The widespread decline in global biodiversity, including farmland biodiversity (Robinson and Sutherland, 2002; Tscharntke et al., 2005), represents a major conservation challenge (Butchart et al., 2010). Approximately 50% of all European species are dependent on agricultural practices (Kristensen, 2003; Stoate et al., 2009), and changes in farming practices (e.g. intensification/extensification of management techniques) can lead to the loss of priority habitats, including grassland habitats such as hay meadows, heathlands, moorlands, chalk and dry grasslands (Henle et al., 2008). Semi-natural grassland habitats act as a refuge for invertebrate, bird and mammal species (O’Neill et al., 2013). The intensification of grassland management (Poschlod and WallisDeVries, 2002), particularly through changes in reseeding and the frequency of new sward establishment (Ridding et al., 2015), grazing and forage systems (Kruess and Tscharntke, 2002), as well as nutrient inputs (Fuller, 1987), has resulted in the loss of a range of grassland habitats and their associated biodiversity (McMahon et al., 2010).
In Ireland, approximately 65% of the land area is dedicated to agricultural production (CSO, 2010; DAFM, 2013a), of which more than 90% is pasture and rough grazing land. Within Irish grassland systems, semi-natural grassland habitats have undergone a significant decline, similar to European trends (Fuller, 1987; Henle et al., 2008; Ridding et al., 2015). For example, of the semi-natural grassland sites recorded in Ireland in the 1970s, approximately 38% no longer supported seminatural grassland communities by 1994 (Byrne, 1997). More recently (1990–2000), semi-natural grassland, heathlands, pastures and mixed farmlands have all decreased, while permanent pastures and arable land in particular have increased (EPA, 2003). In addition, Sheridan et al. (2011) found that the frequency of occurrence of semi-natural grasslands on farms in South-East Ireland was low, and these constituted <1.75% of the average farm area in their study. An evaluation of the status of habitats designated under the Habitats Directive found that all grassland habitats had a “poor” or “bad” conservation status in 2007 (NPWS, 2008), with no improvement by 2013 (NPWS, 2013). Similarly, O’Neill et al. (2013) found that the assessment of overall condition for all five Annex 1 grassland habitats was “unfavourable–bad”. The documented decline in the range and quality of semi-natural grassland habitats highlights the need for evidence-based and well-targeted conservation actions to address this decline.
Agri-environment schemes (AESs) were established (EU Agri-environmental Regulation [90/20788/EEC]) to promote management practices that are more ecological and environmentally beneficial (Kleijn and Sutherland, 2003). These schemes use public funds to pay for private actions by farmers, as a means of ensuring environmental public goods that are external to market systems (Finn and Ó hUallacháin, 2012). EU Member States are obliged to monitor and evaluate the environmental, agricultural and socioeconomic impacts of their agri-environment (AE) programmes (EC Regulation No. 746/96). Evaluation is also necessary to demonstrate value for money to taxpayers, as well as to avoid accusations of trade distortion. However, the effectiveness of AES as a tool to maintain and enhance farmland biodiversity has been questioned (Kleijn and Sutherland, 2003). This may be due to a variety of reasons, including deficiencies in relation to the design, targeting, monitoring, evaluation and flexibility of individual measures and overall schemes.
Considering the prominence of grasslands in Ireland, and the “bad” conservation status of the majority of designated grassland habitats (NPWS, 2013), it is not surprising that measures to address the conservation of grassland habitats have been included in all iterations of Ireland’s AESs: the Rural Environment Protection Scheme (REPS, 1994–2014) and the Agri-Environment Options Scheme (AEOS, 2010–2018). The primary scheme in place during this study in 2013, AEOS, provided three specific AE grassland measures:
- Traditional Hay Meadow (THM)
- Species-Rich Grassland (SRG)
- Grassland with a Natura 2000 designation, which formally received a Natura-based payment (Natura)
Grassland conservation measures are intended to represent a significant financial investment and policy instrument for protection of biodiversity in Ireland. The AE grassland measures were the three most popular measures in AEOS in 2013 (i.e. 51% of participants undertook SRG, 27% undertook THM and 19% undertook Natura measures on their farms) (L. O’Shea, DAFM, personal communication). High participation rates, coupled with payment rates of €314/ ha for SRG and THM and €75/ha for Natura, have resulted in €19.25 million, €6.31 million and €3.03 million, respectively, being spent on these three measures in 2013. More recently, these measures (incorporating slight changes in eligibility and management criteria) have been included in the Green Low-Carbon Agri-environment Scheme (GLAS), the Irish AES launched in 2015.
Two basic requirements of successful conservation measures are that their environmental effectiveness is validated and that they are appropriately costed. The European Court of Auditors report (2011) on AE payments made the following recommendations:
- AE expenditure should be more precisely targeted;
- There should be a higher rate of EU contribution for sub-measures with higher environmental potential;
- There should be a clear distinction between simple and more demanding AE sub-measures; and
- Member States should be more proactive in managing AE payments.
