The Development of Sustainable Agriculture in EU Countries and the Potential Achievement of Sustainable Development Goals Specific Targets (SDG 2)

Abstract

The development of sustainable agriculture is treated as a priority at the EU level, and the importance and role of agriculture, in general, and sustainable agriculture, in particular, is undeniable. The European Commission pushes for sustainability in agriculture in rural areas across the EU through the common agricultural policy (CAP), and the achievement of the 2030 Agenda for Sustainable Development targets becomes essential. Through our research, we aimed to investigate the status of sustainable agriculture development in EU Member States by assessing the current level of the achievement of SDG 2 targets, as well as the potential achievement of these targets by 2030. Based on data collected from Eurostat, we forecast the evolution of indicators for each country considered until 2030 using the ARIMA model and dynamic indicator analysis. The results obtained suggest, on the one hand, the existence of positive developments at the level of European countries, but also highlight a number of existing disparities, together with evidence of potentially significant deviations from the targets assumed by the 2030 Agenda.

1. Introduction

The 21st century is indisputably a century of global challenges, reflected in the complex processes of global warming, global population growth, global political and economic tensions, and the disintegration of the quality of human life, often reflected in the quality of products and services consumed. These are also the challenges that are part of today’s paradigm of life, a paradigm reflected in the global strategies and programs that are shaping a new meaning for human life, as identified in the 2030 Agenda, which includes 17 complex and interlinked goals that structure all economic, social, and environmental aspects into a sustainable whole for the future of the planet. We are therefore living in a period in which we are looking for solutions/strategies that will generate a balance of human existence, a balance that in most cases is sustained by a friendly relationship with the environment, nature, natural resources, flora, fauna, water resources, soil, and subsoil energy resources.

In addition to the above, we identify an increasingly accelerated process of global urbanization, which is reflected in particular in the pressure on food production, in direct competition for urban and peri-urban land, and in the reduction in the land area allocated to food crop production. It is thus undeniable that such a situation is critical, as we also identify problems related to soil quality, but also to the substantially greater increase in demand for the Earth’s land surface—the area lost to urban areas. These situations may trigger additional socially and ecologically inequitable effects in the future that risk setting in motion irreversible negative effects on the planetary ecosystem [1,2,3].

As a corollary to the above, it is increasingly evident that there is a need for a global policy interlinked with regional policies favoring rural and peri-urban agriculture to support local socio-ecological practices as well as circular economies with direct effects on urban resilience through the conservation, restoration, and maintenance of healthy soils [4,5].

In this context, the Sustainable Development Goals of the 2030 Agenda for Sustainable Development come with a set of 17 measures to foster sustainable development in all activities of human life, providing opportunities for sustainable development in all areas of activity, including agriculture. By committing all countries to achieve the Sustainable Development Goals (SDGs) by 2030, we highlight the message of the urgency of using environmental resources in close correlation with measures to reduce non-environmental human pressure on the planet [6].

The 2030 Agenda thus becomes highly relevant for both research and the policy analysis of states involved in supporting the implementation of all sustainable goals in an interconnected and dynamic way, in economic, social, and environmental terms at the global level. Moreover, the 2030 Agenda is an integrated strategy and is frequently described as an “indivisible whole” in which the environmental and economic dimensions of sustainable development are permanently connected and interlinked [7,8].

Regarding the link between sustainable agriculture and food security and the 17 SDGs, Borowski and Patuk (2021) identify three basic groups of factors, namely food security, species and ecosystems, and energy and power [9].

Based on this state of affairs, the present paper includes an analysis of developments but also a forecast of the process by which the specific SDG 2 indicators (“End hunger, achieve food security and improved nutrition and promote sustainable agriculture”) at the EU Member State level will be achieved. The research justifies the role that environmental aspects of food production and food security contribute to the long-term balance of human life, but also to a reduction in disparities between regions/countries, given that “zero hunger” is in fact a response to the continuing pressure of human activities on natural resources increasingly affected by global warming, pollution, drought, floods, and other cataclysms.

The paper is divided into five distinct sections. Following the introduction, Section 2 presents the current literature, Section 3 describes the research methodology, Section 4 discusses the main findings, and Section 5 aggregates the conclusions of the research.

2. Literature Review

In order to reflect as concretely as possible the most relevant studies with direct reference to “End hunger, achieve food security and improved nutrition and promote sustainable agriculture”, a series of research results are presented below to justify the need to develop global agricultural production systems in direct correlation with climate change and food security. This is because achieving the specific objectives defined by the 2030 Agenda becomes the driving force behind the whole mechanism of agricultural production, which is unfortunately also affected by other real, current crises (the COVID-19 pandemic, the war in Ukraine). Equally important to mention is the fact that the achievement of sustainable goals in any area can only be achieved through continuous processes of innovation of sustainable practices and technologies and their rapid implementation in all countries.

Moreover, the need for innovation in agriculture is unquestionably a message but also a firm strategy to increase agricultural productivity and the agricultural economy so that critical problems are solved. This is all the more relevant in a context where innovation and sustainable agricultural productivity are becoming an increasing priority, as the economic models and measures applied to date to develop sustainable agriculture, sustainable productivity, and sustainable business in agriculture have not fully responded to the changes that society is currently undergoing. The transition to 100% sustainable agriculture and also to organic farming based on cost-controlling theory make the present moment a turning point for the evolution of the planet and the continuation of life as sustainably as possible [10,11,12].

Equally important to highlight is the fact that it is becoming increasingly difficult for society, for people in certain regions of the world, to have access to food that can meet their essential daily nutrient intake. For example, recent estimates in 2019 showed that approximately 690 million people worldwide experienced food deprivation (hunger), while nearly 2 billion people were denied the opportunity for adequate food security. This is why the SDG 2 Sustainable Development Goal, called “Zero Hunger”, is one of the 17 Sustainable Development Goals defined to be central to the existence of human life that indisputably marks the quality of life in its deepest sides [13,14].

Reducing hunger, food insecurity and adequate nutrition has only one solution today, and that is to encourage sustainable agriculture by 2030. This is also the reason why the 2030 Agenda, specifically SDG 2, aims at a set of headline targets focusing on eradicating hunger and increasing access to sufficient nutritious food; eradicating all forms of malnutrition; increasing the productivity and incomes of food producers, especially smallholder farmers; ensuring the sustainability of the food production system and implementing sustainable agricultural practices; increasing inter-regional and international investment; preventing trade restrictions; ensuring well-functioning food markets; and facilitating timely access to market information [15,16,17].

We are also witnessing a growing concern of governments around the world for long-term human survival, with a focus on the productive capacity of biodiversity, increasing water balance, reducing soil degradation, increasing global agricultural production, increasing educational progress in sustainable agriculture, etc. In addition, there are even greater concerns about developing countries that are severely affected and where we identify major inequalities both within and between nations. Unfortunately, studies on the relationship between “Sustainable Development Goals” and the interlinked risks associated with global temperature rise are still in their infancy, which is why it is essential that the pace of achieving environmental sustainability starts with identifying local priorities, reducing vulnerabilities, and ensuring the well-being of people on earth [18,19].

Not to be overlooked is the fact that many nations continue to face enormous challenges that exacerbate hunger and food insecurity, including high levels of poverty, losses due to pests and plant-specific diseases, unemployment, social exclusion, corruption, conflict, etc. Finally, yet importantly, we highlight the impact of the COVID-19 pandemic on hunger, food insecurity, and the sustainability of agricultural production processes, the pandemic having further exacerbated the quality of life gap between certain regions and countries of the world [14].

These increasingly evident problems are having a major impact on the achievement of the 2030 targets, which calls for an urgent plan to support countries in implementing the SDGs and integrating food security, nutrition, and sustainable agriculture in a dynamic and coherent way. It is also extremely useful to support the role of small producers in the agricultural sectors, a role that has been identified as being multidimensional and which is reflected in several specific objectives: eliminating hunger and improving nutrition, achieving food security, increasing productivity, increasing income, and promoting sustainable agriculture [20,21].

At the same time, the majority of the world’s poor and undernourished people live in developing countries in rural areas, where agriculture is a key part of their livelihoods and the only method of development. Thus, crops provide food but also income from the exploitation of soil and water resources, which can be particularly beneficial for local development and small farmers. However, it is essential to make greater use of the potential of rural areas to address food, nutrition, and energy security issues in a changing global climate [22].

As Rahaman et al. [23] and Mthembu et al. [24] point out, hunger could kill more people than COVID-19. This is because we are going through a critical period in which the global workforce has lost its jobs. In addition, the proportion of undernourished and hungry people globally has increased due to climate change and violent conflict. Millions of people face chronic malnutrition, which undermines philanthropic and food security efforts.

In order to highlight as deeply as possible the specific aspects of agriculture, its sustainability, and food security that have an impact on hunger and poverty reduction, we present below a series of points of view and results of studies that contribute to better identifying the current state of play of how SDG 2 is reflected in the performance of countries, especially those in the EU.

