A Global baseline for data-driven agronomy interventions

Establishing a global picture of where smallholder farmers are and their patterns of mobile technology access and availability, relative to non-farming populations, is critical for guiding successful data-driven agronomy interventions. These essential baselines include:

• the number of farmers who own mobile phones,
• the number of farmers who have access to the internet,
• the spatial coverage of mobile technologies in farmlands, and
• relative costs of engaging in digital technology for different farming populations across the planet

This baseline information would be of great use for the CoP, the CGIAR’s Big Data in Agriculture Platform and external partners. Our goal is to gain a first estimate of these numbers.

Benchmarking data-driven agronomy services

Although the market for data-driven agronomy services is flourishing, it remains difficult for clients/users to compare these services and to evaluate how reliable and valuable they are.

In other sectors, there is a set of standards the industry has to oblige to, such as electric cars with NEDC or EPA ratings or the mandatory energy performance certificate for buildings in Europe.

The purpose of this joint venture is to explore the opportunities for setting standards for benchmarking and evaluating the services in order to develop standardized protocols that can then be used by organizations to demonstrate the efficiency of their services.

Enabling global discoverability of granular data on yield and management practices

Data-driven initiatives are often limited by low availability of granular data on yield and management practices, even though a lot of this type of data exists already. Over the years it became clear that the value of data lies in what you make of it. 

Therefore, we expect that more and more organizations might be willing to share their data in order to speed up the use of it.

We aim to develop the required infrastructure and guidelines to enable organizations to share their data while addressing the following challenges:

1. Ensure all data shared follows the required the standards of good practices in terms of ontologies, metadata, etc..
2. Make data discoverable without having to centralize this, and
3. Preserve anonymity while allowing in-depth analysis.