Sensitivity of Maize Yield Potential to Regional Climate in the Southwestern U.S.

Abstract

ABSTRACT. The sensitivity of maize yields to the regional climate in the Southwestern U.S. (SWUS)

Regional Variations of Optimal Sowing Dates of Maize for the Southwestern U.S.

“…The ApsimRegions model (Stack and Kafatos, 2013), the regionally extended version of the APSIM model, was run at a total of 958 grid points in the SWUS region, including California (CA), Nevada (NV), and Arizona (AZ) at 32 km spatial resolution over a 21-year period from 1991 to 2011 (Kim et al, 2016). The input data included the daily maximum and minimum temperatures (T max and T min , respectively), surface insolation, and precipitation from North American Regional Reanalysis (Mesinger et al, 2006; http://www.esrl.noaa.…”

“…In order to isolate the effects of climate on maize yield, all management practices except sowing date were fixed in all simulations. Detailed descriptions of the model and its calibration and validation are given by Kim et al (2016). With this model setup, maize yield is simulated without nutrient, water, and pest/disease stresses and, thus, is considered yield potential (Y p ).…”

“…A sensitivity study suggested that the effects of solar radiation on Y p are minimal during warm seasons, except in some coastal regions (Kim et al, 2016). Thus, the simulated maize yield is most sensitive to the averages and the variability of temperature during the growing season.…”

“…Understanding the impacts of temperature variations on crops is important in assessing the impacts of climate change on crop production at regional scales. Recently, actual yield (Y a ), yield potential (Y p ), and yield gap (Y p minus Y a ) parameters have been widely used to assess food security (Van Ittersum and Cassman, 2013;Kim et al, 2016). While Y a is affected by various factors (e.g., climate, water, soil type, nutrition, pest/disease, and genotypes), Y p is determined only by climate variables such as solar radiation and temperature, with non-limiting nutrients and water and controlled biotic stresses (Evans, 1993).…”

“…This may be one of the reasons why previous studies of sowing dates in the SWUS are rarely found. In cases like these, process-based crop models, such as the Agricultural Production Systems sIMulator (APSIM; Keating et al, 2003), are useful tools in evaluating the responses of agricultural systems to variations in sowing dates (e.g., Hansen and Indeje, 2004;Lee et al, 2011;Kim et al, 2016). Process-based crop models have been widely used for simulating and estimating maize Y p at various spatial ranges in the recent past (Chauhan et al, 2013;Mastrorilli et al, 2003;Lv et al, 2015).…”