A New Method for Assessing Land Consolidation Urgency, including Market Value

Abstract

Public funding for land consolidation projects is an instrument in the Common Agricultural Policy of the European Union (CAP). The execution of systematic land consolidation programmes focused on optimising the spatial structure of agricultural areas presents the possibility of improving agricultural production conditions and maximising the efficiency of agriculture at the local, regional, national, and international levels. However, due to limited access to financial resources, it is necessary to delimit priority areas for land consolidation. A contemporary practice based on an assessment of the social support percentage of potential land consolidation projects in individual villages does not represent a real necessity for action. This problem leads to the ineffective utilisation of financial resources and reduces the efficiency of the implemented programmes. We propose a new algorithm for assessing the real needs for land consolidation based on a detailed multi-faceted analysis of the spatial structure of agricultural areas. The research method involved factors describing the spatial structure defectiveness of farms, as well as those determining land quality in relation to investment profitability. Another factor verifying the potential economic rationale of land consolidation is transaction prices, which mostly reflect the agricultural value of the land. The analysis showed that land consolidation in areas with defective spatial structures and relatively high market value should be a priority. The study enabled the extraction of five of the 58 analysed villages, characterised by above-average demand for land consolidation and above-average land prices. This approach will contribute to profit maximisation by increasing the productivity of areas with the highest agricultural suitability.

1. Introduction

Land consolidation, financed by public funds, is a key instrument of national and regional agricultural policy in the context of the sustainable development of rural areas, understood as centres of agricultural production and living space [1,2,3,4,5,6,7,8,9]. In Poland, the rural population accounts for 40% of the total population, whereas 8.4% of the working population is employed in the agricultural sector [10]. Due to the occurrence of unfavourable spatial structures of agricultural farms, particularly in the southern, eastern, and central parts of the country [11], the need to optimise the layout of agricultural production space is reflected in the actual activities of the public administration, carried out within the framework of cyclical programmes and strategic plans being part of the European Union’s Common Agricultural Policy. Public financing of land consolidation projects translates into the widespread availability of instruments to optimise the spatial structures of rural areas. However, the scale of the observed needs, in terms of the correction of defective land layouts, involves the need to select areas intended for inclusion in a land consolidation procedure.

The previous practice of selecting potential objects to be covered by public funding in Poland, adopted for the Rural Development Programme between 2014 and 2020, took into account generalised criteria such as the following:

• Percentage of the number of landowners who have submitted applications for carrying out a land consolidation procedure in the total number of farm owners, or percentage of the area of farms whose owners have submitted the aforementioned applications in the total area of farms;
• Positive impact of the operation on the environment;
• Improvement of landscape values;
• Allocation of land for local public utility purposes;
• Allocation of land to improve hydrographic conditions in terms of water retention [12].

The above assumptions were based solely on public support for the initiative and the quality of the proposed solutions. As a result, it was possible to carry out land consolidation projects in areas featuring a favourable spatial structure of land, including the so-called secondary land consolidation carried out on sites consolidated in the recent past, for which the motivation was the possibility of obtaining funds for the modernisation of the village infrastructure under the so-called post-consolidation development.

With the accession of the Strategic Plan of the Common Agricultural Policy for the years 2023–2027, the operational selection criteria were based on modified criteria, taking into account the following:

• Percentage of the number of landowners who have submitted applications for carrying out a land consolidation procedure in the total number of farm owners, or percentage of the area of farms whose owners have submitted the above-mentioned applications in the total area of farms;
• The area of the designed land consolidation area;
• Average number of cadastral parcels per agricultural farm;
• Average cadastral parcel area in the designed land consolidation area;
• The actual allocation of land in the project for local public utility purposes;
• The actual taking into account of investments that optimise water retention;
• Planned environmental protection solutions (preservation of turf-covered slopes, provision of woodlots and shrubs, and delimitation of buffer zones and the field-forest boundary);
• The actual taking into account of the division of common land, or the disposition of land included in the Agricultural Property Stock of the State Treasury [13].

Detailed expansion of the criteria for selecting objects to be consolidated and the inclusion of selected parameters concerning the condition of the spatial structure are changed to ensure the substantive validity of carrying out land consolidation procedures and ensuring the availability of intervention instruments for areas with the greatest needs.

The approach that assumes the selection of potential objects to be consolidated, based on the assessment of the actual condition of the spatial structure and the delimitation of areas with the most urgent needs, is supported in numerous titles of the global scientific literature. Research on the methodology for assessing and hierarchising land consolidation needs has been conducted, e.g., in China [14,15,16], Croatia [17], Cyprus [18], Turkey [19], Poland [11,20,21,22,23,24,25], Serbia [26], Finland [27], Estonia [28], Lithuania [29], Ukraine [30], and Slovakia [31,32,33].

Tomić et al. used cadastral data to carry out a generalised hierarchisation of land consolidation needs at the national scale for Croatia. The proposed analytical method considers factors such as the proportion of agricultural land in the total area of a particular administrative unit, average cadastral parcel size, regularity of parcel shapes, level of land fragmentation, proportion of land owned by the state, level of regional development, and number of agricultural farms. The authors subject the values of individual indices to standardisation using the zero-minimum method and then calculated the synthetic index value using different statistical methods, such as the Weighted Sum Model (WSM), PROMETHEE, and TOPSIS [17].

Muchová and Petrovič described an original author’s method for examining land consolidation priorities applied to the Žitava River basin in Slovakia. The authors included separated areas (clusters) in the study and analysed parameters such as the total area of land, the area of arable land, the area of permanent crops, the area of forests, the number of parcels that are common land, the average number of parcel co-owners, the number of undetermined landowners, the population of a particular cadastral unit, the population of settlements with an unregulated legal status, the degree of water erosion, and the geomorphological criteria [31]. The validity of considering the factors related to the problematic ownership of land stems from historical determinants, including the collectivisation of agriculture in former socialist Czechoslovakia [34].

