Abstract
Purpose The purpose of this paper is to explore the economic performance of Norwegian crop farms using a stochastic frontier analysis. Design/methodology/approach The analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from 455 Norwegian farms specialized in crop production in eastern and central regions of Norway. Findings The results of the analysis show that the mean efficiency was about 78–81 percent. Farm management practices and socioeconomic factors were shown to significantly affect the economic performance of Norwegian crop farms. Research limitations/implications Farmers are getting different types of support from the government and the study does not account for the different effects of different kinds of subsidy on cost efficiency. Different subsidies might have different effects on farm performance. To get more informative and useful results, it would be necessary to repeat the analysis with less aggregated data on subsidy payments. Practical implications One implication for farmers (and their advisers) is that many of them are less efficient than the estimated benchmark (best performing farms). Thus, those lagging behind the best performing farms need to look at the way they are operating and to seek out ways to save costs or increase crop production. Perhaps there are things for lagging farmers to learn from their more productive farming neighbors. For instance, those farmers not practicing crop rotation might be well advised to try that practice. Social implications For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize farmers appear to bring some benefits in terms of more efficient production that, in turn, increase the supply of some foods so possibly making food prices more affordable. Originality/value Unlike previous performance studies in the literature, the authors estimated farm-level economic performance accounting for the contribution of both an important farm management practice and selected socioeconomic factors. Good farm management practices, captured through crop rotation, land tenure, government support and off-farm activities were found to have made a positive and statistically significant contribution to reducing the cost of production on crop-producing farms in the Central and Eastern regions of Norway.
Highlights
The United Nation has predicted that current world population of 7.6bn is expected to reach 8.6bn in 2030, 9.8bn in 2050 and 11.2bn in 2100 (United Nations, 2017)
The input prices in the cost function in Equation (2) are specified as follows: land price is based on market price for land in terms of rents paid for land at the farm level
Conclusion and implication of the study The aim of this study was to investigate whether farm management and socioeconomic factors contributed to improving the performance of crop farms in Norway
Summary
The United Nation has predicted that current world population of 7.6bn is expected to reach 8.6bn in 2030, 9.8bn in 2050 and 11.2bn in 2100 (United Nations, 2017). Allocative efficiency is achieved when the farm is able to use its inputs according to their respective relative prices Measuring such CE gaps and identifying determinants can be useful to farm managers, policymakers, planners and advisers seeking to help farmers to improve their performance. The primary objectives of the Norwegian agricultural and food policies, as set out in the White paper No 11 (2016 –2017) are: long-term food self-sufficiency along with protection of the environment; creating more added value; and maintenance of small-scale farming in all regions To achieve these objectives the government supports the farmers. Using the BC95 model, a researcher estimates the efficiency score, and can investigate determinants of firm inefficiency (exogenous variables) in a single-step procedure. Our specification of the cost function C, including the prices of inputs (wj) j 1⁄4 1, ..., J, outputs ( yk) k 1⁄4 1, ..., K, and a local wage distribution index (rm) m 1⁄4 1, ..., M, is: XJ
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