This study compared the botanical species richness, abundance and community composition of grassland sites enrolled in three AE grassland measures. We sought to compare the variability in the botanical composition within grasslands undertaking the same management criteria (and thus in receipt of the same payment under AEOS) as well as the variability in botanical composition between grasslands undertaking different grassland measures (and thus receiving different payments). Greater understanding of the botanical composition and quality of grasslands in receipt of AE payments will help inform policymakers on the cost-effectiveness of existing management criteria, as well as helping to identify ways of revising existing measures to improve their cost-effectiveness.
Materials and methods
Site selection was restricted to pastoral drystock farms participating in the AEOS and in receipt of payments for at least one of the three grassland options available under the scheme; THM, SRG, and grassland designated as Natura 2000 site (Natura). Thus, 20 sites representing each of the grassland options were randomly selected, resulting in a total of 60 sites (from counties Roscommon , Kildare , Longford , Laois , Offaly , and Mayo ). Information relating to the condition of the sites prior to entry to AEOS, or on whether the sites had been participants in previous AESs, was not available.
Each grassland measure in AEOS had specific management criteria (abbreviated details are provided in Table 1; additional details can be found in DAFM ). The THM measure required applicants to identify at least three grass species other than Lolium perenne in the sward and specified that the land parcel must not have been cultivated for a minimum of eight years prior to entry into the scheme (DAFM, 2010). The SRG measure required the identification of indicator species as an eligibility criterion for participation and had similar cultivation requirements as for THM. Typically, no pesticides, chemical fertiliser, slurry or farmyard manure (beyond inputs from grazing animals) could be applied to SRG, with the exception being AEOS 3 (third iteration of the scheme; DAFM, 2013b) where up to 30 kg/ha of nitrogen (N) and/or phosphorus (P) could be applied. For THM, up to 30 kg/ha of N could be applied. Compliance with both THM and SRG measures required the continuation of “traditional” grazing practices (without further clarification as to what was considered traditional). THM and SRG had to be closed or cut at certain times of the year, and stocking rates and winter feeding were restricted. Payment rates for THM and SRG were €314/ha (up to a maximum of 10 ha).
Classification and management prescriptions for Traditional Hay Meadow, Species-Rich Grassland and Natura grassland under the Agri-Environment Option Scheme (AEOS), summarised from DAFM (2010)
|Classification and management prescription||Traditional Hay Meadow||Species-Rich Grassland||Natura|
|Eligibility||Three grass species present other than ryegrass||Five indicators from guidelines. <20% negative indicators||Designated as per Natura 2000 specifications|
|Cultivation||No cultivation in past 8 yr||No cultivation in past 8 yr||Cultivation must be avoided|
|Nitrogen||<30 kg/ha N||<30 kg/ha (N or P)||Traditional practices maintained, based on results of soil test|
|Phosphorus||–||<30 kg/ha (N or P)||Traditional practices maintained, based on results of soil test Phosphate level should be less than index 2|
|Closed period||By 15th April||N/A||N/A|
|Grazing/stocking rate||–||No specific details||Traditional grazing practices maintained|
|Mowing||After 15th July||After 15th July||–|
|Additional||Hay must be turned twice||No herbicides permitted. Spot treatment of noxious weeds permitted||No herbicides permitted. Spot treatment of noxious weeds permitted.|
Specifications for Natura grasslands were less explicit. Only designated Natura 2000 sites were eligible. Management prescriptions typically required that traditional management practices be maintained (Table 1; DAFM, 2010). The payment rate was €75/ha (maximum €4,000), and where this measure was selected, all Natura land had to be included (even if the total area of Natura land on the farm exceeded the area of land paid for).
Survey methods for grasslands
Grassland surveys were undertaken on each of the 60 sites between early May and August of 2013 and 2014. One field (site) per AE grassland measure was surveyed per farm. Where a farm had more than one field of any individual AE grassland measure, the largest field was selected. Seventeen farms had more than one AE grassland measure represented (THM and SRG: 8; SRG and Natura: 5; THM and Natura: 3; THM, SRG and Natura: 1), in which case, one field of each AE grassland measure was surveyed. Within each selected field, 20 quadrats (1 m × 1 m) were randomly located. To ensure that the full variability of plant species within the main area of the field was sampled, the quadrats were restricted in their location, i.e., they were (1) a minimum of 10 m apart and (2) a minimum of 10 m away from the base of hedgerows, other field boundaries or gateways. All vascular plants rooted within each quadrat were identified to species according to Stace (1997). Hybridisation of Agrostis species meant that the species were grouped as Agrostis spp. (refer Hubbard, 1984). Abundance values were assigned according to the Braun-Blanquet Scale (Braun-Blanquet et al., 1932). Data analysis techniques required that the Braun-Blanquet values be converted to actual percentage cover values. This was done by assigning a percentage score to the midpoint of each category (according to Wikum and Shanholtzer, 1978). The 20 quadrats per site were summed, and the mean value was used to calculate a mean percentage cover value for each species per site. To address the limitations with species richness metrics (Fleishman et al., 2006), both Shannon diversity index (H′) and effective species richness (expH′) (Jost, 2006) were calculated from the relative abundance data. Effective species richness represents the true diversity rather than an index of diversity; thus, a community with an effective richness of 2.0 is considered twice as diverse as a community with an effective richness of 1.0.