“Agricultural factor income per annual work unit (AWU)”, “Government support to agricultural research and development”, “Area under organic farming”, “Use of more hazardous pesticides”, and “Ammonia emissions from agriculture” belong to the set of indicators that measure SDG 2, both in terms of the current situation and in terms of the targets for 2030 [25].

From the perspective of “Agricultural factor income per annual work unit (AWU)” at the EU level, we can identify successive reforms of the Common Agricultural Policy (CAP), which have been supported in particular by the increasing impact of climate change, especially on the diversity of European agriculture. As a result, we are currently witnessing, on the one hand, the intensification of agricultural activities in some regions and, on the other hand, the marginalization of agriculture and even its abandonment in other regions. These significant differences are increasingly evident between the north-central European regions and the continental peripheries, i.e., the Mediterranean and eastern and northern Scandinavian areas [26].

According to current studies, agricultural sectors occupied by a younger and better-trained population are more likely to achieve high economic performance. Specifically, the chances of achieving high economic performance are almost nine times higher for countries with a highly trained population (the Netherlands—72%, Germany—69%), compared to countries with a poorly trained, aging agricultural population, as we identify in Portugal (72%) and Bulgaria (66%). Similarly, in countries with a high share of agricultural land used in less-favored areas, such as those in the Mediterranean, there is a 94% lower chance of achieving high economic performance. Moreover, a cross-national analysis of farm economic performance has shown that there are significant differences within the EU-27 countries, with the high performers mainly located in the central–northern part of the EU, i.e., Belgium, Czech Republic, Denmark, Germany, France, Luxembourg [26,27].

Another important challenge for the agricultural sector is to keep pace with the ever-growing global population. In this context, providing food for a large population while continuing environmental degradation poses an increasing threat to agricultural production. Moreover, environmental degradation, biodiversity loss, massive deforestation, and carbon emissions affecting agricultural production, as well as cereal and vegetable production at the European level, are deeply interlinked with a number of other variables such as organic farming, renewable energy, political stability, e-governance, and social progress, which also generate a number of other differences between developed and emerging countries, with crucial policy implications for Europe’s agricultural sector [26,28,29,30].

It is therefore undeniable that the resilience of the food system is one of the most important strategic objectives for a sustainable future, as food production depends largely on the type of food product, the supply chain, and the food distribution process, which can vary from city to city, region to region, and country to country. In this respect, we highlight the research carried out in 2022 in nine regions representing different European countries—Wrocław (PL), Oostende (BE), Berlin (DE), Avignon (FR), Copenhagen (DK), Bari (IT), Brasov (RO), Athens (EL), and Barcelona (ES)—which shows that the demand for vegetarian and local food could be satisfied only in the first five of these regions. In addition, if the same number of calories as the current diet is maintained, only the first three countries have enough agricultural land to supply vegetarian ingredients. The results underline the importance of food storage areas and the role of consumer food choices in the diet [31,32].

In terms of “Government support to agricultural research and development”, it should be noted that Europe is a relatively densely populated region with productive agriculture governed by the Common Agricultural Policy (CAP), which includes government-supported nature conservation practices and initiatives. The CAP’s main priority is to reduce the production of agricultural commodities at costs above world prices, but also to create incentives for landowners to adopt and implement voluntary conservation measures [33].

As a result, large-scale landscape conservation strategies have been implemented at the EU level, including the alignment of agricultural and nature conservation policies. In this context, the alignment strategy of all EU Member States aims to financially support farmers to implement larger-scale biodiversity targets and adopt an ecosystem approach [34,35].

In fact, more than half of the European landscape is in agricultural management. This is also the reason why maintaining ecosystems at the European level is a constant concern of agricultural management, with agri-environment schemes (AESs) being set up for this purpose to prioritize ecosystem maintenance. They are also a major source of funding for nature conservation in the European Union (EU) and the largest expenditure on agricultural conservation in Europe [36].

A number of studies published to date report general increases in farmland biodiversity in response to AESs, with the size of the positive effects depending largely on the structure and management of the surrounding landscape. This is important in light of successive EU enlargement and ongoing reforms of the AESs. However, questions remain about the impact of AESs, whether or not they enhance ecosystem services, whether they are more effective in agriculturally marginal areas than in intensively farmed areas, whether they are more or less cost-effective for farmland biodiversity than protected areas, and how much their effectiveness is influenced by farmers [37,38].

Not least, agriculture unfortunately generates about a quarter of global greenhouse gas emissions. This is also the reason why studies show that by 2050, unless appropriate action is taken, agricultural emissions may reach pollution levels that will prevent global climate goals from being met. Clearly, many governments have taken steps to ensure that support to agricultural entities and farms is substantial. Yet only 9% explicitly support conservation, while another 12% support research and technical assistance. In this context, financial support for agriculture needs to be oriented primarily towards climate by increasing the efficient use of land and other natural resources. Governments should also focus on innovation-based projects that are necessary for sustainable farm management [39,40].

Equally important in order to achieve the specific SDG 2 targets is the issue of “Area under organic farming”. From this perspective, agricultural practices need to change radically in order to achieve the Sustainable Development Goals by 2030. The creation of an intensive policy framework that stimulates the rapid transition towards this target is necessary. Moreover, organic farming must provide sufficient and nutritious food for all, while reducing environmental impacts, thus enabling producers to make a decent living. However, how to achieve this is intensely debated and has gaps mainly related to the redesign of farming systems based on agro-ecological principles [41].

The environmental impact of organic farming is also an issue that is constantly debated and there are still conflicting views on how organic farming can contribute to reducing environmental problems and conserving resources. Answers are still being sought on how food security aspects should be included in the assessment of environmental aspects and also how the net environmental impact of agriculture or possible leakage effects due to lower production levels should be taken into account [41,42,43].

Another important issue is the use of more hazardous pesticides. Robin and Marchand [44] find that the use of biological control agents (BCAs) is growing strongly, with a market of €1.9 billion worldwide and €542 million in Europe, and is growing at an estimated average annual rate of 15% to 20%. A number of factors such as scientific progress and environmental characteristics have influenced this positive development. However, a number of barriers still remain that limit the development of BCA, such as market size or variability in effectiveness.

It is thus evident that the development of sustainable agriculture through the identification of plant protection products (PPPs) and non-chemical alternatives is at the heart of the process of achieving long-term environmental conservation goals. However, although a number of significant legislative and policy changes have been made to promote biological control and integrated pest management (IPM) solutions, the literature highlights the disadvantages presented by the European Union’s (EU) two-tier system for the approval of microbial biological control agents (MBCAs) and subsequent microbial biological control products. Equally important is the EU environmental pact set out in the “Farm-to-Fork Strategy” to reduce pesticide use by 50% by 2030, which is a difficult strategy to implement. This is the main reason why it is recommended to simplify the MBCP dossier requirements in several EU Member States to increase their availability and integration into agricultural pest management plans [44,45].

In the same vein, Azadi et al. [46] show that small-scale farmers have significant positive effects on average agricultural productivity, food income, and biofortification. In contrast, farmers with small-scale financial capital generate negative effects on food security. As such, the influential role of these smallholder farmers needs to be considered, as food insecurity leads to socio-economic implications. It is therefore recommended to grow higher-value crops and participate in various income-generating activities such as fisheries and forestry, as the effect of the size of small-scale farmers on global food security is very useful for policymakers to plan for a world without hunger.

The pesticide pollution of groundwater and surface water is also a serious environmental problem that needs attention and an appropriate strategy. Most of the existing best-practice strategies and models unfortunately face the problem of validation due to their complexity, the user subjectivity in their parameterization, and also a lack of empirical data for validation. This is a consequence of the strict regulations for pesticide application, even though imbalances between events considered risky and non-risky are increasingly evident. This is why it is urgent to build predictive models that can be easily applied and that would mitigate all other environmental problems [47,48,49].

As is well known, pesticides are widely used in agriculture to increase the quantity and quality of production, helping to improve food security worldwide. However, the increasing use of pesticides, especially in developing countries, has also increased the risk of toxicity caused by pesticides. As a result, their non-national use has led to pesticide residues in both terrestrial and aquatic ecosystems, tending to bio-accumulate increasingly in the food chain. A revision of legislation to control pesticide use is therefore urgently needed in all countries of the world [50,51].

Although it is an important topic, a priority from the perspective of the sustainability of human life, there are limited reviews of pesticide use in many agricultural countries. This is why pesticide regulation and policy implementation still face real problems. On the other hand, it is widely accepted that each country’s policy on pesticide management directly affects food safety, with governments directly responsible for identifying and implementing additional measures to improve sustainable processes specific to agricultural practices [52,53].

In addition to the above, ammonia emissions from agriculture are also a threat to achieving long-term sustainability targets. It is well known that the share of agriculture in total ammonia emissions in the European Union is very high, averaging 92%. Moreover, most countries are on an upward trend, with agriculture emitting more and more ammonia. Paradoxically, countries that have benefited from high levels of funding for agricultural research and development are actually emitting more and more ammonia from agriculture. It is therefore imperative, in order to avoid intensifying the adverse effects of this phenomenon, that all EU Member States implement effective measures to reduce ammonia emission levels [54,55].

Considering the current state of achievement of the targets, as well as the dynamics of the indicators considered for the analysis, this research aims to assess the development of sustainable agriculture in EU countries and the potential for achieving SDG 2. Taking into account that the evolution of the indicator values in the analyzed period is very likely to be followed in the future, using dedicated econometric tools, this study will assess both the capacity of each EU country to achieve the targets it has set itself and the performance each country could achieve by 2030. Thus, the following research questions have been proposed to be answered through this research, filling some of the existing knowledge gaps:

Research Question 1 (RQ1).

To what extent will EU countries reach the proposed SDG 2 targets by 2030?

Research Question 2 (RQ2).

Based on trend analysis of indicators and projected developments, is it possible to identify EU countries with significant performance in reaching the SDG 2 targets?

It could be noted that although there is substantial literature on the subject of sustainable agriculture, very little research has been published that considers the analysis of the evolution of specific SDG 2 indicators. There is also a lack of complex studies, such as the one presented here, to examine the potential for achieving the targets proposed by the 2030 Agenda, given that there is not much time left until 2030, and there are increasing reports that the possibility of missing these targets is looming.

Taking into consideration all of the above, the present research aims to provide a series of supporting data on the potential evolution of the main indicators characterizing sustainable agriculture in EU countries, as well as to conduct a critical analysis of the state of development and the prospects for achieving the SDG targets by 2030, thus contributing to filling the knowledge gap in the field.

3. Materials and Methods

In order to examine the current state of the sustainable development of agriculture in EU countries and to achieve the objective of this research, i.e., to assess the potential achievement of Sustainable Development Goals specific targets (SDG 2), data collected and published by Eurostat have been considered. Thus, specific data have been aggregated for the period 2007–2021, in order to obtain a more accurate picture of the current and future evolution of the main indicators included in the analysis [25].

In this research, we have approached quantitative analysis based on a hypothetico-deductive model. The first step consisted of familiarizing and analyzing the existing theoretical background and highlighting the relevant background of publications. The theoretical foundations of the hypothetico-deductive model were laid out by reviewing the available literature worldwide and, after a critical analysis of it, aiming at defining the conceptual framework of the research and precisely delimiting the existing elements and terms used, as well as the existing knowledge gap. The next step was to collect and process the available data to be included in the proposed analysis [56,57].

The model proposed for the analysis of the data collected was based on the attempt to investigate and forecast the achievement of the targets assumed by the 27 EU Member States until 2030, in order to get a clearer picture, in time, of the potential non-achievement of the proposed targets. Given that the Paris Climate Accord was adopted in 2015, we aimed to examine the evolution of the selected indicators over an equal period before and after Paris 2015, and the modeling of the available data was carried out from 2007 to the present (with the exception of the indicator regarding the use of more hazardous pesticides, for which data are published from 2011 onwards). Thus, through our analysis, we wanted to highlight the possible increases or decreases in the pace of evolution.

The present research considered 2015 as the reference year for the whole analysis. The literature indicates two econometric time series forecasting models as having relevant results for the purpose of this research: the ETS model (Error Trend and Seasonality, or exponential smoothing) and the ARIMA model (Auto Regressive Integrated Moving Average). Given the complexity of the proposed analysis, as well as the power and accuracy of the forecasts, we decided to apply the ARIMA model available in the SPSS software package [58].

The model used in this research was developed from the method proposed by Box and Jenkins and combines an autoregressive (AR) process with a moving average (MA) model [59,60]. The first component, the autoregressive process, is based on a linear regression of the current value of the series against one or more previous values of the series, generating an AR process of order p (1):

$$T_t =\mathsf{\delta }+{\theta }_1{T}_{t-1}+{\theta }_2{T}_{t-2}+\cdots +{\theta }_p{T}_{t-p}+{\epsilon }_t$$

(1)

where:

• T_t is the time series;
• ε_t is white noise;
• $\delta =\left(1-{\displaystyle \sum }_{i=1}^p{\theta }_i\right)\mu$, with μ denoting the process mean.

The second component models the evolution of the moving average (MA) series, starting from a linear regression of the current value of the series against white noise or random shocks to one or more previous values of the series. The random shocks for each point in time are assumed to come from the same data distribution, most likely following a normal distribution, as in Equation (2):

$$X_t =\mu +A_t-{\theta }_1{A}_{t-1}-{\theta }_2{A}_{t-2}-\cdots -{\theta }_q{A}_{t-q}$$

(2)

where:

• T_t is the time series;
• μ is the mean of the series;
• A_t−i are white noise terms;
• θ₁,…, θ_q are the parameters of the model;
• q is the order of the MA model.

In addition to the results obtained based on the forecast data analyzed for the 2030 horizon, we proposed to further investigate the evolution of the selected indicators using dynamic index analysis. Thus, the analysis considers two critical periods for the considered time horizon, namely 2025 and 2030. The analysis of the values in the indicated periods was related to the year 2015, which was considered the base year for this research.

For the estimation of dynamic indices, we considered the ratio of the selected indicator at a point in time to the value of the indicator in the base year, following Equation (3):

$$DI=\frac{{\mathrm{V}}_n}{{\mathrm{V}}_0}\times 100\%$$

(3)

where:

• V_n is the indicator value in a given moment of time;
• V₀ is the indicator value in the base period.

By means of the dynamic index analysis, individual dynamic indices were calculated for each Member State, together with the possibility of a convergence point between the trend of each extrapolated indicator and the EU average values for the same indicator in the years 2025 and 2030 (as noted in

Table 1. SDG 2.20—augmented Dickey–Fuller statistics.

Countries	Level	1st Difference	Prob. *	Order of Integration
EU-27	−0.715149	−3.968787	0.0405	I(1)
Belgium	−4.049982		0.0092	I(0)
Bulgaria	1.950289	−4.435448	0.0053	I(1)
Czech Republic	−2.127970	−4.593840	0.0040	I(1)
Denmark	−2.307427	−6.797184	0.0004	I(1)
Germany	−3.479037		0.0310	I(0)
Estonia	−4.359811		0.0078	I(0)
Ireland	0.271603	−4.195913	0.0101	I(1)
Greece	−1.319828	−4.854360	0.0026	I(1)
Spain	−0.627454	−4.472492	0.0049	I(1)
France	−1.718573	−3.854289	0.0172	I(1)
Croatia	−0.380010	−3.701780	0.0185	I(1)
Italy	−1.627717	−3.617641	0.0249	I(1)
Cyprus	0.742112	−4.409410	0.0387	I(1)
Latvia	1.559592	−5.515684	0.0009	I(1)
Lithuania	−1.251411	−4.001213	0.0110	I(1)
Luxembourg	−4.065852		0.0090	I(0)
Hungary	−0.764960	−4.420595	0.0402	I(1)
Malta	−1.792836	−5.233564	0.0017	I(1)
Netherlands	−2.818539	−5.555516	0.0008	I(1)
Austria	−4.522842		0.0045	I(0)
Poland	−1.392550	−4.344964	0.0061	I(1)
Portugal	1.209929	−4.121990	0.0016	I(1)
Romania	−1.711694	−3.861012	0.0190	I(1)
Slovenia	1.078330	−5.876949	0.0009	I(1)
Slovakia	−0.599384	−3.841691	0.0145	I(1)
Finland	−1.424042	−4.166373	0.0083	I(1)
Sweden	−4.375063		0.0052	I(0)

Source: own calculations. * MacKinnon (1996) one-sided p-values.

,

Table 2. SDG 2.30—augmented Dickey–Fuller statistics.

Countries	Level	1st Difference	2nd Difference	Prob. *	Order of Integration
EU-27	−0.589750	−3.876283		0.0137	I(1)
Belgium	−1.092602	−4.972482		0.0022	I(1)
Bulgaria	2.699944	−4.297073		0.0049	I(1)
Czech Republic	0.209294	−3.559448		0.0236	I(1)
Denmark	0.716234	−5.691529		0.0007	I(1)
Germany	−0.548142	−4.057910		0.0118	I(1)
Estonia	−1.762163	−2.922697	−4.538323	0.0051	I(2)
Ireland	−1.776444	−3.331903		0.0349	I(1)
Greece	−1.432743	−0.411036	−7.393773	0.0000	I(2)
Spain	−2.225516	−2.251397	−4.142760	0.0146	I(2)
France	−2.269920	−3.956230		0.0119	I(1)
Croatia	−0.018459	−5.812655		0.0006	I(1)
Italy	−1.913124	−1.840440	−6.444156	0.0011	I(2)
Cyprus	−2.438946	−3.491866		0.0265	I(1)
Latvia	−1.572138	−3.170473		0.0480	I(1)
Lithuania	−0.274982	−4.081246		0.0120	I(1)
Luxembourg	−2.394865	−2.600448	−4.290581	0.0076	I(2)
Hungary	−2.902453	−3.186726		0.0491	I(1)
Malta	−2.559582	−5.180378		0.0019	I(1)
Netherlands	−1.551413	−4.080340		0.0096	I(1)
Austria	−2.448347	−4.487729		0.0048	I(1)
Poland	−2.896345	−3.600465		0.0236	I(1)
Portugal	−1.685057	−3.499890		0.0262	I(1)
Romania	−4.006255			0.0135	I(1)
Slovenia	−0.524318	−4.042165		0.0103	I(1)
Slovakia	−3.125427			0.0478	I(1)
Finland	−0.804758	−2.141694	−7.009635	0.0001	I(2)
Sweden	−2.409551	−1.822498	−4.696377	0.0040	I(2)

Source: own calculations. * MacKinnon (1996) one-sided p-values.

