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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #314514
Title: An overview of CERES-Sorghum as implemented in the cropping systems model version 4.5

Author
item White, Jeffrey
item ALAGARSWAMY, GOPAL - Michigan State University
item OTTMAN, MICHAEL - University Of Arizona
item PORTER, CHERYL - University Of Florida
item SINGH, UPENDRA - International Fertilizer Development Center (IFDC)
item HOOGENBOOM, GERRIT - Washington State University

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/31/2015
Publication Date: 8/10/2015
Citation: White, J.W., Alagarswamy, G., Ottman, M.J., Porter, C.H., Singh, U., Hoogenboom, G. 2015. An overview of CERES-Sorghum as implemented in the cropping systems model version 4.5. Agronomy Journal. 107:1987-2002.

Interpretive Summary: Sorghum is the fifth most important grain crop globally and stands out for the diversity of plant types, end-uses, and roles in farming that the crop offers. While presenting many opportunities for producers, this diversity also complicates recommending new options to farmers. Simulation models of crops can help dissect how plant types, crop management, and environment affect production, and they are often used to guide options for field research and to scale-up results from specific environments to larger regions or time spans. We describe the sorghum module of the Cropping System Model (CSM) as implemented in the widely used Decision Support System for Agrotechnology Transfer (DSSAT). Crop growth is simulated based on how efficiently the crop captures light and through photosynthesis, creates new tissue. Development is affected by air temperature and the daylength. Rules to allocate growth to different plant parts are varied depending on growth stages. Routines for climate, soil, crop management, and model controls are shared with other crops in CSM. The performance of the model is illustrated for eight cases involving real-world and hypothetical situations in the US. The examples were selected to indicate potential applications of the model as well as to suggest model features that merit improvement. The examples include growth under well-managed conditions, responses to row-spacing, plant density, sowing date, irrigation, artificial defoliation and increased atmospheric carbon dioxide ([CO2]), and a long-term sorghum and winter wheat rotation. Among the traits and experiments considered, model accuracy was high for phenology, moderate for grain yields, and low for grain size and leaf area of forage sorghum. The CSM-Sorghum model is a promising tool for research on sorghum, especially where variability in weather, soil conditions or crop management are of interest.

Technical Abstract: Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important grain crop globally. It stands out for its diversity of plant types, end-uses, and roles in cropping systems. This diversity presents opportunities but also complicates evaluation of production options, especially under climate uncertainty. Ecophysiological models can assist in dissecting interacting effects 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). Crop growth is simulated based on radiation use efficiency. Development responds to temperature and photoperiod, and partitioning rules are varied depending on growth stages. Routines for climate, soil, crop management, and model controls are shared with modules for other crops in CSM. Modeled responses for eight cases involving real-world and hypothetical situations in the US are presented to indicate potential applications as well as to suggest aspects of the model that merit improvement. These include growth under well-managed conditions, responses to row-spacing, population, sowing date, irrigation, defoliation, and increased atmospheric carbon dioxide ([CO2]), and a long-term sorghum and winter wheat (Triticum aestivum L.) rotation. Among traits and experiments considered, model accuracy was high for phenology (r2 = .96, P < 0.01 for anthesis and r2 = .91, P < 0.01 for maturity), moderate for grain yields (r2-values from .30 to .52, P < 0.01), depending on the simulated experiments, and low for unit grain weight (r2 = .02, NS) and leaf area index for forage sorghum (r2 = .18, NS).