To assess the botanical quality of the sites, plant indicator species for high-quality grassland (hereafter, positive indicators) were based on the High Nature Value plant species list recommended by O’Neill et al. (2013). Negative indicator species (plants associated with Improved Agricultural Grassland (GA1) which are considered negative when they exceed 10% cover; Fossitt, 2000) were also derived from O’Neill et al. (2013) and consisted of the species that were universal to the four occurring Annex 1 grassland habitats in the study (i.e. Cirsium arvense, C. vulgare, L. perenne, Rumex obtusifolius, Senecio jacobaea, Trifolium repens and Urtica dioica).
High soil nutrient levels are often associated with reduced botanical richness and the dominance of a few highly competitive species in the grassland (Kleijn et al., 2009). Soil was sampled at each field to measure the fertility and pH levels. Soil samples consisted of 20 pooled 10-cm-deep cores taken in a “W” formation across the sampled fields. These samples were air-dried and analysed for Morgan’s available P, potassium (K), magnesium (Mg) and pH.
Data was checked for normality using Shapiro–Wilk test with PROC UNIVARIATE in SAS (SAS 9.3.1; SAS Institute Inc., Cary, NC, USA). A comparison of species richness, diversity and effective richness, along with the richness and mean percentage cover of taxonomic groups (i.e. herb, grass, shrub), between each AE grassland measure was undertaken with generalised linear modelling (GLM; normal distribution) and Kruskal–Wallis test (non-normal distribution) in SAS. Shrub cover data were used as a negative indicator of habitat quality; values were zero-inflated, so the data were log-transformed and Poisson distribution used. PROC GLM (normal distribution) and Kruskal–Wallis test (non-normal distribution) were used to analyse variations in P, K, Mg and pH concentrations in soils among each AE grassland measure and also to model correlations between P, K, Mg and pH concentrations and species richness, as well as the richness and relative abundance cover of different taxonomic groups.
Constrained and unconstrained approaches were used to analyse the data using PRIMER v6 and CANOCO 4.5 (CANOCO 4.5; Biometrics – Plant Research International, Wageningen, the Netherlands). Unconstrained analyses – non-metric multidimensional scaling (nMDS) – were used to test differences in the community composition between AE grassland measures. Differences in the botanical composition of the three AE grassland measures were calculated using “analysis of similarity” (ANOSIM) in PRIMER ANOSIM, which is an ANOVA-like analysis specifically developed for ecological data. Non-metric multidimensional scaling plots were produced to display differences in community composition between grassland measures.
Ordination analysis (using CANOCO 4.5) was used to investigate the relationship between plant species composition, AE grassland measure and sampled environmental variables. Detrended correspondence analysis (DCA) indicated that linear methods were most appropriate. Further analysis followed linear unconstrained principal components analysis (PCA) with passive projection of the AE grassland measure.
A total of 143 higher plant species, consisting of 90 herb (including four weed species, as defined by the Noxious Weeds Act 1936), 25 grass, 14 woody, nine sedge, and five rush species were recorded in this study. No Red List plant species (Curtis and McGough, 1988) or Flora (Protection) Order (S.I. No. 356/2015) species were identified on any of the 60 sites. A full species list is provided in Appendix Table A.1
Species richness, diversity and abundance cover
The greatest botanical species richness was found within the Natura grasslands, where 138 species were recorded, with an average (±SE) of 40.05 (±2.06) species per field. We recorded 103 species from the SRG fields, with an average of 33.50 (±1.79) species per field; moreover, 86 species were recorded from the THM, with an average of 29.05 (±1.60) species per field (Table 2). The highest species richness recorded was 59 species in a Natura site, with the lowest being 17 species in a THM site (Table 3). Agrostis spp. constituted the greatest cover for all AE grassland measures (Table 4).
Summary vegetation characteristics of the sampled sites in each of the three grassland measures of AEOS
|Traditional Hay Meadow||Species-Rich Grassland||Natura||F2,57||P-value|
|Mean species richness||29.05 ± 1.60a||33.50 ± 1.79a||40.05 ± 2.06b||9.20||< 0.001|
|Shannon diversity (H′)||2.48 ± 0.07a||2.81 ± 0.07a||3.01 ± 0.05b||18.83||< 0.001|
|Effective species richness (expH′)||12.49 ± 0.87a||17.19 ± 1.03b||20.78 ± 0.98c||18.66||< 0.001|
|Herb richness||18.20 ± 1.32a||19.60 ± 1.37a||26.15 ± 1.26b||10.39||< 0.001|
|Herb cover (%)||37.73 ± 2.42a||38.63 ± 2.30a||47.06 ± 2.21b||4.97||< 0.05|
|Grass richness||10.15 ± 0.30||10.85 ± 0.44||9.80± 0.66||1.19||0.312|
|Grass cover (%)||59.87 ± 2.75a||50.03 ± 2.61b||37.25 ± 2.90c||16.87||< 0.001|
|Grass–sedge–rush richness||12.00 ± 0.52||13.85 ± 0.67||14.10 ± 0.86||2.74||0.073|
|Grass–sedge–rush cover (%)||62.24 ± 2.42a||61.22 ± 2.32a||52.24 ± 2.24b||5.58||< 0.01|
|Shrub richness||0.25 ± 0.20a||0.30 ± 0.73a||1.50 ± 0.51b||4.57||< 0.05|
|Shrub cover (%)||0.03 ± 0.03a||0.16 ± 0.11b||0.70 ± 0.42b||1.98||0.15|
The table presents mean (± s.e.) richness and cover values of different vegetation categories. F-values and significance levels are from general linear models (normal data). Means with the same letter do not differ significantly from one another. Figures in bold indicate significance at P < 0.05.
