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The impacts of agricultural payments on groundwater quality: Spatial analysis on the case of Slovenia
Ecological Indicators 73 (2017) 338–344
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
The impacts of agricultural payments on groundwater quality: Spatial analysis on the case of Slovenia Renata Slabe-Erker ∗ , Tjaˇsa Bartolj, Marko Ogorevc, Damjan Kavaˇs, Klemen Koman Institute for Economic Research, Kardeljeva ploscad 17, 1000 Ljubljana, Slovenia
a r t i c l e
i n f o
Article history: Received 10 December 2015 Received in revised form 19 September 2016 Accepted 28 September 2016 Keywords: Common agricultural policy Agricultural payments Nitrates Pesticides Groundwater quality Spatial panel model
a b s t r a c t The European Common Agricultural Policy still follows its primary goals, i.e. quality food at affordable prices and a decent standard of living for farmers, fifty years after its adoption. Moreover, this policy adapts to the changing needs of society and the new challenges, mostly preservation of the environment, nature and biodiversity in rural areas. Although the Common Agricultural Policy receives the largest share of European budget, the funds are decreasing over time, especially direct payments, which aim to provide basic income support to farmers in the European Union. On the other hand, agri-environmental payments are gaining importance. Policy decision-makers should be interested in the question of impacts of growing eco-conditionality of agricultural spending. New insights would help them to be successful in achieving the goals of sustainable agriculture. The purpose of this paper is to estimate the impacts of production support payments and rural development payments on the quality of groundwater. We use the small EU country Slovenia as an example. The baseline indicators are the level of nitrates and pesticides in groundwater, while the impacts were estimated using spatial error model. The results show that direct payments, coupled subsidies and investment grants raise the level of pesticides in groundwater, but do not have any statistically significant impact on the level of nitrates in groundwater. Furthermore, we did not find any statistically significant effects of agri-environmental payments on decrease of groundwater pollution with nitrates. However, our findings revealed that agri-environmental payments are effective in reducing pesticides in groundwater, although only to a limited extent. These results imply a problem of insufficient targeting of agri-environmental measures on the one hand, and suggest that greening of direct payments is necessary and entirely justified. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction The Common Agricultural Policy (CAP) of the European Union was designed in 1962 to enable good quality food at affordable prices for EU citizens and a decent standard of living for EU farmers. Fifty years later, these goals are still relevant, and the European Union adapts the CAP to the changing needs of society and the new challenges (MAFF, 2015b). Both the public and the policy makers are well aware of the fact that there are other functions of agriculture besides the supply of people with food and maintaining farmers’ incomes. The agriculture preserves the countryside, together with the landscape, demographics and rural economy, as well as natural resources and biodiversity. In addressing these challenges, it is essential for the Member States to promote the
∗ Corresponding author. E-mail addresses: [email protected] (R. Slabe-Erker), [email protected] (T. Bartolj), [email protected] (M. Ogorevc), [email protected] (D. Kavaˇs), [email protected] (K. Koman). http://dx.doi.org/10.1016/j.ecolind.2016.09.048 1470-160X/© 2016 Elsevier Ltd. All rights reserved.
competitiveness and also the sustainability of agriculture and rural areas. With accordance to this, the CAP measures are classified in two Pillars: production support (Pillar I) and rural development (Pillar II). Still, both pillars are intertwined and integrated within the CAP. The primary aim of the Pillar I is to provide basic income support to EU farmers by two main instruments: direct payments to farmers and market measures. In order to be eligible to receive this support, farmers must keep land in good agricultural and environmental condition and meet legislative standards covering the environment, public health, plant health and animal health (crosscompliance conditions). Therefore, direct payments should also affect the delivery of public goods, mostly those related to environment and rural vitality. While the CAP receives the largest share of the EU budget, the funds are decreasing over time, especially direct payments to farmers. In the period from 2007 to 2009, direct payments represented on average 29% of agricultural income in the EU (EC, 2011a). To be less trade distorting the main part of direct payments is decoupled from production (hereinafter referred to as
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direct payments). Only a smaller part of direct payments is still linked to the production of a particular crop or keeping a particular type of livestock. These payments are called coupled subsidies. The measures of common rural development policy (Pillar II) are implemented within the national Rural Development Program (RDP) of the respective Member State which links European and national, local needs. Moreover, the States have to provide cofinancing. Rural development measures are aimed at improving competitiveness and promoting diversification of economic activity, delivering environmental public goods and improving quality of life in rural areas (EC, 2011b). A large part of these measures represents Agri-environmental payments, like payments for organic farming, actions to maintain habitats favourable to biodiversity, management of pastures, crop rotation etc. Therefore, the support for rural development is intended for those farms, which in the management of agricultural land contribute to the conservation of biodiversity, landscape diversity and the protection of soil and water resources. Agri-environmental payments represent 30% of rural development expenditures from the EAGG fund.1 In 2009, the average share of utilised agricultural area under agrienvironmental measures in EU-27 was 21%, with the highest share in Luxembourg and Finland (more than 90%) and the lowest share in Bulgaria, Poland and Netherlands (less than 6%). In terms of area covered by different types of agri-environmental measures, the most important commitments refer to management of landscape, pastures and high nature value farming (39% of the total area committed across the EU-27). This type of commitment was more popular in the new Member States (64%) than in the EU-15 (35.8%) (Eurostat, 2012).2 With the new programming period (2014–2020) the environmental component is gaining even more importance as the support has extended to the field of agriculture’s contribution to climate change mitigation and adaptation. Implementation of the measures financed in the 2007–2013 period in the Member States came to an end. In the beginning of 2015, the European Commission formally confirmed the new Rural Development Program for each Member State for the period 2014–2020 which is the basis for the disbursement of EU funds in this period. Therefore, the time is right to evaluate the impacts of different types of received payments on agri-environmental contents. Increasing the importance of environmental and safety issues for proving eligibility to receive agricultural payments as well as limited empirical evidence on environmental impacts of agricultural expenditures have motivated the present study. Hence, this study aims to numerically examine the impacts of agricultural payments (which are mostly conditional upon environmental standards) on groundwater quality. More precisely, for the EU member state Slovenia, we assess both the impacts of direct payments and coupled subsidies (Pillar I) and agri-environmental payments and investment grants (Pillar II) in the period 2007–2013 on two indicators: the level of pesticides and nitrates in groundwater. The protection of groundwater quality in Slovenia is of upmost importance, because more than 97% of drinking water is abstracted from shallow, unconfined alluvial aquifers and fractured or karstic porosity aquifers (Krajnc et al., 2007). Moreover, more than three quarters of the surface belongs to areas with less favoured conditions for agricultural production (e.g. Alpine region). Consequently, public spending on agri-environmental measures represents the
1
European Agricultural Guarantee and Guidance Fund. Commitments referred to management of landscape, pastures and high nature value farming represent 44% of total area committed in Slovenia in 2009 (ARSE, 2011). In the total area the same area can be counted several times if several types of commitments apply on the same land (Eurostat, 2012). 2
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largest share of the budget for rural development, and the same applies to Europe. In order to avoid, prevent or reduce detrimental concentrations of harmful pollutants in groundwater, in particular the two mentioned above, the European parliament and the Council of the European Union adopted the Groundwater Directive (2006/118/EC). The major source of groundwater contamination is agricultural non-point source pollution. Agricultural pollution with nitrates and pesticides is the most common cause for failing the quality standards, and consequently poor chemical status of groundwater. For that reason, the EU legislation regulates these areas more specifically by Nitrates Directive (91/676/EEC), the Sustainable Use of Pesticides Directive (2009/128/EC) and some other directives. The use of nitrogen fertilizers and pesticides in agriculture brings significant economic benefits to the sector but also risks to the environment and human health. The main source of nitrogen input to agricultural land is application of organic and chemical fertilizers (Korsaeth and Eltun, 2000; Sieling and Kage, 2006), while the main non-agricultural source is related to density of wastewater disposal systems (Wakida and Lerner, 2005). In Slovenia, with extremely dispersed settlement pattern, this could be an important environmental issue. Moreover, the concentration of nitrates in groundwater also depends on natural conditions such as pedological characteristics, climatic and biological factors. On the other hand, pesticides used in agriculture are plant protection products, like weed-killers and plant-growth regulators. Therefore, CAP measures are designed so as to reduce these risks as much as possible. In this paper, the impacts of agricultural payments on the level of pesticides and nitrates in groundwater are assessed with spatial panel data model. The paper is structured as follows. Section 2 presents short literature review. In Sections 3 and 4, the article offers summary statistics of data and description of the estimation method. The results are presented in Section 5. The paper concludes with discussion and implications for policy and practice.
