What you need to know about Africa RISING farm typology work
May 2, 2016 Editor 0Sara Signorelli of IFPRI (left) and Mirja Michalscheck of Wageningen University (photo credit: IITA/Jonathan Odhong’).
Africa RISING is testing alternative technology options with heterogeneous populations of farmers who are likely respond to new technologies differently. The identification of different farmers’ types within the program is crucial to achieving the program’s sustainable intensification and development goals.
In this interview, Mirja Michalscheck of Wageningen University and Sara Signorelli of the International Food Policy Research Institute (IFPRI) explain the vision behind the typology work in Africa RISING; the insights emerging from the studies and how the typology results can be used by the project team.
When and how did the typology work by both IFPRI and WUR begin in Africa RISING?
Mirja: In 2013, Wageningen University (WUR) was asked to carry out a farming systems analysis of smallholders in the Africa RISING communities in northern Ghana. The analysis needed to make a purposeful selection of farm households to represent the diversity of local farming systems. We performed a rapid characterization to group farmers into different types and returned to a few ‘representative farmers’ per type for a detailed characterization. This provided us with valuable first insights into farming systems diversity and local livelihoods. Using the available baseline data, as a project, we have an even better basis for statistical typification of households in our case study sites.
Sara: The discussion started at the program management level around May 2015, and was driven by the recognized need of understanding the characteristics of the farmers in the project, especially in the context of the approaching second phase of the project. The project management team underlined the need to assess the effectiveness and profitability of Africa Rising technologies in relation to the different farmers’ groups that are part of the project. This was driven by the observation that farmers are very diverse and may not all benefit equally from the proposed interventions. Subsequently, WUR and the International Food Policy Research Institute (IFPRI) decided to take the lead and generate typology studies. The data used by IFPRI is taken from the baseline evaluation survey data collected across the five Africa RISING countries (Ghana, Ethiopia, Malawi, Mali and Tanzania), and thus allows the application of the same methodology across the whole program.
What is the value of conducting this typology work for a project like Africa RISING and how do these results practically add value to the intended scaling efforts in phase 2?
Mirja: Typologies are useful for unraveling diversity. By grouping farms or farmers into types of similar characteristics we can examine these groups for typical challenges and opportunities they face towards the different technologies Africa RISING is promoting. A technology might be expensive for one and affordable for another farm type. While it is not possible to offer the same inputs at different prices, one might think of offering microcredit opportunities or facilitating the purchase of inputs, rendering technologies more affordable and accessible. For scaling it is interesting to find ‘geographical hotspots’. By hotspots I mean areas where certain farm types are dominant, e.g. in the Upper East region of Ghana there is a high prevalence of low or medium resource endowed farm households. An adjusted support can hence reach a large number of farms or farmers. I am making the distinction between farms and farmers, since the IFPRI typology is grouping households (farms) while Africa RISING is working with individuals (farmers). During my work in northern Ghana I experienced the complementarity of the two approaches: Resources and decision are shared at household level, but individuals have different production orientations and views on the technologies. If we want to understand and increase technology adoption, we have to work with the diversity within and among households. I believe that typologies are a useful tool in doing so.
Sara: Most of the results produced by the agronomic trials so far are concerned with the average impact of technologies across all beneficiaries. What the typology characterization will allow us to do is to go a step further in the analysis and look at heterogeneity in the effects across farmers with different characteristics. This will help the project in better targeting the technologies towards farmers who are likely to derive the most benefits out of them.
So how can researchers in Africa RISING use the findings of the typology work presently?
Sara: I think the necessary next step is to find a way to operationalize the results in order to make them readily usable by the researchers. This will involve defining a quick procedure to categorize each project beneficiary into one of the established types. A possible solution could be to characterize the project regions on the base of the dominant farmers’ types that live there, and then use this information to plan regional-specific interventions in phase two of the project. In fact, regional or district level targeting may be easier to implement than having one at the household or individual level.
