Identifying Villages for Land Consolidation: A New Agricultural Land Erosion Indicator

: Among the priorities of the European Union’s (EU) Common Agricultural Policy are the willingness to improve the quality of life in rural areas and effectively utilise their resources. Soil quality is one of the major factors that impact the potential level of agricultural crops. Therefore, it is a key determinant of income from agricultural production in a speciﬁc area. The awareness that spatial variations exist in soil quality classes in the study area directly affects the planning of the development of agricultural land and efﬁcient allocation of funds for the spatial redevelopment of rural areas. These data can be used over a very long time in connection with a few changes in land quality. The data on the quality and suitability of soil in the study area were derived from an analysis of map information on land quality and use. The analyses were conducted in 299 villages of the Zamo´s´c district, Lublin voivodeship, in the eastern part of Poland. The study area, extending over more than 187,181 hectares (ha), was divided into more than 280,000 plots for administrative purposes. The paper presents a self-designed agricultural land quality indicator to identify precincts featuring the best soils used in agricultural production. The value of the indicator will oscillate from 0 to 1. The value for an object will be close to or equal to 0 when the area comprises only land showing a high degree of erosion, e.g., light soils with a signiﬁcant slope gradient. The value for an object will be close to or equal to 1 if its area is exclusively or predominantly ﬂat. The highest value of the indicator in the study area was 0.75 and the lowest was 0.26.


Introduction
The soils of Poland are highly varied in terms of quality and spatial distribution. These variations mainly stem from the multiple simultaneous impacts on the soil-forming process. Agriculture underlies the existence of many farms and farmers make a living from it. In Poland, agricultural land covers nearly 52% of the area of the country, whereas all rural areas account for 85% of the total area. It should be remembered that agriculture, as a division of the national economy, has: economic functions (offers job opportunities, influences the national income, and supplies raw materials for trade exchange), social functions (provides a source of maintenance for people, accumulates labour surplus, and supplies food to people), and spatial functions (shapes the natural landscape, maintains the so-called free space, and makes alterations to the natural environment). The rural population of Poland is approximately 15 million, which corresponds to 38% of the country's inhabitants. The development of Polish agriculture is determined by two groups of factors: natural (climate, terrain, soils, hydrological conditions, and slope gradient) and extra-natural factors. The agricultural development determinants include the farming community's potential, national agricultural policy, the agrarian structure of Polish farms, and the general development level of Poland's economy [1]. tion with their properties. Large-scale maps (1:5000 and 1:10 000) are produced locally depending on the needs, mostly for taxation purposes and for soil valuation.
Agricultural soil maps were drawn directly in the field and function as an extremely valuable source of information on soils, in a sense taking their continuing spatial variations into account. Smooth transitions between respective soil complexes stem from specific features of the soil cover and its variations in space [51] (see Figure 1). We obtained the following map from the District Administration Office in Brzozów. The compilers of agricultural soil maps of Poland in the 1960s made every effort to present the spatial distribution of soil as accurately as possible. It should be noted that the accuracy of these maps is evaluated as 10-50 m [52]. The uncertainty about the course of the borderlines stems from the accuracy of agricultural soil contour maps and from the fact that the smoothness of soil cover changes both in geographic space and the space of attributes that describe it is not taken into account [51]. Introducing a blurred boundary will result in data that, contrary to appearances, are more reliable [53] as it refers to areas with 100% certain map contents and areas with uncertain boundaries. This stems from the phenomenon's continuity and the inaccuracy of the map alone. The digitalisation of agricultural soil maps will allow using such data faster and more specifically in various fields of science and areas of the economy, for instance, in comparative estimates for land consolidation purposes. It is very important to ensure that the maps are valid and reliable and feature a specific level of uncertainty of input data and the output agricultural soil map. It is essential to focus specifically on how they are digitalised. The use of existing agricultural soil maps in combination with classification contours from the land and buildings register's database will provide a fuller image of actual soil conditions [51]. The classification of land oriented at soil quality, based on which classification contours are entered into the land and buildings register, is more current in terms of land use [54]. The process of converting agricultural soil maps into vector images must consider changes in land use recorded in the land and buildings register. This ensures reliable and accurate mapping of agricultural soil contours that will contribute to further analyses necessary, among other things, in land consolidation.
