*Article* **Forecasting the Optimal Sustainable Development of the Romanian Ecological Agriculture**

**Ana Ursu <sup>1</sup> and Ionut Laurentiu Petre 1,2,\***


**Abstract:** Organic farming is an important objective of the European Commission, translated into the European Green Pact through the Farm to Fork Strategy and the Biodiversity Strategy, with EU member countries having to find solutions to meet the target of at least 25% of agricultural land being used for organic cultivation by 2030. The aim for Romania can be achieved by modelling the distribution of crops in terms of cultivated areas and production yields obtained in organic and conventional systems according to the population size. Applying quantitative and qualitative analysis of EUROSTAT data for the above-mentioned indicators, the geomean function, linear programming, and the simplex method were used, depending on the set objectives. To demonstrate that organic farming can be sustainable and in line with the three pillars of sustainability, economic, social and environmental, we related the agricultural area to the population of Romania to highlight the average annual growth rate for the 2020–2030 tine horizon. The results showed an increase in agricultural area per capita of 0.708 ha (4.91%), compared to 0.69 ha as the average for the period 2012–2020, which correlated with organic production yields 32% lower than conventional agriculture. Through modelling, the reduction in organic farm yield was found to be less than or equal to the increase in area per capita, thus reaching the proposed target. The results of this study have long-term implications for supporting the transition to organic farming in the sense that the study argues that reaching the target of 25% of agricultural land that can enter organic farming is in line with the sustainability trilogy. The approach used can be followed and replicated according to national agricultural policies.

**Keywords:** modelling organic crops; organic area; strategies; common agricultural policy

#### **1. Introduction**

Agriculture is an important sector for Romania, with an average utilised agricultural area (UAA) of 13.6 million hectares [1]. Agriculture contributes 3.8% toward Romanian GDP (in 2020). Agriculture is an activity that competes for land, so any policy change that affects one land use has the potential to induce changes in the other [2]. Sustainable land use involves considering the range of social, economic and environmental goods and services provided in a given region [3]. Sustainable land use also involves careful consideration of the long-term attributes of resilience and robustness that maintain the underlying ecosystem processes. Population density and GDP are useful indicators in relation to the two dimensions of human activity, the social and economic aspects, which are connected to land use characteristics. The presence of a larger population density requires a higher intensity of land use. On the other hand, increasing economic production requires more intervention on the land [4]. To carry out this study and in line with the set target of increasing arable land in organic farming by 25% by 2030, we analysed the input indicators established for analysis and modelling.

**Citation:** Ursu, A.; Petre, I.L. Forecasting the Optimal Sustainable Development of the Romanian Ecological Agriculture. *Sustainability* **2022**, *14*, 14192. https://doi.org/ 10.3390/su142114192

Academic Editors: Mariarosaria Lombardi, Erica Varese and Vera Amicarelli

Received: 3 October 2022 Accepted: 26 October 2022 Published: 31 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### *1.1. Conventional Agriculture*

The utilised agricultural area (UAA) decreased by 4.98% in 2020 (13,048.80 thousand ha) compared to 2012 (13,733.14 thousand ha), with Romania ranking 6th in the EU-27, after France, Spain, Germany, Poland and Italy (Table 1). Within the structure of land use categories, the largest share, 64%, is occupied by arable land, with a decrease recorded in 2020 (8482.86 thousand ha) of 3.6% compared to 2012 (8797.65 thousand ha). Romania ranks 5th in the EU-27 in this indicator, after France, Spain, Germany and Poland.


**Table 1.** The breakdown of the Romanian utilized agricultural area (UAA).

Source: Calculations based on EUROSTAT data series, years 2012–2022 https://ec.europa.eu/eurostat/ databrowser/product/page/TAG00025\_\_custom\_3351494 (accessed on 14 September 2022) Utilised agricultural area by categories [TAG00025\_\_custom\_3351494].

Although Romania is among the top EU countries in terms of cultivated areas, it has lower production yields per ha. The extremely severe 2020 and 2022 droughts have worsened these deficits [5]. The relative economic performance of organic and conventional agriculture is determined by the ratio of production costs to production value. Both organic and conventional farmers are vulnerable to fluctuations in input and output prices. The future of material prices is uncertain. However, changes in commodity prices may have a greater impact on conventional farmers [6].

#### *1.2. Organic Farming*

Organic farming has been present in Romania since 2000 (17,388 ha) and the land area in production was relatively constant until 2007 (131,529 ha). Since 2007 and until now both the area and the number of organic operators has increased, at a variable pace. In the National Rural Development Plan (NRDP) 2007–2013, Romania did not benefit from compensatory payments, because no measure was implemented in the programme [7]. In the National Rural Development Plan (NRDP) 2014–2020, organic farming benefited from Measure 11- Organic farming, with support being directed towards conversion, methods and maintenance of organic farming practices.

Data presented by "[8]" on organic farming in the EU reveal that at the end of 2020, there were 14.9 million ha (9.2% of total production) of organic land in the European Union managed by more than 349 thousand producers (Table 2, col 2 and col 12). The countries with the largest organic agricultural areas are France (2.5 million ha), Spain (2.4 million ha), Italy (2.1 million ha), and Germany (1.7 million ha). Romania has an organic agricultural area of over 469 thousand ha (3.5%) managed by 9647 producers. The organic areas, for the countries mentioned, are composed of grassland (minimum 26% (Bulgaria) and maximum 89% (Ireland)), arable crops (minimum 21% (Spain) and maximum 74% (Poland)), permanent crops (0% (Ireland) and maximum 39% (Malta)) (Table 2).

From a policy perspective, the Farm to Fork strategy target of having "at least 25% of EU farmland in organic farming by 2030" is seen as a challenge, with many stakeholders questioning whether this ambition can be achieved. According to the data presented in Table 2, the lowest proportions of organic land in the EU are found in Romania (3.5%), Bulgaria (2.3%), Ireland (1.7%) and Malta (0.6%).


**Table 2.** Organic land use in Europe, 2020.

about-us/organic-in-europe/

(accessed on 14 September 2022).

#### *1.3. The Population of Romania*

Romania has an area of 238,369 km<sup>2</sup> and a population recorded in 2020 of 19,281,118 inhabitants, representing approximately 4.3% of the EU-27 population [9].

Rural areas have substantial sources of development, representing 87% of the national territory, and the rural population in 2020 was 8.9 million, approximately 46.4% of the Romanian population.

The rural population decreased (measured in number of inhabitants) by 4.3% due to negative changes in the main demographic indicators: population ageing, declining birth rate and migration of the labour force, especially young people, from villages to cities and especially abroad. It is predicted that some countries in Europe will lose more than 15% of their population by 2050 due to international migration, Romania being one of these countries [10,11].

#### *1.4. Policies and Strategies*

The 2030 Agenda includes global objectives to guide the actions of international communities until 2030, and is relevant for both developed and developing countries. The transition to sustainable food production and agriculture will require major improvements in resource efficiency, environmental protection and system resilience [12].

Increasing the share of organic agriculture in the EU is part of the Action Plan for the development of organic production, with the objective of having "at least 25% of the EU's agricultural land in organic agriculture and a significant increase in organic aquaculture by 2030", contained within the From Farm to Consumer Strategy [13,14].

Organic agriculture is one of the many approaches and paradigms found to fulfil the objectives of sustainable development of agriculture [15,16].

According to IFOAM EU, reaching 25% of organic agricultural land area in the EU by 2030 is achievable if the CAP provides the necessary remuneration for the benefits of ecological conversion and maintenance through existing rural development policies or innovative instruments such as ecological schemes [17].

#### *1.5. The Purpose and Hypothesis of the Research*

In view of the above, the aim of this paper is to determine how organic farming can be developed sustainably, i.e., to determine the size of the areas that can be converted to organic farming so that, on the one hand, the share specified by EU strategies is achieved and, on the other hand, low yields do not affect food security and thus sustainable development objectives.

Based on the information, data and literature, the research hypothesis can be concretized. We believe that in Romania, the ecological agricultural system can be developed, given that the loss of yield can be balanced or cushioned by the fact that the population is decreasing, so there is a possibility that the agricultural area per capita can increase.

In relation to the share of organic farming in the total agricultural area and its expansion, we believe that large areas of grassland and meadows can be converted, as they contribute essentially to this objective.

#### **2. Literature Review**

Studies reveal a multitude of approaches regarding sustainability and ecological agriculture. The dynamics of organic agriculture certification in Romania was studied, starting from the hypothesis that the slow pace of certifications is due to some subjective barriers that can be eliminated if incentive measures are applied to support certification [18].

Regarding the EU Action Plan for organic agriculture, axis 1, stimulating and ensuring consumer confidence in the context of the sustainability and competitiveness of organic farms [19], proposes the implementation of ecological marketing strategies that would stimulate both consumption and production, thus contributing to sustainability and business development.

