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What do we lack in agri-environment schemes? The case of farmland birds in Estonia
Agriculture, Ecosystems and Environment 156 (2012) 89–93
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Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee
What do we lack in agri-environment schemes? The case of farmland birds in Estonia Jaanus Elts ∗ , Asko Lõhmus Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, 46 Vanemuise St., 51014 Tartu, Estonia
a r t i c l e
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Article history: Received 28 November 2011 Received in revised form 24 April 2012 Accepted 25 April 2012 Keywords: Agri-environmental subsidies Estonia EU policy Farmland birds Habitat quality
a b s t r a c t Among 66 Estonian farms, representing three subsidy types, birds (especially Sylvia communis and Saxicola rubetra) were more abundant in organic farms that received the largest subsidies and were typically situated in structurally diverse landscapes with a high share of grasslands. Vertical landscape elements most clearly promoted species richness of breeding birds and S. communis. However, among managementrelated environmental factors considered, only fertilizer input was consistently subsidy-type dependent (highest inputs at medium subsidy levels). High inputs reduced species richness of breeding birds and the density of Alauda arvensis. When environmental factors were accounted for, no significant influences of the subsidy type on birds were observed, but region-specificity remained significant. Our results indicate that differentiation of national subsidies in the AES framework does not necessarily lead to distinct environmental effects, and that targeting of the subsidies on particular species is necessary to effectively improve current environmental conditions. © 2012 Elsevier B.V. All rights reserved.
1. Introduction To promote farming in a way that it supports biodiversity, landscape values, and water, air and soil quality, many regions have introduced agri-environment schemes (AES), including Australia, the European Union, USA, and Switzerland. AES are incentive payments based on voluntary agreements with farmers and land managers to compensate their income losses associated with employing environmentally sustainable land management practices. In the EU, AES are supported through national Rural Development Programmes with EU funding from the European Agricultural Fund for Rural Development. Since the early 1990s, these programs are managed by regional or national authorities under a decentralized management system, subject to approval by the European Commission. Theoretically, AES could be the primary tools for reversing the decline of farmland wildlife in the EU but their scientific basis remains weak (Kleijn et al., 2001, 2003). Their success is usually measured simply by the area under contracts, numbers of enrolling farmers, or the amount of funds spent. Field evidence of biodiversity benefits mostly concern individual species (Peach et al., 2001; Davies et al., 2005), leaving considerable uncertainty about general prospects to mitigate biodiversity loss (Whittingham, 2007; but see Merckx et al., 2009). Therefore, the European Commission is developing more “objective achieved” monitoring and evaluation
∗ Corresponding author. Fax: +372 7422180. E-mail address: [email protected] (J. Elts). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2012.04.023
systems. For the period 2007–2013 (European Commission, 2006) five types of indicators have been proposed, of which the ‘Baseline indicators’ include impact evaluation based on before–after comparisons in relation to the program objectives. Farmland birds are the only wild animals listed among the baseline indicators, but it is still unclear to what extent the schemes do support birds. In some (but not all) parts of Britain, an increase in avian abundance along with the proportion of land included in AES has been demonstrated for upland habitat specialists and species of conservation concern (Dallimer et al., 2010; Davey et al., 2010a). However, the performance may vary among species, so that common species may show an increase, whereas threatened species continue to decline (Birrer et al., 2007; Reid et al., 2007). Also, complex impacts may emerge along food-webs (Reid et al., 2007) and quantitative data over large agricultural landscapes are scarce, especially for the new EU accession countries (Herzon and O’Hara, 2007). All this makes it difficult to properly assess population dynamics of farmland birds in relation to farming impacts in different agricultural systems. The current work is the first to address the effectiveness of AES for enhancing farmland bird populations in a new EU Member State – Estonia. Despite a relatively intensive agriculture during its Soviet past, Estonia has retained a high proportion of extensively managed farmland. The well studied semi-natural grasslands and wooded meadows (e.g. Liira et al., 2009) have only marginal farming value nowadays and their management is mostly driven by conservation interests. However, opening of the EU market and its Common Agricultural Policy (CAP) subsidies have triggered intensification of agricultural production in arable lands. For example,
