A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies
Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight th...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2019/6121467 |
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| author | Devotha G. Nyambo Edith T. Luhanga Zaipuna Q. Yonah |
| author_facet | Devotha G. Nyambo Edith T. Luhanga Zaipuna Q. Yonah |
| author_sort | Devotha G. Nyambo |
| collection | DOAJ |
| description | Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models’ robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables. |
| format | Article |
| id | doaj-art-b32c994bf80149a5b42d2fe429af8456 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-b32c994bf80149a5b42d2fe429af84562025-02-03T06:42:00ZengWileyThe Scientific World Journal2356-61401537-744X2019-01-01201910.1155/2019/61214676121467A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm TypologiesDevotha G. Nyambo0Edith T. Luhanga1Zaipuna Q. Yonah2Information and Communication Science and Engineering, Mandela African Institution of Science and Technology, P. O. Box 447, Arusha, TanzaniaInformation and Communication Science and Engineering, Mandela African Institution of Science and Technology, P. O. Box 447, Arusha, TanzaniaInformation and Communication Science and Engineering, Mandela African Institution of Science and Technology, P. O. Box 447, Arusha, TanzaniaCharacterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models’ robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables.http://dx.doi.org/10.1155/2019/6121467 |
| spellingShingle | Devotha G. Nyambo Edith T. Luhanga Zaipuna Q. Yonah A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies The Scientific World Journal |
| title | A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies |
| title_full | A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies |
| title_fullStr | A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies |
| title_full_unstemmed | A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies |
| title_short | A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies |
| title_sort | review of characterization approaches for smallholder farmers towards predictive farm typologies |
| url | http://dx.doi.org/10.1155/2019/6121467 |
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