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Acta Agron Sin ›› 2009, Vol. 35 ›› Issue (2): 341-347.doi: 10.3724/SP.J.1006.2009.00341


Nrediction of Soybean Growth and Development Stages Using Artificial Neural Network and Statistical Models

ZHANG Jiu-Quan1,2,ZHANG Ling-Xiao2,ZHANG Ming-Hua3, WATSON Clarence4   

  1. 1Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao 266101,China;2Delta Research and Extension Center, Mississippi State University,Stoneville 38776, MS,USA;3Department of Land, Air and Water Resources,University of California, Davis 95616,CA,USA;4 Oklahoma State University, Stillwater 74078-6009,OK,USA
  • Received:2008-01-07 Revised:2008-09-10 Online:2009-02-12 Published:2008-12-12


The prediction of soybean phenology is important in many aspects of soybean production. The study objective was to develop predictive models, using a simple and effective modeling technique, which can allow producers to predict soybean growth and development stages in their fields. The experiments were conducted at the Delta Research and Extension Center in Stoneville, Mississippi (latitude: 33°25' N, longitude: 90°55' W) under irrigated conditions. The models were constructed using four-year field data (1998 to 2001), and validated with the fifth-year data (2002). Potential factors affecting stages of soybean 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 modeling procedures. In addition, stepwise regression (SR) analysis, artificial neural networks (ANN), and interpolation approaches were used to construct the models. The modeling of soybean growth and development processes was separated into two distinct periods: vegetative growth stage (V-stage) and reproductive growth stage (R-stage). The models included ten V-stages (up to V8) and eight R-stages. In the V-stage models, PD (planting date) and mean relative time-span from 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 when 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 modeling.

Key words: Soybean, Growth stage, Maturity group, Artificial neural networks, Modeling, Planting date, Phenology

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