Relationship Between the Integral Indicator of Soil Quality and the Cadastral Value of Agricultural Lands

Abstract

In the current conditions of development of the country’s market economy, the methodological support for cadastral land valuation requires effective modernization and improvement of the existing mechanisms for determining cadastral value for a fair distribution of land tax among landowners. In this regard, the aim of the study was to develop a methodology for taking into account the qualitative state of soils in the cadastral valuation of agricultural lands in the conditions of an active land market, as well as to modernize the method for taking into account the quality of soils within the framework of the income approach in the conditions of a depressed land market. The study was conducted based on a set of scientific methods: the analytical method was used to conduct an analysis of the scientific review of the problem area and to substantiate the relevance of the study, a cycle of laboratory experiments was conducted using mechanical and chemical analyses, the construction of thematic maps was carried out using the dispersion method, the regression modeling method was used to determine the cadastral value of garden plots, and the land rent capitalization method was used to calculate the cadastral value of agricultural land. Research results were as follows: Methodological recommendations were provided for taking into account the quality of soils in the form of an integral indicator of physical and chemical properties in the model for calculating the specific indicator of cadastral value (SICV) of garden and vegetable lands in the conditions of an active land market. The method of accounting for the qualitative state of soil fertility in the form of a weighted quality score of an agricultural land plot was modernized when determining the specific gross income within the framework of the land rent capitalization method used to calculate the SICV. Based on field work and laboratory experiments, current indicators of soil fertility status were obtained, and soil quality scores for Saint Petersburg were calculated. The possibility of using an integral indicator (soil quality score) as a cost factor instead of a large number of fertility status indicators was proven. Also, models for calculating the SICV of garden and vegetable plots were built for the conditions of an active land market, according to which the cadastral value of land plots in Saint Petersburg was calculated for subsequent land taxation. For agricultural lands, using the example of a land plot of a high-commodity agricultural enterprise (Leningrad Region), the cadastral value was also calculated using the proposed income approach method. The scientific significance of the study lies in the improvement of the methodological foundations of cadastral valuation, as well as the technology of taking into account the quality of soils when calculating the cadastral value. The practical significance of the study lies in the applicability of the results of soil quality assessment and models for calculating the SICV for land taxation; individual market valuation for lending, purchase, and sale; lease of agricultural land; and allocation of land plots on account of a land share. In the area of developing a set of melioration measures on agricultural lands, including the development and implementation of agricultural technologies and technical means to improve soil fertility, the results of laboratory studies to determine the physical and chemical properties of soils can be used. The obtained soil quality scores for Saint Petersburg are also applicable to identifying unused and degraded lands for their transfer to other types of use.

1. Introduction

The economy of any state largely depends on the quality, condition, and quantitative diversity of natural resources of the territory of the state. Thanks to natural resources, including mineral, land, forest, and biological resources, various sectors of the economy can function [1]. Particularly relevant is the issue of developing investment projects that provide for a rational and economically advantageous way of developing and using natural resources, including the entire mineral resource complex [2]. This development is largely due to the achievement of the goals of sustainable development of territories [3]. At the same time, the goals of sustainable development are achieved through the linking of indicators that reflect key areas of such development, among which are the economy, society, and the environment [4]. However, in recent decades, the state has increasingly paid attention to issues of assessing land resources, from the point of view of their importance for the effective development of national wealth, food security, and the economic potential of the country. Moreover, the existing shortcomings of the regulatory and methodological support for the assessment of land resources only confirm and indicate the need to improve the existing approaches and methods of cadastral land assessment. This circumstance is due to the fact that in the current conditions of development of a market economy, the question of the procedure for the withdrawal of differential land rent in the form of land tax remains open among the scientific community. The principles of differential land rent, introduced by Karl Marx in 1867, state that the main source of income for agriculture is land rent, the amount of which depends on the natural soil fertility of land plots and their best location (differential land rent of the first kind) [5].

The most complete picture of the current problems in the field of land resource assessment can be seen in numerous original studies of Russian and foreign experience. Among members of the scientific community, there are often controversies regarding the dependence of the economic component of any country on the rational and efficient use of land resources, including their fair assessment. The works of many scientists are devoted to the issues of development and modification of existing methods aimed at taking into account many significant cost factors that have a positive effect on the value of land plots; proposals for completely new and improvements to existing land valuation algorithms; critical analysis of the quality of initial market data; and updating and updating information necessary for conducting an assessment, for example, the results of soil surveys.

However, in modern practical conditions, both market and cadastral valuation of land resources is increasingly based on the subjective opinion of appraisers. This circumstance usually manifests itself in the inclusion of or refusal to include in the cost model one or another cost factor. In support of this, the methodological guidelines for the state cadastral valuation (GKO) contain a list of cost factors, the consideration of which when determining the cadastral value is of a recommendatory nature. For example, A.V. Pylaeva and O.V. Kolchenko talk about the need to supplement the regulated list of cost factors in order to provide information support for the GKO process and solve methodological problems [6]. In this regard, S.V. Gribovsky proposes effective levers for assessing real estate objects, which are manifested, among other things, in solving the problem of providing cadastral appraisers with reliable and complete information about the objects of assessment [7]. H. Tomić, S.I. Mastelic, M. Roic, and J. Sisko substantiated the applicability of the international package of information models for real estate valuation LADM, which allows structuring the initial data, increasing the public significance of the valuation results, and improving the level of transparency of the process of mass valuation of real estate, including land plots [8]. A.E. Smekhnov, E.Y. Kolbneva, and O.V. Gvozdeva propose the creation of an information system containing the initial data necessary for cadastral valuation, which will be updated on an ongoing basis [9].

In solving the problem of modifying the methodological support for cadastral valuation of lands of different categories and types of permitted use, many authors propose alternative methods that, in their opinion, will increase the objectivity of the results obtained. For example, a group of authors (E.N. Bykowa, M.M. Khaykin, Y.I. Shabaeva, and M.D. Beloborodova) proposed a method for determining the cadastral value of lands in the mining industry based on a combination of expert and statistical methods, which led to the effectiveness of the cadastral valuation mechanism and the receipt of more objective results [10]. S.E. Badmaeva and A.Y. Nikolaeva presented improved methodological provisions for the cadastral valuation of lands in populated areas, which make it possible to obtain objective results for the cadastral value of such lands [11]. Using the example of forest lands in Vietnam, Russian researchers are modernizing assessment methods for taking into account the characteristics of the lands in question, such as the state of their vegetation cover, density and composition of plantings, timber reserves, quality class, and other indicators. The authors claim that the presented modification of land assessment technology can increase the objectivity of its results [12,13]. P.M. Sapozhnikov, V.S. Stolbovoy, A.K. Ogleznev, and V.I. Kuzmina, using the Orenburg region as an example, proposed a methodology for cadastral land valuation based on ranking soils according to the intensity of development of negative properties in them and, at the same time, substantiated the applicability of a scale for classifying agricultural lands [14].