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the average wheat yield increased 45% in 2004–2008 compared with the pre-EU accession five-year period in Estonia (1998–2003; FAOSTAT, 2010). The AES are in place since 2004 and include several kinds of subsidies to farmers; however, it is not clear which benefits to biodiversity those complicated systems generate. The only study addressing the effect of Estonian AES on biodiversity found that large homogenous fields generally lacked nesting places and food resources for bumblebees, but the farms that had joined AES had more bumblebee species and higher flower densities (Viik et al., 2007). More general studies in arable areas have documented local and landscape effects on plant communities in field margins (suggesting a subset of ‘high-nature-value species’ for monitoring the effects of land-use intensification; Aavik and Liira, 2010) and the effects of management practices on soil biota (e.g. Truu et al., 2008). By analyzing the case of Estonia, we ask whether higher national AES subsidies benefit birds more than those with lower payments; are those effects region- and taxon-specific; and how are they related to habitat variation. Those questions build on previous coarse-scale Pan-European studies, which have included Estonia, and have reported major, but region-specific, effects of agricultural intensification on bird assemblages (e.g. Guerrero et al., 2011). The environmental factors involved are less clear, because agricultural intensification may both remove and add particular landscape elements. In fact, Geiger et al. (2010) found that organic farms harbored more wild plant and carabid species compared with other AES, but not significantly more bird species. Yet, the Estonian common bird monitoring reveals a recent pronounced drop in farmland bird abundance (Kuresoo et al., 2011). The habitat factors potentially causing such decreases are the reduction of edge structures (Herzon et al., 2008) and of semi-natural habitats, which are also related to fertilizer use (Billeter et al., 2008). Here, we explore a set of such carefully selected environmental factors for a link between their influence to birds and sensitivity to the cost-effectiveness of farming, which could be increasingly affected by increasing the subsidies. In the absence of before–after design, comparing different subsidy levels over several years could distinguish whether the AES are actually causing an improvement in habitat qualities functionally important for birds, or are the subsidies simply applied in higher-quality landscapes where the trend may be even toward agricultural intensification. We compare three subsidy types with different requirements, which collectively should promote mitigation of the environmental impacts of farming. (i) Single Area Payment is a direct payment (total area of 850,000 ha, D 50 ha/year) to all farmers who follow general requirements called Good Farming Practice (e.g. the land should be in a ‘good agricultural condition’). (ii) Environmentally Friendly Production Scheme covers ca. 55% of the farmed area and requires the participants to additionally prepare Nutrient Management Plans (specifying fertilizer use on each field) and Crop Sequence Plans (including the requirements to grow legumes, or mixtures of legumes and graminaceous plants, and not to grow cereals for more than three years successively on the same field). The total application of nitrogen in any form must not exceed an average of 170 kg/ha of cultivated area. Grasslands should be mowed each year before July 31. The support is D 46 ha/year for arable land (except permanent grassland) and D 21 ha/year for permanent grasslands. (iii) Enterprises applying for Organic Farming Support must be approved on the basis of the Organic Farming Act. The payments depend on the type of crop: (a) D 74 ha/year for permanent and natural grasslands where there are at least 0.1 livestock units (at least 50% organically reared) per ha; (b) D 97 ha/year for grains, legumes, industrial crops, potatoes, black fallow and shortterm grasslands; (c) D 241 ha/year for open field vegetables, fodder vegetables, medicinal and aromatic herbs, and fruits and berries. About 10% of the farmed land qualifies under this support scheme.
2. Materials and methods The bird counts were conducted along permanent transects in three regions of Estonia: Jõgeva and Tartu County (hereafter, Jõgeva region), Võru County (hereafter, Võru region) and Saare County (hereafter, Saare region). In each region, there were 22 farms: six farms using Nature Friendly Management, ten Organic Farms and six farms receiving Single Area Payment. All the farms were situated in conventional farmland. On average, arable land covered 87% of the farm area; of that 41% was sown with cereals and 47% with papilionaceous plants or grasses. The majority of the farms received subsidies for their first contract period only (i.e. since 2004), except three organic farms that had received subsidies already before Estonia joined the EU. Jõgeva region (total area 2380 km2 ; mean distance between the transects 9.5 km) had the largest fields and was poor in vertical landscape elements (e.g. tree-lines and hedges), roads, and forest. There were fewer grasslands and the dominant crop was spring barley, but also considerable areas were under rape. In Saare region (906 km2 ; 6.0 km between the transects) the situation was somewhat opposite: fields were small and rich in landscape elements. More than half of the land was covered by grasslands; the dominant crop was spring barley and there was the highest proportion of winter cereals. In Võru region (964 km2 ; 6.2 km between the transects) fields were of moderate size, rich in vertical elements and roads, but with a small share of forested area. The two dominant crops were spring wheat and spring barley. The transects only crossed farmed habitats (i.e., not forest or farmyards); their total length was 57 km (15.6 km at Nature Friendly Farms, 26.6 km at Organic Farms and 14.8 km at farms receiving Single Area Payment). The average length of a transect was 865 m (range 470–2410 m) and, for bird counts, a 100 m wide strip was used (50 m to both sides of the transect line). Bird counts were conducted in four breeding seasons (2006–2009) in each farm. In each season, three censuses (with intervals of at least 10 days) were carried out in each farm, always by the same observer, between mid-May and mid-June. The singing posts and other territorial behavior (nest building, feeding of offspring, aggression) of all bird species were marked on cadastral maps (scale 1:5000), using standard techniques (Bibby et al., 1992). The censuses were conducted between 5 and 10 a.m. in good weather. Maximum annual counts of each species per transect were used to calculate bird densities. The analyses were performed using Statistica 10.0 (StatSoft Inc., 2011) and SYSTAT 10.0 (Cranes Software International Ltd., 2006). The main hypothesis was that the official environmental requirements for subsidy types should be reflected as differences in bird-community characteristics (main