,

Table 3. SDG 2.40—augmented Dickey–Fuller statistics.

Countries	Level	1st Difference	2nd Difference	Prob. *	Order of Integration
EU-27	2.307817	−2.227161	−5.152208	0.0068	I(2)
Belgium	−0.266308	−3.152591		0.0494	I(1)
Bulgaria	−0.948131	−2.834188	−3.650315	0.0260	I(2)
Czech Republic	−2.003064	−1.435331	−3.266132	0.0434	I(2)
Denmark	0.665484	−2.843553	−5.153266	0.0024	I(2)
Germany	2.382453	0.618941	−3.979587	0.0141	I(2)
Estonia	−0.707472	−2.598837	−3.627441	0.0245	I(2)
Ireland	−0.888325	−3.511775		0.0274	I(1)
Greece	−2.332170	−5.481623		0.0012	I(1)
Spain	−2.140347	−3.013486	−4.369480	0.0090	I(2)
France	2.476607	−2.147677	−4.526312	0.0061	I(2)
Croatia	−2.816534	−0.443834	−3.549152	0.0109	I(2)
Italy	0.412980	−3.376868		0.0342	I(1)
Cyprus	−2.542971	−4.109622		0.0115	I(1)
Latvia	−0.396682	−3.179040		0.0497	I(1)
Lithuania	−0.603994	−3.445003		0.0306	I(1)
Luxembourg	1.459733	−3.958208		0.0146	I(1)
Hungary	2.616070	0.178927	−6.650254	0.0005	I(2)
Malta	−1.585370	−0.449815	−9.905468	0.0000	I(2)
Netherlands	2.148422	−1.653955	−3.689596	0.0223	I(2)
Austria	0.492757	−2.171784	−3.682134	0.0225	I(2)
Poland	−7.560346			0.0001	I(1)
Portugal	−1.349243	−3.305593		0.0435	I(1)
Romania	−0.227077	−1.515605	−3.558685	0.0274	I(2)
Slovenia	−0.376852	−2.467688	−4.682542	0.0048	I(2)
Slovakia	−1.165686	−4.419754		0.0084	I(1)
Finland	1.084474	−3.505298		0.0277	I(1)
Sweden	−2.638303	−1.822606	−5.096849	0.0026	I(2)

Source: own calculations. * MacKinnon (1996) one-sided p-values.

,

Table 4. SDG 2.52—augmented Dickey–Fuller statistics.

Countries	Level	1st Difference	2nd Difference	Prob. *	Order of Integration
EU-27	0.685743	−1.776545	−5.154136	0.0069	I(2)
Belgium	1.978934	−3.274512	−6.128748	0.0027	I(2)
Bulgaria	−1.758379	−3.475235		0.0408	I(1)
Czech Republic	−0.054957	−2.046801	−4.919080	0.0089	I(2)
Denmark	−4.505869			0.0110	I(0)
Ireland	−1.752413	−3.349896		0.0481	I(1)
France	−1.966674	−4.750110		0.0082	I(1)
Croatia	−1.111388	−2.543988		0.0109	I(1)
Latvia	−0.765519	−2.493399	−8.099753	0.0005	I(2)
Lithuania	−1.950297	−1.493712	−3.972554	0.0310	I(2)
Luxembourg	−0.842010	−2.833885			I(1)
Hungary	−2.106954	−2.203888	−3.658685	0.0226	I(2)
Netherlands	−1.338804	−2.777931	−6.542682	0.0031	I(2)
Austria	−0.422496	−2.445782	−3.708983	0.0347	I(2)
Romania	−1.483030	−1.642790	−4.203060	0.0244	I(2)
Slovenia	−1.724874	−4.023357		0.0201	I(1)
Slovakia	−1.962538	−2.575341	−5.912032	0.0034	I(2)

Source: own calculations. * MacKinnon (1996) one-sided p-values.

,

Table 5. SDG 2.60—augmented Dickey–Fuller statistics.

Countries	Level	1st Difference	2nd Difference	Prob. *	Order of Integration
EU-27	−1.673800	−1.103817	−4.592524	0.0055	I(2)
Belgium	0.420065	−3.822592		0.0180	I(1)
Bulgaria	−2.374394	−2.461983	−4.225314	0.0111	I(2)
Czech Republic	−1.979266	−3.469851		0.0315	I(1)
Denmark	−1.909215	−2.634004	−5.953133	0.0011	I(2)
Germany	−2.583211	−1.264542	−4.978113	0.0039	I(2)
Estonia	−1.762755	−4.625193		0.0052	I(1)
Ireland	−1.069107	−3.378660		0.0363	I(1)
Greece	−5.683967			0.0012	I(0)
Spain	−1.299368	−4.960414		0.0032	I(1)
France	−3.733788			0.0190	I(0)
Croatia	−1.885548	−3.537635		0.0283	I(1)
Italy	0.666723	−5.682255		0.0061	I(1)
Cyprus	−0.741221	−4.095172		0.0118	I(1)
Latvia	−2.056512	−4.077332		0.0121	I(1)
Lithuania	−1.873213	−4.630769		0.0052	I(1)
Luxembourg	−1.882609	−3.236691		0.0454	I(1)
Hungary	−0.557397	−3.634710		0.0243	I(1)
Malta	−7.450329			0.0002	I(0)
Netherlands	−1.343376	−4.229162		0.0096	I(1)
Austria	−1.267989	−3.613853		0.0251	I(1)
Poland	−2.524774	−3.384556		0.0360	I(1)
Portugal	−2.773760	−2.219694	−4.187302	0.0117	I(2)
Romania	−2.458307	−2.892841	−5.131539	0.0032	I(2)
Slovenia	−3.728260			0.0231	I(0)
Slovakia	−0.952045	−4.047929		0.0127	I(1)
Finland	−0.311066	−4.303135		0.0085	I(1)
Sweden	−3.075135	−5.328529		0.0019	I(1)

Source: own calculations. * MacKinnon (1996) one-sided p-values.

and

Table 6. SDG 6.40—augmented Dickey–Fuller statistics.

Countries	Level	1st Difference	2nd Difference	Prob. *	Order of Integration
EU-27	−0.872799	−5.346925		0.0019	I(1)
Belgium	−1.490370	−4.360583		0.0078	I(1)
Bulgaria	−1.795554	−4.458468		0.0080	I(1)
Czech Republic	0.488363	−3.112074	−5.116314	0.0032	I(2)
Germany	−1.841273	−4.187342		0.0102	I(1)
Estonia	−2.963194	−4.315638		0.0097	I(1)
Ireland	−5.475227			0.0015	I(0)
France	−4.212418			0.0086	I(0)
Cyprus	−1.962445	−3.027550	−4.854113	0.0062	I(2)
Latvia	−3.684569			0.0206	I(0)
Malta	−1.831983	−3.105681	−3.765014	0.0219	I(2)
Austria	−0.304355	−3.503141		0.0299	I(1)
Portugal	−2.994415	−4.683342		0.0048	I(1)
Slovenia	−3.651195			0.0218	I(0)
Slovakia	−2.727314	−3.338532		0.0447	I(1)

Source: own calculations. * MacKinnon (1996) one-sided p-values.

, in the last column, “Conv. 2025” and “Conv. 2030”).

In an ARIMA model, the dependent variable and any independent variables are treated as time, and the application of this model is based on the assumption of the stationarity of the time series. The general model introduced by Box and Jenkins (1976) includes autoregressive as well as moving average parameters, and explicitly includes differentiating in the formulation of the model [59].

The first step in fitting an ARIMA model is to determine the order of differentiation needed to stationarize the time series of the indicators considered for the analysis; that is, to have a constant mean, variance, and autocorrelation through time. The augmented Dickey–Fuller unit root test [61] was used to determine the order of the integration of the selected variables.

Once the stationarity condition was met, the analysis moved to the next step, namely the estimation of the model parameters used. In this case, given the vast scope of the research, i.e., the very large number of variables included in the analysis, we used the automation offered by SPSS software to identify the best-fitting model for each specific indicator at a national level. The results obtained from the application of the method described above are summarized in Table 1, Table 2, Table 3, Table 4, Table 5 and Table 6.

4. Results and Discussion

In order to assess the potential achievement of Sustainable Development Goals specific targets (SDG 2) regarding sustainable agriculture for EU countries, we based our research on the methodological framework described above. Data were collected based on the latest information provided by Eurostat for the period 2007–2021.