Another research study [20] addressed the issue of the limiting factors on the development of the organic food sector. The study used the qualitative analysis method with semi-structured interviews applied to 10 large and medium-sized companies active in the ecological sector. The limiting factors indicated by the managers refer not only to the legislation, the lack of constant supply of organic raw materials and increased competition on the domestic and international markets, but also to the instability of the financial situation, regarding financial liquidity, costs, capital and credits [20].

Rasche and Steinhauser [21], investigated how an increase agricultural area would affect yield differences between conventional and organic systems. Through the accounting tool FABLE, they evaluated the changes in consumption of available calories per personyear/day and the extent of cultivated lands, pastures and areas where natural processes predominate, until the year 2050. It was concluded that by increasing the ecological surface, there will be a caloric deficit of 7–80 kcal/person/day, corresponding to a surface of 1000–5000 km2 of land cultivated. It was also estimated that the deficit would disappear without any changes to the system by 2045 due to demographic and technological development, and that would be no need for additional cultivated land at all if crop productivity were to increase.

Eneizen [22], used exploratory qualitative analysis combined with empirical research results to determine the main obstacles that must be solved for the expansion of ecological agriculture. The findings of the study, based upon interviews with organic farmers, suggest that obstacles to adoption of organic farming are: the absence of an organization to certify organic products, high cost of certification, lack of financing sources, low yield, high price, lack of specific markets for organic food, the low awareness of farmers, unsuccessful agricultural reforms, and lack of coordination between interested parties and institutional changes. The authors recommend that organic farming be carried out by qualified farmers using modern organic farming techniques that can contribute to increasing production yields and cost efficiency. The authors also recommend improving communication between the interested parties of organic agriculture, from farms to markets, including any relevant intergovernmental departments, to develop organic agriculture.

To answer the question of what the contribution of ecological agriculture to the sustainable development of agriculture is, Kilker [23] refers to the trilogy of sustainability, socio-economic and environmental development, which would help producers and exporters to improve their incomes and living conditions, especially in poorer countries. From an economic and social point of view, organic farming reduces the risk of production failure, stabilises profits and improves the quality of life of small farmers' families, while from an ecological point of view, it improves soil fertility and preserves biodiversity, leading to ecosystem stability, reduced susceptibility to drought and pest attack. These benefits appear if production methods adapted to local conditions are applied, synthetic chemical pesticides and fertilizers are avoided, and crop diversity is maximized [24].

In another case study, organic farming is seen as a multi-functional business through which sustainable profits can be obtained, creating economic opportunities for people which can help society develop in a sustainable manner. The research was based on visits to organic farms and organic markets, as well as interviews with farmers. This was a model for the local community and for wider communities, thus contributing to the fulfilment of some among the objectives of sustainable development [25].

Sher [26] investigated the barriers to adopting green entrepreneurial agriculture to obtain economic growth through the minimal use of resources. Of the 34 barriers identified, 20 were considered critical barriers. Based on factor analysis, the 20 barriers were grouped into six major categories: training and development, entrepreneurial orientation, market orientation, customer orientation, innovation orientation, and barriers related to the provision of ecological support. The dominant barrier was training and development, as well as the marginal role of the government in carrying out such efforts.

For Romania, organic farming can become a technological alternative to conventional agriculture, as land conversion is within the reach of managers, and this opportunity is further enhanced by the high level of land fragmentation and the high number of small farms in agriculture [27,28].

#### **3. Materials and Methods**

The focus of this study was to determine the areas cultivated in an ecological system for each crop in Romania in order to reach the threshold imposed by the European Union regulations, regarding the share of organic agriculture in the total agricultural area of 25%. It is desired that development of ecological agriculture results in as little damage as possible in terms of yield and productivity; thus, a sustainable expansion of this farming system is desired.

For this purpose, data taken from European databases (Eurostat) on areas, production, and crop yields in Romania, both for organic and conventional agriculture, were analysed quantitatively and qualitatively in order to determine yield differences.

For the expansion of organic farming to be sustainable, the agricultural area per capita, especially its dynamics, was determined and forecasted for the year 2030, when each Member State must contribute to 25% of the agricultural area being farmed organically. This will compare the potential increase in agricultural area per capita (given the demographic decline in Romania) with the reduction in yields on the organic area (the 25%), so that the reduction in productivity is less than or equal to the increase in area per capita.

The forecast agricultural area per capita will be determined by relating the agricultural area to the population forecast by FAOSTAT, which is forecast using the average annual rate method, this indicator having the following formula [29]:

$$
\mathbb{R} = (\mathbb{Z} - 1) \times 100
$$

and

$$\mathbb{T} = \sqrt[m-1]{\prod I\_{t/t-1}}$$

where: *R*—average rate; *I*—average index; *I*—individual levels of chain-based indices.

Linear programming and the Simplex method were used to determine an optimum yield (tending towards the minimum point), with certain conditions that satisfy both the requirements of European Union regulations and the soil and crop structure specific to Romania.

Programming problems involve the efficient use or allocation of limited resources to achieve desired goals. These problems are characterized by many solutions that satisfy the basic conditions of each problem. Choosing a specific solution as the best solution to a problem depends on the goals or overall objectives contained in the problem statement. The solution that satisfies both the problem conditions and the given objective is called the optimal solution [30].

Linear programming is an important cornerstone of optimization theory. Many realworld problems can be formulated with linear mathematical models. The simplex algorithm is the most used tool for solving linear programming [31].

Maximum efficiency means minimizing effort and maximizing output, and the concept of optimal is defined as a program that minimizes or maximizes an objective function while satisfying all techno-economic constraints.

Assuming that each component of the line vector "c" measures the efficiency of one unit of the output of an activity, then the linear function can be introduced [32]:

$$f\_{(X)} = c\_1 \times X\_1 + c\_2 \times X\_2 + c\_3 \times X\_3 + \dots + c\_n \times X\_n$$

Summarizing, we obtain the following linear programming equations:

$$\left\{ \begin{array}{c} \operatorname{optimum} \left[ f\_{(X)} \right] \quad (A) \\ \sum\limits\_{j=1}^{n} a\_{ij} \times x\_{j} \le b\_{i} \qquad (B) \\ \sum\limits\_{j=1}^{n} a\_{kj} \times x\_{j} \le b\_{k} \qquad (C) \\ \mathbf{x}\_{j} \ge 0 \qquad (D) \\ j = 1, n \end{array} \right\}$$

Relations A–D together constitute the general model of a linear programming problem, each having a specific role: Relation (A) is called the efficiency objective function of the problem, relation B represents resource constraints, and relation C refers to techno-economic constraints.

Constructing the model of the linear programming problem led to the following system of equations. The objective function was minimising yield losses, i.e., losses in organic production compared to conventional production:

$$f\_{\left(X\right)\_{\left(min\right)}} = \frac{\sum\_{i=1}^{n} \Delta^{\boldsymbol{\upvee}} \overline{\boldsymbol{Q}} \times X\_i}{\sum\_{i=1}^{n} X\_i}$$

*Xi*—The variables taken into account (areas of organic crops cultivated in Romania); Δ%*Q*—Relative yield differences for each organic crop compared to the same crop in a conventional system.

For the objective function, it was desired that the weighted average of the yield differences be as small as possible, so each (relative) yield difference between organic and conventional farming for each variable (crop) was multiplied by the area cultivated relative to the total area cultivated organically.

This objective function was conditioned by a series of equations in order to make the expansion of areas sustainable and to be able to determine as correctly as possible the extent of organic crops. Together, the following equations form the system of conditions for the linear programming problem.

$$\left\{ \begin{array}{c} \frac{\sum\_{i=1}^{n} X\_i}{\underline{\Pi} \Pi A} = 0.25\\ \frac{\sum\_{i=1}^{n} \Delta \% \overline{Q \times X\_i}}{\sum\_{i=1}^{n} X\_i} \times 0.25 \le 4.91\% \\\ X\_i \ge X\_{i2020} \\\ \frac{X\_i'}{\Pi A A} \le 0.25 \\\ ('for \ X\_i \text{ with } \Delta \% \overline{Q} > 0) \end{array} \right\}$$

The first condition in the previous system of equations refers to the main target of the European Union strategy, i.e., that the share of organic crops should reach 25%, so that the sum of the organic areas to be established for the year 2030, in relation to the utilised agricultural area (the projected one) should reach 25%.

The second condition is the one that provides a sustainable direction for this expansion of organic areas, i.e., the relative yield gap between organic and conventional agriculture for the 25% of the agricultural area to be less than or equal to 4.91%, which is the potential degree by which the agricultural area per inhabitant will increase by 2030, given that Romania's population is decreasing faster than the agricultural area.

The third equation requires that the organic areas should start from the year 2020, i.e., the last year for which data have been recorded in European statistics, and the last equation requires that the areas of organic crops with positive differences in rankings should not exceed 25%, i.e., the average increase in areas in order to avoid situations in which the development of organic farming is based on 2–3 crops, which currently have very low proportions.

