Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields
Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirec...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Nitrogen |
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| Online Access: | https://www.mdpi.com/2504-3129/6/2/23 |
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| author | Qassim A. Talib Al-Shujairy Suhad M. Al-Hedny Mohammed A. Naser Sadeq Muneer Shawkat Ahmed Hatem Ali Dinesh Panday |
| author_facet | Qassim A. Talib Al-Shujairy Suhad M. Al-Hedny Mohammed A. Naser Sadeq Muneer Shawkat Ahmed Hatem Ali Dinesh Panday |
| author_sort | Qassim A. Talib Al-Shujairy |
| collection | DOAJ |
| description | Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R<sup>2</sup> = 0.773), followed by the ratio (B5/B7) model (R<sup>2</sup> = 0.489), while the inverse log transformation (1/log (B5/B7), R<sup>2</sup> = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems. |
| format | Article |
| id | doaj-art-264aeeda91434c02b52e31b2a47fa9ed |
| institution | DOAJ |
| issn | 2504-3129 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nitrogen |
| spelling | doaj-art-264aeeda91434c02b52e31b2a47fa9ed2025-08-20T03:16:35ZengMDPI AGNitrogen2504-31292025-04-01622310.3390/nitrogen6020023Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat FieldsQassim A. Talib Al-Shujairy0Suhad M. Al-Hedny1Mohammed A. Naser2Sadeq Muneer Shawkat3Ahmed Hatem Ali4Dinesh Panday5College of Environmental Sciences, Al-Qasim Green University, Babil 51013, IraqCollege of Environmental Sciences, Al-Qasim Green University, Babil 51013, IraqDepartment of Combating Desertification, College of Agriculture, Al-Muthanna University, Al-Samawah 66001, IraqCollege of Food Sciences, Al-Qasim Green University, Babil 51013, IraqCollege of Environmental Sciences, Al-Qasim Green University, Babil 51013, IraqRodale Institute, Kutztown, PA 19530, USASoil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R<sup>2</sup> = 0.773), followed by the ratio (B5/B7) model (R<sup>2</sup> = 0.489), while the inverse log transformation (1/log (B5/B7), R<sup>2</sup> = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems.https://www.mdpi.com/2504-3129/6/2/23bootstrappingfertility managementmachine learningrandom forestsoil nitrogen |
| spellingShingle | Qassim A. Talib Al-Shujairy Suhad M. Al-Hedny Mohammed A. Naser Sadeq Muneer Shawkat Ahmed Hatem Ali Dinesh Panday Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields Nitrogen bootstrapping fertility management machine learning random forest soil nitrogen |
| title | Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields |
| title_full | Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields |
| title_fullStr | Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields |
| title_full_unstemmed | Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields |
| title_short | Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields |
| title_sort | bootstrapping enhanced model for improving soil nitrogen prediction accuracy in arid wheat fields |
| topic | bootstrapping fertility management machine learning random forest soil nitrogen |
| url | https://www.mdpi.com/2504-3129/6/2/23 |
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