“Assessing the level of organic farming development in the European countries”

Organic farming is an essential approach to agriculture that seeks to reduce the negative impact of human activities on the environment and ensure the sustainability of food production. The study aims to determine the integral index of the development of organic farming and to create a clustering model of organic farming in European countries. As a research methodology, additive-multiplicative convolution was used to determine the integral index of organic farming development. Cluster analysis (the Ward method and the k-means clustering method) identified respective clusters. The integrated index is based on eight indicators of organic farming from the Eurostat database, 2012–2020, and ranges from zero to one. The following countries have the highest value of the integral index: Italy (0.57), France (0.54), Spain (0.54), Germany (0.45), and Turkey (0.47). Three clusters were identified according to eight indicators of organic agriculture. The first cluster includes countries-leaders in agricultural territories (about 2.1 million hectare) with the highest state financial support for agricultural research and development (1.1 billion euros). The second cluster includes countries with the most minor organic farming operators (50-100 operators). The third cluster includes countries with the highest index of annual income from the sale of farm products (200-220 points) but with the highest level of usage of dangerous pesticides (250 points). The heterogeneity of clusters allows one to determine the strengths and weaknesses of organic farming in European countries.

Assessing the level Assessing the level of organic farming of organic farming development development in the European countries in the European countries

INTRODUCTION
Organic farming is a method of agriculture that strives to create a sustainable and environmentally friendly food production system by minimizing negative impacts on the environment and providing high-quality products to consumers.Organic farmers are turning to more natural methods of controlling pests and improving soil fertility, such as compost, biodegradable fertilizers, and biological pest control.Organic farming typically undergoes certification to meet specific standards set by organizations such as the USDA in the United States or EU Organic in the European Union.
Organic farming occupies a central place in the vast mosaic of European agriculture.As countries today grapple with global climate challenges, assessing the level of development of organic farming becomes a critical lens to understand the dynamics of agricultural development.
The European Commission presented the Action Plan for the Development of Organic Production, which provides that 25% of all agricultural land should be used for organic products by 2030.This plan aims to increase the production and consumption of organic products, achieve 25% of agricultural land under organic farming by 2030, and significantly increase organic aquaculture.Organic production brings several advantages both for consumers of products and for agricultural producers.Organic fields have 30% more biota in livestock than fields under conventional technology.Thus, the level of development of organic farming is a complex phenomenon that includes not only the land areas allocated for the cultivation of organic crops but also the level of sales from organic farming and ecological indicators of producers of organic products.

