| dc.creator |
Nyambo, Devotha |
|
| dc.creator |
Luhanga, Edith |
|
| dc.creator |
Yonah, Zaipuna |
|
| dc.date |
2020-10-07T11:14:30Z |
|
| dc.date |
2020-10-07T11:14:30Z |
|
| dc.date |
2019-05-22 |
|
| dc.date.accessioned |
2022-10-25T09:15:52Z |
|
| dc.date.available |
2022-10-25T09:15:52Z |
|
| dc.identifier |
https://doi.org/10.1155/2019/6121467 |
|
| dc.identifier |
https://dspace.nm-aist.ac.tz/handle/20.500.12479/962 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/94652 |
|
| dc.description |
This research article published by Hindawi, 2019 |
|
| dc.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. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
Hindawi |
|
| dc.subject |
Research Subject Categories::TECHNOLOGY |
|
| dc.title |
A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies. |
|
| dc.type |
Article |
|