Farm Accountancy Data Network Research Articles

Farmers' Financial Literacy—Scale Development and Linkages to Accounting Practices and Financial Outcomes

ABSTRACTThis study investigates the financial literacy (FL) of Swedish farmers, its linkages to farmer characteristics, management accounting practices and farm outcomes by surveying Swedish Farm Accountancy Data Network farmers. Using item response theory, we expand the existing FL measurement specifically to the farming context, assess measurement items and compare shorter and longer scales of FL. Our findings indicate high levels of FL among respondents. FL is positively associated with more extensive management accounting practices, greater farm profitability and lower cost of debt, independent of other farmer characteristics, including education, numeracy, need for cognition, and money vigilance. The findings suggest the relevance of FL as a farmer characteristic for both practices and outcomes, even when included through its shortest and more established measurement scale.

How Can Overinvestment in Farms Affect Their Technical Efficiency? A Case Study from Poland

Farm overinvestment is highly dangerous in how it affects productivity and profitability. Therefore, it imperatively needs to be measured in the context of investment support offered under the Common Agricultural Policy. In turn, technical efficiency is one of the best methods for measuring farm investment. This paper analyzes the technical efficiency of 3273 Polish farms based on unpublished FADN (Farm Accountancy Data Network) data from 2010–2019. The purpose of this study was to indicate the level of, and changes in, technical efficiency of Polish farms in function of their investment levels, with particular emphasis on overinvested holdings. Technical efficiency was examined using stochastic frontier analysis (SFA). The study proved a decline in technical efficiency in underinvested farms and in those affected by absolute overinvestment (i.e., ones where the assets-to-labor ratio grows while labor productivity drops).

Technical efficiency analysis in Italian sheep and goat farms using FADN dataset

The sheep and goat farms are one of the main breeding in many Italian regions located in mountainous and disadvantaged rural areas. One of the main Italian Protected Designation Origin cheese as Pecorino Romano is made by sheep milk from many regions. The main purpose of this research was to asses by a quantitative approach the technical efficiency in sheep and goat farms part of the Farm Accountancy Data Network (FADN) in all Italian regions since 2008 to 2020. By the Multi-direction Efficiency Analysis (MEA) it has been possible to overcome one of the main bottlenecks of the technical efficiency estimation analysing which is the excess in each input able to impact to the technical efficiency. Main results have pointed out that the Italian sheep and goat farms have a higher technical efficiency compared to other studies carried out in other European countries. Land capital, labour and assets have been the main inputs able to impact to the technical efficiency. Furthermore, an important role of subsidies to the sheep and goat Italian farms exists.

Phosphorus removal and use in organic crop farming in the EU

AbstractTo promote sustainable food production, the European Union (EU) aims for a 25% organic agriculture area and a reduction of agricultural nutrient pollution by 50% by 2030. Organic farming is designed to rely on nutrient circularity, it has limited phosphorus (P) sources permitted, influencing the spatial distribution for P demand. Using dataset form the Farm Accountancy Data Network (FADN), this study quantifies and map the amount of P removed through crop harvesting in organic farming across the EU as a proxy for P required to maintain a balanced P budget. Crop harvesting is a major P output variable from agricultural soils and the positive correlation between soil P and P budget suggest that understanding the required amount to maintain a balanced P budget is therefore important for estimating fertiliser demands. On average, 5.6 kg P ha−1 is removed through crop harvesting per country in 26 countries, with fruits and vegetables contributing 36%, cereals 32%, fodder crops 20%, permanent crops 8% and oil crops 4% to the total. Although organic farming restricts off-farm nutrient inputs, on average, 2.1 kg P ha−1 of mineral fertiliser P is used in crop production in each NUTS2 region. Varying national regulations influence the total mineral fertiliser P used per country. Mineral fertiliser P use and P removed through crops provide a lower and upper boundary for P ferilisation in organic farming. Their spatial pattern suggest that increasing organic farming across the EU requires local-scale consideration of P sources, budgets, and spatial constraints to ensure a sustainable P supply.

