Assessing risk of bias of the included papers.
<div><p>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 o...
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2025
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| _version_ | 1852023270011830272 |
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| author | Malontema Katchali (20614838) |
| author2 | Edward Richard (20614841) Henri E. Z. Tonnang (10960695) Chrysantus M. Tanga (7430201) Dennis Beesigamukama (9311524) Kennedy Senagi (20614844) |
| author2_role | author author author author author |
| author_facet | Malontema Katchali (20614838) Edward Richard (20614841) Henri E. Z. Tonnang (10960695) Chrysantus M. Tanga (7430201) Dennis Beesigamukama (9311524) Kennedy Senagi (20614844) |
| author_role | author |
| dc.creator.none.fl_str_mv | Malontema Katchali (20614838) Edward Richard (20614841) Henri E. Z. Tonnang (10960695) Chrysantus M. Tanga (7430201) Dennis Beesigamukama (9311524) Kennedy Senagi (20614844) |
| dc.date.none.fl_str_mv | 2025-01-24T19:07:54Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0292418.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Assessing_risk_of_bias_of_the_included_papers_/28276856 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics Biotechnology Ecology Developmental Biology Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sustainable agricultural practice support vector machines shifted towards efficiencies reduced environmental impact reduce environmental impact european union defined artificial neural networks accepted insect frass improved agricultural productivity quantifying nutrients concentration emerging organic fertilizer organic fertilizer production fertilizer treatment enhance productivity organic fertilizers wider domain results show recent developments random forest paper reviews gradient boosting emerging technologies critically analyzes computational modeling computation modeling challenges associated application conditions 35 studies |
| dc.title.none.fl_str_mv | Assessing risk of bias of the included papers. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_7ff58c5897841a59cd64da381840ce73 |
| identifier_str_mv | 10.1371/journal.pone.0292418.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28276856 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Assessing risk of bias of the included papers.Malontema Katchali (20614838)Edward Richard (20614841)Henri E. Z. Tonnang (10960695)Chrysantus M. Tanga (7430201)Dennis Beesigamukama (9311524)Kennedy Senagi (20614844)GeneticsBiotechnologyEcologyDevelopmental BiologyScience PolicyPlant BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsustainable agricultural practicesupport vector machinesshifted towards efficienciesreduced environmental impactreduce environmental impacteuropean union definedartificial neural networksaccepted insect frassimproved agricultural productivityquantifying nutrients concentrationemerging organic fertilizerorganic fertilizer productionfertilizer treatmentenhance productivityorganic fertilizerswider domainresults showrecent developmentsrandom forestpaper reviewsgradient boostingemerging technologiescritically analyzescomputational modelingcomputation modelingchallenges associatedapplication conditions35 studies<div><p>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.</p></div>2025-01-24T19:07:54ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0292418.t005https://figshare.com/articles/dataset/Assessing_risk_of_bias_of_the_included_papers_/28276856CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/282768562025-01-24T19:07:54Z |
| spellingShingle | Assessing risk of bias of the included papers. Malontema Katchali (20614838) Genetics Biotechnology Ecology Developmental Biology Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sustainable agricultural practice support vector machines shifted towards efficiencies reduced environmental impact reduce environmental impact european union defined artificial neural networks accepted insect frass improved agricultural productivity quantifying nutrients concentration emerging organic fertilizer organic fertilizer production fertilizer treatment enhance productivity organic fertilizers wider domain results show recent developments random forest paper reviews gradient boosting emerging technologies critically analyzes computational modeling computation modeling challenges associated application conditions 35 studies |
| status_str | publishedVersion |
| title | Assessing risk of bias of the included papers. |
| title_full | Assessing risk of bias of the included papers. |
| title_fullStr | Assessing risk of bias of the included papers. |
| title_full_unstemmed | Assessing risk of bias of the included papers. |
| title_short | Assessing risk of bias of the included papers. |
| title_sort | Assessing risk of bias of the included papers. |
| topic | Genetics Biotechnology Ecology Developmental Biology Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sustainable agricultural practice support vector machines shifted towards efficiencies reduced environmental impact reduce environmental impact european union defined artificial neural networks accepted insect frass improved agricultural productivity quantifying nutrients concentration emerging organic fertilizer organic fertilizer production fertilizer treatment enhance productivity organic fertilizers wider domain results show recent developments random forest paper reviews gradient boosting emerging technologies critically analyzes computational modeling computation modeling challenges associated application conditions 35 studies |