Another information resource used in designing the indicator is the Digital Terrain Model, developed using aerotriangulation or laser scanning, available in the central national geodesic and cartographic resources. A Digital Terrain Model (DTM) is a digital representation of terrain by a set of points and an algorithm making it possible to calculate the elevation at any specific point. How a DTM is generated depends on the data in The compilers of agricultural soil maps of Poland in the 1960s made every effort to present the spatial distribution of soil as accurately as possible. It should be noted that the accuracy of these maps is evaluated as 10-50 m [52]. The uncertainty about the course of the borderlines stems from the accuracy of agricultural soil contour maps and from the fact that the smoothness of soil cover changes both in geographic space and the space of attributes that describe it is not taken into account [51]. Introducing a blurred boundary will result in data that, contrary to appearances, are more reliable [53] as it refers to areas with 100% certain map contents and areas with uncertain boundaries. This stems from the phenomenon's continuity and the inaccuracy of the map alone. The digitalisation of agricultural soil maps will allow using such data faster and more specifically in various fields of science and areas of the economy, for instance, in comparative estimates for land consolidation purposes. It is very important to ensure that the maps are valid and reliable and feature a specific level of uncertainty of input data and the output agricultural soil map. It is essential to focus specifically on how they are digitalised. The use of existing agricultural soil maps in combination with classification contours from the land and buildings register's database will provide a fuller image of actual soil conditions [51]. The classification of land oriented at soil quality, based on which classification contours are entered into the land and buildings register, is more current in terms of land use [54]. The process of converting agricultural soil maps into vector images must consider changes in land use recorded in the land and buildings register. This ensures reliable and accurate mapping of agricultural soil contours that will contribute to further analyses necessary, among other things, in land consolidation.
Another information resource used in designing the indicator is the Digital Terrain Model, developed using aerotriangulation or laser scanning, available in the central national geodesic and cartographic resources. A Digital Terrain Model (DTM) is a digital representation of terrain by a set of points and an algorithm making it possible to calculate the elevation at any specific point. How a DTM is generated depends on the data in use: photogrammetric (from photographs), aerial laser scanning, cartographic databases, and land surveying data [55].
Geographic Information Systems (GIS) are used for collecting and integrating data from various sources. As defined by Maguier, a GIS should be understood as a system allow-ing the acquisition, storing, processing, and visualisation of data. The data in Geographic Information Systems are saved as digital spatial databases and descriptive databases interconnected through spatial relationships. Such a systematic registration of spatial data allows spatial analysis models to shape rural spaces. This contributes to increasing the decision-making efficiency based on multi-faceted analyses [56].
Having a spatial distribution map of soils and information about the slope gradient of a specific area derived from DTM processing, including information from Table 1 specifying the intensity of water erosion, if any, one can determine the risk of erosion within the study area. Source: [57] The risk of potential water erosion is marked from 1-5: 1-slight erosion causing small sheet flow only; 2-moderate erosion leading to clear washout of the humus horizon and deterioration of soil properties; 3-medium erosion that can completely reduce the humus horizon and create soil profiles of unclassified types; 4-strong erosion that can contribute to destroying the whole soil profile and even a part of the subsoil, which is associated with changes in soil cover types; 5-very strong erosion with effects resembling those of strong erosion but more intensively expressed and leading to the permanent degradation of ecosystems.
It should be noted that if two degrees of the risk of erosion exist at the same time, the lower degree of erosion is quoted for precipitation levels below 600 mm and the higher-for precipitation levels above 600 mm.
For soil formations from the fifth group on land with a slope gradient > 15 • : for precipitation up to 600 mm, the third degree of erosion intensity is assumed; for rainfall levels from 600 mm to 800 mm, the fourth degree; and for rainfall exceeding 800 mm, the fifth degree.
In addition, the analyses noted the zero (0) degree land unaffected by erosion. Erosion is classified in multiple ways [57], mainly as water erosion, wind erosion, snow erosion, tillage erosion, and mass movements. The most common type of erosion is water erosion caused by precipitation waters (rainwater erosion) and rivers and this erosion leads to the largest loss of eroded material, up to hundreds of tonnes per square kilometre a year.

