Abstract:The study aims to explore the sources of competitiveness of dairy producers before and after the abolition of milk quotas in selected EU member states. The investigation is based on the stochastic frontier modelling of an input distance function in the specification of the four-error-component model. The model is estimated with a multistep procedure employing the generalized method of moments estimator, addressing the potential endogeneity of netputs, and panel data gained from the FADN database. The results r… Show more
“…The way farmers use the milk quota market is associated with farm efficiency. According to Čechura [13], the main driver of productivity growth is the scale effect, which was strengthened after the abolition of milk quotas and positively impacted farms. They also found a positive effect on productivity after the abolition of milk quotas, as also shown in our study…”
Section: Discussionmentioning
confidence: 99%
“…Data for production function estimation were obtained from the Farm Accountancy Data Network (FADN) database [28]. FADN data for evaluating the technical efficiency of dairy farms have been used in many research papers [13,[29][30][31]. FADN data represents EU milk production for 2014-2019 for each Member State at the NUTS 1 level (regional level).…”
Section: Methodsmentioning
confidence: 99%
“…The TE was higher after the abolition of milk quotas (after April 2015). According to Čechura et al [13], the main driver of productivity growth is the scale effect, which could be strengthened after the abolition of milk quotas, positively impacting farms. The development of TE showed an increasing linear trend (as shown in Figure 3).…”
Section: Technical Efficiency Of Eu Dairy Farmsmentioning
confidence: 99%
“…Some studies have evaluated the factors affecting the technical efficiency with respect to the abolition of quotas [11][12][13], to new and old EU member states [14,15], to regions [16][17][18], subsidies drawing [19,20], herd size [18,21,22], the ratio of the total number of dairy cows to the total number of cattle [23], and the economic size [24]. Technical efficiency in agriculture is influenced by many other factors such as technology, quality of factors, management and organisation on the farm, political and institutional conditions, farm economies of scale, etc.…”
This paper aims to analyse the technical efficiency (TE) of dairy farms and find its determinants. To accomplish this problem, the Stochastic Frontier Analysis was applied. The data were obtained from the Farm Accountancy Data Network database for dairy farms (TF15-45—Specialist dairying) for 2004–2019. Dairy farms were divided into four clusters according to their physical size (number of livestock units per farm) and economic size (standard output per farm). The largest farms by physical and economic size are located in Denmark and Cyprus. The smallest, in comparison, are in Bulgaria, Croatia, Latvia, Lithuania, Austria, Poland, Romania, and Slovenia. Farms in the EU are relatively technically efficient, i.e., they use their resources efficiently to produce maximum output (production). However, they have the potential to achieve better economic results and be more competitive, as the size of farms’ is not fully optimised. The abolition of the milk quota can be considered a factor in improving technical efficiency, as the indicator is higher after the abolition. New and old member states have almost comparable technical efficiency levels (the p-value of the t-test is 0.463), with old members having slightly higher level TE. Subsidies have contradictory effects on TE. Farm efficiency with higher subsidies per cow is higher for farms with €51–100/cow. However, as subsidies increase, TE decreases. Only the group of farms with the highest subsidies has a higher TE. More diversified farms are more technically efficient than specialised farms. Milk yield did not influence the analysed indicator. The analysis results can serve the stakeholders as a tool for modelling future agricultural policy, as the European farms are very heterogenous and show different conditions and economic outcomes.