When analyzing the issue of modernizing the methodology of cadastral land valuation, it is necessary to highlight the studies of the authors devoted to taking into account various cost factors that influence the change in the cadastral value of land. J.C. Bencure, N.K. Tripathi, H. Miyazaki, S. Ninsawat, and S.K. Minsun analyzed the influence of geospatial factors (accessibility of main roads, their slope, etc.) on the valuation of land plots, noting both their positive and their negative influences, manifested in the change in the cadastral value [15]. S. Hu and S. Yang et al. identified the influence of land characteristics, location factors, and neighborhood characteristics on urban land value in China [16]. A.L. Zhelyaskov and D.E. Seturidze identified the need to take into account demographic and social indicators and the level of development of infrastructure facilities in the cadastral valuation of agricultural lands [17]. V.P. Vlasenko and Z.R. Sheudzhen proposed taking into account agroecological factors in the cadastral valuation of agricultural lands, confirming their influence on the cadastral value of lands [18].

A number of other researchers are actively engaged in the issues of analysis and accounting of soil quality during cadastral valuation of land plots, including the specifics of conducting soil quality assessment (soil rating). The attention of researchers in the field of soil quality accounting is largely due to a number of burdensome circumstances. Among such circumstances, a complete or partial absence of up-to-date data on soil classification in the territory of many subjects of the Russian Federation is highlighted, including existing data on soil quality in many regions of the country dating back to the 1970s and 1980s, which indicates their obsolescence and only confirms the need for their full updating. In many regions of the country, work on updating soil appraisal has not been carried out since the 1990s, which is due to insufficient funding from regional and local authorities and existing shortcomings in the methodological support of the process of soil sampling and soil appraisal. In this regard, it is worth noting that there are no current soil surveys in the territory of Saint Petersburg, which was chosen as the study area. In practice, this leads to cadastral assessment of agricultural lands, including garden and vegetable garden lands, not taking into account the factors characterizing the quality of the soils. S.A. Galchenko and R.V. Zhdanova are of the opinion that it is precisely in relation to agricultural lands that the data of soil surveys and soil maps should be systematically updated [19].

The team of authors E. Ertunc, A.E. Karkınlı, and A.A. Bozdag in their study identified factors influencing the cost of land, among which they singled out soil fertility, which affects the cost of agricultural land [20]. V.A. Makht and V.A. Rudi, in order to methodically improve the assessment, proposed assessing soils in two stages: soil bonitation and assessment based on crop yield, which would combine data on soil fertility indicators, the use of which would increase the objectivity of the results of cadastral land assessment [21]. Latvian authors have identified the impact of soil quality on the cadastral value of real estate, including land plots. The researchers noted an existing problem in the region, which consisted of the necessary updating of information on soil quality since the last soil surveys were conducted in 1989–1991, which complicates farming in Latvia [22]. E. Lote and F.O. Tavares identified positive–negative dynamics of changes in the value of agricultural land in the province of Huambo (Republic of Angola) from such factors as location, market dynamics, the presence of water on the land, proximity to tourist destinations, and the physical conditions of agricultural fields [23]. H. Huang, G.Y. Miller, B.J. Sherrick, and M.I. Gómez, using the state of Illinois as an example, demonstrated the increase in the value of agricultural land from an increase in the productivity (fertility) of soils, including population density and personal income [24]. In Imo State (Nigeria), the authors were able to prove the influence of soil type, the quality of the planting material, and farm yields on changes in the value of agricultural land [25]. In relation to the value of agricultural land in Germany, the authors identified the influence of such cost factors as the degree of urban sprawl and the development of livestock farming [26]. E. Wójciak and A. Cienciała noted a high degree of correlation between the obtained valuation results and market prices in relation to agricultural land in Poland using the index method technology [27]. In the USA, the impact of agricultural production indicators, including soil quality, crop yields, temperature, precipitation, irrigation requirements, and others, on the cadastral value of agricultural land is noted [28].

From the point of view of the ecological state of soils, A.A. Kharitonov, S.S. Vikin, and N.V. Ershova highlighted the need to take into account various types of pollutants contained in the soil, the accounting of which will be able to adjust the cadastral value of lands [29]. Due to the content of various physical, chemical, and biological indicators in the soil, S. Maurya and colleagues talk about the need for their constant assessment and monitoring, which are constantly subject to change under the influence of natural and anthropogenic factors. For this purpose, the authors propose to create a minimum set of data on soil parameters that will be used to assess their quality [30]. J. Hyun and others also talk about this in their work, proposing to use an index of urban soil quality, including a set of quality factors (bulk density, bulk density, organic matter content, litter layer depth, clay and silt content, hydrolytic activity, cation exchange capacity, etc.) of soils using the example of urban green spaces [31]. F. Li and his colleagues, using correlation analysis, obtained a minimum set of data characterizing soil quality, among which they noted organic carbon, available phosphorus, clay, manganese, and zinc, noting the need to take them into account when assessing land [32]. I. Celik and others, on the basis of comparison of soil quality indicators including physical, chemical, and biochemical properties in Turkish agricultural lands, identified the necessary soil cultivation methods that should be applied for sustainable crop production [33]. Y.D. Smirnov, D.V. Suchkov, A.S. Danilov, and T.V. Goryunova proposed the use of artificially created organomineral soils to increase the productivity of lands subject to negative influences, using large-tonnage waste [34].

The presented analysis of the authors’ studies of Russian and foreign experience allowed the identification of problematic issues of a methodological and practical nature in the field of land resource assessment: shortcomings in the information support of the process of cadastral assessment of land resources within the framework of its methodological support; failure to take into account many significant, from the point of view of market pricing, cost factors in the cadastral assessment of lands; a lack of up-to-date data on soil quality, including its consideration in the assessment of agricultural lands, etc. Despite the significant scientific groundwork in the field of land resource assessment among researchers in relation to a number of the above issues, the issues of information content and methodological support for cadastral assessment of agricultural lands, taking into account the assessment of soil quality, have not yet been resolved in a comprehensive manner.

In this regard, the main objective of this study was to improve the methodological support for the cadastral valuation of agricultural lands, which is based on the consideration of the qualitative state of the soils, as part of the differential land rent of the first type, for different conditions of land market activity.