The issue of ranking land consolidation needs was also addressed in analyses undertaken by Polish researchers. Janus and Taszakowski used a set of original authors’ indices that described the size structure of farms, land fragmentation, soil quality, accessibility of roads, and the results of the analysis of photogrammetric studies in terms of field difficulties in potential land consolidation. Therefore, in addition to the factors determining the demand for land consolidation, the synthetic index also considers the actual technical capacity to perform the work [20].

The hierarchisation of land consolidation needs in the programming of agricultural management practices was also addressed by Stręk and Noga. The authors performed a statistical analysis of 41 indices that characterise land ownership and use structure, the productive value of individual usable agricultural land parcels, the level of land fragmentation, the distribution of non-resident owner ownership, the parcel shape, and the accessibility of roads. Based on the results obtained, the authors divided the localities under study into groups that varied in terms of land consolidation priority [35].

The numerous concepts of the hierarchisation of demand for land consolidation and delimitation of potential objects to be consolidated, observed during the analysis of the global literature on the subject, include, in particular, issues concerning the spatial and ownership structure of land and its agricultural suitability. However, the concept of supplementing this analysis with data from the real estate market has not yet been developed and evaluated. In the authors’ opinion, information about the prices and values of agricultural land may be used to improve the knowledge base for delimiting priority areas for land consolidation and assessing the potential economic profitability of the process. Owing to the observed research gap, relevant research has been conducted in this area.

The subject of this study is the experimental selection of localities intended to be included in a land consolidation procedure using cadastral databases and information on property transaction prices. The authors implemented their own new algorithm, designed for the selection of the studied objects, and an original dedicated method for calculating the land consolidation priority indicator. The analysis also used previously used factors describing the condition of the spatial structure and land productive value of agricultural farms. The researchers conducted a multifaceted analysis of land consolidation priorities for the surveyed areas and compared the information from the study of individual factors. In the authors’ opinion, this concept may be a significant contribution to the present methodology of delimiting preliminary potential land consolidation areas.

2. Materials and Methods

A detailed study was conducted in the Kolbuszowa District, located in the Subcarpathian Voivodeship in southeastern Poland. The study area covered approximately 773.16 km², of which approx. arable land comprised 30.5%, while approx. a total of 25.5% was covered by grassland (meadows and pastures). The locations of the study area are shown in Figure 1.

For this study, spatial data from two distinct thematic categories were acquired. The detailed characteristics of the input data are listed in

Table 1. Description of the data used. Source: authors’ elaboration.

Type of Data	Source	Use
Land and Property Register database	District Administrator’s Office in Kolbuszowa	analysis of factors determining the demand for land consolidation
property price register	District Administrator’s Office in Kolbuszowa	analysis of land prices based on historical transactions

.

As can be seen from the data provided in Table 1, the first downloaded information resource is the Land and Property Register database. The data were sourced from the District Administrator’s Office in Kolbuszowa in the form of a database file including comprehensive, standardised cadastral data comprising graphical and descriptive parts. A set with a structure determined by current legal norms [36] enables us to obtain information on the shape and location of parcels, land valuation classification, and land use. The database also contains comprehensive information on land ownership, which is relevant for the analyses conducted in the thematic area of land consolidation. Using cadastral data as a reliable source of information is a commonly accepted method of data acquisition for spatial and ownership structure analysis [35].

The second category of acquired data is a set of files representing the property price register made available by the District Administrator’s Office in Kolbuszowa. The information includes comprehensive and detailed documentation of historical transactions between 2011 and 2023 concerning undeveloped agricultural land from the area of Kolbuszowa District, which was the maximum dataset available to be obtained in a standard procedure from the local cadastral office. This register, which is maintained based on nationwide standards [36], is in a form that allows a link to the Land and Property Register database to be established.

Due to the specific nature of the processed data and the subject matter of the study, the activities carried out were based on spatial analyses performed by GIS methods, supplemented with elements of statistical analysis and the methodology for updating transaction prices used for the purposes of land valuation [37].

To implement an effective analytical procedure, a preliminary analysis of the structure of the acquired input data was carried out, and a framework plan was defined, including the adaptation of source information, development of original authors’ research tools, and calculation and interpretation of the resulting data. The thematic ranges of the analysis were partly developed on the basis of the combined experience of previous research, reported in the world literature, concerning the methodology of the assessment of land consolidation urgency and the evaluation of the agricultural land spatial structure [17,26,38,39,40]. The adopted action strategy, in schematic terms, is shown in Figure 2.

Given that two separate sources of spatial data on properties were used, information processing was conducted in a two-pronged manner. The first part of the data processing procedure included activities related to objects in the Land and Property Register database (1–5). The second part concerns the property price register (6–8). In the final stage of the procedure, the data were integrated to perform analyses concerning the main research problem.

First, the objects in the Land and Property Register database were converted to a form that enabled the performance of spatial analyses in the GIS environment. The widespread use of the data exchange standard, which assumes the use of the GML format [41], enabled the efficient entry of the input data and initialisation of analysis sequences. The detailed data conversion procedure is illustrated in Figure 3.

The original data were contained in the standardised cadastral and property price register databases. The format of these files depended on the system used by the local cadastral office. In this study, the original data were stored in a Firebird database (FDB) format. Upon the demand by a client, cadastral data may be exported from the database to a standard collective GML file. Depending on the database management program, it is also possible to export the data into other formats, such as vector or tabular files. The GML dataset and the table of transactions were imported into the QGIS programme to enable extraction and combination of the information. To obtain relevant data on the ownership structure, a dedicated SQL query is required. All input data were filtered, integrated, and processed in QGIS 3.32 software using the author’s algorithm. In view of the specific subject matter of the study, concerning land consolidation, the analysed set of cadastral parcels was limited only to agricultural parcels selected based on the information on land use available in the database. Due to the precise regulations on the administration of the county cadastral databased, it was not necessary to provide a preliminary data validation. All the studied objects obtained were suitable for analysis, and the data processing phase was successfully completed.

