Evaluating the CROPGRO Soybean Model for Predicting Impacts of Insect Defoliation and Depodding

• Published in: Agronomy journal Vol. 99; no. 1; pp. 148 - 157
• Main Authors: Timsina, J, Boote, K.J, Duffield, S
• Format: Journal Article
• Language: English
• Published: Madison American Society of Agronomy 2007
• Subjects: Agronomy. Soil science and plant productions; Biological and medical sciences; calibration; crop models; crop yield; CROPGRO soybean model; Defoliation; depodding; Fundamental and applied biological sciences. Psychology; Glycine max; insect pests; model validation; photosynthesis; plant pests; pods; Soybeans; temporal variation; yield losses; Florida; New South Wales; Evaluation; Insecta; Prediction; Glycine max; Modeling; Grain legume; Leguminosae; Defoliation; Arthropoda; Dicotyledones; Agronomy; Angiospermae; Spermatophyta; Simulation model; Invertebrata; Oil plant (vegetal)
• ISSN: 0002-1962; 1435-0645
• DOI: 10.2134/agronj2005.0338

Insect feeding on leaves, pods, and seeds causes significant yield loss in soybean [Glycine max (L.) Merr.]. Robust soybean growth models would be helpful to simulate the effect of defoliation or depodding on soybean growth and yield. The objective of this study was to evaluate the CROPGRO-Soybean model for its ability to predict the impacts of insect defoliation and depodding. Growth data were used to calibrate the model for two cultivars in Griffith, NSW, Australia. The model was evaluated against independent data from defoliation and depodding experiments at Griffith and at Gainesville, FL. The work tested the sensitivity of the model to defoliation and depodding at various growth stages, by comparison to data on manual defoliation of 30 and 60% in Australia and 30 and 70% in the USA, and depodding of 50 and 100% in Australia. The model predicted small yield reductions (4-11%) from 30% defoliation and greater reductions (17-49%) from 60 to 70% defoliation. The model illustrated well the pattern of sensitivity to defoliation: low during vegetative growth, increasing until beginning seed growth, and decreasing thereafter. The model overpredicted yield loss for severe defoliation but predictions improved after model modification to include photosynthetic contribution of green area of stems, petioles, and pods. Depodding predictions were generally accurate, but showed the need to evaluate model ability to add late pods after depodding. We conclude that the CROPGRO-Soybean model has adequate capabilities for use as a tool to predict the effects of timing and intensity of defoliation and depodding.