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<p>The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation, and carbon sequestration. Conventional direct BD measurement methods are often time-consuming, costly, and laborious. In contrast, the use of pedotransfer functions (PTFs) based on readily available soil properties offers a cost-effective alternative approach that can provide indirect quick estimates of soil BD. Although several region-specific PTFs are available, none is tailored for use in the hilly North-Western Himalayas (NWH) area. Thus, this work developed a novel PTF to estimate the soil BD of 300 soil samples collected from the NWH area and analyzed by stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT). The site-specific SR-based equation performed better than the PLSR model by explaining up to 72.2% variability of BD and achieving a coefficient of determination (R<sup>2</sup>) of 0.73, a mean absolute error (MAE) of 0.0379&#xa0;g cm⁻³, and a root mean square error (RMSE) of 0.0487&#xa0;g cm⁻³ for the BD prediction. Soil organic matter and clay contents were the most significant contributors, showing the highest importance in the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation (R<sup>2</sup>: 0.006–0.71, RMSE: 0.0649–0.405 gcm<sup>− 3</sup>, MAE: 0.0512–0.395&#xa0;g cm<sup>− 3</sup>). The promising SR equation and PTF developed in this work showed high accuracy and excellent suitability for stakeholders and professionals in the study area and the scientific community as a reliable BD estimator for hilly soils. This study introduces proposed the first-ever site-specific PTF for BD estimation in hilly soils, contributing to scientific challenge by enhancing soil database and improving BD predictions in complex terrains worldwide. In terms of global significance, the outcomes of this study are essential for soil quality and vulnerability studies, resilience indicators estimates carbon sequestration initiatives, and digital soil mapping, and can also be used to construct a BD database for hilly soils across the globe.</p> Graphical Abstract <p>The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation and carbon sequestration, and soil quality, vulnerability and resilience. Conventional direct methods to measure soil BD are often time-consuming, costly and laborious, whereas pedotransfer functions (PTFs) based on readily available soil properties offer an efficient alternative approach that provides indirect quick estimates of soil BD. However, none of the several region-specific PTFs currently available, is tailored for use in the hilly North-Western Himalayas (NWH) area. A novel PTF was developed in this work to evaluate the BD of 300 soil samples collected from agriculture, horticulture, forest and fallow hilly soils in the North West Himalaya area. Stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT) were tentatively applied for BD estimation of the studied soils, showing that the site-specific SR-based equation performed the best. Various soil properties were evaluated in the models, with soil organic matter and clay contents being the most significant contributors to the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation for the hilly soils studied in this work. In conclusion, the SR equation and PTF developed in this work show excellent and promising suitability for stakeholders and professionals in the study area and for the global scientific community as a reliable BD estimator for hilly soils. Furthermore, the results of this work can be used for soil management, land use planning, and climate change mitigation in fragile ecosystems like the Himalayas.</p>

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Soil Bulk Density Estimation by a Novel Pedotransfer Function Tailored on Hilly Terrains

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月23日

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  • Owais Bashir,
  • Shabir Ahmad Bangroo,
  • Sheikh Amjid,
  • Paolo Nasta,
  • Shahid Shafai,
  • Mario Palladino,
  • Nicola Senesi,
  • Rehana Rasool,
  • Burhan U. Choudhury,
  • Prakash Kumar Jha,
  • Vikas Abrol,
  • Velibor Spalevic,
  • Paul Sestras,
  • Negar Omidvar,
  • Sezai Ercişli,
  • Lizny Jaufer,
  • Brahim Benzougagh,
  • Youssef Bammou,
  • Ksibi Mohamed,
  • Tin Lukić,
  • Shuraik Kader

摘要

The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation, and carbon sequestration. Conventional direct BD measurement methods are often time-consuming, costly, and laborious. In contrast, the use of pedotransfer functions (PTFs) based on readily available soil properties offers a cost-effective alternative approach that can provide indirect quick estimates of soil BD. Although several region-specific PTFs are available, none is tailored for use in the hilly North-Western Himalayas (NWH) area. Thus, this work developed a novel PTF to estimate the soil BD of 300 soil samples collected from the NWH area and analyzed by stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT). The site-specific SR-based equation performed better than the PLSR model by explaining up to 72.2% variability of BD and achieving a coefficient of determination (R2) of 0.73, a mean absolute error (MAE) of 0.0379 g cm⁻³, and a root mean square error (RMSE) of 0.0487 g cm⁻³ for the BD prediction. Soil organic matter and clay contents were the most significant contributors, showing the highest importance in the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation (R2: 0.006–0.71, RMSE: 0.0649–0.405 gcm− 3, MAE: 0.0512–0.395 g cm− 3). The promising SR equation and PTF developed in this work showed high accuracy and excellent suitability for stakeholders and professionals in the study area and the scientific community as a reliable BD estimator for hilly soils. This study introduces proposed the first-ever site-specific PTF for BD estimation in hilly soils, contributing to scientific challenge by enhancing soil database and improving BD predictions in complex terrains worldwide. In terms of global significance, the outcomes of this study are essential for soil quality and vulnerability studies, resilience indicators estimates carbon sequestration initiatives, and digital soil mapping, and can also be used to construct a BD database for hilly soils across the globe.

Graphical Abstract

The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation and carbon sequestration, and soil quality, vulnerability and resilience. Conventional direct methods to measure soil BD are often time-consuming, costly and laborious, whereas pedotransfer functions (PTFs) based on readily available soil properties offer an efficient alternative approach that provides indirect quick estimates of soil BD. However, none of the several region-specific PTFs currently available, is tailored for use in the hilly North-Western Himalayas (NWH) area. A novel PTF was developed in this work to evaluate the BD of 300 soil samples collected from agriculture, horticulture, forest and fallow hilly soils in the North West Himalaya area. Stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT) were tentatively applied for BD estimation of the studied soils, showing that the site-specific SR-based equation performed the best. Various soil properties were evaluated in the models, with soil organic matter and clay contents being the most significant contributors to the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation for the hilly soils studied in this work. In conclusion, the SR equation and PTF developed in this work show excellent and promising suitability for stakeholders and professionals in the study area and for the global scientific community as a reliable BD estimator for hilly soils. Furthermore, the results of this work can be used for soil management, land use planning, and climate change mitigation in fragile ecosystems like the Himalayas.