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Identifying High Nature Value farmlands on a national scale based on multivariate typology at municipality (LAU 2) level

Summary High Nature Value farmlands in Europe are of greatest importance in the conservation of biodiversity. Their environmental importance has been recognized for some time, and has been studied mostly in Western Europe. This article describes the results of multivariate statistical analyses performed on data (13 variables) collected from the latest National Agricultural Census and the CORINE database to provide a typology of farmlands with respect to their nature value at municipality level (LAU 2, Local Administrative Units level 2) across Poland. All municipalities were grouped into eight categories (types). Some of the farmland categories were considered to be High Nature Value farmland (HNVf). The following interrelated variables mostly contributed to the identification of HNVf: share of protected areas and forest, grassland, arable land and fallow, farmland cover diversity, and rate of nitrogen fertilization. HNVf was identified in 958 out of 2173 municipalities, covering 44% of the territory of Poland. The identified HNVf also overlaps partially (61%) with LFAs (Less Favored Areas). Farmlands with the highest nature value are located mostly across mountain and hilly areas, close to forests, and protected areas on lowlands and river valleys. The identified HNV farmlands are characterized by low-input farming systems and a large share of semi-natural habitats with a high landscape mosaic.


Introduction
Farmlands (also called agricultural areas or agricultural land, defined as including Utilizable Agricultural Areas-UAA-and agricultural-ecological wasteland including rough terrain, shrubs, scrubs, peatlands, wetlands, streams, etc.) provide varied habitats for many wild plants and animals. Biodiversity on farmlands has been declining throughout Europe as a result of intensification of agriculture and abandonment of marginal lands since the second half of the 20th century (Keenleyside et al. 2014;Lomba et al. 2014;Benedetti 2017). Due to increasing awareness regarding the declining biodiversity on farmlands, the term High Nature Value farmland (HNVf) has become established (Beaufoy et al. 1994; Andersen et al. 2003). Typical HNVfs are "those areas in Europe where agriculture is a major (usually the dominant) land use and where that agriculture supports, or is associated with, either a high species and habitat diversity or the presence of species of European conservation concern, or both" (Andersen et al. 2003). Thus, HNVf comprises not only the physical farmland that can maintain the biodiversity of plant and animal species and their habitats, but also the accompanying High Nature Value farming system (Paracchini et al. 2008;Strohbach et al. 2015). HNV farming systems support the existence of agriculture-dependent species and their habitat, as they are low-intensity farming systems (Lomba et al. 2014(Lomba et al. , 2015(Lomba et al. , 2017. HNVf is characterized based on the proportion of semi-natural vegetation and the extent of permanently or periodically uncultivated landscape elements in farmland; the density of livestock; and the input of nutrients, agrochemicals, and irrigation in cropping systems (Andersen et al. 2003;Lomba et al. 2014;Benedetti 2017). Andersen et al. (2003) defined three types of HNV farmland, and this categorization has been commonly followed. With subsequent modifications (Paracchini et al. 2008;Lomba et al. 2014) they are as follows: Type 1farmlands under low-intensity management with a high proportion of semi-natural vegetation such as extensive permanent grassland, forest, scrub, etc.; Type 2farmlands dominated by lowintensity farming systems and with a lower proportion of semi-natural vegetation, mosaics (high diversity) of semi-natural and cultivated lands, and presence of landscape elements such as patches of forests and shrubs, field margins, streams, etc.; Type 3farmlands supporting rare species or a high proportion of European or world populations. In practice, the three HNVf types can overlap-this concerns mainly types 1 and 2-making implementation at different spatial scales difficult (Peppiette et al. 2012;Lomba et al. 2014). In Europe, HNVf is estimated to account for ca. 30% of the total area taken up by farmland, with very different shares among the members of the European Union (EU). The largest share of HNVfs is present in Eastern and Southern Europe, especially in mountains and highland areas (EEA 2004;Keenleyside et al. 2014;Oppermann et al. 2012).
HNVfs are most widespread in areas where agricultural production or activity is more difficult because of such factors as low soil fertility, harsh climatic conditions, and steep slopes in mountain areas (Keenleyside et al. 2014;Paracchini et al. 2008).
Identification of HNV farmlands serves chiefly to 1) map the occurrence and distribution of such farmland for appropriate targeting of conservation measures and supporting associated rural communities, and 2) control the impact of humans on the environment, for example by the exclusion of High Nature Value farming areas from intensive agricultural management areas (Andersen et al. 2003;Keenleyside et al. 2014;Lomba et al. 2015;Strohbach et al. 2015;Teillard et al. 2012). Efforts have been initiated to develop approaches and indicators to identify HNVf, mostly in Western Europe, using different methods (Kikas et al. 2018;Brunbjerg et al. 2016;Lomba et al. 2014Lomba et al. , 2015van Doorn and Elbersen 2012).
However, most of the methods used in HNVf studies to date have the aim of obtaining one indicator which shows whether or not farmland is of HNVf type (Lomba et al. 2014;Paracchini et al. 2008;Peppiette et al. 2012;Ribeiro et al. 2014). These methods are developed based on the relationships of nature value characteristics, investigated beforehand, which then are adopted a priori in obtaining the indicator. However, multivariate statistical analyses should deliver better knowledge on the nature value of farmland, its aggregation and regional distribution, and reveal interrelationships between the analyzed characteristics which are assumed to contribute to farmland nature value (Sutkowska et al. 2013;Lomba et al. 2014;Kikas et al. 2018). When identifying HNVf areas, the EU recommends including data on land cover, farming systems, and diversity of wild plant and animal species at farm or administrative unit level (Paracchini et al. 2008;Keenleyside et al. 2014;Brunbjerg et al. 2016;Lomba et al. 2014Lomba et al. , 2017. Across Poland, as in other European countries, farms fall somewhere on a continuum from intensive, highly productive farms with low biodiversity to extensive, low-productive, high-biodiversity HNVfs (Castel et al. 2010;Sutkowska et al. 2013;Lomba et al. 2014). In Poland, agricultural intensity is strongly positively correlated with farm size (Castel et al. 2010). To date, only preliminary mapping of HNVf across Poland has been performed by the National Institute of Geodesy and Cartography Keenleyside et al. 2014). Hence, within this study, the aim is to: • identify and map HNV farmlands for the whole of Poland at municipality level (LAU 2: level 2 of Local Administrative Units according to Eurostat).
• implement a multivariate methodological approach providing information on the likely occurrence and distribution of HNV farmland that is based on available current information on the high nature value of farmland at municipality level.
• assess whether the identified HNVfs lie partially within agricultural areas with various handicaps (low soil fertility, harsh climate, shortage of precipitation, short cropping season, mountainous or hilly landscapes, tendencies towards depopulation) known as Less Favored Areas (LFAs).

