Capturing farm diversity with hypothesisbased 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, 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.Estación Experimental Agropecuaria BarilocheFil: Alvarez, Stephanie. Wageningen University & Research. Farming Systems Ecology; HolandaFil: Timler, Carl J. Wageningen University & Research. Farming Systems Ecology; HolandaFil: Michalscheck, Mirja. Wageningen University & Research. Farming Systems Ecology; HolandaFil: Paas, Wim. Wageningen University & Research. Farming Systems Ecology; HolandaFil: Descheemaeker, Katrien. Wageningen University & Research. Plant Production Systems; HolandaFil: Tittonell, Pablo Adrian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Andersson, Jens A. International Maize and Wheat Improvement Center (CIMMYT); ZimbaweFil: Groot, Jeroen C. J. Wageningen University & Research. Farming Systems Ecology Group, Plant Sciences; Holand

Diversity among smallholder farms and households—Consequences for trade-offs, trajectories, targeting and scaling

United States Agency for International Developmen

A multi-objective model exploration of banana-canopy management and nutrient input scenarios for optimal banana-legume intercrop performance

This study used the multi-objective optimization FarmDESIGN model to exlpore for optimal banana-canopy management and nutrient input scenarios for a banana-bush bean intensification system. Severe leaf pruning was confirmed to negatively impact on farm profitability, while the more profitable un-pruned crop options were unsustainable without external nutrient inputs. Thus, investments in external inputs are crucial for a sustainable banana-intercrop system. The FarmDESIGN model made the trade-offs and synergies in this complex intercrop system explicit, thus was also helpful for field-level decision making

A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions

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 nutrition-related 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

Exploring options for sustainable intensification through legume integration in different farm types in eastern Zambia

In Zambia maize is the main staple food crop and, with a share of 52% in the daily calorie intake of the local population, it is critical for ensuring the national food security (FAOSTAT, 2013). Of the total maize consumed in Zambia, smallholder farmers produce 80% in rain-fed systems under low soil fertility, frequent drought and with a limited use of high yielding varieties or inorganic fertiliser (Sitko et al., 2011). In eastern Zambia, the livelihoods of small-scale farmers depend largely on maize-legume mixed systems characterised by low productivity, extreme poverty and environmental degradation (Sitko et al., 2011). Thus, there seems to be a great need for sustainable intensification of these farming systems, for instance through promoting best practices in maize–legume integration. Maize–legume cropping provides protein-rich food for humans, residues for animal feed, composting and soil amendments and nitrogen inputs through symbiotic fixation by the legume. Sustainable intensification of farming systems can take place through changes in resource use and allocation that increase farm productivity while reducing pressure on local ecosystems and safeguarding social relations. According to Pretty et al. (2011), this entails the efficient use of all inputs to produce more outputs while reducing damage to the environment and building a resilient natural capital from which environmental services can be obtained. Sustainable intensification results from the application of technological and socio-economic approaches that may be categorised into genetic, ecological and socio-economic intensification (The Montpellier Panel, 2013)