While farms in the U.S. can vary by many characteristics such as size, production scale and the products they produce, Penn State researchers say many agricultural policies are designed with a “one size fits all” approach.
A new study led by College of Agricultural Sciences researchers set out to analyze U.S. farming counties to better understand the diversity of farms across the country. They found that counties could be grouped into six main categories, or “clusters,” and that counties could belong to the same cluster while geographically being a great distance apart.
For example, counties in southeastern Pennsylvania tend to have small, labor-intensive, high-value farms, which are more characteristic of many counties on the West Coast than they are of counties much closer by in western Pennsylvania.
This differs from the way the U.S. Department of Agriculture currently groups types of farms, which is by dividing the country into several geographic regions.
David Abler, professor of agricultural, environmental and regional economics and demography, said the findings suggest that the U.S. farm sector is very complex and heterogeneous — knowledge that could help policymakers better identify and respond where agriculture is most impacted by economic changes.
“One example is that inflation has significantly driven up the cost of farm machinery and equipment, and higher interest rates have driven that cost up even further for farms financing new machinery and equipment through loans,” Abler said. “One cluster that our research identifies is highly mechanized farms. Using our findings, policymakers can see which counties are in this cluster and therefore most exposed to higher farm machinery and equipment costs.”
According to Asif Rasool, a doctoral student in energy, environmental and food economics, economists have been trying to identify patterns in U.S. farming since the early 1900s to help understand the differences between farms in both economic performance and the well-being of farm households.
But updated research was hard to find, Rasool said, and many existing studies involved geographically contiguous groups of counties — for example, looking at all the counties in the Northeast as one group instead of analyzing counties individually.
“The United States Department of Agriculture led these efforts previously, but their last study of economic diversity in U.S. agriculture was done more than 30 years ago, and U.S. agriculture has changed a great deal during that time,” Rasool said. “We felt it was time for an up-to-date study of economic diversity in U.S. agriculture using modern methodologies.”
For the study, the researchers compiled data on 2,778 counties in 48 U.S. states, excluding Alaska and Hawaii. Data comprised information on 15 variables related to agriculture, including the agricultural resources of land, labor, machinery, buildings and utilities, as well as the sales generated by those resources.
The researchers then performed a cluster analysis to group counties with similar production potentials or farm resources into different clusters that shared similar characteristics.
“The goals of agricultural policies have evolved over the past few decades, incorporating economic, environmental, social and energy objectives,” Abler said. “Identifying farm sector diversity using advanced methodologies, like the cluster analysis we used, is a vital input into policy analysis.”
The researchers found that farms could be sorted into six clusters.
“Small farms” made up 50% of all farming counties, with an average size of 269 acres smaller than the national average of 594 acres. “Medium-sized farms” made up 7% of the counties, with an average farm size significantly larger than the small farms, and “large farms” made up 5% of all counties, with high levels of assets and land per farm but low levels of assets per acre.
The cluster “highly mechanized farms” made up 25% of all counties, with high values of land, buildings and machinery per farm but low levels of labor per farm. Meanwhile, “small, labor-intensive, high-value farms” made up 8% of all counties. These farms had high values of labor and assets per farm but small values of land area per farm.
Source: psu.edu
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