Ontario Grain Farmer June/July 2021

26 PRECISION TOOLS ARE increasingly common across Ontario’s farm landscape — but disorganized and uncleaned data is a common problem limiting the value of such tools. When trying to improve overall profitability, actionable insights are a product of pairing the right data with the right equation. Mining for the right data, that is, allows farmers to engage in profitability mapping — a process of determining where revenues are coming from, where costs are being incurred, and what corrective actions can be taken. CLEAN AND CONSISTENT Data mining means finding the relevant data amongst inaccurate or otherwise compromised information within a data set. Doing so is critical to ensuring in-field actions are based on accurate information, particularly when combining many data sets. As described by Marty Vermey, Grain Farmers of Ontario’s senior agronomist, many farmers stop short of this process. Even when working with established management zones, matching the right information to the right area can be a challenge. “You can have a whole set of data, but if it doesn’t match your field it’s not worth much. Some people get running and forget to match the data to what the actual field looks like,” Vermey says. Trying to better manage inputs in this case would be a challenge by consequence. Aaron Breimer, manager of Veritas Farm Management, expresses a similar sentiment. He adds seemingly small anomalies such as labeling the same field or input under slightly different names (e.g. simultaneously using 28, 28%, and 28UAN for fertilizer) is a common source of headaches and wasted time. “Keeping data calibrated and organized makes things easier,” says Breimer. “When people do a decent job of that it allows for these types of tools to be used a lot more effectively.” Personal biases are another consideration. In his experience, Breimer says it is not uncommon for farmers to see what they want to see, regardless of whether the information they are analyzing has been cleaned-up. For these reasons, and because detailed data management is inherently time consuming, he encourages those trying to identify where profits and losses are coming from to look for a partner to help them with data management. With relevant and high-quality data, profitability mapping becomes a much more fruitful endeavour. COMBINING PROFITABILITY DATA Breimer describes profitability mapping as “winning the wins and minimizing the cost of losses.” This is done by analyzing the data- derived report card of a given field (yield, fertility, etc.) in conjunction with production efficiency over time. Identifying which areas of the field are most profitable, as well as those which consistently make less or lose money, gives farmers the opportunity to strategically divert resources. Automatically putting more fertilizer on poorer spots is not necessarily the solution, however. Rather than driving costly inputs toward consistently underperforming parts of the field, increasing inputs to higher producing areas could be a more effective way of improving the field’s overall profitability. Rather than focusing on leveling production levels across the field, it is possible to make up for less profitable acres by driving profits on better ones. Trying to fix fertility and other issues is still a possibility. But if corrective measures do not appear to have an impact, other solutions should be considered. Make more, save more THE ADVANTAGES OF PROFITABILITY MAPPING Matt McIntosh Agronomy Finding alternative uses for areas with high fertility but low yield potential, says Breimer, is a possibility, and one which can be made more financially viable with the help of government or conservation programs such as Alternative Land Use Services (ALUS).