Unveiling innovation imperatives in agriculture: A data-driven framework for identifying latent needs and regional priorities

Innovation within a company, sector, or territorial context can arise from identifying specific problems or needs. Performance analysis provides valuable insights, potentially highlighting areas where innovation may be necessary. This paper outlines a methodology designed to identify latent innovation needs at regional, sectoral, and dimensional levels. This methodology is based on critical issues emerging from comparing economic, environmental, and social sustainability indicators of farms. An iterative approach is used for this purpose. Mixed matching algorithms are employed to form the comparison groups, while G-computation is used to calculate the differences. Logistic regression is adopted to calculate the propensity scores, and several generalized linear models are used to estimate the impact of regional localization. Hypothesis tests are then conducted to confirm the statistical significance of the impacts, from which criticality levels are derived. This methodology is applied to data collected by the Italian Farm Accountancy Data Network, which includes over 64,000 observations from two consecutive three-year periods (2016-2018 and 2019-2021). The results highlight the existence of widespread critical issues in Italian agriculture. These problems primarily concern the efficient use of water, whose costs, relative to revenue, exceed, on average, around 40 %, and greenhouse horticulture, which shows an increased criticality level in terms of sustainability of over 50 %. From a regional perspective, Puglia in Southern Italy exhibits the most evident critical issues, with an average criticality level of 30 % that has increased over time. The emerging criticalities are analyzed in relation to the interventions introduced by the 2023-2027 Common Agricultural Policy to verify their coherence and identify possible innovative actions that can be implemented.

The Role of Precision Agriculture Technologies in Enhancing Sustainable Agriculture

Despite the known benefits of precision agriculture, the adoption is challenging due to the cost of investment and the farm sizes. Therefore, profitability is an important aspect to consider. This study aimed to evaluate the net returns, profitability, and investment efficiencies of PA by different economic farm sizes. The study was based on data retrieved from the Farm Accountancy Data Network (FADN) and Eurostat (year 2021). The study examined four countries (Poland, Germany, France, and Romania) under field crop farming using an investment cost of EUR 35,941–EUR 71,883 and a 20% and 15% reduction in the cost of crop protection and fertilizer usage, respectively, without compromising productivity. There is a positive relationship between the adoption of PA and farm returns for larger-scale farms. The result of the profitability and analysis of investment efficiency using Net Present Value (NPV) showed a positive value for economic farm sizes of EUR 100,000 and above. Hence, it is not economically advisable that all farmers use PA technologies with the hope that they will be profitable but with public support (subsidies) more farms will be able to use PA and be profitable. This is also an opportunity to meet the goals of the European Union Green Deal of minimizing emissions that cause climate change.

Technical efficiency and fertilizers use in Italian farms using a machine learning approach

The Common Agricultural Policy has supported a less intense use of fertilizers and chemicals in agriculture in the next five years (2023 – 2027) because of the European Green Deal proposals that are environmental protection-oriented. The most common consequence of input reduction in farms was a direct effect to the technical efficiency in farm. The main purpose of this research was to assess the technical efficiency in a sample of Italian farm part of the Farm Accountancy Data Network (FADN) dataset using the Data Envelopment Analysis (DEA) input-oriented approach and by the machine learning approach such as the iterative decision tree evaluating which quantity of chemical fertilizer in terms of nitrogen (N), phosphorus (P) and potassium (K) has to be reduced to improve the technical efficiency. Results have pointed out that between a reduction of chemical fertilizers and technical efficiency there is a fundamental link and the drop in chemical fertilizers has impacted the technical efficiency in Italian farms part of FADN dataset. Based on these results emerged the need of putting into practice some actions towards farmers to compensate the reduction in technical efficiency and the produced output in the productive process as well.

