Climate change is expected to increase future temperatures, potentially resulting in reduced crop production in many key production regions. Research quantifying the complex relationship between weather variables and wheat yields is rapidly growing, and recent advances have used a variety of model specifications that differ in how temperature data are included in the statistical yield equation. A unique data set that combines Kansas wheat variety field trial outcomes for 1985-2013 with location-specific weather data is used to analyze the effect of weather on wheat yield using regression analysis. Our results indicate that the effect of temperature exposure varies across the September−May growing season. The largest drivers of yield loss are freezing temperatures in the Fall and extreme heat events in the Spring. We also find that the overall effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Our analysis indicates that there exists a tradeoff between average (mean) yield and ability to resist extreme heat across varieties. More-recently released varieties are less able to resist heat than older lines. Our results also indicate that warming effects would be partially offset by increased rainfall in the Spring. Finally, we find that the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts on yields.agriculture | climate change | global warming | wheat | yield T he potential impact of global warming and climate change on socioeconomic outcomes has become an important and growing area of scientific study and evaluation. Separate lines of study include quantifying the likely impact of climatic change on measures of civil conflict (1-5) and agricultural land values, profitability, and/or production efficiency (6-22). Both lines of literature continue to measure, discuss, and debate the effects of warming temperature. An issue that has received much attention in both sets of literature is how best to quantify exposure to extreme temperatures. This is an important concern, as many studies rely on historical spatial and temporal variations in weather outcomes to identify the effects of weather extremes. If these historical extremes are not measured correctly, estimates of their impacts will not be credibly identified, thereby raising doubts regarding any climate change projections based on these impacts.Here we use regression analysis to estimate wheat yields as a function of observed weather variables and forecast yield impacts under a variety of weather scenarios. Our main findings are as follows. First, the effect of temperature exposure varies across the September−May growing season, with the biggest drivers of yield loss being freezing temperatures in the Fall and extreme heat in the Spring. Second, the net effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Thir...

Historical adaptation of sorghum production to arid and semiarid conditions has provided promise regarding its sustained productivity under future warming scenarios. Using Kansas field-trial sorghum data collected from 1985 to 2014 and spanning 408 hybrid cultivars, we show that sorghum productivity under increasing warming scenarios breaks down. Through extensive regression modeling, we identify a temperature threshold of 33°C, beyond which yields start to decline. We show that this decline is robust across both field-trial and on-farm data. Moderate and higher warming scenarios of 2°C and 4°C resulted in roughly 17% and 44% yield reductions, respectively. The average reduction across warming scenarios from 1 to 5°C is 10% per degree Celsius. Breeding efforts over the last few decades have developed high-yielding cultivars with considerable variability in heat resilience, but even the most tolerant cultivars did not offer much resilience to warming temperatures. This outcome points to two concerns regarding adaption to global warming, the first being that adaptation will not be as simple as producers' switching among currently available cultivars and the second being that there is currently narrow genetic diversity for heat resilience in US breeding programs. Using observed flowering dates and disaggregating heat-stress impacts, both pre-and postflowering stages were identified to be equally important for overall yields. These findings suggest the adaptation potential for sorghum under climate change would be greatly facilitated by introducing wider genetic diversity for heat resilience into ongoing breeding programs, and that there should be additional efforts to improve resilience during the preflowering phase.agriculture | climate change | crop | sorghum | warming

This article proposes the use of moment functions and maximum entropy techniques as a flexible approach for estimating conditional crop yield distributions. We present a moment‐based model that extends previous approaches, and is easily estimated using standard econometric estimators. Predicted moments under alternative regimes are used as constraints in a maximum entropy framework to analyze the distributional impacts of switching regimes. An empirical application for Arkansas, Mississippi, and Texas upland cotton demonstrates how climate and irrigation affect the shape of the yield distribution, and allows us to illustrate several advantages of our moment‐based maximum entropy approach.

Temperature increases due to climate change are expected to cause substantial reductions in global wheat yields. However, uncertainty remains regarding the potential role for irrigation as an adaptation strategy to offset heat impacts. Here we utilize over 7000 observations spanning eleven Kansas field-trial locations, 180 varieties, and 29 years to show that irrigation significantly reduces the negative impact of warming temperatures on winter wheat yields. Dryland wheat yields are estimated to decrease about eight percent for every one-degree Celsius increase in temperature, yet irrigation completely offsets this negative impact in our sample. As in previous studies, we find that important interactions exist between heat stress and precipitation for dryland production. Here, uniquely, we observe both dryland and irrigated trials side-by-side at the same locations and find that precipitation does not provide the same reduction in heat stress as irrigation. This is likely to be because the timing, intensity, and volume of water applications influence wheat yields, so the ability to irrigate-rather than relying on rainfall alone-has a stronger influence on heat stress. We find evidence of extensive differences of water-deficit stress impacts across varieties. This provides some evidence of the potential for adapting to hotter and drier climate conditions using optimal variety selection. Overall, our results highlight the critical role of water management for future global food security. Water scarcity not only reduces crop yields through water-deficit stress, but also amplifies the negative effects of warming temperatures.

Understanding extreme weather impacts on staple crops such as wheat is vital for creating adaptation strategies and increasing food security, especially in dryland cropping systems across Southern Africa. This study analyses heat impacts on wheat using daily weather information and a dryland wheat dataset for 71 cultivars across 17 locations in South Africa from 1998 to 2014. We estimate temperature impacts on yields in extensive regression models, finding that extreme heat drives wheat yield losses, with an additional 24 h of exposure to temperatures above 30°C associated with a 12.5% yield reduction. Results from a uniform warming scenario of +1°C show an average wheat yield reduction of 8.5%, which increases to 18.4% and 28.5% under +2 and +3°C scenarios. We also find evidence of differences in heat effects across cultivars, which suggests warming impacts may be reduced through the sharing of gene pools amongst wheat breeding programs.