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Research article - Peer-reviewed, 2023

Incorporating new functions into the WAVES model, to better simulate cotton production under film mulching and severe salinity

Yu, Qihua; Kang, Shaozhong; Zhang, Lu; et al.; et al.

Abstract

Film mulching is widely used as an agronomic practice to counteract water scarcity in arid and semi-arid areas. Although crop models have emerged as powerful tools for system studies and scenarios analysis, they have been rarely used in areas with severe drought and salinization and where mulching is being used as a management practice. An earlier study shown great modelling potential under severe salinity in southern Xinjiang, China. The model is WAVES (the WAter Vegetation Energy and Solute), while evaporation was overestimated in the earlier study without considering the mulching effect. In this study, we used a modified WAVES model by incorporating three functions working on potential evaporation, underlying surface albedo, and soil resistance into it to represent the mulching effect. Calibration and validation were conducted using cotton field experiments from 2 different years. Of the 3 functions evaluated, the one representing potential evaporation reduction exerted the highest modification effect on soil water status. The modified model better simulated evaporation, soil-water content, soil-salt content, leaf area index (LAI), and yield than the original model, decreasing normalized root mean square error (NRMSE) by 173%, 15%, 14%, 9%, and 35%, respectively. The modification effects were most significant during the seedling stage. In addition, the modified model produced a higher realistic evaporation (E)/evapotranspiration (ET) under the film mulching environment. These findings suggest that the modified WAVES model can be applied for crop management under film mulching, particularly in areas with low rainfall and high salinization.

Keywords

Film mulching; Modeling; Evaporation; Soil water; WAVES model

Published in

Agricultural Water Management
2023, Volume: 288, article number: 108470

    UKÄ Subject classification

    Agricultural Science

    Publication identifier

    DOI: https://doi.org/10.1016/j.agwat.2023.108470

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/127264