Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique
Abstract An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research empl...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53410-8 |
| _version_ | 1797274590697750528 |
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| author | Sidhartha Sekhar Swain Tapan Kumar Khura Pramod Kumar Sahoo Kapil Atmaram Chobhe Nadhir Al-Ansari Hari Lal Kushwaha Nand Lal Kushwaha Kanhu Charan Panda Satish Devram Lande Chandu Singh |
| author_facet | Sidhartha Sekhar Swain Tapan Kumar Khura Pramod Kumar Sahoo Kapil Atmaram Chobhe Nadhir Al-Ansari Hari Lal Kushwaha Nand Lal Kushwaha Kanhu Charan Panda Satish Devram Lande Chandu Singh |
| author_sort | Sidhartha Sekhar Swain |
| collection | DOAJ |
| description | Abstract An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio. |
| first_indexed | 2024-03-07T15:00:32Z |
| format | Article |
| id | doaj.art-89f3540de11e4cf09f92449204e263d2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:00:32Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-89f3540de11e4cf09f92449204e263d22024-03-05T19:10:04ZengNature PortfolioScientific Reports2045-23222024-02-0114111810.1038/s41598-024-53410-8Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM techniqueSidhartha Sekhar Swain0Tapan Kumar Khura1Pramod Kumar Sahoo2Kapil Atmaram Chobhe3Nadhir Al-Ansari4Hari Lal Kushwaha5Nand Lal Kushwaha6Kanhu Charan Panda7Satish Devram Lande8Chandu Singh9Division of Agricultural Engineering, ICAR-Indian Agricultural Research InstituteDivision of Agricultural Engineering, ICAR-Indian Agricultural Research InstituteDivision of Agricultural Engineering, ICAR-Indian Agricultural Research InstituteDivision of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research InstituteDepartment of Civil, Environmental and Natural Resources Engineering, Lulea University of TechnologyDivision of Agricultural Engineering, ICAR-Indian Agricultural Research InstituteDivision of Agricultural Engineering, ICAR-Indian Agricultural Research InstituteDepartment of Soil Conservation, National PG College (Barhalganj), DDU Gorakhpur UniversityDivision of Agricultural Engineering, ICAR-Indian Agricultural Research InstituteDivision of Genetics, ICAR-Indian Agricultural Research InstituteAbstract An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.https://doi.org/10.1038/s41598-024-53410-8 |
| spellingShingle | Sidhartha Sekhar Swain Tapan Kumar Khura Pramod Kumar Sahoo Kapil Atmaram Chobhe Nadhir Al-Ansari Hari Lal Kushwaha Nand Lal Kushwaha Kanhu Charan Panda Satish Devram Lande Chandu Singh Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique Scientific Reports |
| title | Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique |
| title_full | Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique |
| title_fullStr | Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique |
| title_full_unstemmed | Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique |
| title_short | Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique |
| title_sort | proportional impact prediction model of coating material on nitrate leaching of slow release urea super granules usg using machine learning and rsm technique |
| url | https://doi.org/10.1038/s41598-024-53410-8 |
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