Vulnerability of grain crops and croplands in the Midwest to climatic variability and adaptation strategies

Abstract

Maize (Zea mays L.) and soybean (Glycine max (L.) Merr.) are the dominant grain crops across the Midwest and are grown on 75% of the arable land with small but economically important crops of wheat (Triticum aestivum L.) and oats (Avena sativa L.) but economically important crops. Historically, there have been variations in annual yields for maize and soybean related to the seasonal weather patterns. Key concerns are the impacts of future climate change on maize and soybean production and their vulnerability to future climate changes. To evaluate these, we analyzed the yield gaps as the difference between the attainable and actual yield at the county level and observed meteorological data to determine which seasonal meteorological variables were dominant in quantifying the actual/attainable yields. July maximum temperatures, August minimum temperatures, and July-August total precipitation were found to be the significant factors affecting the yield gap. These relationships were used to estimate the change in the yield gap through 2100 using both the RCP 4.5 and 8.5 climate scenarios for these variables for selected counties across the Midwest. Yield gaps increased with time for maize Climatic Change (2018) 146:263-275 DOI 10.1007/s10584-017-1997 Contribution from the USDA-ARS and Midwest Climate Hub. Soybean was not as sensitive as maize because the projected temperatures do not exceed optimum temperature ranges for growth and reductions in production that are more sensitive to precipitation changes during the reproductive stages. Adaptation strategies for maize and soybean will require more innovation than simple agronomic management and require the linkage between geneticists, agronomists, and agricultural meteorologists to develop innovative strategies to preserve production in the Midwest.

Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA

“…While earlier PD have been known to yield more, there is the expectation that too early of a PD increases risk due to killing frost or poor soil conditions (Anderson & Vasilas, ; De Bruin & Pedersen, ; De Bruin & Pedersen, ; Robinson, Conley, Volenec, & Santini, ). Our study was focused on a range from average to late planting dates, with the increasing climatic variability, frequent delays in planting are observed across Iowa and the US Midwest (Hamlet, Byun, Robeson, Widhalm, & Baldwin, ; Hatfield et al., ; Kistner et al., ). In four of the seven sites we found an optimum, and in these cases, the optimum was from 1 May to 20 May (DOY 120 to 140).…”

Breeding for resilience to increasing temperatures: A field trial assessing genetic variation in soft red winter wheat

“…An example is the predicted decline of species that control crop pests that will impact agriculture in Europe (Pecl et al., ). Further, Hatfield, Wright‐Morton, and Hall () argue that new synergies between agronomists, geneticists, and agricultural meteorologists must be fashioned if the midwestern United States is to maintain a robust grain production system. Beyond these big picture concerns, at the level of focus of the individual wheat breeder, while it is likely that a warmer climate will result in lower wheat yields (Asseng, Foster, & Turners, ; Zhao et al., ), it is not clear which traits should be the object of a breeding focus.…”

Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity

“…6). While previous studies on vulnerability to climate change in the midwestern United States have focused primarily on mapping changes in projected corn and soybean yield changes, (58,59) this is the first study to (1) examine vulnerability to extreme rain events using both perceived and objective indicators of capacity and (2) map these vulnerabilities for a large part of the upper-midwestern United States. Future research should examine how this study's vulnerability indicators (county level) compare to measures of vulnerability (e.g., yield gap) found in other studies.…”