Data used
In the studies two groups of characteristics (variables) were used. Variables in the first group describe farming systems, and those in the other group characterize land cover. To describe the nature and potential of farming systems existing in Poland, the data collected from the National Agricultural Census carried out in 2010 by the Polish Central Statistical Office (GUS) was analyzed. This is the only complete and current database that characterizes agriculture across the whole country at LAU 2 level (formerly NUTS level 5: municipalities or communes).
The data on individual farms was aggregated to the municipality level during collection, and therefore only the aggregated data is available. The data regarding each municipality was found to be complete; therefore, the authors did not assume any uncertainty. The primary disadvantage of the data used in this study is the aggregation performed at the municipality level, which does not permit investigation of HNVf diversification within a municipality. The data contains information on the structure and size of farmland in the municipality area, livestock numbers, level of nitrogen (N) fertilization, and some aspects of the resources and socioeconomic condition of farms in Poland (Table 1). The data on farms was supplemented with data on vegetation from the European inventory of land cover-the CORINE database (Coordination of Information on the Environment)-from 2012 (this is based on data from a similar period as the census) and with data on protected areas, representing proxies for the biodiversity in each municipality.
General information about farmland cover (farm landscape) diversity was included in the form of the Shannon diversity index (Shannon 1948) calculated from the shares of all forms of Farmland cover by the formula: where pi is the share of the i-th form of Farmland cover. Similarly, crop diversity was calculated from the shares of all crops in the arable area. Based on this information, 13 characteristics (Table 1) Table   1 presents the rationale behind these characteristics.