s.e. = standard error of mean.
Summary of positive and negative indicator species averaged across the sampled sites in each of the three grassland measures of AEOS
|Traditional Hay Meadow||Species-Rich Grassland||Natura|
|Mean species richness||29.05 ± 1.60a||33.50 ± 1.79a||40.05 ± 2.06b|
|Maximum species richness||47||54||59|
|Minimum species richness||17||21||27|
|Positive indicator richness||10.45 ± 0.92a||12.30 ± 1.04a||16.90 ± 1.04b|
|Negative indicator species relative cover (%)||13.60 ± 1.53a||14.07 ± 2.42a||4.90 ± 1.19b|
|Maximum cover (%)||20.68||45.11||17.13|
|Minimum cover (%)||0.94||6.34||0|
|Number of sites with either >10% cover by an individual negative indicator species or >20% cumulative cover of negative indicator species||10||9||1|
|Negative indicator species actual cover (%)||31.59 ± 3.78a||34.57 ± 5.60a||13.75 ± 3.30b|
|Maximum cover (%)||77.38||110.85||15.19|
|Minimum cover (%)||1.50||3.23||0|
|Number of sites with either >10% cover by an individual negative indicator species or >20% cumulative cover of negative indicator species||16||18||9|
The table presents mean (± s.e.) of positive and negative indicator species. F-values and significance levels are from general linear models (normal data). Means with the same letter do not differ significantly from one another. Figures in bold indicate significance at P < 0.05. Positive and negative indicator species are from O’Neill et al. (2013).
Top 10 most abundant (% cover) and frequent (% occurrence) species across the sampled sites in each of the three grassland measures of AEOS
|Traditional Hay Meadow||Species-Rich Grassland||Natura|
Grasslands in the Natura measure were significantly (P < 0.001) more species rich than those in SRG and THM (Figure 1, Table 2). Species richness between SRG and THM was similar. The Shannon diversity (P < 0.001) and effective species richness (P < 0.001) differed significantly between the three AE grassland measures (Table 2). Natura sites were almost twice as diverse as THM sites, with SRG sites intermediate between the two.
Natura sites had a greater (P < 0.001) richness and relative cover of herbs than SRG and THM (Table 2). SRG and THM did not differ significantly for either herb richness or relative cover.
Traditional Hay Meadow sites had significantly greater relative cover of grass than SRG sites, which in turn had a significantly greater cover than Natura sites (Table 2). This was particularly evident in relation to relative cover of L. perenne (P < 0.001), with 6.5%, 3.9% and 1.1% recorded within THM, SRG and Natura sites, respectively. L. perenne was present in 95%, 70% and 25% of THM, SRG and Natura sites, respectively.
Natura grasslands had a greater richness (P < 0.001) of positive indicator species than SRG and THM, both of which were similar (Figure 1, Table 3). The THM and SRG sites also had a greater cover (P < 0.001) of negative indicator species than Natura sites (Table 3).
Soil samples from the three AE grassland measures did not differ in phosphorus (P = 0.371), magnesium (P = 0.954) and pH (P = 0.266) (Table 5). Potassium concentrations were higher (P < 0.05) on SRG and Natura sites than on THM sites (Table 5). There was no significant correlation between the soil data and the botanical community data for each AE grassland measure.
Comparison of soil nutrient levels (mean ± s.e.) between AE grassland measures
|Traditional Hay Meadow||Species-Rich Grassland||Natura|
|Potassium1||76.53 ± 9.74a||100.10 ± 7.89b||82.06 ± 6.30ab|
|Phosphorus1||5.92 ± 0.89||5.74 ± 1.18||6.12 ± 0.69|
|pH||5.74 ± 0.15||5.97± 0.16||6.11 ± 0.16|
|Magnesium||180.41 ± 26.29||188.12 ± 15.64||180.69 ± 20.86|
Analysis was conducted using general linear models (normal) and Kruskal–Wallis test (non-normal).
Means with the same letter do not differ significantly from one another.
s.e. = standard error of mean.
Botanical community structure
There was a significant difference (global R = 0.281) in the botanical composition of the three AE grassland measures (based on ANOSIM in PRIMER). Pairwise comparisons showed a significant difference between SRG and THM (R = 0.215) and between SRG and Natura (R = 0.226). The most significant difference was between THM and Natura (R = 0.461).
