Exploring options for sustainable intensification in different farming system types of four Africa RISING countries

Sustainable intensification is proposed as a promising way to increase the productivity of agricultural systems while reducing pressure on ecosystems, safeguarding equitable relations among societal groups, and supporting the economic viability of households, enterprises, and communities. In sub-Saharan Africa, the identification and dissemination of options for sustainable intensification is hampered by the large diversity within and between farming systems, and their complexity arising from the interactions among different farm components and external factors. This study therefore uses an integrated farming systems approach to identify and assess context-specific improvements that can then be implemented and tested on-farm to foster experiential learning and facilitate adoption. We conducted a farming systems analysis for nine Africa RISING intervention sites across four countries, based on rapid and detailed farm characterizations, followed by model-supported diagnosis, and exploration of options for sustainable intensification. Farm diversity was described and analyzed by means of typologies and cross-site comparisons. Identified constraints varied depending on site and farming system type, but commonly included low input availability, climatic variability, poor soil fertility, sub-optimal livestock feeding, biotic stresses, and poor access to training and technical advice, all impairing farm productivity, returns to labor and capital inputs, income generation and food security. We investigated entry points that tackle the above constraints by exploring alternative farm configurations, technologies and practices for representative farms. By assessing potential impact of these changes on indicators beyond productivity, trade-offs were identified and assessed, for instance between profitability and household food self-sufficiency, and between nitrogen availability for crop uptake and increased nutrient losses. Taking a systems perspective during the entry point evaluation allowed differentiating potential effects on indicators at the field level versus the farm and household level. The exploration of options for specific farming system types now enables more targeted testing of promising innovations with farmers in the second project phase

Drivers of diversification and pluriactivity among smallholder farmers—evidence from Nigeria

Diversification and pluriactivity have become a norm among farm business owners (FBOs) due to persistent low farm income. This study applies the resource-based theory to examine drivers of diversification and livelihood income-oriented towards a sustainable livelihood. Our framework develops hypotheses about the impact of internal and external resources on livelihood choices at the household level. We use a survey of 480 rural Nigerian farmers (agripreneurs), applying a Multivariate Tobit to test our framework. We find that education plays the most significant role in all types of employment options. The more FBOs are educated, the more the likelihood that they will choose non-farm or wage employment. This study revealed that while the agriculture sector’s share of rural employment is declining, non-farm is on the increase. More so, there is a decline in farming among the young generation, marital status bias and gender influence in resource allocation. The socioeconomic (income and food security) and socio-cultural (employment and rural-urban migration) implications of rural sustainability linked to UN Development Goals have been highlighted and analysed in this article

May we participate in your lives?

Smallholder farming systems are diverse. One needs to understand this diversity if one wants to implement development projects or launch a business. Researchers must take into account what farmers do for what reasons. Africa RISING is a research-for-development programme that gathers information at the grass-roots level. One lesson our author learned doing rural research is that the point is not that development agents involve farmers in projects. It is, in fact, the other way round: Success depends on farmers allowing development workers to participate in their lives.<br/

A guideline for the profiling of innovation bundles

Innovation bundles enable different innovations to complement one another and adapt to new contexts they are being introduced. The scalability of a bundle is measured by its ability to adapt to the context in which it is being scaled, respond positively to any system changes, and bring about intended outcomes. Profiling innovation bundles helps to assess the scalability of an innovation bundle to design the best-fit scaling strategies. Innovation bundle profiling includes but is not limited to 1) characterizing the innovation bundle, 2) assessing and enhancing the bundle’s scalability and identifying partnerships to scale the bundle, 3) providing foundation and inputs to design the scaling actions and learning and synergies across work packages, and 4) enhancing reflexivity of the intervention process to ensure that the bundling innovation is participatory, that there are ambassadors of the innovation bundling process, and that financial and human resources are dedicated to the scaling process

A comparison of statistical and participatory clustering of smallholder farming systems - A case study in Northern Ghana

Typologies are often used to understand and capture smallholder farming system heterogeneity, and may be derived using different approaches and methods. This article aims to compare a quantitative, statistical typology based on a survey dataset and multivariate analysis, with a qualitative participatory typology based on informal group sessions and activities with local stakeholders from three communities in Northern Ghana. The statistical typology resulted in six clusters, with farm households categorized on the basis of their structural (resource endowment)- and functional (production objectives/livelihood strategies) characteristics. The participatory typology identified five farm types, based primarily on endowment (farm size, income investment), gender and age-related criteria. While the entire household was adopted as the unit of analysis of the statistical typology, the participatory typology provided a more nuanced differentiation by grouping individual farmers; with possibly several farmer types per household (e.g. 'small' and 'female farmers') as well as 'farm-less' individuals as a result. Other sources of dissimilarity which contributed to limited overlap between the typologies included changes that occurred in the communities between the two data collection efforts and inaccuracies in the data. The underlying causes of the latter seemed to mainly relate to socio-cultural issues that distorted information collection in both typologies; including power and status differences between both the researchers and farmers, as well as the farmers themselves. We conclude that although statistical techniques warrant objectivity and reproducibility in the analysis, the complexity of data collection and representation of the local reality might limit their effectiveness in selection of farms, innovation targeting and out-scaling in R4D projects. In addition, while participatory typologies offer a more contextualized representation of heterogeneity, their accuracy can still be compromised by socio-cultural constraints. Therefore, we recommend making effective use of the advantages offered by each approach by applying them in a complementary manner

Model results versus farmer realities. Operationalizing diversity within and among smallholder farm systems for a nuanced impact assessment of technology packages

