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Article

Reducing Water Resource Pressure and Determining Gross Nitrogen Balance of Agricultural Land in the European Union

by
Wiktor Halecki
,
Konrad Kalarus
*,
Agnieszka Kowalczyk
,
Tomasz Garbowski
,
Justyna Chudziak
and
Beata Grabowska-Polanowska
Institute of Technology and Life Sciences—National Research Institute, Falenty, Al. Hrabska 3, 05-090 Raszyn, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(16), 9216; https://doi.org/10.3390/app15169216 (registering DOI)
Submission received: 15 May 2025 / Revised: 17 July 2025 / Accepted: 19 August 2025 / Published: 21 August 2025
(This article belongs to the Special Issue Plant Management and Soil Improvement in Specialty Crop Production—2nd Edition)
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Abstract

The evaluation of crop production that influences surface and groundwater quality is of growing importance in the context of agricultural sustainability in Europe. The primary aim of this study was to understand the relationship between gross nitrogen surplus in land and nitrate concentrations in surface and groundwater. The analysis was based on datasets collected from 2010 to 2021. Nitrate levels were categorized into three distinct quality classes based on the percentage of monitoring points, reflecting a spectrum from high quality, defined as nitrate levels below 25 mg/dm3, to poor quality, characterized by levels exceeding 50 mg/dm3. Redundancy analysis indicated that Gross Nitrogen Balance, a fertilizer use predictor, partially influences water quality, potentially due to long-term effects. Model selection for Gross Nitrogen Balance based on the AICc information criterion identified catch crops (or green cover), high-intensity agriculture, Natura 2000 sites, nitrogen-fixing plants, organic farming, fast-growing tree plantations, and EU27 states as predictors in the group of supported models. The best-fit model revealed differences between EU27 states for Gross Nitrogen Balance. Catch crops and Natura 2000 sites were also significant predictors, the former associated with a positive and the latter with a negative effect on nitrogen balance. In turn, WEI+ increased with nitrogen balance input but decreased with organic farming, indicating that promoting organic practices could help save water resources. Poland emerged as a country with relatively good water quality compared to several European counterparts, such as Denmark, Belgium, Malta, Czechia, Germany, and Lithuania. The implications of this research extend significantly to evaluation of the effects of the Common Agricultural Policy within the European Union.

1. Introduction

The Common Agricultural Policy (CAP), as the European Union’s (EU) first socio-economic policy, has undergone numerous changes over the years [1]. The current objectives of the CAP include ensuring food security for EU countries, providing fair incomes for farmers, guaranteeing the sustainable development of agriculture and rural areas, and enhancing the competitiveness of agricultural holdings, all while considering environmental protection and landscape values [2]. A significant challenge within this framework is reducing the runoff of nutrients from fields into watercourses and their infiltration into groundwater in lower aquifer layers [3]. A solution involves implementing specific measures, particularly the use of nutrient balances. The gross balance assesses the total nutrient potential in the soil, which is vital for effective fertilization planning. Furthermore, the net balance is crucial as it accounts for nutrient losses, enabling precise and efficient fertilizer use while minimizing negative environmental impacts [4]. From a political assessment standpoint, there is a clear need to calculate the Gross Nitrogen Surplus (GNS). The GNS represents the difference between nitrogen inputs and outputs, measured in kg N per hectare per year. This calculation is important because surpluses indicate environmental risks, especially ammonia emissions, nitrate leaching, or nitrous oxide emissions, while deficits can negatively impact soil fertility. The GNS, which includes nitrogen emissions to both air and water, helps to assess potential nitrate pollution in water bodies [5]. The factors considered in these balance calculations should quantify the nutrients taken up by crops, fodder, and plant residues removed from the field [6].
Concurrently, in the political approach, the section specifying supply factors must meticulously account for all nitrogen delivered to the soil [7]. To maintain soil fertility, the removal of nutrients should be compensated by supplying an equivalent amount. However, not all nitrogen from fertilizers and manure reaches plants; some is inevitably lost [8]. Moreover, yields depend on numerous factors, including weather conditions [9]. More importantly, the risk of nitrogen leaching and runoff varies significantly based on soil type, rainfall, soil saturation, and temperature [10]. One study demonstrated that the Gross Nitrogen Balance indicator effectively showed the relationship between agricultural activity and environmental impact, and helped identify factors determining nitrogen surplus. Therefore, there is a need for nutrient balance analysis, which involves calculating the net balance of nutrients such as nitrogen and phosphorus [11]. The nutrient balance assesses nutrient inputs to the soil (e.g., through fertilizers, sewage sludge, compost) and their losses (e.g., through leaching, erosion, and gas emissions). Another aspect is that net content refers to the amount of nutrients available to plants after accounting for losses like nitrogen volatilization or phosphorus leaching [12]. The net nitrogen balance is calculated by subtracting nitrogen emissions from the Gross Nitrogen Balance (GNB). To accurately measure pollution, the balance per hectare of agricultural land should also be presented. Total nutrient inputs and outputs for the balance (excluding mineral fertilizers) are calculated by multiplying a coefficient by a factor representing the nutrient impact (e.g., nutrient content in kg N or P per ton, or excretion coefficient in kg N or P per head). The coefficients encompass the consumption of inorganic fertilizers and other organic fertilizers (excluding manure) (in tons); livestock population (in 1000 heads); import, export, and manure withdrawal (in tons); crop and fodder production (in tons); plant residues removed from the field (in tons); and the utilized agricultural area [13]. Finally, nitrogen inputs and outputs are estimated for each balance element based on basic data, multiplied by conversion factors for nitrogen content [14]. It is also important to note that the basic data (e.g., fertilizer consumption, livestock numbers, crop production, agricultural area) primarily come from agricultural statistics [15]. To summarize, the coefficients are estimated by research institutes and can be based on models, statistical data, measurements, and expert opinions [16].
Climatic conditions affect the balance through their impact on yields and therefore on nitrogen output [17]. Climate and weather are beyond the farmer’s control [18]. Managing nutrient balances in agriculture is crucial for sustainable development. Therefore, farmers should apply appropriate fertilization practices that minimize nutrient losses and protect water quality [19]. Research has often neglected the comprehensive examination of Gross N budgets related to nitrogen leaching into surface waters, a key factor in protecting aquatic ecosystems in the EU [20,21]. Direct and indirect effects in sustainable management of natural resources can be divided into two categories that differ in terms of occurrence and impact on the environment [22]. Direct effects appear immediately as a result of actions taken, and their impact is felt in the short term in areas such as agriculture, soil, water, and other resources [23,24]. Direct effects include improving nutrient balances, which leads to a quick improvement in soil quality, reducing nutrient leakage, and reducing their leaching to groundwater [25]. Sustainable water management can also yield expected results, as more efficient water use in irrigation systems reduces pressure on water resources and improves crop water efficiency [26]. In contrast, indirect effects are the long-term consequences of actions that appear after some time and have a broader impact on ecosystems, society, the economy, or climate. An example of such effects is water quality protection, where long-term reduction in nutrient leaching contributes to the improvement of the state of aquatic ecosystems, reduction of eutrophication, and restoration of these agroecosystems [27,28,29,30].
Holistic management of natural resources is crucial for sustainability, as it considers both immediate and long-term benefits [31,32,33,34]. This approach supports agricultural sustainability, improves field conditions, and integrates modern technologies for both conservation and efficient resource use [35,36,37]. Additionally, it aligns with and promotes the principles of the circular economy [38]. One aspect of sustainable agriculture is the long-term use of fertilizers, which significantly impacts soil biological activity, particularly the enzymes responsible for nutrient cycling [39]. Changes in nitrogen fertilizer management can also influence water quality across entire watersheds, leading to improved environmental outcomes when implemented correctly [40]. A promising strategy for enhanced nutrient management is the use of controlled-release fertilizers. These fertilizers gradually release nutrients, minimizing losses and improving efficiency within agricultural systems [41].
This study evaluates nutrient management to identify areas for intervention and promote agricultural practices that minimize environmental impact. A knowledge gap exists in analyzing annual and multi-year data to detect trends related to buffer zones, crop rotation, and optimized fertilizer use, which is critical for protecting water quality and availability. The objectives of our research were to (1) assess gross nitrogen balances in European Union countries, (2) verify the effectiveness of actions to reduce nitrogen leaching into surface and groundwater, (3) compare agricultural methods using gross nitrogen indicators to select optimal practices, and (4) determine the extent to which the Water Exploitation Index Plus (WEI+) reflects agricultural water use.

