You don’t have to look very hard to find chemical manufacturers’ advertisements claiming a significant positive yield response (15, 20, 25+ bu./ac) to using one of their fungicide products. There are many effective products on the market that provide good control and protection against fungal pathogens, but advertisement claims based on ‘average trial data’ aren’t guarantees for your fields. Three critical components (a host, favorable environment, and pathogen) must come together at the same time for a plant disease to thrive. These three components are commonly referred to as the Plant Disease Triangle. Management or alteration of just one of these components prevents or reduces disease severity.

We often use the phrase, “Everything agronomic is economic.” What does that really mean? 

First, let's first define agronomics and economics. What is agronomics? That's everything that we do in the field related to making good management decisions. It's deciding how much fertilizer to apply and where to put it, planting rates, crop protection, tillage systems and how to incorporate all of this into the farm. Those all go into how we grow our crop. On the economics side, we’re talking about all of the money involved in farming. Farming is a business, and just like any other business, you need to make sure you have cash flow so you have the opportunity to farm again next year, and the year after that. So, how do we focus on agronomics and economics? We do that by analyzing growers’ data. We use that knowledge to help them make decisions on their farm. 

Knowing what you’ve done on the farm in the last five, 10, or 20 years can provide valuable knowledge as you plan into the future. However, if you never take that data and don't use it to make decisions, it's not doing you any good. It's important to invest time into collecting your farm data. We work with growers to analyze their collected field data. We add costs to the layers of data including product cost, operations cost, management cost if they have any land-specific cost, and tie that to the yield file so we can see what is making agronomic and economic sense on the farm.

It's fairly easy to tell where there are higher yields, but it's a lot harder to know if that yield increase also caused an increase in the pocket book. Did the decision pay for itself? Did you produce enough bushels to offset the cost of production? Every pass across the field matters agronomically, but it also has a cost associated with it. We give you three steps to help combine your farm agronomics and economics below. 

Throughout Premier Crop's nearly 20 year history, we've perhaps been the most diligent at communicating that what we do – big data analysis – would be considered "observational data analysis" by those in the scientific community.

Long before GPS was part of our acronym vocabulary, my early agriculture career started in eastern Iowa and northern Illinois. On one of those scorching hot July days, as you were driving through the area, every so often the road would be higher than the fields and you could visually capture a birds-eye view of the fields below. You could see parts of the fields, where the corn was rolled up tight as the plants went in to "protection" mode – while other parts of the field look perfectly normal. Images like that help make me an advocate that managing parts of the fields differently would make agronomic and economic sense.

I'm always looking for parallels – examples from other industries on how they use data to drive better decisions. While on the road, I listened to several Freakonomics podcasts. One that related well was titled Bad Medicines, Part 2: (Drug) Trials and Tribulations.

Why should you test products on your own farm? Your farm is unique and you have the equipment capabilities and data to conduct those trials. With little risk, you can have a more robust dataset than many companies.  I’ll explain…

Throughout Premier Crop’s nearly 20-year history, we’ve perhaps been the most diligent at communicating that what we do – big data analysis – would be considered “observational data analysis,” which can show relationships and correlations, but stops short of providing cause and effect.