2. Short literature review There are many CAP measures whose objective is to directly or indirectly improve or protect groundwater quality. However, to our knowledge, only a few studies have been carried out that would link individual agricultural payments to groundwater quality. The sparse findings offer a starting point for the research hypotheses on impacts’ evaluation. The CAP’s most important instrument for providing basic income support to farmers and delivering basic public goods is direct payments. These payments are not related to the volume of production. As previously mentioned farmers qualify for the subsidy, if they maintain their land in a good agricultural and environmental condition and comply with the relevant statutory management requirements (Cong and Brady, 2012). The studies mainly examine the impacts of direct payments on production (e.g., Girante, 2008; Monge-Arino, 2007; Peckham, 2010; Sokolova et al., 2015) or, less frequently on the state of the environment. Those that study environmental impacts focus primarily on the type of production (intensive vs. extensive) and the structure of landscape or land abandonment in connection with diversity of habitats (e.g. Cong and Brady, 2012; Nikodemus et al., 2010; Reger et al., 2009). The study of Peckham and Kropp (2012) is related to our field of research, although indirectly. The authors found that direct payments when they were introduced in the USA in 1996, had only a small impact on the use of pesticides and nitrates, but after 2004 their impact became greater and positive (Peckham and Kropp, 2012). However, as far as we know, the problem of groundwa-
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ter pollution in relation to direct payments has not been studied before. Given the request that recipients of direct payments maintain land in good agricultural and environmental condition, and in the absence of researches on this issue, we set the hypothesis that the increase of direct payments has no impact on groundwater quality (Hypothesis 1). There are even fewer studies on impacts of coupled subsidies on the environment and nature. Again, the studies focused mainly on type of production and the structure of landscape in connection with diversity of habitats (e.g. Brady et al., 2003). In Germany, coupled subsidies accelerated the phasing out of agricultural production in areas with low productivity of land and increased the use of land in more productive areas. In this way, the agricultural landscape disappeared and the agricultural production has been intensified. The impact on habitats (diversity, rarity and uniformity of distribution) was negative and medium-sized. If we assume that coupled subsidies are related to more intensive agriculture, we can further assume that increase in coupled subsidies is positively related to the levels of groundwater nitrates and pesticides. To test our claim we have set the Hypothesis 2 that coupled subsidies have no effect on groundwater quality (Hypothesis 2). The objectives of many agri-environmental measures are related to reducing the use of pesticides and nitrates and consequently improving of the quality of water. However, so far the link between agri-environmental payments and water quality has only been studied by Reinhard and Linderhof (2015). They found statistically significant negative relationship between agri-environmental expenditures and nitrogen surplus. The same authors noted that investments may be related to the environment or agricultural intensification. Moreover, the results of their study showed that investments positively affect the level of nitrogen surplus. From these sparse findings we claim that increase in agri-environmental expenditures is negatively related to the levels of groundwater nitrates and pesticides and increase in investment grants is positively related to the levels of groundwater nitrates and pesticides. To test these statements we have set two hypotheses: agri-environmental expenditures and investment grants have no effect on groundwater quality (Hypothesis 3, Hypothesis 4). 3. Data and descriptive statistics Our data set merges SAAMRD data (Slovenian Agency for Agricultural Markets and Rural Development) on actually disbursed applications and ARSE (Agency of the Republic of Slovenia for the Environment) environmental data (ARSE, 2015; MAFF, 2015a; SAAMRD, 2015a,b). We aggregated the panel data for agricultural payments at the farm level to municipality level. The analysed area consists of 210 Slovenian municipalities (LAU 2).3 The financial perspective covered the 2007–2013 period however the first payments in Slovenia were made in 2008. We followed the Common Monitoring and Evaluation Framework logic (Agri, 2006) and focused on the baseline indicator which is the level of nitrates and pesticides in groundwater. This is related to the impact indicator which is the improvement in groundwater quality. For this purpose we used the average annual values of nitrates and pesticides at sampling stations of state monitoring (ARSE, 2015). Table 1 shows the number of approved applications of holdings for agri-environmental payments (AE payments in Table 1), direct payments and coupled subsidies. The majority of holdings that received agri-environmental payments got direct payments as well. The ratio between the holdings, which have been included
3
LAU stands for Local Administrative Units. LAU 2 was previously called NUTS 5.
Table 1 Number of approved applications of holdings by year and type of payment. Year
AE payments
Direct payments
Coupled subsidies
2008 2009 2010 2011 2012 2013 Total
18,383 18,190 18,113 18,048 18,304 16,076 107,114
58,619 59,095 51,904 50,837 51,652 56,740 328,847
27,818 26,237 26,155 25,738 25,329 16,518 147,795
in the agri-environmental measure but have not received direct payments, and those who have received both types of payments is 13 to 100. Similarly, most of the holdings that received coupled subsidies received direct payments as well, with ratio 8 to 100 in favour of combined applications. Fig. 1 shows the average amounts of direct payments, coupled subsidies and agri-environmental payments per hectare of utilised agricultural area. All payments are quite stable over the analysed period. In the case of direct payments, a minor peak is detected in 2009 (i.e. EUR 293.6/ha, at constant prices 2008). The average amount of agri-environmental payments and direct payments has increased in the last year (2013), while the average amount of coupled subsidies has decreased. Agri-environmental payments (AEP) or measures are classified into three groups: negative-impacts-reducing measures, such as integrated crop production and organic farming (AEP I), conservation measures, such as mowing of steep and humpy meadows, mountain pastures and meadow orchards (AEP II), and habitatprotection measures, such as preserving grassland habitats and autochthonous and traditional plants (AEP III). The average annual values of habitat-protection payments (AEP III) per hectare of utilised agricultural area are constantly very low (from EUR 0.19/ha in 2008 to EUR 0.31/ha in 2012). In the observed period the average value of conservation payments (AEP II) per hectare of utilised agricultural area is EUR 18.8. From 2010 onwards, the conservation payments decline. The most agri-environmental payments are aimed at reduction of negative impacts of agriculture on environment (AEP I). The peak is detected in the last year (2013), i.e. EUR 57.5 per hectare of utilized agricultural area. In the case of investment grants per hectare of utilised agricultural area two peaks are detected; for modernisation of agricultural holdings − INV 1 (EUR 27.0/ha) in 2012 and for adding value to agricultural and forestry products − INV 2 (EUR 13.8/ha) in 2010. In 2011, the average value of investment grants decreases to EUR 19.2/ha and EUR 5.8/ha, respectively. Greater variation in average amounts of investment grants was detected for adding value to agricultural and forestry products. Fig. 2 shows the trend in baseline indicators, i.e. annual average levels of nitrogen and the sum of pesticides in groundwater for the period 2007–2013, with spatial pattern in the first and the last year of observation, calculated from the values at sampling points of the state monitoring. Average number of sampling points in the observed period was 154 for nitrates and 72 for pesticides. The presented values are obtained by ordinary kriging interpolation (e.g. Mirzaei and Sakizadeh, 2016). The initial and final state of pollution is expressed as average by municipalities, while the state of pollution by years is expressed as country’s average. Spatial patterns for the levels of nitrates do not change, while spatial patterns for the levels of pesticides show a clear decline, with the largest reduction in North and Northeast Slovenia. There is evident downward trend in levels of pesticides in the whole period, while nitrate levels peaked in 2010, but remained at similar levels compared to 2007 in the last year. A possible explanation for the peak in nitrate levels is the peak in precipitation in 2010 (SORS, 2016) as precipitation surpluses have been found to influence the
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Fig. 1. Average values of agricultural payments in EUR/ha of utilised agricultural area, 2008–2013.
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Note: Amounts are in 2008 constant prices (EUR).