LITERATURE REVIEW
Overall, organic farming is essential in creating sustainable and environmentally friendly food production systems that help preserve natural resources for future generations.The origins of organic farming can be traced back to the early 20th century with the pioneering work of Sir Albert Howard and Rudolf Steiner.Their ideas about natural farming, composting, and biodynamic agriculture laid the philosophical foundations for breeding new varieties of crops (Mandolesi et al., 2022).Titisari et al. (2022), Bazhan et al. (2023), and Carucci et al. (2023) presented the concept of innovative organic farming.Organic farming gained popularity in the 1960s and 1970s, fueled by concerns about the use of synthetic pesticides and fertilizers and a desire to find sustainable alternatives.Thus, the sustainability of agriculture became relevant for organic farming.
Magrin (2022) proposes a method for assessing the sustainability of organic farming in EU countries.Nematollahi 2021).In addition, the topic devoted to producing and using organic seeds is essential.Solfanelli et al. (2022) provide a general assessment of the supply and demand for organic seeds in Europe using the example of twelve important crops for EU organic agriculture.The main obstacle to developing organic farming in this country is the population's low level of general environmental awareness.
One of the key factors in developing organic farming was the establishment of comprehensive policies and regulations.Governments and international organizations have played a critical role in defining and standardizing organic practices.The development of organic product certification systems and the implementation of organic standards have played an important role in strengthening consumer confidence and ensuring the integrity of organic products (Stephenson et al., 2022).Gambelli et al. (2023) analyze the part of regulatory provisions regulating the introduction of organic farming in EU countries using the example of the "TP Organics" programs, the European Technology Platform (ETP).Rudnicki et al. (2021) analyzed the normative regulation of organic farming in Poland and its compliance with EU regulations.Policymakers have also introduced incentives, subsidies, and support programs to encourage the transition from conventional to organic farming.Organic farming has gained recognition for its environmental benefits.It prioritizes soil health, reduction of synthetic chemicals, and conservation of biodiversity (Sarkar et al., 2021).These methods help mitigate soil erosion, water pollution, and greenhouse gas emissions.The degradation of fertile soils remains a severe ecological problem for many regions worldwide.Fenta et al. (2023) investigated runoff change, soil loss, soil organic carbon stocks, and land productivity based on five land use planning and management alternatives in the Aba Gerima watershed of the Upper Blue Nile Basin in Ethiopia.It was quantitatively confirmed that implementing such methods has a positive effect on the qualitative characteristics of the soil.Zhao et al. (2023) investigated how soil organic carbon content changed under the influence of chemical fertilizer treatment and nuclear magnetic resonance.Organic farming systems can be more resilient to climate change and contribute to overall health ecosystems, making them a critical component of sustainable agriculture.However, on the other hand, organic farming can also cause organic pollution in the surrounding area (Hettige et al., 2023), which will threaten the ecological state of the environment.It is also equally important to consider the qualitative composition of the soil and fertilizers applied during organic farming to minimize the negative impact on the general ecological state of the environment (Arynov et al., 2021).
The economic dimensions of organic farming have developed significantly.Originally considered a niche market, organic farming has experienced exponential growth in response to increasing consumer demand for organic products.This growth has created economic opportunities for farmers, processors, and retailers.Kowalska and Bieniek (2022) reviewed the development of organic farming in Europe and the EU to determine incentives for farmers to switch to this type of activity.Thus, with the help of organic farming in the operation of olive farms on the west coast of Palestine (Kashiwagi & Kamiyama, 2023), the level of added value of agriculture began to increase steadily.An equally important issue in this context is the financial sustainability of farms that engage in organic farming (Dono et al., 2022).However, challenges related to economies of scale, price premiums, and market competition remain relevant for developing organic farming.
The development of organic farming is closely related to the growth of consumer demand and awareness.Growing health consciousness, food safety concerns (Mahboubeh et al., 2023), and environmental awareness are driving consumers to choose organic products.Zhou and Ding (2022), using the example of farmers in Anhui province of China, identified central factors that influence their desire to engage in organic farming.These factors include the farmer's education level, political status, family disposable income, and understanding of organic farming and environmental hazards.Consumer demand for organic farming products is gradually gaining popularity.In particular, Jiang et al. (2022) presented the issue of growing organic medicinal plants in the context of actualizing healthcare issues.Prodhan et al. (2023) focus on the development of organic tea cultivation in Bangladesh, researching ways to reduce production volumes and, as a consequence, the export of tea.Zámková et al. (2023) assessed consumer demand for organic products during the spread of the COVID-19 pandemic in the Czech Republic.Accordingly, the younger population is not too inclined to consume organic products, unlike people of average and older age.Consequently, the organic market has expanded, leading to increased investment and participation by both small and large farmers.
Despite its growth, organic farming faces several challenges.The transition from conventional to organic farming can financially and technically challenge farmers (Campuzano et al., 2023).Lack of access to organic markets, price volatility, and competition with traditional agriculture create challenges for producers.In addition, issues related to falsified labeling of organic products and the need for international harmonization of organic standards require constant attention.Considering the relevance of research in organic farming, the purpose of this study is to determine the integral index of the development of organic farming and to create a clustering model of the structure of organic farming in European countries.It may shed light on the unique features of the diversity of progress, challenges, and opportunities for organic farming in European countries.

METHOD
This study assesses the level of development of organic farming using a statistical sample of Eurostat data.In total, data on 34 European countries during the period 2012-2020 are analyzed.As input data for the analysis, 10 indicators of organic farming are used (Table 1).