Achieving the set goal requires solving the following tasks: conduct soil sampling in the territory of the location of land plots of the segment “Gardening and vegetable gardening, low-rise residential development” of Saint Petersburg; perform laboratory experiments to determine the content of physical and chemical indicators in the soil that characterize soil fertility; characterize the condition of the soils of the study area based on prepared thematic maps of fertility indicators; prove the existence of a relationship between crop yields and soil fertility indicators; determine the quality ratings based on a stable correlation between crop yields and physicochemical soil indicators; justify the need to take into account such a cost factor as soil quality in the form of an integral indicator (quality score) in the cadastral valuation of agricultural lands; develop and apply a method for taking into account the quality of soils in the cadastral valuation of garden and vegetable plots for the conditions of the active land market of Saint Petersburg; modify and apply a method for taking into account the quality of soils in determining the cadastral value of agricultural land plots (high-commodity agricultural enterprises) in the conditions of the depressed land market of the Leningrad Region; and analyze and compare the resulting cadastral value of land plots of the segments under consideration with the current results of cadastral valuation.

2. Materials and Methods

2.1. Characteristics of the Study Areas

The study areas were the city of federal significance Saint Petersburg and the Leningrad Region, where the next round of state cadastral valuations of land plots was carried out in 2022. The choice of Saint Petersburg as the object of the study was justified primarily by the fact that this subject of the Russian Federation is one of the leading ones in the field of improving the methodology of cadastral valuation. The State Budgetary Institution “City Department of Cadastral Valuation” is engaged in scientific research on the introduction of new valuation factors and the development of correlation coefficients, including through interaction with higher education institutions that train candidates of science in this area. Testing various methods for determining cadastral values and new assessment factors is associated with the differentiation of the activity of the land market in Saint Petersburg from depressed to active, as well as with the presence of different types of permitted use of land plots in the city. Although Saint Petersburg is a highly urbanized settlement, within its borders, according to the statistical bulletin, agricultural activity is actively developing on the land plots of in horticultural non-profit partnerships (GNPPs), horticultural non-profit partnerships (ANPPs), and territories of individual housing construction (IHC) [35]. Unlike other cities in Russia, Saint Petersburg maintains a database on the yield of agricultural crops grown on the specified land plots. As for other subjects of the Russian Federation, Saint Petersburg lacks the results of soil surveys, which does not allow taking into account the physical and chemical properties of soils during the cadastral valuation of land plots. The presence of local results of soil condition assessment for small territories does not solve the problem, even in its narrow sense. Moreover, the majority of such results are concentrated mainly in the area of assessing the ecological potential of city soils [36]. The objectivity of the cadastral assessment of land in St. Petersburg, taking into account the quality of the soil, will make it possible to halt the current trend toward a reduction in the area of agricultural land, which shows that about 42% of such land is not used for its intended purpose, and 53% of such land is inevitably abandoned [37].

The second object of implementation of the proposed developments was the Leningrad Region, distinguished by the development of the agro-industrial sector of the economy and the location in the territory of the region of a large number of high-commodity agricultural enterprises and organizations conducting active agricultural activities. Every year, the region’s agriculture is actively developing, due to which the sale of agricultural products is increasing. Among the most developed branches of agriculture, livestock farming and crop production predominate [38]. In the state budgetary institution “Leningrad regional institution of cadastral valuation” during the cadastral valuation of the segment “Agricultural use”, the influence of physical and chemical properties of the soil on the cadastral value of land plots is not taken into account. At the same time, attempts to do so are present in the aspect of simple coding of the index of soil varieties (PD-sod-podzolic soils), which does not fully reflect the qualitative state of the soils. Figure 1 shows a zoning map of the territory of Saint Petersburg according to administrative-territorial division (by districts).

2.2. Description of Source Materials for the Study

The scientific research was conducted on the basis of a comprehensive approach to achieving the set objectives. A literature review of Russian and foreign scientific research was conducted using methods of theoretical and practical levels, including methods of analysis, comparison, generalization, induction, deduction, and analogy. The collection of initial market data for modeling cadastral values was carried out on the basis of data from the report on the results of the cadastral valuation for Saint Petersburg and the Leningrad Region, including using data from the online platform for the sale and rental of real estate in Saint Petersburg (CIAN). The soil grading was carried out on the basis of data on the diagnostic characteristics of soils obtained by conducting our own laboratory experiments at the Scientific Center for Assessment of Technogenic Transformation of Ecosystems of the Empress Catherine II Saint Petersburg Mining University. The physicochemical properties of the soils were obtained by conducting mechanical and chemical analyses using various methods, including the sieve method, which was used to determine the granulometric composition of the soils; the gravimetric method, which was used to determine chlorides and sulfates; and the argentometric method, which was used to determine carbonates. The soil moisture content was determined using the thermostat-weight method. The results of the pH of the water and salt extracts of the soils were obtained using a pH meter. The complexometric method was used to determine calcium and magnesium in the soil. The nitrogen content in the soil was determined using the Kjeldahl method, and the mobile phosphorus content was determined using the colorimetric method. The cation exchange capacity (CEC) was obtained by the Bobko–Askinazy–Aleshin method. In order to determine the degree of contamination of the city’s soils with various pollutants, the content of petroleum products in the soil was determined using infrared spectrometry. Thematic maps based on soil diagnostic features were compiled using GIS Axiom 3.6, using software add-ons. Processing, analysis, and structuring of initial market data, as well as modeling of cadastral value of land plots, were carried out in the MS Excel 2503 software product, using correlation and regression analysis.

2.3. Methods for Taking into Account the Quality of Soils in the Conditions of Different Market Segments and Their Market Activity

Depending on the segment and its market activity (frequency of transactions with land plots on the land market), the methods for taking into account the quality of soils in the cadastral valuation of agricultural lands will differ. But in this case, the necessary initial data for the valuation of the said lands are the availability of factors for assessing the qualitative condition of the soils. Within the framework of the study, it was proposed to consider the soil quality score as an integral indicator of such an assessment, the determination of which was carried out using the example of Saint Petersburg through independent soil sampling and laboratory research in the Laboratory for Assessment of Technogenic Transformation of Ecosystems of the Empress Catherine II Saint Petersburg Mining University [40]. Due to the fact that during cadastral valuation, it is necessary to take into account the soil fertility and the influence of natural factors, as factors of part I of differential land rent of type I, the process of conducting soil studies will differ from the methods adopted by GOSTs. The algorithm for assessing the quality of soils by obtaining an integral indicator in the form of a quality score is presented in Figure 2.