Fig. 2. Changes in levels of pesticides (g/l) and nitrates (mg NO3 /l) in groundwater over years.
extent of agricultural nitrate leaching (e.g. Elmi et al., 2002; Salo and Turtola, 2006). Fig. 3 presents the overlap between spatial patterns of total agricultural payments and groundwater pollution. From these patterns we can see that agricultural payments are spatially unevenly distributed. This is due to higher share of arable land in total agricultural land in Northeast Slovenia. Moreover, sampling stations of groundwater state monitoring are located primarily in lowland areas where the share of arable land is usually higher (Juvancic et al., 2012). It is apparent from these figures that payments are more or less concentrated around the sites of higher groundwater pollution. The mismatch between spatial patterns is greater in the case of pollution with nitrates. However, this is expected because of non-agriculture polluters of groundwater with nitrates, such as uncontrolled urban infrastructure (Nolan et al., 1998). 4. Method The References of European Commission and the results of various evaluation studies demonstrate that the major challenge is counterfactual analysis where state intervention (e.g. investment grants, subsidies) is compared with the situation without intervention. In this way, it is possible to estimate which results/impacts can be attributed to public intervention and which are the result
of other factors. To perform the counterfactual analysis a definite “cause and effect chain”, clearly defined that both supported beneficiaries and non-supported groups are required. Different econometric techniques can be used, such as difference-indifference (DID), propensity score matching (PSM), and regression discontinuity (RD). Often the individual methods are combined. However, for the proper implementation of such methods an adequate data quality is needed (Bartolj et al., 2015). The nature of the analysed problem prevents us from using individual farm data, since it is not possible to attribute the impact of an individual farm on groundwater quality using the data from monitoring sites, hence, the spatial analysis is performed at the municipal level. We conducted a spatial panel econometric analysis to assess the impacts of agricultural payments on the quality of groundwater in all municipalities, including those without intervention. The impacts of agri-environmental payments are assessed separately for three groups of objectives: negativeimpacts-reducing measures (areas I), conservation measures (areas II), and habitat-protection measures (areas III). We further assumed that the groundwater quality is affected by municipality agricultural characteristics, such as arable land and livestock units. Moreover, the dispersion of settlement was used to indicate additional non-agricultural sources of nitrogen in urban areas. Also, different development levels of municipalities were taken into
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Fig. 3. Spatial overlap between total agricultural payments (2008–2013) and pesticide/nitrate level in groundwater (2007).
account (Reinhard and Linderhof, 2013). Therefore, it was possible to estimate impacts that can be attributed to agricultural payments
as well as structural and developmental characteristics of municipalities. Although pesticides transfer into the groundwater with percolation through the soil relatively quickly, they remain there for a long time because of the slow decomposition process. Thus, for example, atrazine and its metabolite desethyl-atrazine are still present in groundwater, although the use of atrazine was completely prohibited in 2003 (Mihorko and Gacin, 2013). Similarly, the high concentrations of nitrates retain long after farmers reduce the intensity of fertilization. The latter is one of the main causes for transmission of nitrates in the groundwater. Consequently, when assessing the impact of various payments on the values of pesticides and nitrates in the groundwater their historical values were used. Ordinary kriging was used to interpolate nitrate and pesticide values to a regular grid, and then these values were used to obtain average nitrate and pesticide levels across municipalities, including those without direct measurement of nitrate and pesticide levels. A spatial error model was applied to estimate the impacts of agricultural payments on pesticide and nitrate levels in the groundwater. “This is appropriate when the concern is correcting of the potentially biasing influence of the spatial autocorrelation due to the use of spatial data (irrespective of whether the model of interest is spatial or not)” (Anselin and Baltagi, 2001, pp. 316). We used row-standardized spatial weights matrix (W) based on adjacency between pairs of municipalities. Joint Lagrange multiplier test (Baltagi et al., 2003) was applied to test the null hypothesis of no random effects and spatial autocorrelation. When the test rejects the null hypothesis, we use maximum likelihood estimation, because for a model with spatially auto-correlated error components, ordinary least squares (OLS) on a random effects model is inefficient (Millo and Piras, 2012), while the estimation of an lagged dependent variable model with OLS is only problematic when residual autocorrelation is present (Keele and Kelly, 2006). For the estimation of the spatial autoregressive parameter and the variance components of the disturbance process we used generalized moment estimator (Kapoor et al., 2007). Since the variables are exponentially distributed, their logarithms are calculated. Analysis was performed in R (R Core Team, 2016).
To examine the impact of agricultural payments on the annual values of nitrates and pesticides in groundwater we estimate the following equation:
where Pollutant denotes the annual values of nitrates or pesticides in groundwater referring to municipality i in year t and two time lags of pollutant (t-1 and t-2). We assume that each ecological indicator depends on the agri-environmental payments (AEP), direct payments (DP), coupled subsidies (CS) and investment grants (INV). The impact of agri-environmental payments, direct payments and coupled subsidies is measured with their average values per hectare of utilised agricultural area for each municipality in the previous year and the impact of investments is measured by their average value per hectare of utilised agricultural area in the current year. AL is defined as a share of arable land in utilised agricultural area, LU stands for livestock units per hectare of utilised agricultural area and SD denotes settlement dispersion, which is used to indicate additional non-agricultural sources of nitrogen in urban areas. Settlement dispersion was measured using real estate locations (MESP, 2016) by Clark and Evans aggregation index R, which is a simple measure of clustering or ordering of a point pattern. It is the ratio of the observed mean nearest neighbour distance in the pattern to that expected for a Poisson point process of the same intensity. Average wages (AW) in the model serve as a proxy for development. W represents the spatial weights matrix and is a spatial autocorrelation coefficient. The measurement of spatial autocorrelation describes the overall pattern across a geographic landscape and can be motivated by omitted variable or spatial heterogeneity which affect spatial regression methods less than OLS.
5. Results Estimated coefficients of the equation (1) are presented in Table 2. The increase of direct payments by 1% is followed by 0.357% increase of pesticide levels in the next year. Moreover, the increase of coupled subsidies by 1% is followed by 0.162% increase of pesticide levels in the next year. Similarly, higher investment grants for modernisation of agricultural holdings products slightly increase the pesticide levels in groundwater (0.117). Furthermore, a 1% increase in arable land causes pesticide levels to increase by 0.07%. One the other hand, the increase of agri-environmental payments for conservation measures (such as mountain pastures,
R. Slabe-Erker et al. / Ecological Indicators 73 (2017) 338–344 Table 2 Regression results of agricultural payments on pesticide and nitrate levels in groundwater. Pesticides
Nitrates
LUi
−3.880* (2.254) 0.352*** (0.029) 0.204*** (0.027) 0.357*** (0.105) 0.162*** (0.061) −0.058 (0.038) −0.147*** (0.043) −0.079*** (0.024) 0.017** (0.008) −0.001 (0.010) 0.073* (0.039) –
SDit
–
AWit
0.002 (0.299) 0.001 (0.001) 0.006 (0.023) 58.448*** 0.522 210 5
−0.319 (1.247) 0.471*** (0.032) 0.323*** (0.033) 0.008 (0.059) −0.059 (0.038) 0.011 (0.021) 0.019 (0.023) 0.009 (0.013) −0.002 (0.003) −0.003 (0.005) 0.047** (0.022) 0.064 (0.053) 0.173*** (0.067) 0.133 (0.164) 0.001 (0.001) 0.192*** (0.0434) 13.688*** 0.707 210 5
Const. Pollutantsit-1 Pollutantit-2 DPit-1 CSit-1 AEP Iit-1 AEP IIit-1 AEP IIIit-1 INV 1it INV 2it ALi
Joint LM Test R2 n T
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second pillar measures (investment grants and agri-environmental payments) on indicators of groundwater quality in Slovenia in the period 2007–2013. The impacts were estimated using spatial panel data model at the municipality level. This study indicates that agricultural payments do not have any statistically significant impact on the level of nitrates in groundwater. However, variation in nitrate level in groundwater is largely explained by past levels, arable land and settlement dispersion. Perhaps there is a problem of omitted variable, which is also spatially distributed and affects the value of nitrates, e.g. additional nonagricultural sources of nitrogen in urban areas. However, omitted variable bias affects spatial regression methods less than OLS. The following is a summary of conclusions. Hypothesis 1. that direct payments have no impact on groundwater quality was rejected in the case of pesticides, but could not be rejected in the case of nitrates. Direct payments increase pesticides in groundwater. Hypothesis 2. that coupled subsidies have no impact on groundwater quality was rejected in the case of pesticides, but could not be rejected in the case of nitrates. Coupled subsidies increase pesticides in groundwater. Hypothesis 3. that agri-environmental expenditures have no impact on groundwater quality was rejected in the case of pesticides but could not be rejected in the case of nitrates. Though all groups of agrienvironmental payments decrease pesticides in groundwater, only conservation measures (AEP II) and habitat-protection measures (AEP III) are statistically significant. Hypothesis 4. that investment grants have no impact on groundwater quality was rejected only for investments for modernisation of agricultural holdings (INV 1) in the case of pesticides. These investments increase pesticides in groundwater.
