When I engage in conversations about the sustainability of the dairy industry with people at the community arts studio I frequent, I walk a fine line between education, interest, and trust. That is to say, I share as much useful context as I can to battle misinformation without losing the trust or interest of the person I’m speaking to.
Some of these people have ideas they’ve read about online, heard from a friend, or seen on the news. “Have you guys tried feeding the cows seaweed?” “Can you capture the greenhouse gases emitted by cows in the barn?” “Could you breed cows to make less methane?”. Often I’m impressed and heartened to see the interest these folks have in the industry, and their questions get at an important point. These specific ideas might not be ready for research, or might have already been discussed and found implausible, but their line of questioning gets at a bigger point. How do farmers and the wider dairy industry decide which sustainability measures to research and implement?
In a recent study published by the Journal of Dairy Science, researchers at the University of Wisconsin developed a computer program or “decision support model” to help farmers do just that. Called the DairyPrint model, this tool allows farmers to input specific data about their farm including herd dynamics, housing information, manure management specifics, crop data and more, to form a fully realized model of a farm. In development, researchers found that the theoretical farm producing the least greenhouse gas (GHG) emissions used the lowest NDF-ADF levels in their diets, incorporated 3NOP additives, used sand bedding, and emptied their manure storage in the fall and spring. The highest theoretical GHG emitting farm used the highest NDF-ADF levels in their diets, used sawdust as bedding, and emptied their manure storage only in the fall. Beyond describing the possible highest and lowest GHG emitting farms, this model can suggest to the farmer using it what practice might make the biggest difference in their GHG emissions.
This research was done with a theoretical lens to see if this model could interpret the data it was fed to create an image of a farm. My interest is more often piqued by the discussion of how this tool could be used by farmers. In reading sustainability literature, attending talks and forums, and speaking directly with farmers, I know that decisions that are made usually have an aspect of practicality and must improve margins or efficiency in some way – they can’t just “be sustainable.” While the importance of consumer perception of dairy industry sustainability is of vital importance in the long term, it’s difficult and unrealistic to make expensive and drastic changes based on how an outsider views a farm, a farmer, or an industry.
The potential beauty of the model is in its straightforward product. While the information fed into the model is complex, that data would already be largely coalesced by the farmer. The model would be able to tell a farmer the specific change in practice that they could implement to make as much of a difference in emissions as possible. The issue with this simplicity is in the nuance lost through the model, and through the singular nature of that suggestion.
Let’s take the imaginary farm with the lowest emissions as an example. There may well be a farm that uses these practices already, but there are considerable barriers between this ideal and the reality for many farmers. Sand bedding, for instance, might not work with the manure management system of a farm or might not be available to the farmer. The addition of 3NOP to diets may be too novel and its safety too uncertain for a farmer to be interested in using it. Farmers may be limited in labor or avenues to empty their manure storage in both the spring and the fall.
This research describes exciting advancements in decision making tools for farmers. The decision-making fatigue of farming, of business in general is not often addressed. This model could be a great step in farmer advising and simplifying that decision making process to home in on specific practices that would best serve the farm and farmer. The application of this model is still being developed, and hopefully the more nuanced factors that go into the decision-making process will be ironed out along the way.
— Bridget Craig