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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]

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]

• 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]

• 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]