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This paper is an attempt to understand the impact of public R&D and public infrastructure on the performance of the U.S. agricultural sector during the last part of the twentieth century. A neoclassical Solow growth model is not sufficient for this understanding given the sustained growth performance of the sector. We base our analysis on a well known endogenous growth model, the 'AK model' where nonconvexities are introduced through non-rival inputs. Based on these models and within the dynamic models that rationalize private and public decision making, we have identified three testable hypotheses regarding the aggregate agricultural production technology. They are: 1) increasing returns to scale over all inputs; 2) positive effect of additional units of public inputs on the long-run demand for private capital; and 3) negative impact of public inputs on cost. They are tested using two estimation procedures on two data sets for U.S. agriculture. One, covering the period 1948-1994, developed by USDA, the other, covering the period 1926-1990, from Thirtle et al. Maximum likelihood estimates do not conform to the regularity and behavioral properties of the economic model rendering them unusable for testing these hypotheses. Bayesian estimates, although not totally satisfactory, do not reject the hypotheses after prior imposition of some of the regularity conditions. This supports the notion of an important role for public inputs on the rapid and sustained growth of the sector. We calculate that, on average, one additional dollar spent on public R&D stock reduces private cost by $6.5, implying a return on these public expenses of 190 percent.