Simulation And Model System Of Maize Growth In Tropical Region Of Yunnan

Maize is an important food crop in the world,and the use of crop models to simulate the growth and development process of maize can accurately predict the fertility and yield of maize,providing a scientific basis for maximum agricultural profitability.This paper synthesizes the physiological and ecological research results of maize growth and development and yield formation at home and abroad.Based on the data from maize field experiments on maize cultivars(Huidan 4 and Deyu 6)at the Longchuan and Ruili sites in2010-2013,and at the Mangcheng site in 2013-2014 and 2017-2018 in Yunnan Province,Dehong Dai Jingpo Autonomous Prefecture,Yunnan Province,and historical meteorological data,we constructed a physiological The model parameters and genetic parameters of maize cultivars were tuned with field data to achieve fertility prediction and yield prediction of winter maize in the hot zone of Yunnan,and the prediction accuracy of the model was verified using field data of two cultivars at multiple sites and multiple sowing periods in the validation group.The main research elements are as follows:(1)In this paper,a model for predicting maize fertility in the Yunnan hot zone based on physiological development time was constructed using physiological development time as the time scale.The parameters were debugged by applying field trial data to quantify the specific physiological development times for the two varieties at 4.91,50.5 and 91.5 physiological days for Huidan 4 and 4.8,52.7 and 99.7 physiological days for Deyu 6,respectively.The validation results showed that the NRMSEs of the predicted results of both varieties at the male draw and maturity stage were below 5%,which had good adaptability.(2)This paper combined the modified logistic method-based leaf area index prediction model and the Gaussian integral method-based photosynthetic production and dry matter accumulation prediction model to construct a dry matter accumulation model for maize in the heat zone.The model parameters were debugged by applying the data from the field trials,and the validation results showed that the fitted curves of the leaf area index conformed to the growth pattern of leaf area,and the NRMSE of the dry matter accumulation model were all below 10%,with the RMSE ranging from 563.31 to 972.72 kg/hm2,which laid a good foundation for the subsequent simulation of the model.(3)A distribution index-based dry matter allocation model and yield prediction model for maize cob organs in the hot zone of Yunnan was constructed using physiological development time as the scale.The model parameters were debugged by applying field trial data,and the validation results showed that the NRMSE of the model for predicting the dry matter quality and yield of maize cob organs of two varieties at multi-site and multi-sowing periods in Yunnan were below 10%,and the RMSE ranged from 230.48-845.98 kg/hm2.(4)Using python language and SQL server database,a visualization interface of maize growth simulation model in Yunnan hot zone was developed to realize the functions of site weather query,visualization of maize growth simulation process data,prediction of maize fertility and yield,and parameter debugging.In this study,a maize growth and development simulation system for the hot zone was constructed to realise the prediction of maize fertility,simulation of leaf area index,simulation of dry matter accumulation,simulation of dry matter distribution of cob organs and prediction of yield.The debugging and validation of model parameters and genetic parameters of maize varieties were completed,and the visual interface operating system was established,which provides tools for future research on maize yield.