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A quantification method for cumulative drought effects on summer maize yield incorporating recovery dynamics based on Vegetation Water Index

Author

Listed:
  • Zhang, Yuanda
  • Wang, Peijuan
  • Li, Yang
  • Han, Dianchen
  • Wu, Dingrong
  • Liu, Shuxian
  • Guo, Jianping

Abstract

Effective quantification of drought effects on summer maize (Zea mays L.), incorporating recovery dynamics, is critical for precise agricultural loss assessment and mitigation. To address the limitation of existing drought indices in capturing the dynamic drought effects of drought stress and subsequent recovery, this study developed a novel quantification method of daily DE (drought effect) based on the Vegetation Water Index (VWI). Daily DE values, calculated via proportional interpolation relative to dynamic mild, moderate, and severe drought thresholds, quantitatively captured drought intensity and recovery across growth stages. The DEs for a specific period were accumulated to quantify the combined influence of drought intensity and recovery dynamics affecting maize. Cumulative DEs for three stages were computed, one for critical water requirement stage (stem elongation (V6)–milk (R3)) and two for non-critical water requirement stages (planting (V0)–V6 and R3–physiological maturity (R6)). Cumulative DEs for three stages, ratio of summer maize pixels (Rmp), and geographical coordinates were employed as input variables to simulate maize yield incorporating Linear, SVM (Support Vector Machine), LightGBM (Light Gradient Boosting Machine), and Random Forest models. The SHAP (SHapley Additive exPlanations) analysis was conducted for revealing the contributions of each factor. The results indicated that: (1) The DE effectively tracked the drought evolution in real-time, and showed strong spatiotemporal consistency with soil moisture; (2) Cumulative DEs provided a more accurate depiction of the cumulative drought impacts on crops and the mitigation effectiveness during a certain period; (3) The Random Forest model achieved superior yield simulation accuracy (R² = 0.90, MAPE = 4.25 %, RMSE = 339.57 kg/ha); (4) SHAP analysis revealed that the cumulative DEs during V6–R3 stage had the greatest impact on yield, followed by the cumulative DEs during V0–V6 and R3–R6 stages, Rmp, latitude, and longitude. This quantification framework transformed qualitative drought monitoring into a dynamic, physiologically interpretable assessment tool. It provided a scientific basis for precise irrigation scheduling and targeted drought mitigation strategies, thereby enhancing agricultural resilience in the face of drought.

Suggested Citation

  • Zhang, Yuanda & Wang, Peijuan & Li, Yang & Han, Dianchen & Wu, Dingrong & Liu, Shuxian & Guo, Jianping, 2025. "A quantification method for cumulative drought effects on summer maize yield incorporating recovery dynamics based on Vegetation Water Index," Agricultural Water Management, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:agiwat:v:320:y:2025:i:c:s0378377425005797
    DOI: 10.1016/j.agwat.2025.109865
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