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<p>Soil fertility is critical for sustainable agriculture, especially in arid and semi-arid regions where environmental constraints affect crop productivity. This study assesses the Soil Fertility Index (SFI) for wheat cultivation in Khuzestan Province using six machine learning (ML) models: Random Forest (RF), Support Vector Machine (SVM), Cubist (CB), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XG). Empirical Bayesian Kriging (EBK) was hybridized with the models to improve accuracy and reduce uncertainty. 89 environmental covariates—including radar and optical remote sensing (RS) data, climatic variables, and topographic attributes—were used as proxies for soil-forming factors. The RF model showed the best individual performance (R<sup>2</sup> = 0.77, RMSE = 3.76, CCC = 0.88), while the hybrid RF-EBK model achieved higher accuracy (R<sup>2</sup> = 0.84, RMSE = 2.98, CCC = 0.91), outperforming all others. Hybridization notably improved weaker models such as k-NN-EBK. Relative importance analysis highlighted RS covariates—such as the normalized difference vegetation index (NDVI), soil brightness index (BI), and green–red vegetation index (GRVI)—as dominant predictors, exceeding the impact of climatic and topographic variables. The spatial prediction map indicated that 3.3% of soils had very low fertility, 59.6% had low fertility, and 37.1% had medium fertility. Uncertainty assessment based on the prediction interval coverage probability (PICP) showed that 91.1% of RF-EBK predictions fell within the 90% interval, suggesting high confidence in the spatial results. This study demonstrates the value of advanced ML models combined with uncertainty analysis in supporting sustainable agricultural decisions in environmentally constrained regions. </p>

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Improved soil fertility mapping for wheat cultivation in the southwest agricultural plain of Iran: integration of comparative modeling techniques and environmental variables

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Zeinab Zaheri Abdehvand,
  • Kazem Rangzan,
  • Danya Karimi,
  • Seyed Roohollah Mousavi

摘要

Soil fertility is critical for sustainable agriculture, especially in arid and semi-arid regions where environmental constraints affect crop productivity. This study assesses the Soil Fertility Index (SFI) for wheat cultivation in Khuzestan Province using six machine learning (ML) models: Random Forest (RF), Support Vector Machine (SVM), Cubist (CB), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XG). Empirical Bayesian Kriging (EBK) was hybridized with the models to improve accuracy and reduce uncertainty. 89 environmental covariates—including radar and optical remote sensing (RS) data, climatic variables, and topographic attributes—were used as proxies for soil-forming factors. The RF model showed the best individual performance (R2 = 0.77, RMSE = 3.76, CCC = 0.88), while the hybrid RF-EBK model achieved higher accuracy (R2 = 0.84, RMSE = 2.98, CCC = 0.91), outperforming all others. Hybridization notably improved weaker models such as k-NN-EBK. Relative importance analysis highlighted RS covariates—such as the normalized difference vegetation index (NDVI), soil brightness index (BI), and green–red vegetation index (GRVI)—as dominant predictors, exceeding the impact of climatic and topographic variables. The spatial prediction map indicated that 3.3% of soils had very low fertility, 59.6% had low fertility, and 37.1% had medium fertility. Uncertainty assessment based on the prediction interval coverage probability (PICP) showed that 91.1% of RF-EBK predictions fell within the 90% interval, suggesting high confidence in the spatial results. This study demonstrates the value of advanced ML models combined with uncertainty analysis in supporting sustainable agricultural decisions in environmentally constrained regions.