In order to conduct soil sampling, it was necessary to first conduct a reconnaissance of the area in order to survey the territories of GNPPs, ANPPs, and IHC; selection of the location of the test sites and drawing up a scheme of the soil sampling route. An important aspect is to take into account their coordinate location [41]. Considering the fact that a sufficient number of GNPPs, ANPPs, and IHC territories can be located within the boundaries of several soil varieties, soil sampling should be carried out within the zone of each of the soil varieties (without crossing the boundaries of the land plots of the property holders) and the boundaries of GNPPs, ANPPs, and IHC.

The cycle of laboratory experiments was characterized by the implementation of continuously sequential stages and actions. The first stage of research consisted of the primary processing of soil samples by drying them to an air-dry state. The second stage involved the preparation of soil extracts after careful processing and cleaning of dried samples from debris and plant elements. The third stage involved the direct determination of soil condition indicators in the samples. Practical implementation at this stage was carried out through the use of laboratory equipment and glassware, chemical reagents, and solutions, as well as ready-made test kits. Thus, the use of the MOS-120N weighing moisture analyzer was necessary to determine the soil moisture; using a spectrophotometer, the content of mobile phosphorus in the soil was determined; the cation exchange capacity was analyzed using a fluorescent-photometric liquid analyzer FLUORATA-02-3M; and the pH level in the soil, including in the acidic environment, was measured with a pH meter. Chemical compounds of salt, which included sulfates, chlorides, and carbonates, were determined using ready-made test kits.

Thematic maps were constructed in GIS Axiom 3.6. To prepare the maps, geoinformation layers with attributive and graphical information on soil varieties of Saint Petersburg and land plots of GNPPs, ANPPs, and IHC, within the boundaries of which soil sampling was carried out, as well as data on soil fertility indicators, presented in tabular form, were used.

The relationship between crop yields and soil quality indicators was determined based on data on the average long-term crop yields of crops grown in the territory of GNPPs, ANPPs, and IHC of Saint Petersburg. By conducting a regression analysis, the type of relationship between crop yields and soil quality indicators was established.

As part of the soil appraisal, a comparative assessment of soils was performed based on their productive capacity and potential fertility. During the work, it was necessary to determine the quality score, the calculation of which was performed using the following formula:

$$B_i={\displaystyle \frac{P_f}{P_{ref}}}·100$$

(1)

where $B_i$ is the quality score for a certain characteristic; $P_f$ is the actual value of the feature in a given soil; and $P_{ref}$ is the maximum or reference value of a feature, taken as 100 points.

After completing the sequential steps of the algorithm presented above, it was necessary to construct a model of the specific indicator of cadastral value (SICV) of land plots of the segment under consideration, using the method of statistical (regression) modeling within the framework of a comparative approach.

Table 1. List of cost factors taken into account in the 2022 GKO [39].

Name of the Factor	Unit of Measurement
Quantitative Influence of local centers (X1)	m
Social infrastructure (X2)	m
Proximity to water bodies (X3)	m
Recreational areas (X4)	m
Location within the boundaries of a cottage settlement (X5) Engineering infrastructure (X6)	1/0 m
Qualitative
Assessment zone (X7)	1–4
Presence of encumbrances from utilities (X8)	1/0
Additional discount on the area of the plot (X9) Soil quality (X10)	RUB/m2 B

presents a list of cost factors that were taken into account by the State Budgetary Institution “City Cadastral Valuation Department” in the 2022 GKO. It is worth noting that the presented factors included a new factor under consideration, “soil quality” (X₁₀). To identify the relationship between the factors, a correlation analysis was carried out using significance coefficients, and factors were tested for multicollinearity. Such testing helped to identify highly correlated factors, the value of the correlation coefficient of which was more than (0.75). The presence of multicollinearity between factors can usually lead to both unstable estimates of regression coefficients and problems in the validation and interpretation of the constructed model [42].

Based on the results of checking factors for multicollinearity and in the event of its absence, a regression model of the SICV of the land plots under consideration was constructed, and an assessment of its statistical significance was carried out. The criteria for assessing the statistical significance of the model were the calculated value of the Fisher F-criterion, the value of the determination coefficient R², the correspondence of the values of cost factors and objects of assessment to the normal distribution law, and autocorrelation in the residuals.

The final stage was to conduct an analysis of the comparison of the cadastral value of land plots in the considered segment of Saint Petersburg with the current results of the GKO.

For the segment “Agricultural use”, which included high-commodity agricultural enterprises and organizations, it was proposed to use the income approach to assessment, based on the calculation of the specific gross income (SGI) through the average weighted quality score. Since different soil varieties can be located on the territory of agricultural enterprises and peasant (farming) households, in this regard, it was impossible to use the quality score of one of them, but it was necessary to determine the average weighted quality score. According to the methodological guidelines for the GKO, in order to calculate the SICV of agricultural lands, it was necessary to determine the specific indicator of land rent, the calculation formula for which is presented below:

$$SILR=SGI-{SC}_{cult}-{SC}_{fert}$$

(2)

where $SILR$ is the specific indicator of land rent, RUB/ha; SGI is the specific gross income, RUB; ${SC}_{cult}$ is the specific costs of cultivation and harvesting of agricultural crops, RUB; and ${SC}_{fert}$ is the specific costs of maintaining soil fertility, RUB.

To calculate the specific gross income, it was proposed to express the value of the standard yield through the quality score and its price. Thus, the calculation of the SGI was performed according to the following formula:

$$SGI=P_{qs}·{BB}_{\mathrm{w}eigh}·P_{\mathrm{c}rop}$$

(3)

where $P_{qs}$ is the price of a crop quality point, p/score; ${BB}_{\mathrm{w}eigh}$ is the weighted quality score, score; and $P_{\mathrm{c}rop}$ is the selling price of an agricultural crop, RUB/ha. In this case, the calculation of the average weighted quality score was carried out according to the following formula:

$${BB}_{weigh}={\displaystyle \frac{B_{sv1}{·b}_1+B_{sv2}·b_2+\dots +B_{svn}·b_n}{b_1+b_2+\dots +b_n}}$$

(4)

where ${BB}_{\mathrm{w}eigh}$ is the weighted quality score, $B_{svn}$ is soil variety quality score and b is the indicator of the influence of a given feature on the yield of agricultural crops.