2.1. Parcel Shape Analysis

The first analysis of land spatial structure, carried out using cadastral data, was a complex, multifaceted analysis of the parcel shape. For the purposes of this study, the method proposed by Demetriou was used, which assumes the calculation of an integrated parcel shape index (PSI) based on the following factors:

• Number of edges shorter than 20 m ($w_1)$;
• Number of acute angles ($w_2)$;
• Number of concave angles ($w_3)$;
• Number of vertices (boundary points) ($w_4)$;
• Compactness ($w_5$);
• Regularity ($w_6$) [42].

The final PSI value for parcel i was calculated based on the formula below:

$${PSI}_i=\frac{\sum _{j=1}^m{P}_{ij}{w}_j}{m}$$

(1)

where

• PSI_i—parcel shape index for parcel i;
• i—index of the parcel;
• j—index of the parameter;
• m—number of the parameters;
• $P_{ij}$—standardised score of each parameter;
• $w_j$—weight of the parameter.

The particular parameters ($w_1–w_5)$ are calculated by the separate formulas [42]:

$$w_1=0.99+1.49\ast \left({10}^{-2}\ast {x_1}^{1.5}\right)-0.46\ast \left({x_1}^{0.5}\right)$$

(2)

$$w_2={\left(1+6.05\ast x_2+2.71\ast {x_2}^2\right)}^{-1}$$

(3)

$$w_3={\left(1+6.05\ast x_3+2.71\ast {x_3}^2\right)}^{-1}$$

(4)

$$\begin{array}{c}{w}_4=14.45-407.76\ast {x_4}^{-1}+4280.97\ast {x_4}^{-2}-20959.323\ast {x_4}^{-3}+49414.25\ast {x_4}^{-4}\\ -45677.80\ast {x_4}^{-5}\end{array}$$

(5)

$$\begin{array}{c}{w}_5=1467298744.97\ast {x_5}^6+4133386014.178\ast {x_5}^5-45406553.82\ast {x_5}^4\\ +2435303.92\ast {x_5}^3-65445.193\ast {x_5}^2+831.98\ast x_5-3.91\end{array}$$

(6)

where

• $x_1—$number of boundaries with length < 25 m;
• $x_2—$number of acute angles ≤ 80°;
• $x_3—$number of re-entrants (215°; 360°);
• $x_4—$number of boundary points;
• $x_5—$density indicator.

The density indicator ($x_5)$ is calculated as the result of the equation:

$$x_5=\frac{S}{L^2}$$

(7)

where

• S—area of the parcel;
• L—perimeter of the parcel.

The last parameter, which characterises the parcel shape regularity, has not been specified precisely in the equations. However, based on the provided definition, regarding the regular shape as a polygon with rotational symmetry, equal edge lengths, and equal measures of internal angles [42], the authors developed their own formula:

$$x_6=\frac{\sum _{i=1}^{n_j}{\left(\sqrt{{\left(X_{P_i}-X_D\right)}^2+{\left(Y_{P_i}-Y_D\right)}^2}-\sum _{i=1}^{n_j}\frac{\sqrt{{\left(X_{P_i}-X_D\right)}^2+{\left(Y_{P_i}-Y_D\right)}^2}}{n}\right)}^2}{n}$$

(8)

where

• n—number of vertices of the parcel (except the rejected vertices with the interior angles in the range of 170° to 190°);
• ${X_P}_i$—X coordinate of the i-th vertex of the parcel;
• ${Y_P}_i$—Y coordinate of the i-th vertex of the parcel;
• $X_D$—X coordinate of the geometric centre of the parcel;
• $Y_D$—Y coordinate of the geometric centre of the parcel.

The applied method enabled the calculation of a synthetic index that determined the agricultural suitability of parcels in terms of the shape of their contours. This concept is based on the assumption that an optimal agricultural parcel should have a regular shape similar to a rectangle. Irregular and fragmented contours of parcels are considered unfavourable because of potential difficulties in the performance of agricultural operations [43].

The analysis yielded a set of spatial data representing the parcels under study, supplemented with the value of the calculated PSI index, which takes a value in the form of a floating-point number from 0 to 1, where the higher the value, the better the shape. For further research, the nominal PSI value was replaced with the C_PSI, calculated as follows:

$$C_{PSI}=1-PSI$$

(9)

A spatial representation of the study results, in cartographic terms, is shown in Figure 4.

The obtained results show a significant predominance of areas covered by parcels with highly unfavourable shapes, including excessive elongation, characterised by the so-called ribbon pattern of land. Approximately 78% of the studied area is covered by parcels with a C_PSI index of 0.50 or more, and approximately 26% of the county consists of parcels with an index of 0.90 or more. The arithmetic average value of the calculated C_PSI for the parcels under study was 0.56, while the parcel area-weighted average was 0.71. The high index values indicate the validity of correcting parcel shapes, which is one of the basic tasks of the land consolidation procedure, whose main task is to improve farming conditions in rural areas [23], covering approximately 93% of the total area of Poland (as of 2020) [44].

2.2. Analysis of Parcel Access to Public Roads

Many years of research have shown that rural areas in southern, southeastern, eastern, and central Poland are characterised by a very high percentage of parcels without direct access to a public road. Therefore, when assessing the defectiveness of the spatial structure of rural areas, it is necessary to determine the number and percentage of parcels without access to a road, as this is standard practice during the implementation of land consolidation projects in Poland to ensure access to public roads for each of the designed parcels [45].

To efficiently delimit parcels without direct access to public roads, a spatial analysis was conducted using land-use data. The algorithm recognised the roads by the “OFU” (land use designation) attribute, and the type of owner was obtained with an SQL query. This method enabled the extraction of only roads that were registered in the cadastral database as public roads. All parcels communicated by private roads, informal or established as the necessary road easements in the context of land consolidation work planning, are regarded as parcels without a connection to the public road. Thus, the state registered in the cadastral database was the only fundamental data source for this type of analysis.

A binary-type variable C_ATR, for which the value of “0” for a particular parcel indicates observed access to a road while the value of “1” indicates no access to a road, was adopted as the study result. A spatial representation of the processed analysis is shown in Figure 5.