effect) or their trends (subsidy type × year interaction). To test that, repeated measures general linear models (GLMs; Type III approach) were compiled where every bird-community variable (response variables; data from the four years explicitly included as repeated measures) was explained by the three subsidy types and three regions (categorical predictors), management-related environmental factors (continuous predictors). Bird densities were expressed per 10 ha and, for each year and farm, the maximum count of the three censuses was used. The following bird community characteristics were analyzed: species richness of breeders, number of all bird individuals registered (independent of their breeding status), and the density of four typical and most numerous farmland breeders – skylark (Alauda arvensis), lapwing (Vanellus vanellus), whinchat (Saxicola rubetra) and common whitethroat (Sylvia communis). The environmental variables included six landscape characteristics on each transect (150 m wide strip; expressed per 10 ha) and the average fertilizer use in the land parcels crossed by the transect. Land use types and landscape elements were initially assessed from the vector-shaped Estonian Basic Map (1:10,000) and then
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Table 1 Medians (quartile range in parentheses) of the bird and management-related environmental factors measured by subsidy type. For the bird data, four-year averages per farm were used. Significant p-values (based on general discriminant analysis; df = 2, 56) are given in bold; n – the number of farms. Subsidy type
p
Nature Friendly (n = 22)
Organic (n = 22)
Single Area Payment (n = 22)
No. of breeding species No. of all counted birds Skylark, pairs/10 ha Lapwing, pairs/10 ha Whinchat, pairs/10 ha Whitethroat, pairs/10 ha
4.6 (3.5–5.8) 21.4 (16.1–33.3) 4.5 (3.0–5.7) 0 (0–0.6) 0.6 (0–1.6) 0.5 (0.3–1.4)
5.0 (3.8–8.0) 26.1 (18.6–37.7) 4.5 (2.9–6.7) 0 (0–0.6) 1.4 (0.9–2.4) 0.7 (0.3–1.2)
3.4 (2.5–5.8) 21.7 (13.7–31.4) 4.1 (3.8–5.2) 0.3 (0–1.2) 0.6 (0.3–0.7) 0.4 (0–0.9)
Nitrogen use (kg/ha) Length of vertical landscape elements/10 ha No. of stone heaps/10 ha Length of ditches/10 ha Cover of farmyards (%) No. of single trees/10 ha Length of roads/10 ha Cover of spring cereals (%)
39.2 (26.3–63.0) 406 (214–732) 0.0 (0–1.0) 110 (0–459) 0.02 (0.01–0.04) 1.7 (0–3.0) 234 (186–586) 0.41 (0.27–0.63)
4.6 (0–20.7) 601 (113–897) 0.5 (0–1.4) 102 (0–258) 0.00 (0–0.04) 2.3 (0.9–6.5) 242 (92–382) 0.23 (0.07–0.38)
32.1 (12.4–50.7) 244 (0–772) 0.0 (0–1.9) 0 (0–221) 0.00 (0–0.01) 1.1 (0–2.2) 208 (29–393) 0.36 (0.21–0.71)
reclassified according to habitat requirements of farmland bird. The landscape characteristics included: total length of (i) vertical elements (hedges, tree lines with mature trees, stone walls), (ii) drainage ditches, and (iii) roads (including footpaths depicted on the map); numbers of (iv) stone heaps and (v) single trees; (vi) share of farmyards (% of the total area). Crops were mapped during the fieldwork and classified as spring crops, winter crops, or other. Of these, the cover of spring cereals (%) of the total area was used in the analysis. Data about fertilizer use (obtained from the Estonian Agricultural Registers and Information Board) comprised the amounts and types of fertilizer applied in 2005–2008, application date (if known), the proportions of chemical elements in mineral fertilizers. To make different fertilizers comparable, their amounts were recalculated as nitrogen used (N kg/ha); the coefficients for organic fertilizers follow Viil and Võsa (2005). Differences in the management-related environmental factors among subsidy types were tested using general discriminant analysis (GDA), where the variables (ii), (iv) and (vi) above were included as discrete variables due to their non-normal distribution. The pair-wise correlations of the explanatory variables were not strong (r < 0.5) and all the variables were therefore considered.
3. Results Among the 66 farms under study, birds tended to be consistently (though not significantly) most numerous in Organic Farms, especially the whitethroat and the whinchat (Table 1) that are typical species of heterogenous landscapes. The landscape of Organic Farms was rich in landscape elements, and cereal fields covered smaller areas (grasslands were more common). However, fertilizer use was the only environmental variable that significantly differed among the subsidy types – highest inputs were applied in farms receiving Nature Friendly Payment (Table 1). Those farmlands were also rather intensively drained when compared to Single Area Payment lands. Four management-related environmental factors covaried significantly with the bird variables (Table 2). Higher fertilizer input was related to reduced species richness of breeders and skylark density. Abundance of vertical landscape elements promoted both the species richness and the density of the whitethroat. The total length of ditches was positively related to the total abundance of all birds, but negatively related to the breeding density of the skylarks. Additionally, marginal positive relationships were detected between the share of farmyards and the density of all breeding birds, and between the density of single trees and of the whitethroat.
0.041 0.841 0.904 0.338 0.529 0.310 0.059 0.081
When the environmental factors were accounted for, no significant influences of the subsidy type on bird-community variables were observed any more (Table 2). Skylark density was explained by a significant year × subsidy type interaction, but the pattern of this relationship was unclear (increasing difference in the first years disappeared in the end of study period). However, (i) three bird variables varied regionally (of these, breeding density of the whinchat was not affected by any other environmental variable), (ii) density of the skylark varied annually, and (iii) significant year × region interactions emerged for the number of all birds observed, the density of the skylark and of the whinchat (suggesting somewhat different population fluctuations of those species in the three regions). None of the studied predictors had significant effects on the density of lapwing.