When AE grassland measures were looked at individually, PCA and MDS analysis indicated that there was a close association between THM grassland and a number of grass species, in particular, grasses associated with improved pasture (e.g. L. perenne, Agrostis spp. and Poa trivialis; Table 4). Grasses were distinctly more closely associated with THM than with Natura grasslands, reflecting the results in Table 2. Natura grasslands were strongly associated with a number of herb species, especially those associated with semi-natural habitats (e.g. Senecio aquaticus, Succisa pratensis, Mentha aquatica and Caltha palustris). The community composition of SRG contained a combination of both grasses and herbs; moreover, it appeared to be intermediate in composition between the grass-dominated sward of the THM and the herb-rich sward of the Natura grassland. Species-Rich Grassland showed similar results as THM in grass species richness (Table 2); however, Figure 2 highlights that the grasses present in SRG (e.g. Anthoxanthum odoratum, Festuca spp.) are associated with low levels of nutrient inputs, in contrast to the grass species associated with THM grasslands.
The nMDS plot (Figure 3) shows that although there is a close association between the individual sites and certain plant species; there is also a certain level of overlap in community structure among the AE grassland measures. A majority of the THM sites were closely associated with species of improved grassland (as mentioned previously); however, some sites had a closer association with the botanical community of SRG grassland. Similarly, some Natura sites were closely associated with the plant communities of THM or SRG. The composition of the SRG sites is intermediate between those of THM and Natura.
Agri-environment schemes are an important policy mechanism for the protection of natural resources and provision of ecosystem services (Finn and Ó hUallacháin, 2012), with measures targeted at the conservation of semi-natural grasslands vital to the maintenance and enhancement of these habitats (Ridding et al., 2015). Considering that the majority of High Nature Value farmland is believed to be outside of Natura 2000 areas (Keenleyside et al., 2014; Walsh et al., 2015; Matin et al., 2016), there is a strong need for AE measures and nature conservation strategies that target species and habitats outside of designated protected areas (Jackson et al., 2009).
Design and management of AE grassland measures
The European Court of Auditors (2011) highlighted that to ensure the environmental effectiveness of AESs, schemes should be appropriately designed and managed, as well as including the provision of specific objectives, clear justification and having a good ability to assess the intended environmental objectives.
The objective of the THM measure under AEOS is not clearly specified through quantitative targets; however, we assume that it is similar to the objective for THM under REPS, i.e. “to encourage a grassland management system that results in a more diverse sward with an increase in fora and fauna”. The lack of clarity in relation to a specific, measurable objective makes it difficult to determine whether objectives for THM are being achieved. In our study, THM sites had a relatively diverse sward, containing on average >10 species that indicated high-quality grassland. Some sites were very species rich; two sites had an average ≥40 species per field, including 18 indicator species of high-quality grassland. However, more than half of the THM sites had >30% cover of negative indicator species. A number of THM sites had reduced species richness (e.g. three sites had ≤20 species per field), with the cover and botanical communities resembling those of more agronomically productive swards. In the absence of specific, measurable objectives, it is difficult to conclude the extent to which this measure is effective.
The most stringent eligibility criterion for THM was that sites had to contain “at least three grass species (other than ryegrass)” (Table 1). Thus, e.g. sites containing a high cover (>10%) of negative indicator grass species (e.g. Dactylis glomerata, Arrhenatherum elatius; as in O’Neill et al., 2013), or grass species associated with improved agricultural grassland (e.g. Holcus lantus, Phleum pratense, Poa annua; as in Fossitt, 2000) would satisfy the eligibility criteria for THM in AEOS. Two sites in the current study had more than 72% and 76% cover of species associated with improved agricultural grassland (the GA1 grassland category in Fossitt, 2000), yet were fully compliant with the botanical eligibility criteria for THM in AEOS. A lack of stringent eligibility criteria could result in the fact that AE measures are not targeted to the sites with greatest environmental potential.
Effective AE measures require valid cause-and-effect relationships between the prescribed management practices and the intended outcomes. In a sample of European AESs, Primdahl et al. (2010) found that biodiversity measures were least likely to have evidence-based models of such causeand-effect relationships. Under AEOS, the management specifications for THM, particularly where sites are relatively species poor, are unlikely to be sufficient to either enhance the species richness or encourage the establishment of positive indicator species (Bakker and Berendse, 1999; Fritch et al., 2011). The latest AES – GLAS (DAFM, 2015) – has introduced a further relaxation of THM management specifications, with permissible nitrogen application rates being increased from 30 (REPS and AEOS) to 40 kg N/ha (GLAS). With regard to SRG, permissible nitrogen application rates have increased from 0 (in REPS) to 30 (in AEOS) and 40 kg N/ha (in GLAS). Nitrogen application, even at low levels, is strongly associated with an increase in nitrophilus grasses, an increase in competition and ultimately an overall decline in species diversity (Kleijn et al., 2009; Bobbink et al., 2010; Bobbink and Hettelingh, 2011).