The study reveals that 40,649 cadastral parcels had no direct access to a public road, accounting for 41.2% of the total number of parcels in the study area and 30.0% of the area of the studied lands. The lack of direct access to a road contributes to limitations in farm management, which may generate additional costs associated with the establishment of easements, a decrease in the productive value of the burdened parcel, and potential conflicts between landowners. The significant proportion of parcels without access to a road among the total number of parcels indicates the validity of correcting the defective spatial structure in the land consolidation process [46].

2.3. Land Dispersion Analysis

Land dispersion, also defined as one of the manifestations of internal farm fragmentation [47], occurs when farm parcels are distributed over an extensive area [48]. A negative consequence of land dispersion is the higher cost of agricultural activity, resulting from the increased cost of transport between parcels and the time-consuming nature of agricultural practices [49].

The methodology for assessing land dispersal can vary depending on the purpose of the analysis, type of available data, and specific nature of the area under study. The practice of assessing the spatial structure of rural areas, carried out at the preparatory stage of a land consolidation procedure, is usually based on an analysis of the distance between the agricultural land of farms and their farmstead centres [25,50]. Although many researchers have approved this method, it is necessary to consider the limitations of the assumed simplification. For example, in the case of a farm consisting of two adjacent, significantly elongated parcels, one of which contains a farmstead centre, the measured distance is relatively high, whereas the parcels are not dispersed. However, this simplified method was implemented because of the general characteristics of the values obtained, which are used only for village classification.

The study used a land dispersion index calculated as the average distance between the farm’s agricultural parcel centroids and the centroid of its farmstead centre, which is understood as a parcel with buildings necessary for carrying out agricultural activities (the so-called farm buildings). The affiliation of parcels to individual farms was determined based on the relevant relationships in the cadastral database. The target land dispersion index C_DSP, calculated for each identified farm based on the following formula, was determined empirically based on the distribution of values calculated in the course of this analysis. The coefficients of the polynomial and border values, enabling the normalisation of the scores, were assumed on the basis of the percentiles of the distances between a parcel and the farmstead centre, aggregated for each village from the studied population. Owing to the regional diversity of the spatial structures of agricultural areas, there is no versatile, unequivocal method of arbitrary normalisation and interpretation of the obtained values. The mathematical model of the normalisation used in this study is provided in Formula (10).

$$C_{DSP}=\left\{\begin{array}{c}0, if D\le 0;\\ 0.000000000049\ast D^3-0.000000443435\ast D^2\\ +0.001187851618\ast D+0.013479552666, \\ if D\in \left(0;1825⟩\right.;\\ 1, if D>1825\end{array}\right.$$

(10)

where

• C_DSP—land dispersion index;
• D—average distance between a parcel and the farmstead centre.

The results obtained for all farms under study are shown in Figure 6.

The presence of areas with a significantly higher land dispersion index can provide initial information on the possible locations of land requiring consolidation [49].

2.4. Land Fragmentation Analysis

The term ‘land fragmentation’ is defined in various ways in the world literature. Van Dijk distinguishes four types of fragmentation depending on the origins of the phenomenon [47]. The study area is characterised by the internal fragmentation of farms, typical of southeastern Poland, and manifested by the division of a farm into a large number of parcels that are often isolated from each other.

To assess the degree of land fragmentation, an analysis of cadastral parcels on the farms under study was conducted. As a result of the analysis, the C_FRG index with values determined based on empirical Formula (11) was calculated for each farm. The methodological assumptions for the formula designed by the authors were developed similarly to the study of the land dispersion (Section 2.3).

$$C_{FRG}=\left\{\begin{array}{c}0, if n=1;\\ 0.278415998077\ast ln\left(n\right)+0.1046119524,if n\in \left(1;24⟩\right.;\\ 1, if n>24\end{array}\right.$$

(11)

where

• C_FRG—land fragmentation index;
• n—number of cadastral parcels being part of a particular farm;

A spatial representation of the calculations is shown in Figure 7.

According to this study, an average farm comprises approximately three cadastral parcels. The average area of a cadastral parcel was approximately 0.44 ha. However, the occurrence of areas with significantly higher farmland fragmentation, particularly in the northeastern part of the county, was noted.

2.5. Calculation of the Synthetic Land Consolidation Need Index

The values of the calculated indices C_PSI, C_ATR, C_DSP, and C_FRG were averaged to obtain a synthetic C index indicating the land consolidation urgency for a particular area. Owing to the varying and ambiguously defined spatial resolution of each of the partial measures, a logical sum of the spatial sets was used in the calculations. The calculated C-index was subjected to cartographic visualisation, and its spatial representation is shown in Figure 8.

A particularly high demand for land consolidation, manifested by clearly unfavourable (high) values of individual land spatial structure indices and an elevated value of the synthetic C index, is observed in the eastern part of the county. In this area, the land is characterised by highly unfavourable (irregular and excessively elongated) parcels and considerable land fragmentation within farms, including significant distances between agricultural parcels and farmstead centres. The intensity of the factors that reduce the production values of farms indicates that the correction of the defective spatial structure of agricultural land through a consolidation procedure should be prioritised.

2.6. Preselection of Transaction Data

The data acquired from the property price register, which includes the complete range of transactions for undeveloped land, were subjected to expert verification and selection. Nearly 3800 data points were obtained in the form of transaction prices between 2011 and 2023. Only agricultural land transactions (1408 records) were selected from these data. Due to the agricultural nature of the communes situated in this county, a considerable number of the properties being the object of trade are properties purchased for the purpose of farm extension [51,52]. Only the land intended for agricultural production, according to the entries in the area development plans, was used for the analysis. However, in the absence of a plan, it was land without a building permit.

Therefore, the transactions qualified for the analysis were those concluded under free market conditions and assessed as a representative source of data on the prices of property used for agricultural production in the study area. The spatial distribution of the analysed transactions with a heatmap as a background is shown in Figure 9.

