regions in the United States. We combined a tested spatial framework, Technology Extrapolation Domain (TED), with crop modeling, long-term (30-y) daily weather records, and soil and management databases to calculate the frequency of ENVTs per crop for major harvested areas. Each ENVT was determined based on the intensity of drought and heat stress during key crop stages for yield determination. The ENVT repeatability was calculated based on the frequency of the most dominant ENVT in each TED. We found that inter-annual variation in drought and heat stress was larger than spatial variation. Our ENVTs explained 2x to 7x larger portion of the variance in actual yield compared to the existing TED framework that is based on long-term annual climate means and soil water storage. For maize and soybean, ca. 30% of their harvested area was located in TEDs with highly repeatable ENVTs (>66% of years). In contrast, only 15% of the wheat harvested area was located in TEDs with high ENVT repeatability. In comparison to the TED framework, the ENVTs defined here can help better capture G×E×M interactions and determine the environmental correlation between testing sites and target production environments. [ABSTRACT FROM AUTHOR]

A random field model, consisting of a regional trend component and a local interaction component, describes the spatial pattern of corn and wheat yields in the High Plains. The presence of spatial autocorrelation in the local component raises interesting statistical estimation problems. Temporal variations in the slope and interaction parameters are a function of climatic fluctuations. The local component reflects intercounty spatial autocorrelation in precipitation patterns. [ABSTRACT FROM AUTHOR]

• Soil moisture has the potential to be used as a measurement of the resiliency. • We examined the applicability of four soil moisture metrics to measure resiliency. • These metrics helped with identifying promising treatments to address resiliency. • No-till and reduced input treatments were the most and least promising. Measuring and improving resiliency is the key goal of climate risk management in rainfed agriculture. Currently, available metrics are generally used to qualitatively measure the resilience of agricultural systems at broader scales. Moreover, these metrics showed non-linear responses to climate variability, thus often fail to capture the temporal dynamics of resilience at field scales. Our objective for this study is to combine a few soil moisture-based metrics to gauge the resiliency of three promising rainfed agricultural treatments, namely the no-till, organic, and reduced input treatments against the conventional treatment. These treatments have been established at the Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) experiment in a randomized complete block design with six blocks per treatment. All four of these treatments consisted of maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum aestivum) in rotation. Long-term (1993–2018) soil moisture data from this experiment was collected to compute the soil moisture metrics while the total crop biomass, crop yield, and soil organic carbon data were statistically analyzed to evaluate the robustness of the metrics to gauge the resiliency of these systems. Results have shown that, among the soil moisture metrics, the mean relative difference, the Spearman's rank correlation coefficient, and soil water deficit index were suitable, while the index of temporal stability was not suitable to gauge the resiliency of different rainfed agricultural systems. The no-till treatment was identified as the most resilient treatment in terms of soil moisture retention, effectiveness for drought mitigation, and crop yields. Meanwhile, the reduced input treatment was the least resilient in terms of soil water conservation and drought recovery. The results of this study can be extended to other Midwest regions of the United States and similar climatological areas around the world. [ABSTRACT FROM AUTHOR]

• Quantified irrigation's role in stabilizing U.S. agricultural yields. • Irrigation-Induced Reduction in Yield Variability (IIRYV) computed for nine crops. • Derived from 220,000 historical records, mean U.S IIRYV was 41 % (range: 0–90 %). • Maize and cotton demonstrated the highest and lowest IIRYV. • IIRYV increased during 1950–2015 for most crops, albeit crop and space variation. Irrigation contributes to enhance and sustain agricultural production in the U.S. across all aridity regimes, via mitigation against interannual environmental variability. Currently, a quantitative understanding of the role of irrigation in stabilizing agricultural yields is lacking, limiting the realization of irrigation-adoption impacts. Here we use >220,000 historical (1950–2015) county-year irrigated and rainfed yield records for the top-nine U.S. crops [maize (Zea mays L.), soybean (Glycine max (L.) Merr.), spring wheat (Triticum aestivum L.), winter wheat (Triticum aestivum L.), alfalfa (Medicago sativa L.), sorghum (Sorghum bicolor (L.) Moench), cotton (Gossypium hirsutum), barley (Hordeum vulgare L.) and oats (Avena sativa)]. To comprehensively quantify the "Irrigation-Induced Reduction in Crop Yield Variability" (IIRYV, defined as the percent reduction in crop yield variability when irrigated with respect to rainfed yield variability). Averaged across all crops, national-level IIRYV was 41 %, which varied from 0 to 90 % across various regions and crops. IIRYV was highly crop-specific, and maize and cotton crops demonstrated the highest and least magnitudes. IIRYV substantially varied spatially within the constituent growing regions for each crop, and thus national scale assessments masked significant spatial differences. IIRYV was subject to interannual (temporal) variability; however, in general, IIRYV increased over time (1950–2015) for most crops. These findings will help in the evaluation of the contribution of historical irrigation development policy and adoption in the U.S in mitigating external shocks (variability) in U.S. agricultural productivity. The demonstration of spatial and temporal dynamics in IIRYV will aid in irrigation-water allocations and adoption by prioritizing irrigation development for vulnerable crops and regions. [ABSTRACT FROM AUTHOR]

The article discusses the history of the establishment of the Northern Regional Research Center (NRRC) in Peoria, Illinois. Topics covered include its formation in December 1940. Also mentioned is the facility's focus on developing new industrial methods and value-added products from the surpluses of wheat as well as corn in the Midwest area of the U.S.