Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review.
Organic fertilizers have been identified as a sustainable agricultural practice that can enhance productivity and reduce environmental impact. Recently, the European Union defined and accepted insect frass as an innovative and emerging organic fertilizer. In the wider domain of organic fertilizers,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292418 |
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| author | Malontema Katchali Edward Richard Henri E Z Tonnang Chrysantus M Tanga Dennis Beesigamukama Kennedy Senagi |
| author_facet | Malontema Katchali Edward Richard Henri E Z Tonnang Chrysantus M Tanga Dennis Beesigamukama Kennedy Senagi |
| author_sort | Malontema Katchali |
| collection | DOAJ |
| description | Organic fertilizers have been identified as a sustainable agricultural practice that can enhance productivity and reduce environmental impact. Recently, the European Union defined and accepted insect frass as an innovative and emerging organic fertilizer. In the wider domain of organic fertilizers, mathematical and computational models have been developed to optimize their production and application conditions. However, with the advancement in policies and regulations, modelling has shifted towards efficiencies in the deployment of these technologies. Therefore, this paper reviews and critically analyzes the recent developments in the mathematical and computation modeling that have promoted various organic fertilizer products including insect frass. We reviewed a total of 35 studies and discussed the methodologies, benefits, and challenges associated with the use of these models. The results show that mathematical and computational modeling can improve the efficiency and effectiveness of organic fertilizer production, leading to improved agricultural productivity and reduced environmental impact. Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. These models have been used in quantifying nutrients concentration/release, effects of nutrients in agro-production, and fertilizer treatment. This paper also discusses prospects for the use of these models, including the development of more comprehensive and accurate models and integration with emerging technologies such as Internet of Things. |
| format | Article |
| id | doaj-art-84a15da7ee6648248551399fa0129c47 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-84a15da7ee6648248551399fa0129c472025-02-05T05:32:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e029241810.1371/journal.pone.0292418Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review.Malontema KatchaliEdward RichardHenri E Z TonnangChrysantus M TangaDennis BeesigamukamaKennedy SenagiOrganic fertilizers have been identified as a sustainable agricultural practice that can enhance productivity and reduce environmental impact. Recently, the European Union defined and accepted insect frass as an innovative and emerging organic fertilizer. In the wider domain of organic fertilizers, mathematical and computational models have been developed to optimize their production and application conditions. However, with the advancement in policies and regulations, modelling has shifted towards efficiencies in the deployment of these technologies. Therefore, this paper reviews and critically analyzes the recent developments in the mathematical and computation modeling that have promoted various organic fertilizer products including insect frass. We reviewed a total of 35 studies and discussed the methodologies, benefits, and challenges associated with the use of these models. The results show that mathematical and computational modeling can improve the efficiency and effectiveness of organic fertilizer production, leading to improved agricultural productivity and reduced environmental impact. Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. These models have been used in quantifying nutrients concentration/release, effects of nutrients in agro-production, and fertilizer treatment. This paper also discusses prospects for the use of these models, including the development of more comprehensive and accurate models and integration with emerging technologies such as Internet of Things.https://doi.org/10.1371/journal.pone.0292418 |
| spellingShingle | Malontema Katchali Edward Richard Henri E Z Tonnang Chrysantus M Tanga Dennis Beesigamukama Kennedy Senagi Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. PLoS ONE |
| title | Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. |
| title_full | Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. |
| title_fullStr | Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. |
| title_full_unstemmed | Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. |
| title_short | Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. |
| title_sort | mathematical and computational modeling for organic and insect frass fertilizer production a systematic review |
| url | https://doi.org/10.1371/journal.pone.0292418 |
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