2.7. Update of Transaction Prices

Owing to the considerable time span between the transactions under study, the recorded prices were updated to the date of carrying out the study (10 July 2023). The procedure was carried out using a linear regression model, defined by mean values and standard deviations in marginal distributions determined on the basis of the results from the sample and taking into account the (Pearson) complete correlation coefficient [37], using the following formula:

$$c_{i\left(t\right)}=c_i+B\ast \left(t_a-t_i\right)$$

(12)

$$B=r\ast \frac{\sigma \left(c\right)}{\sigma \left(t\right)}$$

(13)

where

• $c_i$—transaction price of the i-th property;
• $B$—regression coefficient;
• $t_a$—date of update (expressed in months);
• $t_i$—date of transaction (expressed in months);
• $r$ is the coefficient of the correlation between the unit transaction price and date [53].

The correlation coefficient determines the direction and strength of the relationships between the analysed variables, which, in a particular case, will enable an assessment of whether the set of prices needs to be updated. The strength of the correlation was considered at five levels: r < 0.2, no correlation; 0.2 ≤ r < 0.4, weak correlation; 0.4 ≤ r < 0.7, moderate correlation; 0.7 ≤ r < 0.9, strong correlation; r ≥ 0.9, very strong correlation.

Due to the spatial variation of the factors that determine property prices, the update was carried out separately for each of the communes in the county under study. The obtained correlation coefficient value confirms the relationship between the date of the transaction and the transaction price. The obtained index value was higher than 0.2. For most communes, a moderate correlation coefficient of more than 0.4 (moderate correlation) was obtained. In only two cases, the coefficient value fell into the second range (weak correlation).

After eliminating the “time” factor as an important element differentiating transaction prices, the updated prices were further analysed using GIS tools.

2.8. Delimitation of Priority Areas and a Ranking of Villages According to the Market Value of Land

The calculated and updated transaction prices of land were georeferenced based on information on cadastral parcel identifiers. The created point layer was then interpolated using the Voronoi polygon method to obtain a map that preserves the spatial continuity of transaction price values for the entire study area.

The values of the unit transaction price attribute were classified using the equinumerous interval method, and the outlier records (exceeding the value of the average price increased or decreased by three times the deviation standard for the population) were excluded, yielding the P attribute taking values from the range (0, 1). The rules for assigning the P attribute values to the polygons representing the spatial distribution of the transaction prices of land are provided in

Table 2. P attribute values as a function of the transaction price. Source: authors’ elaboration.

Price Range (PLN/ha)	P
0.00–10,000.00	0.0
10,000.01–15,000.00	0.1
15,000.01–20,000.00	0.2
20,000.01–25,000.00	0.3
25,000.01–30,000.00	0.4
30,000.01–40,000.00	0.5
40,000.01–60,000.00	0.6
60,000.01–80,000.00	0.7
80,000.01–100,000.00	0.8
100,000.01–150,000.00	0.9
>150,000.00	1.0

.

The cartographic visualisation of the Voronoi polygons representing the unit transaction prices of land, converted into the P index form, is shown in Figure 10.

Particularly high transaction prices of land, exceeding a value of PLN 100,000 per ha, were observed in the area of the town of Kolbuszowa and in its immediate vicinity. High transaction prices are also observed in the southwestern part of the county. Relatively low prices were observed in the eastern part of the study district. Section 3 provides a detailed interpretation of the graphically presented distribution of land transaction prices in the context of demand for land consolidation.

2.9. Analysis of Arable Land’s Productive Value

In view of the need to provide details of the conclusions formulated based on the analysis of the spatial distribution of property transaction prices and the demand for land consolidation, the study also covered the productive value of agricultural areas. This parameter is both a factor that potentially determines land prices and a criterion for the validity of carrying out a land consolidation procedure.

For the purposes of this study, a method proposed by Leń was employed, which enables efficient identification of the productive value of agricultural land (arable land and grassland) based on pedological land classification data available in the cadastral database maintained at the county level [54]. Pre-defined scores of the individual land classes, used for the purposes of land value estimation carried out as part of the land consolidation procedure, were applied as a measure of the land productive value [55]. To enable a comparison of the obtained results with the results of other analyses, the scores were subjected to unitisation with zero minimum, performed according to the following formula:

$$Q_i=\frac{x_i-x_{min}}{x_{max}-x_{min}}$$

(14)

where

• $Q_i—$value of the variable after unitisation for the i-th element of the set (agricultural land suitability index);
• $x_i—$original value of the variable for the i-th element of the set;
• $x_{min}—$minimum variable value in the set;
• $x_{max}—$maximum variable value in the set [11].

The method adopted for calculating the Q index values for the individual land classes is provided in

Table 3. The Q index values of agricultural land suitability were based on the scores of the individual land classes. Source: [55], authors’ elaboration.

Land Use Type	Land Class	Score	Q
arable land	I	100	1.00
	II	92	0.91
	IIIa	83	0.80
	IIIb	70	0.65
	IVa	57	0.49
	IVb	40	0.29
	V	30	0.18
	VI/VIz	18	0.04
grassland (meadows, pastures)	I	90	0.88
	II	80	0.76
	III	65	0.59
	IV	45	0.35
	V	38	0.27
	VI	15	0.00

.

A spatial representation of the value of the Q index calculated for land in the study area is provided in Figure 11.

As a result of the preliminary analysis of the spatial distribution of the value of the Q index that represents the productive value of agricultural land, it was concluded that land with low and very low suitability for agriculture is found almost throughout the entire study area.

2.10. Data Synthesis

As a result of carrying out the activities described in detail in Section 2.1, Section 2.2, Section 2.3, Section 2.4, Section 2.5, Section 2.6, Section 2.7, Section 2.8 and Section 2.9, extensive data were obtained on the multifactorial specificity of land consolidation needs, the characteristics of the distribution of transaction prices of agricultural land, and agricultural land suitability for the study area of the Kolbuszowa District. The information obtained in the form of a set of georeferenced indices was characterised by varying the spatial resolution and non-uniform spatial coverage. To enable the integration of the developed geographical information and its correct interpretation, the data were subjected to spatial generalisation, assuming the area of a cadastral district (a single village or town) as the basic spatial unit of the study area. Out of the Kolbuszowa District under analysis, 52 cadastral districts were separated, for which substitute indices, based on the averaged values of C, P, and Q indices and weighted by the areas of the existing elementary polygons, were calculated. The results of this analysis, which achieves one of the main research objectives of this study, are provided in Section 3.

