Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields
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| Publicado no: | Nitrogen vol. 6, no. 2 (2025), p. 23-37 |
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| Autor principal: | |
| Outros Autores: | , , , , |
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3223927473 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-3129 | ||
| 024 | 7 | |a 10.3390/nitrogen6020023 |2 doi | |
| 035 | |a 3223927473 | ||
| 045 | 2 | |b d20250401 |b d20250630 | |
| 100 | 1 | |a Al-Shujairy Qassim A. Talib |u College of Environmental Sciences, Al-Qasim Green University, Babil 51013, Iraq; qassim.talib@environ.uoqasim.edu.iq (Q.A.T.A.-S.); ahmedenviron94@gmail.com (A.H.A.) | |
| 245 | 1 | |a Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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 (R2 = 0.773), followed by the ratio (B5/B7) model (R2 = 0.489), while the inverse log transformation (1/log (B5/B7), R2 = 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. | |
| 651 | 4 | |a Iraq | |
| 653 | |a Outliers (statistics) | ||
| 653 | |a Wheat | ||
| 653 | |a Accuracy | ||
| 653 | |a Agricultural production | ||
| 653 | |a Datasets | ||
| 653 | |a Models | ||
| 653 | |a Agricultural ecosystems | ||
| 653 | |a Landsat | ||
| 653 | |a Arid zones | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Nitrogen | ||
| 653 | |a Satellite imagery | ||
| 653 | |a Data processing | ||
| 653 | |a Monoculture | ||
| 653 | |a Soil improvement | ||
| 653 | |a Spectral reflectance | ||
| 653 | |a Contamination | ||
| 653 | |a Machine learning | ||
| 653 | |a Remote sensing | ||
| 653 | |a Spatial data | ||
| 653 | |a Soils | ||
| 653 | |a Environmental conditions | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Fertilizers | ||
| 653 | |a Soil conditions | ||
| 653 | |a Arid regions | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Al-Hedny, Suhad M |u College of Environmental Sciences, Al-Qasim Green University, Babil 51013, Iraq; qassim.talib@environ.uoqasim.edu.iq (Q.A.T.A.-S.); ahmedenviron94@gmail.com (A.H.A.) | |
| 700 | 1 | |a Naser, Mohammed A |u Department of Combating Desertification, College of Agriculture, Al-Muthanna University, Al-Samawah 66001, Iraq; mohammed.naser@mu.edu.iq | |
| 700 | 1 | |a Shawkat Sadeq Muneer |u College of Food Sciences, Al-Qasim Green University, Babil 51013, Iraq; sadeq.muneer@fosci.uoqasim.edu.iq | |
| 700 | 1 | |a Ali Ahmed Hatem |u College of Environmental Sciences, Al-Qasim Green University, Babil 51013, Iraq; qassim.talib@environ.uoqasim.edu.iq (Q.A.T.A.-S.); ahmedenviron94@gmail.com (A.H.A.) | |
| 700 | 1 | |a Panday Dinesh |u Rodale Institute, Kutztown, PA 19530, USA | |
| 773 | 0 | |t Nitrogen |g vol. 6, no. 2 (2025), p. 23-37 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223927473/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223927473/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223927473/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |