Effects of Insurance Adoption and Risk Aversion on Agricultural Production and Technical Efficiency: A Panel Analysis for Italian Grape Growers

This article aims to evaluate the effect of insurance on production, technical efficiency, and input use of Italian specialised-quality grape growers. A panel instrumental variable stochastic frontier approach is applied over the years 2008–2017 using data from the Farm Accountancy Data Network. The results show the requirement to correct for the endogeneity that stems from insurance adoption. Insurance has an enhancing effect on production and efficiency and reduces the use of intermediate inputs. It suggests that insurance helps to diminish the risk-averse farmers’ suboptimal input use due to the presence of uncertainty. Crop insurance leads risk-averse farmers to behave as if they were risk neutral and employs the profit-maximising input vector. Therefore, by reducing the risks linked to the uncertainty of outcomes, crop insurance leads grape growers to go in the direction of profit maximisation.

Main Factors That Explain the Use of Fertilisers on Farms in the European Union

A deeper assessment of the main determinants associated with the use of fertilisers, for example, in the European Union farms may bring relevant insights about the respective frameworks and support to find more sustainable solutions. In this context, the main objective of this study is to identify factors that influence the use of fertilisers in the agricultural sector of the European Union regions. To achieve this objective, statistical information, at farm level, from the European Farm Accountancy Data Network was considered. These data were first analysed through exploratory approaches and after assessed with classification and regression tree methodologies. The results obtained provide interesting insights to promote a more sustainable development in the European farms, namely supporting the policymakers to design more adjusted measures and instruments. In addition, the fertilisers costs on the European Union farms are mainly explained by crop output, costs with inputs, current subsidies, utilised agricultural area, and gross investment.

The Competitiveness of Agriculture in EU Member States According to the Competitiveness Pyramid Model

Agriculture in the European Union is highly differentiated, and one of the objectives of the Common Agricultural Policy is to improve agricultural competitiveness. Therefore, surveys regarding the competitiveness of agriculture and grouping countries of the European Union (EU) according to similar characteristics of agriculture are very valuable. They help make strategic decisions concerning the agricultural sector. This paper aims to evaluate the agricultural competitiveness of EU member states in 2010–2019. Data used is derived from the Eurostat and FADN (Farm Accountancy Data Network). The study employed a competitiveness pyramid model based on two groups of factors–competitiveness sources (bottom of the pyramid) and competitiveness effects. Partial components allocated to the groups mentioned above of factors were used to calculate a synthetic measure to determine the level of agricultural competitiveness in respective countries. The studies revealed that EU agriculture varies both in terms of resources and relationships between production factors, as well as the efficiency of their utilization. A clear difference in the level of competitiveness occurred between old and new member states, although some new countries ranked relatively high in terms of competitiveness sources (Czechia and Poland). Belgium scored highest for the synthetic measure of agricultural competitiveness in 2010–2019, and Cyprus had the lowest. It was demonstrated that human resources were of utmost importance in the structure of competitiveness sources. In turn, the average holding area determined the management conditions to the highest extent.

Assessment of Farm Accountancy Data Network System in Croatia

Croatia is the newest member of the EU Farm Accountancy Data Network (FADN). So far, no comprehensive evaluation of the national FADN system has been conducted, and this study based on a survey of staff involved and a desktop analysis was conducted to fill this gap. The three main elements of the Croatian FADN system are the National FADN Committee, the Ministry of Agriculture as Liaison Agency (LA) and the Croatian Bureau for statistics. All FADN survey activities are carried out by LA and the main burden of data collection lies with the data collectors and the farms. The system operates in accordance with relevant EU legislation and currently its main objective appears to be the fulfilment of its obligations to the FADN network. Other benefits like analysis of CAP measures etc. are not yet exploited. The main challenges are further development of quality assurance systems, the motivation of staff and farms to participate, the increased use of FADN data, and the expected changes in transformation of the FADN into the sustainability data network (FSDN). Croatia will successfully meet these challenges if it strengthens its capacity for FADN research and data analysis and establishes an effective quality assurance system.

The impact of geographical indications on farm incomes in the EU olives and wine sector

PurposeGeographical indications (GIs) are expected to stimulate rural development by increasing the viability and resilience of farms in disadvantaged and remote areas. However, little quantitative evidence exists to support this expectation. This study fills this knowledge gap by quantitatively analyzing the effect of GI adoption on farm incomes in the EU olives and wine sectors.Design/methodology/approachThe analysis uses data from the Farm Accountancy Data Network and EUROSTAT and an endogenous switching regression model to analyze the impact of GI adoption on farm incomes for specialized quality wine and olives producers in the year 2014.FindingsThe results show that GI adoption significantly improves farm incomes in both the olives and the wine sector.Research limitations/implicationsThe research uses data from the farm accountancy data network (FADN). This is seen as a limitation of the analysis. The research raises some concerns about the appropriateness of FADN for the assessment of farmers' involvement in food quality schemes and a reconsideration of FADN as a tool for farm performance analysis is advised.Originality/valueThis is one of few quantitative studies of the impact of geographical indications on farm performance. Furthermore, it gives insights into the mechanisms by which GI can affect farm incomes.

