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    Want to Know the Farmer? Start by Knowing the Fields.

    Posted by FMiD Team on Feb 20, 2020


    Want to Know the Farmer? Start by Knowing the Fields.


    We talk a lot at Farm Market iD about how our data is “built from the ground up.” But why is that so important, especially in marketing and sales?

    The key reason: farmers make their key purchasing decisions based on the land.

    When you’re marketing or selling to farmers, you’re connecting with them on a variety of levels:

    • Farmers are consumers, buying products and services for themselves and their businesses
    • Farmers are often property owners , which gives them both the stressors and advantages that come with owning rather than leasing properties
    • Farmers are often business owners, meaning that concepts like profit and loss and tracking business expenses matter to them
    • Farmers are family members, related to each other and working together on the family business

    But none of these attributes are unique to farmers.

    What makes a farmer unique is the fact that their material success is based primarily on the land they operate.

    It’s based on how much land they have, how productive that land is, the market forces that value the crops grown there, and many more factors.

    Thus, understanding the fields will help you understand the farmer in a way that name and address simply won’t. Here are some specific reasons why.


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    The Unmatched Accuracy of Field-Level Insights

    While there are many advantages to relying on field-level insights, perhaps the most powerful is accuracy.

    When compared to the alternative -- self-reported and surveyed data -- insights drawn from analysis of the fields and their connections to particular growers are the most accurate in the industry.

    A great example of this comes through duplication of acres. Because we use field-level data, we can de-duplicate fields that probably won’t show up in a survey.

    Suppose two brothers farm 5,000 acres together and a publication surveyed them. They each respond 5,000. Or maybe one responds with 5,000, thinking of the total acres, and the other responds 2,500 because he’s thinking about it as split with his brother.

    Either way, that survey result could give their combined operation anywhere from 7,500 to 10,000 acres, when in reality they only farm 5,000. A 2,500- to 5,000-acre discrepancy no small rounding error.

    On the other hand, because we build our database from the ground up, that duplication isn’t going to happen. We start with the land, then attribute growers to that land, not the other way around.

    Another factor to consider is that 100 acres of land doesn’t mean 100 acres of crop. Farmers will often carve out certain areas that aren’t planning -- whether it’s getting to the edge of the road or coming to a ditch line.

    Field detail on a map

    Here's an example of a field boundary covering areas that aren't being planted right now.

    Instead, we look at the actual area that’s being planted. From there, we can see what’s been planted historically, and from there estimate how much of the land will be relevant to the farmer and, thus, to your discussions.

    Figuring Out How Farmers Make Decisions

    Farmers make decisions based on their fields. What’s more, the potential decisions available to them are also set by their fields and how they’re performing.

    For example, a large farming operation with over a thousand acres won’t be managed monolithically. It’s just not possible. For some of these operations, there are fields that will be miles apart.

    Fields on one end of the farm are going to have different factors impact their performance than the other end:

    • Microclimate
    • Soil productivity
    • Field slope
    • Irrigation

    There are very different things you would put into the field -- very different decisions you’d make -- depending on context.

    Plus, many farmers don’t simply manage a contiguous operation. They may have many fields in one location, yes, but there could be others in other states or counties that they manage from a distance or simply hire someone to operate them.

    These nuances in how the fields impact the farmers’ decisions will never come up when all you know is that they farm 2,000 acres.

    A spread-out operation

    Here's an example of a group of fields that a single grower operates -- and they're spread out across a wide swath of geography.

    Another example: proximity to ethanol plants, elevators, or feedlots -- points of sale for the market. If one field is 100 miles away from the nearest ethanol plant, but another field is 10 miles away -- within the same operation and same grower’s portfolio -- the closer field is going to be a good candidate for ethanol corn, while the farther field isn’t.

    On the other hand, that field that’s 100 miles from the ethanol plant may be 20 miles from a feedlot or elevator, so it could be a good candidate for feed corn.

    Knowing the grower is a good start. But you really don’t have the level of insight needed to make customized, tailored sales decisions when all you have is name and address.

    Understanding each field both on its own and in the context of the larger operation is just as important in agriculture as understanding the customer, the farmer, who’s making those decisions.

    How to Put These Insights into Action

    You need to know farmers’ fields in order to understand their decision-making process. That’s all well and good, but we’re talking about a lot of details here.

    How do we take this data, and put it into something actionable?

    One example of the kinds of inferences that are available from geospatial field data is the Lost Acres of 2019. Last year, we experienced record floods in the Midwest, which wiped out nearly 20 million acres according to the USDA.

    Understanding which growers experience those losses could be invaluable information when you’re trying to figure out the best way to communicate with them.

    It’s the difference between a farmer who had half of their crops wiped out, versus one who just lost a hundred acres in the context of a multi-thousand acre operation.

    Using our field-level information combined with our NDVI vegetation data, we were able to identify fields that were historically productive but had, in 2019, had no production. This definition would include not only Prevented Plant, but also failed or cover crop acres.

    So if you’re a chemical company and you provide chemicals that help to burn down the weeds on unplanted fields, it would be good to understand which of those fields were unplanted so you know which ones to prioritize. You sell them the chemicals so they’ll be ready to till in the fall.

    Alternatively, if you’re selling seed, understanding which fields have been unplanted may impact whether you sell them a weed-resistant seed.

    The more information you have on the grower, the better able you are to communicate helpful products for their operation. Starting at the field-level gives you more accurate, comprehensive and detailed insights to guide these decisions.

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