Materials and Methods
The study area was the district of Zamość (187,181 ha) in the south-eastern part of the Lublin voivodeship, east of Poland. This is one of the largest districts in the voivodeship, consisting of 304 localities (5 towns and 299 villages). It is divided into more than 280,000 plots in total, of which 212,000 are agricultural plots accounting for 75.7% of all the plots in the study area. An average farmstead in the study area extends over 1.73 ha. Figure 2 illustrates the spatial location of the study area in Europe. than 280,000 plots in total, of which 212,000 are agricultural plots accounting for 75.7% of all the plots in the study area. An average farmstead in the study area extends over 1.73 ha. Figure 2 illustrates the spatial location of the study area in Europe. The study area features varied terrain relief and soil types. According to the vertical reference frame PL-KRON86-NH, the lowest point in the study area is situated approximately at 185.3 m and the highest at 386.7 m above sea level. The mean elevation is 243.7 m above sea level. The relief map ( Figure 3) shows that plains are located in the central part of the area's clear belt extending from the north-west towards its eastern parts. Areas with a bigger slope are situated in the northern, southern, and south-western parts of the examined district. The study area features varied terrain relief and soil types. According to the vertical reference frame PL-KRON86-NH, the lowest point in the study area is situated approximately at 185.3 m and the highest at 386.7 m above sea level. The mean elevation is 243.7 m above sea level. The relief map ( Figure 3) shows that plains are located in the central part of the area's clear belt extending from the north-west towards its eastern parts. Areas with a bigger slope are situated in the northern, southern, and south-western parts of the examined district.
The study implies that the analysed area is highly varied in terms of soil type and quality. The biggest group is light formations (61,530 ha), sand (32,225 ha), loess soils and heavy loess formations (22,095 ha), heavy formations (15,763 ha), loam (7230 ha), medium formations (1907 ha), and clay (410 ha). No soil class is assigned to an area of approximately 39,334 ha as this land is meant for roads, buildings, water reservoirs, and watercourses. The said land is not further taken into account in this study. Figure 4 shows the distribution of soils in the study area.
This paper attempts to create a universal land quality indicator (Di) to constitute an underlying feature for identifying rural areas in land consolidation projects. Such an indicator will allow for the identifying of villages with the highest land quality potential that can be used in agricultural production for land consolidation purposes.  The study implies that the analysed area is highly varied in terms of soil type and quality. The biggest group is light formations (61,530 ha), sand (32,225 ha), loess soils and heavy loess formations (22,095 ha), heavy formations (15,763 ha), loam (7230 ha), medium formations (1907 ha), and clay (410 ha). No soil class is assigned to an area of approximately 39,334 ha as this land is meant for roads, buildings, water reservoirs, and watercourses. The said land is not further taken into account in this study. Figure 4 shows the distribution of soils in the study area.  The study implies that the analysed area is highly varied in terms of soil type and quality. The biggest group is light formations (61,530 ha), sand (32,225 ha), loess soils and heavy loess formations (22,095 ha), heavy formations (15,763 ha), loam (7230 ha), medium formations (1907 ha), and clay (410 ha). No soil class is assigned to an area of approximately 39,334 ha as this land is meant for roads, buildings, water reservoirs, and watercourses. The said land is not further taken into account in this study. Figure 4 shows the distribution of soils in the study area.  Agricultural soil maps illustrate spatial variations in the soil habitat and contain synthetic information on soil's physical properties and suitability for agriculture. Agricultural soil maps present soil complexes consisting of various types and genera of the soil of similar use. The allocation to respective complexes is based on: type, genus, class, and variety of soil; physical and chemical properties of soil; climate conditions; water regime; situation in terrain.
Possible applications of maps and map annexes include using them in the land consolidation process in order to estimate the value of an agricultural property, for design amelioration, to set up the border between agricultural land and forestland as well as to determine the lines of rural development, in the valorisation and protection of land, in spatial planning, for the transformation of agricultural plots into building land, for the design of roads, and measuring the performance of earthworks. Agricultural soil maps also show the location of precinct limits, limits of possession, built-up land, roads, and water bodies.
The methodology of designing the land quality indicator was divided into two stages. The first stage described the model data used for determining the indicator's value and how they were produced using GIS tools. The second stage is a theoretical description of how the indicator was developed and its final shape.
In order to determine the land quality indicator, it is necessary to find data on: the registered surface area of each village for which the indicator is to be determined, with less areas not affected by erosion or where erosion is insignificant for agricultural crops, e.g., built-up land, roads, swamps, peatland, water bodies, etc. using Equation (1): where: P c -surface area of soil within the study object for which the coefficient of erosion was specified; P m i -total surface area of soil within the study object; P e i -surface area of soil within the study object for which no coefficient of erosion was specified (soil not taken into account in calculating wsjgr).