4. Discussion The general result of our study is that a prescription-based differentiation of subsidies in the AES framework does not necessarily provide similarly distinct environmental effects. Among the environmental conditions relevant for birds, only fertilizer use – the only factor considered that is directly regulated in the subsidy schemes – differed among the subsidy types in Estonia, but even those differences did not correspond to the subsidy amounts. At the same time, several environmental factors analyzed were related to bird-community characteristics; thus they were ecologically relevant, yet not much affected by the subsidies. Organic Farms may be an exception: they tended to support more birds than the other farm types; they also had the most heterogenous landscapes, and grassland as the predominant crop type (including more than 70% of legumes in our sample). However, those effects were statistically not significant, which confirms that the current practice and consequences of organic farming are highly variable (Bengtsson et al., 2005). Furthermore, no subsidy-type effects remained on the bird-community characteristics when those environmental factors were taken into account, i.e. it is not likely that we missed some fundamental, subsidy-type related influences. The potential of CAP for affecting the European environment is huge: it represents roughly one-third of the EU budget, while only less than 1% is spent on the special financial instrument for environmental protection (LIFE+). Of the CAP budget, 73% was spent on direct payments in 2009 (EUR-LEX, 2009). It has been criticized that, in terms of biodiversity, these payments are given to land managers without clear and reasonable objectives (e.g. Vepsäläinen et al., 2010). However, our study additionally shows that even farm-level differentiation based on explicit requirements can be
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Table 2 General linear models of the effects of subsidy type, region, year, fertilizer input, and landscape structure on bird-community variables in 66 farms. Significant p-values are given in bold and directions of the effects of continuous variables are indicated. In case of subsidy type and region df = 2, in case of interactions df = 4. The bird variables comprise four-year data as repeated measures. Explanatory variables
Response variables No. of breeding species
No. of all birds
Breeding density, pairs/10 ha Skylark
Subsidy type Region Year Year × region Year × subsidy type Nitrogen use (kg/ha) Length of vertical landscape elements/10 ha No. of stone heaps/10 ha Length of ditches/10 ha Cover of farmyards (%) No. of single trees/10 ha Length of roads/10 ha Cover of spring cereals (%)
0.971 0.605 0.226 0.290 0.704 −0.047 +0.003 0.869 0.947 0.083 0.241 0.124 0.999
insufficient when those requirements are too weak or only loosely linked with ultimate conservation needs (such as the status of bird populations). To be effective, differentiation of agri-environment subsidies should be performed at proper spatial scales. At the farm scale, flat-rate payments obviously depend on how the eligible area is determined. In Estonia, the paying agency was excluding all ‘unproductive’ parts of the field from area eligible for support. Thus, in case of such payment type, farms in heterogenous and more natural landscapes would receive less total support than large farms in homogenous, structurally poor landscapes. This creates a pressure to farmers to reduce the abundance of, for example, field margins and vertical landscape elements such as single trees and hedges, i.e., the places where most of the biodiversity is concentrated (Marshall and Moonen, 2002; Manning et al., 2006). To address this, key structures (e.g. field margins, within-field grass strips) should be explicitly included either in delineating the eligible area or in the AES requirements, which should also include specific, robust indicators of heterogeneity. The latter should address the potential problems of another extreme – a universally fine-grained landscape. For example, grassland specialist species, which nest and forage on the ground, tend to prefer homogenous landscapes (Morris and Gilroy, 2008) and keep some distance from vertical landscape elements. Some large-sized migrant species also avoid heterogenous farmland at stopover sites (Dänhardt et al., 2010) and, in general, small and medium-sized birds benefit from extensive management most (Concepción and Díaz, 2011). Hence, the challenge for effective AES is to balance the ecological necessity for complex multi-scale, species-specific approaches and practical administrability, given also that farmers are critical toward formal requirements and increasingly oppose any additional restrictions and obligations (Henle et al., 2008). We cannot fully exclude the possibility that the lack of evidence for the success of differentiated AES for birds in Estonia may be partly due to local reasons. Notably, there can be a lag period in population response (see also Lukasch et al., 2011), while our study only spanned over the first five years since the introduction of the AES. Particularly the creation of an improved landscape structure can be very time-consuming (but note that fertilizer inputs did not follow the subsidy amounts either). A specific technical problem was that we probably missed an appropriate study scale for wideranging bird species (such as lapwing or Eurasian curlew Numenius arquata). This could explain our failure to identify factors relevant for the lapwing. Instead of farm-type related differences in bird-community characteristics, there was significant regional variation within
0.633 0.026 0.519 0.015 0.977 0.800 0.607 0.505 +0.025 0.899 0.488 0.110 0.796
0.307 0.040 0.021 0.001 0.043 −0.023 0.276 0.589 −0.010 0.756 0.617 0.412 0.753
Lapwing
Whinchat
0.508 0.249 0.256 0.133 0.183 0.903 0.965 0.437 0.690 0.159 0.863 0.580 0.835
0.163 0.014 0.185 0.048 0.198 0.621 0.495 0.999 0.703 0.973 0.346 0.248 0.112
White throat 0.768 0.321 0.121 0.062 0.473 0.339 +0.015 0.127 0.356 0.139 0.074 0.933 0.233
Estonia (i.e. at distances of 100–200 km). Interestingly, the species richness of breeders did not differ among the regions, while it responded to two important variables (fertilizer use and the abundance of vertical elements). Thus, the species richness might constitute an indicator of wider applicability than those based on bird densities. The regional differences in bird densities may result both from natural conditions (especially landscape, also local climate) and different efficacy of agri-environmental subsidies (e.g. Davey et al., 2010b). Birds may also have regionally different limiting factors, which may create distinct responses to anthropogenic pressures (Väli et al., 2004). For example, fields in Jõgeva region were more intensively managed, including grasslands, which explains why breeding densities of the whinchat were much lower than in Võru region. To conclude, despite several reforms during last decades, CAP still lacks a systematic link between the level of payments and environmental performance. In Estonia, the additional subsidy for nature-friendly production was even accompanied with higher environmental impacts due to increased fertilizer inputs. We also suspect that eligibility problems and misimplementation of cross compliance rules may exclude significant areas of valuable habitat from support and therefore incentivize their conversion to less wildlife-friendly land uses. Yet, these technical problems do not compromise the general necessity for diverse approaches within AES and special efforts to promote low-intensity farming are clearly crucial for the European bird diversity (e.g. Doxa et al., 2010). Acknowledgments This work could not have been done without the thorough field work by Riho Marja, Mati Martinson, Uku Paal, Enn Soom, Tõnu Talvi and Veljo Volke. Riho Marja also helped in calculating landscape information and the Estonian Agricultural Research Centre kindly allowed us to use their agri-environment monitoring data on birds. Comments of two anonymous referees significantly improved our manuscript. This research was financially supported by the Estonian Ministry of Education and Science (project SF0180012s09), the Estonian Science Foundation (grant 7402), and European Union through the European Regional Development Fund (Centre of Excellence FIBIR). References Aavik, T., Liira, J., 2010. Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries. Agric. Ecosyst. Environ. 135, 178–186.
