Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Frontiers in Plant Science

Plant Science

Guangmei Wang¹  Shide Dong²  Zewei Jiang³  Jie Zhang³  Yi Xu³  Shihong Yang⁴  Mohamed Abdalla⁵  Pete Smith⁵  Qingqing Pang⁶ 

[1] CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, China;CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, China;Shandong Saline-Alkali Land Modern Agriculture Company, Dongying, China;College of Agricultural Science and Engineering, Hohai University, Nanjing, China;College of Agricultural Science and Engineering, Hohai University, Nanjing, China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China;Cooperative Innovation Center for Water Safety & Hydro Science, Hohai University, Nanjing, China;Institute of Biological & Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom;Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, China;

关键词: salinity;    evapotranspiration;    drip irrigation;    cotton;    ensemble machine learning;   

DOI  :  10.3389/fpls.2023.1143462

 received in 2023-01-13, accepted in 2023-05-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.

【 授权许可】

Unknown   
Copyright © 2023 Jiang, Yang, Dong, Pang, Smith, Abdalla, Zhang, Wang and Xu