Sen et al. (2026) Benchmarking farmers’ irrigation decisions using farm competition data and a crop growth model

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Irrigation Science
• Year: 2026
• Date: 2026-08-05
• Authors: Rintu Sen, Saleh Taghvaeian, Daran R. Rudnick, Christopher A. Proctor, 杨海顺, Abia Katimbo, Chuck Burr, Derek M. Heeren
• DOI: 10.1007/s00271-026-01161-x

Research Groups

• University of Nebraska-Lincoln (UNL), Testing Ag Performance Solutions (UNL-TAPS) program.

Short Summary

This study benchmarks actual farmer irrigation practices against optimal amounts for maize production in Nebraska using the DSSAT CERES-Maize model to quantify water management efficiency.

Objective

• To determine optimal irrigation amounts and quantify the deviations of actual farmer irrigation decisions from these optima to identify opportunities for sustainable water management.

Study Configuration

• Spatial Scale: Farm-level sites across Nebraska, USA.
• Temporal Scale: Multi-year period.

Methodology and Data

• Models used: DSSAT CERES-Maize (calibrated for cultivar traits and site-specific conditions).
• Data sources: University of Nebraska-Lincoln’s Testing Ag Performance Solutions (UNL-TAPS) program.

Main Results

• Model Validation: The DSSAT CERES-Maize model showed strong agreement with observed phenology and yield (NRMSE < 5%, d > 0.80).
• Irrigation Deviations: Seasonal differences between applied and optimal irrigation ranged from −175 mm to 306 mm, with a mean difference of 4 mm.
• Over-irrigation: 54% of farmer teams applied more water than optimal, with a mean over-irrigation of 58 mm.
• Variability: Actual irrigation showed significantly higher variability (CV = 44%) compared to optimal irrigation (CV = 21%), indicating that behavioral factors heavily influence irrigation decisions.

Contributions

• Improves upon previous benchmarking studies by replacing simplified modeling assumptions with a calibrated, validated crop model, providing a more accurate assessment of the magnitude of over-irrigation in Nebraska maize production.

Funding

• Not specified in the provided text.

Citation

@article{Sen2026Benchmarking,
  author = {Sen, Rintu and Taghvaeian, Saleh and Rudnick, Daran R. and Proctor, Christopher A. and 杨海顺 and Katimbo, Abia and Burr, Chuck and Heeren, Derek M.},
  title = {Benchmarking farmers’ irrigation decisions using farm competition data and a crop growth model},
  journal = {Irrigation Science},
  year = {2026},
  doi = {10.1007/s00271-026-01161-x},
  url = {https://doi.org/10.1007/s00271-026-01161-x}
}