3. Results

The completed research procedure, described in Section 2, yielded detailed output data providing information necessary for the assessment of the villages under study in terms of the spatial structure of agricultural areas, demand for land consolidation, and distribution of transaction prices of undeveloped land property intended for agricultural use. In the course of the integration of the results of multi-stage analyses, two basic summaries of indices calculated for cadastral districts (villages or towns) of Kolbuszowa District were obtained. A map of the assessment of land consolidation priority (C index) is shown in Figure 12. A map of the interpolated, averaged transaction prices (P-index) is shown in Figure 13. An index of the cadastral district designations and a comparison of the values of both indices are provided in

Table 4. Ranking of villages under study in terms of aggregated C and P index values. Source: authors’ elaboration.

Item	Locality Name	Area (ha)	C	Ranking Position According to C	P	Ranking Position According to P
1.	Wola Raniżowska	3325.21	0.73	1	0.33	50
2.	Lipnica	3883.43	0.72	2	0.32	51
3.	Mazury	1158.43	0.67	3	0.40	40
4.	Wilcza Wola	4694.43	0.66	4	0.50	27
5.	Staniszewskie	844.62	0.66	5	0.36	46
6.	Kopcie	1065.93	0.65	6	0.39	42
7.	Cmolas	3167.71	0.64	7	0.72	9
8.	Mechowiec	599.91	0.63	8	0.51	25
9.	Werynia	1948.27	0.63	9	0.44	33
10.	Zarębki	703.91	0.61	10	0.52	23
11.	Kłapówka	409.71	0.60	11	0.61	18
12.	Dzikowiec	1408.53	0.60	12	0.35	47
13.	Kupno	1253.21	0.60	13	0.46	30
14.	Hucisko	791.05	0.60	14	0.35	49
15.	Świerczów	729.41	0.60	15	0.40	41
16.	Widełka	2725.11	0.59	16	0.46	31
17.	Poręby Dymarskie	2124.21	0.58	17	0.69	14
18.	Poręby Kupieńskie	1510.57	0.57	18	0.50	28
19.	Krzątka	5408.81	0.57	19	0.66	15
20.	Ostrowy Tuszowskie	2747.32	0.57	20	0.71	11
21.	Zielonka	1716.47	0.57	21	0.29	52
22.	Raniżów	2028.56	0.56	22	0.35	48
23.	Bukowiec	296.13	0.55	23	0.70	13
24.	Domatków	943.14	0.55	24	0.56	20
25.	Kolbuszowa Górna	1843.33	0.55	25	0.51	26
26.	Siedlanka	545.14	0.55	26	0.40	38
27.	Huta Przedborska	497.15	0.54	27	0.44	34
28.	Majdan Królewski	1117.53	0.54	28	0.79	3
29.	Ostrowy Baranowskie	2346.31	0.54	29	0.71	12
30.	Brzostowa Góra	1722.74	0.54	30	0.73	8
31.	Kosowy	870.99	0.54	31	0.49	29
32.	Kolbuszowa Dolna	921.72	0.53	32	0.63	16
33.	Trzęsówka	1528.34	0.53	33	0.75	7
34.	Płazówka	289.61	0.53	34	0.40	39
35.	Wola Rusinowska	1244.29	0.53	35	0.63	17
36.	Przedbórz	1452.92	0.52	36	0.44	32
37.	Trześń	747.79	0.52	37	0.36	45
38.	Nowa Wieś	1050.69	0.51	38	0.55	22
39.	Komorów	1469.92	0.51	39	0.72	10
40.	Zapole	359.90	0.51	40	0.43	36
41.	Rusinów	515.25	0.50	41	0.58	19
42.	Kolbuszowa	797.99	0.50	42	0.97	1
43.	Leszcze	697.25	0.50	43	0.41	37
44.	Hadykówka	506.67	0.50	44	0.83	2
45.	Niwiska	2388.05	0.49	45	0.43	35
46.	Huta Komorowska	4047.08	0.48	46	0.77	5
47.	Jagodnik	490.51	0.48	47	0.77	6
48.	Toporów	482.95	0.48	48	0.78	4
49.	Nowy Dzikowiec	214.97	0.47	49	0.39	43
50.	Przyłęk	2393.22	0.46	50	0.56	21
51.	Hucina-Staszówka	696.45	0.46	51	0.52	24
52.	Korczowiska	589.04	0.40	52	0.39	44

.

The highest values of the land consolidation demand index (C) were noted in the eastern part of the county (particularly in the localities of Wola Raniżowska and Lipnica, with C index values of 0.73 and 0.72, respectively). At the same time, this area is characterised by the lowest unit transaction prices of agricultural land. A minimum P index value of 0.29 was noted for the village of Zielonka. On the other hand, relatively high land prices were observed in the northwestern part of the county, which is characterised by a favourable spatial structure of agricultural areas. A generalised statistical analysis of the calculated values of the C and P variables is presented in

Table 5. A simplified statistical description of the C and P indices was calculated for the cadastral districts of the Kolbuszowa District. Source: authors’ elaboration.

Variable	Minimum	Maximum	Average Value	Standard Deviation	r-Pearson’s Correlation Coefficient
C	0.40	0.73	0.56	0.07	−0.35
P	0.29	0.97	0.54	0.16

.

The land consolidation demand index (C) takes values from the range of 0.40–0.73. The average value was 0.76, whereas the standard deviation was 0.07. As for the land unit transaction price index (P), values ranging from 0.29 to 0.97 were obtained. The average index value is 0.54, with a standard deviation of 0.16. The calculated coefficient of correlation between two variables, equal to −0.35, indicates an average negative correlation [37].

Based on the calculated values of the indices describing the study characteristics, the cadastral districts were divided into four groups. The classification was developed in accordance with the rules listed in

Table 6. Classification of the cadastral districts under study as determined by the values of C and P indices. Source: own elaboration.