Significance levels: * < 0.1; ** < 0.05; *** < 0.01.
mowing of steep and humpy meadows and preserving of extensive grassland) by 1% decreases pesticide levels in groundwater by 0.015% in the next year. Additionally, a 1% increase in agrienvironmental payments for habitat-protection measures, such as preserving grassland habitats, causes a minor reduction of pesticide levels in groundwater, i.e. by 0.08%. The model explains 52% of pesticides variation and spatial errors are not statistically significant. Joint Lagrange multiplier tests rejected the null hypothesis of no random effects and no spatial autocorrelation. In contrast to pesticides, there is no statistically significant connection between the level of nitrates in groundwater and agricultural payments. Nitrate levels are explained only by its past levels (two time lags), share of arable land in utilised agricultural area and the settlement dispersion. A 1% increase in arable land causes nitrate levels to increase by 0.05%. The results reveal that the increase of settlement dispersion by 1% is followed by 0.17% increase of nitrate levels. The model explains 70% of nitrates variation and spatial error coefficient is positive and statistically significant. Positive spatial autocorrelation coefficient confirms spatial heterogeneity, which can be interpreted as an autoregressive-in-space error structure, with decreasing intensity as we move further away. Joint Lagrange multiplier tests rejected the null hypothesis of no random effects and no spatial autocorrelation. 6. Conclusion and discussion In the present paper, we evaluated the impacts of the CAP first pillar measures (direct payments and coupled subsidies) and CAP
Interestingly, the above findings are not consistent with a study conducted by Reinhard and Linderhof (2015). Authors found a significantly negative effect of agri-environmental expenditures on nitrogen surplus, while we did not find statistically significant effects of agri-environmental payments on decrease of groundwater pollution with nitrates. However, our findings revealed that agri-environmental payments are effective in reducing pesticides in groundwater, although only to a limited extent. Unfortunately, this cannot be argued with certainty for agri-environmental payments relating to negative-impacts-reducing measures (AEP I). Similarly, Reinhard and Linderhof (2015) demonstrated that investments positively affect the level of nitrogen surplus, while we didn’t find statistically significant effect of investments on groundwater pollution with nitrates. But we discovered that investments for modernization of agricultural holdings increase pesticides in groundwater. These results imply a problem of insufficient targeting of agri-environmental measures, especially those aimed at reduction of negative impacts of agriculture on environment, such as integrated crop production and organic farming, preservation of crop rotation and greening of arable land. In their study, Reinhard and Linderhof (2015, pp. 25) stated that “data on AEM [agri-environmental measures] spending disaggregated to various activities could improve econometric estimation”. For that reason the impacts of agri-environmental payments were assessed separately for three groups of objectives: negativeimpacts-reducing measures (areas I), conservation measures (areas II), and habitat-protection measures (areas III). Reinhard and Linderhof (2015) who did not find spatial effects in their panel data specification argued that these are more likely to be observed on local levels. In line with their reasoning, our analysis confirmed the presence of spatial autocorrelation at a municipality level.
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Regarding the production support measures, it appears that compensation effect of cross-compliance requirements of direct payments is not adequate. Our findings, which are in line with Peckham and Kropp (2012), highlight the still insufficient environmental orientation of production support measures. The evidence suggests that a new policy instrument of the CAP’s first pillar—greening (which is directed to the provision of environmental public goods) is necessary and entirely justified. Moreover, from the environmental perspective, the policy of decoupling agricultural support from production decisions should be further pursued. The current study contributes to the knowledge by addressing important agri-environmental issues and limited evidence on impacts of agricultural expenditures on groundwater quality. We believe that this exercise is useful and beneficial in directing rural development policy and setting regionally tailor-made and voluntary measures for other European regions which are facing the rising issues regarding sustainable development of agriculture. Acknowledgements The article is based on research funded by the Ministry of Agriculture, Forestry and Food (Grant No. 2330-14-000334, Financial support: EUR 79.426). The Ministry of Agriculture, Forestry and Food had been involved in collection of data used in this article. References ARSE (Agency of the Republic of Slovenia for the Environment), 2011. Kazalci okolja [Environmental indicators]. Available at: http://kazalci.arso.gov.si/. ARSE (Agency of the Republic of Slovenia for the Environment), 2015. Povpreˇcne letne vrednosti nitratov in pesticidov na merilnim mestih drˇzavnega monitoringa podzemnih voda za obdobje 2007–2014. Baza podatkov [Database on nitrates and pesticides]. Agri, E.C.D.G., 2006. Handbook on Common Monitoring and Evaluation Framework. Guidance document, Brussels. Anselin, L., Baltagi, B., 2001. A companion to theoretical econometrics. Companion to Theoretical Econometrics. Baltagi, B.H., Song, S.H., Koh, W., 2003. Testing panel data regression models with spatial error correlation. J. Econ. 117, 123–150. Bartolj, T., Slabe-Erker, R., Koman, K, Kavaˇs, D., Cunder, T., Bedraˇc, M., 2015. Izdelava sprotnega vrednotenja programa razvoja podeˇzelja 2007–2013 v letu 2014, Vrednotenje vplivov plaˇcil I. stebra SKP in investicijskih ukrepov na uspeˇsnost izvajanja KOP (PRP 2007–2013). [The ongoing evaluation of Rural Development Program 2007–2013 in 2014] Ljubljana: Kmetijski inˇstitut Slovenije, 111 p. Brady, M., Kellermann, K., Sahrbacher, C., Jelinek, L., 2003. Impacts of decoupled agricultural support on farm structure, biodiversity and landscape mosaic: some EU results. J. Agric. Econ. 60 (3), 563–585. Cong, R., Brady, M., 2012. How to design a targeted agricultural subsidy system: efficiency or equity? PLoS One 7 (8). Elmi, A.A., Madramootoo, C., Egeh, M., Liu, A., Hamel, C., 2002. Environmental and agronomic implications of water table and nitrogen fertilization management. J. Environ. Qual. 31, 1858–1867. European Commission, 2011. CAP towards 2020 impact assessment. Direct payments. European Commission, 2011. Impact assessment—Common Agricultural Policy towards 2020. Eurostat, 2012. Agri-environmental indicator–commitments. Available at: http:// ec.europa.eu/eurostat/statistics-explained/index.php/Agri-environmental indicator - commitments, (accessed 31.05.2016). Girante, M.J.S., 2008. Three essays on the production effects of decoupled payments: Do decoupled payments matter for acres and yields? ProQuest Dissertations & Theses A&I. Juvancic, L., Travnikar, T., Glavan, M., Cvejic, R., Pintar, M., 2012. Targeting and spatial impacts of agri-environmental support—spatial econometric analysis of agri-environmental measures in Slovenia. In: 132nd Seminar of the EAAE, October 25–27, 2012, Skopje, Republic of Macedonia.