In general, the formula for calculating the SICV of agricultural lands, taking into account the average weighted quality score, looks like this:

$$SICV={\displaystyle \frac{P_{qs}·{BB}_{\mathrm{w}eigh}·P_{\mathrm{c}rop}-{SC}_{cult}-{SC}_{fert}}{CR}}$$

(5)

where $P_{qs}$ is the price of a crop quality point, p/score; ${BB}_{\mathrm{w}eigh}$ is the weighted average soil quality index; $P_{\mathrm{c}rop}$ is the selling price of an agricultural crop, RUB; ${SC}_{cult}$ is the specific costs of cultivation and harvesting of agricultural crops, RUB; ${SC}_{fert}$ is the specific costs of maintaining soil fertility, RUB; and CR is the capitalization ratio, %.

Based on the results of determining the SICV of land plots in the “Agricultural Use” segment, their cadastral value was calculated, and a comparative analysis of the obtained cadastral value of agricultural land plots in the Leningrad Region was carried out with the current results of the GKO.

3. Results

At the first stage of the study, information about soils located within the city boundaries was used to conduct soil sampling (see

Table 2. Soil name [43,44].

Soil Name	Area, ha
Gleyic Podzols (1)	1618
Arenosols (2)	2074
Cambisols (3)	1372
Gleyic Albeluvisols Abruptic (4)	4932
Calcaric Leptosol (Skeletic) (5)	397
Haplic Regosol (6)	8516
Podzols (7)	9550
Pedo-allochthonous gray-humus urbistratified (8)	7214

).

After that, soil sampling was carried out on the territory of land plots of the segment “Gardening and vegetable gardening, low-rise residential development” of Saint Petersburg, during which 112 soil samples were taken. Figure 3 shows the location of soil sampling sites on the territories of GNPPs, ANPPs, and IHC.

The second stage of the study involved conducting a series of laboratory experiments to determine the content of quality indicators in the soil. The results of the laboratory studies are provided in Appendix A. Based on the results of the cycle of laboratory experiments, a database of indicators of the fertility of agricultural lands in Saint Petersburg was published [44].

At the third stage, the soil condition in the study area was analyzed based on constructed thematic maps based on soil fertility indicators. Figure 4 shows a thematic map based on soil moisture content.

It is worth noting that the values of soil moisture content varied from 20.5% to 56%. The most humid soils were concentrated in the central areas of the city on the northwestern and southeastern sides. At the same time, soil samples taken in the locations of GNPPs, ANPPs, and IHC located near water bodies turned out to be the most humid. Figure 5 shows a thematic map of the content of mobile phosphorus in the soil. According to the presented map, the content of mobile phosphorus in the soil varied from 77.8 kg to 166.8 kg. Most of the soils in Saint Petersburg had a high content of mobile phosphorus. The highest values of mobile phosphorus in the soil were found in the southwestern part of the city in the Petrodvortsovy and Krasnoselsky districts.

At the fourth stage of the study, data on the average long-term yield of agricultural crops in the city, which were contained in statistical bulletins, were used to obtain the correlation dependence [35].

Table 3. Data on the average long-term yield of agricultural crops in the territory of Saint Petersburg.

Yield of Agricultural Crops, kg/ha	2016	2017	2018	2019	2020	2021	2022	2023	Average Long-Term Yield
Potatoes	19,470	24,740	26,640	0	15,030	12,730	0	0	12,400
Open-ground vegetables	42,040	45,210	54,680	0	19,630	24,650	0	0	23,300
Perennial grasses for hay	0	0	0	0	0	4240	3450	2950	1300
Perennial grasses for green fodder, silage, haylage	0	0	0	10,460	0	14,490	0	5670	3800
Cereals and legumes	2040	4110	3810	0	0	0	0	0	1200
Annual grasses for silage, haylage, green fodder	0	0	0	0	0	0	0	12,840	1600

presents data on the average long-term yield of agricultural crops.

Of the three types of agricultural crops presented in Table 1, the most common were vegetable crops, including open-ground vegetables and potatoes. Based on the data presented in Table 3 and Appendix A, the dependence of agricultural crop yields on soil fertility indicators was obtained. Using regression analysis, the linear dependence equation presented below was obtained. It is worth noting that along with the linear function, power, logarithmic, and exponential functions are also provided [45]. However, preference was given to the linear dependence, which is due to the value of the determination coefficient R²:

Y = 164.8 − 39.4·X₁ − 54.4·X₂ + 340.3·X₃ − 76.9·X₄ − 249.0·X₅ + 229.7·X₆

(6)

where Y is the crop yield, X₁ is the humus content, X₂ is the humidity, X₃ is the granulometric composition, X₄ is sulfate, X₅ is the pH of salt extract, X₆ is magnesium.

To carry out the grading, data on soil fertility indicators obtained during laboratory experiments were used (see

Table 4. Data on soil fertility indicators in Saint Petersburg.

Soil Varieties	Area of Soil Varieties, ha	Humus Content in Soil, %	Humidity, %	Power of the Humus Horizon, cm	Granulometric Composition, %	Chlorides, mg/kg	Sulfates, mg/kg	Carbonates, %	Electrical Conductivity, mS/m	pH of Aqueous Extract	pH of Salt Extract	Cation Exchange Capacity, meq/100 g	Content of Mobile Phosphorus, mg/kg	Nitrogen Content, %	Potassium, mg/kg	Magnesium, %
1	1618	2	20.5	23	24	31.31	75.26	0.98	0.14	7.7	7.1	172	137.0	0.29	5	4.51
2	2074	3	31.5	30	16	14.68	79.59	0.62	0.09	7.2	6.7	72	87.6	0.24	3	3.30
3	1372	4	34.7	17	45	102.95	60.29	0.80	0.13	7.0	7.2	96	70.9	0.17	3	2.60
4	4932	4	24.9	15	30	66.77	92.47	2.21	0.04	7.6	6.8	99	154.9	0.24	3	3.09
5	397	4	34.1	23	43	40.02	117.29	1.13	0.12	6.7	6.6	107	136.6	0.19	3	3.33
6	8516	4	27.6	23	44	25.56	134.40	1.30	0.11	7.1	6.8	100	166.7	0.36	3	2.58
7	9550	3	26.9	20	29	76.81	75.41	1.89	0.09	7.0	7.2	104	163.4	0.39	5	4.18
8	7214	4	48.7	18	40	16.24	99.84	1.36	0.12	6.9	6.6	102	136.6	0.35	4	5.72

).

Based on the data presented in Table 4, soil quality scores and their quality classes were determined (see

Table 5. Results of soil assessment in the territory of Saint Petersburg.

