Paper Withdrawn
Tuesday, November 14, 2006
141-1
Development and Application of a Crop Management Knowledge Model.
Yan Zhu1, Jing Cao1, Yongchao Tian1, Xia Yao1, Xiaojun Liu1, and Weixing Cao2. (1) Dep. of Agronomy, Nanjing Ag. Univ., Nanjing, 210095, China, (2) Hi-Tech Key Lab of Information Agriculture of Jiangsu Province, Nanjing Agricultural Univ, Nanjing, Jiangsu, 210095, China
By using systems analysis and dynamic modeling techniques, knowledge models with temporal and spatial characteristics were developed as decision-making tools for quantitative design of cultural pattern in digital farming of wheat, rice, cotton, and rapeseed crops. The fundamental relationships and algorithms between crop growth indices and management criteria to cultivars, ecological environments and production levels were derived from the existing literature and research data. Then, knowledge model systems for quantitative crop management was established using Visual C++. The system designs a cultural management plan for general management guidelines and crop regulation indices for time-course control criteria during the wheat-growing period. The cultural management plan module included the sub-models to determine target grain yield and quality, cultivar choice, sowing date, population density, sowing rate, fertilization strategy, and water management, while the crop regulation indices module included the sub-models for suitable development stages, dynamic growth indices, source-sink indices, and nutrient indices. Evaluation of the knowledge models by design studies on the basis of the data sets of different eco-sites, cultivars, and soil types indicated a good performance of the model system in recommending the growth indices and management criteria under diverse conditions. Practical application of the knowledge model in comparative field experiments produced yield gains of 2% to 16% in wheat and of 1% to 13% in rice. Thus, the knowledge model system could overcome some of the difficulties of traditional crop management patterns and expert systems, and lay a foundation for precision and/or digital crop management.