Kapoor, M., Kelejian, H.H., Prucha, I.R., 2007. Panel data models with spatially correlated error components. J. Econ. 140, 97–130. Keele, L., Kelly, N.J., 2006. Dynamic models for dynamic theorires: the ins and outs of lagged dependent variables. Political Anal. 14 (2), 186–205. Korsaeth, A., Eltun, R., 2000. Nitrogen mass balances in conventional, integrated and ecological cropping systems and the relationship between balance calculations and nitrogen runoff in an 8-year field experiment in Norway. Agric. Ecosyst. Environ. 79, 199–214. Krajnc, M., Gacin, M., Krsnik, P., Sodja, E., Kolenc, A., 2007. Groundwater quality in Slovenia assessed upon the results of national groundwater monitoring. Eur. Water 19 (20), 37–46. MAFF (Ministry of Agriculture, Forestry and Food), 2015a. Baza podatkov o investicijskih plaˇcilih za investicijske ukrepe 121, 122, 123 in 125 za obdobje 2007–2013 [Database on investments grants]. MAFF (Ministry of Agriculture, Forestry and Food), 2015b. MAFF Website. Available at: http://www.mkgp.gov.si/si/delovna podrocja/kmetijstvo/skupna kmetijska politika 50 let zive zgodovine/, (accessed 1.06.15.). MESP (Ministry of the Environment and Spatial Planning), 2016. The Surveying and Mapping Authority of the Republic of Slovenia. Available at: http://www.eprostor.gov.si/si/dostop do podatkov/mapa/brezplacni podatki/obcine/, (accessed 18.08.16). Mihorko, P., Gacin, M., 2013. Ocena kemijskega stanja podzemnih voda v Sloveniji v letu 2012 [Assessment of the chemical status of groundwater in Slovenia in 2012]. Ljubljana, ARSO. Millo, G., Piras, G., 2012. splm: Spatial Panel Data Models in R. J. Stat. Software 47 (1), 1–38. Mirzaei, R., Sakizadeh, M., 2016. Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environ. Sci. Pollut. Res. 23 (3), 2758–2769. Monge-Arino, F., 2007. Decoupled payments and agricultural output: A dynamic optimization model for a credit-constrained farming household. ProQuest Dissertations & Theses A&I. Nikodemus, O., Bell, S., Peneze, Z., Kruze, I., 2010. The influence of European union single area payments and less favoured area payments on the Latvian landscape. Eur. Countryside 2 (1) (25-n/a). Nolan, B.T., Ruddy, B.C., Hitt, K.J., Helsel, D.R., 1998. A national look at nitrate contamination of ground water. Water Cond. Purif. 39 (12), 76–79. Peckham, J.G., Kropp, J.D., 2012. Decoupled direct payments under base acreage and yield updating uncertainty: an investigation of agricultural chemical use. Agric. Resour. Econ. Rev. 41 (2), 158–174. Peckham, J.G., 2010. The effects of decoupled payments on production decisions under base acreage and yield updating uncertainty: An investigation of agricultural chemical use. ProQuest Dissertations & Theses A&I. R Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Reger, B., Sheridan, P., Simmering, D., Otte, A., Waldhardt, R., 2009. Potential effects of direct transfer payments on farmland habitat diversity in a marginal European landscape. Environ. Manag. 43 (6), 1026–1038. Reinhard, S., Linderhof, V.G.M., 2013. Using spatial econometrics in impact assessment. SPARD project (No. Deliverable D4. 5, p. 16). LEI, part of Wageningen UR. Reinhard, S., Linderhof, V., 2015. Convergence of EU nitrogen surplus: the RDP indicator of water quality. Ecol. Indicators 59, 19–26. SAAMRD (Slovenian Agency for Agricultural Markets and Rural development) 2015a. Baza podatkov o KOP plaˇcilih 2007–2013 [Database on agri-environment payments in the period 2007–2013]. SAAMRD (Slovenian Agency for Agricultural Markets and Rural development) 2015b. Baza podatkov o neposrednih plaˇcilih (drobnica, govedo, eko0) 2007–2013 [Database on direct payments in the period 2007–2013]. SORS (Statistical Office Republic of Slovenia), 2016. SORS Website. Available at: http://pxweb.stat.si/pxweb/Dialog/varval.asp?ma=0156104S&ti=&path=./ Database/Arhiv/01 ozemlje podnebje/10 01561 podnebni kazalniki/&lang=2, (accessed 19.08.16). Salo, T., Turtola, E., 2006. Nitrogen balance as an indicator of nitrogen leaching in Finland. Agric. Ecosyst. Environ. 113, 98–107. Sieling, K., Kage, H., 2006. N balance as an indicator of N leaching in an oilseed rape – winter wheat – winter barley rotation. Agric. Ecosyst. Environ. 115, 261–269. Sokolova, E., Kirovski, P., Ivanov, B., 2015. The role of EU direct payments for production decision-making in Bulgarian agriculture. Agric. For. 61 (4), 145–152. Wakida, F.T., Lerner, D.N., 2005. Non-agricultural sources of groundwater nitrate: a review and case study. Water Res. 39 (1), 3–16.
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
The impacts of agricultural payments on groundwater quality: Spatial analysis on the case of Slovenia Renata Slabe-Erker ∗ , Tjaˇsa Bartolj, Marko Ogorevc, Damjan Kavaˇs, Klemen Koman Institute for Economic Research, Kardeljeva ploscad 17, 1000 Ljubljana, Slovenia
a r t i c l e
i n f o
Article history: Received 10 December 2015 Received in revised form 19 September 2016 Accepted 28 September 2016 Keywords: Common agricultural policy Agricultural payments Nitrates Pesticides Groundwater quality Spatial panel model
a b s t r a c t The European Common Agricultural Policy still follows its primary goals, i.e. quality food at affordable prices and a decent standard of living for farmers, fifty years after its adoption. Moreover, this policy adapts to the changing needs of society and the new challenges, mostly preservation of the environment, nature and biodiversity in rural areas. Although the Common Agricultural Policy receives the largest share of European budget, the funds are decreasing over time, especially direct payments, which aim to provide basic income support to farmers in the European Union. On the other hand, agri-environmental payments are gaining importance. Policy decision-makers should be interested in the question of impacts of growing eco-conditionality of agricultural spending. New insights would help them to be successful in achieving the goals of sustainable agriculture. The purpose of this paper is to estimate the impacts of production support payments and rural development payments on the quality of groundwater. We use the small EU country Slovenia as an example. The baseline indicators are the level of nitrates and pesticides in groundwater, while the impacts were estimated using spatial error model. The results show that direct payments, coupled subsidies and investment grants raise the level of pesticides in groundwater, but do not have any statistically significant impact on the level of nitrates in groundwater. Furthermore, we did not find any statistically significant effects of agri-environmental payments on decrease of groundwater pollution with nitrates. However, our findings revealed that agri-environmental payments are effective in reducing pesticides in groundwater, although only to a limited extent. These results imply a problem of insufficient targeting of agri-environmental measures on the one hand, and suggest that greening of direct payments is necessary and entirely justified. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction The Common Agricultural Policy (CAP) of the European Union was designed in 1962 to enable good quality food at affordable prices for EU citizens and a decent standard of living for EU farmers. Fifty years later, these goals are still relevant, and the European Union adapts the CAP to the changing needs of society and the new challenges (MAFF, 2015b). Both the public and the policy makers are well aware of the fact that there are other functions of agriculture besides the supply of people with food and maintaining farmers’ incomes. The agriculture preserves the countryside, together with the landscape, demographics and rural economy, as well as natural resources and biodiversity. In addressing these challenges, it is essential for the Member States to promote the
∗ Corresponding author. E-mail addresses: [email protected] (R. Slabe-Erker), [email protected] (T. Bartolj), [email protected] (M. Ogorevc), [email protected] (D. Kavaˇs), [email protected] (K. Koman). http://dx.doi.org/10.1016/j.ecolind.2016.09.048 1470-160X/© 2016 Elsevier Ltd. All rights reserved.
competitiveness and also the sustainability of agriculture and rural areas. With accordance to this, the CAP measures are classified in two Pillars: production support (Pillar I) and rural development (Pillar II). Still, both pillars are intertwined and integrated within the CAP. The primary aim of the Pillar I is to provide basic income support to EU farmers by two main instruments: direct payments to farmers and market measures. In order to be eligible to receive this support, farmers must keep land in good agricultural and environmental condition and meet legislative standards covering the environment, public health, plant health and animal health (crosscompliance conditions). Therefore, direct payments should also affect the delivery of public goods, mostly those related to environment and rural vitality. While the CAP receives the largest share of the EU budget, the funds are decreasing over time, especially direct payments to farmers. In the period from 2007 to 2009, direct payments represented on average 29% of agricultural income in the EU (EC, 2011a). To be less trade distorting the main part of direct payments is decoupled from production (hereinafter referred to as
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direct payments). Only a smaller part of direct payments is still linked to the production of a particular crop or keeping a particular type of livestock. These payments are called coupled subsidies. The measures of common rural development policy (Pillar II) are implemented within the national Rural Development Program (RDP) of the respective Member State which links European and national, local needs. Moreover, the States have to provide cofinancing. Rural development measures are aimed at improving competitiveness and promoting diversification of economic activity, delivering environmental public goods and improving quality of life in rural areas (EC, 2011b). A large part of these measures represents Agri-environmental payments, like payments for organic farming, actions to maintain habitats favourable to biodiversity, management of pastures, crop rotation etc. Therefore, the support for rural development is intended for those farms, which in the management of agricultural land contribute to the conservation of biodiversity, landscape diversity and the protection of soil and water resources. Agri-environmental payments represent 30% of rural development expenditures from the EAGG fund.1 In 2009, the average share of utilised agricultural area under agrienvironmental measures in EU-27 was 21%, with the highest share in Luxembourg and Finland (more than 90%) and the lowest share in Bulgaria, Poland and Netherlands (less than 6%). In terms of area covered by different types of agri-environmental measures, the most important commitments refer to management of landscape, pastures and high nature value farming (39% of the total area committed across the EU-27). This type of commitment was more popular in the new Member States (64%) than in the EU-15 (35.8%) (Eurostat, 2012).2 With the new programming period (2014–2020) the environmental component is gaining even more importance as the support has extended to the field of agriculture’s contribution to climate change mitigation and adaptation. Implementation of the measures financed in the 2007–2013 period in the Member States came to an end. In the beginning of 2015, the European Commission formally confirmed the new Rural Development Program for each Member State for the period 2014–2020 which is the basis for the disbursement of EU funds in this period. Therefore, the time is right to evaluate the impacts of different types of received payments on agri-environmental contents. Increasing the importance of environmental and safety issues for proving eligibility to receive agricultural payments as well as limited empirical evidence on environmental impacts of agricultural expenditures have motivated the present study. Hence, this study aims to numerically examine the impacts of agricultural payments (which are mostly conditional upon environmental standards) on groundwater quality. More precisely, for the EU member state Slovenia, we assess both the impacts of direct payments and coupled subsidies (Pillar I) and agri-environmental payments and investment grants (Pillar II) in the period 2007–2013 on two indicators: the level of pesticides and nitrates in groundwater. The protection of groundwater quality in Slovenia is of upmost importance, because more than 97% of drinking water is abstracted from shallow, unconfined alluvial aquifers and fractured or karstic porosity aquifers (Krajnc et al., 2007). Moreover, more than three quarters of the surface belongs to areas with less favoured conditions for agricultural production (e.g. Alpine region). Consequently, public spending on agri-environmental measures represents the
1
European Agricultural Guarantee and Guidance Fund. Commitments referred to management of landscape, pastures and high nature value farming represent 44% of total area committed in Slovenia in 2009 (ARSE, 2011). In the total area the same area can be counted several times if several types of commitments apply on the same land (Eurostat, 2012). 2
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largest share of the budget for rural development, and the same applies to Europe. In order to avoid, prevent or reduce detrimental concentrations of harmful pollutants in groundwater, in particular the two mentioned above, the European parliament and the Council of the European Union adopted the Groundwater Directive (2006/118/EC). The major source of groundwater contamination is agricultural non-point source pollution. Agricultural pollution with nitrates and pesticides is the most common cause for failing the quality standards, and consequently poor chemical status of groundwater. For that reason, the EU legislation regulates these areas more specifically by Nitrates Directive (91/676/EEC), the Sustainable Use of Pesticides Directive (2009/128/EC) and some other directives. The use of nitrogen fertilizers and pesticides in agriculture brings significant economic benefits to the sector but also risks to the environment and human health. The main source of nitrogen input to agricultural land is application of organic and chemical fertilizers (Korsaeth and Eltun, 2000; Sieling and Kage, 2006), while the main non-agricultural source is related to density of wastewater disposal systems (Wakida and Lerner, 2005). In Slovenia, with extremely dispersed settlement pattern, this could be an important environmental issue. Moreover, the concentration of nitrates in groundwater also depends on natural conditions such as pedological characteristics, climatic and biological factors. On the other hand, pesticides used in agriculture are plant protection products, like weed-killers and plant-growth regulators. Therefore, CAP measures are designed so as to reduce these risks as much as possible. In this paper, the impacts of agricultural payments on the level of pesticides and nitrates in groundwater are assessed with spatial panel data model. The paper is structured as follows. Section 2 presents short literature review. In Sections 3 and 4, the article offers summary statistics of data and description of the estimation method. The results are presented in Section 5. The paper concludes with discussion and implications for policy and practice.
