USDA ERS - Agricultural Productivity in the U.S.

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Agricultural Productivity in the U.S.

Overview

It is widely agreed that increased productivity is the main contributor to economic growth in U.S. agriculture. This data set provides estimates of productivity growth in the U.S. farm sector for the 1948-2009 period, and estimates of the growth and relative levels of productivity across the States for the period 1960-2004. Note that this data series has been revised (see the Documentation and Methods for details).

The level of U.S. farm output in 2009 was 170 percent above its level in 1948, growing at an average annual rate of 1.63 percent. Aggregate input use increased a mere 0.11 percent annually, so the positive growth in farm sector output was very substantially due to productivity growth. This contrasts with a 3.6-percent annual output increase in the private nonfarm sector, with productivity growth accounting for a little more than a third of the economic growth. But what exactly is productivity?

Single-factor measures of productivity, such as corn production per acre (yield or land productivity) or per hour of labor (labor productivity), have been used for many years because the underlying data are often easily available. While useful, such measures can also mislead. For example, yields could increase simply because farmers are adding more of other inputs, such as chemicals, labor, or machinery, to their land base. USDA produces measures of total factor productivity, taking account of the use of all inputs to the production process.

Specifically, annual productivity growth is the difference between growth of agricultural output and the growth of all inputs taken together (methods for combining inputs are described in the Documentation and Methods). Productivity therefore measures changes in the efficiency with which inputs are transformed into outputs. USDA also produces State-level productivity measures-annual productivity growth rates as well as cross-State differences in levels of productivity, or differences in output per unit of combined inputs. Input measures are adjusted for changes in their quality, such as improvements in the efficacy of chemicals and seeds, changes in the demographics of the farm workforce, or innovations in machinery design. As a result, agricultural productivity is driven by innovations in onfarm tasks, changes in the organization and structure of the farm sector, research aimed at improvements in farm production, and/or random events like weather.