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Understanding and modeling soybean (Glycine max., L. Merr.): Growth and development under optimum conditions

Tri D Setiyono, University of Nebraska - Lincoln


A new soybean model was developed by incorporating existing and new approaches. Data for model development were obtained from a long-term ecological intensification experiment (1999-2005) at Lincoln, NE. ^ The phenology was simulated as a function of temperature and photoperiod distinguishing floral induction and post-induction processes for the time of flowering. The component accurately simulated the dynamics of vegetative development, final node number and the occurrence of major reproductive stages with root mean square errors of 1.8 days for major phenological stages in the long-term experiment. ^ Leaf area index (LAI) was simulated considering potential growth driven by temperature and water stress and also the dry matter available for leaf growth. The rate of potential gross leaf area expansion was simulated using the first derivative of a logistic function and accounting for plant population density. Leaf area senescence was also simulated using a logistic function, assuming monocarpic senescence began at flowering stage (R1). LAI simulation with the proposed model had average RMSE of 0.59 m m-2. ^ Leaf photosynthesis was simulated as a function of solar radiation, temperature, and RH, ambient CO2 concentration, and phenology using a modified Farquhar approach. Maintenance respiration and partitioning of dry matter and the conversion from leaf to canopy level photosynthesis rate were simulated using an approach similar to that in the WOFOST model. ^ Seed growth was simulated by combining the concept of hydraulic model of pod set (Sheldrake, 1979) and assimilated partitioning driven seed number determination (Charles-Edwards et al., 1986) and phenology driven mean individual seed growth. ^ The proposed model simulated total above ground and seed dry matter with reasonable accuracy under high-yielding environments while requiring minimum cultivar specific input parameters. The model, therefore, is particularly suited for applications related to soybean management aiming at high yield ^

Subject Area

Agriculture, Agronomy|Biology, Ecology|Engineering, Agricultural

Recommended Citation

Setiyono, Tri D, "Understanding and modeling soybean (Glycine max., L. Merr.): Growth and development under optimum conditions" (2007). ETD collection for University of Nebraska - Lincoln. AAI3254330.