Mirja: I think it would be interesting to match the existing trial results with the IFPRI typology: We could check whether there is a disparity in farm types between their performance in different Baby and Up scaled trials. The recently completed adoption survey can add context to this exercise and might help in understanding which technologies work best for the different types of farmers.
What are the similarities and the differences of the typology work you have both done?
Sara: I think that the main commonality between the two bits of work is that they both used household endowments as the first level of characterization. Even though the collection of endowment information differs across the two methods, they are both driven by the same guiding principle. On the other hand, they also present notable differences. The typologies developed by WUR in the Ghanaian context are based on in-depth participatory discussions with a limited number of farmers, conferring a considerable richness of details to the results but also rendering them highly context specific and thus hardly generalizable. On the contrary, IFPRI’s typologies are based on survey data and thus are much less thorough in describing the local realities, but given the systematic statistical approach taken, they produced comparable results across the five project countries. In this sense I think that these two studies are complementary and add value to each other.
Mirja: We have used different data sets and different approaches, so our findings provide different viewpoints but they concern the same local farming systems. The knowledge we gained through the different viewpoints provides us with a rich picture that we, as a project, can use to better understand the combination of technologies and farmers we work with.
What are some of the key findings and outcomes that you would like to highlight from both typology works?
Mirja: I am using the typologies to perform a nuanced impact assessment of the different Africa RISING technologies. The same technology often has a very different impact on farms of different farm types. For some low resource endowed farms, an increased fertilizer application on maize may double operating profits but in high resource endowed farms, the same measure typically has a much lower impact, due to higher baseline input levels and the high profitability of other crops cultivated such as rice and groundnuts. I found it particularly interesting to observe how important crop soil fertility measures (e.g. legume integration and integrated soil fertility management) are for low resource endowed farmers, since they have no or only little livestock and therefore no manure to add to their fields.
Sara: One of the striking results that emerged was the fact that highly endowed farmers are not necessarily scoring the highest across the other sustainable intensification domains. While the economic and productivity aspects seems to go in the same direction in most cases, it seems that they are not highly correlated with the level of education and the fertility of the soils (the results vary country by country). In addition, the level of female responsibility in managing plots and livestock seems to be the highest among the low-endowment households, which are also more frequently headed by women. These results underline the fact that there is no group that is performing well across the board but that each one of them needs support in his specific weaknesses.
What next for the two sets of typology characterization and clusters?
Mirja: I would like to know from the local experts of our project what they learned from the different approaches. What were the most interesting new insights for them? Which questions are still to be answered concerning farming systems diversity in our project sites? Personally, I am interested in diving deeper into intra-household diversity to understand how decisions are made and how adoption takes place. I will use the IFPRI types as a starting point for this investigation.
Sara: I think it is highly encouraging that, despite using different methodologies, the typology results from WUR and IFPRI in Ghana seems to be pointing in the same direction. After the appropriate validation of the results by the local teams, the next step would be to use the outcomes of the two studies in order to increase the efficacy of the Africa Rising interventions, especially in the light of the scaling that will take place in phase two.
- Social learning for farming systems – Insights from Africa RISING in Ethiopia
- Innovation and R4D platforms in Africa RISING: questioning sustainability
- Connecting research-in-development dots and investing in dynamic scaling pathways – Africa RISING phase2 kicks off with science for impact event
- Africa RISING Ethiopia project external review report
- Africa RISING Ethiopia reviews progress and plans for 2016 and beyond
- On the trail of adoption data: Africa RISING embarks on study to evaluate use of improved technologies in northern Ghana
Subscribe to our stories
- Opportunities and Challenges for Data-Driven Agricultural Innovation June 21, 2017
- Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond. June 21, 2017
- Leveraging ‘suptech’ for financial inclusion in Rwanda June 21, 2017
- Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda. June 21, 2017
- WHO cone bio-assays of classical and new-generation long-lasting insecticidal nets call for innovative insecticides targeting the knock-down resistance mechanism in Benin. June 14, 2017