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Contents lists available at SciVerse ScienceDirect
Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee
What do we lack in agri-environment schemes? The case of farmland birds in Estonia Jaanus Elts ∗ , Asko Lõhmus Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, 46 Vanemuise St., 51014 Tartu, Estonia
a r t i c l e
i n f o
Article history: Received 28 November 2011 Received in revised form 24 April 2012 Accepted 25 April 2012 Keywords: Agri-environmental subsidies Estonia EU policy Farmland birds Habitat quality
a b s t r a c t Among 66 Estonian farms, representing three subsidy types, birds (especially Sylvia communis and Saxicola rubetra) were more abundant in organic farms that received the largest subsidies and were typically situated in structurally diverse landscapes with a high share of grasslands. Vertical landscape elements most clearly promoted species richness of breeding birds and S. communis. However, among managementrelated environmental factors considered, only fertilizer input was consistently subsidy-type dependent (highest inputs at medium subsidy levels). High inputs reduced species richness of breeding birds and the density of Alauda arvensis. When environmental factors were accounted for, no significant influences of the subsidy type on birds were observed, but region-specificity remained significant. Our results indicate that differentiation of national subsidies in the AES framework does not necessarily lead to distinct environmental effects, and that targeting of the subsidies on particular species is necessary to effectively improve current environmental conditions. © 2012 Elsevier B.V. All rights reserved.
1. Introduction To promote farming in a way that it supports biodiversity, landscape values, and water, air and soil quality, many regions have introduced agri-environment schemes (AES), including Australia, the European Union, USA, and Switzerland. AES are incentive payments based on voluntary agreements with farmers and land managers to compensate their income losses associated with employing environmentally sustainable land management practices. In the EU, AES are supported through national Rural Development Programmes with EU funding from the European Agricultural Fund for Rural Development. Since the early 1990s, these programs are managed by regional or national authorities under a decentralized management system, subject to approval by the European Commission. Theoretically, AES could be the primary tools for reversing the decline of farmland wildlife in the EU but their scientific basis remains weak (Kleijn et al., 2001, 2003). Their success is usually measured simply by the area under contracts, numbers of enrolling farmers, or the amount of funds spent. Field evidence of biodiversity benefits mostly concern individual species (Peach et al., 2001; Davies et al., 2005), leaving considerable uncertainty about general prospects to mitigate biodiversity loss (Whittingham, 2007; but see Merckx et al., 2009). Therefore, the European Commission is developing more “objective achieved” monitoring and evaluation
∗ Corresponding author. Fax: +372 7422180. E-mail address: [email protected] (J. Elts). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2012.04.023
systems. For the period 2007–2013 (European Commission, 2006) five types of indicators have been proposed, of which the ‘Baseline indicators’ include impact evaluation based on before–after comparisons in relation to the program objectives. Farmland birds are the only wild animals listed among the baseline indicators, but it is still unclear to what extent the schemes do support birds. In some (but not all) parts of Britain, an increase in avian abundance along with the proportion of land included in AES has been demonstrated for upland habitat specialists and species of conservation concern (Dallimer et al., 2010; Davey et al., 2010a). However, the performance may vary among species, so that common species may show an increase, whereas threatened species continue to decline (Birrer et al., 2007; Reid et al., 2007). Also, complex impacts may emerge along food-webs (Reid et al., 2007) and quantitative data over large agricultural landscapes are scarce, especially for the new EU accession countries (Herzon and O’Hara, 2007). All this makes it difficult to properly assess population dynamics of farmland birds in relation to farming impacts in different agricultural systems. The current work is the first to address the effectiveness of AES for enhancing farmland bird populations in a new EU Member State – Estonia. Despite a relatively intensive agriculture during its Soviet past, Estonia has retained a high proportion of extensively managed farmland. The well studied semi-natural grasslands and wooded meadows (e.g. Liira et al., 2009) have only marginal farming value nowadays and their management is mostly driven by conservation interests. However, opening of the EU market and its Common Agricultural Policy (CAP) subsidies have triggered intensification of agricultural production in arable lands. For example,
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the average wheat yield increased 45% in 2004–2008 compared with the pre-EU accession five-year period in Estonia (1998–2003; FAOSTAT, 2010). The AES are in place since 2004 and include several kinds of subsidies to farmers; however, it is not clear which benefits to biodiversity those complicated systems generate. The only study addressing the effect of Estonian AES on biodiversity found that large homogenous fields generally lacked nesting places and food resources for bumblebees, but the farms that had joined AES had more bumblebee species and higher flower densities (Viik et al., 2007). More general studies in arable areas have documented local and landscape effects on plant communities in field margins (suggesting a subset of ‘high-nature-value species’ for monitoring the effects of land-use intensification; Aavik and Liira, 2010) and the effects of management practices on soil biota (e.g. Truu et al., 2008). By analyzing the case of Estonia, we ask whether higher