ArticleFrontiers in plant science2023
Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 11 citations in OpenAlex.
- Tissue-Specific RNA-Seq Analysis of Cotton Roots' Response to Compound Saline-Alkali Stress and the Functional Validation of the Key GenePlants (Basel, Switzerland) · 2025Article
- A longitudinal analysis of soil salinity changes using remotely sensed imageries.Scientific reports · 2024Article
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9 authors at 5 institutions in 2 countries.
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Abstract
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,
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