Therefore, the research stages to be presented will start with the determination and forecast of the dynamics of the agricultural area per inhabitant, so that on the basis of the expected increase in the indicator, it will be possible to determine the percentage that Romania can assume in terms of productivity losses on the 25% of the organic areas. Subsequently, the data on yield loss for each crop will be entered into the linear programming model and these conditions related to the proportion of area and the correlation of losses with agricultural area per capita will be introduced in order to determine the exact size of the ecological area for each crop analysed.

#### **4. Results**

To identify the areas that should be extended for each organic crop in Romania to reach the threshold of 25% of the agricultural area, we started with a quantitative analysis of statistical data on both organic and conventional agriculture.

It can be assumed that organic farming is in its infancy, even if there are data as early as 2012, or perhaps organic farming existed in practice before this period, but this statement is based on the proportion of organic areas in the total utilised agricultural area, as shown in Table 3.

**Table 3.** Dynamics of total area under organic farming in Romania, hectares.


Source: processing based on Eurostat data.

The area cultivated organically in Romania increased from 103 thousand hectares in 2012 to approximately 276 thousand hectares in 2020, which represents an increase of 168%. We also observed an average annual growth of 13.1% during the period analysed. However, it can be seen that the expansion of the organic land area has not been constant and strictly increasing; there is a decrease in the middle of the period, with 2016 and 2017 recording slightly smaller areas. These years coincided with the interval between the two programming periods of the Common Agricultural Policy. The subsidies and funds for agriculture were lower in this period. The standard deviation was approximately 50 thousand hectares, a variation of ±28%.

Table 3 shows the proportion of organic area in the total agricultural area in Romania, which increased from 0.75% to 2.11%. However, as mentioned above, this proportion is low compared to other EU countries, so the development of organic farming up to 25% of the agricultural area will be a challenge.

In order for this development to be sustainable, without economic (drastic reduction in yields), social (transition to food insecurity) and environmental (high resource consumption) implications, it is hoped that there is a possible situation in which the difference in yield and decrease in productivity for that 25% of the agricultural area is covered by the increase in agricultural area per capita, given the demographic decline in Romania.

This will determine the agricultural area per inhabitant by 2030, the deadline for meeting the EU biodiversity strategy target.

From Figure 1 it can be seen that utilised agricultural area and population are both decreasing, but by analysing the trend equation of the two indicators, we found that population is decreasing faster than the utilised agricultural area. Over the period 2012–2020, the utilised agricultural area decreased from 13.73 million hectares to approximately 13.05 million hectares, representing a decrease of 4.95% and an average annual rate of change of −0.64%.

**Figure 1.** Dynamics of agricultural land use and population in Romania. Source: Eurostat data.

Based on the average annual rate of change in utilised agricultural area, as well as FAOSTAT population forecasts, which estimate that the population will reach 18.3 million in 2030, it was possible to determine and forecast the agricultural area per capita and the dynamics of this indicator.

In regard to the period 2012–2020, for which precise data have been recorded for both utilised agricultural area and population, there were no increases as perhaps expected, given the steady decrease in population, because the utilised agricultural area has also fluctuated with both negative and positive variations. The utilised agricultural area per capita ranged from 0.68 hectares per capita to 0.71 hectares per capita, with an average of 0.69 hectares per capita over the period and a standard deviation of 0.01 hectares per capita from this average, giving a variation of ±1.6%. (Figure 2).

**Figure 2.** Determining and forecasting the agricultural area used per capita (ha/capita).

Forecasting this indicator on the basis of the utilised agricultural area determined on the basis of the average annual rate of change and on the basis of the population according to the FAO forecast, it is estimated that the agricultural area per capita will follow an increasing trend until 2030, reaching a level of 0.71 hectares per capita.

In order to determine the degree of sustainability in terms of yield reduction for organic farming, the dynamics of the agricultural area per capita was determined, i.e., the relative difference between the target year (2030) and the last year with exact data, i.e., 2020, the agricultural area per capita will be expected to increase by 4.91%, which allows for a slight decrease in agricultural productivity given the characteristics of the organic farming system.

Next, the areas and yields for all crops recorded in the Eurostat databases, both for organic and conventional farming, were researched in order to finally determine the yield differences, which are essential in the second part of the work on minimizing the decrease in agricultural productivity in organic versus conventional farming, depending on the areas of the crops studied. The determination of the yields for the two cropping systems in agriculture and their levels can be seen in Tables A1 and A2.

Table 4 presents the percent yield differences for each crop in Romania grown organically, according to the Eurostat data, compared to conventional yields, for the period 2012–2020, where data were available. Analysing the average percent differences across years, there are organic crops for which yields are higher than in the conventional system. These crops include berries (excluding strawberries), whose yield in organic system was 158% higher; a second crop is hops, but the average was determined over a short period of time, so there is a larger margin of error. The average yield of organic hops is 65% higher. Oats and spring cereal mixtures had yields in organic system higher by 9.26%. While all these crops were higher yielding in organic systems, their area share was not very high. The situation is different for grain maize and corn-cob-mix, which is only 2% higher yielding in organic systems, but the area cultivated is about 19% of the total organic area, being the largest single crop.

However, for the most part, organic crop yields are lower than conventional yields; among the closest but still lower yields are sunflower (−4.34%), oilseed rape (−10.9%) and common wheat (−12.46%). At the other end of the scale, there are crops whose organic yields are much lower, more than half, especially for fruit and vegetables, where organic yields are more than 70% lower than conventional ones.

On average, for all the organic crops analysed, there was a yield gap of 32% against organic compared to conventional farming. However, it should be noted that this is a simple arithmetic average; without considering the share of cultivated areas, by taking this average weighted by the areas of the main crops, the yield in organic farming was about 10% lower than in conventional farms, so, as is natural, farmers turned to crops with potential to risk as little as possible and eliminate losses. However, given that Romania must expand its organic farming area, farmers will no longer be able to focus on certain crops, and expansion will most likely widen the gap between weighted yields.

At the same time, in addition to determining the yield differences, which will represent the coefficients of each variable of the linear programming function, the weight of each crop will also be used (Figure 3), given that, until now, there are areas cultivated in an organic system, these will have to be extended from now on, so the values of the variables will have to be higher than or at least equal to those at present.

As mentioned above, the area under organic maize has a significant share, but this crop is in first place, with a share of 19% of the total area under organic cultivation, followed by wheat and spelt, with 15.7%, then sunflower with 8.1%, followed by plants harvested green from arable land with 6.5%, barley with 3.4%, rapeseed with 3%, and then hops with only 0.002%.

Therefore, having created this context with which we can realize and determine the areas that should be cultivated in 2030 in order to reach the European Union target, we constructed a linear programming model that led to the following system of equations.

**Table 4.** Determination of yield differences for each organic crop in Romania compared to the yield in the conventional system (%).



#### **Table 4.** *Cont.*

Source: authors' calculations, x—no data available.

**Figure 3.** Share of main organic crops in Romania.

The variables considered and the main coefficients of the variables or equations are presented in Table A3.

Table A3 shows the 40 variables, i.e., the 40 crops grown organically in Romania, for which data were available, with the related coefficients, i.e., the relative difference in yield between organic and conventional farming, and the share of each crop in the total agricultural area used. All these crops will be included in the Simplex method, and the change in each area for each crop will fulfil both the conditions presented above and the objective function.

Following the application of the simplex method, which led to the optimal solution that fulfils both the objective function (Table 5), where productivity do not decrease very much, and the set of conditions imposed, the areas for the 40 organic crops were identified for which Romania would reach the share of 25% of the utilised agricultural area.


**Table 5.** Solution of the objective function as a function of each variable.


**Table 5.** *Cont.*

Source: authors' calculations.

As can be seen from Table 5, the total organic area would need to be 3.241 million hectares to reach the 25% share. Of this total, the largest and most extensive crop share should be plants harvested green from arable land with 1.014 million hectares, which represents 31.29% of the organic area and 7.8% of the total utilised agricultural area.

Grain maize and corn-cob-mix is the second most important crop, with an area of 614.4 thousand hectares, i.e., a share of 4.7% of the utilised agricultural area. Wheat and spelt is in third place with an organic area of 507.5 thousand hectares, representing 3.9% of the utilised agricultural area.

According to the optimal solution, i.e., according to the values of the 40 variables, this results in an objective function value of −19.64%, i.e., the smallest difference in yield/productivity between organic and conventional farming according to the organic crop structure shown in Table 5 and Figure 4. With this organic crop structure, the first condition is met, i.e., the organic area is 25% of the utilised agricultural area; the second condition is also met at the limit, but it can be seen that this yield decrease of 19.64% applied to 25% of the agricultural area results in a fixed total decrease in agricultural production of 4.91%, which does not exceed the increase of agricultural area per capita, so we can consider that this extension of agricultural area can be considered as sustainable. The other conditions have also been met.