Soil Variety	Area, ha	Average Weighted Bonitet Score	Soil Class
Gleyic Podzols (1)	1618	63	VII
Arenosols (2)	2074	64	VII
Cambisols (3)	1372	77	VIII
Gleyic Albeluvisols Abruptic (4)	4932	72	VIII
Calcaric Leptosol (Skeletic) (5)	397	84	IX
Haplic Regosol (6)	8516	83	IX
Podzols (7)	9550	70	VII
Pedo-allochthonous gray-humus urbistratified (8)	7214	92	X

).

In the framework of the SICV modeling, 300 land plots were selected, which met the requirements of sufficiency and representativeness of the sample. In this case, when analyzing cost factors, they were divided into quantitative and qualitative, the values of which were specified discretely; for example, the factor (evaluation zone) was given a price zone number in the range from 1 to 4. For the factors “encumbrances from utility lines” and “location within the boundaries of a cottage settlement”, the last of which characterized the location of land plots within the boundaries of cottage settlements in the city, the characteristics 1/0 were specified, which indicated the presence or absence of the feature, respectively. Based on the analysis and processing of the initial market data on cost factors, the values of the significance coefficients were obtained using correlation analysis, and such factors were checked for multicollinearity (see

Table 6. Testing factors for multicollinearity.

	Y	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
Y	1
X1	0.59	1
X2	0.47	0.34	1
X3	0.36	0.18	0.04	1
X4	0.37	0.14	0.22	0.06	1
X5	0.45	0.06	0.02	−0.08	0.08	1
X6	0.50	0.36	0.42	0.16	0.09	0.06	1
X7	−0.65	−0.54	−0.49	−0.28	−0.07	−0.12	−0.59	1
X8	−0.02	0.04	−0.20	−0.01	0.11	−0.08	−0.02	0.13	1
X9	0.45	0.33	0.87	−0.02	0.27	0.06	0.37	0.47	0.19	1
X10	0.34	0.38	0.04	0.22	0.02	0.08	0.06	0.26	0.17	0.08	1

).

To check the factors for multicollinearity, we showed the absence of a strong relationship between the factors, and therefore, all the analyzed factors were included in the model of the model of the specific indicator of the cadastral value of land plots. Then, a regression analysis was carried out in the MS Excel software product, using the internal add-in “Data Analysis—Regression”. An important element in the process of regression analysis was the consistent inclusion of each cost factor in the cadastral value model, where at the next stage of factor input, the quality of the resulting model was checked. In this case, the quality criteria were the t-statistics (t > 3) and the significance of F (α = 0.05). The results of regression modeling are given in

Table 7. Results of regression analysis.

Regression Statistics
Multiple R	0.88
R-squared	0.77
Normalized R-squared	0.76
Standard error	1680.82
Observations	300
	Coefficients	Standard Error	t—Statistics	p—Meaning
Y—intersection	5777.80	1171.03	4.93	0.00
Influence of local centers (X1)	1097.92	119.36	9.19	0.01
Social infrastructure (X2)	5.44	5.46	0.99	0.03
Proximity to water bodies (X3)	116.06	27.37	4.24	0.00
Recreational areas (X4)	236.42	26.64	8.87	0.01
Location within the boundaries of a cottage settlement (X5)	−898.73	780.37	−1.15	0.05
Engineering infrastructure (X6)	1.17	1.12	1.05	0.04
Assessment zone (X7)	−1522.27	145.27	−10.47	0.05
Presence of encumbrances from utilities (X8)	38.76	236.99	0.16	0.05
Additional discount on the area of the plot (X9)	0.36	3.08	0.11	0.01
Soil quality (X10)	22.52	12.96	1.74	0.03

.

The results of the regression analysis showed that when the factor under consideration was included in the cadastral value model, the value of the determination coefficient became equal to 0.82, which indicated the significance of such a model and the need to take such a factor into account in the cadastral valuation of land plots. Based on the results presented in Table 7, a linear regression model equation was compiled, which had the following form:

Y = 5777.80 + 1097.92·X₁ + 5.44·X₂ + 116.06·X₃ + 236.42·X₄ − 898.73·X₅ + 1.17·X₆ − 1522.27·X₇ + 38.76·X₈ + 0.36·X₉ + 22.52·X₁₀

(7)

where Y is the SICV, RUB/m²; X₁ is the influence of local centers; X₂ is the social infrastructure; X₃ is the proximity to water bodies; X₄ is recreational areas; X₅ is the location within the boundaries of a cottage settlement; X₆ is engineering infrastructure; X₇ is the assessment zone; X₈ is the presence of encumbrances from utilities; X₉ is the additional discount on the area of the plot; and X₁₀ is the soil quality.

The next step was to test the constructed regression model for its statistical significance, where “Significance F” did not exceed the reliability of the model (α = 0.05) (see

Table 8. Results of statistical significance of the model.

	df	SS	MS	F	Significance F
1	2	3	4	5	6
Regression	10	27,937	2795	98.9	0.00
Remainder	289	817	28.3
Total	299	36,102

).

Next, the constructed model was tested for compliance of the values of cost factors and prices of similar objects with the normal distribution law, which showed that the function obeyed the normal distribution law since the values of statistical significance did not exceed (p < 0.05) for each of the cost factors (see

Table 9. Results of statistical significance of factors.

	p-Value
Y—intersection	0.00
Influence of local centers (X1)	0.00
Social infrastructure (X2)	0.03
Proximity to water bodies (X3)	0.02
Recreational areas (X4)	0.02
Location within the boundaries of a cottage settlement (X5)	0.03
Engineering infrastructure (X6)	0.02
Assessment zone (X7)	0.03
Presence of encumbrances from utilities (X8)	0.00
Additional discount on the area of the plot (X9)	0.03
Soil quality (X10)	0.03

).

Then the constructed model was tested for autocorrelation in the residuals, for which the Durbin–Watson statistical criterion was used since there was a free term in the equation (see

Table 10. Results of testing the model for autocorrelation in residuals.

Indicators	Meaning
dobserv	1.15
D1	1.40
D2	1.62

).

The results of the check showed the absence of autocorrelation in the residuals of the regression model. After the stage of constructing the regression model, the regression coefficients were determined, and the values of the SICV of land plots were calculated, on the basis of which the cadastral value of such land plots was determined (see Table 6).

At the next stage, the values of the SICV were calculated by substituting into the equation of the linear regression model each of the factors selected at the stage of regression analysis, including the new analyzed factor “soil quality” (see

Table 11. Results of determining the SICV of land plots.

