• ISSN 1008-505X
  • CN 11-3996/S
严定春, 朱艳, 曹卫星, 王绍华. 水稻适宜养分指标动态的知识模型研究[J]. 植物营养与肥料学报, 2005, 11(5): 590-596. DOI: 10.11674/zwyf.2005.0504
引用本文: 严定春, 朱艳, 曹卫星, 王绍华. 水稻适宜养分指标动态的知识模型研究[J]. 植物营养与肥料学报, 2005, 11(5): 590-596. DOI: 10.11674/zwyf.2005.0504
YAN Ding-chun, ZHU Yan, CAO Wei-xing, WANG Shao-hua. A dynamic knowledge model on appropriate nutrient indexes in rice[J]. Journal of Plant Nutrition and Fertilizers, 2005, 11(5): 590-596. DOI: 10.11674/zwyf.2005.0504
Citation: YAN Ding-chun, ZHU Yan, CAO Wei-xing, WANG Shao-hua. A dynamic knowledge model on appropriate nutrient indexes in rice[J]. Journal of Plant Nutrition and Fertilizers, 2005, 11(5): 590-596. DOI: 10.11674/zwyf.2005.0504

水稻适宜养分指标动态的知识模型研究

A dynamic knowledge model on appropriate nutrient indexes in rice

  • 摘要: 在分析研究水稻栽培理论与技术资料的基础上,通过定量描述水稻群体地上部植株氮、磷、钾养分积累量和养分浓度动态与品种类型、生态环境因子和生产技术水平之间的动态关系,以生理发育时间为主线,建立了系统化和广适性的水稻适宜群体地上部植株养分指标的动态知识模型。本模型可为不同条件和产量目标下水稻栽培过程中的苗情诊断与生长调控提供定量化的养分指标动态体系。利用南京和常德2个不同生态点的常年逐日气象资料和品种资料对所建知识模型进行了实例分析,养分积累动态和养分浓度动态的RMSE分别为8.97.kg/hm2和0.32%,表明本知识模型对不同条件下的水稻植株养分指标适宜动态具有较好的预测性和指导性。

     

    Abstract: Rice (Oryza sativa L.) is the most important food crop in the world and China. Studying the dynamics of nitrogen, phosphorus, and potassium nutrient accumulation and analyzing nutrient content in above ground plant is very important, which could help farmers to make rational decision on fertilizer application. Based on the analysis and refinement of rice raising rationale and latest technical research, a general knowledge model for nutrient index dynamics in rice was developed through the quantitative description of dynamic relationship of N, P and K accumulation and concentration in the above-ground part of rice to varieties, ecological environment factors and technique levels. The knowledge model was established using Visual C++ as programming language, and encapsulated in the form of automation with the standard of COM (Component Object Model) on operational platform as Windows 2000 in Chinese version, which could be used to predict the time-course dynamics of above-ground plant nutrient contents and accumulation for the purpose of nutrient (diagnosis) and growth regulation under different target yields with different rice varieties, environmental condition and production levels. Case studies were carried out using the daily meteorological data and research results in normal climatic year of Nanjing and Changde in China. Results showed that RMSE of nutrient accumulation and concentration trend were 8.97 kg/hm2 and 0.32%, respectively, indicating that the time-course dynamics of nutrient index designed by the knowledge model for two eco-sites and different rice cultivars were overall consistent with the practical growth patterns (under) specific production systems. Therefore, the quantitative knowledge model would overcome the weakness of traditional crop expert system with poor spatial and temporal adaptation, such as specific site limitation, massive knowledge base and low quantification, and lay a foundation for the digital decision support on rice management.

     

/

返回文章
返回