干旱区生态环境知识资源中心(Arid): Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Arid

DOI	10.3389/fpls.2023.1143462
	Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning
	Jiang, Zewei; Yang, Shihong; Dong, Shide; Pang, Qingqing; Smith, Pete; Abdalla, Mohamed; Zhang, Jie; Wang, Guangmei; Xu, Yi
通讯作者	Yang, SH
来源期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
出版年	2023
卷号	14
英文摘要	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, R-2 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 R-2 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%similar to 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.
英文关键词	salinity evapotranspiration drip irrigation cotton ensemble machine learning
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:001012304300001
WOS关键词	PREDICTION
WOS类目	Plant Sciences
WOS研究方向	Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396598
推荐引用方式 GB/T 7714	Jiang, Zewei,Yang, Shihong,Dong, Shide,et al. Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning[J],2023,14.
APA	Jiang, Zewei.,Yang, Shihong.,Dong, Shide.,Pang, Qingqing.,Smith, Pete.,...&Xu, Yi.(2023).Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning.FRONTIERS IN PLANT SCIENCE,14.
MLA	Jiang, Zewei,et al."Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning".FRONTIERS IN PLANT SCIENCE 14(2023).