Research And Development Of Crop Irrigation Decision Support System Based On Meta-analysis And AquaCrop Model

Due to global climate change,water use in agriculture is increasing and poses a huge challenge to food security,so improving water use efficiency(WUE)is key to sustainable agricultural development.Deficit irrigation is a water-saving irrigation strategy proposed against the limitation of water resources.Its principle is to carry out irrigation in the critical period of crop water demand and reduce irrigation in other non-critical periods according to the law of water demand in the growth process of crops,so as to reduce irrigation water consumption and improve crop water use efficiency.In addition,intelligent irrigation decision making techniques with the goal of precision irrigation are gaining increasing attention.In this thesis,the yield and water effects of winter wheat and summer maize in northern China under deficit irrigation were quantified using the Meta-analysis and the results were evaluated using the AquaCrop model.Maize was selected as a typical crop,and the crop parameters in the AquaCrop model were optimised using the M-Elite Coevolutionary Algorithm(MECA)based on meteorological data from 66 meteorological stations in Huang-Huai-hai region from 2000to 2020,and the global evolutionary algorithm for the optimal irrigation strategy(GET-OPTIS)to simulate the optimal irrigation strategy for maize growth from 2011 to 2020,and analysed the spatial variability and reliability of the irrigation strategy optimisation results.Finally,a crop irrigation decision support system based on Meta-analysis and AquaCrop model was built using database technology and web platform technology,with a view to providing theoretical guidance and technical support for farmers’irrigation management production practices.The main findings of the thesis are as follows:(1)The effects of deficit irrigation on yield and water use efficiency of winter wheat and summer maize in major grain producing areas in northern China were evaluated based on Meta-analysisThe results of Meta-analysis showed that under deficit irrigation,the average yield of wheat and maize was reduced by 7.10%and 18.71%,while the average water use efficiency of wheat and maize was increased by 9.25%and 6.38%,respectively.This study revealed the heterogeneity and effectiveness of deficit irrigation for wheat and maize in different regions and hydrological years,and proposed suitable deficit levels and irrigation strategies.The optimal deficit irrigation system in different regions is as follows:Irrigation was carried out twice in wheat growth period with irrigation quota of 100-200 mm,and supplemental irrigation generally occurring during critical water demand periods.Maize yields and water use efficiency were higher when the supplemental water(sum of irrigation and precipitation)was 80%of the fully irrigated water.(2)The results of the Meta-analysis were validated using the AquaCrop model,and the irrigation system of maize in Huang-Huai-hai region was optimized based on GET-OPTIS.The simulation results of AquaCrop model were consistent with the results of Meta-analysis.There was no significant difference in crop yield between deficit irrigation and full irrigation.The yield of wheat under deficit irrigation was slightly higher than that under full irrigation,while the yield of maize under deficit irrigation was lower than that under full irrigation,and WUE of both crops under deficit irrigation was higher than that of the other two irrigation modes.The results of crop growth simulation after optimization of irrigation system showed that the average WUE of maize in Huang-Huai-hai region was 1.42kg/m~3 and the average yield was 6.327t/ha,which could reach the average yield of maize per unit area in China.The irrigation water was controlled below 80%of the full irrigation water requirement,which could ensure stable yield of maize and improve water use efficiency at the same time.(3)A decision support system for crop irrigation based on Meta-analysis and AquaCrop model was established.According to the results of optimized maize irrigation,the irrigation database was established,and the cloud platform of field irrigation management was built with Spring Boot,Vue.js and My SQL frameworks and technologies.By connecting the field wireless valve controller,electric butterfly valve,soil temperature and humidity collector and other hardware equipment,the data summary management and view,account management,cloud monitoring,irrigation remote control and other functions are realized.The platform can be accessed through a browser,with good access and convenient management.Users can break away from the limitations of time and space,realize remote view of irrigation knowledge data and make irrigation decisions according to the actual growth of crops.The research results have certain practical significance for promoting the optimization and intelligence of farmland irrigation management.