Yields variability as a determinant of production risk in agriculture

Agricultural activity is exposed to a number of adverse external factors known as production risks, which affect the quantity and value of production. One of the measures of production risk assessment in crops production is yields variability. The aim of the study is to determine the scope of the variability of the yields of selected agricultural crops on the basis of various sources of data covering the years 2015–2019. The study uses aggregated official statistics data compiled by Statistics Poland, published in the Statistical Yearbook of Agriculture, and individual data from the Polish Farm Accountancy Data Network (FADN), which takes into account commercial farms. The analysis of the differences in crops variability determined on the basis of different sources of information was carried out using basic statistical parameters: the arithmetic mean, standard deviation and the coefficient of variation. The research shows that the variability of yields in Poland depends not only on the type of crops, but also on the location of the production. Moreover, it indicates a greater stability of crops production among commercial farms when compared with farms in general. The comparison of the values of the coefficient of variation calculated from individual data with the values calculated on the basis of the average yields indicates fundamental differences which should be taken into account when applying the variation coefficient to assess production risk and, consequently, to optimise insurance tools.

Embedding animal welfare in sustainability assessment: an indicator approach

In line with growing consumer interest in sustainable food production, a number of farm-level sustainability indicator studies have been published in recent years. Despite the importance of animal welfare, many such frameworks fail to adequately take account of it, mostly due to difficulties in accessing suitable data. This paper demonstrates that it is possible to develop indicators of animal welfare that can be embedded within a wider sustainability framework using a representative farm-level dataset such as the European Union (EU) Farm Accountancy Data Network (FADN) for Ireland, the Teagasc National Farm Survey. The paper presents a set of sustainability indicators for dairy farms in Ireland for the 2014–2017 period and examines the impact of policy reform on sustainability performance. Headline results show that welfare standards on dairy farms in Ireland have remained stable over the period despite the considerable intensification of the dairy sector following EU milk quota removal. Furthermore, dairy farms that have expanded herd size significantly have improved welfare standards more than farms that have not increased production. An analysis of synergies and trade-offs between the various aspects of sustainability reveals that positive correlations exist between welfare standards and economic and environmental performance. The analysis facilitates the identification of win-win farm-level strategies that can be adopted to improve economic, environmental and animal welfare outcomes. The framework developed here presents opportunities for evaluating policy impacts at the farm level on various aspects of sustainability. The use of the FADN demonstrates the capacity to extend such an approach across the EU.

One or many European models of agriculture? How heterogeneity influences income creation among farms in the European Union

Agricultural structures are quite heterogeneous across the European Union (EU), and it is likely that the underlying technology also differs across regions. In this article, we claim that the heterogeneity of agriculture across the EU affects the process of income creation (i.e. the relative importance of the factors of farm income differ for different agricultural models). A panel of farms representative for 125 regions reporting to the EU Farm Accountancy Data Network (FADN) during the period from 2007 to 2018 is used. In this article, those regions are grouped into three clusters. A system generalised method of moments (GMM) panel estimator is applied to each cluster. The results showed that total factor productivity (TFP), relative prices and agricultural subsidies make different contributions to farm net value added (FNVA). In particular, the income growth of farms in regions dominated by large farms seems to react more to marginal changes of the explanatory variables.

Data envelopment analysis methods in sustainable agricultural development research

Sustainable development of business entities can be analysed in terms of three dimensions, i.e., economic, social and environmental ones. The economic dimension of sustainable development can be assessed, inter alia, by entities’ technical efficiency defined as the relation of outputs to inputs. One of the methods that is used to assess the technical efficiency of business entities compared to other entities is the Data Envelopment Analysis (DEA) method. The aim of the chapter is to determine the relative technical efficiency of representative agricultural farms from the individual European Union countries in 2018. Moreover, the scale efficiency indexes and the area of scale effects (increasing or decreasing) of the analysed farms were also determined. In the study the data from the Farm Accountancy Data Network (FADN) for 2018 were applied. In order to achieve the assumed research goals, the input-oriented DEA model was used, and the technical efficiency indexes of farms were estimated with the assumption of constant return to scale (CRS) and variable return to scale (VRS). This allowed, among others, for indicating the countries with farms achieving the highest technical efficiency (Belgium, Spain, Italy, Malta and Netherlands assuming CRS, and Belgium, Spain, Italy, Malta and Netherlands, Greece, Ireland, Romania and Slovenia assuming VRS), the lowest technical efficiency (the Czech Republic and Slovakia) within surveyed group of farms. All relatively inefficient farms (except Slovakia) functioned in the area of increasing economies of scale.

Technological Heterogeneity in Pig Farming: A Metafrontier Approach—Perspectives from Hungary and Poland

Despite remaining the most important type of animal production, pig production in Poland and Hungary declined after their accession to the European Union (EU) in 2004. This paper investigated the evolution of the technical efficiency of the pork industry in both countries. We applied stochastic frontier analysis, which takes into account heterogeneity in the production environment and production functions in both countries—true random effects, and a metafrontier model. We employed farm-level data from the Farm Accountancy Data Network sample during the period 2004–2015. Results illustrate the differences in production function in both countries and technological decline throughout the period of analysis. Furthermore, farms in Hungary were more technologically developed as well as less efficient in relation to the country frontier; however, the higher technological level resulted in generally greater efficiency in relation to the metafrontier. Our results suggest that different policy measures would be effective in the countries under analysis.