A study was conducted in Stoneville, Mississippi, USA, under irrigated conditions to develop predictive models, using a simple and effective model technique which can allow producers to predict soyabean growth and development stages in their fields. The models were constructed using 4-year field data (1998-2001) and validated with the fifth year data (2002). Potential factors affecting stages of soyabean growth and development were considered for developing the models. Affecting factors, such as weeds, insects, diseases and drought stress, were controlled optimally to simplify the modelling procedures. In addition, stepwise regression (SR) analysis, artificial neural networks (ANN), and interpolation approaches were used to construct the models. The modelling of soyabean growth and development processes was separated into 2 distinct periods: vegetative growth stage (V-stage) and reproductive growth stage (R-stage). The models included 10 V-stages (up to V8) and 8 R-stages. In the V-stages models, PD (planting date) and mean relative time-span for planting to a particular stage were the only significant parameters, whereas in R-stage models, PD and MG (maturity group) were significant. The models obtained accurate predictions were only using PD, MG and mean relative time-span from planting to a particular stage. The ANN method provided the greatest accuracy in predicting phenological events, indicating that the ANN method can be effectively applied in crop modelling.
