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spring freezes (Carter, 1995;Kunkel and Hollinger, 1995), and excessive precipitation and flooding during Weather and climate have had major influences on crop production the growing season (Kunkel et al., 1994). in the Upper Great Lakes states of Michigan, Minnesota, and Wiscon-Analyses of the impact of weather and climate on sin during the past century. However, isolation of the impact of weather is made difficult by the confounding effects of technological agriculture for extended time periods have been freimprovements in agriculture, which have resulted in significant grain quently constrained by the lack of quality long-term yield increases. The objective of this study was to identify climatologiclimatological data series and the limited number of cal impacts involved with the production of three crops commonly experimental treatment combinations available from grown in the region-alfalfa (Medicago sativa L.), maize (Zea mays field experiments. In addition, it is difficult to isolate L.), and soybean [Glycine max (L.) Merr.]-without the influence of the impact of weather due to the confounding effects technology, and trends of relevant agroclimatological variables during of technological improvements in agriculture (e.g., imthe period 1895-1996. The models DAFOSYM, CERES-Maize, and proved varieties, increased rates of fertilization), which SOYGRO models were used to simulate crop growth, development, have resulted in significant yield increases during the and yield of the three crops, respectively. Regionally, low precipitation past century (Thompson, 1986). An alternative strategy and moisture stress were chief limitations to simulated crop yields. is the use of crop simulation models that are based on Simulated maize and soybean yield series were found to increase with time an average of 11.4 kg ha Ϫ1 yr Ϫ1 and 4.9 kg ha Ϫ1 yr Ϫ1 , respectively, the underlying physiological processes governing plant across the study sites during the study period. These increases were growth and development. Such models, if properly caliassociated with average study period increases in total seasonal precipbrated and tested, allow a user to easily investigate the itation of 0.4 mm yr Ϫ1 and decreased total seasonal potential evapoeffects of individual input variables by holding all others transpiration of 0.2 mm yr Ϫ1 . No consistent trends were found for constant and provide a more convenient, less expensive alfalfa. The simulated yield results support previous research identitool than long-term field research in the evaluation of fying a period of benign climate, which favored crop production in crop response to environmental and management facthe region from 1954 to 1973, and was preceded and followed by tors (Angus, 1991). Crop simulation models can also be periods of relatively greater yield variability.used to investigate multiple factors and their interactions at various hierarchical levels, including farm, regional, and national scales.

Sorghum [Sorghum bicolor (L.) Moench] is the fi ft h most important grain crop globally. It stands out for its diversity of plant types, end-uses, and roles in cropping systems. Th is diversity presents opportunities but also complicates evaluation of production options, especially under climate uncertainty. Ecophysiological models can dissect interacting eff ects of plant genotypes, crop management, and environment. We describe the sorghum module of the Cropping System Model (CSM) as implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) to illustrate potential applications and suggest areas for model improvement. Crop growth is simulated based on radiation use effi ciency. Development responds to temperature and photoperiod. Partitioning rules vary with growth stages, respecting mass balance and maintaining functional equilibrium between roots and shoots. Routines for climate, soil, crop management, and model controls are shared with other crops in CSM. Modeled responses for eight real-world and hypothetical cases are presented. Th ese include growth under well-managed conditions, responses to row-spacing, population, sowing date, irrigation, defoliation, and increased atmospheric carbon dioxide concentration ([CO 2 ]), and a long-term sorghum and winter wheat (Triticum aestivum L.) rotation. Among traits and experiments considered, model accuracy was high for phenology (r 2 = 0.96, P < 0.01 for anthesis and r 2 = 0.91, P < 0.01 for maturity), moderate for grain yields (r 2 values from 0.30 to 0.52, P < 0.01), depending on the simulated experiments, and low for unit grain weight (r 2 = 0.02, not signifi cant, NS) and leaf area index for forage sorghum (r 2 = 0.18, NS). V alued for its heat and drought tolerance, sorghum is the fi ft h most important grain crop globally aft er wheat, rice (Oryza sativa L.), maize (Zea mays L.), and barley (Hordeum vulgare L.) (FAOSTAT, 2015). Among cereal crops, sorghum stands out for its diversity of plant types, cropping systems, growing environment, and end-uses (Dahlberg et al., 2011). Sorghum is variously grown to provide grain, forage, sugar, and bioenergy feedstocks, and crop architecture and other traits vary accordingly. While this diversity presents opportunities, it complicates attempts to assess potential impacts of innovations, especially as aff ected by climate uncertainty.Th e CSM (Jones et al., 2003) as implemented in the DSSAT has submodels that allow simulation of more than 25 crop species, including sorghum (Hoogenboom et al., 2011). Th e sorghum submodule uses shared routines for model control (including input and output), soil physical and chemical processes, evapotranspiration, and all aspects of crop management including tillage, planting, fertilization, irrigation, mulching, and other practices. Eight subroutines describe sorghum-specifi c crop processes. Th e shared routines simplify model improvement and simulating cropping sequences and rotations with diff erent crops and management practices. While based on the widelyused Crop ...

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