2. Materials and Methods

2.1. Statistical Analysis: Data on Surface Water Quality and Fertilizer Management

To determine whether the nitrate content in groundwater differs from that in surface water and whether the Gross Nitrogen Surplus (Gross N) modifies nitrate content in water, a redundancy analysis (RDA) was performed [42]. The nitrate content in water is expressed as a percentage of monitoring points for a given country in three categories: high-quality water (<25.0 mg/dm3), medium quality (≥25 and <50 mg/dm3), and poor quality (≥50 mg/dm3) (categories according to the Nitrates Directive (91/676/EEC, 1991 [43])). The nitrate content in the water considered in this analysis is the average from 2019 to 2021, while the Gross N balance was given for European countries as the average from 2016 to 2019. The three-year average for water quality was based on later years, while the four-year average for Gross N also included earlier years. This is because the effects of Gross N may not have been fully revealed if we had measured water quality when pollutants were only just being introduced into the environment. The three-year average for water quality was based on later years, while the four-year average for Gross N also included earlier years. This is the correct approach, since the effects of Gross N may not be fully revealed if we measure water quality when pollutants are only just being introduced into the environment. The four-year average of Gross N allows us to capture its impact over the long term, which is especially important in the case of groundwater, which can respond with a delay to introduced contaminants.
The influence of Gross N and the existence of a difference between surface and groundwater in nitrate content was checked using permutation tests by performing 999 permutations [42]. Mahalanobis distance was used as a measure of dissimilarity (difference); it handles highly correlated data, such as the percentage share in categories [44]. Using general linear models (GLM), factors influencing the water exploitation index (WEI+), Gross Nitrogen Surplus (Gross N), high quality of surface water (nitrate content <25.0 mg/dm3), and high quality of groundwater (nitrate content <25.0 mg/dm3) were tested. The mentioned variables are dependent variables. The initial selection of factors (i.e., predictors), which are independent variables, was limited by the quality of available data and aimed to focus on the most important factors for evaluating the effectiveness of CAP measures and to eliminate spurious dependencies (e.g., water is not used in agriculture in Natura 2000 areas). The Water Exploitation Index (WEI) is a basic measure, calculating gross freshwater abstraction against long-term average renewable resources. The Water Exploitation Index Plus (WEI+) is an advanced version that provides a more accurate picture by measuring net water consumption (accounting for returns) as a percentage of available renewable resources, better reflecting actual water scarcity.
For each dependent variable, a global model containing all included predictors was calculated. Then, a model selection procedure was carried out according to the Akaike information criterion, corrected for small samples (AICc) [45]. The information criterion allows for the selection of the model that best describes the data. For seven predictors, it is already possible to construct 128 models, and for 10 predictors, as many as 1024 models. Models with a delta AICc < 2, which describes how subsequent models differ in terms of explaining the data from the best model, are considered comparably good models [45]. As a result of the selection procedure, the best model was chosen, which was finally calculated and its results provided. Such an approach is particularly important in practice when clear recommendations for the CAP are needed, especially when the best model is the most parsimonious one.
Testing of selected predictors in one analysis was possible due to the fact that these variables were mostly no more than moderately correlated (<0.5) (Table A1) [46]. This allows them to be tested together in the same model. Variance Inflation Factor (VIF) analysis resulted in findings consistent with the results from the correlation analysis (Table A1). In all the global models considered, almost all variables had VIF values less than 5, with many even having values below 2. One or two variables had a VIF greater than 5; however, these specific variables were not included in the set of supported models. An exception is the variable catch crop, which was part of the best model for predicting surface water quality. However, it does not occur together with variables to which it shows a high correlation (see Table A1). Recalculation of the VIF of the best model revealed a VIF of 2.48 for this particular variable. The adjusted Generalized VIF (GVIF) values for the categorical factors country and year were between 1.09 and 1.42 and between 1.02 and 1.47, respectively. These results indicate that multicollinearity is not a problem in our analyses [46]. With regard to heteroscedasticity, Levene’s test indicated minor violations of the homoscedasticity assumption in a case involving the categorical variable representing EU27 states. However, these deviations were not considered to meaningfully affect model validity. The between-country differences in the dependent variable were substantial and substantively meaningful, supporting the inclusion of this variable despite limited variance heterogeneity. Moreover, as GLMs accommodate non-constant variance structures through the specification of appropriate distributions and link functions, such deviations are unlikely to bias parameter estimation [46].

