I spent four years working in a crop protection company’s Canadian office and was always amazed at how marketing departments could spin the data.
I often marveled at what they must have thought about the gullibility of their farmer customers.
It’s 25 years later and I still see this strategy.
Crop protection companies have incredible databases of information about the performance of their products. However, they usually keep these cards close to their vests and only play the ones their marketing departments see as the most beneficial to them.
That is not to say that they are providing misinformation. In most cases, it is how the data is presented that I question.
As a consulting agronomist, I would love to have access to some of that data. A large database of information allows me and my client to develop plans using solid data and probabilities for estimating results and economic returns rather than a SWAG (scientific wild ass guess).
Rigas Karamanos developed a tool using nitrogen returns when he worked at Westco Fertilizers. It used yield data from trials from 1989 to 2004 and is excellent for determining optimum nitrogen levels at various fertilizer and grain prices. It can be found at www.gov.mb.ca/agriculture/crops/soil-fertility/nitrogen-rate-calculator.html.
A similar strategy can be taken with crop protection products.
For example, I have put together a database of 40 trials using a commonly used fungicide for cereals from North Dakota and Western Canada.
A summary of the data found that the fungicide treatment gave an average 7.6 percent yield increase. This is not surprising because the trials spanned a number of years, some wet and some dry, and were applied regardless of disease pressure. Eleven of the trials showed less than two percent yield increase.
Would you use a fungicide providing an average of 7.6 percent return? It would probably depend on the cost of the fungicide, the price of wheat and the likelihood of leaf disease in your area.
So, with my limited computer skills and an Excel spreadsheet, I have prepared a simple tool that allows me to dig into the data to answer some of the questions that a grower might pose.
For example, what would be the returns on a 50 bushel per acre wheat crop if wheat prices were $5 per bu.?
With my simple calculator, I can determine an estimated return of $7 per acre using a chemical and application cost of $12. I can also determine that 16 out of the 40 trials would have lost money.
To the next question, “is breaking even good enough?” I would say no. I recommend that a grower should demand at least a $1.50 return for the last dollar invested.
Using this criterion, we are down to eight out of 40 trials showing a positive return, which is one in five odds of winning.
Changing the wheat price to $7 per bu. increases the average return to $14.50, which is more than a $2 return for every $1 invested. That means 24 of the 40 trials showed a $1.50 return for every $1 invested. Now our odds are up to three in five.
These examples show the benefits of this kind of data when used in a calculator. I would challenge crop protection companies to use their databases to develop calculators, which would be a better way for producers to make application decisions.