Dynamic simulation of the dry weight of soybean stems in intercropping systems

The intercropping of maize and soybean is practiced in several parts of the world. Using models for forecasting soybean growth is a good cropping technique; however, few research models are available that predict soybean growth in monocropping and intercropping practices. This study was based on the systematic observations of Nandou 032-4, Jiuyuehuang, and Nandou 12. The growth of soybean stems can be quantitatively simulated by the patterns of growth in monocropping and intercropping cultivation modes. A dynamic model was established using Richards equation for model building, where the time step was represented by the physiological development time (PDT), describing the elongation of soybean stem internodes and the enlargement of the dynamic processes at the internodes, in monocropping and intercropping practices. In the dynamic model, the time step was estimated by the growth and development taking place on the actual number of days by logistic equations, for predicting the dry weight of the soybean stem in monocropping and intercropping cultivation practices. From the model, the potential length and thickness of the internodes gradually increased as the number of soybean internodes increased and reached a maximum at the ninth and seventh internodes, respectively, and then gradually decreased. Therefore, the potential length of the ninth internode and the potential thickness of the seventh internode were identified as the potential genetic parameters that can be used to calculate the potential length and thickness of other internodes. At the same time, the potential dry weight of the stem was used to determine the potential dry weight of other stems. The inspection results showed that different varieties of soybean have different cultivation patterns. The model reflected a satisfactory prediction rate. This model, developed using soybean varieties having different modes of cultivation, internode lengths, internode thicknesses, physiological development times, and dry weights of the stem, has good predictability and reliability, and the stem growth dynamics predicted by the model conformed well to the biological rules of stem growth, highlighting the biological significance of the model.