The slope gradient of the study area was determined using the Digital Terrain Model with a 1 × 1 m resolution. Using GIS functions to determine spaces with a specific slope range and reclassify DTM spaces, the vectors corresponding to the specific slope gradient could be delimited.
As shown by the study (Table 2), 100,574.0 ha is an area with small differences in height, corresponding to 54.0% of the total analysed area. More than 25.0% of the study area is land with slopes ranging from 3 • to 6 • , which in terms of area is 47,148.0 ha. More than 22,000 ha is land with slopes from 6 • to 10 • . The areas with downslopes exceeding 10 • cover nearly 16,000 ha, which accounts for more than 8.5% of the total surface of the study area.
For an agricultural soil map and a vector image of the terrain slope within a uniform reference system, GIS tools (Tabulate Intersection in ArcGIS Pro) were used to calculate the intersection of these two elements and create a table showing the surface area of the respective soils, including the slope gradient, at the same time individually for a specific village. Table 3 presents an example of the above-mentioned data. The values of the erosion coefficient (vi) were adopted after Table 1.
The agricultural land quality indicator (w sjgr ) describing the best areas in terms of soil erosion is: where v i -value of the coefficients of erosion; v imin -minimum value of the coefficients of erosion adopted in the analysis; P mi -total surface area of soil within the study object; P c -surface area of soil within the study object for which the coefficient of erosion was specified.
The form of the equation is the quotient between the total surface area of soil within the study object for which the coefficient was specified P c , multiplied by the value of the lowest erosion coefficient, adopted in the analysis v imin (in our case v imin = 1), to the sum, which is the multiplied result of the area of individual soils of the tested object P mi and their erosion coefficient v i . The numerator value in the equation will always be less or equal to the denominator value.
The value of the indicator will oscillate from 0 to 1. The value for an object will be close to or equal to 0 when the area comprises only land showing a high degree of erosion, e.g., light soils with a big slope gradient. The value for an object will be close to or equal to 1 if its area is exclusively or predominantly flat. When considering the values of the erosion coefficients for flat areas (up to 3 • ), it can be concluded that the soil type is not significant in this case. The subject research was conducted in 2022.

Results and Discussion
The above-presented model used for determining the value of the agricultural land quality indicator was designed by us. The study covered 299 villages in the district of Zamość. Appendix A presents the precinct's identifier and the indicator value calculated based on Equation (2); their spatial distribution is illustrated in Figure 5.

Results and Discussion
The above-presented model used for determining the value of the agricultural land quality indicator was designed by us. The study covered 299 villages in the district of Zamość. Appendix A presents the precinct's identifier and the indicator value calculated based on Equation (2); their spatial distribution is illustrated in Figure 5. According to the study results, the mean quality indicator of agricultural land for the study area is 0.26. In addition, for 275 villages, which corresponds to 92% of all the villages covered by the study, the indicator's value did not exceed 0.30. This testifies to the fact that the study area is predominantly land with varied terrain relief exposed to erosion.
Terrain relief is the main element of the natural environment determining the possibilities of agricultural development. It is an essential factor shaping the soil cover, water conditions, and temperature distribution and it is closely linked to all the elements of nature. Terrain relief also directly affects field work methods used in rural areas and even the selection of adequate agricultural machines on farms.
The study indicates that Zrąb Szosa (ID: 062010_2.0030) is a precinct with the most significant height difference, for which the indicator was only 0.198. The examined village features diverse terrain relief where land with a slope gradient exceeding 6° cover an area of 176.0 ha, which corresponds to 56% of the total area.
Terrain relief, along with the lack of permanent structure of the vegetation cover, is favourable to water erosion and makes it difficult to conduct field work in uplands. It is essential to plant adequate crops, apply contour ploughing, maintain wide balks, and introduce buffer strips.
