If you spend any time scrolling social media for agricultural content, you’ll see lots of posts advertising “Smart Farming” tools that can help you “Make smarter decisions with the help of data…”
It all sounds so… magical!
Unfortunately, farming is not magic. Farm management is a complex business and farmers are often told that technology is here to help. But when we ask farmers how happy they are with their digital tools they are usually less than thrilled.
One area where digital tools—”smart farming” tools—could help is by simplifying farm recordkeeping. In this article we will briefly explore how and why farmers keep records and how emerging technologies, including AI, can change the game.
All farmers keep at least some records of their farm activities. Farm recordkeeping can look like sticky notes and pocket diaries, spreadsheets and integrated forms, or fully integrated farm management software, with tractor, sensor, and remote-sensing data all talking to each other. Decisions about agricultural recordkeeping tools use are usually a matter of why a farmer is keeping records and their comfort level with the available tools.
Published in 2024, the chart below shows that many farmers’ present-day farm recordkeeping is on paper, templates, and spreadsheets, though farmers are increasingly adopting digital recordkeeping techniques.
Range of farm recordkeeping options, taken from Basir et. al., “From pen and paper to digital precision: a comprehensive review of on‑farm recordkeeping” (2024).
There are many reasons farmers keep records. Besides compliance and certification, farm recordkeeping makes good business sense.
USDA Organic certification is the one of the most commonly known reasons to keep records, but many different certification programs exist, including Certified Naturally Grown, GlobalGAP, and Salmon Safe. Any producer seeking a certification must prove adherence to that certification’s standards by keeping comprehensive records of farming practices, materials used, and sales. This can be done using many digital farm recordkeeping software tools or using document templates, like those provided by the USDA (below).
Likewise, farms may be subject to various regulations, where lack of compliance can result in fines (ex. tracking chemical applications), requiring detailed recordkeeping to prove compliance. In these cases, farm recordkeeping is a necessary part of farm management.
Example of a USDA document template available to support organic producers.
There is a (growing?) segment of consumers and food processors with significant interest in purchasing sustainably grown food. This can include food grown organically (mentioned above) or other sustainable agriculture practices without existing certifications (ex: sourcing regeneratively grown food for farm-to-table or food-is-medicine). Precise farm recordkeeping can provide transparency and traceability to build trust between producers, consumers, and food processors, opening up premium market opportunities.
Keeping accurate records can help farmers manage their resources (inputs, labor, time) more effectively. Some examples of how farm recordkeeping can help include:
Tracking expenses, revenue and profits can help farmers identify inefficiencies in their operations, leading to better financial decision making. Detailed farm accounting also makes it easier to apply for loans or other forms of financial assistance and properly file taxes.
By and large, farmers don’t want to sit behind a computer, but digital recordkeeping actually makes it easier to achieve all of the recordkeeping goals above without a lot of screentime. Mobile apps and digital tools make it easy to record activities and automate repetitive tasks. Standardized data fields and automated calculations improve the accuracy and consistency of farm records. Shared access to up-to-date information makes it easier to collaborate on day-to-day farm activities.
Under the hood, there are additional benefits to digital recordkeeping. By making it easier to query, retrieve, and transfer data, digital recordkeeping enables integration between many different tools. It’s what allows your tractor, sensor, and remote-sensing data to all talk to each other. It can also allow you to pre-populate regulatory and certification forms at the click of a button instead of tracking down each record individually. These are just some of the benefits of recordkeeping in farming.
There are a lot of digital decision-support platforms out there that compile your data and make it usable. These can come with remote-sensing data, integrated sensors, or AI analytics to provide you with powerful decision-making support; diagnosing problems and recommending solutions in real-time. Access to digital solutions for agricultural recordkeeping often comes at a significant cost, making them viable only to farms of a certain size or type. They may only be useful to those farms that match the farms these tools were trained on (ex: row crop farmers in the Midwest).
At the end of the day, the real-time, integrated, magical decision-support tools advertised all over social media provide the same benefits as those mentioned in the “Planning and Efficiency” section above. The difference is in their ability to automate the process, providing you with suggestions and insights before you ask for them. Conversely, precise recordkeeping in the absence of these decision-support platforms requires that you actively analyze your data yourself to gain insights into how to improve your farms operations.
Besides the “why,” there are a few other factors that affect farmers’ decisions about recordkeeping.
Scale and Complexity
Tracking activity at scale on a thousand row crop acres may seem more challenging than tracking farm activities on a 3-acre market garden. However, the complexity of tracking successional plantings, weedings, and harvests across dozens of beds presents a significant set of challenges. The factors of scale and complexity are primary drivers in farmers’ recordkeeping choices.
Future Uses
Consider where your farm business is headed. The recordkeeping choices you make today won’t prevent you from changing tactics later. But taking the time to consider how you may want/need to use farm records in the future may help make more appropriate decisions today.
Personal Comfort
Finally, recordkeeping comes down to personal preference. When we ran the “nutrient variability” project for the Bionutrient Food Association (box, below), we interacted with hundreds of producers, many of whom simply did not want to use digital tools on their farms. That same hesitancy to use digital tools may be amplified in the current wave of AI tools, with many farmers being unwilling to give AI tools access to their data.
In the past, translating data from handwritten records (ex: field journals) into the structured data required by farm-management software and decision-support tools was challenging and time-consuming. However, the ability of large language models (LLM)—a type of AI—to extract and structure that data is getting better every day. Similarly, you can use AI tools for farm recordkeeping to merge data from different spreadsheets (ex: production and sales spreadsheets) and assemble the data to build your own decision-support tools, customized to the data that you already collect, and targeted to output the insights that you want.
What does all this mean for you? Well, it means the recordkeeping decisions made yesterday may not be the best fit for your operation tomorrow. You likely also have questions about how AI is transforming farm paperwork and whether you want to use this technology in your own farm recordkeeping. We can help sort through this ever-changing landscape and make the recordkeeping decisions that are right for your farm management.
Oh, and I should note that we do not develop or build farm-recordkeeping software, so a call with us does not involve a hard sell!
Book a time with Greg or Book a time with Dan or email us at info@our-sci.net
| Linking farm practices and soil health to nutritional variation in the food supply. |
| From 2018 to 2022, Our Sci worked with the Bionutrient Institute (BI) to conduct a large-scale survey of the nutritional variation in the food supply chain. In short, producers submitted crop and soil samples to the BI lab in Ann Arbor, MI. Those samples were measured for numerous crop and soil health parameters and added to a dataset of thousands of samples across dozens of crops. To link crop nutritional outcomes to management practices, we collected detailed records about how each crop sample was managed. Participating farmers were asked to complete a survey detailing management practices such as tillage type and intensity, fertilizer management, and weeding and irrigation schedules. Check out our publication in Nature: Science and Communications here. |
December 12, 2025
