Hybrid-maize—a maize simulation model that combines two crop modeling approaches

Authors

H. S. Yang, University of Nebraska-LincolnFollow
A. Dobermann, University of Nebraska-Lincoln
John L. Lindquist, University of Nebraska-LincolnFollow
Daniel T. Walters, University of Nebraska-LincolnFollow
Timothy J. Arkebauer, University of Nebraska-LincolnFollow
Kenneth G. Cassman, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

5-10-2004

Comments

Published in Field Crops Research 87:2–3 (May 10, 2004), pp. 131-154; doi:10.1016/j.fcr.2003.10.003. Copyright © 2003 Elsevier B.V. Used by permission. http://www.sciencedirect.com/science/journal/03784290

Abstract

A new maize (Zea mays L.) simulation model, Hybrid-Maize, was developed by combining the strengths of two modeling approaches: the growth and development functions in maize-specific models represented by CERES- Maize, and the mechanistic formulation of photosynthesis and respiration in generic crop models such as INTERCOM and WOFOST. It features temperature-driven maize phenological development, vertical canopy integration of photosynthesis, organ-specific growth respiration, and temperature-sensitive maintenance respiration. The inclusion of gross assimilation, growth respiration and maintenance respiration makes the Hybrid- Maize model potentially more responsive to changes in environmental conditions than models such as CERES-Maize. Hybrid-Maize also requires fewer genotype-specific parameters without sacrificing prediction accuracy. A linear relationship between growing degree-days (GDD) from emergence to silking and GDD from emergence to physiological maturity was used for prediction of day of silking when the former is not available. The total GDD is readily available for most commercial maize hybrids. Preliminary field evaluations at two locations under high-yielding growth conditions indicated close agreement between simulated and measured values for leaf area, dry matter accumulation, final grain and stover yields, and harvest index (HI). Key areas for further model improvement include LAI prediction at high plant density, and biomass partitioning, carbohydrate translocation, and maintenance respiration in response to above-average temperature, especially during reproductive growth. The model has not been evaluated under conditions of water and/or nutrient stress, and efforts are currently in progress to develop and validate water and nitrogen balance components for the Hybrid- Maize model.