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Estimation of onion crop evapotranspiration and crop coefficients using weighing lysimeters and machine learning models in semi-arid region

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Abstract

Water scarcity is a significant challenge in Iran’s agricultural sector, particularly for onion (Allium cepa L.) cultivation, which is a vital crop for the country’s economy and diet. However, there is a lack of standardized data on onion evapotranspiration (ETc) in semi-arid conditions, making precise irrigation management difficult. To address this gap, a two-year field experiment was conducted at the Kooshkak Agricultural Research Station, Shiraz University, Iran to measure ETc using digital weighing lysimeters based on the water balance method and to develop predictive models using machine learning algorithms. The ETc of onion in the first and second years were 447.1 mm and 432.2 mm, respectively. Soil evaporation accounted for 36.6% and 32.8% of the total ETc in the first and second years, respectively. The average of single crop coefficient values for the initial, mid, and late growth stages across both years were 0.41, 0.68, and 0.51, respectively. Additionally, the basal crop coefficient values for the initial, mid, and late growth stages were 0.10, 0.51, and 0.37, respectively. To estimate ({text{E}text{T}}_{text{c}}) using easily accessible parameters, five machine learning algorithms were developed: Artificial Neural Network, Support Vector Machine, Decision Tree, Random Forest, and Lasso Regression. These models utilized meteorological variables (temperature, relative humidity, wind speed, net radiation) and crop parameters (leaf area index and plant height) as input features and measured ({text{E}text{T}}_{text{c}}) was utilized as target outputs for calibrating and validating the model. Among the algorithms, the RF and DT achieved the highest predictive accuracy (R2 = 0.98, NRMSE = 0.04), followed by ANN (R2 = 0.97, NRMSE = 0.07), SVR (R2 = 0.97, NRMSE = 0.08), and LASSO (R2 = 0.85, NRMSE = 0.18). Using lysimeter measurements as reliable reference data to evaluate machine-learning models provides a dependable framework for optimizing irrigation scheduling and enhancing water-use efficiency in onion cultivation under semi-arid conditions.

Data availability

Data will be available upon reasonable request from the corresponding author.

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Acknowledgements

The authors also thank the support of Shiraz University Research Council, Drought Research Center and Center of Excellence for On-Farm Water Management. The authors thank Mr. Hoseini for his great help and collaboration during the experiment.

Funding

This research was funded by Shiraz University under Grant no. 2GCB1M222407. Second author has received research support.

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Authors and Affiliations

  1. Water Engineering Department, School of Agriculture, Shiraz University, Shiraz, Iran

    Saba Hashempour Motlagh Shirazi, Fatemeh Razzaghi & Ali Reza Sepaskhah

  2. Drought Research Center, Shiraz University, Shiraz, Iran

    Fatemeh Razzaghi & Ali Reza Sepaskhah

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  1. Saba Hashempour Motlagh Shirazi
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  2. Fatemeh Razzaghi
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  3. Ali Reza Sepaskhah
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Contributions

Saba Hashempour: The conception and design of the study, acquisition of data, analysis, interpretation of data, and writing the first draft of manuscript; Fatemeh Razzaghi: The conception and design of the study, acquisition of data, analysis and interpretation of data, supervising, and revising the article critically; Ali Reza Sepaskhah: The conception and design of the study, and revising the article critically.

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Fatemeh Razzaghi.

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Shirazi, S.H.M., Razzaghi, F. & Sepaskhah, A.R. Estimation of onion crop evapotranspiration and crop coefficients using weighing lysimeters and machine learning models in semi-arid region.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-43887-w

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Keywords

  • Random Forest
  • Decision Tree
  • Crop coefficient
  • Semi-arid agriculture
  • Irrigation optimization

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