Based on the results of our research, the first important observation is that most European countries are nowadays showing constant concern for sustainable agricultural development, with the latest developments in the conflict on the EU’s eastern border highlighting the importance of agriculture and ensuring adequate food security. Based on the results of our research, we are convinced that in the coming years, the concerns about sustainable agriculture and the convergence towards the 2030 Agenda for Sustainable Development targets will intensify in the 27 EU countries and beyond.

The results of the research have been summarized, for each indicator selected for analysis, in the following tables. In the first column of each table, the values of the analyzed indicator for the year 2015 have been written; in the second column, the most recent published values of the respective indicator have been recorded; and in the fourth and fifth columns, the estimated values for the years 2025 and 2030 have been inserted (

Table 7. SDG 2.20—agricultural factor income per annual work unit—AWU (index, 2010 = 100).

Countries	2015	2021	2025	2030	2021/2015	2025/2015	2030/2015	Trend	Conv. 2025	Conv. 2030
EU-27	113.29	137.73	146.58	161.06	1.22	1.29	1.42	UP	-	-
Belgium	92.36	87.13	83.63	79.73	0.94	0.91	0.86	DOWN	YES	YES
Bulgaria	156.04	329.58	353.91	439.39	2.11	2.27	2.82	UP	NO	NO
Czech Republic	137.98	166.52	181.59	201.65	1.21	1.32	1.46	UP	NO	NO
Denmark	69.52	53.15	56.34	35.32	0.76	0.81	0.51	DOWN	YES	YES
Germany	82.64	91.04	91.92	85.71	1.10	1.11	1.04	NONE	YES	YES
Estonia	100.40	118.37	95.65	88.06	1.18	0.95	0.88	DOWN	YES	YES
Ireland	119.68	172.04	171.81	193.13	1.44	1.44	1.61	UP	NO	NO
Greece	96.40	111.89	140.14	170.53	1.16	1.45	1.77	UP	NO	NO
Spain	125.24	132.81	144.74	159.65	1.06	1.16	1.27	UP	YES	YES
France	107.16	125.63	125.50	135.02	1.17	1.17	1.26	UP	YES	YES
Croatia	105.73	144.93	161.27	181.69	1.37	1.53	1.72	UP	NO	NO
Italy	132.17	134.48	134.48	134.48	1.02	1.02	1.02	NONE	YES	YES
Cyprus	101.61	142.23	157.79	178.54	1.40	1.55	1.76	UP	NO	NO
Latvia	130.67	205.41	293.47	411.49	1.57	2.25	3.15	UP	NO	NO
Lithuania	135.36	181.93	137.48	137.48	1.34	1.02	1.02	DOWN	YES	YES
Luxembourg	96.96	108.19	122.44	130.52	1.12	1.26	1.35	UP	YES	YES
Hungary	151.91	211.23	228.85	264.30	1.39	1.51	1.74	UP	NO	NO
Malta	93.59	67.66	61.56	51.43	0.72	0.66	0.55	DOWN	YES	YES
Netherlands	101.64	86.47	93.03	91.06	0.85	0.92	0.90	NONE	YES	YES
Austria	84.28	98.50	91.87	88.51	1.17	1.09	1.05	DOWN	YES	YES
Poland	111.93	153.07	185.92	212.73	1.37	1.66	1.90	UP	NO	NO
Portugal	116.36	155.55	174.93	204.10	1.34	1.50	1.75	UP	NO	NO
Romania	116.22	164.47	165.66	186.21	1.42	1.43	1.60	UP	NO	NO
Slovenia	114.10	103.71	128.44	138.78	0.91	1.13	1.22	UP	YES	YES
Slovakia	142.86	215.11	263.06	315.96	1.51	1.84	2.21	UP	NO	NO
Finland	68.42	65.34	58.64	47.47	0.95	0.86	0.69	DOWN	YES	YES
Sweden	107.14	102.50	104.06	105.31	0.96	0.97	0.98	NONE	YES	YES

Source: Eurostat, own calculations.

,

Table 8. SDG 2.30—government support for agricultural R&D (Euro/inhabitant).

Countries	2015	2021	2025	2030	2021/2015	2025/2015	2030/2015	Trend	Conv. 2025	Conv. 2030
EU-27	5.9	7.3	8.4	9.7	1.24	1.42	1.64	UP	-	-
Belgium	3.3	5.2	5.7	6.6	1.58	1.74	2.01	UP	NO	NO
Bulgaria	2.4	4.2	4.7	5.6	1.75	1.95	2.34	UP	NO	NO
Czech Republic	4	6.2	6.9	7.9	1.55	1.74	1.97	UP	NO	NO
Denmark	16.5	23.8	19.1	20.8	1.44	1.16	1.26	UP	YES	YES
Germany	10	13.5	15.7	18.4	1.35	1.57	1.84	UP	YES	YES
Estonia	6.5	7.6	6.6	6.4	1.17	1.02	0.99	NONE	NO	NO
Ireland	19.5	16.8	17.9	16.8	0.86	0.92	0.86	DOWN	YES	YES
Greece	2.8	5.3	4.3	4.7	1.89	1.52	1.69	UP	NO	NO
Spain	8.7	10.4	12.0	13.9	1.20	1.38	1.60	UP	YES	YES
France	5.9	5.2	6.6	7.2	0.88	1.12	1.22	UP	NO	NO
Croatia	1.1	2.8	3.7	4.7	2.55	3.37	4.31	UP	NO	NO
Italy	4	4.9	5.3	5.4	1.23	1.33	1.36	UP	NO	NO
Cyprus	7	7.3	5.2	2.6	1.04	0.74	0.37	DOWN	NO	NO
Latvia	4.4	5.8	5.8	6.6	1.32	1.32	1.51	UP	NO	NO
Lithuania	2.6	3.3	3.7	4.2	1.27	1.43	1.63	UP	NO	NO
Luxembourg	0.3	0.3	0.3	0.2	1.00	0.98	0.73	DOWN	NO	NO
Hungary	1.7	3.4	4.0	4.4	2.00	2.38	2.60	UP	NO	NO
Malta	1.6	1.9	2.4	2.9	1.19	1.49	1.80	UP	NO	NO
Netherlands	5.9	12.9	7.7	6.8	2.19	1.30	1.15	NONE	NO	NO
Austria	3.8	4.9	4.2	4.1	1.29	1.10	1.07	UP	NO	NO
Poland	1.6	0.1	2.5	3.0	0.06	1.57	1.86	UP	NO	NO
Portugal	1.8	1.7	1.1	0.7	0.94	0.62	0.40	DOWN	NO	NO
Romania	1.2	1.1	0.5	0.1	0.92	0.39	0.05	DOWN	NO	NO
Slovenia	2.9	6.5	6.0	6.8	2.24	2.07	2.34	UP	NO	NO
Slovakia	1.9	1.9	2.4	2.5	1.00	1.26	1.31	UP	NO	NO
Finland	17.7	12.6	10.7	8.4	0.71	0.61	0.47	DOWN	NO	NO
Sweden	4.8	7.7	5.8	6.1	1.60	1.21	1.27	UP	NO	NO

Source: Eurostat, own calculations.

,

Table 9. SDG 2.40—area under organic farming (%).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Conv. 2025	Conv. 2030
EU-27	6.56	9.08	12.03	14.98	1.38	1.83	2.28	UP	-	-
Belgium	5.17	7.25	9.12	10.98	1.40	1.76	2.12	UP	NO	NO
Bulgaria	2.37	2.30	3.07	3.84	0.97	1.30	1.62	UP	NO	NO
Czech Republic	13.68	15.33	16.03	16.73	1.12	1.17	1.22	UP	YES	YES
Denmark	6.33	11.45	13.93	16.41	1.81	2.20	2.59	UP	YES	YES
Germany	6.34	9.59	15.55	21.60	1.51	2.45	3.41	UP	YES	YES
Estonia	15.68	22.41	27.68	32.96	1.43	1.77	2.10	UP	YES	YES
Ireland	1.65	1.66	1.91	2.17	1.01	1.16	1.31	UP	NO	NO
Greece	7.69	10.15	9.87	10.68	1.32	1.28	1.39	UP	NO	NO
Spain	8.24	9.98	12.28	14.58	1.21	1.49	1.77	UP	YES	NO
France	4.54	8.71	13.18	17.66	1.92	2.90	3.89	UP	YES	YES
Croatia	4.94	7.21	7.40	7.59	1.46	1.50	1.54	UP	NO	NO
Italy	11.79	15.97	19.07	22.18	1.35	1.62	1.88	UP	YES	YES
Cyprus	3.72	4.37	6.46	7.73	1.17	1.74	2.08	UP	NO	NO
Latvia	12.29	14.79	17.36	19.94	1.20	1.41	1.62	UP	YES	YES
Lithuania	7.11	8.00	9.35	10.69	1.13	1.31	1.50	UP	NO	NO
Luxembourg	3.21	4.63	5.41	6.19	1.44	1.69	1.93	UP	NO	NO
Hungary	2.43	6.03	7.66	9.28	2.48	3.15	3.82	UP	NO	NO
Malta	0.25	0.62	0.46	0.52	2.48	1.84	2.07	UP	NO	NO
Netherlands	2.67	3.95	5.03	6.11	1.48	1.88	2.29	UP	NO	NO
Austria	20.30	25.69	29.03	32.37	1.27	1.43	1.59	UP	YES	YES
Poland	4.03	3.52	4.63	5.12	0.87	1.15	1.27	UP	NO	NO
Portugal	6.53	8.05	8.30	9.18	1.23	1.27	1.41	UP	NO	NO
Romania	1.77	3.45	6.30	9.15	1.95	3.56	5.17	UP	NO	NO
Slovenia	8.85	10.29	11.98	13.67	1.16	1.35	1.54	UP	NO	NO
Slovakia	9.47	11.67	12.58	14.14	1.23	1.33	1.49	UP	NO	NO
Finland	9.91	13.93	16.75	19.57	1.41	1.69	1.97	UP	YES	YES
Sweden	17.14	20.31	20.13	19.95	1.18	1.17	1.16	UP	YES	YES

Source: Eurostat, own calculations.