**Figure 4.** Structure of ecological surfaces according to the optimal solution.

#### **5. Discussion**

The study was based on the premise that the share of agricultural land cultivated organically should reach 25% of the country's agricultural land by 2030, and the aim of the study was to determine the amount of each crop that should be cultivated organically in order to reach this share with certain sustainability restrictions.

Given that the average yield of organic crops is just over 30% lower, this would negatively affect food security and sustainable development goals. Thus, in determining the size of the areas cultivated for each crop, it was decided to minimise productivity losses so that, applying these losses to the 25% of the area, the total loss of production would be less than or equal to the gain in agricultural area per inhabitant determined by the decrease in Romania's population.

After identifying the optimal solution, when the crop structure and the size of the crop areas resulting in the smallest loss of productivity, which is about 20%, are applied to the 25% area, then a total loss of 5% of agricultural production results, which is recovered by increasing the agricultural area per capita.

Furthermore, following the identification of the optimal solution, it is observed that the hypothesis becomes true, namely that the main crop to be cultivated in the highest proportion will be the one related to green plants, thus leading us to the idea of cultivating and converting the cultivated areas to grassland and meadows.

The economic impact, not just the technical one, must also be discussed. Given the loss of yield and the high cost of inputs in organic farming, the final cost per unit of product will be higher, and this will be reflected in the market price. As the price of organic products is higher than conventional products, this means a higher value for organic products per unit of product, but this price aspect has not been taken into account, as this is the only technical condition of the European Union's area ratio strategies. If the price of products, the value of production and the price difference between the two systems had been taken into account, the crop structure would probably have looked different, with uncertainty as to whether the main condition of area assurance would still be met. Unfortunately, however, this analysis could not be carried out due to the limitations of the static data, as there are no price data for organic products.

#### **6. Conclusions**

In this paper, the aim was to determine the ecological areas that should be cultivated in Romania by 2030, so that their share meets the targets of the European Union strategy for biodiversity, namely 25%. Although this share represents the EU average of organic crop area in the total agricultural land, this paper assumes that Romania should ensure this share in a sustainable way.

Given that organic crop yields are lower than conventional ones, in order to achieve sustainable growth, the agricultural area per capita in Romania was determined, so even if the dynamics of agricultural area and population are decreasing, the rate of population decline is faster, so the indicator of agricultural area per capita is expected to increase in the period 2020–2030, with a forecasted increase of up to 5%. This would therefore be considered as the upper limit of yield losses in organic farming for the 25% of the area.

In order to determine as accurately and optimally as possible the areas of organic crops to be sown, the average yields per hectare for the intersection of crops recorded in Romania between the two systems were analysed, thus determining the relative differences in productivity between the organic and conventional systems for each crop and the average relative difference, which was 32% against organic farming.

In addition, we mention the fulfilment of the proportion of 25% of the total agricultural area as organic cultivation, as well as the difference in yield for the section of 25% of the agricultural area as less than or equal to the growth rate of agricultural area per capita, so that sustainable growth of organic agriculture will be present in Romania.

It was concluded that the organic area would have to increase by 11.7 times, i.e., to reach a size of 3.24 million ha, to ensure the proportion recommended in the EU strategy. Solving the linear programming problem led to the determination of the size of the areas to be cultivated organically for each crop in order to fulfil the objective function of minimising yield loss. This gives the smallest yield loss in organic farming compared to conventional farming, 16.94%. At the same time, this yield decrease, applied to 25% of the agricultural area, leads to a loss of up to 5% of production, which is sustainably covered by the increase in agricultural area per capita due to the decrease in population.

**Author Contributions:** Conceptualization, A.U. and I.L.P.; methodology, I.L.P.; software, I.L.P.; validation, A.U. and I.L.P.; formal analysis, A.U.; investigation, A.U.; resources, A.U. and I.L.P.; data curation, I.L.P.; writing—original draft preparation, A.U. and I.L.P.; writing—review and editing, A.U. and I.L.P.; visualization, A.U. and I.L.P.; supervision, A.U. and I.L.P.; project administration, A.U.; funding acquisition, A.U. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by the ADER 23.1.1 project "Technical-economic substantiation of production costs and estimates of the valorisation prices of the main crop and livestock products obtained in conventional and organic farming", by the Ministry of Agriculture and Rural Development (MADR), phase 4, 2022.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Conventional crop yield (t/ha).


**Table A1.** *Cont.*



**Table A1.** *Cont.*

Source: authors' calculations.

**Table A2.** Organic crop yield (t/ha).


#### **Table A2.** *Cont.*



**Table A2.** *Cont.*

Source: authors' calculations, x—no data available.

#### **Table A3.** Definition of variables and initial coefficients.



#### **Table A3.** *Cont.*

#### **References**


**Raluca Georgiana Robu \*, Ana-Maria Holobiuc, Alina Petronela Alexoaei, Valentin Cojanu and Dumitru Miron**

Department of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania

**\*** Correspondence: raluca.robu@rei.ase.ro

**Abstract:** This article contributes to the discussion about the socioeconomic factors that reinforce pesticide dependence in the European Union and hinder the transition to more sustainable agricultural practices in light of the European Union's Green Deal objective of reducing the use of pesticides by 50% by 2030. The analysis has a two-pronged purpose: (1) to identify the determinants of pesticide consumption in the European Union by conducting a set of four seemingly unrelated regressions and (2) to emphasize the existence of regional patterns across EU countries formed by the factors that significantly impact pesticide consumption based on a cluster analysis. Per capita GDP, selling prices, population, and real income positively influence pesticide use, whereas subsidies and organic agricultural area negatively influence them. Pesticide use is most affected by GDP per capita and least affected by subsidies. Cluster analysis highlights regional differences reflected in three clusters: (1) the most recent EU member states, (2) the European countries with large population levels, and (3) the countries with the highest GDP per capita. Our findings may contribute to the EU's capacity to generate policy changes at the member state level and can be built into recommendations to address the persistent overuse of pesticides.

**Keywords:** Green Deal; Farm-to-Fork; pesticides; sustainable crop production; sustainable production policies

#### **1. Introduction**

The term 'sustainability' in agriculture describes the need to meet the food needs of the growing human population while ensuring minimal impact on the environment and people as well as profitability for producers [1]. Most researchers agree that sustainability in the agricultural field should, by definition, address the environmental, economic, and social issues associated with its practice [2]. Food systems refer to the entire range of activities from production to consumption [3] that contain the 'environment, people, inputs, processes, infrastructures, institutions' and the 'socioeconomic and environmental outcomes' [4]. They are widely spread across multiple economic territories in various geographic regions, a fact that leaves them exposed to various risks [5]. The growing demand for food, which mainly comes from the growing population, simultaneously exerts pressures to keep food prices affordable to all people which is opposed by pressure to keep the businesses of agri-food producers profitable and address climate change issues. These pressures impact food systems at an unprecedented level and emphasize the need for sustainability. Furthermore, prices for agri-food inputs and outputs have increased significantly in recent years because of the COVID-19 pandemic and the war in Ukraine; these events have caused severe shortages in the supply chain [6,7].

Contrary forces are at work when attempting to ensure the sustainability of agriculture. Several agricultural practices that are employed to ensure that food is affordable and available to the growing population, while also generating enough revenue for farmers to maintain agricultural production, have a detrimental effect on both human health and

**Citation:** Robu, R.G.; Holobiuc, A.-M.; Alexoaei, A.P.; Cojanu, V.; Miron, D. Regional Patterns of Pesticide Consumption Determinants in the European Union. *Sustainability* **2023**, *15*, 2070. https://doi.org/ 10.3390/su15032070

Academic Editors: Mariarosaria Lombardi, Erica Varese and Vera Amicarelli

Received: 29 November 2022 Revised: 17 January 2023 Accepted: 18 January 2023 Published: 21 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the environment. According to the European Environment Agency, most of the existing operations of the EU agri-food systems have a direct impact on the deterioration of the environment and climate in Europe. These activities also have a negative impact on biodiversity and climate change and cause pollution [4]. Various pesticides on the market have been banned because of their extremely severe effects on both human health and the environment; other available pesticides are fake. Pesticides that are used legally may also cause diseases of different types and intensity levels [8]. Estimates indicate that there are 168,000 deaths worldwide and one to two million cases of pesticide poisoning each year [8].

Fertilizers and pesticides are the two types of chemicals that are widely used in current agricultural systems. The former increase soil fertility, allowing crops to produce higher yields, while the latter protect crops from diseases and pests [9]. Therefore, the use of pesticides in agriculture, along with other measures, has positive effects on the level of crop production. Production per unit of input is often referred to as intensity in agriculture [10]. Higher intensity can be obtained mainly through mechanization [11], improved seed productivity [12], reduced crop cycle, increased fertilizer consumption [13,14], and reduced losses due to the use of pesticides [15].

