NEPC 2013 Annual Meeting: Decision-Making Economics

Jennifer Colby, Program Coordinator of the Vermont Pasture Network at UVM, Colchester, took the floor to wrap up the
Developing Economic and Energy Tools to Aid Farmer Decision-Making session.

She gave some history on dairy farm economics. Agricultural economists have looked at these economic indicators:

  • Debt to asset ratio (Karszes et al., Kriegel, Parsons)
  • Paid labor expense per hundredweight
  • Grain as % of milk
  • Milk per cow
  • Net profit per cow
  • Labor and machine per cow

However, this has been a dairy focus. It has extensive records so it is a great snapshot of grazing dairy farm profitability running now over a decade of results. Expanding economic analysis into non-dairy is challenged by a lack of farm numbers in the New England area but not in the Northeast Region.

“Economic uncertainty and financial performance” research by Cannella, 2009, observed that it is a challenge to measure economic performance when averaging the best and the worst. It makes it hard to identify the attributes contributing to the best, and why the worst are the worst. What makes that difference? Daily decisions.

Jennifer asked the question, “What are day-to-day economic decisions?” She gave some examples:

  • Value of a grazing day versus a day feeding stored feed,
  • Cost of timing forage harvest, and
  • Renting versus purchasing equipment; versus purchasing forages.

Then she asked, “What do YOU use as a daily indicator?” For a grass farmer during the growing season, it should be how high the grass is, its stage of maturity, and how much of it is like that.

Historical energy measurements are fuel use, electricity use (often tied to milking parlor), and labor expense. Energy data pros & cons:

Pros:

  • regularly collected
  • they’re easier to get quickly
  • give a financial picture
  • “Headquarters”

Cons:

  • single bottom-line
  • “landscape-level”
  • different farm sizes, types

Energy data is easily collected at the headquarters scale for a farm. At the landscape level, the collection of enough or all relevant data is more uncertain or is not readily available or apparent (overlook an energy input). There is also a choice one has to make on how large a landscape is appropriate. Different farm sizes and types can lead to an unrepresentative average if all the data is thrown together. Yet, it also presents problems if farm sizes and types are segregated out as a discrete data blocks. Sample size of each block of data may not be large enough to have a very sensitive statistical analysis.

In closing Jennifer asked the question, “How to address the challenge?” The CIG Energy project goals are to use this opportunity to look at energy use in a new way and incorporate that perspective into day-to-day farm-level decisions. Many of the farmer members of the Consortium could participate in this project for their own edification and give the project the requisite sample size and scope.

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