2.2. Data Management

In the entire dataset, 35.4% of the data was missing (Figure A1), which constitutes a generally moderate level of gaps. On the other hand, gaps for individual variables ranged from 1% to 78%. Gaps for forested areas exceeded 60%, forcing the exclusion of this variable from the analysis. Similarly, the level of gaps for nitrate content in groundwater and surface water was over 70%, making it impossible to perform a full dataset analysis. The analysis was performed for the years for which data were available. Only for this range of years were a few gaps filled with imputed values. The final results of the analysis for nitrate content in groundwater and surface water were based on a small dataset, partially imputed. These results should be treated in light of this and as indicative. Data from the EEA (European Environment Agency) necessary to present the NUE (Nitrogen Use Efficiency) were downloaded. Data on physical and chemical water quality parameters at the municipal level are not available. A similar situation applies to the quality of fertilization and the use and consumption of fertilizers.
Our analysis exclusively utilized pre-processed data. The INTEGRATOR model was employed to determine nitrogen (N) leaching to groundwater and N runoff to surface water. This was achieved by first establishing N fluxes through a soil balance approach, involving the calculation of N budgets across approximately 40,000 unique combinations of soil type, administrative region, slope, and altitude. Data inputs drew from Eurostat 2010 statistics on fertilizer, manure, and biosolids application, as well as EMEP (European Monitoring and Evaluation Programme) for N deposition data. The dataset does not separate nitrogen sources into manure or synthetic fertilizers. The N surpluses were subsequently calculated as the difference between N input and crop N removal. Finally, the INTEGRATOR model specifically quantified the biological processes related to the fate of the N surplus, which directly encompassed its losses to both groundwater and surface water. This data, featuring a 1 km spatial resolution in the EPSG:3035 coordinate reference system, is obtainable from the EEA and is freely available for public access under their standard re-use policy. Nitrogen runoff to surface water and leaching to groundwater was quantified in kg/ha/year. We utilized QGIS 3.34.8 to process this data and generate the map.
Analysis of the balance results in nutrient management efficiency and environmental impact was performed. Ideal budgets account for all nutrient flows, including crop residues, while practical implementation often excludes these benefits. Definitions should be clear, and nutrient flows must be defined for Gross Nutrient Balance for the relevant area. Guidelines for implementation and data collection are provided, with some flows subject to future improvements. Indicator changes should be linked to key success factors in sustainable resource management, enhancing nutrient balances, and reducing water resource pressure. The parameters in Table A2 were selected to influence CAP policies for groundwater and surface water quality improvement.

2.3. Factors Affecting Renewable Freshwater Resources, and Nutrient Balances in European Countries

WEI+ (Water Exploitation Index Plus) measures water use in relation to renewable freshwater resources, which is important for irrigation and fertilization management in agricultural areas. High-intensity agriculture requires precise fertilizer management to avoid groundwater contamination. Organic farming reduces the use of chemical fertilizers, lowering the risk of water pollution. Managing excess nitrogen and phosphorus is crucial for protecting water and soil quality. Afforested areas and the introduction of catch crops or green cover help reduce erosion and nutrient leaching, thus improving water quality. The catch crop variable includes legumes, grasses, and brassicas. These crops are typically employed during non-growing seasons to mitigate nitrate loss through leaching and runoff. Their effectiveness is context-dependent, varying with region, crop rotation, and soil type (Table A2).
The global model for WEI+ included the following fixed factors: high-intensity agriculture (%UAA), organic farming (%UAA), Gross Nitrogen Balance (Gross N), year, and European country. The global model for Gross Nitrogen Balance (Gross N) included the following fixed factors: high-intensity agriculture (%UAA), organic farming (%UAA), permanent grasslands (%UAA), Natura 2000 (%UAA), catch crops or green cover (% of arable land), fallow land (% of arable land), landscape elements (% of arable land), nitrogen-fixing plants (% of arable land), fast-growing tree plantations (% of arable land), year, and European country. The models for nitrate content in groundwater and surface water also included the same predictors. The models for these dependent variables were made using the available, limited dataset (see Appendix A, Table A2). Variables, if necessary, were transformed by natural logarithm (WEI+) or square root (nitrates in surface water and nitrates in groundwater) to maintain the distribution of dependent variables consistent with the Gaussian curve. The described analyses were based on data from 2010 to 2019. Data for high-quality water were available only for the years 2012, 2017, and 2019.
In the RDA (redundancy analysis), missing data were supplemented with available data from the year within the studied range or the nearest year to that range, most often data for the year 2015. For dealing with missing data in model selection and GLM analyses, we applied Multiple Imputation by Chained Equations (MICE Forest), which relies on the Random Forest algorithm [47,48]. This approach imputes missing values through iterative construction of prediction models for each variable with missing data using available information in the other variables. This is performed in multiple rounds (iterations), and in each iteration, new updated estimates are used for making better predictions. After a predefined number of iterations (usually 10–50), the imputed values stabilize and are used as final.
MICE Forest creates multiple versions of the dataset, each with slightly different imputed values. In our study, we created ten such datasets and randomly selected one for further analysis. By using this approach, we are able to incorporate the uncertainty of missing data. A major advantage of this method is that it does not rely on distributional assumptions of the variables, as it skillfully handles complex, nonlinear relationships. Additionally, it proves to be effective even when there are outliers in the data. However, its performance is ideal when the dataset is comparatively large and the percentage of missing values is within a reasonable limit. In our study, the amount of missing data ranged from small to moderate and thus made this method suitable. Imputation quality was evaluated via an examination of the patterns of missing data (see Figure A1 in Appendix A) and by checking the stability of the imputation results over successive iterations (convergence of the imputation process).
Definitions of abbreviations and variables used in the analysis are provided in the glossary and in Appendix A (Table A2), which outlines the interpretation of variables in the context of the Common Agricultural Policy. Missing data in the dataset are documented in Figure A1 of the same material. The RDA was calculated using Canoco for Windows 4.5 [42]. GLMs were calculated using STATISTICA 13.3 [49]. Model selection and MICE Forest imputation procedures were run in R 4.5.0 software [50] using the MuMIn 1.48.11 package [51] and mice 3.17.0 package [52].