2. Short literature review There are many CAP measures whose objective is to directly or indirectly improve or protect groundwater quality. However, to our knowledge, only a few studies have been carried out that would link individual agricultural payments to groundwater quality. The sparse findings offer a starting point for the research hypotheses on impacts’ evaluation. The CAP’s most important instrument for providing basic income support to farmers and delivering basic public goods is direct payments. These payments are not related to the volume of production. As previously mentioned farmers qualify for the subsidy, if they maintain their land in a good agricultural and environmental condition and comply with the relevant statutory management requirements (Cong and Brady, 2012). The studies mainly examine the impacts of direct payments on production (e.g., Girante, 2008; Monge-Arino, 2007; Peckham, 2010; Sokolova et al., 2015) or, less frequently on the state of the environment. Those that study environmental impacts focus primarily on the type of production (intensive vs. extensive) and the structure of landscape or land abandonment in connection with diversity of habitats (e.g. Cong and Brady, 2012; Nikodemus et al., 2010; Reger et al., 2009). The study of Peckham and Kropp (2012) is related to our field of research, although indirectly. The authors found that direct payments when they were introduced in the USA in 1996, had only a small impact on the use of pesticides and nitrates, but after 2004 their impact became greater and positive (Peckham and Kropp, 2012). However, as far as we know, the problem of groundwa-
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ter pollution in relation to direct payments has not been studied before. Given the request that recipients of direct payments maintain land in good agricultural and environmental condition, and in the absence of researches on this issue, we set the hypothesis that the increase of direct payments has no impact on groundwater quality (Hypothesis 1). There are even fewer studies on impacts of coupled subsidies on the environment and nature. Again, the studies focused mainly on type of production and the structure of landscape in connection with diversity of habitats (e.g. Brady et al., 2003). In Germany, coupled subsidies accelerated the phasing out of agricultural production in areas with low productivity of land and increased the use of land in more productive areas. In this way, the agricultural landscape disappeared and the agricultural production has been intensified. The impact on habitats (diversity, rarity and uniformity of distribution) was negative and medium-sized. If we assume that coupled subsidies are related to more intensive agriculture, we can further assume that increase in coupled subsidies is positively related to the levels of groundwater nitrates and pesticides. To test our claim we have set the Hypothesis 2 that coupled subsidies have no effect on groundwater quality (Hypothesis 2). The objectives of many agri-environmental measures are related to reducing the use of pesticides and nitrates and consequently improving of the quality of water. However, so far the link between agri-environmental payments and water quality has only been studied by Reinhard and Linderhof (2015). They found statistically significant negative relationship between agri-environmental expenditures and nitrogen surplus. The same authors noted that investments may be related to the environment or agricultural intensification. Moreover, the results of their study showed that investments positively affect the level of nitrogen surplus. From these sparse findings we claim that increase in agri-environmental expenditures is negatively related to the levels of groundwater nitrates and pesticides and increase in investment grants is positively related to the levels of groundwater nitrates and pesticides. To test these statements we have set two hypotheses: agri-environmental expenditures and investment grants have no effect on groundwater quality (Hypothesis 3, Hypothesis 4). 3. Data and descriptive statistics Our data set merges SAAMRD data (Slovenian Agency for Agricultural Markets and Rural Development) on actually disbursed applications and ARSE (Agency of the Republic of Slovenia for the Environment) environmental data (ARSE, 2015; MAFF, 2015a; SAAMRD, 2015a,b). We aggregated the panel data for agricultural payments at the farm level to municipality level. The analysed area consists of 210 Slovenian municipalities (LAU 2).3 The financial perspective covered the 2007–2013 period however the first payments in Slovenia were made in 2008. We followed the Common Monitoring and Evaluation Framework logic (Agri, 2006) and focused on the baseline indicator which is the level of nitrates and pesticides in groundwater. This is related to the impact indicator which is the improvement in groundwater quality. For this purpose we used the average annual values of nitrates and pesticides at sampling stations of state monitoring (ARSE, 2015). Table 1 shows the number of approved applications of holdings for agri-environmental payments (AE payments in Table 1), direct payments and coupled subsidies. The majority of holdings that received agri-environmental payments got direct payments as well. The ratio between the holdings, which have been included
3
LAU stands for Local Administrative Units. LAU 2 was previously called NUTS 5.
Table 1 Number of approved applications of holdings by year and type of payment. Year
AE payments
Direct payments
Coupled subsidies
2008 2009 2010 2011 2012 2013 Total
18,383 18,190 18,113 18,048 18,304 16,076 107,114
58,619 59,095 51,904 50,837 51,652 56,740 328,847
27,818 26,237 26,155 25,738 25,329 16,518 147,795
in the agri-environmental measure but have not received direct payments, and those who have received both types of payments is 13 to 100. Similarly, most of the holdings that received coupled subsidies received direct payments as well, with ratio 8 to 100 in favour of combined applications. Fig. 1 shows the average amounts of direct payments, coupled subsidies and agri-environmental payments per hectare of utilised agricultural area. All payments are quite stable over the analysed period. In the case of direct payments, a minor peak is detected in 2009 (i.e. EUR 293.6/ha, at constant prices 2008). The average amount of agri-environmental payments and direct payments has increased in the last year (2013), while the average amount of coupled subsidies has decreased. Agri-environmental payments (AEP) or measures are classified into three groups: negative-impacts-reducing measures, such as integrated crop production and organic farming (AEP I), conservation measures, such as mowing of steep and humpy meadows, mountain pastures and meadow orchards (AEP II), and habitatprotection measures, such as preserving grassland habitats and autochthonous and traditional plants (AEP III). The average annual values of habitat-protection payments (AEP III) per hectare of utilised agricultural area are constantly very low (from EUR 0.19/ha in 2008 to EUR 0.31/ha in 2012). In the observed period the average value of conservation payments (AEP II) per hectare of utilised agricultural area is EUR 18.8. From 2010 onwards, the conservation payments decline. The most agri-environmental payments are aimed at reduction of negative impacts of agriculture on environment (AEP I). The peak is detected in the last year (2013), i.e. EUR 57.5 per hectare of utilized agricultural area. In the case of investment grants per hectare of utilised agricultural area two peaks are detected; for modernisation of agricultural holdings − INV 1 (EUR 27.0/ha) in 2012 and for adding value to agricultural and forestry products − INV 2 (EUR 13.8/ha) in 2010. In 2011, the average value of investment grants decreases to EUR 19.2/ha and EUR 5.8/ha, respectively. Greater variation in average amounts of investment grants was detected for adding value to agricultural and forestry products. Fig. 2 shows the trend in baseline indicators, i.e. annual average levels of nitrogen and the sum of pesticides in groundwater for the period 2007–2013, with spatial pattern in the first and the last year of observation, calculated from the values at sampling points of the state monitoring. Average number of sampling points in the observed period was 154 for nitrates and 72 for pesticides. The presented values are obtained by ordinary kriging interpolation (e.g. Mirzaei and Sakizadeh, 2016). The initial and final state of pollution is expressed as average by municipalities, while the state of pollution by years is expressed as country’s average. Spatial patterns for the levels of nitrates do not change, while spatial patterns for the levels of pesticides show a clear decline, with the largest reduction in North and Northeast Slovenia. There is evident downward trend in levels of pesticides in the whole period, while nitrate levels peaked in 2010, but remained at similar levels compared to 2007 in the last year. A possible explanation for the peak in nitrate levels is the peak in precipitation in 2010 (SORS, 2016) as precipitation surpluses have been found to influence the
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Fig. 1. Average values of agricultural payments in EUR/ha of utilised agricultural area, 2008–2013.