By contrast, the highest indicator-amounting to 0.75-was recorded for Kolonia Horyszów Polski (ID: 062009_2.0007) and its value was unique on the scale of the whole area. Such a high indicator value was, in the first place, due to the fact that for 279.0 ha, which accounts for more than 78% of the village's total area, the slope gradient was smaller than 3°. The second village with the highest value of the agricultural land quality indicator (wsjgr = 0.50) is the precinct of Sitaniec Wolica (ID 062014_2.0020). Variations in According to the study results, the mean quality indicator of agricultural land for the study area is 0.26. In addition, for 275 villages, which corresponds to 92% of all the villages covered by the study, the indicator's value did not exceed 0.30. This testifies to the fact that the study area is predominantly land with varied terrain relief exposed to erosion.
Terrain relief is the main element of the natural environment determining the possibilities of agricultural development. It is an essential factor shaping the soil cover, water conditions, and temperature distribution and it is closely linked to all the elements of nature. Terrain relief also directly affects field work methods used in rural areas and even the selection of adequate agricultural machines on farms.
The study indicates that Zrąb Szosa (ID: 062010_2.0030) is a precinct with the most significant height difference, for which the indicator was only 0.198. The examined village features diverse terrain relief where land with a slope gradient exceeding 6 • cover an area of 176.0 ha, which corresponds to 56% of the total area.
Terrain relief, along with the lack of permanent structure of the vegetation cover, is favourable to water erosion and makes it difficult to conduct field work in uplands. It is essential to plant adequate crops, apply contour ploughing, maintain wide balks, and introduce buffer strips.
By contrast, the highest indicator-amounting to 0.75-was recorded for Kolonia Horyszów Polski (ID: 062009_2.0007) and its value was unique on the scale of the whole area. Such a high indicator value was, in the first place, due to the fact that for 279.0 ha, which accounts for more than 78% of the village's total area, the slope gradient was smaller than 3 • . The second village with the highest value of the agricultural land quality indicator (w sjgr = 0.50) is the precinct of Sitaniec Wolica (ID 062014_2.0020). Variations in the slope gradient for two examples of the examined objects are presented in Figure 6. The respective colours mark areas within specific slope ranges designated as percentages. The precinct Zrąb Szosa clearly shows a more considerable differentiation in the slope angle than Kolonia Horyszów Polski, which is reflected by a significantly lower w sjgr value. However, flat land does not mean that erosion will not occur there. In such areas, closed drainage can also be a problem as causes ponds of stagnating water that prevent farmers' field work. Here, an amelioration procedure is required to drain the land. The said phenomenon can be taken into account in the presented method by increasing the value of parameter v i . In summary, terrain relief causes several obstacles to agricultural production in lowlands and uplands.

Conclusions
Land consolidation is deemed an important and, simultaneously, very difficult land survey and legal procedure. It refers to property law and causes new land to use governance within the consolidated area, which increases the efficiency of land management and facilitates the multi-faceted development of rural areas.
Accounting for the limited financial and human resources, it is necessary to identify areas where land consolidation should be implemented first. According to long-term surveys, important factors taken into account when identifying villages for consolidation include fragmentation, scattering of land, and incorrect geometry of plots for which, following the consolidation works, the parameters are substantially improved.
Following a review of numerous scientific publications and analyses, we noted an essential need for designing an indicator that would allow us to identify areas (villages) where land consolidation works should be conducted in view of their terrain relief and soil quality. These are very important features that will make it possible to specify the study area precisely. The designed original agricultural land erosion indicator is particularly useful in areas with varied terrain relief and a variety of soils since land quality is one of the key factors impacting agricultural crop yield. The soil conditions in terms of soil quality and use can be described according to soil quality classes, agricultural soil complexes, and water regimes. Low soil classes, poor agricultural soil complexes, and excesses or deficiencies of water (depending on the soil) considerably impact the type and amount of a farm's crops.
Therefore, it is a key determinant of income from agricultural production in a specific area. The awareness that spatial variations exist in soil quality classes in the study area actually affects the planning the development of agricultural land and efficient allocation of funds for the spatial redevelopment of rural areas.
The proposed indicator was tested for 299 villages in the district of Zamość, situated in the Lublin voivodeship in eastern Poland. The designed agricultural land quality indicator is universal. Since it is not limited to the study area, it can also be applied to other study regions such as communes, districts, voivodeships, and countries, which makes it very advantageous.
To sum up, in the context of identifying villages for land consolidation, the proposed agricultural land quality indicator is useful for delimiting areas that can be included to improve the spatial structure of rural areas. Thanks to analyses followed by rural management works, rural areas become competitive, and activity in such areas will generate a beneficial effect and upgrade the living standard of the local inhabitants.