Farmers’ evolving attitudes on environmental conditionality in the CAP: a farm level examination of temporal shifts

AbstractGiven the anticipated shift in the allocation of resources under the common agricultural policy (CAP) toward increasing positive environmental outcomes, this study explores farmer attitudes toward the current principle of connecting direct payments to environmental conditionality, known as cross compliance. Furthermore, we explore whether there have been changes in farmer attitudes toward cross compliance over time by posing identical questions to farmers in both 2010 and 2018. A supplementary survey was designed and administered in conjunction with the core EU Farm Accountancy Data Network (FADN) to assess farmers’ attitudes to linking CAP payments to environmental conditionality. The findings of a generalized ordered logit model indicate a high level of support among farmers for linking direct payments to good agricultural practices and cross compliance standards. Factors influencing farmers’ acceptance of cross compliance can be categorized into several domains, including farm and farmer characteristics, social and institutional attributes, and economic factors. Notably, the level of subsidy dependency emerges as a highly significant factor, with farmers who rely more heavily on direct payments being more inclined to disagree with cross compliance requirements.

FARManalytics – A bio-economic model to optimize the economic value of sustainable soil management on arable farms

Soil quality is an important determinant of agricultural productivity and environmental quality. Despite its importance, few economic models incorporate sustainable soil management. The objective of this study is to develop and illustrate FARManalytics: a bio-economic model to gain quantitative insight in the economic value of sustainable soil management. First, we defined a comprehensive set of chemical, physical and biological soil quality indicators and quantitative rules on how these indicators respond to farmers’ production management over time. Second, we introduce an economic calculation framework that enables accurate calculation of the contribution of different production management decisions towards farm income using Activity-Based-Costing. The set of soil quality indicators and economic calculations serve as the basis for the bio-economic model FARManalytics, which consists of two modules: (1) the PMcalculator, a module that calculates the impact of current production management on soil quality and farm economics and (2) the PMoptimizer, a module that uses Mixed-Integer-Linear-Programming to maximize farm incomewithin predefined soil quality indicator constraints. The decision variables are the crop rotation, cover crops, manure & fertilizer application and crop residue management. We illustrate the added value of the model by applying it to an extensive and intensive farm type, both on clay and sandy soil. These farm types are derived from the Farm Accountancy Data Network (FADN) in the Netherlands. FARManalytics demonstrates that it is possible to increase farm income with up to €940 ha−1 year−1 on clay soil and up to €683 ha−1 year−1 on sandy soil, while meeting all soil quality targets except subsoil compaction vulnerability. The latter was among the most limiting soil quality indicators for the farm types in this study, together with soil organic matter input, wind erosion vulnerability and plant-parasitic nematodes. FARManalytics integrates the impact of production management decisions on soil quality and economics at farm level. Combined with representative farm types, the bio-economic modeling approach of FARManalytics can provide useful information for policy support. FARManalytics can also be tailored to provide decision support for individual farms, based on data that is commonly available on arable farms at low cost.

Agricultural production and biodiversity conservation: A typology of Swiss farmers’ land use patterns

Understanding farmers' land use behaviour is a prerequisite for designing effective policies that aim to protect and enhance biodiversity in agriculture. We develop a typology of Swiss farmers' land use patterns in terms of their agricultural production and biodiversity conservation. We contribute by adopting a comprehensive perspective encompassing not only EFAs (Ecological Focus Areas) but also non-EFAs. Relying on a sample of 2341 Swiss farm observations from the Farm Agri-Environmental and the Farm Accountancy Data Network, we conducted – for each agricultural region (plain, hill and mountain) – a K-means clustering to identify farmland use patterns. We considered four clustering variables, namely agricultural production intensity, the extent of a farm's participation in agri-environmental payment schemes and the impact of farm agricultural practices on the organismal biodiversity of 1) EFAs and 2) non-EFAs. The analysis reveals four distinct farmland use patterns beyond the classical dichotomy of low shares of EFAs and high agricultural production intensity versus high shares of EFAs and low agricultural production intensity. Three of the four land use patterns are similar across all agricultural regions. Our findings show that biodiversity enhancement is possible outside of EFA direct payment programmes. One cluster succeeded in exhibiting both a high agricultural production intensity and a high overall biodiversity score, which highlights that these dimensions are not mutually exclusive. The low or moderate use intensity of mineral fertilisers, pesticides and purchased feedstuffs in combination with a high use efficiency of these inputs seems to be the key to reconciling agricultural production and biodiversity conservation.

