From targeting to tailoring : baskets of options for legume cultivation among African smallholders

Ronner, E.

doi:10.18174/430727

Cited by 5 publications

(5 citation statements)

References 150 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the inevitable movements of farmers across clusters, it is key to understand the best-before date of a farm typology [47]. The best-before date greatly depends on the rate at which the different indicators used to differentiate the production systems change [100]. In this study, it means that significant changes in productivity, commercialization levels, farm management practices, and production scale and intensity would render the typology less accurate, and this would necessitate conducting a new typology study.…”

Section: Discussionmentioning

confidence: 99%

Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda

et al. 2021

View full text Add to dashboard Cite

Despite the huge potential for milk production, interventions to improve productivity in sub-Saharan Africa (SSA) are barely based on specified farm classifications. This study aimed to develop robust and context-specific farm typologies to guide content of extension farm advice/services in Uganda. From a sample of 482 dairy farmers, we collected data on farmer socio-demographics, farm management practices, ownership of farm tools and facilities, willingness to pay for extension services, milk production, and marketing. Farm typologies were obtained based on principal component and cluster analyses. Thereby, of the three dairy production systems that emerged, small-scale, largely subsistence yet extensive and low productive farms were more prominent (82.6%). Farms that were classified as large-scale, less commercialized yet extensive with modest productive systems were more than the medium-scale commercial farms with intensive and highly productive systems. However, the later were considered to potentially transform dairy farming in Uganda. It was also predicted that the validity of our farm classification may persist until half of the farms have moved between clusters. The study gives new insights on dairy production systems in Uganda, which can be used to organize more targeted research on farmers’ extension needs for facilitating delivery of relevant and effective extension services and designing appropriate extension policies.

show abstract

Section: Discussionmentioning

confidence: 99%

Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Agronomic research definitely has been successful in identifying general productivity constraints, addressing these in most cases by technological solutions and recommendations originating from Green Revolution technology packages (Tittonell and Giller, 2013). Recommendations, however, have no universal validity and implementing these recommendations in sub Saharan Africa is difficult due to non-uniform, site specific conditions (Dea and Scoones, 2003;Giller et al, 2008;Ronner, 2018). In past decades much effort has been made to support effective implementation of recommendations, but real impact on crop yield still appears limited (Dalal-Clayton and Dent, 2001;Giller et al, 2011;Giller et al, 2009).…”

Section: Farmers and Researchersmentioning

confidence: 99%

Farmers in the lead : Assessing effectiveness of participatory experimentation inTigray, Northern Ethiopia

Kraaijvanger¹

View full text Add to dashboard Cite

The study area 1.10 Thesis outline 2 Comparison of methods to identify crop productivity constraints in developing countries. A review 2.1 Introduction 2.2 Heuristic model and prototype methods 2.3 Different use of methodologies: a review 2.4 Comparing different methods for the identification of productivity constraints in Tigray 2.5 Discussion 2.6 Conclusion 3 Identifying crop productivity constraints and opportunities using Focus Group Discussions: A case study with farmers from Tigray 3.1 Introduction 3.2. Material and methods 3.3 Results 3.4. Discussion 3.5. Conclusion 4 The importance of local factors and management in determining wheat yield variability in on-farm experimentation in Tigray, northern Ethiopia 4.1 Introduction 4.2 Materials and methods 4.3 Results and Discussion 4.4 Conclusion viii 5 Four years of farmer experimentation on soil fertility in Tigray, northern Ethiopia: Trends in research strategies 5.1 Introduction 5.2 Method 5.3 Results 5.4 Discussion 5.5 Conclusion 6 Grain productivity, fertilizer response and nutrient balance of farming systems in Tigray, Ethiopia: A multi-perspective view in relation to soil fertility degradation 6.1 Introduction 6.2 Materials and method 6.3 Results 6.4 Discussion 6.5 Conclusion 7 Considering change: Evaluating four years of participatory experimentation with farmers in Tigray (Ethiopia) highlighting both functional and human-social aspects 7.1 Introduction 7.2 Methods 7.3 Results and Discussion 7.4 Conclusion 8 Synthesis 8.1 The research project: an overview 8.2 Assessing effectiveness: putting pieces together 8.3 Reflection on process and points of departure 8.4 Impact, livelihood development and scaling 8.

show abstract

“…Baskets of options have featured in a range of recent work, including papers by Giller et al (2011), Falconnier et al (2017), Ronner (2018), and Descheemaeker et al (2019), who couple them with iterative, co-learning cycles. The recognition of the importance of tailoring options to local contexts is reflected in the shift from more general ‘best-bet’ options to ‘best fits’ – options that are assessed for their suitability to fit within a particular context or niche (Giller et al, 2011; Ojiem et al, 2006).…”

Section: Recommendations Options and Basketsmentioning

confidence: 99%

Basket of options: Unpacking the concept

Ronner¹,

Sumberg

Glover

et al. 2021

Outlook Agric

View full text Add to dashboard Cite

How to stimulate technological change to enhance agricultural productivity and reduce poverty remains an area of vigorous debate. In the face of heterogeneity among farm households and rural areas, one proposition is to offer potential users a ‘basket of options’ – a range of agricultural technologies from which potential users may select the ones that are best suited to their specific circumstances. While the idea of a basket of options is now generally accepted, it has attracted little critical attention. In this paper, we reflect on outstanding questions: the appropriate dimensions of a basket, its contents and how they are identified, and how a basket might be presented. We conceive a basket of options in terms of its depth (number of options related to a problem or opportunity) and breadth (the number of different problems or opportunities addressed). The dimensions of a basket should reflect the framing of the problem or opportunity at hand and the objective in offering the basket. We recognise that increasing the number of options leads to a trade-off by decreasing the fraction of those options that are relevant to an individual user. Farmers might try out, adapt or use one or more of the options in a basket, possibly leading to a process of technological change. We emphasise that the selection (or not) of specific options from the basket, and potential adaptation of the options, provide important opportunities for learning. Baskets of options can therefore be understood as important boundary concepts that invite critical engagement, comparison and discussion. Significant knowledge gaps remain, however, about the best ways to present the basket and to guide potential users to select the options that are most relevant to them.

show abstract

From targeting to tailoring : baskets of options for legume cultivation among African smallholders

Cited by 5 publications

References 150 publications

Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda

Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda

Farmers in the lead : Assessing effectiveness of participatory experimentation inTigray, Northern Ethiopia

Basket of options: Unpacking the concept

Contact Info

Product

Resources

About