Adaptation of the CROPGRO growth model to velvet bean (Mucuna pruriens). II. Cultivar evaluation and model testing - Hartkamp, A.D.; Hoogenboom, Gerrit; Gilbert, R.A.; Benson, T.; Tarawali, Shirley A.; Gijsman, A.J.; Bowen, W.; White, Jeffrey W. (Elsevier, 2002)
Velvet bean (Mucuna pruriens (L.) DC. cv.-group utilis) is widely promoted in tropical and sub-tropical regions as a green manure cover crop that can reduce weed growth and soil erosion and enhance soil fertility. To provide these benefits, the crop must attain rapid ground cover and develop substantial aboveground biomass. To assist biophysical targeting of the crop to environments that can provide adequate growth conditions, the CROPGRO model was adapted to simulate velvet bean growth and development. This paper evaluates the performance of the model for phenology, growth, senescence and N accumulation for multiple locations that represent a range of environmental and agronomic management scenarios. Vegetative development, as described by main stem leaf appearance rate, varied linearly with thermal time. Time to flowering showed departures from the linear photoperiod response used in the model. Additional research is required to determine whether the crop is influenced by factors besides photoperiod and air temperature, especially water and nutrient deficits. The linear response to photoperiod did, however, provide reasonable values for partitioning to vegetative, reproductive and senesced materials. Simulation of nitrogen concentration for various plant components matched observed data. Sensitivity analyses evaluating the ability of the crop to provide ground cover, intercept light and develop adequate growth for soil protection and weed suppression indicated that a mean temperature of over 22 °C and a soil moisture holding capacity of at least 100 mm are required. The CROPGRO model proved to be a reliable decision support tool for guiding analyses of velvet bean response to crop management and environmental conditions. Further research, however, is warranted to improve its predictive capability, especially for phenology.