national AES subsidies benefit birds more than those with lower payments; are those effects region- and taxon-specific; and how are they related to habitat variation. Those questions build on previous coarse-scale Pan-European studies, which have included Estonia, and have reported major, but region-specific, effects of agricultural intensification on bird assemblages (e.g. Guerrero et al., 2011). The environmental factors involved are less clear, because agricultural intensification may both remove and add particular landscape elements. In fact, Geiger et al. (2010) found that organic farms harbored more wild plant and carabid species compared with other AES, but not significantly more bird species. Yet, the Estonian common bird monitoring reveals a recent pronounced drop in farmland bird abundance (Kuresoo et al., 2011). The habitat factors potentially causing such decreases are the reduction of edge structures (Herzon et al., 2008) and of semi-natural habitats, which are also related to fertilizer use (Billeter et al., 2008). Here, we explore a set of such carefully selected environmental factors for a link between their influence to birds and sensitivity to the cost-effectiveness of farming, which could be increasingly affected by increasing the subsidies. In the absence of before–after design, comparing different subsidy levels over several years could distinguish whether the AES are actually causing an improvement in habitat qualities functionally important for birds, or are the subsidies simply applied in higher-quality landscapes where the trend may be even toward agricultural intensification. We compare three subsidy types with different requirements, which collectively should promote mitigation of the environmental impacts of farming. (i) Single Area Payment is a direct payment (total area of 850,000 ha, D 50 ha/year) to all farmers who follow general requirements called Good Farming Practice (e.g. the land should be in a ‘good agricultural condition’). (ii) Environmentally Friendly Production Scheme covers ca. 55% of the farmed area and requires the participants to additionally prepare Nutrient Management Plans (specifying fertilizer use on each field) and Crop Sequence Plans (including the requirements to grow legumes, or mixtures of legumes and graminaceous plants, and not to grow cereals for more than three years successively on the same field). The total application of nitrogen in any form must not exceed an average of 170 kg/ha of cultivated area. Grasslands should be mowed each year before July 31. The support is D 46 ha/year for arable land (except permanent grassland) and D 21 ha/year for permanent grasslands. (iii) Enterprises applying for Organic Farming Support must be approved on the basis of the Organic Farming Act. The payments depend on the type of crop: (a) D 74 ha/year for permanent and natural grasslands where there are at least 0.1 livestock units (at least 50% organically reared) per ha; (b) D 97 ha/year for grains, legumes, industrial crops, potatoes, black fallow and shortterm grasslands; (c) D 241 ha/year for open field vegetables, fodder vegetables, medicinal and aromatic herbs, and fruits and berries. About 10% of the farmed land qualifies under this support scheme.
2. Materials and methods The bird counts were conducted along permanent transects in three regions of Estonia: Jõgeva and Tartu County (hereafter, Jõgeva region), Võru County (hereafter, Võru region) and Saare County (hereafter, Saare region). In each region, there were 22 farms: six farms using Nature Friendly Management, ten Organic Farms and six farms receiving Single Area Payment. All the farms were situated in conventional farmland. On average, arable land covered 87% of the farm area; of that 41% was sown with cereals and 47% with papilionaceous plants or grasses. The majority of the farms received subsidies for their first contract period only (i.e. since 2004), except three organic farms that had received subsidies already before Estonia joined the EU. Jõgeva region (total area 2380 km2 ; mean distance between the transects 9.5 km) had the largest fields and was poor in vertical landscape elements (e.g. tree-lines and hedges), roads, and forest. There were fewer grasslands and the dominant crop was spring barley, but also considerable areas were under rape. In Saare region (906 km2 ; 6.0 km between the transects) the situation was somewhat opposite: fields were small and rich in landscape elements. More than half of the land was covered by grasslands; the dominant crop was spring barley and there was the highest proportion of winter cereals. In Võru region (964 km2 ; 6.2 km between the transects) fields were of moderate size, rich in vertical elements and roads, but with a small share of forested area. The two dominant crops were spring wheat and spring barley. The transects only crossed farmed habitats (i.e., not forest or farmyards); their total length was 57 km (15.6 km at Nature Friendly Farms, 26.6 km at Organic Farms and 14.8 km at farms receiving Single Area Payment). The average length of a transect was 865 m (range 470–2410 m) and, for bird counts, a 100 m wide strip was used (50 m to both sides of the transect line). Bird counts were conducted in four breeding seasons (2006–2009) in each farm. In each season, three censuses (with intervals of at least 10 days) were carried out in each farm, always by the same observer, between mid-May and mid-June. The singing posts and other territorial behavior (nest building, feeding of offspring, aggression) of all bird species were marked on cadastral maps (scale 1:5000), using standard techniques (Bibby et al., 1992). The censuses were conducted between 5 and 10 a.m. in good weather. Maximum annual counts of each species per transect were used to calculate bird densities. The analyses were performed using Statistica 10.0 (StatSoft Inc., 2011) and SYSTAT 10.0 (Cranes Software International Ltd., 2006). The main hypothesis was that the official environmental requirements for subsidy types should be reflected as differences in bird-community characteristics (main effect) or their trends (subsidy type × year interaction). To