The paper follows the following stages to analyze the indicators for the sampled countries:
Stage 1. Normalize input data using the maximin method.In the first stage, it is necessary to normalize the array of input indicators due to their different nature of origin.Since indicators describe one phenomenon under study, they usually differ in absolute units of measurement.Thus, the possibility to compare indicators with each other disappears.Therefore, data normalization allows bringing all indicators to the same area of their change and, as a result, makes them commensurate.
To normalize the data, it is necessary to know precisely the limits of change of each indicator.Therefore, it is required to determine their minimum and maximum values.Thus, the limits of normalization will be selected.Formulas for normalizing indicators-stimulators and indicatorsdestimulators are as follows: where x ĩkstim , x ĩkdestim -normalized values of stimulating indicators and destimulating indicators, respectively; x ik -initial values of indicators; x maxi , x mini ,the maximum and minimum value of indicators.
In the second stage, the study calculates the weighting coefficients for each indicator, which indicates the level of development of organic farming.A successful solution for determining the weighting coefficients of the indicators that will form the basis of the integral index of the development of organic farming is the use of factor analysis, which is based on the method of principal components.With the help of this econometric approach, one can not only identify the weighting factors in a reasoned way but also select the most relevant input indicators, which can be used in the future during the clustering of countries.
The optimal number of indicators is determined based on the factor loadings of each indicator, which is included in several factors (components) that allocate more than 70% of the cumulative variance and are correlated by their values with the Kaiser and Kettel criteria.Thus, the weighting factors are determined using: where w i -the weighting factor for the variable і; FL і -statistically significant factor loading value of the і variable; p k -share of the total variance allocated by the k factor.
Having the values of the weighting coefficients, there can be the next step -the calculation of the integral index of the development of organic farming (the sum of the products of the weighting coefficients and the normalized values of the input indicators), which is based on: where IIOF -integral index of development of organic farming; x ik -normalized value of the i indicator for the k country; w i -weight factor of the i indicator.
After the fourth stage, which involves the calculation of the integral index of the development of organic farming, the next stage clusters the studied countries according to the indicators of organic farming selected in the previous step.
Hierarchical methods include the intergroup communication, the "nearest neighbor" method, the "distant neighbor" method, the method of weighted and unweighted pairwise averages, the method of weighted and unweighted centroids, and the Ward method.All of the listed methods make it possible to build dendrograms (a visual representation of the functional relationships between the studied indicators, which made it possible to form the corresponding clusters of countries).Ward's method was chosen from all existing hierarchical methods to carry out two-stage clustering.It differs from all other methods of this group because it uses variance analysis methods to estimate the distance between clusters.Due to the sum of these distances between indicators, the redistribution of countries in clusters is detailed.Those clusters that give the smallest increase in the total sum of variances are combined into each new cluster.
Unlike Ward's, the k-means clustering method is more sensitive to outliers and requires prior determination of the number of clusters.Therefore, there is a need for preliminary clustering by the method of hierarchical analysis, which will allow for obtaining the optimal number of clusters.Along with the identified disadvantages of the k-means method, its advantages include that it is pretty easy to use, uses Euclidean distances as metrics, and provides the possibility of visual interpretation of the obtained results by constructing a graph of the average values of clusters.
Thus, after passing all the research stages, an integral index of the development of organic farming is obtained for 34 countries participating in this analysis.A two-stage clustering of these countries identifies centers of similarity regarding organic farming development.It will allow the European community to consider potential opportunities for improving organic farming practices.All calculations are based on the software complex Stata/ SE 12.0.