The Common Agricultural Policy (CAP) had a major contribution to agriculture intensification [16,17], providing incentives to farmers to increase productivity. The drawback, which was later acknowledged, is that agriculture intensification further degrades the soil, decreasing the concentration of organic resources [18,19], thus increasing the need for additional compensation. Current food support policies do not meet the requirements of a modern food system and have not kept up with the rapid structural changes that affect food systems or the difficulties caused by these changes [20]. Market price support, production payments, and the unrestricted use of inputs are among the most distorting government interventions and the most environmentally damaging agricultural support programs. They create incentives for increased input consumption, for the allocation of land to subsidized crops, and for the introduction of additional land for agricultural production. In the absence of adequate limitations, payments based on variable inputs could encourage the overuse of pesticides, resulting in significant damage to freshwater ecosystems and biodiversity [20].

Against the backdrop of growing concerns around the impact of pesticide use on human health and the environment, many sectoral policies address the current status of agriculture and biodiversity in Europe, encourage a systemic approach to the sustainability of the agriculture and food sector, and outline the primary objectives centred on sustainability. By 2030, the Green Deal, Farm-to-Fork, and biodiversity programs hope to reduce the loss of nutrients from both mineral and organic fertilizers by at least 50%, while ensuring that soil fertility does not deteriorate in the process. This ambitious target, along with other EU objectives, such as reaching 25% organic agriculture, is expected to result in a reduction in pesticide use. The use of organic farming is generally minimal or non-existent [21]. However, many aspects are likely to be contentious, given the dispute over the production impacts of all new regulations. A macrolevel assessment based on the Green Deal objectives (that is, 50% reduction in overall pesticide use and risks, 50% reduction in more hazardous pesticides, 20% reduction in fertiliser use, 10% of agricultural land under organic production), concluded that the Farm-to-Fork and biodiversity strategies will result in a 'decrease of the volumes produced per crop in the entire EU on average ranging from 10 to 20%' and prices for some crops (for example, wine, olives and hops) will increase. As a result, exports of EU crops will fall, while imports will increase (the volume of imports of products can double) [22].

Throughout the EU, agri-food systems differ significantly and their progress in terms of sustainable development is strongly influenced by these differences. According to [23], pesticide sales between 2011 and 2020 emphasise substantial regional differences in both the absolute amount of pesticides used (per hectare of agricultural land) and in total sales. Sales have increased on the markets of certain member states (for example, Latvia, Austria, Germany, France, and Hungary) and fell in others (for example, Czech Republic, Portugal, Denmark, Romania, Belgium, Ireland, Italy, Sweden, Slovenia, Netherlands, and Cyprus). The difficulty of setting national targets in CAP national strategic plans (in this case, for the use of pesticides) is increased because there are differences in both national evolutions and absolute quantities of pesticides (per hectare of agricultural land and overall). This applies to all quantitative targets of the Green Deal currently specified at the EU level [24]. Although member states are required to adopt legally binding targets for the achievement of the overall EU targets, in regard to determining national targets, members have the flexibility to take into account their own national circumstances, including their level of pesticide use and their historical level of progress.

This article aims to serve as a starting point to reveal the socio-economic and political factors that reinforce pesticide dependence in the European Union and determine the slow pace in the transition to more sustainable agricultural practices. The analysis is narrowed to pesticide consumption since pesticides are the most widely used tool in intensive agriculture and because the European Green Deal includes more stringent reduction targets. The paper has two main objectives: (1) to identify pesticide consumption determinants in the European Union (EU) by running an empirical model based on panel data for EU27 between 2001 and 2019; and (2) to emphasize the existence of some regional patterns across EU countries and classify the EU27 member states into broad categories according to the factors that had been shown to significantly affect pesticide consumption. By achieving these objectives, cluster characteristics can be developed into recommendations to combat the persistent overuse and reliance on chemical pesticides at regional levels.

Our findings may contribute to the EU's capacity to generate policy change at the member state level and may be useful to a wider audience interested in the restraints national states face in adapting their policies to meet the Grean Deal objectives. This paper contributes to the body of knowledge in three ways. First, it provides a comprehensive analysis of the multiple factors that contribute to pesticide use in agriculture, in contrast to the majority of existing studies that discuss pesticide consumption from either micro or macro-economic perspectives. Second, the article looks at the EU market and provides, for the first time, a cluster analysis applied to the determinants of pesticide use in agriculture. Lastly, the original elements of the paper reside in the complementarity between regression and cluster analysis, with the purpose of determining common regional patterns for member states. The article is structured as follows: Section 2 presents a literature review on frequent determinants of pesticide consumption; Section 3 is dedicated to the methodological framework; Section 4 presents the findings of the regressions and the cluster analysis; and the last section brings together final observations and conclusions.

#### **2. Review of the Scientific Literature**

#### *2.1. Determinants of the Use of Pesticides in Agriculture*

The section presents a review of the literature on the impact of various economic and social factors on the widespread use of pesticides in agricultural production and provides the scientific basis for the regression analysis.

Given the multifaceted and transdisciplinary nature of pesticide dependence, it is impossible to identify a rigorous review of generally accepted and standardized variables. Although a wide range of studies have sought social, economic, and political explanations, most of these studies have focused on a small number of factors or have approached the topic from a microlevel perspective and emphasize the role of farmer decision making [25–29]. To our knowledge, no analysis has been performed at the EU level on the determinants of pesticide use in agriculture. Most studies have addressed the environmental and health impact of pesticide use or have performed a comparative analysis of National Action Plans of member states [30–32]. One general observation from the literature reviews is that, while some research investigates the factors of pesticides and fertilizers together, others study them individually. The widespread use of pesticides among intensive agricultural tools and the larger reduction target outlined in the European Green Deal regulations required a focus on analysing the determinants of pesticide consumption. Depending on

data collection possibilities, we tested the impact of several factors on the use of pesticides in European agricultural practices at country level, continuing with a cluster analysis to highlight possible geographical patterns of these determinants.

As described in the Introduction, the use of pesticides reduces crop losses and contributes to an increase in overall crop yield [33,34]. The primary factors that have caused an increase in agricultural productivity have deep roots and do not all operate in the same way under comparable conditions.

In the literature, the most cited determinant for the widespread use of pesticides is the growing demand for food from the growing population at the global level, which puts greater pressure on increasing crop yields and using resources more efficiently [35]. At the European level, between 1960 and 2020, the population has decreased overall, while net migration to Europe increased during the same period [36]. At the same time, trade liberalization offers opportunities for European farmers to supply foreign markets [37], especially from Eastern Europe [38,39]. To some, this reduces the relevance of the link between the population of a country and the pressure to use various methods that improve productivity. With these constraints, we will test the impact of the population on the use of pesticides in the same country.

Demand and production for food, in general, and for healthy food, more specifically, varies between countries, depending on the country's wealth. Economic development determines an inverse U-shaped evolution of the curve that describes the use of pesticides. The least developed countries have a small consumption of chemicals in agriculture because the prices of these inputs are prohibitive; developed countries are heavy users of pesticides, while the most developed countries use them more efficiently to increase production [40,41]. Ref. [42] observed an increase in the use of pesticides in the least developed countries as their trade connectivity with developed countries improves, resulting in increased imports of pesticides and increased exports of agricultural products. Ref. [43] found an increase in the market for organic products in rich countries in western Europe as they have a higher demand for healthy food. Using GDP per capita as the independent variable, our aim is to determine whether there is a causal relationship between economic growth and pesticide use based on the references mentioned above.

From a supply perspective, the available workforce in agriculture was reduced by the urbanization process [44] (later in the case of Eastern European countries), imposing the need for higher labour productivity. The issue of labour scarcity in agriculture has been partly diminished since 1990 by the migration of low-wage labour to rural areas either from the same country or to less developed countries [45] as well as technological progresses that have improved productivity through mechanization [11]. Ref. [46] underlined a stronger effect of mechanization when labour is scarce or expensive, while pesticides are used more intensively when land is expensive. On many occasions, chemical inputs were the solution to increasing productivity in order to face increasing labour costs [38], higher land prices, and growing competition [47–49].

Another set of complementary factors with potential impact on pesticide use are land fragmentation and farm size. When land is fragmented, it becomes more difficult to use farming equipment efficiently, which decreases productivity and increases costs [50]. The results of a survey of Chinese farmers point out that the use of pesticides does not necessarily depend on farm size but rather on certain psychological aspects: in order to preserve the soil, farmers use them less frequently when they believe the land to be clean, but more frequently when they believe their chances of remaining in agriculture are limited [51]. At the same time, recent research emphasizes the possibility of increased crop yields when using traditional farming methods. For example, crop rotation favours organic farming [52] and the participation of household labour in small farms improves farm efficiency [52]. Therefore, smaller traditional farms are more suitable for organic farming, while economies of scale can be obtained when farms are larger. Once the land is introduced into the organic farming circuit, producers cannot maintain certifications as organic farmers unless they change the type or amount of pesticides they use; this

production system should maintain profitability over the long term [53]. The transition to organic farming imposes certain costs and changes in farm structure [54] that are not justified in the short term. In our analysis, we use the total organic agricultural area as a factor that is expected to negatively impact the use of pesticides. Many farms indicate a high level of farm fragmentation, so we initially tested the impact of the number of producers in agriculture (as a proxy for farm size) on the use of pesticides, but it turned out to be insignificant, as we eliminated the indicator from the final regression.

