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<p>Sustainable agricultural management in arid and semi-arid regions is increasingly constrained by land degradation, particularly secondary salinization, poor drainage, and structural deterioration. Reliable soil quality assessment is therefore crucial for food security; however, widely adopted generic indices often fail to capture the unique geomorphological and climatic complexities of dryland agroecosystems such as the Mesopotamian Plain. To address this critical gap, this study developed the Maysan Agricultural Soil Quality Index (MASQI), an automatic and location-specific spatial framework for agricultural soil quality evaluation in Maysan Governorate, Iraq. A total of 230 georeferenced pedons were analyzed using 11 variables Minimum Data Set comprising key physicochemical indicators. Soil indicators were standardized using linear scoring techniques, while Principal Component Analysis (PCA) was employed to derive objective parameter weights and minimize expert-driven subjectivity. These outputs were integrated into a custom Geographic Information System (GIS) toolbox, the “Automated Soil Quality Spatial Modeler” (ASQSM), that fully automated index generation and spatial classification. Results showed 54.39% of the studied agricultural lands fall within poor quality classes, primarily driven by high salinity and inadequate drainage. Independent validation using field surveys and farmer assessments demonstrated significant agreement between predicted classes and ground-truth observations, evidenced by a Spearman’s Rank Correlation of 0.761. Complementing these findings, a Local Indicators of Spatial Association (LISA) analysis revealed statistically significant spatial clustering (<i>p</i> &lt; 0.05), effectively identifying specific contiguous zones of high soil quality alongside localized areas of soil degradation. Ultimately, the MASQI framework provides a reproducible and scalable decision-support system for identifying priority reclamation zones and optimizing sustainable agricultural management, offering potential for application across salinity-affected drylands.</p> Graphical Abstract <p></p> <p>The provided graphical abstract visually encapsulates the comprehensive methodological framework and the technological innovation introduced in this study for assessing agricultural soil quality in arid environments. The left panel illustrates the rigorous, traditional workflow required to develop the Maysan Agricultural Soil Quality Index (MASQI). This complex phase involves processing 11 standardized physicochemical soil rasters and applying multivariate statistical modeling, specifically Principal Component Analysis (PCA), to derive objective statistical weights, thereby eliminating subjective bias. However, manually integrating these weights via traditional map algebra is a highly repetitive and time-consuming process. To bridge this complexity gap, the right panel highlights the study’s core innovation: the Automated Soil Quality Spatial Modeler (ASQSM) toolbox. This custom-built, open-access GIS spatial toolbox provides an automated, one-click solution that seamlessly applies PCA-derived weights to input rasters, instantaneously generating a high-resolution soil quality map. Crucially, the final cartographic output underwent rigorous ground-truthing through comprehensive field surveys and direct farmer assessments. Field validation confirmed a statistically significant agreement and reliable predictive performance, as evidenced by a rank-based correlation metric (Spearman’s = 0.761). Furthermore, localized spatial autocorrelation was confirmed using the Local Indicators of Spatial Association (LISA) analysis, demonstrating a realistic and statistically significant alignment between the model’s predictions and actual land degradation states. Ultimately, this graphical abstract demonstrates how advanced statistical theory is translated into a highly accurate, user-friendly spatial guide, offering a vital decision-support system for sustainable agricultural investment and land reclamation in the Mesopotamian Plain.</p>

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MASQI: An Automated Spatial Framework for Agricultural Soil Quality Assessment in Arid Lands

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Heba Al-Hashimi,
  • Tariq Al-Mawla,
  • Shahbaz Khan,
  • Rizwan Niaz

摘要

Sustainable agricultural management in arid and semi-arid regions is increasingly constrained by land degradation, particularly secondary salinization, poor drainage, and structural deterioration. Reliable soil quality assessment is therefore crucial for food security; however, widely adopted generic indices often fail to capture the unique geomorphological and climatic complexities of dryland agroecosystems such as the Mesopotamian Plain. To address this critical gap, this study developed the Maysan Agricultural Soil Quality Index (MASQI), an automatic and location-specific spatial framework for agricultural soil quality evaluation in Maysan Governorate, Iraq. A total of 230 georeferenced pedons were analyzed using 11 variables Minimum Data Set comprising key physicochemical indicators. Soil indicators were standardized using linear scoring techniques, while Principal Component Analysis (PCA) was employed to derive objective parameter weights and minimize expert-driven subjectivity. These outputs were integrated into a custom Geographic Information System (GIS) toolbox, the “Automated Soil Quality Spatial Modeler” (ASQSM), that fully automated index generation and spatial classification. Results showed 54.39% of the studied agricultural lands fall within poor quality classes, primarily driven by high salinity and inadequate drainage. Independent validation using field surveys and farmer assessments demonstrated significant agreement between predicted classes and ground-truth observations, evidenced by a Spearman’s Rank Correlation of 0.761. Complementing these findings, a Local Indicators of Spatial Association (LISA) analysis revealed statistically significant spatial clustering (p < 0.05), effectively identifying specific contiguous zones of high soil quality alongside localized areas of soil degradation. Ultimately, the MASQI framework provides a reproducible and scalable decision-support system for identifying priority reclamation zones and optimizing sustainable agricultural management, offering potential for application across salinity-affected drylands.

Graphical Abstract

The provided graphical abstract visually encapsulates the comprehensive methodological framework and the technological innovation introduced in this study for assessing agricultural soil quality in arid environments. The left panel illustrates the rigorous, traditional workflow required to develop the Maysan Agricultural Soil Quality Index (MASQI). This complex phase involves processing 11 standardized physicochemical soil rasters and applying multivariate statistical modeling, specifically Principal Component Analysis (PCA), to derive objective statistical weights, thereby eliminating subjective bias. However, manually integrating these weights via traditional map algebra is a highly repetitive and time-consuming process. To bridge this complexity gap, the right panel highlights the study’s core innovation: the Automated Soil Quality Spatial Modeler (ASQSM) toolbox. This custom-built, open-access GIS spatial toolbox provides an automated, one-click solution that seamlessly applies PCA-derived weights to input rasters, instantaneously generating a high-resolution soil quality map. Crucially, the final cartographic output underwent rigorous ground-truthing through comprehensive field surveys and direct farmer assessments. Field validation confirmed a statistically significant agreement and reliable predictive performance, as evidenced by a rank-based correlation metric (Spearman’s = 0.761). Furthermore, localized spatial autocorrelation was confirmed using the Local Indicators of Spatial Association (LISA) analysis, demonstrating a realistic and statistically significant alignment between the model’s predictions and actual land degradation states. Ultimately, this graphical abstract demonstrates how advanced statistical theory is translated into a highly accurate, user-friendly spatial guide, offering a vital decision-support system for sustainable agricultural investment and land reclamation in the Mesopotamian Plain.