Simulated farm model foresees the future

The effect of policies on farm production and cash flow can change from one year to the next. Congressmen and lobbyists in Washington, D.C., must be able to see what impact their policies will have on U.S. farmers.

The University of Georgia has developed a farm simulator model that allows U.S. policymakers and lobbyists to see what repercussions their policies will have on the farm community. The Georgia model focuses on peanut farms and represents what current and future farm operations will look like.

According to the University of Georgia, the computerized farm model uses real data collected from farmers. Once the economic data is fed into the model, a person can view different scenarios of how various economic outcomes and production practices can affect farm operations.

“It allows us to determine the impact of agricultural policy changes on farms,” says Bob Redding, agricultural lobbyist in Wash-ington, D.C. “A closer view allows determination policy changes on a specific farm size.”

Redding says the peanut farm model allows him to develop agricultural policy positions. For instance, he has used it often to determine price and payment-limit-policy change and new program options to see how those will affect producers.

Recently, he used the model to determine the impact of a proposed federal-rotation program.

Simulated model

Stanley Fletcher, director of the National Center for Peanut Competitiveness in Tifton, Ga., and economist with the Georgia College of Agricultural and Environmental Sciences (CAES), initiated the Georgia farm model in 2002. Along with other Georgia economists, he developed the peanut farm model using similar nationally recognized models for other crops established by The Agricultural & Food Policy Center in the Department of Agricultural Economics at Texas A&M University at College Station, Texas. The farmer-supported Georgia Peanut Commission and the National Peanut Board through the Southeastern Peanut Research Initiative funded the peanut farm model.

Nineteen panels made up of about 100 farmers from Virginia to New Mexico come together for four to six hours in various groups to share their knowledge of base allotments, crop costs, production practices, farm locations, loan availability, equipment value, historical risks and other information, says Allen McCorvey, CAES economic research coordinator at the National Center for Peanut Competitiveness.

“We can change these variables and see what the economic viability can be,” he says.

When compiling the data, the groups target specific regions where peanuts are grown. Before data is approved, all panel members from each group must agree on what is presented. Once agreed upon by the panels, the University of Georgia records and stores the data in Fortran spreadsheets. That data can be easily fed into the simulator farm model. Panel members meet and update the data every two to three years.

McCorvey says farmers, other than grower panels and county cooperative extension agents who recommended the panels, have no access to the data except what the trained users of the farm model send them. However, farmers have requested that economists run specific scenarios based on the data available.

Farmers do receive simulated reports that will benefit their operations. “We try to keep them in the loop on what will be applicable to them,” McCorvey says.

In the simulated Georgia model, the common element involves peanut production, but the other commodity variables can include cattle, corn, cotton, fruit, soybeans and vegetables. The farm model is flexible enough to represent many situations such as the impact from a rise in input costs, the effect of a downward trend in commodity prices, the learning aspect from new technology on the farm, and the impact of a drought situation and water restrictions.

“We feel these farms represent significant parts of farming in the southern United States,” McCorvey says. “We have come to believe they are a good average of what farms represent and what the entire industry is doing.”

Before energy prices started to rise in 2004, he says farms were in a good economic position as commodity prices rose. When the energy prices increased, McCorvey says they made assumptions of what the impact would be on farms. Using the supplied data, he says the farm model analyzed the situation based on rising energy cost assumptions and found it would cause a negative impact on farm operations, something most farmers don’t want to hear.

In the past couple of years, commodity prices have increased or remained steady, but input costs have continued to rise and weakened the profit picture, signaling agriculture sectors are headed for trouble. The outcome of higher energy costs has exceeded even what the economists first thought, and McCorvey says production costs now are setting record highs. He adds that the increased commodity prices will lure some farmers into a false perception if they do not monitor rising input costs closely.

“With commodity prices rising, which is a good thing, and input costs have risen, some of our farms are not as viable as they would think,” McCorvey says.

Some farm model scenarios involve big changes while others don’t. Even if congressional members believe a scenario will change using different data, sometimes they find it doesn’t or find it changes on a larger scale than they thought. For instance, depending on the scenario and data used, the outcome could reveal a decline in economic viability rather than an increase.

While the scenario outcomes aren’t always rosy, McCorvey says the model has been effective in answering questions for state and federal representatives, as well as lobbyists and farm leaders. They can see how a particular regulatory and policy issue will affect the nation’s farmers.

Last year, the university prepared 37 farm model reports that went to congressional members and lobbyists at the federal capitol.

From the feedback he has heard from these users, McCorvey says they believe the model reports available to them have made their job easier and hate to see their lives without them.

Developers of the farm model can turn over data quickly on a timely basis. For instance, many congressional members and lobbyists have sought out the data while working on the current farm bill, scheduled to be completed in 2008. They can see what a peanut farm operation will look like in 2009 and beyond.

Twice a year, McCorvey sends out a benchmark based on assumptions and price projects looking 10 years into the future. The baselines for assumptions and price projects come from the Food and Agricultural Policy Research Institute at the University of Missouri-Columbia and Iowa State University. He says all the universities share data among themselves when asked.

Take action

Redding encourages policymakers who plan to apply new policy proposals to contact the university and see how the data fed into the farm model can benefit them. “The university has done a great job in turning around policy data with the virtual farm project,” he says. “Timing is critical in periods such as the farm bill.”

He believes the simulated model could benefit other commodities. “Frankly, some policy positions that have been offered may, on their face, be viewed as having a certain impact on production agriculture, but once the policy proposal is applied to the virtual farm project it may not necessarily have this perceived impact,” he says.

For more information about the representative farm model, contact McCorvey at 229-386-7291 or allemcc@uga.edu.

The author is a freelance writer in Danville, Va