The crop light temperature yield potential simulation optimization method based on random forest
In order to effectively reduce the amount of data required for crop simulation and improve computing efficiency,a model for estimating the light-temperature yield potential of winter wheat was established based on machine learning.Taking 129 agro-meteoro-logical stations in the winter wheat region of China from 1980 to 2009 as the research object,the characteristic variables of tempera-ture,sunshine hours,latitude and longitude,etc.,which had a great influence on the simulation of photoperiod yield potential were selected.Based on the input and output data of WheatGrow model,the random forest model(RF_GS)and the random forest model(RF_Mon)with the variables of growing season and month were established.Finally,the performance of the random forest model was evaluated by root mean square error(RMSE).The results showed that the random forest model could reduce the data requirement un-der the premise of ensuring the simulation accuracy,and the accuracy of RF_GS was better than that of RF_Mon.The results of the variable importance test and partial dependence plots showed that latitude,sunshine duration in the growing season,sunshine dura-tion in May and minimum temperature in March had a great influence on photoperiod yield potential simulation.If the range of model validation data exceeded the range of training data,the random forest model's accuracy could not be guaranteed.
crop modelWheatGrow modelrandom forest modellight temperature yield potentialsimulation optimization method
NETL
NSTL
万方数据