A lack of relevant and reliable management information is also problematic for some species-rich sites. The majority of surveyed Natura sites had a diverse botanical sward (Table 2), containing on average almost 17 positive indicator species for priority grasslands (e.g. Dactylorhiza fuchsii, Mentha aquatica, Succisa pratensis and Galium palustre). However, a small number of the surveyed Natura grasslands (Figure 3) had plant communities that were more closely related to THM swards or improved agricultural grassland. It is also concerning that 15% of Natura grassland sites had shrub and bracken cover exceeding the 5% threshold for designated habitats recommended by O’Neill et al. (2013). Considering the lack of clarity in relation to the objectives of the Natura measure and the lack of specific details in relation to management prescriptions (Table 1), this is not surprising. In the absence of specific management prescriptions, some farmers with Natura sites may be reluctant to undertake any management in these areas due to concerns that they might inadvertently be in violation of cross-compliance or Habitats Directive obligations. This lack of active management can lead to these grasslands becoming rank, losing botanical richness (and herb richness in particular), culminating in scrub encroachment. O’Neill et al. (2013) and Ridding et al. (2015) highlighted that abandonment, particularly of wet grasslands, was a problem, resulting in swards becoming rank, with negative implications for many insect and bird species.
Targeting and value for money
A key question for the design of AESs is: how to target participation and resources to those sites with the greatest environmental potential? Grassland conservation options were the most popular measures in AEOS at the time of the study (2013). Grassland measures were also the most popular options in GLAS, with Low Input Permanent Pastures (similar to SRG) being the most popular measure in Tranche 1, resulting in the participation target being met. This resulted in changes to Tranche 2 of GLAS, where the maximum permissible area for Low Input Permanent Pastures was reduced from 10 to 5 ha. It is understandable that there is a need to have participation targets and to avoid oversubscription to a reduced number of measures. However, where there is likelihood that a measure will be oversubscribed, there is an opportunity to target participation to those sites with the greatest environmental potential, as opposed to basing participation on a random selection. In this study, e.g. SRG sites that had only four positive indicator species were as likely to be included in AEOS as those with 24 positive indicator species. Where there is likely to be oversubscription, more targeted selection could prioritise entry of higher-quality sites by modifying the eligibility criteria; this would also help fulfil the recommendations of the European Court of Auditors that AE expenditure should be more precisely targeted.
Successful conservation measures should assess the environmental effectiveness of AESs and be appropriately costed. Where a number of measures are available to effectively achieve the same biodiversity objective, it is desirable that the most ecologically effective and least costly options are selected. The species richness and the richness of positive indicator species were greater (though not significantly so) on SRG sites compared to THM sites, yet payment rates (under both AEOS and GLAS) for both measures are the same. This suggests that the SRG measure provides better value for money than the THM measure in terms of conservation of overall botanical richness.
The European Court of Auditors (2011) recommended higher payments for measures with higher environmental potential. The results in the current study found that Natura sites had significantly higher species richness, higher richness of positive indicator species and significantly lower cover of negative indicator species than either THM or SRG sites. Despite this, payment rates for Natura sites (€75/ha) were lower than those for THM and SRG (€314/ha). Farmers could select the THM or SRG measure for their Natura sites; however, this could potentially involve a change in the ‘traditional’ management of the Natura sites (e.g. a change in traditional grazing or mowing dates), with potential negative implications for biodiversity.
Payment by actions or payment by results?
Challenges lie in appropriately rewarding those sites that are delivering the greatest environmental benefit or, alternatively, incentivising site management to increase their provision of environmental benefits. We found high variability in the botanical composition between sites in the same measure and subject to the same management criteria (and thus in receipt of the same payment under AES). This highlights some of the limitations with AE measures that are based on payment by action. For example the current study identifies the contrasting situation of a THM site containing only five positive indicator species and >50% cover of negative indicator species being in receipt of the same payment rate (€314/ha) as a THM site with 19 positive indicator species and <10% cover of negative indicator species. A possible approach to address these challenges is to progress towards measures and schemes that are based on “payment by results”, as opposed to “payment by action” (Schroeder et al., 2013). Payment-by-results approaches are based directly on the delivery of an ecosystem service; the more of the service that is provided, the higher the payment. They are more quantitative in their objectives, have measurable attributes that can be used to determine eligibility and – in turn – facilitate monitoring to assess effectiveness, and they also allow payment rates to be set in a more objective, transparent manner (refer Klimek et al., 2008; McGurn and Moran, 2013). They allow greater flexibility for farmers to innovate and achieve the environmental targets, resulting in greater uptake and a better working relationship with farmers because the payment rates are more transparent, with a reduced need for penalties (Klimek et al., 2008; Burton and Schwarz, 2013). However, there are some potential limitations associated with the payment by results approaches, particularly where delivery of positive results may be beyond the control of farmers (but this is less likely to be the case with measures for the conservation of grassland habitats) or when there are difficulties with developing lists of suitable indicator species. Nevertheless, evidence from the “Burren Farming for Conservation Programme” and further afield (Fleury et al., 2015; Russi et al., 2016) indicate that these limitations can be addressed (especially for grassland habitats) and that the benefits outweigh the limitations. Matzdorf et al. (2008) concluded that AE schemes could be improved by refocusing on a payment by result approach, as opposed to the more traditional payment by action method.