№	Cadastral Number	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	Slp, m2	S(market)	SICV2022, RUB/m2	SICVnew, RUB/m2
1	78:42:0018315:58	1.02	1.63	2.07	1.2	1	230.00	1	1	12	72	358	1,868,454.55	6181.39	6900.48
2	78:42:0018315:59	0.98	1.53	1.35	1.3	1	180.03	2	0	10	72	355	1,843,797.79	6071.87	5218.52
3	78:36:0013376:55	2.98	1.47	1.32	1.3	1	111.16	3	1	7	63	603	1,469,451.12	4031.11	5642.89
4	78:38:0021241:102	0.22	0.29	2.59	0.5	0	13.55	2	0	10	64	984	3,690,370.97	5354.63	4855.95
5	78:40:0900207:10	0.65	1.06	0.86	3.4	0	23.11	4	0	11	83	757	1,569,006.60	2102.55	3211.95
6	78:14:0007701:49	0.80	6.51	1.74	1.1	0	123.43	4	1	25	70	595	1,308,630.21	3093.41	2833.24
7	78:34:0004265:296	0.71	18.82	3.57	3.7	1	265.08	4	0	71	63	642	2,377,598.20	4870.01	2715.60
8	78:37:1741002:42	1.41	2.19	0.36	1.6	0	37.28	4	0	17	84	812	1,844,345.21	2923.82	3610.18
9	78:12:0721502:233	1.36	5.12	3.08	1.3	0	200.16	4	0	36	84	461	2,041,666.34	4778.87	4013.57
10	78:12:0007153:44	2.76	3.22	1.03	3.3	1	85.08	4	0	2	92	743	2,407,892.56	3718.12	4909.49

).

Taking into account the data presented in Table 11, the cadastral value of the land plots under consideration was determined (see

Table 12. Results of determining the cadastral value of land plots in Saint Petersburg.

№	Cadastral Number	Slp,m2	SICV2022, RUB/m2	CV2022, RUB	SICVnew, RUB/m2	CVnew,RUB
1	78:42:0018315:58	358	6181.39	2212937.62	6900.48	2470370.19
2	78:42:0018315:59	355	6071.87	2155513.85	5218.52	1852574.78
3	78:36:0013376:55	603	4031.11	2430759.33	5642.89	3402663.64
4	78:38:0021241:102	984	5354.63	5268955.92	4855.95	4778252.54
5	78:40:0900207:10	757	2102.55	1591630.35	3211.95	2431446.68
6	78:14:0007701:49	595	3093.41	1840578.95	2833.24	1685778.22
7	78:34:0004265:296	642	4870.01	3126546.42	2715.60	1743415.59
8	78:37:1741002:42	812	2923.82	2374141.84	3610.18	2931468.43
9	78:12:0721502:233	461	4778.87	2203059.07	4013.57	1850257.06
10	78:12:0007153:44	743	3718.12	2762563.16	4909.49	3647747.95

) using the following formula:

$$CV=SICV·S_{lp}$$

(8)

where $CV$ is the cadastral value of a land plot, RUB; $SICV$ is the specific indicator of cadastral value, RUB/m²; and $S_{lp}$ is the area of the land plot, m².

The inclusion of such a cost factor as soil quality in the cadastral value model as an integral indicator made it possible to obtain the cadastral value of land plots in the segment “Gardening and vegetable gardening, low-rise residential development” in the range of their market value. This circumstance undoubtedly had a positive trend both in the future differentiation of the land tax amount and in reducing the tax burden on the owners of such land plots.

At the next stage of the study, the proposed improved method for determining the cadastral value of agricultural lands in the conditions of the depressed land market of the Leningrad Region was tested based on the income approach taking into account the average weighted quality score as an indicator allowing for a comprehensive assessment of soil quality. It should be noted that since the territories of agricultural fields used in agriculture can be located within the boundaries of several soil varieties, the value of the average weighted quality score was used in this regard. Figure 6 shows an example of the location of an agricultural field within the boundaries of several soil varieties.

Table 13. Calculation of the average weighted quality score.

№ Land Plot	Soil Index	Area, ha	Quality Score, B	Overall Field Quality Score, B
1103006	Pd1c/M	12.8	65	64
	Pdgg/M	0.7	57
	Pdgt/M	1.2	60
	Pd1c/k	2.1	64
	Pdgc/M	3.4	62
-	-	20.2	-	-

shows the calculation of the average weighted quality score using formula (4) using the example of one agricultural field.

To calculate the (SGI), the values of the selling prices of agricultural crops were used $\left(P_{crop}\right)$; the most relevant ones for 2024 were used. To determine the specific costs of maintaining soil fertility (${SC}_{fert}$), data on the removal of the active substance of nutrients (kg) per unit (t) of agricultural produce were used since the specific costs of maintaining fertility were calculated based on the market value of fertilizers and the active substance that must be added to neutralize the removal of nutrients. To calculate the specific costs of cultivating agricultural crops (${SC}_{cult}$), the specific weight of costs in the price per unit of sold products was used: for grain crops, potatoes, and hay (agricultural crops grown on the territory of land plots of agricultural enterprises, for which the SICV was determined), the specific weights were 77%, 88%, and 77%, respectively. The results of determining the SICV and cadastral value of agricultural land plots are presented in

Table 14. Results of determining the cadastral value of land plots in the Leningrad Region.

№	Cadastral Number	Slp,m2	SICV2022, RUB/m2	CV2022, RUB	SICVnew, RUB/m2	CVnew,RUB
1	47:14:0702001:149	21,986	5.93	130,679.41	6.03	132,646
2	47:14:0702001:286	20,651	5.93	122,383.34	5.56	114,845
3	47:14:0702001:364	492,881	5.93	2,922,784.33	5.29	2,606,654
4	47:14:0702001:365	800,364	5.93	4,736,368.09	6.10	4,885,728
5	47:14:0702001:682	803,262	5.93	4,727,698.43	5.85	4,697,541

.

It is worth noting that according to the current rules for determining the SICV within the framework of the GKO of the Leningrad Region, the SICV was determined by districts without taking into account any adjustments for the characteristics of land plots, for example, the quality of soils within the boundaries of a land plot. Within the framework of the proposed methodology, the SICV values may vary depending on the results obtained, which subsequently affects the cadastral value of the land plot. The results of determining the cadastral value of agricultural land plots differed from the current cadastral value determined within the framework of the GKO of 2022 in the Leningrad Region by 5–10%, which confirmed the need to take into account the quality score when determining the cadastral value of the land plots in question.