,

Table 10. SDG 2.52—use of more hazardous pesticides (index, 2015–2017 average = 100).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Conv. 2025	Conv. 2030
EU-27	103	74	34	min.	0.72	0.33	min.	DOWN	-	-
Belgium	107	58	min.	min.	0.54	min.	min.	DOWN	YES	YES
Bulgaria	57	122	170	207	2.14	2.98	3.63	UP	NO	NO
Czech Republic	108	77	55	33	0.71	0.51	0.30	DOWN	NO	NO
Denmark	96	101	49	15	1.05	0.51	0.16	DOWN	NO	NO
Germany	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Estonia	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Ireland	113	72	58	44	0.64	0.52	0.39	DOWN	NO	NO
Greece	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Spain	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
France	106	76	70	57	0.72	0.66	0.54	DOWN	NO	NO
Croatia	106	77	84	78	0.73	0.79	0.74	DOWN	NO	NO
Italy	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Cyprus	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Latvia	91	134	182	227	1.47	2.00	2.49	UP	NO	NO
Lithuania	90	68	11	min.	0.76	0.12	min.	DOWN	YES	YES
Luxembourg	132	58	7	min.	0.44	0.05	min.	DOWN	YES	YES
Hungary	98	90	103	111	0.92	1.05	1.13	UP	NO	NO
Malta	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Netherlands	97	81	min.	min.	0.84	min.	min.	DOWN	YES	YES
Austria	99	86	63	39	0.87	0.63	0.39	DOWN	NO	NO
Poland	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Portugal	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Romania	88	86	126	147	0.98	1.44	1.67	UP	NO	NO
Slovenia	91	86	117	132	0.95	1.29	1.45	UP	NO	NO
Slovakia	94	91	119	133	0.97	1.27	1.41	UP	NO	NO
Finland	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Sweden	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

Source: Eurostat, own calculations. min. denotes a zero or negative value.

,

Table 11. SDG 2.60—ammonia emissions from agriculture (kg/ha).

Countries	2015	2019	2025	2030	2019/2015	2025/2015	2030/2015	Trend	Conv. 2025	Conv. 2030
EU-27	20.4	19.7	16.4	13.6	0.97	0.80	0.67	DOWN	-	-
Belgium	48.1	44.5	41.7	39.3	0.93	0.87	0.82	DOWN	NO	NO
Bulgaria	7.4	7.2	7.4	7.5	0.97	1.01	1.02	UP	YES	YES
Czech Republic	28.6	21.8	22.2	21.4	0.76	0.78	0.75	DOWN	NO	NO
Denmark	24.4	24.7	23.6	22.6	1.01	0.97	0.93	DOWN	NO	NO
Germany	32.7	29.9	24.2	19.5	0.91	0.74	0.60	DOWN	NO	NO
Estonia	9.2	9.3	8.6	8.2	1.01	0.94	0.89	DOWN	YES	YES
Ireland	26.6	27.4	30.3	32.0	1.03	1.14	1.20	UP	NO	NO
Greece	10.8	11.1	12.3	13.3	1.03	1.14	1.24	UP	YES	YES
Spain	18.0	18.4	19.3	20.0	1.02	1.07	1.11	UP	NO	NO
France	19.6	18.8	19.6	19.8	0.96	1.00	1.01	UP	NO	NO
Croatia	20.0	21.0	15.3	10.5	1.05	0.76	0.52	DOWN	YES	YES
Italy	26.4	24.7	24.9	24.2	0.94	0.94	0.92	DOWN	NO	NO
Cyprus	61.6	51.9	38.2	23.3	0.84	0.62	0.38	DOWN	NO	NO
Latvia	7.7	7.7	8.3	8.8	1.00	1.08	1.14	UP	YES	YES
Lithuania	11.5	10.8	10.2	9.5	0.94	0.88	0.83	DOWN	YES	YES
Luxembourg	38.1	38.4	38.8	39.1	1.01	1.02	1.03	UP	NO	NO
Hungary	13.2	13.7	14.7	15.7	1.04	1.12	1.19	UP	YES	NO
Malta	110.1	105.3	112.7	118.9	0.96	1.02	1.08	UP	NO	NO
Netherlands	62.9	58.9	39.1	22.6	0.94	0.62	0.36	DOWN	NO	NO
Austria	21.5	22.0	24.5	26.5	1.02	1.14	1.23	UP	NO	NO
Poland	19.8	20.6	20.6	20.8	1.04	1.04	1.05	UP	NO	NO
Portugal	12.8	12.0	12.4	12.2	0.94	0.97	0.95	DOWN	YES	YES
Romania	12.2	11.5	10.2	9.1	0.94	0.84	0.75	DOWN	YES	YES
Slovenia	35.3	34.5	33.6	32.8	0.98	0.95	0.93	DOWN	NO	NO
Slovakia	13.5	14.8	15.6	16.4	1.10	1.16	1.21	UP	YES	NO
Finland	13.2	12.4	11.3	10.3	0.94	0.85	0.78	DOWN	YES	YES
Sweden	15.3	15.0	15.0	14.9	0.98	0.98	0.98	DOWN	NO	YES

Source: Eurostat, own calculations.

and

Table 12. SDG 6.40—nitrate in groundwater (mg/L).

Countries	2015	2019	2025	2030	2019/2015	2025/2015	2030/2015	Trend	Conv. 2025	Conv. 2030
EU-27	21.20	20.65	21.48	21.71	0.97	1.01	1.02	UP	-	-
Belgium	27.90	28.25	26.43	25.05	1.01	0.95	0.90	DOWN	NO	NO
Bulgaria	31.26	29.75	33.47	35.74	0.95	1.07	1.14	UP	NO	NO
Czech Republic	18.15	17.67	16.80	15.94	0.97	0.93	0.88	DOWN	YES	YES
Denmark	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Germany	26.89	26.28	28.04	29.51	0.98	1.04	1.10	UP	NO	NO
Estonia	4.52	5.02	5.19	5.43	1.11	1.15	1.20	UP	YES	YES
Ireland	14.21	14.42	12.74	12.50	1.01	0.90	0.88	DOWN	YES	YES
Greece	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Spain	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
France	18.17	18.21	18.48	18.45	1.00	1.02	1.02	UP	YES	YES
Croatia	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Italy	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Cyprus	70.48	48.84	80.29	92.11	0.69	1.14	1.31	UP	NO	NO
Latvia	4.16	4.69	5.03	5.29	1.13	1.21	1.27	UP	YES	YES
Lithuania	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Luxembourg	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Hungary	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Malta	59.85	59.43	64.02	66.70	0.99	1.07	1.11	UP	NO	NO
Netherlands	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Austria	23.62	21.80	19.62	17.83	0.92	0.83	0.75	DOWN	YES	YES
Poland	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Portugal	16.76	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Romania	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Slovenia	19.50	16.19	15.49	14.41	0.83	0.79	0.74	DOWN	YES	YES
Slovakia	12.84	12.58	12.52	11.93	0.98	0.98	0.93	DOWN	YES	YES
Finland	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Sweden	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

Source: Eurostat, own calculations.

).

The agricultural factor income per annual work unit (AWU) is used to monitor progress towards achieving SDG 2, which is one of the European Commission’s priorities under the European Green Deal initiative. Improving agricultural productivity and food producer incomes are key factors in sustaining economic development and ensuring food security, objectives that have become increasingly important in recent years.

Income from agricultural production is more volatile than in other economic sectors, mainly due to the volatility of agricultural prices and the risks arising from climate change. One of the five major objectives of the Common Agricultural Policy (CAP) is to ensure a fair standard of living for farmers. Through the specific financial framework of the CAP, EU subsidies and direct payments have become an important part of farmers’ incomes.