This study found significant differences in the species richness and botanical composition of grasslands enrolled in three different grassland conservation measures supported by AEOS. Compared to THM and SRG, Natura sites had significantly higher species richness, more positive indicator species and lower cover of negative indicator species. Despite this, Natura sites were in receipt of lower payment rates (per area) than THM and SRG.
The lack of clarity in relation to specific, measurable objectives makes it difficult to determine whether objectives for grassland measures are being achieved. Furthermore, the lack of a specific objective has resulted in relatively undemanding eligibility criteria for THM. This means that sites containing, for instance, a high cover of negative indicator grass species or grass species associated with improved agricultural grassland are eligible for inclusion. Even where compliance and participation are high, if a measure is not appropriately designed, it is unlikely to have the desired environmental benefits (Carlin et al., 2010; Ó hUallacháin and Finn, 2011). This is evident with THM, where despite the high compliance and participation rates, the relatively undemanding eligibility criteria in particular, coupled with under-researched management prescriptions, are unlikely to result in an increase in positive indicator species in swards where they do not currently exist. This is not to say that there are no biodiversity benefits associated with the measure, but rather the high rate of financial payment could be associated with a higher level of provision of environmental benefits.
This work was funded by Teagasc (The Irish Agriculture and Food Development Authority) under the Walsh Fellowship Scheme. We would like to acknowledge support from the Teagasc Advisory and two anonymous reviewers for comments on the manuscript. Finally, we would like to thank the farmers and landowners of the study sites.
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Presence and absence of botanical species within each AE grassland measure
|Taxonomic group||Common name||Taxonomic name||THM||SRG||Natura|
|Grass||Bent grass##||Agrostis spp||*||*||*|
|Grass||Marsh foxtail||Alopecurus geniculatus||*||*||*|
|Grass||Meadow foxtail##||Alopecurus pratensis||*||*||*|
|Grass||Sweet vernal##||Anthoxanthum odoratum||*||*||*|
|Grass||False oat||Arrhenatherum elatius||*||*||*|
|Grass||Quaking grass##||Briza media||*||*||*|
|Grass||Soft brome||Bromus hordeaceus||*||*||*|
|Grass||Crested dogs tail||Cynosurus cristatus||*||*||*|
|Grass||Tufted hair grass||Deschampsia cespitosa||*||*|
|Grass||Flote grass||Glyceria fluitans||*||*||*|
|Grass||Reed sweet- grass||Glyceria maxima||*|
|Grass||Downy oat grass##||Helictotrichon pubescens||*||*||*|
|Grass||Yorkshire Fog||Holcus lanatus||*||*||*|
|Grass||Creeping soft grass||Holcus mollis||*|
|Grass||Perennial ryegrass||Lolium perenne||*||*||*|
|Grass||Purple moor grass##||Molinia caerulea||*||*||*|
|Grass||Reed Canary grass||Phalaris arundinacea||*|
|Grass||Common reed||Phragmites australis||*||*||*|
|Grass||Annual meadow grass||Poa annua||*||*||*|
|Grass||Smooth meadow grass||Poa pratensis||*||*||*|
|Grass||Rough meadow grass||Poa trivialis||*||*||*|
|Sedge||Glaucous sedge##||Carex flacca||*||*||*|
|Sedge||Tawny sedge||Carex hostiana||*||*||*|
|Sedge||Oval sedge||Carex ovalis||*|
|Sedge||Tussock grass||Carex paniculata||*||*||*|
|Sedge||Flea sedge##||Carex pulicaris||*|
|Sedge||Yellow sedge##||Carex viridula||*||*|
|Sedge||Common cotton-grass||Eriophorum angustifolium||*||*|
|Sedge||Hare’s-tail cotton-grass||Eriophorum vaginatum||*|
|Rush||C. Spike rush||Eleocharis palustris||*||*|
|Rush||Sharp-flowered rush##||Juncus acutiflorus||*||*||*|
|Rush||Jointed rush##||Juncus articulatus||*||*||*|
|Rush||Soft rush||Juncus effusus||*||*||*|
|Rush||Hard rush||Juncus inflexus||*||*||*|
|Herb||Lady’s mantle||Alchemilla vulgaris||*|
|Herb||Wild angelica||Angelica sylvestris||*||*|
|Herb||Fool’s water-cress||Apium nodiflorum||*|
