November 25, 2021Time to read 10 min
Index
  1. Introduction
  2. The importance of farm-level data
  3. IDH Farmfit's mission
  4. Key reasons for primary farm-level data collection
  5. Methodology, challenges and best practices
  6. Next steps
  7. More information

The importance of data and lessons learned by IDH Farmfit

 

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Do you know why farm-level data is crucial to increasing the livelihood of farmers? And how IDH Farmfit currently collects this data to achieve the desired insights and results?

While smallholder farmers (SHFs) are key players in agricultural value chains, they often lack access to affordable and high-quality goods and services (e.g., inputs, credit, markets, and information) that would enable them to improve their farms and incomes. Key barriers, such as risks associated with smallholder farming, keep the private sector from fully investing in service provision to SHFs.

This problem is compounded by a limited understanding of what works best for the private sector and for SHFs, due in large part to the limited quantity, quality, and interoperability of farm-level data. In this report, we argue that accurate farm-level data is critical to creating financially viable and investable smallholder engagement models that improve farmer livelihoods.

 

This report:

  • Highlights the importance of data for improving SHF livelihoods, as well as key challenges
  • Explains how in our work, farm-level data is not just valuable to evaluate the impact of interventions focused on improving smallholder engagement models, but also critical to understand and analyse these engagements models in the first place. This allows us and other stakeholders to make informed decisions on interventions and investments in smallholder farming.
  • Provides transparency on IDH Farmfit’s methodological approach to primary data collection at farm level, including challenges and best practices, thereby offering guidance and tools to others seeking to collect such data; and
  • Invites our peers to engage with IDH Farmfit to optimize our data-driven journey, to share insights and create alignment in the industry, and to reflect on the future role of primary data in smallholder agriculture.

 

 

In Section 1 of this report, we argue that farm- level data is critical to creating financially viable and investable smallholder engagement models that improve farmer livelihoods. We discuss the importance of high quality data and the challenges of collecting it from SHFs, which include logistical issues of reaching research participants, especially in rural or remote areas; lack of available farm-level records; complexity and vast differences in measurement approaches; costs of collecting primary data; and the underdeveloped regulatory environment governing the collection, use, and sharing of farm-level data.

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In Section 2, we illustrate how and why data is critical for IDH Farmfit to achieve its mission, which is to improve farmer livelihoods by contributing to the transformation of smallholder agriculture markets. Through analytics, technical assistance, and catalytic investment funding, we support and guide the private sector in making their service delivery to SHFs profitable and investable while also improving farmers’ livelihoods.

Yet, a solid understanding of farmers’ socio-economic characteristics and agronomic behavior is crucial for designing and implementing sustainable, scalable, and investable Service Delivery Models (SDMs) that enhance farmers’ income and resilience.

We explain why the collection of farm-level data is a critical component of our private sector engagements.

 

In Section 3, we delve deeper into the value to our work:

  • For IDH Farmfit’s engagements with the private sector— specifically, for high-quality analyses of Service Delivery Model (SDMs) and informed Technical Assistance (TA) to make SDMs investment ready, scalable, and effective;
  • For generating aggregated insights to drive change in service markets for Smallholder Farmers (SHFs); and
  • For evaluating the impact of interventions on farmer livelihoods and adapting interventions accordingly

 

 

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In Section 4, we provide an overview of our methodological approach to data collection. We discuss how a well developed research design, data collection instrument, and collection process, help us tackle challenges associated with agricultural data (such as a lack of quality, comparable, and interoperable farm-level data).

Particularly, we elaborate on our approach to collecting standardized farm-level data while ensuring that our farm-level survey is adapted to local nuances and measurement units. In this way, we can collect high quality data on a certain crop or in a geographical region, while ensuring that these questions remain standardized and comparable across SDM analyses.

 

Finally, we conclude this report with a call for action. We are keen on engaging with other development organizations and knowledge partners to optimize our data-driven journey, share insights and lessons, and create alignment in the industry.

We would like to start a conversation with other actors to learn from one another, for instance on how to deal with:

  • Farm-level data ownership, privacy and how to share information back to farmers
  • Representation of women in farm-level surveys
  • The use of technologies for data collection (e.g. Farmer information management systems)
  • Measuring concepts around farmer livelihoods such as living income, gender inclusion, food security and climate resilience

To conclude, we seek to contribute to the use of increasingly harmonized language and methodologies and hope that the data and insights generated in this manner will become increasingly interoperable across the sector. As such, the ease and value of benchmarking can be continuously strengthened and the impact on farmer livelihoods can be maximized.

Any thoughts on the above topics. Feel free to reach out to IDH Farmfit or me, Charlotte Keijser.

More information

Charlotte Keijser

Innovation Manager