Agricultural Data- A non-depreciating asset

Dr. Shashi Kumar Sharma

With a network of 63 State Agricultural Universities (SAUs), India has one of the largest publicly funded agricultural education and research systems in the world. Each university operates within a unique agro-climatic setting, serving regional farming needs while also contributing to broader goals of food and nutritional security.

Since agriculture is a state subject, most of these universities depend on funding from their respective state governments, despite being modelled on the Land-Grant system of the United States established under the Morrill Acts (1862, 1890). At present, many SAUs are facing financial stress due to inadequate funding, which often covers little beyond salaries, accounting for nearly 87% of total expenditure, leaving limited resources for research and infrastructure.

Under these circumstances, these institutions must expand their internal revenue streams. While universities have made efforts to generate income, traditional approaches such as farm-based activities or sales of farm produce have yielded only modest returns. What is needed is not incremental adjustment, but a fundamental shift in how these institutions view and leverage their inherent strengths.

One such strength lies largely in the overlooked ‘data’ that these institutions are developing. Over decades, SAUs have built an enormous repository of field-based knowledge—on crops, soils, weather patterns, pest dynamics, and farming practices. This data is not incidental; it is the outcome of carefully designed experiments, long-term observations, and region-specific research. Yet, much of it remains confined to theses, reports, and academic publications, with little direct use beyond the immediate research context.

This is a missed opportunity. Data, unlike most institutional assets, does not depreciate with use. It becomes more valuable over time. Each new season, each additional dataset, improves its depth and reliability. In an era of climate uncertainty and market volatility, such data can serve as a powerful foundation for better decision-making.

The challenge, however, is not simply about access, but about use. Raw data, in isolation, has limited value. It must be organised, validated, and interpreted before it can guide action. This is where the idea of agricultural intelligence becomes important—turning accumulated data into practical tools that can support farmers, planners, and agribusinesses.

The scope is considerable. Long-term trials can inform crop selection, fertilizer use, and irrigation planning. Climate-linked datasets can help anticipate risks and guide adaptation strategies. Plant disease records can support early warning systems. Post-harvest data can improve storage, quality, and market outcomes. Even forestry datasets are gaining relevance in emerging areas such as carbon markets and rural enterprises.

Despite this potential, much of the data within universities remains fragmented across departments. The result is a paradox: institutions rich in knowledge, but limited in its application. Addressing this gap requires a change in approach. Universities must begin by bringing their data together—digitising it, standardising formats, and enabling internal access. This is not merely a technical exercise, but an institutional one. Dedicated units may be needed to manage data, develop applications, and connect research with real-world needs.

Such efforts can also open new revenue pathways. Instead of relying solely on traditional sources, universities can offer services based on their data—advisories, forecasts, and decision-support tools tailored to farmers, agri-businesses, and policymakers. These could be delivered through subscription models, collaborative platforms, or partnerships with private firms. In this way, data moves from being a passive resource to an active institutional asset.

Evidence from the United States shows that universities do derive tangible financial returns from research data and intellectual property, primarily through licensing, patents, and technology transfer. Most universities typically earn a few million dollars annually from such activities, while leading institutions generate over $100 million through licensing revenues and equity in research-driven startups. However, studies indicate that universities capture only a small fraction—around 15–20%—of the total economic value created from their innovations, with the larger share accruing to industry partners. This highlights both the potential of data-driven revenue models and the need for stronger institutional frameworks to retain greater value within the academic system.

However, universities cannot do this alone. Most SAUs have strong domain expertise but limited capacity in areas such as data management and digital platform development. Bridging this gap will require collaboration. Partnerships with technology firms, agri-tech companies, and startups can help translate scientific data into usable applications.

Such collaborations must be approached carefully. Universities must retain control over their data, even as they work with external partners. Clear agreements on ownership, revenue sharing, and data use are essential. This concern is not hypothetical. It is already evident in academic publishing, where researchers often transfer intellectual property rights to publishers as part of publication agreements. Over time, this has led to a situation where publicly funded research becomes privately controlled. Universities need to reconsider this practice and adopt policies that ensure they retain ownership of their data and knowledge outputs.

Ultimately, the question is not whether agricultural universities have the resources to become more self-reliant—they clearly do. The question is whether they are willing to rethink how those resources are used. The answer will depend on leadership. Recognising data as a strategic asset is the first step. Building systems that can use it effectively is the next. This will require a combination of institutional reform, technological adoption, and carefully designed partnerships.

Agriculture itself is changing. It is becoming more information-driven, more precise, and more dependent on timely decisions. Institutions that can convert knowledge into usable insights will shape this transition.

India’s agricultural universities already hold a critical advantage: decades of credible, field-tested data rooted in local realities. If used well, this can support not only farmers and policymakers, but also the universities themselves.

The future of these institutions will not be decided by how much research they produce, but by how effectively they ensure that this knowledge reaches the field. Turning data into decisions is no longer just an academic exercise—it is the key to both relevance and sustainability.