RESULTS
Among the indicators of organic farming presented in Table 1, indicators Org_farm6 (nitrates in groundwater), Org_farm7 (ammonia emissions from agricultural activities), and Org_farm8 (use of dangerous pesticides) are inherently disincentives about the environment and the ecological situation as a whole.They contribute to the accumulation and removal of hazardous substances.However, in modern organic farming, these indicators demonstrate the pace of intensification of its development.Therefore, this study considers them as stimulator indicators, so the array of input data is normalized using the formula 1.Having received the normalized values of the input array of data, it is necessary to proceed to the next stage -determining weighting factors.Factor analysis was carried out with the help of the corresponding module of the Statstica 12 software complex.The graph of stony scree, which is based on the value of the Kettel criterion, allows one to single out the optimal number of factors (components), which in the future must be considered when calculating weighting factors (Figure 1).
On the X-axis are the serial numbers of all factors (components), which correspond to the number of indicators that are the basis of the calculation.The values of the Kettel criterion are presented along the Y-axis, the optimal value of which is one.Thus, if one move along the line of the graph from the left side to the right, there should be a place where the value of the Kettel criterion remains below unity.In this case, the fourth factor meets the specified condition, which indicates their optimal number.This conclusion can be confirmed using the Kaiser criterion, which corresponds to the eigenvalues of the factors.If the Kaiser criterion is higher than one, the corresponding factor should be left for further research.Otherwise, the factors are not taken into account.In addition, the cumulative variance allocated by the factors should exceed 70% to determine the optimal number of factors.Table 2 shows the eigenvalues of the factors (Kaiser's criterion) and the selected variance of the factors.The cumulative variance value (last column of Table 3) of 74.32% corresponds to the fourth factor.In addition, starting from the fifth factor, the eigenvalue (Kaiser's criterion) is lower than one.Therefore, the optimal number of factors for fur-research corresponds to four.Next, Table 3 presents the factor loadings of the first four factors needed to calculate the weights.
To determine the weighting factors according to formula 3 from Table 4, it is necessary to choose the highest factor load values by module and calculate the weighting factors.Because indicators Org_farm4 (the number of producers of agricultural products) and Org_farm10 (the share of the total agricultural area used for organic farming) have factor loadings less than 0.7 (absolute value) within all four factors, they should be excluded from the further stages of the study.Therefore, the integral index of the development of organic farming is calculated based on eight indicators.The results of the obtained weighting coefficients for the investigated indicators of organic farming are presented as a linear graph in Figure 2.  The study further analyzes the obtained weighting coefficients.Three of the eight indicators of organic farming have weighting factors greater than 0.2: • organic farming operators, according to the status of the registration process (Org_farm1)weighting factor 0.24; • the area of organic farming crops (Org_farm2)weighting factor 0.21; • ammonia emissions from agricultural activities (Org_farm7) -weighting factor 0.22.
The weight coefficients of the remaining five indicators are less than 0.1: • the area of agricultural territories excluding vegetable gardens (Org_farm3) -weighting factor 0.06; • index of annual income from the sale of agricultural products per unit of labor (Org_ farm5) -weighting factor 0.08; • nitrates in groundwater (Org_farm6)weighting factor 0.06; • use of dangerous pesticides (Org_farm8)weighting factor 0.09; • state financial support for agricultural research and development (Org_farm9)weighting factor 0.05.
Weighting coefficients of indicators of organic farming related to emissions of harmful substances (nitrates, pesticides) have minimal values, except ammonia emissions from agricultural activities.
To complete the fourth stage of the analysis, it is necessary to calculate the integrated index of the development of organic farming IIOF.As the targeted research period is 2012-2020, the study searched for the value of the integral index in 2012, 2016, and 2020, respectively.Therefore, several leading countries can be singled out with confidence.Their list includes Italy, France, Spain, Germany, and Turkey.These five countries demonstrate that the value of the IIOF integral index is at least 0.3.
An essential feature of the transformation of organic farming for the studied countries is that qualitative positive changes in this sector marked the period from 2012 to 2016, as practically all countries demonstrated positive dynamics of changes in the integral index during this period.
At the next research stage, for a better understanding of the European organic farming environment segmentation, there is clustering analysis.The chosen method of hierarchical clustering (Ward's method) will allow one, at the first step, with the help of the resulting dendrogram, to visually assess the optimal number of clusters that stand out among the 34 European countries.Clustering is based on eight indicators that are part of the integral index of organic farming development.Three After a visual assessment of the constructed dendrograms in 2012, 2016, and 2020, the studied countries were regrouped into three clusters according to the level of development of organic farming.Next, using the k-means method, the study conducts the qualitative composition of each selected cluster in 2012, 2016, and 2020 (Tables 4-6).During the studied period, the composition of the clusters in 2012 and 2016 practically did not differ (except for the transfer of Poland from the first cluster in 2012 to the second cluster in 2016): the first cluster included six (five) countries, the second -13 ( 14) countries, to the third -15 countries.In 2020, there were noticeable changes in the qualitative composition between the second and third clusters, while the first design remained stable.
The study further analyzes the average values of indicators of the development of organic farming, which were the basis of clustering (Figures 6-8).
According to the results of the average values of the indicators of organic farming, which were the basis of the clustering, the first cluster included precisely those countries that had the maximum value of five of the eight indicators.These indica-     The quality of the obtained clustering results can be confirmed using variance analysis.The results of variance analysis for clustering analysis as of 2012, 2016, and 2020 are presented in Tables 7-9. http://dx.doi.org/10.21511/ee.15(1).2024.05 Note: 1 -Org_farm1, 2 -Org_farm2, 3 -Org_farm3, 5 -Org_farm5, 6 -Org_farm6, 7 -Org_farm7, 8 -Org_farm8, 9 -Org_farm9.Cluster 1 is marked in blue, cluster 2 in red, and cluster 3 in green.Since the significance level of p for all indicators of organic farming, which are the basis of clustering, is less than 0.05.This indicates the statistical significance of qualitative differences between the presented clusters of European countries.

DISCUSSION
A comparison of the results obtained within the scope of this study with similar scientific directions allowed this study to note the following.Gmitrowicz-Iwan and Ligęza (2023) analyzed the influence of the chemical treatment plant of the nitrogen fertilizer plant on the water quality of the adjacent river and lake systems and the neighboring territories of Poland.The cluster analysis determined that if reservoirs are damaged, for example, during a flood, an ecological disaster will occur not only for the Vistula but also for the Baltic Sea, affecting millions of people.However, this study did not isolate the impact on the ecosystem of the lakes and adjacent territories of other factors not related to the emission of harmful chemicals, as well as how this will be reflected in the pace of development of organic farming in Poland.This moment is critical because, since 2012, Poland has lost its leading position in terms of organic farming.At this time, this study places all the necessary emphasis on forming an integral index of the development of organic farming.