Potential impacts of the Common Agricultural Policy's Income Stabilisation Tool on farmers' incomes and crop diversity: A French case study

AbstractThis paper analyses the potential impacts of a hypothetical implementation of the Income Stabilisation Tool (IST) in France for the field crops sector. The IST is a risk management tool available within the 2014–2020 Common Agricultural Policy (CAP) to support farmers facing a severe drop in their incomes. This analysis was conducted using a farm‐level model relying on expected utility theory and based on positive mathematical programming with risk. The model was applied to a sample of 1375 field crop farms in France derived from Farm Accountancy Data Network (FADN) data. Simulation results show that the uptake rate of the tool is relatively low, less than 37% in all scenarios. It is strongly dependent on CAP public support and on how much premium farmers have to pay. Highest uptake rates are observed in farms specialising in Other Field Crops, such as potatoes, pulses and sugar beet, and farms located in regions highly exposed to climatic risks. Previous experience with insurance favours the acceptance of the IST. Model results also show that the IST improves adopters' income and reduces income inequality. However, its impacts on crop diversity, measured by the Shannon index, are negative.

Are Organic Farms Less Efficient? The Case of Estonian Dairy Farms

The paper investigates the technical efficiency of conventional and organic dairy farms in Estonia in the period 2006‒2015 using Farm Accountancy Data Network. We analyse self-selection into organic farming using the propensity-score-matching approach and explicitly test the hypothesis that organic and conventional farms apply homogeneous technology. We find that organic farms are less efficient. However, the difference in technical efficiency between organic and conventional farms decreases substantially when the technical efficiency assessment incorporates the use of the appropriate technology. The lack of growth of technical efficiency over time indicates that there might be a lack of knowledge in organic milk production that hinders its development. Since technical efficiency increases with farm size, it is important that organic dairy farms increase their scale.

Conversion to organic farming: Does it change the economic and environmental performance of fruit farms?

This paper compares the performance of conventional and organic fruit farms in Spain, using a set of base indicators to assess their economic and environmental performance on a per hectare basis. Composite indicators are also calculated to measure the overall economic and environmental performance of both production systems. Comparisons are made using propensity score matching to minimize the non-randomization biases caused by structural differences between the samples of conventional (n = 552) and organic (n = 127) fruit farms sourced from the Spanish Farm Accountancy Data Network (RECAN). The results based on per hectare metrics point to modest changes in the performance of farms that converted to organic farming. This is mainly because most converted farms were former conventional farms characterized by lower profitability and less intensive input use, consequently minimizing the effects of the conversion process. Nevertheless, the conversion outcomes exhibit a degree of variability depending on the specific type of fruit production. Economic gains are only discernible in the case of nut farms, whereas fruit and tropical fruit farms tend to yield the most favorable results per hectare from an environmental perspective.

Irrigation water economic value and productivity: An econometric estimation for maize grain production in Italy

Climate change, characterized by rising temperatures and limited precipitation, has intensified the demand for irrigation water while simultaneously restricting its availability. This challenge poses significant risks to agricultural and food production, particularly in the Mediterranean regions where, recently, water deficits have led to substantial production losses and quality issues. Water is a critical determinant of crops' economic viability, especially for water-intensive crops, making it essential to estimate its economic relevance, especially in the absence of reliable water market prices. This study has two primary objectives: first, to evaluate the shadow price of irrigation water for maize grain at the farm level, which is defined as the value generated by the marginal unit of water consumed; and second, to analyse its heterogeneity. Leveraging a Farm Accountancy Data Network (FADN) panel of 1625 Italian farms over a decade (2010–2020), an econometric production function approach is employed. Moreover, quantile regressions reveal variations in the shadow price linked to geographical, managerial, and structural farm characteristics. Our findings underscore water’s key role in economically viable maize grain production, significantly enhancing the productivity of other inputs like fertilizers and pesticides. The average shadow price is 0.29 €/m³, with a median of 0.20 €/m³ and water total productivity accounts for one-third of maize’s average gross output. Quantile regressions uncover how factors like geographic location, altitude, farm management, irrigation water source, and farm size influence the distribution of water productivity, reflecting either efficient use or scarcity of this resource. Our estimation provides valuable insights for policymakers by offering accurate shadow price estimates for irrigation water in Italian maize grain production. Furthermore, it enhances our understanding of irrigation water’s role in the economic viability of this crop, while contributing to support evidence-based water management strategies, identifying vulnerable areas and farms and allowing for future methodological developments.

