Pedosphere 17(2): 172--181, 2007
ISSN 1002-0160/CN 32-1315/P
©2007 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| A knowledge model system for wheat production management |
ZHU Yan1,2 , CAO Wei-Xing1,2, DAI Ting-Bo1,2, TIAN Yong-Chao1,2 and YAO Xia1,2 |
1 Hi-Tech Key Laboratory of Information Agriculture of Jiangsu Province, Nanjing Agricultural University, Nanjing 210095 (China). E-mail: yanzhu@njau.edu.cn;
2 Key Laboratory of Crop Growth Regulation of Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095 (China) |
| ABSTRACT |
| A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat management, as a decision-making tool in digital farming. The fundamental relationships and algorithms of wheat growth indices and management criteria to cultivars, ecological environments, and production levels were derived from the existing literature and research data to establish a knowledge model system for quantitative wheat management using Visual C++. The system designed 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 submodels to determine target grain yield and quality, cultivar choice, sowing date, population density, sowing rate, fertilization strategy, and water management, whereas the crop regulation indices module included submodels for suitable development stages, dynamic growth indices, source-sink indices, and nutrient indices. Evaluation of the knowledge model by design studies on the basis of data sets of different eco-sites, cultivars, and soil types indicated a favorable performance of the model system in recommending growth indices and management criteria under diverse conditions. Practical application of the knowledge model system in comparative field experiments produced yield gains of 2.4% to 16.5%. Thus, the presented knowledge model system overcame some of the difficulties of the traditional wheat management patterns and expert systems, and laid a foundation for facilitating the digitization of wheat management. |
| Key Words: expert system, knowledge model, quantitative decision-making, regulation index, wheat |
| Citation: Zhu, Y., Cao, W. X., Dai, T. B., Tian, Y. C. and Yao, X. 2007. A knowledge model system for wheat production management. Pedosphere. 17(2): 172-181. |
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