CONCLUSION
The purpose of this paper is to assess the level of development of organic farming in European countries.Thus, the paper proposed a scientific and methodological approach to determining the level of development of organic farming in European countries: an integral index of the development of organic farming.Based on eight indicators of organic farming in 34 European countries, it allowed distinguishing leading and outsider countries.There are five countries with the highest level of development of organic farming for the period 2012-2020: Italy (0.57), France (0.54), Spain (0.54), Germany (0.45), and Turkey (0.47).There is a two-step clustering analysis using the Ward method and k-means method on a sample of countries.The findings show three clusters based on the eight indicators.The first cluster includes five countries with the highest area of agricultural territories (about 2.1 million ha) and state financial support for agricultural research and development (1.1 billion euros).The second cluster includes 18 countries with the lowest values of organic farming operators (50-100 operators).The third cluster includes 11 countries with a high value of the index of annual income from the sale of farm products (200-220 points) and the level of usage of dangerous pesticides (250 points).
This scientific approach can be a valuable tool for policymakers, researchers, and stakeholders to identify patterns, trends, and areas for improvement in promoting and enhancing organic farming across different regions.Further analysis and interpretation of the specific indicators and characteristics within each cluster would provide deeper insights into the factors influencing the development of organic farming in these countries.
et al. (2021) investigate a tripartite agricultural supply chain consisting of agribusiness, organic farming, and traditional farming.Kociszewski et al. (2023) present the evolution of the formation of organic farming in Poland during 2011-2021.A similar study, but about Turkey, was conducted by Akyüz and Theuvsen (2021), about Portugal -Salavisa et al. (2021), Kazakhstan -Grigoruk et al. (2021), and African countries -Schader et al. (

Stage 2 .
Identify relevant indicators using the method of principal components.Stage 3. Determine weighting coefficients for each indicator during the resolution of the integral indicator of the development of organic farming.http://dx.doi.org/10.21511/ee.15(1).2024.05Stage 4. Calculate the integral indicator of the development of organic farming using the method of additive-multiplicative convolution.Stage 5. Cluster the studied countries according to pre-selected indicators (Stage 2) using Ward and k-means methods.Stage 6. Analyze the obtained results.

Figure 2 .
Figure 2. Weighting coefficients of indicators of organic farming

Figure 3 .Figure 4 .
Figure 3. Dendrogram of the distribution of 34 European countries by indicators of organic farming in 2012

Figure 5 .Table 4 .Cluster 1
Figure 5. Dendrogram of the distribution of 34 European countries by indicators of organic farming in 2020

Figure 6 .
Figure 6.Average values of indicators of organic farming, which were the basis of clustering of the studied sample of countries in 2012

Figure 7 .
Figure 7. Average values of indicators of organic farming, which were the basis of clustering of the studied sample of countries in 2016

Figure 8 .
Figure 8.Average values of indicators of organic farming, which were the basis of clustering of the studied sample of countries in 2020

Table 1 .
An array of input dataSource: Eurostat.

Table 3 .
Factor loadings of indicators of organic farming

Table 5 .
Clustering of the studied European countries by the k-means method as of 2016

Table 6 .
Clustering of the studied European countries by the k-means method as of 2020

Table 7 .
Variance analysis for clustering countries in 2012

Table 8 .
Variance analysis for clustering countries in 2012

Table 9 .
Variance analysis for clustering countries in 2020 Guo et al. (2023)s that are not members of the EU.The study offers a comprehensive analysis of the organic farming market for a broader range of European countries, which allows choosing more flexible mechanisms of influence on the level of organic farming.Reidsma et al. (2023) presented 10 European agricultural management systems to support the stable and sustainable development of arable farming.Using a retrospective approach, they identified relevant future strategies that will contribute to strengthening sustainability attributes.The proposed systems make it possible to form guidelines for other European countries but do not consider the current level of organic farming in such countries.This study allows one to solve this problem thanks to the defined integral index of organic farming.Guo et al. (2023)show that renewable energy exacerbates the biodiversity crisis, and organic farming contributes to conserving European biodiversity.This study can be improved by including the integral index of organic farming calculated in this study.It is the basis for potential scientific collaboration.