The great detective and precision agriculture

It’s not enough to just analyze the data that is collected by modern agricultural technology.

Eventually, producers need to make a decision, whether it is doing the same thing they have done for the last 20 years or doing something different.

Computers and software do many things well, such as processing numbers for analysis, but they won’t make a decision, especially about farming.

It will be up to the grower and/or agronomist to do that.

How do we connect the analysis on the computer with the decision that a grower has to make? It’s a step that I call “interpretation.”

Interpretation tries to make sense of the analysis and explain it because the analysis may not be perfectly clear.

Several years ago I received Volume I of Sherlock Holmes: The Complete Novels and Stories as a gift. It contains almost 40 of Sir Arthur Conan Doyle’s short stories about Sherlock Holmes. As I’ve read it, the similarities between these stories and the vast array of detective and forensic TV programs was amazing.

The key point in these stories and TV shows is one that I have tried to make with students: don’t make a decision until you have analyzed and interpreted all of the available information, and then let the facts drive the decision.

In Doyle’s works and most forensic TV shows, the main character (Sherlock) collects information and asks all sorts of seemingly non-related questions. Usually there is a supporting character (Watson) that offers suggested solutions that are mostly incorrect. All of the characters have the same information and data, but it is Sherlock who comes up with the correct interpretation.

A common thread is that the supporting characters make up their mind first about the suspect and then try to find the data or interpret the analysis to match that decision.

Sherlock keeps an open mind and lets the data analysis tell him who done it.

It isn’t that Sherlock is smarter than everybody else. In fact, he considers most of what he does as relatively simple in an arrogant sort of way, thus the often quoted though inaccurate “elementary, my dear Watson” (not once have I read that quote in any Sherlock Holmes novels or stories).

Sherlock is able see details more clearly and make correct inferences from them and thus interprets the data differently.

Too many growers have already made the decision made when they start looking at the data and analysis. When they look at the data, they are looking at justification for their decision, which of course they find.

So how can interpretation help?

An interpretative map takes the analysis techniques discussed in my last column and objectively creates a map that lays out that decision in clear terms. It still does not make the decision, but it will use the data to create a “no-brainer.”

Suitability maps are a great example of this. They answer the question, “where is the most suitable area to put (or do)?”

This could be used to determine the most suitable placement of a new wetland to mitigate crop inputs from leaching or running into open water sources. Factors that define a wetland determine the maps that are needed to make a wetland suitability map.

For example, let’s say soil permeability is one of the factors that go into deciding where to locate a wetland. Consequently, a soil type map with permeability would be important.

Slope would be another factor, and a map for that would also be needed.

Once all the factors of a wetland have been defined and a map has been created for each factor, then producers can analyze them and add them together.

The result is an interpretative map showing specific areas where a grower could put a wetland. It turns a subjective guesstimate into an objective data driven decision, clearly showing the best or most suitable area.

An instability map is another example. It would be important to know which areas of a field are consistently high yielding compared to those areas that are inconsistent, or unstable, when making decisions on seed and fertilizer rates.

This again makes use of map-matics, which means we are using mathematical operations to analyze maps.

Four or more years of yield data are normalized, added together and then divided by the number of years to get an average. This provides information about areas that are historically high or low, but says nothing about the stability.

One way of calculating the stability would be to subtract each year’s yield from the average yield and adding together the absolute value. This shows areas that vary greatly in yield from year to year.

Adding the average yield map to the stability map shows areas that are consistently high.

Some growers and consultants have told me that they use this type of map to create seed rate prescriptions. They increase the seed rate in areas that have a consistently high average yield and lower the seed rate in areas that have been unstable and produce consistently low yield.

Most importantly, this provides an objective use of the data. The grower receives an explanation or interpretation of 10 years of yield data that helps to clarify a decision on seeding rates.

As Grissom, a Sherlock-like character in the TV show CSI, once said: “Let the facts tell you what to think.”

We need more Sherlock Holmes in precision agriculture, or at least people who know how to let the facts speak for themselves. Precision happens when interpretation happens.

Terry A. Brase is an educational consultant, former precision agriculture educator and author. BrASE LLC. Contact him at

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