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Note: Amounts are in 2008 constant prices (EUR).
Fig. 2. Changes in levels of pesticides (g/l) and nitrates (mg NO3 /l) in groundwater over years.
extent of agricultural nitrate leaching (e.g. Elmi et al., 2002; Salo and Turtola, 2006). Fig. 3 presents the overlap between spatial patterns of total agricultural payments and groundwater pollution. From these patterns we can see that agricultural payments are spatially unevenly distributed. This is due to higher share of arable land in total agricultural land in Northeast Slovenia. Moreover, sampling stations of groundwater state monitoring are located primarily in lowland areas where the share of arable land is usually higher (Juvancic et al., 2012). It is apparent from these figures that payments are more or less concentrated around the sites of higher groundwater pollution. The mismatch between spatial patterns is greater in the case of pollution with nitrates. However, this is expected because of non-agriculture polluters of groundwater with nitrates, such as uncontrolled urban infrastructure (Nolan et al., 1998). 4. Method The References of European Commission and the results of various evaluation studies demonstrate that the major challenge is counterfactual analysis where state intervention (e.g. investment grants, subsidies) is compared with the situation without intervention. In this way, it is possible to estimate which results/impacts can be attributed to public intervention and which are the result
of other factors. To perform the counterfactual analysis a definite “cause and effect chain”, clearly defined that both supported beneficiaries and non-supported groups are required. Different econometric techniques can be used, such as difference-indifference (DID), propensity score matching (PSM), and regression discontinuity (RD). Often the individual methods are combined. However, for the proper implementation of such methods an adequate data quality is needed (Bartolj et al., 2015). The nature of the analysed problem prevents us from using individual farm data, since it is not possible to attribute the impact of an individual farm on groundwater quality using the data from monitoring sites, hence, the spatial analysis is performed at the municipal level. We conducted a spatial panel econometric analysis to assess the impacts of agricultural payments on the quality of groundwater in all municipalities, including those without intervention. The impacts of agri-environmental payments are assessed separately for three groups of objectives: negativeimpacts-reducing measures (areas I), conservation measures (areas II), and habitat-protection measures (areas III). We further assumed that the groundwater quality is affected by municipality agricultural characteristics, such as arable land and livestock units. Moreover, the dispersion of settlement was used to indicate additional non-agricultural sources of nitrogen in urban areas. Also, different development levels of municipalities were taken into
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Fig. 3. Spatial overlap between total agricultural payments (2008–2013) and pesticide/nitrate level in groundwater (2007).
account (Reinhard and Linderhof, 2013). Therefore, it was possible to estimate impacts that can be attributed to agricultural payments
as well as structural and developmental characteristics of municipalities. Although pesticides transfer into the groundwater with percolation through the soil relatively quickly, they remain there for a long time because of the slow decomposition process. Thus, for example, atrazine and its metabolite desethyl-atrazine are still present in groundwater, although the use of atrazine was completely prohibited in 2003 (Mihorko and Gacin, 2013). Similarly, the high concentrations of nitrates retain long after farmers reduce the intensity of fertilization. The latter is one of the main causes for transmission of nitrates in the groundwater. Consequently, when assessing the impact of various payments on the values of pesticides and nitrates in the groundwater their historical values were used. Ordinary kriging was used to interpolate nitrate and pesticide values to a regular grid, and then these values were used to obtain average nitrate and pesticide levels across municipalities, including those without direct measurement of nitrate and pesticide levels. A spatial error model was applied to estimate the impacts of agricultural payments on pesticide and nitrate levels in the groundwater. “This is appropriate when the concern is correcting of the potentially biasing influence of the spatial autocorrelation due to the use of spatial data (irrespective of whether the model of interest is spatial or not)” (Anselin and Baltagi, 2001, pp. 316). We used row-standardized spatial weights matrix (W) based on adjacency between pairs of municipalities. Joint Lagrange multiplier test (Baltagi et al., 2003) was applied to test the null hypothesis of no random effects and spatial autocorrelation. When the test rejects the null hypothesis, we use maximum likelihood estimation, because for a model with spatially auto-correlated error components, ordinary least squares (OLS) on a random effects model is inefficient (Millo and Piras, 2012), while the estimation of an lagged dependent variable model with OLS is only problematic when residual autocorrelation is present (Keele and Kelly, 2006). For the estimation of the spatial autoregressive parameter and the variance components of the disturbance process we used generalized moment estimator (Kapoor et al., 2007). Since the variables are exponentially distributed, their logarithms are calculated. Analysis was performed in R (R Core Team, 2016).
To examine the impact of agricultural payments on the annual values of nitrates and pesticides in groundwater we estimate the following equation:
where Pollutant denotes the annual values of nitrates or pesticides in groundwater referring to municipality i in year t and two time lags of pollutant (t-1 and t-2). We assume that each ecological indicator depends on the agri-environmental payments (AEP), direct payments (DP), coupled subsidies (CS) and investment grants (INV). The impact of agri-environmental payments, direct payments and coupled subsidies is measured with their average values per hectare of utilised agricultural area for each municipality in the previous year and the impact of investments is measured by their average value per hectare of utilised agricultural area in the current year. AL is defined as a share of arable land in utilised agricultural area, LU stands for livestock units per hectare of utilised agricultural area and SD denotes settlement dispersion, which is used to indicate additional non-agricultural sources of nitrogen in urban areas. Settlement dispersion was measured using real estate locations (MESP, 2016) by Clark and Evans aggregation index R, which is a simple measure of clustering or ordering of a point pattern. It is the ratio of the observed mean nearest neighbour distance in the pattern to that expected for a Poisson point process of the same intensity. Average wages (AW) in the model serve as a proxy for development. W represents the spatial weights matrix and is a spatial autocorrelation coefficient. The measurement of spatial autocorrelation describes the overall pattern across a geographic landscape and can be motivated by omitted variable or spatial heterogeneity which affect spatial regression methods less than OLS.
