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.

No abstract

No abstract

No abstract

Assessing progress towards healthier people, farms and landscapes through nutrition-sensitive agriculture (NSA) requires transdisciplinary methods with robust models and metrics. Farm-household models could facilitate disentangling the complex agriculture-nutrition nexus, by jointly assessing performance indicators on different farm system components such as farm productivity, farm environmental performance, household nutrition, and livelihoods. We, therefore, applied a farm-household model, FarmDESIGN, expanded to more comprehensively capture household nutrition and production diversity, diet diversity, and nutrient adequacy metrics. We estimated the potential contribution of an NSA intervention targeting the diversification of home gardens, aimed at reducing nutritional gaps and improving livelihoods in rural Vietnam. We addressed three central questions: (1) Do 'Selected Crops' (i.e. crops identified in a participatory process) in the intervention contribute to satisfying household dietary requirements?; (2) Does the adoption of Selected Crops contribute to improving household livelihoods (i.e. does it increase leisure time for non-earning activities as well as the dispensable budget)?; and (3) Do the proposed nutritionrelated metrics estimate the contribution of home-garden diversification towards satisfying household dietary requirements? Results indicate trade-offs between nutrition and dispensable budget, with limited farm-household configurations leading to jointly improved nutrition and livelihoods. FarmDESIGN facilitated testing the robustness and limitations of commonly used metrics to monitor progress towards NSA. Results indicate that most of the production diversity metrics performed poorly at predicting desirable nutritional outcomes in this modelling study. This study demonstrates that farm-household models can facilitate anticipating the effect (positive or negative) of agricultural interventions on nutrition and the environment, identifying complementary interventions for significant and positive results and helping to foresee the trade-offs that farm-households could face. Furthermore, FarmDESIGN could contribute to identifying agreed-upon and robust metrics for measuring nutritional outcomes at the farm-household level, to allow comparability between contexts and NSA interventions.