Agriculture Data Ecosystem


Now that we’ve looked at the range of different sources and types of agriculture data it is time to look at the larger digital ecosystem that creates, collects, manages and ultimately uses agricultural data.


Agri-Food Value Chain

Before we jump into thinking about the data ecosystem, let’s stop briefly to think about the agri-food value chain. This is a concept that you are no doubt familiar with. The value chain tracks food from the farm through various value-added logistical and processing steps through to the end consumer. You can see a simplified version of the agri-food value chain in the illustration below.


While it is fair to say that this is a pretty simplified version of the agri-food value chain, notice how the typical way of depicting the agri-food value chain is focused on the movement of the farm products or food only. It really doesn’t have a lot to say about how data or information is flowing between various stakeholders along the value chain.


Agriculture Data Ecosystem and Data Value Chain

Now as we turn our attention to the agriculture data ecosystem we will focus on the data flows and relationships between stakeholders where data is exchanged. Keep in mind that in many cases where data is flowing there will not be any food product involved.  

Take for example a cloud-based farm management information system (FMIS). In this case, data is sent from the farm to the software provider (e.g. John Deere for OpsCentre, Bayer for Climate FieldView, or Farm Credit Canada for AgExpert Field) who stores and analyzes the data on behalf of the grower. Data is exchanged to support your farm’s operation, but it is not directly connected to shipping out crops, livestock, or other farm products to your customers.

With that in mind, take a look at the illustration below which maps out the data ecosystem and data flows in and around a typical grain and oilseed farm. This illustration does not capture all data flows and relationships but it shows the different ways that a farm is linked to a number of different external companies, service providers and agtech vendors through various devices, pieces of machinery and software tools. 


Farms, and farmers, play a central role in the agriculture data ecosystem as the data originators, or the primary data contributors. In that sense, growers play a similar role in the data ecosystem as they do in the larger agri-food value chain as the originator of food products.

As you think about the farm data ecosystem, the role of software can sometimes be hard to see when compared to devices, sensors, equipment and agricultural machinery. But software has a key role as the tool used to manage, analyze and visualize your agriculture data. Software on-farm can take many forms including farm management information systems (FMIS), precision agronomy applications, accounting software,  input inventory systems, device specific apps (e.g. weather station monitoring mobile app), livestock barn HVAC software, in-barn camera and other security systems, and even Excel, or another spreadsheet program.  

Agtech software is increasingly delivered from the cloud (see  🌐⁠⁠Data Infrastructure⁠ ) as software-as-a-service (SaaS). This is important for two reasons:

1. When agricultural data is uploaded to the cloud to be managed by cloud-based SaaS systems, it has effectively left the farm. As we will learn in later modules of this course,  the way your data is handled by the service provider (i.e. agtech software company) is defined through the data use agreement, and other contracts, you enter into when you start using the software service. Therefore, it is important to pay attention to these agreements.

2. The infrastructure used to run the agtech SaaS software is most likely coming from one of a small number of global big technology companies, rather than directly from the agtech software provider. This arrangement is not unique to agtech, in fact it is the most common way for websites and other internet services to be constructed across all sectors of the economy. For example, based on a  2023 market study , Amazon Web Services ( AWS ) provides 32% of all cloud computing services, followed closely by Microsoft Azure at 23% and Google Cloud at 10%. This observation is not intended as a criticism as this approach brings with it certain advantages including economies of scale and cybersecurity best practices. It is simply a fact worth knowing.

 Software-as-a-Service (SaaS)

Software-as-a-service (SaaS) is a way of delivering software over the Internet, rather than installing software on your local computer or laptop. You are no doubt using SaaS software on a daily basis - almost all of the apps on your mobile phone are built this way. The other important element of SaaS is that the data related to the software is stored in the cloud and payment for SaaS software is usually done through a subscription.

Take a look at the image below to get an overview of the wider agriculture technology landscape. This landscape scan was done in 2020 by a US-based  venture capital firm  so no doubt the individual companies listed will have changed and evolved. At the same time, the wider categories are likely to be more stable and are a good guide to the different digital agriculture products and services available in the marketplace.  

Source:  Culterra Capital 


What about the Data Value Chain?

Finally, it is worth noting that the idea of a value chain can still be used to understand the way data is exchanged between different stakeholders within the agriculture data ecosystem. For example, as data flows between your farm and one of your agtech vendors, a service provider, or a trusted advisor, value is added to the data by processing, storage and analysis operations. We already encountered this idea in Module 1 through 📈⁠⁠The Data Lifecycle⁠ . So, in a way the agriculture data ecosystem is built up of a number of different data value chains where value is added to data as it flows between different stakeholders.

Studying the Agriculture Data Ecosystem

Academic researchers are increasingly interested in studying how the agriculture data ecosystem operates. This type of research is important as digital agriculture technology becomes more common and the use of data in farming and within the agri-food value chain becomes more valuable.

If you are interested in checking out an influential paper in this area of research, this paper, " Big Data in Smart Farming - A Review",  is available free of charge. Section 4.3 is especially relevant to our discussion of the agriculture data ecosystem.

Source: Sjaak Wolfert, Lan Ge, Cor Verdouw, Marc-Jeroen Bogaardt, Big Data in Smart Farming – A Review, Agricultural Systems, 153:69-80, 2017.


Next:  ⁠⁠Exercise: Mapping Your Farm's Agriculture Data Ecosystem⁠