Knowledge Model-Based Decesion Support System For Cotton Management

This research focused on applying the system analysis principle and mathematical modeling technique to study knowledge expression system for crop cultivation management. Based on extensively collecting, understanding, analysis, and integration of expert's knowledge and experience, literature and experiment data for cotton cultivation management, the dynamic relationships of cotton growth and management indices to variety types, ecological environments and production levels were quantified, and a dynamic knowledge model for cotton management (CottonKnow) was developed. By further incorporating the rule-based knowledge system for cotton management, a comprehensive and intelligent knowledge model-based decision support system for cotton management (KMDSSCM) was established with component design.The dynamic knowledge model with temporal and spatial characters for cotton management includes three modules as pre-sowing plan design, dynamic development indices, diagnosis and regulation. The knowledge model for pre-sowing plan design includes submodels of target yield calculation, variety selection, sowing or transplanting date, population density and sowing rate, fertilization and water management strategy. The knowledge model for the dynamics of main development indices includes submodels of suitable development stages, plant height, leaf area index, dry matter accumulation, numbers of fruit branch, square and boll, plant nutrient accumulation. The knowledge model for diagnosis and regulation includes calculation of differences in growth indices, possibility of regulation and intensity of regulation practices.The submodel for target yield prediction was developed through integrating the effects of cotton radiation-temperature-water yield potential, soil fertility, average yield of last three years and production technique levels. The sub-model for variety selection was established by qualifying the relationships of variety characters to eco-environments through the combined effects of disease and insect resistances, yield and quality traits. The sub-model for suitable sowing or transplanting date was developed according to the principle of determining variety maturity characters from planting style, variety type fromvariety maturity characters and user's requirement, and sowing date from variety and target yields. The sub-model for design of population density was developed according to the principle of determining boll number from target yield, fruit node from boll number, fruit branch from fruit node and population density from fruit branch by integrating the effects of sowing date, cutout date, effective temperature accumulation above 12 , variety type, and fertilizer and water management levels. Sowing rate was then decided by integrating the effects of different soil water and salt contents, pH, temperature and sowing style on seedling emergence rate with relative weight method. According to the principle of nutrient balance and water requirement in cotton, the sub-model for fertilization and water management was developed by integrating the effects of soil characters, variety traits and yield target. The submodel can make decisions on the suitable total nutrient and water rates and distributions among main growth stages, ratio of organic to inorganic nitrogen, and the ratio of base to topdressing fertilizer.Based on the effective temperature accumulation above 12 and variety maturity characters, physiological time for predicting the development processes from sowing to boil opening under different environments was determined. The knowledge model for the dynamics of main development indices as plant height, leaf area index, dry matter accumulation, numbers of fruit branch, square and boll was developed based on the physiological time and target yield and quality. In addition, the dynamic relationships between plant nutrients and dry matter accumulation was quantified. All these sub-models provide the reference standards for quantitative and dynamic growth diagnosis and management regulation.