Group	C	P	Number of Localities
A	$C_i\le \mu \left(C\right)$	$P_i>\mu \left(P\right)$	17
B	$C_i>\mu \left(C\right)$	$P_i>\mu \left(P\right)$	5
C	$C_i\le \mu \left(C\right)$	$P_i\le \mu \left(P\right)$	13
D	$C_i>\mu \left(C\right)$	$P_i\le \mu \left(P\right)$	17

.

The distribution of the values of C and P indices for the cadastral districts under study, taking into account the developed classification, is also visualised in the form of a diagram in Figure 14.

The spatial representation of the completed classification of the analysed cadastral districts in cartographic form is shown in Figure 15.

The “A”-type localities, characterised by low demand for land consolidation and high prices of land, are located only in the northwestern part of the county (12 localities) and in the immediate vicinity of the town of Kolbuszowa (5 localities) which is the seat of the county under analysis. For the areas located near the town, it is reasonable to assume that the main determinant of land prices is the favourable location. In other cases, high land prices coincide with the satisfactory quality of the spatial structure of agricultural areas, which translates into a lack of noticeable need for land consolidation.

The “B”-type localities make up the least numerous groups. Of the five cadastral districts, one was located in the northern part of the county, one in the western part, one in the southeastern part, and two in the central part of the county. This group includes localities with high land transaction prices and a high need for land consolidation. A detailed determination of the reasons behind this phenomenon requires an additional analysis of the agricultural stability of land in terms of geomorphological and soil conditions. With the assumption of high productive values for the land of this group, carrying out the land consolidation procedure in this case should be considered particularly reasonable from an economic point of view.

The “C” group includes 13 localities with low needs for land consolidation and low transaction prices. Ten localities assigned to this group are located in the southwestern part of the county, two in the central part, and one in the eastern part. Despite the relatively favourable spatial structure of farms, arable land in the localities in this category does not achieve high selling prices. This phenomenon may be due to the low productivity of the soil and other factors limiting the development of agriculture, which cannot be corrected in the course of the land consolidation procedure.

The “D” group, which comprises 17 localities, includes cadastral districts with high demand for land consolidation and low transaction prices of land. The localities classified in this group were mostly located in the eastern and central parts of the county, with only two localities located in the western part. The group included six consecutive cadastral districts placed in the highest positions in the ranking of demand for land consolidation. A defective spatial structure, including excessive elongation, irregular parcel shape, and land fragmentation and dispersion, which hinders agricultural activity, may lead to a reduction in property values. This unfavourable phenomenon can be eliminated or limited through a land consolidation procedure, leading to the optimisation of the usable value of agricultural land by correcting the spatial structure of farms. However, particularly low transaction prices of agricultural property may indicate geomorphologically or sozologically based limitations on agricultural productivity. Therefore, the areas assigned to the “D” group should be subjected to a particularly thorough analysis of the agricultural suitability of land and to the delimitation of the so-called problem areas of agriculture, which may potentially be excluded from agricultural production and used for alternative purposes [23].

Owing to the observed ambiguity of information derived from the analysis of agricultural land’s spatial structure and property transaction prices, it is necessary to carry out an additional analysis of agricultural land suitability. It was assumed that a detailed study on the quality of land intended for agricultural purposes could provide important information on the reasons behind the variation in land prices and contribute to the correct delimitation of potential objects to be consolidated. The previously developed classification of cadastral districts under study, based on the values of the C and P indices, was supplemented with another spatial data layer representing the usable (productive) value of land via the Q index calculated based on the pedological classification of land. A generalised statistical summary of the Q index aggregated for cadastral districts is provided in

Table 7. Generalised statistical summary of the Q index. Source: authors’ elaboration.

Variable	Minimum	Maximum	Average Value	Standard Deviation
Q	0.09	0.34	0.20	0.06

.

The aggregated Q index takes values from 0.09 to 0.34, with an arithmetic mean of 0.20 and a standard deviation of 0.06. The obtained results confirm the preliminary hypothesis that land in the area of the county under study has a relatively low productive value. The average index value roughly corresponds to class V (poor soils) [56] for both arable land and grassland.

r—Pearson’s correlation coefficients were also calculated for the developed Q index in relation to variables C and P, shown in

Table 8. r—Pearson’s coefficients of correlation among the C, P, and Q indices aggregated for cadastral districts. Source: authors’ elaboration.

Variable	C	P	Q
C	1.00	−0.35	0.23
P	−0.35	1.00	−0.10
Q	0.23	−0.10	1.00

.

A weak positive correlation (correlation coefficient of 0.23) was noted between the variables C and Q. On the other hand, no unequivocal correlation was observed between the variables P and Q (r-Pearson’s coefficient of −0.10).

The spatial visualisation of the developed classification of localities, along with the aggregated Q index values, is shown in Figure 16.

A visual analysis of the above summary reveals no clear relationship between the production value of agricultural land and land prices or land consolidation requirements. To analyse the obtained results in detail, the Q index values were aggregated according to the selected groups of localities. Aggregation was performed using an average weighted by the area of the cadastral districts included in each group. The calculated aggregated Q index values for each group are listed in

Table 9. Aggregated Q index values for individual groups of localities. Source: authors’ elaboration.

Group	Q
A	0.19
B	0.14
C	0.21
D	0.22

.

In view of the specific nature of the subject matter of the ongoing research, particular attention was paid to the analysis of the localities assigned to groups “B” and “D”, which include cadastral districts with above-average demand for land consolidation.

As can be seen from the obtained numerical data, the lowest agricultural land suitability index value of 0.14 was noted for group “B”. Although the specific nature of the spatial structure of the localities of this group indicates the validity of correcting the defective layout of farms, the low productive value of the land may be related to the lack of economic rationale for undertaking complex and costly agricultural management practices. The presumed reason for the high property transaction prices for “B”-type localities, irrespective of the land functional quality, may include their low accessibility or the possibility for potential conversion of the purchased land for non-agricultural purposes.