Agricultural production in Northern Ghana is dominated by smallholder farm systems, which are characterized by low inputs and low outputs, declining soil fertility, large yield gaps and limited adoption of agricultural technologies. There is an urgent need for alternative farm designs that are more productive, yet more sustainable. Technology packages for sustainable intensification are promoted by an R4D project in the Upper East, Upper West and Northern Regions of Ghana. In this paper, we analyse differences in perceived suitability, and modelled technical impact per technology package. We used a locally validated framework to categorise farm systems diversity that considers both, the horizontal (between households) and vertical (within households) dimension of diversity. Farm households were classified along a gradient of resource endowment. We selected one representative farm per type and per region to assess and compare their socio-economic and environmental performance (farm profitability, labour and soil organic matter inputs) using the whole-farm model Farm DESIGN. We then used Farm DESIGN to assess the potential impact of five proposed technology packages and to explore promising alternative farm configurations. We discussed model assumptions and results with farmers, including alternative cropping patterns and trade-offs. We evaluated the packages with different household members using a weighted scoring technique, subsequently juxtaposing model results with farmer perceptions. Large differences prevailed among and within farms per type and per region, with low resource endowed farms being projected to benefit most in relative and least in absolute terms from an adoption of the packages. Farmer feedback confirmed the accuracy of alternative farm configurations, as determined by the model. However, the feedback also revealed that the most profitable farm designs would be hard to attain in reality, particularly for members of low and medium resource endowed households, due to high initial investment costs. Within households, women were more positive about the packages than men, since men heavily penalized extra costs and labour, translating into a greater congruence of model results with the male evaluation. We discuss the importance of distinguishing between technical (technology i.e. purchased tools and inputs) and managerial (techniques e.g. row planting) package components. We conclude that operationalizing inter- and intra-household diversity is a fundamental step in identifying sensible solutions for the challenges smallholder farm systems face in Northern Ghana

Model results versus farmer realities. Operationalizing diversity within and among smallholder farm systems for a nuanced impact assessment of technology packages

Agricultural production in Northern Ghana is dominated by smallholder farm systems, which are characterized by low inputs and low outputs, declining soil fertility, large yield gaps and limited adoption of agricultural technologies. There is an urgent need for alternative farm designs that are more productive, yet more sustainable. Technology packages for sustainable intensification are promoted by an R4D project in the Upper East, Upper West and Northern Regions of Ghana. In this paper, we analyse differences in perceived suitability, and modelled technical impact per technology package. We used a locally validated framework to categorise farm systems diversity that considers both, the horizontal (between households) and vertical (within households) dimension of diversity. Farm households were classified along a gradient of resource endowment. We selected one representative farm per type and per region to assess and compare their socio-economic and environmental performance (farm profitability, labour and soil organic matter inputs) using the whole-farm model Farm DESIGN. We then used Farm DESIGN to assess the potential impact of five proposed technology packages and to explore promising alternative farm configurations. We discussed model assumptions and results with farmers, including alternative cropping patterns and trade-offs. We evaluated the packages with different household members using a weighted scoring technique, subsequently juxtaposing model results with farmer perceptions. Large differences prevailed among and within farms per type and per region, with low resource endowed farms being projected to benefit most in relative and least in absolute terms from an adoption of the packages. Farmer feedback confirmed the accuracy of alternative farm configurations, as determined by the model. However, the feedback also revealed that the most profitable farm designs would be hard to attain in reality, particularly for members of low and medium resource endowed households, due to high initial investment costs. Within households, women were more positive about the packages than men, since men heavily penalized extra costs and labour, translating into a greater congruence of model results with the male evaluation. We discuss the importance of distinguishing between technical (technology i.e. purchased tools and inputs) and managerial (techniques e.g. row planting) package components. We conclude that operationalizing inter- and intra-household diversity is a fundamental step in identifying sensible solutions for the challenges smallholder farm systems face in Northern Ghana

Characterising the diversity of smallholder farming systems and their constraints and opportunities for innovation : A case study from the Northern Region, Ghana

<p>Typologies may be used as tools for dealing with farming system heterogeneity. This is achieved by classifying farms into groups that have common characteristics, i.e. farm types, which can support the implementation of a more tailored approach to agricultural development. This article explored patterns of farming system diversity through the classification of 70 smallholder farm households in two districts (Savelugu-Nanton and Tolon-Kumbungu) of Ghana's Northern Region. Based on 2013 survey data, the typology was constructed using the multivariate statistical techniques of principal component analysis and cluster analysis. Results proposed six farm types, stratified on the basis of household, labour, land use, livestock and income variables, explaining the structural and functional differences between farming systems. Types 1 and 2 were characterized by relatively high levels of resource endowment and oriented towards non-farm activities and crop sales respectively. Types 3 and 4 were moderately resource-endowed with income derived primarily from on-farm activities. Types 5 and 6 were resource constrained, with production oriented towards subsistence. The most salient differences among farm types concerned herd size (largest for Type 1), degree of legume integration (largest for Types 2-4), household size and hired labour (smallest household size for Types 4 and 6, and largest proportion of hired labour for Type 4), degree of diversification into off/non-farm activities (highest for Type 1 and lowest for Type 5) and severity of resource constraints (Type 6 was most constrained with a small farm area and herd comprised mainly of poultry). It was found that livelihood strategies reflected the distinctive characteristics of farm households; with poorly-endowed types restricted to a 'survival strategy' and more affluent types free to pursue a 'development strategy'. This study clearly demonstrates that using the established typology as a practical framework allows identification of type-specific farm household opportunities and constraints for the targeting of agricultural interventions and innovations, which will be further analysed in the research-for-development project. We conclude that a more flexible approach to typology construction, for example through the incorporation of farmer perspectives, might provide further context and insight into the causes, consequences and negotiation of farm diversity.</p