The changes in farm structure have been observed in Lithuania as well as in other Central and Eastern European countries. These changes, to a high extent, have been driven by decreasing profitability of the small farms. In this paper, we look into the changes in the profitability of Lithuanian family farms across different farm size groups. Farm size is measured in terms of the standard output. The period covered is 2005–2016. The index decomposition analysis model and Shapley value are adapted for the analysis. The proposed framework ensures complete decomposition among other desirable properties. The decomposition of the changes in profitability was carried out following the DuPont identity. The results suggest that for small (respectively large) farms the asset turnover (respectively profit margin) component appear more important, whereas the leverage effect remained minimal irrespectively of the farm size group.
This article presents insights into the efficiency of Lithuanian family farms. The research covers the years 2004-2009 and is based on farm-level Farm Accountancy Data Network data. Bootstrapped data envelopment analysis was employed to obtain efficiency scores, whereas stochastic kernels and multivariate methods, viz. fuzzy clustering and nonparametric regression, were utilized to assess the impact of selected determinants on efficiency. Clustering analysis indicated that the efficiency change paths specific for the analyzed sample differed in both their average levels and ranges. Furthermore, it was concluded that Lithuanian agricultural policy should focus on increasing the efficiency of crop farms. Cluster analysis suggested that production subsidies might be having a negative effect on efficiency.JEL classifications: C440, C610, Q100, Q120
Abstract:China has experienced an uninterrupted growth of grain output during the past decade. However, a long-term analysis indicates fluctuations in productivity and output levels, as well as dramatic shifts in grain crop mix and regional distribution. This paper, therefore, re-examines the major factors behind the dynamics in China's grain production over the period of 1978-2013. The Index Decomposition Analysis technique, facilitated by means of Logarithmic Mean Divisia Index, is employed to factorize the changes in China's grain output into four effects, i.e., yield effect, area effect, crop-mix effect and spatial distribution effect. The results show that yield effect, having been the major driver behind the growth, is experiencing a declining trend over time, with crop-mix effect gaining increasing importance. The results also indicate that changes in crop-mix caused an increase in the total grain output during 2003-2013, however this was due to abandonment of soybean farming, which is not sustainable in terms of self-sufficiency. The effect of spatial distribution has been diminishing ever since 1984. Therefore, re-allocation of areas sown is not likely to damper the sustainability of grain farming.
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