Regarding the drivers behind the transition to organic farming, it can be found that this depends on the possibility of making a profit coming from two main directions: higher selling prices and support payments for organic farming [55]. Normally, when product prices grow on the market, we would expect organic crop production to grow because, although the total costs in this segment are higher, farmers would have increased opportunities to make profit. However, more farmers are stimulated to meet market opportunities when they appear [56], and they continue producing even more inorganic products, which means a higher total pesticide usage. Other authors found that low-income farmers use more inorganic fertilizers, even in unnecessary amounts [57,58], while [59] identified a negative impact of farmer income on the efficiency of pesticide use. In our analysis, we test the impact of sales prices and real income on the consumption of pesticides.

Subsidies represent a solution for farmers to either: (1) invest in new technologies [60] that boost productivity as an alternative to the use of more chemicals or (2) sell organic farm products at competitive prices regardless of the technologies used [61,62]. However, the impact of subsidies on the consumption of pesticides should be interpreted according to the conditions applied by the states that offered subsidies in a given period of time. In the 1990s, eastern European countries withdrew many subsidies previously given to farmers to buy chemicals to improve agricultural productivity [63]. The policy's withdrawal in 2013, when the European Union introduced green payments (focusing on measures such as crop diversification) in the Common Agriculture Policy, led to a substantial decrease in yield [64]. Different types of pesticide subsidies have been applied in several European countries, including lower VAT rates (e.g., Austria, Belgium, Bulgaria, Croatia, France, Ireland, Italy, Poland, Portugal, Romania and Spain) [65]. However, some authors believe that that improved market access for organic products have greater overall effects than subsidies in reducing chemical use [66]. In other cases, the total value of available subsidies is not very relevant if the conditions imposed on farmers who access them are very restrictive or the contract terms are not flexible enough [67]. Taking these limits into account, in the current article, subsidies on agricultural products are expected to have a significant influence on pesticide consumption.

Specific social characteristics also determine the choice of certain types of farming method. For example, older, more experienced, and more educated farmers would be more prone to take the hard way and implement traditional methods [68]. The general education level of the population positively influences the demand for healthy food and therefore negatively influences the use of pesticides [66].

The main determinants of pesticide application identified in the literature were summarized in Table 1.

The intricacy of multiple problems that must be addressed simultaneously, as well as the link between different strategies, highlight the necessity for research that examines pesticide use in the context of the agricultural system and on a regional scale. Multicriteria evaluation and decision support systems, in conjunction with pest monitoring programmes, can aid in the development of region-specific and long-term policies that are coordinated within an EU framework.

#### *2.2. Cluster Analysis of Pesticide Consumption in Agriculture*

A cluster analysis of agricultural systems provides categorization (and grouping) of countries, which can then serve as the basis for policymakers interested in establishing targets that clusters can strive to attain within certain time periods. This is of utmost importance since, on some occasions, management practices are not standardized, knowledgesharing and learning from best practices are not well established, and systems frequently operate unaware and unconcerned about the performance of others around them.


**Table 1.** Selection of Primary Determinants in Pesticide Application.

(a) economic factors, (b) social factors, (c) socio-demographic factors. Source: authors' computation based on literature review.

Cluster analysis have been used to identify patterns of energy and land use in agriculture [70], heavy metal sources in soils [71], and farmer search behaviour of various types of information, including pesticide use [72]. To our knowledge, cluster analysis has not been applied to the determinants of pesticide use in agriculture grouped at the geographical level.

#### **3. Data and Methodology**

#### *3.1. Methods*

Given the ambitious goal of the European Union to reduce the use of chemical pesticides by 50% by 2030, the primary objective of this paper is to understand the influence of a set of economic and social variables on their use in the EU by conducting several regression analyses. We have considered the evolution of pesticide consumption between 2000–2019, and estimations were performed on panel data extracted from international databases [73,74] for all 27 member states (excluding the United Kingdom). The time frame is long enough to draw meaningful conclusions that are helpful in understanding the perspectives of the European Union in the field of agriculture.

Estimates were made using seemingly unrelated regressions, a method that takes into account heteroskedasticity and correlations between errors. The empirical study initially focuses on the influence of GDP per capita and selling prices of crop production on the use of pesticides. Given the complexity of the topic and the variety of factors that influence the use of pesticides, we have expanded our model with other variables referring to the governmental support granted to agriculture and (subsidies on agricultural crops) and a social-demographic variable (population in each member state) (Equation (2)). Furthermore, given the EU's ambitious goal of improving the health of its citizens, we subsequently included an independent variable related to the organic crop area in Equation (3). Lastly, the FAO-calculated index of real income factors in agriculture was added as an explanatory variable to account for the impact of factor productivity on pesticide use [75]. Initially, we have estimated the regression by including independent variables: employment in agriculture (1000 persons), labour force participation rate in rural areas (% of total population ages 15+), number of producers in agriculture, export value index for agricultural products (2014–2016 = 100) and number of people who completed tertiary education. However, these variables had no significant impact in the regression and were not preserved in the estimations presented below, representing one of the limits of the research. With the purpose of examining the determinants of the use of chemical pesticides in the EU (27) between 2000 and 2019, we have estimated an empirical model based on panel data, gradually extending the equations with explanatory variables, specifically related to the agricultural sector or aiming at the macroeconomic and social framework, as follows:

$$Pesisides\_{i,t} = a + \beta\_1(GDP\ per\ capita\_{i,t}) + \beta\_2(Selling\ prices\_{i,t}) + u\_{i,t} \tag{1}$$

*Pesticidesi*,*<sup>t</sup>* = *a* + *β*1(*GDP per capitai*,*t*) + *β*2(*Selling pricesi*,*t*) + *β*3(*Subsidiesi*,*t*) + *β*4(*Populationi*,*t*) + *ui*,*<sup>t</sup>* (2)

$$\begin{array}{ll}\text{Pesticides}\_{i,t} = & a + \beta\_1 (\text{GDP per capita}\_{i,t})\\&+ \beta\_2 (\text{Selling price}\_{i,t}) + \beta\_3 (\text{Sulsidies}\_{i,t}) + \beta\_4 (\text{Population}\_{i,t}) + \beta\_5 (\text{Organic}\_{i,t}) + u\_{i,t} \end{array} \tag{3}$$

$$\begin{array}{l} \text{Pesticides}\_{i,t} = & a + \beta\_1 (GDP \text{ per } capita\_{i,t}) \\ &+ \beta\_2 (Selling \text{ prices}\_{i,t}) + \beta\_3 (Subsides\_{i,t}) + \beta\_4 (Population\_{i,t}) \\ &+ \beta\_5 (Organic\_{i,t}) + \beta\_6 (Real \text{ income}\_{i,t}) + u\_{i,t} \end{array} \tag{4}$$

where *a* = constant, *ui*,*<sup>t</sup>* = error term; *t* = 1, . . . , T (years); *i* = 1, . . . , N (countries) The variables, definitions, sources, and the expected influence are presented in Table 2.


**Table 2.** Variables, definitions, and sources used in the first empirical model.

Source: Authorial computation.

Panel data for all variables were tested for stationarity by using the Levin, Lin & Chu unit root test [76]. By applying this test, we have started from the hypothesis that the data have a unit root and are not stationary. Initially, we tested for stationarity using an individual intercept and trend, then with an individual intercept or no regressors. If the data did not show stationarity at level, we have checked for the first difference. The data were tested at level, initially including the trend and intercept in the equations. If using this variant revealed that the panel data have a unit root, we resorted to including only intercept or no regressor in the equation. Given the results obtained by applying the root tests (Prob. < 5%), we concluded that the data is stationary at level for all variables, except for the organic agricultural area (Table 3).

**Table 3.** Stationarity test.


Source: Authorial computation.

#### *3.2. Cluster Analysis*

A cluster analysis was carried out based on the most relevant factors in the use of inorganic pesticides that had a significant influence in the regressions (*p*-value less than 10%). The analysed variables were the following: GDP per capita, population, sales prices, subsidies, organic agricultural area, index of real income of factors in agriculture, all having equal weight in the cluster formation. Cluster analysis is applied at a one-year level. The most recent year for which statistics were available was chosen (2019 in most cases, except France, where the last available data for selling prices are from 2016). Cyprus and Malta were excluded from the cluster analysis due to the lack of data on sale prices.