3. Results

3.1. Nitrogen Runoff to Surface and Leaching to Groundwater

High nitrogen runoff concentrations into surface water have been observed in agricultural areas of the Netherlands, Belgium, northern regions of France, and southern Poland. The maximum levels ranged between 24.6 and 71 kg/ha/year, while the average values were between 9.1 and 15 kg/ha/year (Figure 1). The highest levels were observed in the northern Netherlands, ranging from 82 to 161.7 kg/ha/year. In Italy, concentrations varied significantly from region to region. In the United Kingdom and Ireland, values averaged between 5.5 and 31.8 kg/ha/year, while Poland recorded levels ranging from 15.2 to 31.8 kg/ha/year, which was typical for average values in European countries (Figure 2).
The RDA analysis showed a significant impact of the Gross Nitrogen surplus (Gross N) on water quality, treated as nitrate content in water (F = 4.550, p = 0.032). The first ordination axis (horizontal axis) explained 9.2% of the variance in water quality data, of which 100% was explained by the relationship between Gross N and water quality (Figure 3). The first ordination axis describes the Gross N gradient, which has only a minor impact on water quality. The second ordination axis (vertical axis) explained 74% of the variance in water quality data and represents the water quality gradient (Figure 3). The RDA (redundancy analysis) revealed a strong positive correlation of poor water quality with medium water quality, i.e., the more monitoring points are characterized by poor water quality, the more other monitoring points are likely to belong to the medium water quality category in terms of nitrate content, rather than to the high-quality category.
The diagram also shows that poor and medium water quality is associated with higher Gross N, while high water quality is associated with lower Gross N. The permutation test revealed a weak statistical difference (F = 2.562, p = 0.41) between groundwater and surface water quality in Europe. Groundwater has slightly better quality than surface water. This difference is clear in the case of Poland (Figure 3). In the first quadrant of the ordination diagram, there are countries with poor water quality, partly due to higher Gross N values. In the second quadrant, there are countries with poor water quality, but Gross N has a marginal impact on shaping this quality. In the third quadrant, there are countries with high water quality, partly due to lower Gross N values, while in the fourth quadrant, there are countries with good water quality despite high Gross N values. Poland has relatively good water quality compared to Europe as a whole. The poorest water quality is found in Denmark, Belgium, Malta; in the case of surface water, the poorest quality is found in the Czech Republic, Germany, and Lithuania, and in the case of groundwater, Greece.

3.2. Drivers of Gross Nitrogen Across European Countries

For the analysis of factors influencing Gross N, model selection revealed seven supported models (delta AICc < 2). We present statistical model comparison results, where each row represents a different combination of environmental predictors (indicated by numbers), with columns showing the degrees of freedom (df), log-likelihood (logLik), AICc score, delta AICc, and model weight, with the best-fit model and the predictors corresponding to agricultural practices (Table 1).
The best-fit model successfully explained Gross N with significant contributions from the intercept (p < 0.001), country (p < 0.001, Figure 4), catch crops or green cover (slope = 0.725, p = 0.014), and Natura 2000 (slope = −0.951, p < 0.001). However, organic farming did not make a significant contribution (slope = 0.684, p = 0.097) (Table 2).
The model selection for WEI+ revealed that the best-fit model included Gross N, high-intensity agriculture, organic farming, and country. This model had an AICc of 240.81 and a weight of 0.69. The second-best model, which included an additional predictor, had 31 degrees of freedom, a log-likelihood of −86.06, an AICc of 242.45, a delta of 1.64, and a weight of 0.31. Model selection for WEI+ revealed two supported models (delta AICc < 2) (Table 3).
The results of the best-fit model showed differences between European countries (Figure 5) concerning WEI+, a positive impact of Gross N (slope = 0.003), and a negative impact of organic farming (slope = −0.020). The intercept had an F-value of 95.795 (p < 0.001). Country was highly significant, with an F-value of 167.081 (p < 0.001, Figure 5). Gross N had an F-value of 5.231 (p = 0.023), and organic farming had an F-value of 5.746 (p = 0.017) (Table 4).
For high-quality groundwater and surface water in terms of nitrate content, detailed results are not presented, as the data quality was insufficient, and the analysis results were inconclusive. Only the most important results of the best-fit models are presented below. Model selection for groundwater revealed two supported models, with the best model containing only one predictor—European country. Model selection for surface water revealed seven supported models, with the best model containing four predictors, but only one—European country—was statistically significant. The only significant factor shaping groundwater quality and surface water quality was the European country (F26,54 = 30.09, p < 0.001, N = 81; F26,51 = 5.744, p < 0.001, N = 81) (Figure 6).
The differences presented as percentages in Figure 6 should be interpreted in the following way. The y-axis expresses the percentage of high-quality water, but the bars do not directly indicate the percentage difference between EU27 states in a relative sense. Instead, they show absolute values i.e., the proportion (in %) of high-quality waters in each state. If the x-axis represents the mean value for each country and the standard error (SE), then each country’s bar on the y-axis shows its own value, not the deviation in percent from the base mean. The difference between a state and the average can be described in percentage points, not percentage (which would imply a relative difference). The bars indicate the percentage of high-quality waters in each EU state. In summary, differences between EU states should be read as percentage points, not as percentages.

4. Discussion

4.1. Limiting Nutrient Leakage to Surface Waters and Reducing Their Leaching to Groundwater

Since GNS (Gross Nitrogen Surplus) reflects nitrogen balance rather than direct water pollution, it only indicates potential risk. It accounts for soil changes, atmospheric emissions, and runoff, though ideally, soil nitrogen reserves would be included in nutrient budgets. Operational implementation involves assessing fertilization, yields, soil conditions, and climatic data through multi-stage processes using appropriate formulas and models. Denmark has a high widespread groundwater monitoring well density—29.7 per 1000 km2, over eight times Germany’s. From 2016 to 2020, 14.4% of tested wells exceeded the nitrate limit of 50 mg/L [53].
The EU Nitrates Directive has had mixed success in reducing groundwater nitrate levels. From 1992 to 2019, nitrate hotspots covered 401,000 km2, with 47% outside designated NVZs (Nitrate Vulnerable Zones). Only 5% of 2019 hotspots may meet standards by 2040, highlighting the need for better NVZ designation and monitoring [54]. The “country” factor had the strongest influence, with a very high F-value (167.081) and a p-value of < 0.001, highlighting the crucial role of national-level differences. Both Gross N (F = 5.231, p = 0.023) and organic farming (F = 5.746, p = 0.017) also significantly predicted WEI+, indicating that both macro-level context and specific agricultural practices were important drivers of the indicator (Table 4).
Germany has struggled with nitrogen surpluses due to intensive agriculture and excessive fertilizer use. Areas exceeding the EU nitrate limit of 50 mg NO3-N/L have required targeted reduction measures. The findings supported the implementation of the Water Framework Directive, emphasizing the need for improved agricultural practices and regional prioritization of interventions [55].
Risk management is essential due to changing climatic conditions. Sustainable agriculture is the foundation of ensuring food security, protecting the environment, and promoting the health of rural communities. To effectively monitor and assess progress in this area, the use of appropriate impact indicators is necessary. Increased EU agricultural production since the 1940s via more N fertilizer has harmed air/water quality and ecosystems/health. An EU model calculated “safe N boundaries” based on thresholds for biodiversity (requiring a 31% average N input cut), surface water quality (43% cut needed), and groundwater (threshold exceeded on 18% of farmland). The findings, based on ~40,000 soil–climate combinations, suggest that targeted N reduction policies are better than current flat-rate approaches [20].
We selected a database with multiple indicators to show which are the best at assessing surface and groundwater impact (Table A2).
The results of this work will help identify areas requiring additional support and better tailor our environmental protection strategies. Another example justifying this type of research is the logic of the discussed interventions, such as interventions related to establishing field shelterbelts and agroforestry systems, which can have both a direct impact on biodiversity and an indirect impact on soil stabilization and water retention, contributing to reducing erosion and improving water quality. Furthermore, support programs for water retention can directly influence the reduction of pressure on water resources, while their effects may also be visible in the longer term, in the form of improved water availability for agriculture and increased efficiency of its use. These instruments create a support system that achieves direct results and supports the long-term sustainable development of rural areas and water environment protection. Selecting the best model does not always mean choosing the one that best fits the data. When AICc (Akaike information criterion, corrected for small samples) values differ minimally (1452.47 vs. 1452.85), the risk of selecting a suboptimal model is high. Alternatives include complex selection methods or weighted parameter averaging. However, when prioritizing key results, opting for the model with the fewest predictors is a reliable approach, as models with ΔAICc < 2 are similarly effective. The best-fit model for WEI+ (Water Exploitation Index Plus) showed significant contributions from the intercept (F = 95.795, p < 0.001), country (F = 167.081, p < 0.001), Gross N (Gross Nitrogen Balance) (F = 5.231, p = 0.023), and organic farming (F = 5.746, p = 0.017). The best-fit model for WEI+ included Gross N, high-intensity agriculture, and organic farming (Table 4). The highest reported WEI+ value was for Cyprus (Figure 5).
This method was used to identify and compare approaches for detecting trend reversals in groundwater quality data from 12 monitoring points, each assumed to equally represent the groundwater body in Northern Italy. Sen’s slope was then used to project future nitrate concentrations. These projections indicated that by 2021, six monitoring points would exceed 37.5 mg/L nitrate, and by 2027, the same six points (representing 50% of the area) would exceed the 50 mg/L quality standard [56]. According to our data, Malta has the lowest-value water among all European countries in terms of nitrate content in groundwater and surface water (Figure 6).