Based on the data processed, it is possible to observe a division of the Member States compared to the European average, which is increasing. Thus, 16 countries have registered an increasing trend, and this trend is forecasted to continue until 2030. Of these, however, only 12 countries are forecasted to remain higher than the EU average by 2030 (Bulgaria, Czech Republic, Ireland, Greece, Croatia, Cyprus, Latvia, Hungary, Poland, Portugalia, Romania, and Slovakia). The analysis also reveals that for seven EU countries (Belgium, Denmark, Estonia, Lithuania, Malta, Austria, and Finland), the agricultural factor income per annual work unit is forecast to be on a downward trend, while for four EU countries (Germany, Italy, Netherlands, and Sweden), the trend estimated on the basis of existing data is uncertain or stationary (Table 7).

In general, for the 27 countries under analysis, the estimated trend is maintained over the whole time horizon, except for two countries, Lithuania and Greece. Thus, in the year 2021, Lithuania registers a value of the agricultural factor income per annual work unit above the EU average, but the forecasted downward trend includes it in the group of European countries with values below the European average. Greece, on the other hand, in 2021 records a value of agricultural factor income per annual work unit below the European average, but the forecast until 2030 places it in the group of countries with values above the EU average.

From the interpretation of the results of the analysis, it can be seen that the concentration of countries with a higher agricultural factor income per annual work unit than the EU average coincides with a clustering of countries in terms of their level of economic performance. In other words, lower-performing countries from an economic point of view correspond to higher values of the agricultural factor income per annual work unit and vice versa for higher-performing countries. The analysis of the data not only shows that this trend will continue until 2030 but also suggests that the current differences will increase.

A second indicator relevant to the research carried out is government support for agricultural R&D. There are numerous initiatives at the European Commission level to support agricultural research, and all stakeholders are interested in supporting these initiatives.

The European Commission is keen to encourage and provide support for agricultural research; to make food production more sustainable and less dependent on pesticides. The EU is also reviewing its policy on genetically modified organisms (GMOs) in a bid to catch up with the rest of the world and allow the use of the latest gene-editing technologies for precision plant breeding. In addition, €10 billion from the Horizon Europe program is earmarked to fund agricultural research.

However, the analysis of the available data indicates a significant gap between the Member States, in the sense that in 2021 only 9 countries out of the 27 examined reported for this indicator a value higher or equal to the EU average (Denmark, Ireland, Germany, Netherlands, Finland, Spain, Sweden, Estonia, and Cyprus), the remaining 18 countries were below the European average. By contrast, for the 2030 horizon, forecasts indicate a reduction in the number of countries with a value of government support for agricultural R&D per capita above the EU average to only four (Denmark, Germany, Ireland, and Spain) (Table 8).

As regards the trend in the evolution of the values of the indicator considered, the results of the survey indicate that for 19 countries, an upward trend is expected to be recorded until 2030, i.e., the efforts of the European Commission are expected to materialize and contribute to the development of R&D activity. However, there are also seven EU countries (Ireland, Finland, Cyprus, Greece, Portugal, Romania, and Luxembourg) for which a reduction in government support to agricultural R&D is estimated by 2030, which should give the responsible stakeholders food for thought so that they can adopt corrective measures and support in time, in order to prevent the occurrence of significant chronic gaps with the other Member States.

The third indicator investigated in this research, the area under organic farming, is not explicitly mentioned in the 2030 Agenda, but can be considered as part of the overall indicator of SDG 2.4.1 “Proportion of productive and sustainable agricultural area”. The EU sees organic farming as a production method that places the greatest emphasis on environmental protection and, as far as animal production is concerned, on animal welfare considerations, and is an integral part of a sustainable agricultural system. This is also the reason why Eurostat provides data on the area under organic farming at the EU level.

The EU’s “Farm-to-Fork” strategy for sustainable food is a key component of the European Green Pact, aiming to reduce pesticide use, reduce nutrient losses, and reduce the use of fertilizers and antibiotics. The Farm-to-Fork Strategy sets a target of achieving 25% of all EU farmland under organic farming by 2030.

From the analysis of the collected data, some relevant conclusions can be drawn. The first significant conclusion is that all 27 Member States are expected to increase the area allocated to sustainable agriculture by 2030, with forecasts indicating an upward trend for this indicator (Table 9).

A second conclusion refers to the fact that in 2020, 13 Member States had an area under organic farming higher than the EU average, while the remaining 14 European countries had an area under organic farming lower than the EU average (9.08%). However, by 2030, the forecast analyses indicate a reduction in the number of countries with an area under organic farming above the EU average from 13 to 10, with Slovakia, Slovenia, Greece, and Spain falling below the average and France rising above the average.

A third conclusion of the analysis of the area under organic farming, through the Farm-to-Fork Strategy, is that in 2020, only one country will reach the proposed target of at least 25% (Austria, 25.69%), while in 2030, only two countries are projected to reach this threshold (Estonia, 32.96%, and Austria, 32.37%). It is also worth noting that the EU average is far from the proposed target, both in 2020 (9.08%) and in the forecast for 2030 (14.98%). It is clear that strong and sustained measures are needed to increase the pace of the adoption of sustainable agriculture and accelerate the conversion of the area under organic farming.

The fifth indicator used in our research refers to the use of more hazardous pesticides, which is considered a key component of the “European Green Deal”. In addition, the EU “Farm-to-Fork Strategy” for sustainable food foresees a 50% reduction in the use of chemical pesticides and the use of more hazardous pesticides by 2030.

From the preliminary analysis of the data available, it can be seen that a significant number of Member States (11 out of 27 countries, representing 40.7%) do not provide information on the use of more hazardous pesticides. Of the 16 European countries that reported data for this indicator for the year 2020, 12 countries recorded higher values of the use of hazardous pesticides, and only four countries (Ireland, Lithuania, Belgium, and Luxembourg) recorded lower values than the EU average (Table 10).

By 2030, the results of the forecast model indicate that, if the trend of the values recorded up to 2020 is followed, only four countries out of the 16 with reported data (i.e., the Netherlands, Lithuania, Belgium, and Luxembourg) will reach a minimum of the more hazardous pesticides use index. Of the twelve countries projected to record values above the EU average, six countries are expected to follow a downward trend in the period 2021–2030 (Croatia, France, Ireland, Austria, Czech Republic, and Denmark), while, for the remaining six EU countries, an upward trend in the use of the more hazardous pesticides index is expected (Latvia, Bulgaria, Romania, Slovakia, Slovenia, and Hungary).

Undoubtedly, in the case of this indicator, urgent measures need to be taken, first to provide access to data on the use of more hazardous pesticides. Secondly, there is a need for the greater involvement of decision-makers and stakeholders in order to reduce the use of these hazardous pesticides at an accelerated pace, given the particularly negative impact on the health and well-being of the population and the harmful effects on the environment.

Ammonia is naturally present in essential biological processes and is not a problem if it exists in low concentrations. However, the volatilization of ammonia into the atmosphere has negative consequences for agriculture, ecosystems, and human health. Ammonia emissions can lead to an increase in acid deposition and nutrients in soil or water, with a strong negative impact on ecosystems (Table 11). Ammonia, in combination with other air pollutants (such as sulfuric particles), contributes to respiratory diseases. Ammonia pollution from agriculture represents a high cost to society. According to the European nitrogen assessment, €12 per kg of nitrogen emitted for damage to health and €2 for damage to ecosystems is estimated [62].

At the EU level, measures implemented under the Common Agricultural Policy (CAP) have focused on reducing ammonia emissions from agriculture in the EU since the 1990s. The National Emission reduction Commitments Directive (NEC Directive) sets national emission reduction commitments for Member States for five important air pollutants, including ammonia.

Our research provides key information on the evolution of ammonia emissions from agriculture between 2007 and 2019, as well as the forecast of the evolution of this indicator for Member States until 2030. Thus, in 2019 (respectively, the most recent year for which data are published), out of the 27 Member States, 13 reported a level of ammonia emissions from agriculture below the EU average (19.7 kg/hectare), and 14 EU countries reported a level above the European average. Moreover, in the same year, 2019, the dispersion of values of this indicator at the EU level ranged from 7.2 kg/hectare in Bulgaria to 105.3 kg/hectare in Malta.

Regarding the forecast of the evolution of ammonia emissions from agriculture for the 2030 horizon, the research results suggest that 15 of the European countries included in the analysis will register a downward trend, but the remaining 12 countries will register an upward trend. Based on these estimates, we can say that in 2030 only nine Member States will reach ammonia emissions from agriculture values lower than the EU average, while for the remaining countries, higher values are estimated.

It is obvious that even in the case of this indicator, which has a significant impact on human health and the environment, more attention is needed, as well as the adoption of firm measures to correct the negative deviations from the European average and to change the projected upward trend into a downward trend of ammonia emissions from agriculture.

Regarding the sixth indicator included in our analysis, the amount of nitrates in groundwater, the Water Framework Directive is the main European legislation aimed at preventing water pollution (Table 12). The EU Biodiversity Strategy 2030 supports the implementation of the Water Framework Directive target, calling on Member States to restore freshwater ecosystems [63].

