Jia et al. (2026) Calibrating DSSAT–CROPGRO–Tomato to optimize stage-specific deficit irrigation for processing tomato across hydrological year types

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-09-22
• Authors: Zhecheng Jia, Yue Wen, Yonghui Liang, Hao Jia, Jinzhu Zhang, Zhenhua Wang
• DOI: 10.1016/j.agwat.2026.110790

Research Groups

• College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang, China
• College of Hydraulic and Civil Engineering of Xinjiang Agricultural University, Urumqi, Xinjiang, China
• Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi, Xinjiang, China
• Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang, China
• Engineering Technology Center in the Corps for Comprehensive Utilization of Saline-Alkali Land, Shihezi, Xinjiang, China

Short Summary

This study calibrated and applied the DSSAT–CROPGRO–Tomato model for processing tomato under drip irrigation in Xinjiang, China, integrating field experiments, long-term hydrological year classification, and stage-specific deficit irrigation scenarios. It found that optimal deficit irrigation strategies are highly dependent on hydrological year type, recommending different regimes for wet/normal versus dry years to balance water saving and yield stability.

Objective

• To calibrate the DSSAT–CROPGRO–Tomato model for drip-irrigated processing tomato using field experiments with stage-specific deficit irrigation.
• To evaluate the calibrated model using calibration and validation datasets for key growth and yield indicators.
• To classify long-term weather records into wet, normal, and dry hydrological year types based on growing-season precipitation.
• To use the calibrated model to assess yield and water productivity responses to 50 stage-specific irrigation regimes under representative hydrological years.

Study Configuration

• Spatial Scale: Field experiments conducted at the irrigation experimental station of the Wuyi Agricultural Regiment in Toutun River District, Urumqi, Xinjiang, China (southern margin of the Junggar Basin and northern slope of the Tianshan Mountains). Long-term weather data for the region.
• Temporal Scale: Field experiments in 2019 and 2020. Long-term daily weather data for a 38-year historical period for hydrological year classification. Growing season for processing tomato was approximately 151 (156) days to 273 (278) days of the year.

Methodology and Data

• Models used: DSSAT–CROPGRO–Tomato model, GLUE (Generalized Likelihood Uncertainty Estimation) procedure for cultivar genetic coefficient calibration.
• Data sources:
  • Field experiments (2019, 2020) on 'Heinz 1015' processing tomato under full irrigation and eight stage-specific deficit drip irrigation treatments.
  • Field observations: Leaf area index (LAI), aboveground dry matter, fresh fruit yield, fruit dry matter, phenological development (flowering, fruit setting, fruit expansion, red-ripening), and gravimetric soil water content (at 20, 40, 60 cm depths).
  • Weather data: Long-term daily solar radiation (MJ⋅m⁻²⋅d⁻¹), maximum daily temperature (°C), minimum daily temperature (°C), and daily precipitation from the China Meteorological Science Data Sharing Service Network (http://data.cma.cn/) at Urumqi and national meteorological stations.
  • Soil properties: Characterized over 0–100 cm depth, including hydraulic properties and initial nutrient status.

Main Results

• GLUE-based calibration yielded stable genetic coefficients for most phenological and growth parameters (e.g., FL-SH, FL-SD, SD-PM, FLLF, SLAVR, SFDUR with coefficients of variation below 5%).
• The calibrated DSSAT–CROPGRO–Tomato model satisfactorily reproduced observed dynamics of leaf area index, aboveground dry matter, fruit yield, and phenological development during both calibration (2019) and independent validation (2020).
• Hydrological classification of 38-year growing-season precipitation identified representative wet (141.28 mm), normal (109.77 mm), and dry (80.71 mm) years.
• Scenario analysis with 50 stage-specific irrigation regimes showed that yield and water productivity responses were strongly dependent on hydrological year type.
• In wet and normal years, moderate deficit irrigation during non-critical stages (seedling and red-ripening) improved water productivity with acceptable yield loss.
• In dry years, irrigation strategies needed to be more conservative, prioritizing higher irrigation levels during critical stages (flowering, fruit setting, and fruit expansion) to maintain yield stability.
• Recommended irrigation regimes reduced irrigation by 7.02–11.47% while limiting yield loss to 3.57–5.09%. Specifically:
  • For wet and normal years, treatment T40 (irrigation coefficients of 0.875, 1.000, 0.875, 0.750 for seedling, flowering-fruit setting, fruit expansion, and red-ripening stages, respectively) was recommended, resulting in 4.27–5.09% yield loss and 11.46–11.47% water saving.
  • For dry years, treatment T32 (irrigation coefficients of 1.000, 1.000, 0.875, 0.875) was recommended, leading to 3.57% yield loss and 7.02% water saving.

Contributions

• This study is the first to integrate DSSAT–CROPGRO–Tomato model calibration, long-term hydrological year classification, and stage-specific deficit irrigation scenarios to develop climate-adaptive irrigation scheduling for processing tomato in Xinjiang, China.
• It provides a robust framework for extending limited field observations to broader weather and management scenarios, addressing the challenge of interannual hydroclimatic variability.
• The research quantifies the trade-off between yield loss and water productivity improvement under different hydrological year types, offering specific, data-driven recommendations for irrigation management.
• It highlights the importance of stage-specific irrigation regulation tailored to hydrological conditions for optimizing water saving and yield stability in arid regions.

Funding

• The Research on Whole-Process Digital Precision Regulation Technologies and Equipment for Agricultural Drip Irrigation in Arid Areas, Major Science and Technology Project of the Ministry of Water Resources (SKS-2025072).

Citation

@article{Jia2026Calibrating,
  author = {Jia, Zhecheng and Wen, Yue and Liang, Yonghui and Jia, Hao and Zhang, Jinzhu and Wang, Zhenhua},
  title = {Calibrating DSSAT–CROPGRO–Tomato to optimize stage-specific deficit irrigation for processing tomato across hydrological year types},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110790},
  url = {https://doi.org/10.1016/j.agwat.2026.110790}
}