Challenges
As we will discuss throughout the rest of the course, putting agriculture data to work in your farm business requires time, money and concerted effort. With that in mind, we will wrap up this module by looking at some of the current challenges connected to the collection and use of agriculture data.
1. 
Costs and Return of Investment
Purchasing new machinery or equipment can be a significant cost. Not to mention that many digital agriculture technologies may also require an ongoing software subscription. Getting the most out of your agriculture data may also require working with trusted advisors who can provide data collection or analysis services. There is also likely to be a learning curve associated with these new technology and data-intensive approaches, which can extend the time required to get a return on your initial investment.
In a 2021 ICTC survey of 310 agri-food technology companies, 71% of respondents cited cost of equipment and/or installation as a barrier they faced when adopting new technologies or automation. Similar results were found in a 2021 American survey of 610 farmers, with 79% of respondents indicating that they would start or increase their use of precision farm management technologies if they could acquire the equipment needed (softwares, sensors, etc.) at no charge or an incentivized discount.
2. 
Knowledge and Available Support
Working with large volumes of agriculture data can be a new skill set for many farms. As a result, access to solid information and support is often cited by farmers as a challenge associated with the adoption of digital agriculture technologies. In 2021, 53% of American farmers surveyed cited a lack of training or understanding as a key barrier they face in collecting data. 76% of respondents expressed interest in training and technical resources as an incentive to collect and share more data about their operations.
This is especially true when we remember that the goal is not just collecting data, but also getting value from it in the form of insights and improved decision making.
That is one reason we have created this training course and we will spend more time in 📊Module 3: Working with Data looking at the role of trusted advisors and agtech companies in supporting your on-farm data management activities. Other training opportunities are often available through grower associations or continuing education opportunities at colleges or universities.
3. 
Quality and Completeness
To really create value with your agriculture data you need to have solid data collection and data management practices that produce high quality, accurate and complete data. What does this mean in practice? It means limited errors, proper calibration, regular data collection over the appropriate time periods and spatial distribution. We will spend a lot more time looking at data quality in 📊Module 3: Working with Data , but for now the key phrase to remember is simply: garbage in - garbage out.
4. 
Interoperability and Data Portability
It is intuitive to say that everyone working with digital tools wants the various devices and data they generate to work seamlessly together. Unfortunately, this remains a work in progress for digital agriculture technologies. There are a number of contributing factors, including the diverse types of data types present on-farm, the number of different machinery and technology companies active in the industry, and a limited number of widely accepted standards for agriculture data.
At a basic level, this can mean that a file coming from one machinery brand may need to be converted before it is used by another piece of equipment, or a software tool. The agtech industry is actively working to improve the level of interoperability through industry consortia like AgGateway , the AEF AgIn project , as well as more formal standardization .
Interoperability can also be a challenge for data portability, which is the ability to easily take your data from one software tool, device or service provider to another. Data portability is especially important in the situation where you decide to stop using one agtech company’s services. Most agtech companies will provide you with the option to request a copy of your data, but this data can be challenging to use if it is provided in a proprietary format that other agtech tools cannot easily use.
For a deeper dive into interoperability, see Software is Feeding the World #134:
5. 
Internet Connectivity
Poor internet access and cellular network connectivity can limit the ability of some agricultural equipment or in-field sensors to gather data. A lack of wireless connectivity in particular may mean that you need to manually transfer data between equipment or other tools like in-field sensors. A 2021 survey of American farmers reported that poor internet connectivity was a barrier to utilizing their farm data for 63% of farmers.
Robust internet connectivity also supports other important business functions (e.g. video conferencing) and contributes to the quality of life for people living in rural communities. For more information on this issue, check out this recent media coverage:
You can also look at a recent report on the topic from the Agricultural Producers Association of Saskatchewan (APAS) and ongoing research by the Regional & Rural Broadband Project .
Next: 🌎Agriculture Data Ecosystem
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