4.2. Improving the Nutrient Balance on Agricultural Land

Balancing nutrients on farmland improves soil health and water quality by preventing erosion and reducing pollution. Better fertilization management and buffer zones minimize nutrient runoff. Sustainable practices, efficient irrigation, and organic farming further protect water resources, promoting biodiversity and stable agricultural ecosystems (Table 3). Both Gross N and organic farming significantly influence WEI+ (p = 0.023 and p = 0.017), shaping water resource dynamics. The highly significant country effect (p < 0.001) underscores the need to consider national and environmental factors in water management strategies (Table 4). Between 2012 and 2021, nine EU countries cut external environmental costs of N fertilizer production, leading to an overall EU reduction despite increased use in 18 states. Romania and Spain saw increases. The Netherlands/Belgium had the highest costs per hectare (intensive agriculture), while Portugal/Romania had the lowest [57].
Actual risks depend on weather, soil, and farming practices, with EU agro-environmental indicators providing a clearer picture. Spain is tackling its heavily nitrate-polluted aquifers using EU-aligned strategies. The model indicates that 90% could recover in 6–12 years by increasing nitrogen efficiency (30% less fertilization) and halving losses [58].
There are three basic approaches to estimating nutrient budgets: the farm budget, the soil budget, and the land budget. Data on harvested crops are available from European sources and are needed for crop production. Nutrient content coefficients in crop production vary between countries, partly reflecting differences in farming practices and climate, and partly resulting from the methodology used (measurements, scientific research, expert opinions). The impact of missing data on plant residues on the gross nutrient balance is therefore likely to be small. Estimates of the production and consumption of grasslands have a major impact on the balance outcome. The best-fit model explained Gross N with significant contributions from the country, cover crops, and Natura 2000. Organic farming was not significant (Table 2). The p-value of 0.097 for organic farming in our regression analysis, while not meeting the conventional 0.05 threshold for statistical significance, suggests a marginally significant association with Gross N. This result could indicate a subtle underlying relationship that our model, or current sample size, does not fully capture with high confidence. However, we must also consider the possibility that this marginal association is spurious, influenced by unmeasured confounding factors not accounted for in our pre-processed dataset.
Our results show that organic farming is linked to lower use of water resources, as expressed by a decrease in the Water Exploitation Index Plus (WEI+). This result is in line with the general expectation that organic farming systems tend to have more efficient water management practices and higher soil organic matter content, thereby increasing water retention. Although only a marginal significance was found for the effect on the Gross Nutrient Balance (GNB), this could be due to the character of the “organic farming” variable itself. As noted, this category not only includes farms that have completed the transition to organic practices but also areas that are still in conversion, where nutrient management practices might not yet fully meet organic regulations. This variability presumably softened the detectable impact and indicates that more specific data are required for a proper assessment.
The widening share of Utilized Agricultural Area (UAA) for organic farming in the European Union, which rose between 2012 and 2020 from 5.88% to 9.10% (according to Eurostat), highlights the growing policy and market relevance of this farming method. Against this positive trend and in view of the potential environmental benefits, an orientation towards organic farming appears justified even in regions of high nutrient excesses. As already noted, organic farming can evolve in regions suffering from nutrient pollution, subject to adequate institutional support, technical guidance, and financial support for regenerative practices like composting and soil replenishment. While outcomes can vary according to local conditions and a long-term commitment to sustainable methods, organic farming remains a crucial tool in the context of the Common Agricultural Policy (CAP) and its environmental ambitions.
Results showed that required N inputs are 27% above actual, while critical N inputs were 43% (surface water) below actual, but 1% above for groundwater. A 30% average N input reduction is estimated to be necessary for air and water quality, varying regionally. High N input regions in Ireland and the Netherlands mostly exceed critical levels. EU-wide N use efficiency (NUE) was 61% and needs to rise to 72–74% to protect surface water. While NUE improvements can reduce environmental impact and potentially increase yields, some areas (15–25%) may need N input cuts, impacting yields, to meet surface water criteria [59].
The Farm to Fork strategy, aiming to cut fertilizer use and nutrient losses, has faced criticism for potential economic and agricultural harm. Modeling of fertilizer reduction scenarios (20% uniform and combined restrictions) showed a 6–9% drop in crop production and a rise in grassland, with nitrogen losses decreasing by 9–20%, below the 50% target [60].
An important success factor in assessing CAP SP (Common Agricultural Policy Strategic Plans) actions is supporting organic farming, as well as taking care of the development of protected areas. These areas are associated with improving water quality and can first mitigate the effects of improper fertilizer management and strengthen the effects of sustainable fertilizer use in the long term [61]. Model selection based on Akaike’s information criterion is an important procedure for validating CAP SP actions. It allows for the detection of factors affecting water quality and fertilizer management, and actions that are most effective for the success of CAP SP policy (Table 4). Quantitative impact assessment ensures effective evaluation of sustainable resource management. Proper nutrient balance in agriculture minimizes losses, protects water quality, and improves environmental health. Excess nitrogen can harm water and air quality, while deficits affect soil fertility [62].
Sustainable management of natural resources has a direct effect on impact indicators [63]. Improving nutrient balances of agricultural land leads to better soil and water quality through nutrient management, which limits negative impacts on waters [64]. Reducing nutrient leaching is decisive for maintaining soil balance and protecting water resources through protective actions [65]. Reducing pressure on water resources is achieved by monitoring and limiting water consumption in agriculture, which is necessary for managing the risk of atmospheric droughts [66,67,68]. These actions, in the face of climate change, are crucial for sustainable water management [69,70].