Since the variables were different sizes and to prevent large-scale variables from dominating the cluster formation, the data have first been normalized to have a mean of 0 and a variation of 1. We have used SAS software and applied the Ward minimum variance method, which groups observations based on the minimum distance between them (the distance being the ANOVA sum of squares). Clusters are grouped in subsequent stages at each level of the hierarchy until we obtain the optimum number of clusters, which have the maximum distance between them [77].

#### **4. Results and Discussion**

#### *4.1. Regression Analysis*

Table 4 illustrates the statistical description of the variables included in the empirical model. At first glance, the differences between the minimum and maximum values emphasize the heterogeneous evolution among the member states in the field of macroeconomic, social, and agricultural related variables. The average amount of pesticides used per hectare in the European Union was 3.1 kg, with significant differences between member states. For example, countries such as the Netherlands and Belgium had a consumption of pesticides greater than 12 kg per hectare, while the Baltic States and Bulgaria recorded values under half kilogram per hectare. In the field of economic development, the GDP per capita suggests a heterogeneous evolution among member states, with an average income of 24,279 euros for the time interval 2000–2019. In terms of agricultural sector performance, the average selling prices for agricultural products were 15 euros per 100 kg, with Italy and France recording the highest performances. Referring to the amount of subsidies granted to agriculture, the average value recorded between 2000 and 2019 was 331 million euros, with significant differences between member states. The highest amounts were given to France

(5121 million euros) and Germany (3335 million euros), while the countries that benefited the least from the subsidies were Slovakia, Denmark, and Ireland. For the entire period, the average surface aimed at organic agriculture was 337,059 hectares per member state, while in the field of real income in agriculture, the mean value of the index was 102.48 euros.


**Table 4.** Statistical description of the variables in the empirical model.

Source: Authorial computation.

Given the estimated values of the coefficients presented in Table 5, the equations are as follows:

*Pesticidesi*,*<sup>t</sup>* = −5.6136 + 0.2252(*GDP per capitai*,*t*) +0.5727(*Selling pricesi*,*t*) + *ui*,*<sup>t</sup>* (5)

$$\begin{array}{l} \text{Ps:} \text{Sides}\_{i,t} = -8.9782 + 0.5787(\text{GDP per capita}\_{i,t})\\ \quad + 0.1098(\text{Selling price}\_{i,t}) - 0.0024(\text{Sulsities}\_{i,t}) + 0.2312(\text{Population}\_{i,t}) + u\_{i,t} \\ \quad \text{PS:} \text{Sides}\_{i,t} = -9.4161 + 0.5466(\text{GDP per capita}\_{i,t})\\ \quad + 0.2299(\text{Selling price}\_{i,t}) - 0.01224(\text{Sulsities}\_{i,t}) + 0.4083(\text{Population}\_{i,t})\\ \quad - 0.1948(\text{Organic}\_{i,t}) + u\_{i,t} \end{array} \tag{7}$$

$$\begin{array}{l} \text{Pesticides}\_{i,t} = -10.8320 + 0.5097(\text{GDP per capita}\_{i,t})\\ \quad + 0.0829(\text{Selling prices}\_{i,t}) - 0.0067(\text{Sulsides}\_{i,t}) + 0.4327(\text{Population}\_{i,t})\\ \quad - 0.1902(\text{Organic}\_{i,t}) + 0.3710(\text{Real income}\_{i,t}) + u\_{i,t} \end{array} \tag{8}$$

Table 5 and the estimation presented above illustrate the results of the empirical analysis aimed at identifying pesticide determinants of use in the European Union. Pesticide use was mainly influenced by the economic performance of the member states, particularly the level of GDP per capita. Consequently, an increase of one euro of GDP per capita will determine a rise of 0.5 kg of pesticide use per hectare, according to Equation (4). The increase in GDP per capita had a positive and strong influence on the use of pesticides in the European Union, confirming that many developed countries are still heavy users of pesticides. Our results are in line with [78], which also showed a positive connection between pesticide consumption, population and GDP per capita for several countries, including Europe, between 1990 and 2014. We cannot firmly contradict [40,41] which showed an inverted U-shaped evolution of pesticides along with GDP growth because we only checked a linear relationship applying a regression model for the entire time frame. Looking at the primary data that we used in the sample, one can find that several countries have reduced pesticide consumption over recent years (from 2017, 2018, or 2019) after increasing it between 2010 and 2016: Austria, Belgium, Bulgaria, Czech Republic, France, Germany, Italy, Netherlands, Poland, Portugal, and Sweden. Other countries have continued to use more pesticides until 2019: Croatia, Estonia, Latvia, Romania, Slovakia, and Spain. Although GDP per capita has continuously grown in the mentioned countries from 2010 to 2019, we can only associate a decrease in pesticide use with a higher GDP per capita for two or three years. This trend should be followed for a few more years to be able to draw more pertinent conclusions.


**Table 5.** Regression output.

Note: robust standard errors and t-statistics are in parentheses. \*—*p*-value < 1%, \*\*—*p*-value < 5%, \*\*\*—*p*-value < 10%. Source: Authorial computation.

The analysis shows that farmers tend to use more chemical pesticides as the population of EU member states increases, indicating a potential increase in food demand. The population growth of one unit generates a 0.43 kg per hectare increase in pesticides (Equation (4)). This finding confirms the relationship between the increase in demand and the response to improve productivity (in line with [69]). Consequently, European farmers still place a priority on producing a large amount of food, while the production of healthy, high-quality food (including few or no pesticides) is less attractive. The increase in demand (expressed as a higher quantity) is still a more interesting opportunity compared to the advantages of organic agricultural production. The profits obtained from organic farming may not yet be enough to determine the specialization in this area. For example, ref. [79] found a similar profit per surface of cultivated land for conventional and organic farming in certain regions in Germany.

Another factor that had a high influence on the dependent variable was real income, which determined an increase in pesticide use of 0.37 kg per hectare (Equation (4)). Moreover, having a high statistical significance in Equation (1), the resulting coefficients suggest that an increase with 1 euro of selling prices per 100 kg determines a rise in the quantity of pesticide per hectare of 0.57 kg. Therefore, when profits are higher, producers prefer to retain them rather than invest in the switch to organic farming, confirming that the

behaviour observed by [56], as presented in the literature review, continues to manifest in the same way. When income increases, the preoccupation with efficiency diminishes and more pesticides are wasted, similar to what [59] found in Chinese agricultural practices.

Although not statistically significant in the second and fourth equations, subsidies granted to agricultural products tend to negatively influence the consumption of pesticides. Their growth in one unit leads to a decrease in pesticide consumption of 0.0067 kg per hectare. This result is probably due to the orientation of these funds toward organic farming. This is an interesting finding because it confirms the fact that if a certain conditionality is imposed on accessing subsidies, such as following sustainable farming methods, it would motivate farmers to embrace them and still have sufficient profit. Ref. [80] also identified direct subsidies to be effective in determining a switch to organic farming practice, but that it also caused a price decrease for both organic and conventional farming output. The decrease in the output price for organic products makes them more available to consumers, but when prices for conventional farming products are also lower, the profits for this specialization also decrease. This is when subsidies directed to organic farming compensate for the profit loss. Ref. [55] pointed out that both increased prices and subsidies for organic farming are effective methods to increase profits and encourage farmers use sustainable production methods.

The results also suggest that the increase in the area intended for organic agriculture tends to negatively influence the consumption of chemical pesticides, as the independent variable is significant in the estimations. More specifically, the expansion with one hectare of organic farming creates a decrease in pesticide use of 0.19 kg per hectare (Equation (4)). This relationship is obvious because organic farming means using fewer synthetic pesticides [81]. The question that needs to be further explored is whether an increase in the surfaces dedicated to organic agriculture determines a progressively higher reduction of chemical pesticide use as more farmers learn from the experience and as certain economies of scale or scope can be obtained. However, such an analysis can only be performed when and where sustainable agriculture is more widely spread.

The values of the coefficient of determination (R2) imply that the model explains the variation of the dependent variable in a percentage that spans from 34% (Equation (1)) to 45% (Equation (4)). To check the validity of the model, we tested the classical linear regression assumptions. The general form of the multiple linear regression is as follows:

$$Y\_{i,t} = a + \beta X\_{i,t} + u\_{i,t}$$

where *Yi* = dependent variable; *Xi*,*<sup>t</sup>* = independent variable; *ui*,*<sup>t</sup>* = error term *t* = 1, ... , T (time); *i* = 1, . . . , N (cross-sections).