“…The way farmers use the milk quota market is associated with farm efficiency. According to Čechura [13], the main driver of productivity growth is the scale effect, which was strengthened after the abolition of milk quotas and positively impacted farms. They also found a positive effect on productivity after the abolition of milk quotas, as also shown in our study…”
Section: Discussionmentioning
confidence: 99%
“…Data for production function estimation were obtained from the Farm Accountancy Data Network (FADN) database [28]. FADN data for evaluating the technical efficiency of dairy farms have been used in many research papers [13,[29][30][31]. FADN data represents EU milk production for 2014-2019 for each Member State at the NUTS 1 level (regional level).…”
Section: Methodsmentioning
confidence: 99%
“…The TE was higher after the abolition of milk quotas (after April 2015). According to Čechura et al [13], the main driver of productivity growth is the scale effect, which could be strengthened after the abolition of milk quotas, positively impacting farms. The development of TE showed an increasing linear trend (as shown in Figure 3).…”
Section: Technical Efficiency Of Eu Dairy Farmsmentioning
confidence: 99%
“…Some studies have evaluated the factors affecting the technical efficiency with respect to the abolition of quotas [11][12][13], to new and old EU member states [14,15], to regions [16][17][18], subsidies drawing [19,20], herd size [18,21,22], the ratio of the total number of dairy cows to the total number of cattle [23], and the economic size [24]. Technical efficiency in agriculture is influenced by many other factors such as technology, quality of factors, management and organisation on the farm, political and institutional conditions, farm economies of scale, etc.…”
This paper aims to analyse the technical efficiency (TE) of dairy farms and find its determinants. To accomplish this problem, the Stochastic Frontier Analysis was applied. The data were obtained from the Farm Accountancy Data Network database for dairy farms (TF15-45—Specialist dairying) for 2004–2019. Dairy farms were divided into four clusters according to their physical size (number of livestock units per farm) and economic size (standard output per farm). The largest farms by physical and economic size are located in Denmark and Cyprus. The smallest, in comparison, are in Bulgaria, Croatia, Latvia, Lithuania, Austria, Poland, Romania, and Slovenia. Farms in the EU are relatively technically efficient, i.e., they use their resources efficiently to produce maximum output (production). However, they have the potential to achieve better economic results and be more competitive, as the size of farms’ is not fully optimised. The abolition of the milk quota can be considered a factor in improving technical efficiency, as the indicator is higher after the abolition. New and old member states have almost comparable technical efficiency levels (the p-value of the t-test is 0.463), with old members having slightly higher level TE. Subsidies have contradictory effects on TE. Farm efficiency with higher subsidies per cow is higher for farms with €51–100/cow. However, as subsidies increase, TE decreases. Only the group of farms with the highest subsidies has a higher TE. More diversified farms are more technically efficient than specialised farms. Milk yield did not influence the analysed indicator. The analysis results can serve the stakeholders as a tool for modelling future agricultural policy, as the European farms are very heterogenous and show different conditions and economic outcomes.
“…Also, policies aimed at supporting investments that embody innovations tend to facilitate productivity [27]. In addition, there is evidence that the policy shift that unwound the milk quota system (before it was ended in 2015) was found to have led to an improvement in productivity [1,28,29], while tradability of milk quota was found to reduce allocative inefficiencies and allow resources to flow to more efficient farms [30,31].…”
This study examines the farm-level factors that influence differences in total factor productivity (TFP) on dairy farms. To this end, a fixed-effects regression approach is applied to panel data for dairy farms obtained from the Farm Accountancy Data Network for Northern Ireland over the period of 2005 to 2016. The findings are largely consistent with existing empirical evidence, showing that herd size, milk yield, stocking density, and share of hired labour have a positive and statistically significant impact on TFP, while labour input per cow, purchased feed input per cow, and share of direct payments in total farm output have a negative and statistically significant impact. The more complex relationships, namely age, education, and investment, have been unpacked using interaction terms and nonlinear approximation. The impact of age is negative, and the drag on productivity grows as age increases. Capital investment and education both have a positive impact on farm-level TFP, as well as on their interaction. Policy recommendations on strategies and best practices to help dairy farms tackle productivity constraints are suggested.
The Luenberger–Hicks–Moorsteen (LHM) total factor productivity (TFP) indicator has sound theoretical properties, but its decomposition yields indeterminate components of technical change and scale efficiency change that can become infeasible. The current paper decomposes the approximating Bennet indicator, which results in determinate components of technical change, technical efficiency change, scale efficiency change and mix efficiency change that are always feasible. The application focuses on the German dairy‐processing sector, an important postfarm supply chain actor. We compute 558 growth rates for the period 2011–2020. The results show that the LHM‐approximating Bennet indicator decreases by on average 1.14% p.a., with substantial annual fluctuations. The underlying components of output‐ and input‐oriented technical change also fluctuate substantially, and often conflict. Moreover, output‐ and input‐oriented TFP efficiency change fluctuate moderately on average, which is mainly driven by scale efficiency change and mix efficiency change. The components of technical efficiency change remain relatively stable on average. Indeterminateness is a relevant problem when decomposing the original LHM indicator for the current sample: depending on the specification, the proportion of infeasibilities when decomposing the original LHM indicator ranges between 6.09% and 15.95%. Our proposed determinate decomposition is thus a valuable complement. [EconLit Citations: D24, D25, Q13].
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