Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development

PLoS One. 2018 May 15;13(5):e0194757. doi: 10.1371/journal.pone.0194757. eCollection 2018.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture / methods*
  • Biodiversity*
  • Conservation of Natural Resources
  • Crops, Agricultural / growth & development*
  • Decision Making*
  • Farms / organization & administration*
  • Humans
  • Socioeconomic Factors

Grant support

The fieldwork of this study was conducted within the Africa RISING/SIMLEZA research-for-development program in Zambia that is led by the International Institute of Tropical Agriculture (IITA). The research was partly funded by the United States Agency for International Development (USAID; https://www.usaid.gov/) as part of the US Government’s Feed the Future Initiative. The contents are the responsibility of the producing organizations and do not necessarily reflect the opinion of USAID or the U.S. Government. The CGIAR Research program Humidtropics and all the donors supported this research through their contributions to the CGIAR Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. For a list of Fund donors please see: https://www.cgiar.org/funders/.