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In recent years, as drought intensifies and agricultural water consumption increases, it is of great significance to optimize the irrigation schedule to ensure regional food security. This paper constructs the distributed AquaCrop model and multi-objective genetic algorithm (NSGA - Ⅱ) simulation - optimization (MGSO) model to facilitate the development of a rational irrigation schedule. The distributed AquaCrop model considers the spatial variability of soil, climate, crops, and management practices, which can be batch calibrated using the XGBoost method. The MGSO model is for irrigation schedules under the combination of different Precipitation and ET 0. In this paper, crop yield, ET, and water use efficiency (WUE) were simulated and analyzed in 13 irrigation zones in Northeast China, where the existing irrigation schedules were analyzed and optimized. The results showed that the distributed AquaCrop model could simulate regional crops well. Crop yields in the study area ranged from 3 to 10ton/hm2. The western part of Heilongjiang province and the northern part of Jilin province has a higher yield. The simulation results of Heilongjiang Province are more accurate, and the relative error is minor. The joint distribution model constructed by the Frank Copula function can describe the joint probability distribution characteristics of precipitation and ET 0. According to the simulation results, each typical station has a different performance under the existing irrigation schedule under different situations. The crop yield and WUE of some stations changed significantly. The maximum and minimum yield difference was 22% for Harbin, 32% for Heihe, and 21% for Dunhua. It is mainly due to the irrigation amount in some scenarios that do not meet crop water requirements. Under the optimized irrigation schedule, the crop yield and WUE in different scenarios have been improved by the MGSO model in the Harbin station [Display omitted] • Distributed AquaCrop model was established based on batch parameter calibration processing. • Use a copula based joint probability of precipitation and ET 0 to define climate conditions. • New irrigation schedule considering both precipitation and ET 0 can improve yield and WUE of Crop. [ABSTRACT FROM AUTHOR]

Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment. [ABSTRACT FROM AUTHOR]