First, we have verified whether there is serial independence, which assumes that the errors are distributed independently. To test the first-order correlation, we used the Durbin–Watson test. The value of around 2 confirms that there is no first-order correlation between errors, so we consider that the empirical model is valid. Subsequently, we have tested the validity of the model by looking at another assumption of the linear regression model, the multicollinearity, which implies that the explanatory variables are not correlated. To identify multicollinearity, we used the VIF test (variance inflation factors). As described in Table 6, the results of the centred VIF are around 1 for all variables. Consequently, we have concluded that there is not a highly collinear relationship between explanatory variables that could bias the estimates. We have also tested another assumption of the linear regression model—the homoscedasticity—which assumes that all error terms have the same variance, respectively *var*(*εi*,*t*) = *σ*<sup>2</sup> = constant for all *t* [73]. The histogram confirms that the residuals have a normal distribution, with a probability value above 5%. Finally, we have tested whether the explanatory variables are representative. The redundant variable test illustrates that the variable related to the population is significant, as we have rejected the null hypothesis. Moreover, we were also interested in determining if another variable initially taken into consideration for the estimation was omitted in the model. By accepting

the null hypothesis (Prob. > 5%), we concluded that agriculture (% of GDP) would not be significant (Table 7).

**Table 6.** Variance Inflation Factors.


**Table 7.** Coefficient diagnosis.


Source: Authorial computation.

#### *4.2. Cluster Analysis*

The next step involved clustering the European countries based on the factors of pesticide consumption that we found in the regression models. The goal was to identify similar arrangements of these drivers so that comparable pesticide mitigation strategies could be developed. Based on the Ward method's application of the following criteria, we have obtained three clusters, as shown in Table 8. Pseudo F statistic (14.6) and cubic cluster criterion (0.28) were high showing a high separation between clusters, while pseudo Tsquared was low (5.4) indicating that the variance between clusters relative to the variance within clusters is low. Figure 1 illustrates the cluster formation stages. The red line marks the stage in which the three clusters were obtained.

**Table 8.** The groups obtained in the cluster analysis.


Source: Authorial computation.

**Figure 1.** Hierarchical clustering tree. Source: Authorial computation.

Based on the average values that the variables take in each country (Table 9), we further comment on the main characteristics and propose a set of recommendations in each case.

**Table 9.** Average values of variables in each cluster.


Source: Authorial computation.

The first cluster is the most homogeneous (semi-partial R-squared is 0.0506) and contains three of the founding member states (Italy, Germany, and France) together with Spain, which joined the EU in 1986. These are the countries with the largest population in our sample (64 million people on average) and the largest organic crop area (1.97 million hectares on average), which is not surprising given the large territory of these countries. The total subsidies for agriculture are the highest in this cluster (an average of 253.2 million euros), especially in the case of France (306.16 million euros) and Spain (301.93 million euros), although this does not translate into lower selling prices for agricultural products. Compared to other clusters, this one exhibits the largest average value for selling prices (18.85 euros per 100 kg). However, since subsidies are expressed in absolute terms and are not related to either the quantity of products or the cultivated surface, we cannot establish a clear connection between subsidies and sales prices. The average GDP per capita (30,427.50 euros) and the average real income of the factors in agriculture are at middle levels among the three clusters, indicating good possibilities to develop organic agriculture, but also putting the producers in a more comfortable situation, which brings little motivation to switch to sustainable agriculture.

Most of the newest EU members (Poland, Lithuania, Slovenia, Estonia, Latvia, Czech Republic, Hungary, Slovakia, Romania, Bulgaria, and Croatia) and two South European countries (Greece and Portugal) are included in the second cluster, which has the highest disparities, as suggested by the highest semipartial R-squared in the dendrogram (0.05060). This cluster has the smallest average GDP per capita (14,486.92 euros). Their population (9.5 million people on average) and organic crop farming area (291,459 hectares on average) are between the other clusters, but closer to the average figures in the third cluster. The level

of subsidies is larger than in the third cluster (94.26 million euros on average). The average of the index of real income of factors in agriculture per annual work unit is the highest for this cluster, showing that farmers obtain high productivity and good development possibilities in agriculture. Although these countries lag in terms of economic development, which could indicate a lower demand for organic food, they could serve other developed markets through the single market as well.

The third group consists of Northern and Western European countries and, among the three clusters, it occupies a middle position in terms of homogeneity (semipartial R-squared equals 0.06094). Its members (Ireland, Luxembourg, Finland, Denmark, Netherlands, Belgium, Sweden, Austria) are characterized by having the highest GDP per capita (48,998.75 euros on average), a smaller population compared to the other clusters (8 million people on average), the lowest level of subsidies for agriculture (3.79 million euros on average), and the lowest real income in agriculture (an average index of 104.87). This occurs against a backdrop of limited agricultural specialization, which is specific to developed countries with small surfaces, low population, or scarce population in general. However, existing agricultural production, even if smaller, has the prerequisites to be turned into organic agriculture, improving the quality of the products, and addressing high income markets. Indeed, the average organic crop area (265,544 hectares) is close to the one reported by the countries in the second cluster. Austria (671,703 hectares) and Sweden (613,964 hectares) even have a higher organic crop area than any country in the second cluster. The selling prices of farm products are the smallest in the case of this cluster (an average value of 15.38 euros per 100 kg), and when combined with the small level of subsidies for agriculture and small real income, they indicate a lower profitability of agriculture currently. Although prices are low, an increase in direct subsidies might be possible to counteract this disadvantage for producers.

#### **5. Conclusions and Recommendations**

This paper consists of an analysis built up in two main stages. The first carried out a set of four seemingly unrelated regressions aimed at identifying the impact of various economic and social determinants on the consumption of pesticides in EU member countries, and the second retained the determinants with a significant impact to be used as factors in the cluster analysis. It resulted in three main clusters that place the member countries on different levels of similar conditions that determine the current level of pesticide consumption and represent barriers or opportunities to switch to sustainable agricultural practices.

Our study revealed that wealthy countries use more pesticides in agriculture, but on a downward trend over the previous two or three years, as wealthier consumers can afford healthier food. GDP per capita had the greatest impact on pesticide use (a 0.50 coefficient in the fourth equation). A larger population determines the use of more pesticides (coefficient: 0.43), establishing the link between food demand and productivity pressure.

From the supply perspective, results showed a positive and asymmetrical influence of sale prices of agricultural products (*p*-value < 10%) and real income of agricultural components (*p*-value less than 1%) on the inputs of pesticides. This emphasises the fact that improved market opportunities, expressed through favourable prices, motivate farmers to produce more, sell more and gain more profit.

As organic crop area grows, pesticide input decreases, showing that organic farming experience encourages sustainable pest control. Although conventional agriculture is still profitable, subsidies, especially those targeted at sustainable production techniques, are the only economic leverage that can push farmers to investigate alternative pest management methods and reduce synthetic pesticide use.

The cluster analysis resulted in three country clusters on which we can draw the following conclusions and recommendations.



Originality elements for this study derive from the complementary study of determinants of pesticide consumption that considers the synergies between the agricultural, macroeconomic, and social levels, and the analysis of regional differences reflected in the cluster analysis. Consequently, our findings can contribute to the creation of targeted national sustainable production policies and the design of practical measures by providing specific quantitative information.

The analysis takes into consideration crop production as a whole without being able to differentiate between organic and conventional farming. Such a distinction would have been useful for variables such as sales prices, real income, and subsidies. In the case of some explanatory variables (subsidies on agricultural products), data for some member states were not available for the entire period of time. However, we were aware of this down side at the beginning of this study and believed that the estimation would not be biased.

Given the impossibility of capturing the multitude of factors that influence pesticide consumption, another limit of the study derives from the restrained set of parameters included in the empirical models. Future research can address the study of other social, economic and technical factors specific to the farm environment that affect the acceptance of sustainable production methods, continuing a previous work on good practices for lowering the use of pesticides and fertilizers [82].

Another important research direction would be to deepen the examination of the conditions under which subsidies or other forms of public financial support would be efficient in extending sustainable agriculture practices. Diversified farming systems that combine conventional and sustainable agriculture production methods are perhaps worth considering; this was also identified as a research direction in [83].

Eliminating pesticide use is difficult. Farmers have few pest and weed control choices after years of relying on them. Many farmers, especially those who have invested in conventional farms, cannot afford alternative pest treatment equipment and longer production time. However, a rise in organic farms, knowledge of sustainability in modern agriculture, and government-sponsored efforts are propelling the biopesticide business and pushing more farmers to adopt sustainable agricultural production.

**Author Contributions:** Conceptualization, V.C. and D.M.; methodology, V.C., R.G.R., A.P.A., and A.-M.H.; software, A.-M.H. and R.G.R.; validation, V.C., D.M., R.G.R., and A.P.A.; formal analysis, V.C.; investigation, A.P.A., R.G.R., and A.-M.H.; resources, A.-M.H. and R.G.R.; data curation, A.- M.H., R.G.R., A.P.A., and V.C.; writing—original draft preparation, R.G.R., A.P.A., and A.-M.H.; writing—review and editing, R.G.R., A.P.A., V.C., and D.M.; supervision, V.C. and D.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Authors may provide access to data necessary to validate their conclusions to the extent that their legitimate interests or constraints are safeguarded.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
