Put Me In, Coach Farmer: How Agriculture’s Version of Sabermetrics is Driving Decision-Making on the Farm

By Nick Weber

Monsanto Corporate Engagement

Ever heard of BABIP (batting average on balls in play), WAR (wins above replacement) or LIPS (late-inning pressure situation)?

These acronyms are part of stats used for baseball, or “sabermetrics,” and they are just a few of the many data points that are analyzed to measure a baseball player’s output on the field. For many, the use of analytics in baseball is seen as cutting edge.

But there’s another industry, maybe somewhat surprising, that is using the latest techniques in data science to help its talent evaluators be successful—agriculture.

Farmers are using a variety of data points to grow food efficiently using fewer natural resources. But what is being measured, and how is it helping farmers? Let’s dive into agriculture’s version of sabermetrics.

Pairing field stats and seed stats

Each acre of land—about the size of a football field—has a lot of characteristics that could be considered when preparing the year’s crop. For example, one section of the field may be higher in elevation than another. Another section may have more nutrients, such as nitrogen, phosphorous and potassium, in it. There may be several types of soil within an acre, sometimes within even a quarter acre. All of these characteristics play a part in how a crop grows.

And just like fields, the various seeds that farmers plant have unique characteristics. One type of corn seed may grow really tall, while another type has a better ability to withstand certain weather conditions.

It doesn’t end there. These seeds can then be paired with the field characteristics mentioned above. Certain seeds grow better in a silt-loam soil versus a clay soil. The data points that could help a farmer can number in the thousands. And farmers, along with industry, are using the data points to become better at placing the right product on the right field while optimizing the use of natural resources.

Here are a few examples of how farmers are using the data available to them to help inform their planting decisions:

When to Plant

Farmers can analyze how certain seeds perform if planted early or late in the season. For example, in this graph, Monsanto researchers studied the impact of planting dates between two hybrids to determine if there is a difference in crop yield. When corn hybrid A is planted early relative to the normal planting period, it has a nearly 12-bushel advantage over hybrid B. This type of information can help a farmer at planting time. If he has good weather that enables him to plant early, he may consider choosing hybrid A. But if the weather is poor, and he has to plant corn late, the choice between hybrid A and B may not be that significant.

Soil Type

Farmers can evaluate whether a certain seed product will perform better in certain soil types. In the graph below, researchers compared the impact of soil type between two hybrids. In this example, hybrid A performs best in a loam soil compared with hybrid B.


Farmers can analyze how a hybrid may perform under certain conditions, such as drought conditions or extreme heat. In this graph, we evaluated various hybrids according to a range of events, such as hybrid performance under parameters including heat, time of planting and soil type. A farmer can then use this type of information to select a hybrid that fits his farm, climate and area of the country.

Though sometimes complex, all of this data can help farmers score big.

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