Date of Award

Spring 2015

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Agricultural Economics

First Advisor

Michael Langemeier

Second Advisor

Paul Preckel

Third Advisor

Yelto Zimmer

Committee Chair

Michael Langemeier

Committee Co-Chair

Paul Preckel

Committee Member 1

Yelto Zimmer

Abstract

Comparing detailed production costs and input use across countries allows producers to see if they are competitive with similar operations in other countries. This thesis looks at the cost efficiency of a sample of corn and soybean farms representing 16 countries to understand efficiency and cost trends at the farm-level, compare these farms, and identify factors that might explain farm-level performance. This analysis delves into detailed production data to calculate an important international benchmark, which is currently not available. ^ The data consist of a six-year (2008-2013) unbalanced panel of 35 corn-producing farms and 16 soybean-producing farms, which come from a dataset managed by the Agri-Benchmark Network at the Thünen Institute (TI) of Farm Economics. Data Envelopment Analysis (DEA) techniques are used to construct a non-parametric efficiency frontier and compute technical (TE), allocative (AE), and cost efficiency (CE) scores under variable returns to scale for each farm and each year. I consider two models: a single output/ multi-input model for corn and soybean production separately, as well as a two-output/ multi-input model for corn and soybeans together. Two measures of output are tested; first, implicit output, which is calculated as gross revenue divided by farm-gate price (tons per hectare), and second, total output produced (tons). Inputs for the farms are aggregated into seven categories, including seed; fertilizers; crop protection; labor; land; fixed capital (includes machinery, building and their related depreciation, repairs, and maintenance, and finance costs); and other direct inputs (includes drying energy costs, irrigation, crop insurance, and finance costs on direct inputs). All inputs are considered to be variable. ^ Efficiency scores as well as input cost shares are analyzed. A panel-data Tobit regression model is applied to the farms’ efficiency scores to determine causality of input cost shares and selected farm characteristics on technical and cost efficiency scores. Similar to previous research, a wide range of efficiency scores were found. Efficiency scores are between 0 and 1.0, where 0 signifies completely inefficient and 1.0 is efficient. The full unbalanced panel of 139 corn farms, had average technical, allocative, and cost efficiency scores of 0.951, 0.783, and 0.749, respectively, over the 2008-2013 period. The full unbalanced panel of 78 soybean farms had average technical, and cost efficiency score of 0.936, 0.787, and 0.752, respectively, over the 2008-2013 period. Using Tobit regression analysis, certain farm characteristics were found to be significantly related to efficiency. Crop protection cost was strongly negatively related to technical efficiency for corn production. Among farm characteristics, total farm size had a strong and positive relationship to TE on corn farms, while crop intensity was positively related to TE on soybean farms. Rain also had a positive relationship to CE on soybean farms.

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