Ahmad et al. (2026) Modeling mustard water use and its effects on soil water content and subsequent maize performance under projected climate scenarios

Identification

• Journal: European Journal of Agronomy
• Year: 2026
• Date: 2026-01-02
• Authors: Uzair Ahmad, Xuejun Dong, Shah Jahan Leghari, Gerrit Hoogenboom
• DOI: 10.1016/j.eja.2025.127971

Research Groups

• Texas A&M AgriLife Research & Extension Center, Uvalde, TX, USA
• College of Mechanical and Electronical Engineering, Northwest A&F University, Yangling, China
• Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA

Short Summary

This study calibrated and evaluated the DSSAT model using field data to simulate mustard cover crop effects on soil water content and subsequent maize performance under projected climate scenarios in semi-arid southwest Texas. It found that mustard cover cropping improved soil water content and maize yield under moderate to high rainfall, but had neutral effects in drier conditions, with maize irrigation demand projected to increase by 20% by 2100.

Objective

• To quantify the effects of mustard cover crops on root-zone soil water content (SWC) in semi-arid maize cropping systems.
• To evaluate the influence of residual soil moisture from mustard on subsequent maize growth, yield, and water use efficiency (WUE).
• To simulate and analyze soil water dynamics and maize performance under future climate scenarios using DSSAT coupled with seven Global Climate Models (GCMs).

Study Configuration

• Spatial Scale: Field experiments at the Texas A&M AgriLife Research and Extension Center in Uvalde, Texas, USA (29°13' N, 99°45' W, 276 m elevation), with 18 plots (9 m × 18 m). Soil water content was measured across the 0–120 cm profile.
• Temporal Scale: Field data collected from 2019 to 2020 for model calibration and evaluation. Climate projections from GCMs were analyzed for five time slices: baseline (1986–2005), reference (2006–2024), mid-century (2025–2050), near-future (2051–2070), and far-future (2071–2100).

Methodology and Data

• Models used:
  • Decision Support System for Agrotechnology Transfer (DSSAT) v4.8.2
  • CERES-Maize model (for maize simulation)
  • CROPGRO-canola module (as a proxy for mustard simulation)
  • Seven Global Climate Models (GCMs) from the CMIP5 ensemble: GFDL-ESM2M, CanESM2, ACCESS1.0, MPI-ESM-LR, CMCC-CM, IPSL-CM5A-LR, and NorESM1-M.
• Data sources:
  • Field observations (2019–2020) of soil water content (SWC), leaf area index (LAI), aboveground biomass (AGB), and grain yield for maize and mustard.
  • Soil chemical and physical properties (e.g., texture, bulk density, pH, organic matter, nutrient concentrations).
  • Daily meteorological data (precipitation, maximum and minimum temperature, solar radiation) from the study site.
  • Bias-corrected and downscaled climate projections from CMIP5 GCMs under RCP 4.5 and RCP 8.5 scenarios.

Main Results

• The DSSAT model accurately simulated LAI for maize (RMSE = 0.28) and mustard (RMSE = 1.42), and AGB for maize (RMSE = 1092.51 kg ha⁻¹) and mustard (RMSE = 772.54 kg ha⁻¹). Simulated SWC closely matched observed values.
• Maize yield is projected to peak in 2050 under RCP 4.5 and decline under RCP 8.5.
• Mustard cover cropping improved root-zone SWC by 0.02–0.04 m³ m⁻³ and subsequently increased maize yield by 3% under wet-class GCM projections (moderate to high rainfall scenarios).
• Under drier conditions or dry-class GCM projections, mustard cover cropping had neutral effects on SWC (as low as 0.13 m³ m⁻³) and maize yield.
• Maintaining SWC above 0.27 m³ m⁻³ is critical for maize yield stability in the region.
• Maize irrigation demand is projected to increase by 20% by 2100, while mustard irrigation demand is projected to increase by 32.14% under RCP 8.5 by 2100.
• Sensitivity analysis showed that maize yield is strongly influenced by genetic parameters P1, P5, and G2, which regulate phenology and grain filling duration.

Contributions

• This study is novel in integrating DSSAT with downscaled, bias-corrected climate projections from seven GCMs to simulate mustard-maize interactions in terms of soil water dynamics and crop productivity.
• It applied a DSSAT–GCM ensemble approach to simulate soil water dynamics and crop responses in a mustard-maize cropping system under semi-arid conditions, providing insights into how mustard cover crops influence soil water retention and maize performance under projected rainfall scenarios.
• The research is among the few to use GCMs within the DSSAT framework to simulate SWC and evaluate results against field-collected data in southwest Texas, informing context-specific strategies for climate adaptation.

Funding

• U.S. Department of Agriculture’s National Institute of Food and Agriculture, project “Increasing WUE for Crop Production: An Approach of Soil-Plant Water Relations” (award number TEX0-1-9574).

Citation

@article{Ahmad2026Modeling,
  author = {Ahmad, Uzair and Dong, Xuejun and Leghari, Shah Jahan and Hoogenboom, Gerrit},
  title = {Modeling mustard water use and its effects on soil water content and subsequent maize performance under projected climate scenarios},
  journal = {European Journal of Agronomy},
  year = {2026},
  doi = {10.1016/j.eja.2025.127971},
  url = {https://doi.org/10.1016/j.eja.2025.127971}
}