5. Results Estimated coefficients of the equation (1) are presented in Table 2. The increase of direct payments by 1% is followed by 0.357% increase of pesticide levels in the next year. Moreover, the increase of coupled subsidies by 1% is followed by 0.162% increase of pesticide levels in the next year. Similarly, higher investment grants for modernisation of agricultural holdings products slightly increase the pesticide levels in groundwater (0.117). Furthermore, a 1% increase in arable land causes pesticide levels to increase by 0.07%. One the other hand, the increase of agri-environmental payments for conservation measures (such as mountain pastures,
R. Slabe-Erker et al. / Ecological Indicators 73 (2017) 338–344 Table 2 Regression results of agricultural payments on pesticide and nitrate levels in groundwater. Pesticides
Nitrates
LUi
−3.880* (2.254) 0.352*** (0.029) 0.204*** (0.027) 0.357*** (0.105) 0.162*** (0.061) −0.058 (0.038) −0.147*** (0.043) −0.079*** (0.024) 0.017** (0.008) −0.001 (0.010) 0.073* (0.039) –
SDit
–
AWit
0.002 (0.299) 0.001 (0.001) 0.006 (0.023) 58.448*** 0.522 210 5
−0.319 (1.247) 0.471*** (0.032) 0.323*** (0.033) 0.008 (0.059) −0.059 (0.038) 0.011 (0.021) 0.019 (0.023) 0.009 (0.013) −0.002 (0.003) −0.003 (0.005) 0.047** (0.022) 0.064 (0.053) 0.173*** (0.067) 0.133 (0.164) 0.001 (0.001) 0.192*** (0.0434) 13.688*** 0.707 210 5
Const. Pollutantsit-1 Pollutantit-2 DPit-1 CSit-1 AEP Iit-1 AEP IIit-1 AEP IIIit-1 INV 1it INV 2it ALi
Joint LM Test R2 n T
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second pillar measures (investment grants and agri-environmental payments) on indicators of groundwater quality in Slovenia in the period 2007–2013. The impacts were estimated using spatial panel data model at the municipality level. This study indicates that agricultural payments do not have any statistically significant impact on the level of nitrates in groundwater. However, variation in nitrate level in groundwater is largely explained by past levels, arable land and settlement dispersion. Perhaps there is a problem of omitted variable, which is also spatially distributed and affects the value of nitrates, e.g. additional nonagricultural sources of nitrogen in urban areas. However, omitted variable bias affects spatial regression methods less than OLS. The following is a summary of conclusions. Hypothesis 1. that direct payments have no impact on groundwater quality was rejected in the case of pesticides, but could not be rejected in the case of nitrates. Direct payments increase pesticides in groundwater. Hypothesis 2. that coupled subsidies have no impact on groundwater quality was rejected in the case of pesticides, but could not be rejected in the case of nitrates. Coupled subsidies increase pesticides in groundwater. Hypothesis 3. that agri-environmental expenditures have no impact on groundwater quality was rejected in the case of pesticides but could not be rejected in the case of nitrates. Though all groups of agrienvironmental payments decrease pesticides in groundwater, only conservation measures (AEP II) and habitat-protection measures (AEP III) are statistically significant. Hypothesis 4. that investment grants have no impact on groundwater quality was rejected only for investments for modernisation of agricultural holdings (INV 1) in the case of pesticides. These investments increase pesticides in groundwater.
Significance levels: * < 0.1; ** < 0.05; *** < 0.01.
mowing of steep and humpy meadows and preserving of extensive grassland) by 1% decreases pesticide levels in groundwater by 0.015% in the next year. Additionally, a 1% increase in agrienvironmental payments for habitat-protection measures, such as preserving grassland habitats, causes a minor reduction of pesticide levels in groundwater, i.e. by 0.08%. The model explains 52% of pesticides variation and spatial errors are not statistically significant. Joint Lagrange multiplier tests rejected the null hypothesis of no random effects and no spatial autocorrelation. In contrast to pesticides, there is no statistically significant connection between the level of nitrates in groundwater and agricultural payments. Nitrate levels are explained only by its past levels (two time lags), share of arable land in utilised agricultural area and the settlement dispersion. A 1% increase in arable land causes nitrate levels to increase by 0.05%. The results reveal that the increase of settlement dispersion by 1% is followed by 0.17% increase of nitrate levels. The model explains 70% of nitrates variation and spatial error coefficient is positive and statistically significant. Positive spatial autocorrelation coefficient confirms spatial heterogeneity, which can be interpreted as an autoregressive-in-space error structure, with decreasing intensity as we move further away. Joint Lagrange multiplier tests rejected the null hypothesis of no random effects and no spatial autocorrelation. 6. Conclusion and discussion In the present paper, we evaluated the impacts of the CAP first pillar measures (direct payments and coupled subsidies) and CAP
Interestingly, the above findings are not consistent with a study conducted by Reinhard and Linderhof (2015). Authors found a significantly negative effect of agri-environmental expenditures on nitrogen surplus, while we did not find statistically significant effects of agri-environmental payments on decrease of groundwater pollution with nitrates. However, our findings revealed that agri-environmental payments are effective in reducing pesticides in groundwater, although only to a limited extent. Unfortunately, this cannot be argued with certainty for agri-environmental payments relating to negative-impacts-reducing measures (AEP I). Similarly, Reinhard and Linderhof (2015) demonstrated that investments positively affect the level of nitrogen surplus, while we didn’t find statistically significant effect of investments on groundwater pollution with nitrates. But we discovered that investments for modernization of agricultural holdings increase pesticides in groundwater. These results imply a problem of insufficient targeting of agri-environmental measures, especially those aimed at reduction of negative impacts of agriculture on environment, such as integrated crop production and organic farming, preservation of crop rotation and greening of arable land. In their study, Reinhard and Linderhof (2015, pp. 25) stated that “data on AEM [agri-environmental measures] spending disaggregated to various activities could improve econometric estimation”. For that reason the impacts of agri-environmental payments were assessed separately for three groups of objectives: negativeimpacts-reducing measures (areas I), conservation measures (areas II), and habitat-protection measures (areas III). Reinhard and Linderhof (2015) who did not find spatial effects in their panel data specification argued that these are more likely to be observed on local levels. In line with their reasoning, our analysis confirmed the presence of spatial autocorrelation at a municipality level.
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Regarding the production support measures, it appears that compensation effect of cross-compliance requirements of direct payments is not adequate. Our findings, which are in line with Peckham and Kropp (2012), highlight the still insufficient environmental orientation of production support measures. The evidence suggests that a new policy instrument of the CAP’s first pillar—greening (which is directed to the provision of environmental public goods) is necessary and entirely justified. Moreover, from the environmental perspective, the policy of decoupling agricultural support from production decisions should be further pursued. The current study contributes to the knowledge by addressing important agri-environmental issues and limited evidence on impacts of agricultural expenditures on groundwater quality. We believe that this exercise is useful and beneficial in directing rural development policy and setting regionally tailor-made and voluntary measures for other European regions which are facing the rising issues regarding sustainable development of agriculture. Acknowledgements The article is based on research funded by the Ministry of Agriculture, Forestry and Food (Grant No. 2330-14-000334, Financial support: EUR 79.426). The Ministry of Agriculture, Forestry and Food had been involved in collection of data used in this article. References ARSE (Agency of the Republic of Slovenia for the Environment), 2011. Kazalci okolja [Environmental indicators]. Available at: http://kazalci.arso.gov.si/. ARSE (Agency of the Republic of Slovenia for the Environment), 2015. Povpreˇcne letne vrednosti nitratov in pesticidov na merilnim mestih drˇzavnega monitoringa podzemnih voda za obdobje 2007–2014. Baza podatkov [Database on nitrates and pesticides]. Agri, E.C.D.G., 2006. Handbook on Common Monitoring and Evaluation Framework. Guidance document, Brussels. Anselin, L., Baltagi, B., 2001. A companion to theoretical econometrics. Companion to Theoretical Econometrics. Baltagi, B.H., Song, S.H., Koh, W., 2003. Testing panel data regression models with spatial error correlation. J. Econ. 117, 123–150. Bartolj, T., Slabe-Erker, R., Koman, K, Kavaˇs, D., Cunder, T., Bedraˇc, M., 2015. Izdelava sprotnega vrednotenja programa razvoja podeˇzelja 2007–2013 v letu 2014, Vrednotenje vplivov plaˇcil I. stebra SKP in investicijskih ukrepov na uspeˇsnost izvajanja KOP (PRP 2007–2013). [The ongoing evaluation of Rural Development Program 2007–2013 in 2014] Ljubljana: Kmetijski inˇstitut Slovenije, 111 p. Brady, M., Kellermann, K., Sahrbacher, C., Jelinek, L., 2003. Impacts of decoupled agricultural support on farm structure, biodiversity and landscape mosaic: some EU results. J. Agric. Econ. 60 (3), 563–585. Cong, R., Brady, M., 2012. How to design a targeted agricultural subsidy system: efficiency or equity? PLoS One 7 (8). Elmi, A.A., Madramootoo, C., Egeh, M., Liu, A., Hamel, C., 2002. Environmental and agronomic implications of water table and nitrogen fertilization management. J. Environ. Qual. 31, 1858–1867. European Commission, 2011. CAP towards 2020 impact assessment. Direct payments. European Commission, 2011. Impact assessment—Common Agricultural Policy towards 2020. Eurostat, 2012. Agri-environmental indicator–commitments. Available at: http:// ec.europa.eu/eurostat/statistics-explained/index.php/Agri-environmental indicator - commitments, (accessed 31.05.2016). Girante, M.J.S., 2008. Three essays on the production effects of decoupled payments: Do decoupled payments matter for acres and yields? ProQuest Dissertations & Theses A&I. Juvancic, L., Travnikar, T., Glavan, M., Cvejic, R., Pintar, M., 2012. Targeting and spatial impacts of agri-environmental support—spatial econometric analysis of agri-environmental measures in Slovenia. In: 132nd Seminar of the EAAE, October 25–27, 2012, Skopje, Republic of Macedonia.
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