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<p>Stress from climate change is now seriously affecting agriculture worldwide, so it is essential to have practical tools to measure and evaluate its impact on crop productivity. To better capture agroclimatic stress and its implications for crop productivity, we introduce a new composite metric, the Agricultural Productivity Stress Index (APSI), which is constructed using All Sky Surface Photosynthetically Active Radiation total (PAR), Wind Direction at 10&#xa0;m (WD10), and the Warm Spell Duration Index (WSDI). First, all indicators were standardized to allow for fair comparison worldwide and throughout time. Then, they were merged objectively using Triple Collocation (TC) and Scaled Triple Collocation (STC) methods that do not require any ground-truth reference. Long-term climate data spanning from 1991 to 2022 in the six important agricultural regions were used to assess APSI performance based on Correlation Analysis, Error Variance Analysis, Nash-Sutcliffe Efficiency (NSE), the Taylor Diagram, and trend analysis through the Mann-Kendall test and Sen’s Slope estimation. According to all evaluation metrics, STC generally improved APSI estimation by reducing error variances, strengthening correlations, and enhancing NSE values, particularly for WD10, while maintaining consistent performance for PAR. However, its impact on WSDI remained less consistent, with only slight and location-dependent improvements. These findings suggest that STC enhances the robustness of APSI through WD10 and PAR, whereas further refinement is required to better capture WSDI variability. An analysis of climate trends revealed that PAR and WD10 decreased significantly on most sites, while WSDI increased in Quetta, suggesting more heat exposure in that region. Overall, the APSI framework gives a powerful, flexible, and information-driven method for figuring out agricultural stress related to climate variability.</p>

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Integrating Standardized Climate Indicators Using Triple and Scaled Triple Collocation to Develop an Agricultural Productivity Stress Index

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Muhammad Asim Khan,
  • Jianyi Lin,
  • Mohammed M. A. Almazah,
  • Ijaz Hussain,
  • Hanen Louati,
  • Mhassen. E. E. Dalam

摘要

Stress from climate change is now seriously affecting agriculture worldwide, so it is essential to have practical tools to measure and evaluate its impact on crop productivity. To better capture agroclimatic stress and its implications for crop productivity, we introduce a new composite metric, the Agricultural Productivity Stress Index (APSI), which is constructed using All Sky Surface Photosynthetically Active Radiation total (PAR), Wind Direction at 10 m (WD10), and the Warm Spell Duration Index (WSDI). First, all indicators were standardized to allow for fair comparison worldwide and throughout time. Then, they were merged objectively using Triple Collocation (TC) and Scaled Triple Collocation (STC) methods that do not require any ground-truth reference. Long-term climate data spanning from 1991 to 2022 in the six important agricultural regions were used to assess APSI performance based on Correlation Analysis, Error Variance Analysis, Nash-Sutcliffe Efficiency (NSE), the Taylor Diagram, and trend analysis through the Mann-Kendall test and Sen’s Slope estimation. According to all evaluation metrics, STC generally improved APSI estimation by reducing error variances, strengthening correlations, and enhancing NSE values, particularly for WD10, while maintaining consistent performance for PAR. However, its impact on WSDI remained less consistent, with only slight and location-dependent improvements. These findings suggest that STC enhances the robustness of APSI through WD10 and PAR, whereas further refinement is required to better capture WSDI variability. An analysis of climate trends revealed that PAR and WD10 decreased significantly on most sites, while WSDI increased in Quetta, suggesting more heat exposure in that region. Overall, the APSI framework gives a powerful, flexible, and information-driven method for figuring out agricultural stress related to climate variability.