The Concept and Pertinence of Data Integration in Agricultural Finance

Jul 15, 2021 | Blog, GIS in Agriculture

The Concept and Pertinence of Data Integration in Agricultural Finance

Data integration is a process by which disparate data sets are brought together into a central repository for more effective presentation or analysis. While data integration can be beneficial to many industries, it’s becoming increasingly important in agriculture. 

As the Sustainability Ag Research Action Team (SARAT) of the National Corn Growers Association explains:


“Agriculture has become an expert at collecting data but continues to fall short of the economic and environmental promise the information bonanza represents.”


For ag finance professionals, this data presents an opportunity to make better lending and investment decisions, and reduce overall portfolio risk. As climate change, water scarcity, and other transition risks threaten the stability of the agricultural sector, it’s imperative for ag professionals to make better use of the data at hand.

This post will explore the current limitations of data integration in ag finance, and how ag professionals can gather, integrate, manage, and analyze data more effectively.

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The Current State of Data Integration in Agriculture

One of the main challenges facing the agricultural sector is disparate, siloed data that makes it hard for banks and lenders to account for all of the risk factors that can affect an agricultural portfolio. For example, groundwater GIS data may be publicly available, but it isn’t always presented in a format that’s easy to read or navigate, nor does it actively give bank or investor data subjective context.

Likewise, growers, suppliers, and other ag professionals may have access to internal data, such as environmental data collected from field sensors, or ESG data collected for reporting purposes. Even if this data is shared, it may not be presented in a standardized format, which makes it harder to connect it with existing data and unlock relevant insights.

The SARAT team points out that “integrating data from outside the farm is also critical such as in the research field” but “will require some standardization.” Additionally, ag professionals can benefit from using an API, or Application Programming Interface, which allows users to integrate third-party data in a secure manner.


The Importance of Un-siloed Data

Un-siloed data can provide more value to ag finance professionals, and contribute to the resiliency of the agricultural industry as a whole. In a report called Feed the Future by U.S. Aid, the writers note that this data is especially vital in developing countries:

“The widespread adoption of mobile phones in developing and emerging markets…. [has led to] greater financial inclusion, more precision in agriculture, better data collection and analysis, and more effective information dissemination.”


Lenders and investors can use un-siloed data to digitize the loan and investment process, reducing borrower exposure to water stress and climate risk and, therefore lessen financial risk.


As climate uncertainty leads to higher stakes and higher risk, there will be a greater need for integrated datasets that can be shared with multiple stakeholders.



Examples of Siloed Data

Agricultural data comes in many forms, but can largely be broken down into three main categories, which correspond with the kinds of data that are collected in the course of ESG reporting: environmental, social, and governance.

In the agricultural sector, much of this information is geospatial in nature and should be analyzed in a map-based format.



Environmental data encompasses everything from on-the-ground measurements such as water and soil quality to long-term climate trends and weather patterns.

As an article in Frontiers puts it, these kinds of measurements “allow the farmer to treat the production field as the heterogeneous surface it is (fertility, water, plant pathogens, slope, surface runoff, drainage, etc., which are all highly variable throughout the field) instead of as a homogeneous surface as it was treated in the past.”


Other examples of environmental data, relevant to ag finance professionals, include:

  • Water sources and water rights for agriculture
  • Pumping restrictions due to droughts
  • Watershed boundaries
  • Wildfire risk
  • Flood zones
  • Endangered species zones


Environmental data is key to sustainable agriculture and water conservation because it allows farmers to reduce inputs while maintaining consistent yields.



Social data is becoming increasingly important as investors and consumers alike seek out greater transparency around companies’ ESG initiatives and socially responsible business practices. This data can include everything from employment statistics in a specific farming region to reports on agricultural land ownership by gender.

For example, the Food and Agriculture Organization of the U.N. collects a wide range of social data, including “income, poverty, employment, and social protection, with a special focus on rural areas.” Ag banks and lenders could monitor data such as charges in farmworker employment by country as a result of water stress and reduced productivity.

In California, pumping restrictions under the Sustainable Groundwater Management Act (SGMA) have led to an increase in fallowed land. Fewer acres in production means less farm labor is needed, which impacts the rural economy. During the 2012-2016 drought, the state lost over 10,000 jobs, and rural Latino communities were hit hardest.

By monitoring acres of fallowed land as a proxy for employment data, ag professionals can anticipate these trends and work to mitigate the impact on farming communities.



According to a report by S&P Global, “Governance data, unlike environmental or social data, has been compiled for a longer period of time and the criteria for what comprises good governance … has been more widely discussed and accepted.”

Still, governance in ag finance is changing rapidly, with many countries introducing new ESG reporting requirements and mandating climate risk disclosures. At the same time, some local watersheds are introducing new groundwater management practices and pumping restrictions due to water scarcity and aquifer depletion.

Ag banks and lenders need to know which policies apply to the farming operations in their portfolios and how they will affect different parcels of farmland.


This data might include:

  • Pumping restrictions under SGMA
  • Watershed boundaries
  • Historical water delivery information



What Data Integration Can Offer Ag Finance Businesses

In many cases, there’s significant overlap between these datasets, with each type of data providing additional context for other types of data. For example, regulations are often a response to environmental data, so by staying on top of regulatory trends, ag banks and lenders may be able to anticipate risks before their competitors.

When data is kept in silos and considered independently, its usefulness is limited. From a financial risk mitigation perspective, it would benefit ag banks and lenders to integrate this data in a way that puts existing portfolios and datasets into perspective.


Data integration can:

  • Provide valuable insights and allow stakeholders to see things more clearly
  • Save time and money when it comes to research and decision-making
  • Empower accurate, educated, and informed decisions
  • Improve efficiency by auto-populating reports with existing data
  • Build financial resilience through risk analysis, team collaboration, and data management


Data integration is the future of data management and the key to staying competitive in ag finance. By integrating their datasets now, ag banks and lenders can bring their loan decisioning process and portfolio analytics into the 21st century and make better financial decisions.



The Bottom Line

Historically, agricultural data has been difficult to process, due to a lack of transparency and standardization across datasets. Social, environmental, and governance data may be collected by different parties and presented in incompatible ways. Data integration allows ag banks and lenders to gain deeper insights into data by merging and presenting multiple sources of information in a consistent, easy-to-understand way.


In doing so, data integration unlocks the potential of in-house data with third-party data.


In particular, geospatial tools like AQUAOSO’s GIS Connect platform allow users to integrate third-party datasets using a secure API, and export data into shareable PDF reports. GIS tools help lenders and investors save time and money by bringing strategic decision-making into the 21st century.

Reach out to the team at AQUAOSO with any questions, or download a free ebook or white paper from the Resources page.

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