4. Discussion

Based on the results of a cycle of laboratory studies, up-to-date data on the state of soil fertility in the territory of Saint Petersburg were obtained. The results of the study showed a high content of mobile phosphorus in the soil and cation exchange capacity, and the pH level in the soil corresponded to the normal acid–base balance of the soil. As part of the laboratory work, soil pollution indicators were also determined, including the content of oil and oil products, since the industrial and manufacturing sectors of the economy are actively developing in the city. However, the results showed an insignificant oil content, which corresponded to the maximum permissible concentration.

Based on the data on soil fertility status and average long-term yield of open-ground vegetables grown in the territory of Saint Petersburg, a linear relationship was established between them. A comparison of the dependence of the crop yield on the indicators of soil fertility obtained in the study for Saint Petersburg with the results of work [46] showed a similarity in its linear form, but the composition of the indicators was somewhat different since M.Y. Azarova and E.V. Pismennaya also took into account the density of the soil. K. Juhos, S. Szabó, and M. Ladányi also arrived at a linear dependence between the soil pH, the humus content, physical clay, the topographic position of soils, and the yield of grain crops for the eastern part of Hungary [47], which corresponded to the type of dependence for the Leningrad region in the present study.

As a result of the regression analysis, a model of the cadastral value of land plots was obtained taking into account the cost factor “soil quality”. The results of the analysis of the statistical significance of the model (Table 8) indicated high reliability of the results, the absence of randomness, and the presence of a pattern (significance < 0.05). Checking the model for autocorrelation in the residuals using the Durbin–Watson criterion showed its absence. The implementation of the resulting model made it possible to clearly see the difference between the cadastral value obtained within the framework of this study and that according to the results of the GKO in 2022. The range of differences in values varied from 5% to 12% for all studied GNPPs. X. Zhang and his colleagues also came to the conclusion that it is necessary to consider not a complex of physical and chemical properties but an integral index of soil fertility as a cost factor, thereby noting the possibility of conducting a more accurate assessment of soil fertility [48].

The coefficient of the regression equation for X₁ showed that the closer location of the local center to the land plot for each km increased its SICV by 1097 RUB. The coefficients for X₂ and X₆ showed an increase in the SICV of a land plot by an average of 5 RUB and 1 RUB with a change in the distance every km to social and engineering infrastructure facilities, respectively. The coefficient for X₃ showed that when the location of a land plot is closer to a water body, its SICV increased by 116 RUB. The coefficient for X₄ showed the proximity of the recreational zone increased the SICV of the land plot by 236 RUB for each km; the coefficient for X₅ indicated that if the land plot was not located within the boundaries of a cottage village (distinguished by an increased level of comfortable living for the population), then the SICV of the land plot was reduced by 898 RUB; the coefficient for X₇ showed a decrease in the SICV by 1522 RUB when a land plot fell, depending on the deterioration of the prestige of the assessment zone. The coefficient for X₈ showed an increase in the SICV by 38 RUB in the absence of utility facilities within the boundaries of the land plot, the coefficient for X₉ showed a decrease in the SICV with an increase in the area of the land plot, and the coefficient for X₁₀ showed that increasing soil quality by 1 point increased the SICV by 22 RUB.

When comparing the results of determining the cadastral value within the framework of the study and the cadastral value determined based on the results of the GKO 2022, a change in the determination coefficient (R²) of the regression model from 0.63 to 0.77 was noted, which indicated an increase in the quality of the model, its high significance compared with the model used by the state budgetary institution.

Within the framework of the income approach, when applying the land rent capitalization method, the cadastral value of agricultural land plots in the Leningrad Region was determined taking into account the average weighted quality score. The difference between the obtained cadastral value and the value calculated within the framework of the GKO 2022 was about 10%. The change in cadastral value was due to the quality of the soils within the boundaries of the assessed land plots, which was presented as an average weighted quality score.

The results of the conducted study allowed us to prove the previously put forward hypothesis about the necessity of taking into account the cost factor “soil quality” in the cadastral valuation of agricultural lands. The proof of the hypothesis is based on the following facts: first, in obtaining a linear dependence of the yield of open-ground vegetables on the physicochemical properties of the soil; second, the results of constructing a regression model confirmed the significance of the proposed cost factor in the cadastral valuation of agricultural lands in Saint Petersburg; and third, taking into account the average weighted quality score when determining the cadastral value of agricultural lands in the Leningrad Region showed its influence on the change in the value of such lands.

5. Conclusions

Based on the above material, we can conclude the following:

First, as a result of the laboratory research cycle, up-to-date data on soil fertility indicators were obtained. Based on the results obtained, a structured and relational database of indicators of the fertility of agricultural lands in Saint Petersburg was created, which was subsequently published.

Second, a linear relationship was proven between the yield of open-ground vegetables and the indicators of the state of soil fertility of agricultural land plots in Saint Petersburg.

Third, based on field work, a range of laboratory studies, and an assessment of soil quality, for the first time in the last 45 years, soil quality ratings for Saint Petersburg were obtained.

Fourth, the need to take into account the cost factor “soil quality” when calculating the cadastral value of agricultural land plots in order to extract part of the differential land rent of the first type, received due to natural fertility, through land tax, was substantiated.

Fifth, a methodology has been developed for taking into account the qualitative condition of soils in the cadastral valuation of agricultural lands in the conditions of an active land market, and a method for taking into account the qualitative condition of soils in the form of an average weighted quality score of a land plot for agricultural use has been modernized when determining the specific gross income within the framework of the method of capitalization of land rent in the conditions of a depressed land market.

Sixth, the cadastral value of land plots in the segments “Gardening and vegetable gardening, low-rise residential development” and “Agricultural use” of Saint Petersburg and the Leningrad region was determined.

Seventh, a comparison of the obtained values of the cadastral value of agricultural land plots with the current results of cadastral valuation in Saint Petersburg and the Leningrad Region was performed, which showed that taking into account the quality of soils can change the land tax within the range of 5 to 12%.

Eighth, the results of the study made it possible to prove the previously put forward hypothesis about the need to take into account the cost factor “soil quality” in the cadastral valuation of agricultural lands.

The practical significance of the study lies in the applicability of the results of soil quality assessment and models for calculating the SICV for land taxation, individual market valuation for lending, purchase and sale, lease of agricultural land, allocation of land plots on account of a land share. In the field of developing a set of melioration measures on agricultural lands, including the development and implementation of agricultural technologies and technical means that ensure an increase in soil fertility, the results of laboratory studies to determine the physical and chemical properties of soils can be used. The obtained quality scores for Saint Petersburg are also applicable to identify unused and degraded lands for their transfer to other types of use.