4.3. Limitations of This Study

Data-driven insights into what significantly affects agricultural outputs can guide policymakers in creating regulations and incentives that support the most effective and sustainable practices. The RDA (redundancy analysis) proved to be well-suited for the studied variables and allowed the detection of non-obvious relationships in the case of correlated data that could not be analyzed, for example, using regression analysis. The obtained data were arranged in different ways and had numerous gaps, which prevented effective analysis. Some variables had data over a wide range of years, while other variables had a narrower range of available data, often including more recent data from 2021 to 2022. However, this range overlapped to a small extent with the range of years of other variables for which earlier years were given. It was difficult to extract a common range of years for all selected variables, covering the years 2010–2019. However, the problem of data gaps remained (Figure A1).
The study includes the preparation of a methodology for estimating the impact of CAP SP (Common Agricultural Policy Strategic Plans) on the goal of supporting sustainable development of natural resources, such as water, soil, and air, and efficient management of them, including by reducing dependence on chemical means (CS5). EU legal frameworks require member states to evaluate their strategic plans. According to Article 2 of the Commission Implementing Regulation (EU) 2022/1475 [71], member states should conduct evaluations of their strategic plans during the implementation period. EU Regulation 2021/2115 [72], Article 140(1) mandates member states to evaluate their CAP Strategic Plans, assessing their effectiveness, efficiency, relevance, coherence, EU value added, and impact on achieving CAP objectives. Future data collection must prioritize complete and accurate information for nitrogen assessment, specifically Gross Nitrogen Balance and agricultural practice measures such as tree cover (percentage of land coverage by tree canopy). To improve environmental indicators across European countries, it is essential to collect detailed data on various factors, especially buffer zones, crop rotation, and fertilizer usage reduction within each country. To obtain more accurate data at the municipal level, it is worth authorities starting to collect their own data, collaborating with local organizations and universities, and using new satellite technologies to determine selected indicators. An alternative way is to develop data at a more general level, e.g., at the voivodeship level.
Agriculture is the largest water consumer in many areas and pressures water resources, especially in arid regions where irrigation competes with urbanization and tourism. Traditional irrigation systems maintain diverse landscapes and habitats. The Water Framework Directive regulates water withdrawal and use, aiding climate change adaptation. Nitrogen pollution sources include sewage, waste dumps, pastures, septic systems, and urban drainage. These aspects should be analyzed before attributing nitrogen pollution to agriculture and calculating the balance of it. Agriculture should adapt to resource use without increasing pressure on surface and aquifer water in areas where climate change significantly impacts water availability. This includes managing all agricultural water uses, such as irrigation, storage, transport, farm consumption, livestock raising, cleaning, value-added activities, and water used by the food and timber industries. The CAP supports some non-agricultural water use, but this is minimal compared to the demands of agriculture. Many CAP actions aimed at water management are implemented locally, yet the absence of detailed local data makes it challenging to accurately track the water consumption of these smaller-scale non-agricultural activities and to assess the true efficacy of policy interventions.
The results allowed for adjusting interventions to national conditions and the specificity of rural and environmentally protected areas. Important variables shaping water resources were Natura 2000 areas and organic farming. Optimizing fertilizer management is necessary, as high fertilizer use in agriculture is linked to higher water use (WEI+ index), leading to excessive water resource exploitation, as seen in Cyprus. The analysis indicated a focus on eco-schemes related to organic farming and extensive use of meadows and pastures, field shelterbelts, and permanent grasslands.
Additionally, the use of catch crops or green cover needs further study. Regrettably, cover crops are linked to declining water quality in the sense of rising Gross N index. The reason for this could be that cover crops or green manure plants are plowed under and handled like fertilizer, which could account for the adverse effects found in the analyses. This finding should, however, be taken cautiously since the cover crops variable had 59% missing data, which renders the imputation relatively uncertain. While the MICE method using Forest is robust and can account for missing data, its performance is less reliable where there is a high proportion of missing values [47,48]. Based on the methodological literature, imputations can be trusted in general when the proportion of missing data is less than about 30–40% [47,48]. Above this threshold, imputed values tend to be more sensitive to the observed data structure and imputation model assumptions. Thus, the relation between cover crops and GNB should be considered preliminary and interpreted in light of high imputation uncertainty.
We also examined the following indicators: percentage of agricultural land protected under the Natura 2000 program, percentage of arable land covered with forests, percentage of arable land sown with catch crops or covered with green cover, percentage of arable land lying fallow, percentage of arable land sown with nitrogen-fixing plants, and plantation of fast-growing trees. Deleting missing cases (i.e., removing from the dataset every case for which there is a missing value for at least one of the studied variables) would result in the removal of at least 50% of cases from the entire set, significantly reducing it. Statistical analyses conducted on a small dataset could not provide clear results from which to draw correct conclusions and recommendations for CAP SP. The outlined problem was solved using the MICE (Multiple Imputation by Chained Equations) Forest multiple imputation algorithm, which considers complex relationships between all variables in the set, taking into account different types of data, such as continuous variables, categorical variables, and binary or ordinal variables. The imputation for each variable is carried out using the Random Forest model, which captures nonlinear relationships between variables and performs well with highly variable data. In the first step, the data are divided into variables with missing values and variables that contain the full set. The analyzed set always included information about the year of measurement and the country in which the measurement was made. Variables with missing values are treated as dependent variables, while the remaining variables are treated as independent variables. For each variable with missing cases, a Random Forest model is built based on complete data, using independent variables (i.e., those without missing data) as predictors, and variables with missing values as variables to be predicted. Random Forest learns from the relationships between these variables and generates a set of decision trees, which is then used to impute the missing data. After the Random Forest model is trained, the missing values in the dependent variable are predicted based on this model and inserted into the dataset. The values predicted by Random Forest are imputations of the missing data (Figure A1).

