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Improving crop modeling in saline soils by predicting root length density dynamics with machine learning algorithms

Author

Listed:
  • Dong, Liming
  • Lei, Guoqing
  • Huang, Jiesheng
  • Zeng, Wenzhi

Abstract

Crop modeling is an effective tool for simulating crop growth under various agricultural water and salinity management practices. However, most crop models fail to describe the root dynamics in response to soil stresses adequately. To address this issue, field experiments were conducted by planting sunflowers in saline soils. Three machine learning (ML) models of random forest (RF), gaussian process regression (GPR), and extreme gradient boosting (XGBoost) were initially introduced for predicting root length density (RLD). Then, by coupling with a crop model SWAP, the soil salt content (SSC), soil water content (SWC), and crop growth indicators of leaf area index (LAI) and dry matter (DM) were simulated. Results show that RF and XGBoost models could predict RLD more accurately than the GPR model, with root mean square error (RMSE) lower than 0.473 cm cm-3. Compared to using a typical cubic polynomial function (CPF) of RLD in the SWAP model, similar SWC and SSC simulation results were obtained based on the ML models. However, for the crop growth simulation, the performances of ML models were significantly better than the CPF. Especially for LAI simulation in the high salinity fields, the relative root mean square error (RRMSE) in the RF model was 0.222–0.282 lower than in the CPF. Moreover, compared to the XGBoost model of RLD, more accurate and stable simulation results of SWC, SSC, and LAI were obtained based on the RF model. These results illustrate that ML models, especially the RF model, can be used to quantify RLD dynamics and improve crop modeling performances.

Suggested Citation

  • Dong, Liming & Lei, Guoqing & Huang, Jiesheng & Zeng, Wenzhi, 2023. "Improving crop modeling in saline soils by predicting root length density dynamics with machine learning algorithms," Agricultural Water Management, Elsevier, vol. 287(C).
  • Handle: RePEc:eee:agiwat:v:287:y:2023:i:c:s0378377423002901
    DOI: 10.1016/j.agwat.2023.108425
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    References listed on IDEAS

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    1. Yu, Qihua & Kang, Shaozhong & Hu, Shunjun & Zhang, Lu & Zhang, Xiaotao, 2021. "Modeling soil water-salt dynamics and crop response under severely saline condition using WAVES: Searching for a target irrigation volume for saline water irrigation," Agricultural Water Management, Elsevier, vol. 256(C).
    2. Timsina, Jagadish & Dutta, Sudarshan & Devkota, Krishna Prasad & Chakraborty, Somsubhra & Neupane, Ram Krishna & Bishta, Sudarshan & Amgain, Lal Prasad & Singh, Vinod K. & Islam, Saiful & Majumdar, Ka, 2021. "Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency," Agricultural Systems, Elsevier, vol. 192(C).
    3. Miltiadis Iatrou & Christos Karydas & George Iatrou & Ioannis Pitsiorlas & Vassilis Aschonitis & Iason Raptis & Stelios Mpetas & Kostas Kravvas & Spiros Mourelatos, 2021. "Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems," Agriculture, MDPI, vol. 11(4), pages 1-17, April.
    4. Genxiang Feng & Zhanyu Zhang & Zemin Zhang, 2019. "Evaluating the Sustainable Use of Saline Water Irrigation on Soil Water-Salt Content and Grain Yield under Subsurface Drainage Condition," Sustainability, MDPI, vol. 11(22), pages 1-18, November.
    5. Carcedo, Ana J.P. & Bastos, Leonardo M. & Yadav, Sudhir & Mondal, Manoranjan K. & Jagadish, S.V. Krishna & Kamal, Farhana A. & Sutradhar, Asish & Prasad, P.V. Vara & Ciampitti, Ignacio, 2022. "Assessing impact of salinity and climate scenarios on dry season field crops in the coastal region of Bangladesh," Agricultural Systems, Elsevier, vol. 200(C).
    6. Verma, A.K. & Gupta, S.K. & Isaac, R.K., 2012. "Use of saline water for irrigation in monsoon climate and deep water table regions: Simulation modeling with SWAP," Agricultural Water Management, Elsevier, vol. 115(C), pages 186-193.
    7. Lei, Guoqing & Zeng, Wenzhi & Yu, Jin & Huang, Jiesheng, 2023. "A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields," Agricultural Water Management, Elsevier, vol. 277(C).
    8. Amirhossein Hassani & Adisa Azapagic & Nima Shokri, 2021. "Global predictions of primary soil salinization under changing climate in the 21st century," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    9. Goutam Konapala & Ashok K. Mishra & Yoshihide Wada & Michael E. Mann, 2020. "Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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