Statistical analysis
To perform the analysis, the 13 variables described in Table 1 regarding 2173 rural and rural-urban municipalities were used. First, the frequency distribution of each variable within all the municipalities was analyzed using the normal probability plot technique (Z-score scaling). The distribution of some variables turned out to have many values that were very distant from the mean for all of the municipalities. Such cases are considered as outliers, and were excluded from further multivariate analyses based on a qq-plot for each variable according to the methodology proposed by Wilk and Gnadeskian (Pecher et al. 2013;Wilk and Gnanadesikan 1968). This decision resulted from the need to ensure reliability of inference, using in this study statistical methods which require normality of the distribution. On the other hand, outlier municipalities can be identified and/or interpreted as indicating their assignment to non-HNVf. Analysis of characteristics related to agricultural land use and agricultural intensity indicating non-HNVf (e.g. low share of farmland in the municipality area, high nitrogen input and/or high livestock density) made it possible to exclude 68 municipalities.
Among them, 14 entries had a very low share of farmland in the municipality area (<10%) and thus a very high share of forest (municipality group O1). This group of municipalities was assumed to include HNVf, although the available data was not sufficient to properly assess the nature value of these agro-ecosystems. These municipalities were considered to be typical forest areas, not generally displaying farmland attributes. Furthermore, 54 entries with high farming intensity were qualified as non-HNVf (municipality group O2). In addition, there were municipalities with exceptionally high shares of conventional (market and intensive) orchards. In Poland, market orchards are concentrated in several relatively small regions. In 91% of the municipalities the share of orchards in farmland was found to be less than 5%, and there are few municipalities where orchards constitute more than 50% of agricultural area. Thus, in the studies on HNVf identification, the share of orchards in the farmland area was treated as subject to a cutoff. The frequency distribution of the share of orchards in the farmland area was investigated across all of the municipalities, and those of them with a share of orchards higher than 30% were excluded from the analyzed data set (municipality group O3, considered as non-HNVf). Through these procedures, 95 outlying municipalities were identified.   1 Grassland area is the total area of permanent grassland 2 O1, O2 and O3 denote the groups of excluded LAU 2 units 3 NA -FCD and CD Indices were not calculated due to the unavailability of data for proportions of some land or crop categories in LAU 2 units assigned to O1. cluster analysis (Castel et al. 2010;Pecher et al. 2013;Ribeiro et al. 2014). These procedures are considered to be very effective to perform numerical taxonomy of multivariate entries like farms or area units (Mądry et al. 2013). In this study PCA identifies original variables that are meaningful in the discrimination of municipalities' farmlands according to the nature value of farming systems in each municipality. The set of 13 characteristics was analyzed using PCA as described by Pecher et al. (2013). The PCA results were subsequently employed in a hierarchical clustering approach using the Ward algorithm with squared Euclidean distance for the 13 standardized variables, to classify the municipalities into homogeneous groups with relatively equal size. The optimal number of clusters was determined based on two criteria: the pseudo-F statistic and pseudo-t2 statistic. Through these analyses, several groups of municipalities were distinguished, each of them specifying a farmland category/type. Some of the farmland categories were identified to be HNVf or potential HNVf. PCA and cluster analysis were performed using SAS 10 software (SAS 2012). Maps showing areas of municipalities with PC1 scores and maps presenting the groups identified via cluster analysis were prepared using QGIS 2.1 software (Figures 1   and 2).
The degree to which one municipality is similar to other nearby municipalities was also assessed. In the case of spatial pattern analysis, Moran's I spatial autocorrelation coefficient was calculated for all characteristics (Table 1) and the nearest neighbor ratio was calculated (Mitchel 2005) for each of the municipality groups distinguished by the cluster analysis (Table 2)  The obtained typology represents categories of farmland in terms of the nature value of their farming system. This provides a basis to recognize HNVf and to create a map of HNVf and non-HNVf at municipality level. Moreover, the recognized HNVf can be categorized according to the types suggested by Andersen et al. (2003).
In addition, to check the most common hypothesis concerning consistency between the spatial extent of HNVf and less favored areas (LFAs) (areas with natural handicaps to agricultural intensification in Poland, identified by the Ministry of Agriculture and Rural Development and supported by EU subsidies), four groups of municipalities were formed and mapped: "HNVf and LFA," "HNVf but non-LFA," "non-HNVf but LFA," and "non-HNVf and non-LFA."

Descriptive statistics for the municipalities
On average, 26% of Poland's area was covered by forests, and 65% by farmlands.
11 ha, with average nitrogen fertilization of 60 kg/ha and average livestock density (mostly cattle and pigs) of 0.60 head of livestock/ha of farmland.
However, municipalities were very diverse in terms of land cover structure indicators, with respect to both the total area of the municipality and its farmland (the coefficient of variation-CV-was found to range from 27% to 142%; Table   1), and in terms

Characteristics contributing most to the identification of HNVf
Based on the elbow criterion, only the first principal component (PC1) describes variation of all the original characteristics more than a single original characteristic (eigenvalue 4.42). It accounts for 34.6% of the total variability between municipalities (Figure 3). PC1 is mostly related to the proportion of different land types within both the whole municipality area and farmland, land cover diversity, and livestock density were found to be weakly correlated with PC1, although in other studies (Andersen et al. 2003;Lomba et al. 2014;Paracchini et al. 2008) livestock density has been considered to be an important indicator of HNVf. One reason for this difference may be the exclusion of 25 outlying municipalities due to their high livestock density.

Typology of municipalities' farmland based on cluster analysis
In the hierarchical cluster analysis, eight homogeneous groups of municipalities (without the excluded ones) were identified according to the pseudo-F and pseudo-t2 statistics. The optimal number of clusters was indicated by the highest value of the pseudo-F statistic and a low value of the pseudo-t2 statistic. Table 2 profiles these groups with the three excluded ones (O1, O2 and O3) by presenting the group-means for each of the characteristics. The first four groups (1-4) and O1 were qualified as HNVf. Groups 5-8, O2 and O3 were specified as non- HNVf.
The nearest neighbor ratio for all groups was found to be less than 1, which means that the spatial distribution of the distinguished groups (clusters) was not random. This confirms the cluster analysis results presented in Figure 2. The strongest spatial clustering was observed for groups 1 and 8 (hotspots of municipalities for these groups were observed), whereas for group 2 the spatial pattern was most random.