5. Conclusions

Improving the nutrient balance of agricultural land is crucial for reducing water pollution. This study assessed the Common Agricultural Policy’s objectives in European countries, analyzing agricultural practices such as high-intensity agriculture, organic farming, and Natura 2000, alongside Gross N (nitrogen input–output budget) and water nitrate levels. The article assessed the impact of EU policies on nutrient management, evaluating the practical application of CAP strategic plans. Effective nitrogen management supports sustainable farming practices, which can improve surface and groundwater quality and limit resource inefficiency. One aspect of this investigation involved conducting a redundancy analysis to assess differences in nitrate levels between groundwater and surface water. Moreover, the study employed Generalized Linear Models to dissect various contributing factors, including the Water Exploitation Index, Gross N, and in the soil. The findings from this analytical framework revealed a concerning correlation between elevated Gross N levels and deteriorating water quality—the RDA (F = 4.550, p = 0.032). The findings of this research can serve as a valuable resource for policymakers and agricultural stakeholders.
The analysis showed that subsidies for organic farming improved surface water quality by reducing chemical fertilizer use. It also explored whether technology enhances water resource efficiency and reduces pressure in high-demand regions. Our method allows for evaluation of the effectiveness of measures aiming to reduce nutrient pollution. Implementing and optimizing agricultural practices, such as establishing buffer zones, practicing crop rotation, and reducing fertilizer usage, are essential strategies for protecting both surface and groundwater. The findings indicated that tree cover as a percentage of land coverage by tree canopy is effective in supporting policies aiming to improve water quality. Regulations and incentives for farmers in regions with higher surface water pollution are particularly important. On the other hand, for the validation of CAP measures, other predictors should be sought. Improving water quality is a cornerstone of sustainable agriculture, as excessive fertilization can lead to contamination of surface and groundwater.
To effectively mitigate water pollution risks, future studies must prioritize assessing cover crops and their impact on water quality. A challenge lies in the current limitation of improved spatial-temporal data, which is often only available at the national level. Therefore, we strongly recommend that future research gather local and sub-national data to validate Gross N as a reliable, long-term indicator for water quality, thereby addressing these critical data gaps. Monitoring the Gross Nitrogen Balance on agricultural land is essential for tracking fertilizer usage and its potential impact on water quality. This indicator enables the assessment of nutrient leaching levels, such as nitrogen, from agricultural land into surface water across European Union countries.
This study confirms that cover crops are a significant predictor in regression models assessing soil health, nutrient retention, and biomass accumulation. In particular, species with high allelopathic potential (Avena strigosa) and those with rapid biomass accumulation (Fagopyrum esculentum or Raphanus sativus) warrant deeper investigation due to their dual roles in weed suppression and soil carbon enhancement. We also recommend that use of leguminous cover crops such as Vicia sativa should be studied further to understand their nitrogen fixation dynamics and long-term impact on soil fertility. Future modeling efforts should incorporate species-specific traits, termination timing, and regional climate variability to improve predictive accuracy and guide tailored cover crop recommendation.

Author Contributions

Conceptualization, W.H., K.K. and A.K.; Writing—original draft preparation, W.H. and K.K.; Formal analysis, K.K.; Writing—review and editing, W.H., K.K., A.K., T.G., J.C. and B.G.-P.; Methodology, W.H. and K.K.; Validation, W.H., K.K. and J.C.; Visualization, W.H. and K.K.; Supervision, W.H. and K.K.; Software, K.K. and W.H.; Investigation, W.H., K.K. and A.K.; Resources, W.H., K.K. and B.G.-P.; Data curation, W.H., K.K. and T.G. 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

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Pearson’s Correlation Coefficients Between Investigated Variables. The statistically significant correlations (p < 0.05) are marked in red.
Table A1. Pearson’s Correlation Coefficients Between Investigated Variables. The statistically significant correlations (p < 0.05) are marked in red.
Variable High-Intensity AgricultureOrganic Farming Gross Nitrogen Balance (Gross N)Permanent Grassland (TUZ) Natura 2000Afforested Areas Catch Crops or Green Cover Fallow Land Landscape Elements Nitrogen-Fixing PlantsFast-Growing Tree Plantations
High-intensity agriculture1.0000.1250.395−0.153−0.425−0.1250.3700.232−0.0180.1620.311
Organic farming0.1251.000−0.158−0.1640.253−0.464−0.3320.045−0.3770.685−0.202
GrossNitrogen Balance(Gross N)0.395−0.1581.0000.056−0.588−0.3040.882−0.525−0.132−0.386−0.458
Permanent grassland (TUZ)−0.153−0.1640.0561.0000.0190.144−0.185−0.4970.730−0.305−0.238
Natura 2000−0.4250.253−0.5880.0191.000−0.129−0.5390.142−0.3610.2040.090
Afforested areas−0.125−0.464−0.3040.144−0.1291.000−0.3650.0290.5800.0890.300
Catch crops or green cover0.370−0.3320.882−0.185−0.539−0.3651.000−0.194−0.295−0.525−0.348
Fallow land0.2320.045−0.525−0.4970.1420.029−0.1941.000−0.1390.1550.517
Landscape elements−0.018−0.377−0.1320.730−0.3610.580−0.295−0.1391.000−0.2350.176
Nitrogen-fixing plants0.1620.685−0.386−0.3050.2040.089−0.5250.155−0.2351.0000.054
Fast-growing tree plantations0.311−0.202−0.458−0.2380.0900.300−0.3480.5170.1760.0541.000
Table A2. Definition of variables used in the study.
Table A2. Definition of variables used in the study.
DefinitionUnitVariable
The ratio of freshwater consumption to renewable freshwater resources in a given region. WEI+ measures water use as a percentage of available renewable freshwater at the river basin level and for each of the four quarters of the year (three consecutive months).%WEI+ (Water Exploitation Index Plus)
Percentage of agricultural land used intensively for farming.% of agricultural land (UAA)High-intensity agriculture
Percentage of agricultural land under organic production.% of agricultural land (UAA)Organic farming
Amount of nitrogen used in agriculture per hectare annually.kg N/ha/yearGross Nitrogen Balance (Gross N)*
Percentage of monitoring points in surface water monitoring with nitrate concentrations below 2 mg/L, in accordance with Directive 91/676/EEC.% of monitoring pointsNitrates in surface water–high quality
Percentage of monitoring points in groundwater monitoring with nitrate concentrations below 50 mg/L, in accordance with Directive 91/676/EEC.% of monitoring pointsNitrates in groundwater–high quality
Percentage of permanent grassland relative to the total area of agricultural land.% of agricultural land (UAA)Permanent grassland (TUZ)
Percentage of agricultural land under protection within the Natura 2000 program.% of agricultural land (UAA)Natura 2000
Percentage of arable land covered by forests.% of arable landAfforested areas
Percentage of arable land covered by catch crops or green cover.% of arable landCatch crops or green cover
Percentage of arable land lying fallow, uncultivated.% of arable landFallow land
Percentage of arable land containing landscape elements such as hedges, field margins, and ponds.% of arable landLandscape elements
Percentage of arable land planted with nitrogen-fixing crops, such as peas, lupins, and alfalfa.% of arable landNitrogen-fixing plants
Percentage of arable land planted with fast-growing trees, such as energy willows. These are specialized crops where fast-growing tree species are planted to produce large amounts of wood in a shortened production cycle (up to 60 years), mainly for industries based on physical–chemical wood processing.% of arable landFast-growing tree plantations
Gross N (GNB)* = Ninput − Noutput [22], where Ninput = nitrogen from mineral fertilizers, manure, biological fixation, atmospheric deposition, and seeds/planting materials; Noutput = nitrogen removed through crop harvest and grazing.
Applsci 15 09216 g0a1
Figure A1. Missing data from the whole dataset in this paper.
Figure A1. Missing data from the whole dataset in this paper.
Applsci 15 09216 g0a1