test that, repeated measures general linear models (GLMs; Type III approach) were compiled where every bird-community variable (response variables; data from the four years explicitly included as repeated measures) was explained by the three subsidy types and three regions (categorical predictors), management-related environmental factors (continuous predictors). Bird densities were expressed per 10 ha and, for each year and farm, the maximum count of the three censuses was used. The following bird community characteristics were analyzed: species richness of breeders, number of all bird individuals registered (independent of their breeding status), and the density of four typical and most numerous farmland breeders – skylark (Alauda arvensis), lapwing (Vanellus vanellus), whinchat (Saxicola rubetra) and common whitethroat (Sylvia communis). The environmental variables included six landscape characteristics on each transect (150 m wide strip; expressed per 10 ha) and the average fertilizer use in the land parcels crossed by the transect. Land use types and landscape elements were initially assessed from the vector-shaped Estonian Basic Map (1:10,000) and then
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Table 1 Medians (quartile range in parentheses) of the bird and management-related environmental factors measured by subsidy type. For the bird data, four-year averages per farm were used. Significant p-values (based on general discriminant analysis; df = 2, 56) are given in bold; n – the number of farms. Subsidy type
p
Nature Friendly (n = 22)
Organic (n = 22)
Single Area Payment (n = 22)
No. of breeding species No. of all counted birds Skylark, pairs/10 ha Lapwing, pairs/10 ha Whinchat, pairs/10 ha Whitethroat, pairs/10 ha
4.6 (3.5–5.8) 21.4 (16.1–33.3) 4.5 (3.0–5.7) 0 (0–0.6) 0.6 (0–1.6) 0.5 (0.3–1.4)
5.0 (3.8–8.0) 26.1 (18.6–37.7) 4.5 (2.9–6.7) 0 (0–0.6) 1.4 (0.9–2.4) 0.7 (0.3–1.2)
3.4 (2.5–5.8) 21.7 (13.7–31.4) 4.1 (3.8–5.2) 0.3 (0–1.2) 0.6 (0.3–0.7) 0.4 (0–0.9)
Nitrogen use (kg/ha) Length of vertical landscape elements/10 ha No. of stone heaps/10 ha Length of ditches/10 ha Cover of farmyards (%) No. of single trees/10 ha Length of roads/10 ha Cover of spring cereals (%)
39.2 (26.3–63.0) 406 (214–732) 0.0 (0–1.0) 110 (0–459) 0.02 (0.01–0.04) 1.7 (0–3.0) 234 (186–586) 0.41 (0.27–0.63)
4.6 (0–20.7) 601 (113–897) 0.5 (0–1.4) 102 (0–258) 0.00 (0–0.04) 2.3 (0.9–6.5) 242 (92–382) 0.23 (0.07–0.38)
32.1 (12.4–50.7) 244 (0–772) 0.0 (0–1.9) 0 (0–221) 0.00 (0–0.01) 1.1 (0–2.2) 208 (29–393) 0.36 (0.21–0.71)
reclassified according to habitat requirements of farmland bird. The landscape characteristics included: total length of (i) vertical elements (hedges, tree lines with mature trees, stone walls), (ii) drainage ditches, and (iii) roads (including footpaths depicted on the map); numbers of (iv) stone heaps and (v) single trees; (vi) share of farmyards (% of the total area). Crops were mapped during the fieldwork and classified as spring crops, winter crops, or other. Of these, the cover of spring cereals (%) of the total area was used in the analysis. Data about fertilizer use (obtained from the Estonian Agricultural Registers and Information Board) comprised the amounts and types of fertilizer applied in 2005–2008, application date (if known), the proportions of chemical elements in mineral fertilizers. To make different fertilizers comparable, their amounts were recalculated as nitrogen used (N kg/ha); the coefficients for organic fertilizers follow Viil and Võsa (2005). Differences in the management-related environmental factors among subsidy types were tested using general discriminant analysis (GDA), where the variables (ii), (iv) and (vi) above were included as discrete variables due to their non-normal distribution. The pair-wise correlations of the explanatory variables were not strong (r < 0.5) and all the variables were therefore considered.
3. Results Among the 66 farms under study, birds tended to be consistently (though not significantly) most numerous in Organic Farms, especially the whitethroat and the whinchat (Table 1) that are typical species of heterogenous landscapes. The landscape of Organic Farms was rich in landscape elements, and cereal fields covered smaller areas (grasslands were more common). However, fertilizer use was the only environmental variable that significantly differed among the subsidy types – highest inputs were applied in farms receiving Nature Friendly Payment (Table 1). Those farmlands were also rather intensively drained when compared to Single Area Payment lands. Four management-related environmental factors covaried significantly with the bird variables (Table 2). Higher fertilizer input was related to reduced species richness of breeders and skylark density. Abundance of vertical landscape elements promoted both the species richness and the density of the whitethroat. The total length of ditches was positively related to the total abundance of all birds, but negatively related to the breeding density of the skylarks. Additionally, marginal positive relationships were detected between the share of farmyards and the density of all breeding birds, and between the density of single trees and of the whitethroat.
0.041 0.841 0.904 0.338 0.529 0.310 0.059 0.081
When the environmental factors were accounted for, no significant influences of the subsidy type on bird-community variables were observed any more (Table 2). Skylark density was explained by a significant year × subsidy type interaction, but the pattern of this relationship was unclear (increasing difference in the first years disappeared in the end of study period). However, (i) three bird variables varied regionally (of these, breeding density of the whinchat was not affected by any other environmental variable), (ii) density of the skylark varied annually, and (iii) significant year × region interactions emerged for the number of all birds observed, the density of the skylark and of the whinchat (suggesting somewhat different population fluctuations of those species in the three regions). None of the studied predictors had significant effects on the density of lapwing.
