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

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.

Reducing agro-environmental trade-offs through sustainable livestock intensification across smallholder systems in Northern Tanzania

“…Exploratory statistics with the R statistical programming software (R Core Team, 2013) were conducted to describe the general farming systems. A quantitative, multivariate statistics method was used to construct a smallholder livestock systems typology (Alvarez et al, 2018). Expert knowledge and literature review resulted in the selection of 12 variables for the typology construction, which were extracted or calculated from the dataset (Table 1).…”

“…Cattle number was closely correlated with total TLU (R2 = 0.93) and therefore not included. As multivariate analyses are sensitive to exceptional observations, the dataset was curated for missing and outlying data (Alvarez et al, 2018). The following farms were removed: five farms without livestock (TLU = 0), two farms with missing data, and six farms with exceptional data (two farms with >4 improved cattle, two farms with >25,000 kg cereal residue fed, one farm with >1000 kg other residue fed, and one farm with >3000 kg legume residue fed).…”

“…Farming system types are a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods and constraints, and for which similar development strategies and interventions would be appropriate. Different approaches can be used to construct farm typologies, such as qualitative, participatory, expert-based and quantitative typologies (Alvarez et al, 2018;Kuivanen et al, 2016;Tittonell et al, 2010). In this study, we chose a predominantly structural, quantitative approach to construct a farm typology, validated by local experts, and opted to model 'real' instead of 'constructed' farms.…”

Farm typology analysis and technology assessment: An application in an arid region of South Asia

“…Recognizing variability within and among farms and across localities is the first step in designing interventions and policies to help poor farmers (Ruben and Pender, 2004;Mutoko et al, 2014). Farm household typologies can help summarize this variability and diversity among different farming systems (Kuhn and Offutt, 1999;Alvarez et al, 2018). Capturing this heterogeneity is an essential first step in the analysis of potential technological interventions and policy support.…”

Farmer perspectives on sustainable crop-livestock integration in cereal based farm systems of Nepal

“…For each district, we built a farm typology using multivariate analysis: a principal component analysis (PCA) was performed to identify non-correlated explanatory variables, followed by a hierarchical clustering (HC) to group the farms. The clustering algorithm finds the most homogeneous groups possible, minimizing the intra-group heterogeneity and maximizing inter-group heterogeneity (Alvarez et al, 2018). The software R was used for the statistical analysis (version 3.4.0, R Development Core Team, 2017; ade4 package) (Dray and Dulfur, 2007).…”