A Knowledge Model And Decision Support System For Rice Management

This research focused on applying the system analysis principle and mathematical modeling technique to study of knowledge expression system for rice cultivation management. Based on understanding, analysis, extraction and integration of experts' knowledge and experience, literature and experiment data for rice cultivation management, the dynamic relationships of rice growth and management indices to variety types, ecological environments and production levels were analyzed, and a dynamic knowledge model for rice management was developed. By further integrating the knowledge base expression system for rice management, a comprehensive and intelligent knowledge model and component-based decision support system for rice management (KMDSSRM) was established.The dynamic knowledge model with temporal and spatial characters for rice management includes two modules as cultural technique plan design and suitable dynamic growth index prediction. The knowledge model for cultural technique plan design includes submodels of target yield calculation, variety selection, sowing date, population density, fertilization strategy, water management. The knowledge model for the dynamics of main development indices includes submodels of population stem and tiller number, leaf area index, dry matter accumulation, and nutrient status for aboveground plant.The submodel for target yield prediction was based on the quantitative calculation of dynamic yield increment through integrating the effects of highest historical yield, average yield of last three years, soil fertility, water management levels and production technology level. The submodel for variety selection was established by quantifying the relationships of variety characters to eco-environment through integrating the effects of growth duration, yield and quality traits, salt and disease resistances, and photoperiod sensitivity with relative weighing method, which depends on safe emergence, strong seedling, safe full heading, suitable farming system, high quality, yield, and resistance. In the submodel for the design of suitable sowing date, suitable sowing date of rice was determined so that there should be strong seedlings and safe jointing. The submodel quantitatively characterized the impact of genotypic differences and eco-environment on sowing date by yearlyaccumulative temperature above 10, thermal interval for leaf emergence, maximum leaf age in seedling bed, leaf number on main stem, internode number, length of whole growth period, the accumulative temperature needed for the variety between jointing and maturity, and seedling nursery method. Population density was determined by final spikes and effective tillers per plant, and the theoretical main stem and tiller number per plant was quantified by the synchronization relationship between tiller occurrence and leaf appearance on main stem, the percentage of final spikes to effective tillers was quantitatively characterized by the tillering ability, field level, sowing and transplanting depth, soil nutrient, and temperature at the stage of tillering. The submodel for fertilization strategy was developed with principle of nutrient balance and by combining the effects of soil characters, target yield, planting method, harvest index, water management level and fertilizer application. In the submodel, the impact of genotypic differences on nutrient requirement and uptake were characterized by nutrient content in grain and straw and nutrient uptake efficiency, and the nutrients supplied by soil were quantified according to the soil characters and soil nutrient contents. The submodel for fertilization strategy can make decisions on the suitable total rates of nitrogen, phosphorus and potassium, ratio of organic to inorganic nitrogen, and the ratio of base to topdressing nitrogen, phosphorus and potassium. The submodel for water management was based on water balance theory.Based on the dynamic relationship of population main stem and tiller number, leaf area index and dry matter accumulation to variety type, eco-environment factors and...