|Herb||Lesser water-plantain||Baldellia ranunculoides||*|
|Herb||Marsh marigold##||Caltha palustris||*||*|
|Herb||Black Knapweed##||Centaurea nigra||*||*||*|
|Herb||Mouse ear||Cerastium fontanum||*||*||*|
|Herb||Meadow thistle##||Cirsium dissectum||*|
|Herb||Marsh thistle||Cirsium palustre||*||*||*|
|Herb||Hawksbeard spp||Crepis spp||*||*||*|
|Herb||Smooth hawksbeard##||Crepis capillaris||*||*||*|
|Herb||Common spotted orchid##||Dactylorhiza fuchsii||*||*||*|
|Herb||Wild carrot##||Daucus carota||*||*|
|Herb||Greater willowherb,||Epilobium hirsutum||*|
|Herb||Marsh willowherb,||Epilobium palustre||*||*|
|Herb||Meadow sweet##||Filipendula ulmaria||*||*||*|
|Herb||Marsh bedstraw##||Galium palustre||*||*||*|
|Herb||Heath bedstraw##||Galium saxatile||*||*|
|Herb||Greater horsetail||Equisetum telmateia||*||*||*|
|Herb||Square stalked St John’s Wort||Hypericum tetrapterum||*||*||*|
|Herb||Marsh pennywort##||Hydrocotyle vulgaris||*||*|
|Herb||Cats ear##||Hypochaeris radicata||*||*|
|Herb||Yellow Iris||Iris pseudacorus||*||*|
|Herb||Field scabious##||Knautia arvensis||*||*|
|Herb||Ox-eye daisy##||Leucanthemum vulgare||*||*|
|Herb||Ragged robin##||Lychnis flos-cuculi||*||*||*|
|Herb||Purple loosestrife||Lythrum salicaria||*|
|Herb||Pineapple weed||Matricaria discoidea||*||*|
|Herb||Water mint##||Mentha aquatica||*||*|
|Herb||Bog bean||Menyanthes trifoliata||*|
|Herb||Field forget-me-not||Myosotis arvensis||*||*||*|
|Herb||Water forget-me-not||Myosotis scorpioides||*|
|Herb||Grass of Parnassus||Parnassia palustris||*||*|
|Herb||Amphibious Bistort||Persicaria amphibia||*|
|Herb||Red leg||Persicaria maculosa||*||*||*|
|Herb||Common butterbur||Petasites hybridus||*|
|Herb||Mouse ear Hawkweed##||Pilosella officinarum||*||*|
|Herb||Common butterwort||Pinguicula vulgaris||*|
|Herb||Ribwort plantain##||Plantago lanceolata||*||*||*|
|Herb||Greater plantain||Plantago major||*||*||*|
|Herb||Trailing tormentil##||Pontentilla angelica||*|
|Herb||Marsh cinequefoil##||Potentilla palustris||*||*|
|Herb||Creeping cinquefoil||Potentilla reptans||*|
|Herb||Meadow buttercup##||Ranunculus acris||*||*||*|
|Herb||Creeping buttercup||Ranunculus repens||*||*||*|
|Herb||Yellow rattle##||Rhinanthus minor||*||*||*|
|Herb||Common sorrel||Rumex acetosa||*||*||*|
|Herb||Sheep sorrel||Rumex acetosella||*||*||*|
|Herb||Marsh ragwort||Senecio aquaticus||*||*|
|Herb||Marsh woundwort||Stachys palustris||*||*|
|Herb||Greater stitchwort||Stellaria holostea||*||*||*|
|Herb||Devil’s-bit scabious##||Succisa pratensis||*||*|
|Herb||Dandelion||Taraxacum officinale agg.||*||*||*|
|Herb||Water speedwell||Veronica anagallis-aquatica||*|
|Herb||Germander speedwell||Veronica chamaedrys||*||*||*|
|Herb||Thyme-leaved speedwell||Veronica serpyllifolia||*||*||*|
|Weed||Creeping thistle||Cirsium arvense||*||*||*|
|Weed||Spear thistle||Cirsium vulgare||*||*||*|
|Weed||Broadleaved dock||Rumex obtusifolius||*||*||*|
|Legume||Meadow vetchling##||Lathyrus pratensis||*||*||*|
|Legume||Bird’s-foot trefoil##||Lotus corniculatus||*||*||*|
|Legume||Black medic||Medicago lupulina||*||*||*|
|Legume||Red clover##||Trifolium pratense||*||*||*|
|Legume||White clover **||Trifolium repens||*||*||*|
|Legume||Tufted vetch##||Vicia cracca||*||*||*|
|Legume||Bush vetch||Vicia sepium||*||*|
|Legume||Beaked hawksbeard||Crepis vesicaria||*|
|Shrub||Bell heather||Erica cinerea||*||*|
|Shrub||Bog myrtle||Myrica gale||*||*|
|Shrub||Downy birch||Betula pubescens||*|
|Shrub||Pedunculate Oak||Quercus robur||*|
Kruskal–Wallis test (non-normal distribution).
Kruskal–Wallis test (non-normal distribution).
Plant indicator species for high-quality grassland based on the High Nature Value plant species list recommended by O’Neill et al. (2013).
Plant indicator species for high-quality grassland based on the High Nature Value plant species list recommended by O’Neill et al. (2013).
Plant indicator species for high-quality grassland based on the High Nature Value plant species list recommended by O’Neill et al. (2013).