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Figure 1. Nitrogen runoff to surface water in agricultural areas in Europe. Source: EEA—Mean values for the years 2008–2019.
Figure 1. Nitrogen runoff to surface water in agricultural areas in Europe. Source: EEA—Mean values for the years 2008–2019.
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Figure 2. Nitrogen leaching to groundwater in agricultural areas in Europe. Source: EEA—Mean values for the years 2008–2019.
Figure 2. Nitrogen leaching to groundwater in agricultural areas in Europe. Source: EEA—Mean values for the years 2008–2019.
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Figure 3. Ordination diagram of the RDA analysis for the quality of groundwater and surface water in terms of nitrate content. HQ—high water quality, MQ—medium water quality, PQ—poor water quality. Blue points indicate surface water, brown points indicate groundwater. Country codes: BE—Belgium, CZ—Czech Republic, DK—Denmark, DE—Germany, EE—Estonia, IE—Ireland, EL—Greece, ES—Spain, FR—France, HR—Croatia, IT—Italy, CY—Cyprus, LV—Latvia, LT—Lithuania, AT—Austria, PL—Poland, PT—Portugal, RO—Romania, SI—Slovenia, SK—Slovakia, FI—Finland, SE—Sweden. Sample size—N = 47.
Figure 3. Ordination diagram of the RDA analysis for the quality of groundwater and surface water in terms of nitrate content. HQ—high water quality, MQ—medium water quality, PQ—poor water quality. Blue points indicate surface water, brown points indicate groundwater. Country codes: BE—Belgium, CZ—Czech Republic, DK—Denmark, DE—Germany, EE—Estonia, IE—Ireland, EL—Greece, ES—Spain, FR—France, HR—Croatia, IT—Italy, CY—Cyprus, LV—Latvia, LT—Lithuania, AT—Austria, PL—Poland, PT—Portugal, RO—Romania, SI—Slovenia, SK—Slovakia, FI—Finland, SE—Sweden. Sample size—N = 47.
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Figure 4. Differences across EU27 states in Gross N. Mean along with standard error (SE).
Figure 4. Differences across EU27 states in Gross N. Mean along with standard error (SE).
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Figure 5. Differences across EU27 states in WEI+. Mean along with standard error (SE).
Figure 5. Differences across EU27 states in WEI+. Mean along with standard error (SE).
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Figure 6. Differences across EU27 states in high water quality in terms of nitrate content in groundwater and surface water. Mean along with standard error (SE). Groundwater is represented by orange bars, surface waters by blue bars.
Figure 6. Differences across EU27 states in high water quality in terms of nitrate content in groundwater and surface water. Mean along with standard error (SE). Groundwater is represented by orange bars, surface waters by blue bars.
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Table 1. Results of model selection for Gross N. The best-fit model is in bold.
Table 1. Results of model selection for Gross N. The best-fit model is in bold.
WeightDeltaAICclogLikdfPredictor
0.2202353.03−1141.34311357
0.170.552353.58−1142.9030137
0.131.092354.12−1140.603213,567
0.131.142354.16−1141.91311237
0.131.152354.17−1140.633212,357
0.121.322354.34−1142.00311367
0.111.442354.46−1140.783213,457
Predictor codes: 1—catch crops or green cover, 2—high-intensity agriculture, 3—Natura 2000, 4—nitrogen-fixing plants, 5—organic farming, 6—fast-growing tree plantations, 7—country.
Table 2. Results of the best-fit model explaining Gross N. N = 270.
Table 2. Results of the best-fit model explaining Gross N. N = 270.
ParameterdfFp
Intercept1243.825<0.001
Country2649.751<0.001
Catch crops or green cover16.1210.014
Natura 2000115.51<0.001
Organic farming12.7750.097
Error240
Table 3. Results of model selection for WEI+. The best-fit model is in bold.
Table 3. Results of model selection for WEI+. The best-fit model is in bold.
WeightDeltaAICclogLikdfPredictors
0.690240.81−86.5130134
0.311.64242.45−86.06311234
Predictor codes: 1—Gross N, 2—high-intensity agriculture, 3—organic farming, 4—country.
Table 4. Results of the best-fit measure for WEI+ using general linear model (GLM). N = 270.
Table 4. Results of the best-fit measure for WEI+ using general linear model (GLM). N = 270.
ParameterdfFp
Intercept195.795<0.001
Country26167.081<0.001
Gross N15.2310.023
Organic farming15.7460.017
Error241
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Halecki, W.; Kalarus, K.; Kowalczyk, A.; Garbowski, T.; Chudziak, J.; Grabowska-Polanowska, B. Reducing Water Resource Pressure and Determining Gross Nitrogen Balance of Agricultural Land in the European Union. Appl. Sci. 2025, 15, 9216. https://doi.org/10.3390/app15169216

AMA Style

Halecki W, Kalarus K, Kowalczyk A, Garbowski T, Chudziak J, Grabowska-Polanowska B. Reducing Water Resource Pressure and Determining Gross Nitrogen Balance of Agricultural Land in the European Union. Applied Sciences. 2025; 15(16):9216. https://doi.org/10.3390/app15169216

Chicago/Turabian Style

Halecki, Wiktor, Konrad Kalarus, Agnieszka Kowalczyk, Tomasz Garbowski, Justyna Chudziak, and Beata Grabowska-Polanowska. 2025. "Reducing Water Resource Pressure and Determining Gross Nitrogen Balance of Agricultural Land in the European Union" Applied Sciences 15, no. 16: 9216. https://doi.org/10.3390/app15169216

APA Style

Halecki, W., Kalarus, K., Kowalczyk, A., Garbowski, T., Chudziak, J., & Grabowska-Polanowska, B. (2025). Reducing Water Resource Pressure and Determining Gross Nitrogen Balance of Agricultural Land in the European Union. Applied Sciences, 15(16), 9216. https://doi.org/10.3390/app15169216

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