Detailed characterization of the four categories of farmlands recognized as HNVf
Category 1  As farm size does not provide enough information to discriminate between HNVf types 1 and 2, a strong correlation between farm size and farming intensity allows one to assert only the dominance of attributes of one HNVf type over the other in the analyzed areas. HNVf type 2 includes farmlands more often intensively managed than the semi-natural HNVf type 1. In practice, HNVf type 1 and type 2 can overlap, making implementation at different spatial scales difficult (Peppiette et al. 2012;Lomba et al. 2014).
Category 3 farmland is similar to the category 2 farmland identified in this analysis. It is characterized by a lower proportion of semi-natural vegetation in comparison with category 2, but with a significant mosaic of agricultural landscape and extensive mixed crop-livestock farming systems. In general, category 3 farmland may be regarded as HNVf type 2 according to Andersen et al. (2003) and Lomba et al. (2014). It is found in 216 municipalities, which is 9.9% of the total. It is located mainly in southeastern and eastern Poland, and is accompanied by category 2 HNVfs, sometimes filling spaces between HNVfs of categories 2 and 4 ( Figure 2). to these regions to support the maintenance of low-intensity agriculture.
The remaining categories of farmland were classified as non-HNVfs, and are characterized by a low (category 5) to extremely low (category 8) proportion of semi-natural vegetation, few or very few landscape elements, moderate to very low land cover diversity (uniform agricultural landscape), intensively or highly intensively managed livestock and/or mixed farming systems, accompanied by moderate to very low cover of public forests and protected areas. These areas may be used for future agricultural intensification based on local and national requirements.

Relations between HNVf and LFA
The comparison of HNVf municipalities, identified in this study as groups 1, 2, 3, and 4 (Table 2)   There is a large share of HNVf in non-LFAs, which corresponds to extensive, small-farm agriculture established in areas with a potential for high agricultural production. Most of the High Nature Value but non-LFA municipalities are located in the southeast of Poland in the Sandomierz Basin (marked in yellow in Figure 4), where HNVf exists on fertile soils, providing an exception to the rule that HNVf is present mostly in LFAs (Keenleyside et al. 2014;Lomba et al. 2014;Pointerau et al. 2007;Sutkowska et al. 2013). The chief characteristic of these areas is the structure of land ownership created by the Habsburg Empire in the 19th century and preserved in the 20th century. The area is divided into very small farms (with areas of around 1 ha) belonging to different owners. This makes it impossible to implement high-intensity, profitable agriculture. It is very important to learn the potential risks to the existence of these regions and possible directions of their development, so as to work on appropriate support strategies to make these farmlands more efficient without damaging their nature value.
There are also some municipalities recognized as HNVf but not LFA located around Warsaw, Radom, and Częstochowa.
They are scattered across central and western Poland (marked in blue in Figure   4). Municipalities recognized as non-HNVf but as LFAs were mostly found in municipality groups 5 or 6, hence they are dominated by low-or moderateintensity farming systems. Non-HNVf municipalities without natural handicaps (non-LFA) are characterized by high-intensity farming systems (groups 7 or 8), and these areas should be protected to maintain biodiversity without decreasing their agricultural production.
In spite of the use of complete data on Polish farm resources, farming, and land cover features, there are still some valuable data missing-such as the proportion of extensive and semi-extensive permanent grasslands, farmer's forest, shrubs, biodiversity and distribution of various species of flora and fauna, grazed cattle, use of pesticides-for defining criteria for the reliable identification of HNVf. However, the analysis provides a well approximated identification of the extent and character of HNVf. It may contribute to methodological progress in this area, especially in the identification of HNVf areas having a limited quantity and quality of data relating to the nature value of farmland. In addition, a very important advantage of using the census data is that these data are updated every 8-10 years, which enables the monitoring of changes with respect to HNVf over time. Similar data is available in most countries; therefore, the methodology could also be adapted by them.

Conclusions
In this article, indicators relating to the farmland cover pattern, land cover diversity and intensity of farming systems were used in the identification of HNVf at municipality (LAU 2) level. Multivariate procedures were found to be appropriate for providing a typology of farmlands with respect to their nature value at that level. Thus, these procedures can be replicated in future periods both in Poland and in other EU member states. The map of likely occurrence and distribution of HNVf may be used as a reference point for future monitoring of the distribution of HNV farmland across the country in order to assist with planning and policy development for the rural environment. According to the best knowledge of the authors, this is the first report of such a multivariate study on the spatial distribution and characterization of HNV farmlands in Poland.