4. Discussion The general result of our study is that a prescription-based differentiation of subsidies in the AES framework does not necessarily provide similarly distinct environmental effects. Among the environmental conditions relevant for birds, only fertilizer use – the only factor considered that is directly regulated in the subsidy schemes – differed among the subsidy types in Estonia, but even those differences did not correspond to the subsidy amounts. At the same time, several environmental factors analyzed were related to bird-community characteristics; thus they were ecologically relevant, yet not much affected by the subsidies. Organic Farms may be an exception: they tended to support more birds than the other farm types; they also had the most heterogenous landscapes, and grassland as the predominant crop type (including more than 70% of legumes in our sample). However, those effects were statistically not significant, which confirms that the current practice and consequences of organic farming are highly variable (Bengtsson et al., 2005). Furthermore, no subsidy-type effects remained on the bird-community characteristics when those environmental factors were taken into account, i.e. it is not likely that we missed some fundamental, subsidy-type related influences. The potential of CAP for affecting the European environment is huge: it represents roughly one-third of the EU budget, while only less than 1% is spent on the special financial instrument for environmental protection (LIFE+). Of the CAP budget, 73% was spent on direct payments in 2009 (EUR-LEX, 2009). It has been criticized that, in terms of biodiversity, these payments are given to land managers without clear and reasonable objectives (e.g. Vepsäläinen et al., 2010). However, our study additionally shows that even farm-level differentiation based on explicit requirements can be
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Table 2 General linear models of the effects of subsidy type, region, year, fertilizer input, and landscape structure on bird-community variables in 66 farms. Significant p-values are given in bold and directions of the effects of continuous variables are indicated. In case of subsidy type and region df = 2, in case of interactions df = 4. The bird variables comprise four-year data as repeated measures. Explanatory variables
Response variables No. of breeding species
No. of all birds
Breeding density, pairs/10 ha Skylark
Subsidy type Region Year Year × region Year × subsidy type Nitrogen use (kg/ha) Length of vertical landscape elements/10 ha No. of stone heaps/10 ha Length of ditches/10 ha Cover of farmyards (%) No. of single trees/10 ha Length of roads/10 ha Cover of spring cereals (%)
0.971 0.605 0.226 0.290 0.704 −0.047 +0.003 0.869 0.947 0.083 0.241 0.124 0.999
insufficient when those requirements are too weak or only loosely linked with ultimate conservation needs (such as the status of bird populations). To be effective, differentiation of agri-environment subsidies should be performed at proper spatial scales. At the farm scale, flat-rate payments obviously depend on how the eligible area is determined. In Estonia, the paying agency was excluding all ‘unproductive’ parts of the field from area eligible for support. Thus, in case of such payment type, farms in heterogenous and more natural landscapes would receive less total support than large farms in homogenous, structurally poor landscapes. This creates a pressure to farmers to reduce the abundance of, for example, field margins and vertical landscape elements such as single trees and hedges, i.e., the places where most of the biodiversity is concentrated (Marshall and Moonen, 2002; Manning et al., 2006). To address this, key structures (e.g. field margins, within-field grass strips) should be explicitly included either in delineating the eligible area or in the AES requirements, which should also include specific, robust indicators of heterogeneity. The latter should address the potential problems of another extreme – a universally fine-grained landscape. For example, grassland specialist species, which nest and forage on the ground, tend to prefer homogenous landscapes (Morris and Gilroy, 2008) and keep some distance from vertical landscape elements. Some large-sized migrant species also avoid heterogenous farmland at stopover sites (Dänhardt et al., 2010) and, in general, small and medium-sized birds benefit from extensive management most (Concepción and Díaz, 2011). Hence, the challenge for effective AES is to balance the ecological necessity for complex multi-scale, species-specific approaches and practical administrability, given also that farmers are critical toward formal requirements and increasingly oppose any additional restrictions and obligations (Henle et al., 2008). We cannot fully exclude the possibility that the lack of evidence for the success of differentiated AES for birds in Estonia may be partly due to local reasons. Notably, there can be a lag period in population response (see also Lukasch et al., 2011), while our study only spanned over the first five years since the introduction of the AES. Particularly the creation of an improved landscape structure can be very time-consuming (but note that fertilizer inputs did not follow the subsidy amounts either). A specific technical problem was that we probably missed an appropriate study scale for wideranging bird species (such as lapwing or Eurasian curlew Numenius arquata). This could explain our failure to identify factors relevant for the lapwing. Instead of farm-type related differences in bird-community characteristics, there was significant regional variation within
0.633 0.026 0.519 0.015 0.977 0.800 0.607 0.505 +0.025 0.899 0.488 0.110 0.796
0.307 0.040 0.021 0.001 0.043 −0.023 0.276 0.589 −0.010 0.756 0.617 0.412 0.753
Lapwing
Whinchat
0.508 0.249 0.256 0.133 0.183 0.903 0.965 0.437 0.690 0.159 0.863 0.580 0.835
0.163 0.014 0.185 0.048 0.198 0.621 0.495 0.999 0.703 0.973 0.346 0.248 0.112
White throat 0.768 0.321 0.121 0.062 0.473 0.339 +0.015 0.127 0.356 0.139 0.074 0.933 0.233
Estonia (i.e. at distances of 100–200 km). Interestingly, the species richness of breeders did not differ among the regions, while it responded to two important variables (fertilizer use and the abundance of vertical elements). Thus, the species richness might constitute an indicator of wider applicability than those based on bird densities. The regional differences in bird densities may result both from natural conditions (especially landscape, also local climate) and different efficacy of agri-environmental subsidies (e.g. Davey et al., 2010b). Birds may also have regionally different limiting factors, which may create distinct responses to anthropogenic pressures (Väli et al., 2004). For example, fields in Jõgeva region were more intensively managed, including grasslands, which explains why breeding densities of the whinchat were much lower than in Võru region. To conclude, despite several reforms during last decades, CAP still lacks a systematic link between the level of payments and environmental performance. In Estonia, the additional subsidy for nature-friendly production was even accompanied with higher environmental impacts due to increased fertilizer inputs. We also suspect that eligibility problems and misimplementation of cross compliance rules may exclude significant areas of valuable habitat from support and therefore incentivize their conversion to less wildlife-friendly land uses. Yet, these technical problems do not compromise the general necessity for diverse approaches within AES and special efforts to promote low-intensity farming are clearly crucial for the European bird diversity (e.g. Doxa et al., 2010). Acknowledgments This work could not have been done without the thorough field work by Riho Marja, Mati Martinson, Uku Paal, Enn Soom, Tõnu Talvi and Veljo Volke. Riho Marja also helped in calculating landscape information and the Estonian Agricultural Research Centre kindly allowed us to use their agri-environment monitoring data on birds. Comments of two anonymous referees significantly improved our manuscript. This research was financially supported by the Estonian Ministry of Education and Science (project SF0180012s09), the Estonian Science Foundation (grant 7402), and European Union through the European Regional Development Fund (Centre of Excellence FIBIR). References Aavik, T., Liira, J., 2010. Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries. Agric. Ecosyst. Environ. 135, 178–186.
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