In contrast to category “B” localities, the “D” group is characterised by the highest aggregated Q index value of 0.22. This group also included two cadastral districts with the highest noted index values of 0.34 for the locality of Kupno and 0.32, respectively. The high demand for land consolidation works and relatively high agricultural land suitability indicate the potential economic efficiency of agricultural management practices. The optimisation of fragmented and dispersed agricultural holdings distributed in the so-called ribbon pattern can also contribute to an increase in the value of agricultural land, which currently remains at a relatively low level.

4. Discussion

This study yielded a detailed, multi-aspect description of the county under study in terms of both the factors that characterise the spatial structure of agricultural land and the distribution of property transaction prices. The use of standardised cadastral data enabled the performance of a number of spatial and statistical analyses [57], leading to important conclusions regarding the relationships between the overall condition of the spatial structure of farms [58] and their potential value as projected based on historical transactions. The demonstrated correlation between the land consolidation work demand index and transaction prices of agricultural properties does not represent a strong linear relationship. However, this may indicate the validity of optimising the spatial structure of land to increase its value [59]. In the authors’ opinion, the observed relationship may be implemented as one of the factors used for predicting the economic potential of land consolidation, especially for comparing the costs with the potential land value gains. This hypothesis was confirmed by another analysis that considered the assessment of land productive value as determined by the pedological classification. The indices of soil quality (productive value) are used as a significant determinant for the selection of potential objects to be consolidated by a considerable number of researchers, including Leń [23], Janus and Taszakowski [20], and Uyan [60]. The original authors’ detailed indices describing soil quality include factors such as physicochemical and granulometric parameters, as well as susceptibility to erosion. The pedological classification used in this analysis is the result of an expert assessment of land recorded in a standardised cadastral database. The use of predefined patterns for assessing soil quality limits the assessment details to some extent. However, an important benefit of adopting this methodology is that it significantly streamlines the research procedure and ensures the universality of the methodology proposed [61]. Another potential limitation of the method proposed in this article may be non-standard, as it is difficult to detect factors modifying transaction prices in a way different from the general trends, determining the assumptions of the research. The proposed solution, which is likely to be developed in future research stages, may be an extension of the adopted scheme through a detailed statistical analysis of the relationship between the transaction price and its determinants.

The proposed division of the localities under study into groups distinguished on the basis of an assessment of the spatial structure of farms, which determines the degree of demand for land consolidation works, and an analysis of the distribution of property transaction prices, which provides the basis for determining land value, enabled a preliminary delimitation of areas requiring the performance of agricultural management practices, as well as the determination of the trends in their implementation. An analogous approach was used for the selected referenced titles. Methods that assume the grouping of localities with similar characteristics are proposed, e.g., by Stręk and Noga. The authors emphasise that combining potential objects to be consolidated into groups enables the proper selection of operational methods for the inclusion of an area covering more than one cadastral unit in the land consolidation procedure [35]. The grouping method proposed in this study may also be used for alternative purposes, based on an analysis of two categories of factors.

The consideration of the actual land transaction prices, which reflect the action of the factors that determine their value, is an important addition to the information on the validity of commencing land consolidation works in terms of their profitability from the perspective of both landowners and public administration bodies that implement agricultural policy at the regional and national levels. The proposed method can be implemented to delimit areas where land consolidation would be most profitable for landowners. Thus, the authors recommend evaluating the proposed solution on an extended data sample and implementing a method for improving the procedure of selecting potential land consolidation areas. The methodology of the research described may also be supplemented with other factors that determine the urgency of land consolidation and the value of the parcels. A future analysis of the correlations between particular indices may reveal the unobvious determinants of land value and avoid potential misinterpretations. For example, in some cases in Poland, agricultural lands characterised by lower soil classes may reach higher transaction prices owing to the possibility of parcel destination conversion.

The acquired information on the market value of land can also be used in the course of land consolidation procedures, at the stage of estimating the land of individual farms to correctly separate equivalent land [62,63,64,65,66,67,68,69,70,71,72]. The method presented may also be used for land consolidation profitability assessment on the condition that the analysis is conducted before and after the procedure. In the authors’ opinion, a long-term observation of the transaction prices and spatial structure characteristic changes carried out in the consolidated areas would be an important source of information about the real efficiency of the works. In the literature, there are some scientific reports on land consolidation evaluation [22]; however, they usually focus only on agricultural aspects.

5. Conclusions

The complex nature of agricultural management practices necessitates the use of concepts and tools to ensure the high efficiency of ongoing tasks aimed at the sustainable development of rural areas. The issue of planning and carrying out land consolidation works, which is one of the key issues of national and regional agricultural policy, involves the need to ensure maximum efficiency of the work being carried out, which is manifested in economic and social benefits. The limited financial resources dedicated to agricultural management practices imply the need to develop an efficient action strategy based on the substantive preselection of potential objects to be consolidated.

The proposed solution, which includes a detailed analysis of the spatial structure of the village, supplemented with a study of the spatial distribution of land transaction prices, helped obtain preliminary information that characterises local agriculture in terms of the economic rationale for carrying out agricultural management practices. Taking into account the transaction prices of real estate, which are a reliable reflection of real market value basing (for example, agricultural production suitability), enabled the enhancement of the method of delimitation of a group of localities where potential land consolidations are supposed to be the most profitable. From an extensive set of 52 villages, areas characterised by an unfavourable spatial structure and high transaction prices related to the potential agricultural use value were selected. The use of data derived from the county cadastral database with a structure and content in line with national standards proves the universality of the developed method and the possibility of applying analogous schemes of procedures to study areas located in different regions of the country, provided that necessary technical modifications are made in any country and region of the world where agricultural activity is carried out. The authors suggest continuing the research on the factors determining land consolidation priority to eliminate the limitations of some of the data sources and maximise the reliability and versatility of the results obtained.

The multifaceted strategy for the delimitation of agricultural land intended for the priority optimisation of production conditions is a prerequisite for the effective implementation of agricultural policy aimed at ensuring food security and creating economic development at the regional, national, and international levels. Taking measures to modernise agriculture in the context of the sustainable development of the regional economy should, therefore, be considered a necessary instrument for implementing the conditions necessary to ensure a stable future for society.