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Agricultural technologies can benefit farmers, businesses, consumers, and the environment in low- and middle-income countries (LMICs). However, companies that control the flow of data to and from farmers are more integrated than ever.

In an era where a small number of agtech providers increasingly hold unprecedented control over farmer data, digital agriculture is on a trajectory to evolve in ways that may inhibit farmer opportunity—unless digital agriculture stakeholders take deliberate steps to change the trajectory of data governance in the agtech sector.

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Farmers face a paradox: while the use of data and agtech holds much potential to strengthen the agriculture sector, farmer capacity and data governance challenges raise the possibility that farmers will not benefit economically from their own data.

AgriTech Data Governance Models

Given agtech’s breakneck forward momentum, implementing fair and equitable data governance models that prioritize farmer participation while guarding farmers against potential disadvantages and exploitation is crucial. For this reason, there is a pressing need to unpack data governance practices and challenges within the agriculture sector.

The terms data governance, data stewardship, and data management are often used interchangeably, with no broad consensus on their meaning.

• Data governance is a collection of policies, practices, roles, and responsibilities that establish the authority to manage data. Executed according to agreed-upon approaches that describe who can take what actions with what information, when, under what circumstances, and using what methods, it is a critical part of the digital governance process.
• Data management is governance in action, applying policies and practices that manage the data lifecycle and information assets.
• Data stewardship is the responsible use, collection, and management of data in a participatory and rights-preserving way. Data stewards ensure the quality of data sharing, holding, privacy, and control across parties, providing data collectors with more consistent and reliable data. To that end, data stewards generally (1) build data opportunities to unlock the value of data; (2) manage data to ensure representation, usability, and quality; (3) define guidelines for quality, usability, safety, and transparency; and (4) help protect the rights of of individuals and communities.

However, because these three terms largely originated in anglophone Western research, they are largely unknown to many LMIC practitioners and communities.

Farmer-Centric Data Governance Report

The objective of the Farmer-Centric Data Governance: Towards A New Paradigm report is to showcase farmer-centric data governance models and the enabling factors needed for their implementation.

It aims to raise awareness around the current political economy of agricultural data and its implications; identify user-centric data governance models and mechanisms, particularly in LMICs; demonstrate the purpose, value, benefits, and challenges of these models for all stakeholders; and identify appropriate and relevant actionable principles, recommendations, and considerations related to user-centric data governance in the agriculture sector for the donor community.

The report’s main recommendations include:

1. User-centric data governance models should be integrated into digital agriculture programs given their immense potential to shift the current paradigms of information imbalances to benefit farmers, communities, and societies.
2. Companies and organizations that handle farmer data need to foster trust with farmers throughout the data lifecycle.
3. Farmer-centric data governance approaches must pursue more consistent, higher- quality data sharing, interoperability, and defragmentation of data.
4. Meaningful participation must strongly tie farmers to data governance.
5. Recognition of the vital role that data stewards play is required within agriculture programs, particularly their role as trusted intermediaries between farmers, data collectors, and data generators.
6. Local context, culture, and existing practices should be centered to determine outcomes, implications, and impact when considering data governance models.
7. User-centric models are not a panacea “one-size-fits-all” solution, although they are important tools for deeper inclusion of farmers and other agriculture sector stakeholders in data governance.
8. More research is needed to further identify training and capacity-building requirements and the financial sustainability of user-centric data governance initiatives.

A lightly edited synopsis of Farmer-Centric Data Governance: Towards A New Paradigm, which was funded by USAID and produced under DAI’s Digital Frontiers Project. The contents are the responsibility of Development Gateway and do not necessarily reflect the views of USAID or the United States Government.