Farming of Medicinal and Aromatic Plants in Italy: Structural Features and Economic Results

In recent years, the primary sector in Italy and elsewhere has been profoundly affected by climate change and a deep economic crisis, mainly linked to stagnating prices and rising production costs. Because of this situation, we are witnessing renewed interest in alternative agricultural productions, which are characterized by their resilience and sustainability, including medicinal and aromatic plants (MAPs). This sector is characterized by a certain heterogeneity due to the great variety of species and their wide range of uses. Although these characteristics contribute to the sector’s economic success, they also hinder its study due to commodity complexity and limited data availability. At the farm level, the situation is complicated by the fact that MAP cultivation is often embedded in complex cropping systems, and more rarely, is practiced exclusively or predominantly. In light of these considerations, we concentrated solely on the agricultural phase of the supply chain, using data available in the Farm Accountancy Data Network. We aimed to examine the main structural characteristics and economic outcomes of Italian farms that grow MAP, as well as the profitability of some of the species. To ensure accurate species classification, only MAPs exclusively designated for botanical use in the Italian National List were considered. The analysis of farm economic performance indicators (gross output, variable costs, gross margins, etc.) focused mainly on the species most represented in the sample: saffron, rosemary, lavender, oregano, and sage. The results indicate that the total gross output and gross margin show the best performance in the case of saffron (66,200 and 57,600 EUR/ha, respectively) and rosemary (27,500 and 22,000 EUR/ha, respectively). However, for saffron, the biggest cost concerns propagation (purchase of bulbs), amounting to 50% of the variable costs, whereas fertilization ones are particularly high for sage and rosemary.

Upscaling environmental incentives in the Common Agricultural Policy: an assessment of the potential of transfers from the first to second pillar

Agri-environment-climate measures and organic farming support have been the main contractual instruments promoting environment-friendly agricultural practices in the European Union since the 90s. They are insufficient in reaching significant environmental improvements, partly because underfunded. Using French panel data from the farm accountancy data network, we evaluate the impact of a budget transfer from income support to environmental incentives on contract uptake. We apply a generalised Tobit model to estimate the adoption probability and the acceptable farm-level payment triggering this adoption and simulate a transfer from direct payments to organic farming support and agri-environment-climate measures budget. Results suggest this mechanism increases adoption. Decreasing direct payments affects participation probabilities and acceptable farm-level payments, differently depending on the type of environmental contract, the type of direct payment and the farm technical orientation. We evaluate several transfer scenarios and provide ex-ante elements on how it could help reaching the Green Deal organic target.

Determinants of Financial Security of European Union Farms—A Factor Analysis Model Approach

The objective of this study was to assess the level of financial security of farms and identify its determinants based on factor analysis. The data used in this research were obtained from the European FADN (Farm Accountancy Data Network). Factor analysis (FA) was employed to reduce the number of variables that potentially determine the financial security of farms. The results indicate that the surveyed entities maintained financial security between 2014 and 2021. This study suggests that it is necessary to examine these factors separately for farms engaged in crop farming and animal production. The results obtained for all farms were less satisfactory than those that took into account the specifics of agricultural production. This study addresses a gap in the literature by including highly correlated variables in the analysis of the determinants of financial security. Factor analysis is used to reduce the number of variables without losing important information. Firstly, seventeen variables related to the financial security of all farms were assigned to six factors. These were income and self-financing of operations; area and subsidies; long-term investments and financial decisions consequences; economic size, taxes, and non-breeding livestocks; investment activity; and inputs, stock, short-term loans, and labor. Then, the determinants of the financial security of farms were examined, taking into account the specialization of activities. For crop-producing farms, six factors were identified, including three that were identical to those for all farms: income and self-financing of operations; long-term investment and financial decisions consequences; and investment activity. In addition, the following items were specified: area, subsidies, non-breeding livestocks, and taxes; economic size, inputs, and labor; and stock and short-term loans. The correlated variables in the case of livestock production combined into factors in a different way. In this case, four factors were distinguished: economic size, non-breeding livestocks, income, and self-financing of operations; operational activities of animal production; long-term investment and financial decisions consequences; and investment activity. Financial security is a complex matter that can be affected by a range of factors related to agricultural activities.

