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Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development
Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, dat...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5953459/ https://www.ncbi.nlm.nih.gov/pubmed/29763422 http://dx.doi.org/10.1371/journal.pone.0194757 |
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author | Alvarez, Stéphanie Timler, Carl J. Michalscheck, Mirja Paas, Wim Descheemaeker, Katrien Tittonell, Pablo Andersson, Jens A. Groot, Jeroen C. J. |
author_facet | Alvarez, Stéphanie Timler, Carl J. Michalscheck, Mirja Paas, Wim Descheemaeker, Katrien Tittonell, Pablo Andersson, Jens A. Groot, Jeroen C. J. |
author_sort | Alvarez, Stéphanie |
collection | PubMed |
description | Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia’s Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies. |
format | Online Article Text |
id | pubmed-5953459 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59534592018-05-25 Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development Alvarez, Stéphanie Timler, Carl J. Michalscheck, Mirja Paas, Wim Descheemaeker, Katrien Tittonell, Pablo Andersson, Jens A. Groot, Jeroen C. J. PLoS One Research Article Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia’s Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies. Public Library of Science 2018-05-15 /pmc/articles/PMC5953459/ /pubmed/29763422 http://dx.doi.org/10.1371/journal.pone.0194757 Text en © 2018 Alvarez et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alvarez, Stéphanie Timler, Carl J. Michalscheck, Mirja Paas, Wim Descheemaeker, Katrien Tittonell, Pablo Andersson, Jens A. Groot, Jeroen C. J. Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development |
title | Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development |
title_full | Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development |
title_fullStr | Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development |
title_full_unstemmed | Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development |
title_short | Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development |
title_sort | capturing farm diversity with hypothesis-based typologies: an innovative methodological framework for farming system typology development |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5953459/ https://www.ncbi.nlm.nih.gov/pubmed/29763422 http://dx.doi.org/10.1371/journal.pone.0194757 |
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