At the same time, at the EU level, under the Drinking Water Directive, a maximum concentration of 50 mg/L of nitrates in groundwater used for drinking water is accepted. The Nitrates Directive requires the designation of vulnerable zones based on this threshold for all waters, including groundwater [64].

As for the indicator regarding the use of more hazardous pesticides, a limited number of EU countries have available data on nitrates in groundwater, with only 13 of the 27 European countries included in the analysis publishing relevant information. Of the 13 countries that published data in 2019, half were below the European average and half above the EU average value. Only one country, Malta, reported in 2019 a level of 59.43 mg/L of nitrates in groundwater, above the limit of 50 mg/L set by the Drinking Water Directive.

A worrying result based on the research is the estimated trend of this indicator until 2030. Thus, of the thirteen Member States that provided public data on the level of nitrates in groundwater, six countries are forecasted to register a decreasing trend of values, but for the remaining seven European countries, the results of our research indicate a downward trend, and for Cyprus and Malta the limit of 50 mg/L is forecasted to be exceeded.

It is imperative that policymakers take note of these negative trends and act immediately to limit the amounts of nitrates in groundwater in all EU countries. In the case of countries for which an increasing trend is estimated, increased attention is needed to eliminate the factors that may favor this evolution and to identify concrete measures to reverse the estimated trend.

Also in relation to this indicator, it should be mentioned that the proposed 8th Environmental Action Programme at the EU level sets the environmental policy agenda for the years 2021–2030 and defines the six priority objectives, of which two objectives are directly related to water. Thus, one priority objective refers to reducing pollution to zero for air, water, and soil, and the second priority objective refers to protecting, conserving, and restoring biodiversity and enhancing natural capital. It can thus be seen that environmental protection and the promotion of sustainable agriculture are of particular importance in the European Union [65].

Critical factors such as food production, increasing demand, rising food prices, and supply chain security, coupled with the struggle to conserve natural resources and achieve Sustainable Development Goals, have led the EU to step up efforts to adopt sustainable agricultural practices.

However, there is some progress at the EU Member State level that is mainly driven by the use of digital tools, mainly artificial intelligence in the agri-food sector, which is also a promising tool to support cost-effective solutions for a green ecosystem and achieve the Sustainable Development Goals of SDG 2.

Our findings thus highlight the importance of sustainable agriculture even in developing countries, where more policy support programs are expected to encourage farmers to adopt sustainable agricultural practices.

Nevertheless, due to the nature of the agriculture and food industry and the difficult times the EU is currently going through, production management, storage, transport, waste disposal, and the environmental effects of waste production are sustainability issues that the EU will need to address in the 2030s.

It is also worth noting that increasing sustainable agricultural production and investment in specific infrastructure, which implies reducing environmental degradation in EU member countries, is one of the strategies identified, but one that is subject to much debate due to the characteristics of these countries, as well as their agrarian nature and their excessive dependence on natural resources in particular. Therefore, we believe that the indicators highlighted are directly or indirectly related to the environment and contribute indisputably to informing long-term decisions at the European policy level.

Food production, consumption, and the recycling of food waste in EU countries are inseparable parts of the circular economy from the production stage, which is why food security strategies make EU agricultural policy an inseparable part of the sustainable growth of national economies.

The implications of the results of this research for stakeholders, both theoretical and practical, are profound and can contribute significantly to increasing the potential for achieving the SDG 2 targets. The breadth of the analysis, both in scope and time horizon, provides valuable information for stakeholders, who can adapt their actions and decisions to correct potential negative trends highlighted by the research results and also sustain or amplify positive effects.

Firstly, given that improving agricultural productivity is a key factor in sustaining economic development and ensuring food security is a key priority at the EU level, it is important for decision-makers to be aware of the projected results. The investments made so far, as part of the Common Agricultural Policy, have proven to contribute substantially to increasing food security, but the results obtained highlight the need for support to improve this indicator for a number of Member States, in particular for those for which a negative AWU trend is estimated.

The present study also provides important information on the impact of agricultural activity on human health and the environment, the results suggesting that more attention is needed, as well as the adoption of firm measures to correct the negative deviations in terms of reducing the use of more hazardous pesticides and forecasting the evolution of ammonia emissions from agriculture and nitrate in groundwater. Particular importance is given at the EU level to reducing pollution and increasing health and living standards for European citizens. The results of our research suggest, in most cases, that the indicators analyzed are on a positive trend for the coming years, but also highlight a number of potential negative deviations, which may have a significant negative impact on the environment and inhabitants. We strongly support the adoption of measures to halt phenomena with potential negative effects on the environment and the adoption of measures to support agricultural producers for a faster transition to sustainable agriculture through the adoption of specific techniques or the use of environmentally friendly products.

5. Conclusions

The importance of sustainable agriculture in EU countries, and the continuing concerns expressed by all decision-makers at the EU level, are self-evident. However, as the results of our research indicate, there are many discrepancies between Member States in terms of the current state of development and the potential to achieve the specific targets of SDG 2 by 2030.

Given the discrepancies between European countries in terms of the sustainable development of agriculture, as well as the growing importance of this extremely important sector, our research aims to provide insight into the potential evolution of the main relevant indicators until 2030. The results obtained fill a knowledge gap and will certainly support all stakeholders in making the most appropriate decisions.

The research started by modeling the data available from Eurostat in terms of the level of specific indicators for SDG 2. The results obtained indicate a number of positive developments towards 2030 but also a number of potential negative developments, with significant risks for people and the environment.

Based on the two research questions (RQs), we can state that the results obtained provide consistent answers, providing access to a series of meaningful results for the scientific community and useful information for policymakers, thus opening up potential new research directions, the better adaptation of public policies, or better support for targeted actions to correct potential negative developments.

In relation to RQ1, the results of the present research indicate the existence of a relatively high potential for meeting the proposed targets for the specific SDG 2 indicators but, at the same time, also highlight the potential slippages that may manifest themselves in the next interval, until 2030. If these negative deviations are not corrected in time, the likelihood that the respective Member States will reach the targets proposed for 2030 will be considerably reduced.

Furthermore, as regards the identification of high-performing countries in terms of reaching the SDG 2 targets (RQ2), the research results indicate that the Member States with a clear advantage over the rest of the European countries in terms of the evolution of specific indicators of sustainable agriculture cannot be designated. Through the analysis, we were able to highlight the performance of the countries for each indicator, both in terms of potential positive and negative developments, providing support for the designation of net winners or losers. Efforts at both the European Commission and Member State levels to support sustainable agriculture and the implementation of dedicated strategies and policies are to be appreciated, and the research results show the effects of these measures and progress towards the 2030 horizon.

Thus, as regards the percentage of area under organic farming, the research results suggest a unanimously positive evolution, with all 27 Member States showing constant concern for the continuous improvement of the existing situation and the increase in these areas dedicated to sustainable agriculture.

The research also indicates a certain degree of clustering of EU countries in terms of current and projected values for the indicators of agricultural factor income per annual work unit and ammonia emissions from agriculture. The results suggest, for each indicator, the creation of two groups of countries: high performers and countries that present a number of challenges in reaching a certain level of performance. In order to mitigate these differences, policymakers need to intervene by adopting concrete support measures to reduce the existing differences and converge towards a higher level of performance, as is the case in the extreme variations between EU countries in the case of ammonia emissions from agriculture.

Therefore, a combination of appropriate policies and technologies should be adopted that can be addressed by all EU Member States and, in particular, by the less-developed economies, such as the introduction of an emissions tax, a total ban on solid urea, the establishment of conservation areas, the provision of incentives for suppliers of sustainable commodities, and improving private sector participation in sustainable agriculture supply chains.

A third conclusion of the research is that there is a significant concentration of government support to agricultural R&D, with only four European countries (Denmark, Germany, Ireland, and Spain) expected to be above the EU average (9.7 Euro/inhabitant) by 2030, and the remaining 23 countries surveyed below this average. Moreover, in 2021, the gap between the highest (Denmark) and lowest (Poland) values of this indicator is 23.8 to 0.1 Euro/inhabitant, and by 2030, it is estimated that this gap will remain at approximately the same ratio. These aspects also require an in-depth analysis to identify the best solutions to reduce the development gaps between European countries, in order to lead to a harmonious and balanced development of sustainable agriculture and of the whole society at the EU level.

The results of this research should also be seen in light of the inherent limitations specific to this type of predictive analysis. A lack of or inconsistencies in available data, forecasting errors in the model used, or political, economic, or social factors may influence the results obtained, as well as future developments.

However, we hope that the results we have obtained can add to the knowledge gap on the development of sustainable agriculture in EU countries and the potential achievement of SDG 2’s specific targets. The information provided in this way can inform a number of actions or decisions of the parties involved and, at the same time, we hope that it will open up new research directions to deepen certain aspects raised, thus contributing in an important way to the promotion of organic agriculture and sustainable development. Achieving the targets set by the 2030 Agenda for Sustainable Development is becoming a struggle against time, and the negative effects of moving away from these targets will manifest themselves not only in our generation but also in future generations.