Attia et al. (2025) Mapping spatial zones of climate vulnerability and adaptive potential for major crops in the Texas high plains

Identification

• Journal: Modeling Earth Systems and Environment
• Year: 2025
• Date: 2025-09-11
• Authors: Ahmed M. Attia, Prem Woli, Charles R. Long, F. M. Rouquette, Gerald R. Smith, Amir M. H. Ibrahim
• DOI: 10.1007/s40808-025-02605-7

Research Groups

• Texas A & M AgriLife Research and Extension Center, Overton, USA
• Department of Soil and Crop Sciences, Texas A & M University, College Station, USA

Short Summary

This study integrates process-based crop modeling with geospatial analysis to identify spatial zones of climate vulnerability and adaptive potential for four major crops in the Texas High Plains, revealing significant regional variability in yield responses that necessitate targeted adaptation strategies.

Objective

• To simulate historical and future crop yields using the DSSAT model across all counties in the Texas High Plains.
• To classify and map vulnerable and adaptive zones based on projected yield trajectories.
• To assess the relative impacts of climate drivers on productivity trends to inform regional adaptation planning.

Study Configuration

• Spatial Scale: Texas High Plains (THP), approximately 8.9 million hectares, covering 48 counties. Simulations were conducted on a 12 km resolution grid and aggregated to the county level.
• Temporal Scale:
  • Baseline: 1991–2020
  • Mid-century: 2031–2060
  • End-century: 2070–2099
  • Climate scenarios: Representative Concentration Pathway 4.5 (RCP 4.5) and 8.5 (RCP 8.5)

Methodology and Data

• Models used:
  • Decision Support System for Agrotechnology Transfer (DSSAT) v. 4.8, including CERES-Sorghum, CERES-Maize, CROPGRO-Cotton, and CERES-Wheat modules.
  • Spatial clustering techniques: Global Moran’s I and Getis-Ord Gi* statistics.
• Data sources:
  • Climate projections: Multivariate Adaptive Constructed Analogs (MACA) dataset, statistically downscaled from five Global Circulation Models (GCMs) (IPSL-CM5A-MR, MIROC5, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0) to approximately 6 km resolution. Daily maximum/minimum temperature (°C), precipitation (mm), solar radiation (MJ m⁻² day⁻¹), wind speed at 2 m (m s⁻¹), and relative humidity (%).
  • Soil data: WISE database (ISRIC SoilGrids initiative), providing soil texture, bulk density, organic carbon content, pH, field capacity, wilting point, and saturated hydraulic conductivity across multiple soil layers.
  • Crop management: A 4-year improved rotation system integrating cover crops, with specific planting/harvesting dates and nitrogen fertilization rates for each crop, simulated under dryland (rainfed) conditions.

Main Results

• Wheat: Vulnerability was concentrated in southern counties, with projected yield decreases of 10–30% under RCP 8.5 relative to the 1991–2020 baseline. Northern counties showed yield increases of 30–50% under RCP 4.5 by mid-century and under RCP 8.5 by end-century.
• Cotton: Projected to increase by 20–40% across most counties under both RCP 4.5 end-century and RCP 8.5 mid- and end-century. Localized vulnerability may emerge in southwestern THP under RCP 8.5 by end-century.
• Grain Sorghum: Projected to increase by 10–20% in eastern and northern counties under RCP 4.5, but under RCP 8.5, widespread yield declines exceeding 40% are expected by end-century, largely attributed to reduced rainfall and increased temperature stress during the growing season (due to late June planting).
• Maize: Showed spatially variable yield changes, with several southern counties exhibiting positive trends (e.g., 50–100% yield increases by mid-century under RCP 8.5), primarily associated with projected precipitation increases during the growing season and early planting (April). Northern counties showed declines up to 50% by end-century under RCP 8.5.
• Spatial Clustering: Global Moran’s I values indicated strong spatial clustering of similar yield changes for all crops (ranging from 0.405 to 0.911, p < 0.001). Getis-Ord Gi* statistics successfully classified counties into vulnerable, adaptive, stable, and more stable climate impact zones.
• Precipitation Trends: Yield responses generally tracked changes in growing season precipitation, but C3 crops (wheat, cotton) showed yield improvements in some drier regions under RCP 8.5, suggesting a compensatory role of elevated atmospheric CO₂ fertilization.

Contributions

• Provides a spatially explicit, county-level assessment of climate change impacts on major crop yields in the Texas High Plains, integrating advanced crop modeling with geospatial statistical analysis.
• Identifies and maps specific climate impact zones (vulnerable, adaptive, stable, more stable) for winter wheat, cotton, maize, and grain sorghum, offering a nuanced understanding of regional climate risk beyond aggregated averages.
• Offers actionable insights for developing targeted adaptation strategies, including climate-resilient crop varieties, optimized irrigation management, crop diversification, and adaptive land use planning, to enhance agricultural resilience in semi-arid environments.
• Integrates an improved crop rotation system with cover crops and DSSAT-calibrated cultivars specifically adapted to the region's semi-arid climate.

Funding

• Funding for this research was provided by Texas A&M AgriLife Research Center in Overton.

Citation

@article{Attia2025Mapping,
  author = {Attia, Ahmed M. and Woli, Prem and Long, Charles R. and Rouquette, F. M. and Smith, Gerald R. and Ibrahim, Amir M. H.},
  title = {Mapping spatial zones of climate vulnerability and adaptive potential for major crops in the Texas high plains},
  journal = {Modeling Earth Systems and Environment},
  year = {2025},
  doi = {10.1007/s40808-025-02605-7},
  url = {https://doi.org/10.1007/s40808-025-02605-7}
}