Developing a real-time self-organizing algorithm for irrigation planning of rapeseed cultivation

Abstract

Sustainable planning of water allocation in the agricultural sector requires attention to soil, plant, climate and their limitations. This study was conducted in order to develop a real-time framework for simulating soil–water balance in the root zone, crop growth curve and irrigation planning of rapeseed cultivation in Henan Province, China during a cropping season from March to October 2022. Simulation of production functions with field information calibration at daily time step was developed to accurately estimate the simulation of crop growth and soil water balance. Particle swarm optimization (PSO) algorithm is incorporated as an efficient tool to evaluate the water productivity as objective function in a self-organizing framework. Choosing the appropriate planting date for rapeseed cultivation at the beginning of the growing season was evaluated to increase the use of precipitation for canopy cover growth and thus reduce irrigation water consumption. The results showed that the proposed model increased water productivity by 23% as the objective function, and evaporation from the soil surface decreased by 16%. The maximum difference between the irrigation depth in the optimal and existing strategies was 41 mm in the germination stages until the seed-filling stage, which caused a decrease in final biomass and plant transpiration. HIGHLIGHTS An optimization framework was used to find the sustainable strategy of water distribution in rapeseed cultivation.; A daily time step modeling was developed to estimate the crop growth process.; Improving the planting date increased water productivity and effective use of rainfall.;

Keywords

• canopy cover
• objective functions
• particle swarm optimization
• water use efficiency