Non-monetary motivations of the EU agri-environmental policy adoption. A causal forest approach

This paper investigates the non-monetary motivations of farmers' adoption of agri-environmental policies. Unlike the monetary (income) motivations, non-monetary drivers can not be directly observed but can be identified from observational data within appropriate quasi-experimental designs. A theoretical justification of farmers' choices is first formulated and a consequent natural experiment setting is derived. The latter admits heterogeneous, i.e. Individual, Treatment Effects (ITE) that, in turn, can be interpreted in terms of more targeted and tailored policy expenditure. A Causal Forest (CF) approach is adopted to estimate these ITEs for both the treated and not treated units. The approach is applied to two balanced panel samples of Italian Farm Accountancy Data Network (FADN) farms observed over the 2008–2018 period and concerns agri-environmental policies delivered through the Common Agricultural Policy (CAP). Results show how heterogeneous the farmers’ response and the associated non-monetary motivations can be, thus indicating room for a more efficient policy design.

Importance and determinants of cooperation between fruit farms and the institutional environment

The development of farms requires support from the institutional environment of agriculture. Institutions help introduce innovations and adapt their functioning to the requirements of the European Union. The aim of the research was to determine the factors affecting the cooperation of orchard farms with the institutional environment and the economic effects of this cooperation. The article's novelty answers two questions: what factors on the farm affect cooperation with the institutional environment, and whether such cooperation affects the economic results of orchard farms? The study covered 45 farms from the Małopolska and Pogórze Region in Poland, specializing in cultivating fruit trees and shrubs, which in 2018 kept accounts under the Farm Accountancy Data Network (FADN). The descriptive method, chi-square test, and Spearman's rank correlation coefficient were used in the data analysis. An original relationship index was used to assess fruit producers' cooperation with agricultural organizations. A backward stepwise multiple regression model was used to identify the factors influencing its level. The research showed a statistically significant relationship between the relationship index with the organization and utilized agriculture area, total workload, the value of subsidies to operating activities, direct costs, and the farmer's age. The regression model shows that the value of subsidies to operating activities explains 28.09% of the variability of the relationship index (Ri). Based on the analysis, it can be concluded that the cooperation of fruit producers with the institutional environment positively impacts orchard farms' economic and financial results.

Subsidies and the income inequality in the Hungarian wine sector

The paper investigates the impact of different sources of income on wine farm total income inequality in Hungary using Farm Accountancy Data Network data for the period 2013-2019. The decomposition of the Gini coefficient is applied to focus on the impact of the Common Agricultural Policy (CAP) shift from market to government budgetary support on wine farm total income inequality. Off-farm income has a rather stable impact on wine farm total income inequality. CAP Pillar 1 subsidies have remained more important than CAP Pillar 2 subsidies, both in the structure of wine farm total income and in the reduction of wine farm total income inequality. The most striking finding is regarding a shift in wine farm market income from a negative (losses) to a positive (profit) value and its increasing role in wine farm total income inequalities. The 20% of the largest wine farms created from almost 90% to less than 80% of wine farm total incomes between 2013 and 2019, but during the same period their participation in CAP subsidy payments was reduced much more from more than 80% to around 60%. Subsidies from Pillars 1 and 2 were reduced, and wine market income increased wine farm total income inequality, while it remained constant for off-farm income. The wine farm market income has driven wine farm total income inequalities. This might strengthen because of the ongoing market selection process with the exit of less efficient and loss-making wine farms and the increasing role of surviving profitable wine farms. This market selection process can be related to managerial, entrepreneurial, and innovation activities based on the differentiation and segmentation of wine farm products and their market incomes.