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Modeling Temperature Responses of Leaf Growth, Development, and Biomass in Maize with MAIZSIM
Author(s) -
Kim SooHyung,
Yang Yang,
Timlin Dennis J.,
Fleisher David H.,
Dathe Annette,
Reddy Vangimalla R.,
Staver Kenneth
Publication year - 2012
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2011.0321
Subject(s) - phenology , biomass (ecology) , environmental science , leaf area index , canopy , agronomy , climate change , crop , growing degree day , ecology , biology
Mechanistic crop models capable of representing realistic temperature responses of key physiological processes are necessary for enhancing our ability to forecast crop yields and develop adaptive cropping solutions for achieving food security in a changing climate. Leaf growth and phenology are critical components of crop growth and yield that are sensitive to climate impacts. We developed a novel modeling approach that incorporates a set of nonlinear functions to augment traditional thermal time methods (e.g., growing degree days) for simulating temperature responses of leaf expansion and phenology in maize or corn ( Zea mays L.). The resulting leaf expansion and phenology models have been implemented into a new crop model, MAIZSIM, that simulates crop growth based on key physiological and physical processes including C 4 photosynthesis, canopy radiative transfer, C partitioning, water relations, and N dynamics for a maize plant. Coupled with a two‐dimensional soil process model, 2DSOIL, MAIZSIM was applied to simulate leaf growth, phenology, biomass partitioning, and overall growth of maize plants planted at two field sites on the Eastern Shore of Maryland and in Delaware for 3 yr of data. The model parameters were estimated using data from outdoor sunlit growth chambers and the literature. No calibration was performed using the field data. The MAIZSIM model simulated leaf area, leaf addition rate, leaf numbers, biomass partitioning and accumulation with reasonable accuracy. Our study provides a feasible method for integrating nonlinear temperature relationships into crop models that use traditional thermal time approaches without sacrificing their current structure for predicting the climate change impacts on crops.