Agroecological Assessment of Arable Lands in the Leningrad Region of Russia under the Influence of Climate Change

Abstract

The paper presents an analysis of the influence of climatic characteristics on the rating of land suitability for agricultural use. Soil fertility is one of the most important factors in land productivity and crop capacity; it is a complex value that depends not only on agrophysical and agrochemical soil properties but also on other natural factors, such as climate. There are different methodical approaches for a quantitative assessment of fertility level. The objectives of the research were to understand whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use in the example of the Leningrad region. The estimation and comparison between Semenov–Blagovidov’s method of quality land estimation and Karmanov’s method of appraisal of soils are given in this article. Karmanov’s method is highlighted in this paper for its ability to assess soil’s ecological indices more effectively than traditional methods. The research suggested that climate change may lead to increased variability in soil quality, with potential benefits for agriculture under certain climate scenarios, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields. The results of such assessment show that the performed calculation models can be used to forecast crop yields for future periods.

1. Introduction

Global warming and associated climatic changes have serious consequences that affect or threaten important social, economic, and environmental aspects of life. As observed in all industry sectors, agricultural development is affected by climate change, which has a negative impact on land and water resources [1,2]. Therefore, the effective use of land and water resources and their protection, as well as the improvement of soil fertility in the context of climate change, will stimulate further development of agricultural sectors and determine the prospects of the industry.

The current state of soil resources is alarming, as over the last 30–50 years, soils have become poor in humus and nutrients, subject to salinization, water and wind erosion, polluted with trace metals, fluorides, agrochemicals, etc. [3]. Therefore, it is of great importance to correctly describe trends of processes occurring in the soil and to determine relationships between them, to manage soil processes, to improve the state of arable land under climate change, as well as its rational use and development of optimal agro-reclamation measures aimed at preventing negative factors [4].

The dependence of agricultural productivity on climate change is largely determined by the geographical location of the territory. Moreover, an agroecological system’s productivity largely determines crop yields. Productivity depends not only on agrophysical and agrochemical soil properties but also on the heat and moisture that plants supply and the amount of solar radiation received during the growing season. Soil fertility determines the productivity of agricultural lands [5]. At the same time, land productivity is not only soil fertility but also the soil’s ecological component state of the agroecological system. Considering the concept of “productivity” in this way, a land assessment according to soil bonnet, which is quite common in practical agronomy, is possible to use in the following analysis [6].

Carrying out a comprehensive score and cost assessment of soil development over time and taking into account various factors will make it possible to solve a new applied problem of agrosoil science, agroecology, and agricultural economics—monetization of ecosystem services of agroecosystem components whereas it is necessary to introduce effective environmental management systems in the agro-industrial complex [7]. It is necessary to differentiate and expand the estimated indicators with the intensification of agriculture, as V.I. Kiryushin [8] suggested using the experience of agroecological assessment, typology, and classification of agro-landscapes and soils, and the soil research databases available to create a system for a comprehensive assessment of agricultural land.

Environmental factors of productivity are heterogeneous in space, and agroecological factors can change over time, including as a result of human activities. The morphological soil characteristics are relatively stable in time and space (within certain soil areas). The main agrochemical indicators may change as a result of anthropogenic impact when mineral and organic fertilizers are used. Climatic parameters vary in micro- and mesorelief and are unstable over time. Therefore, it seems interesting to analyze the spatial and temporal variability of soil value (soil bonitet) under the conditions of predicted climate changes for a specific territory, for example, in the Leningrad region. Moreover, while there are numerous investigations on vegetation dynamics in connection with global warming, there are practically no such forecasts for soil cover. This is primarily due to the fact that soil is a complex bio-inert formation, which is formed over a long period of time. Currently, no studies have been carried out on the quantitative analysis and forecast of changes in the properties and quality of soils due to climate warming. There are only a few works on the forecast of qualitative changes in soil properties and soil formation processes during global climate change [9,10]. In particular, it was shown that the increase in the sum of active temperatures, the decrease in soil freezing depth, and the increase in the vegetation period would lead to the activation of the productivity process and change in the direction of the soil formation process [10]. However, such assumptions are exclusively qualitative, whereas no one has studied quantitative regularities. Therefore, in order to achieve this goal, it is necessary to decide whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use within the Leningrad region.

The parameterization of soil formation processes has been the most important task of fundamental and applied soil science throughout the development of this science. The parametrization of processes is necessary for their quantitative and qualitative assessment, which can later be used to manage processes and the agroecological potential of agroecosystems. This task is solved in several ways: (1) parametrization of the soil-forming potential components, (2) soil rating, and (3) assessment of ecosystem services, including functional, depository, spatial, providing, and resource ones. The present article is directed to the soil bonitet and methodological approaches to rating.

Analysis of Approaches and Methods for Rating Land Suitability for Agricultural Use

With the increasing intensity of farming and trends to conservation and rational use of natural resources, the importance of agroecological and economic land assessment is significantly increasing. Traditional approaches relied on soil appraisal, rating assessments of land suitability for agricultural use, various indices, and other integral assessments requiring direct indicators of natural factors and conditions of economic activity in general and farming in particular.

Quantitative assessments of land suitability have a relatively short history. At the same time, they are necessary for numerous applications: cadastral assessments, determination of land structure, crop selection, and placement. They can be applied at the field, farm, and district levels and are determined by summarizing a set of indicators relevant to the objective.

The history of land assessment and soil rating begins in the 1960s–1970s, when a variety of groups, classifications, and methods for soil and land assessment were proposed in various countries. The method recommended in the 1970s by the FAO for rating land suitability for agricultural use was most widely used abroad. According to that method, each rating indicator receives a certain score (rating), and the land unit index is calculated as the arithmetic mean of the total number of scores (LUI—land unit index). Based on this index, the land suitability is assessed in four categories [11,12,13]. Several land assessment systems were used in the United States, including the bonitet classification system with three appraisal categories: classes, subclasses, units of land suitability, and the well-known score appraisal [14].

The highest level in Russia’s land assessment and soil rating was reached in the projects of on-farm land management of collective and state farms in the 1970s–1980s, where the features of economically efficient and least environmentally hazardous land use were fully disclosed and carried out by GIPROZEM. A great experience in this area has also developed in the process of development, design, and mastering of adaptive landscape-farming systems in the methodology of V.I. Kiryushin [8]. Taking into account this experience, he proposed a methodology of integrated assessment of agricultural land, largely taking into account the economic features of agricultural production.

Attempts at quantitative landscape-based agroecological assessment have been made in different countries in the last third of the 20th century. The Slovak LANDEP [15], the American METLAND [16,17], and the Canadian ABC [18,19] methodologies are well known. However, nowadays, integrated landscape and planning approaches have somewhat lost their position.

At present, emphasis is placed on the assessment and planning of the production process in different landscape conditions. AgroSim and CERES (Crop Environment Resource Synthesis) [20] are world widely known; in Russia, the Agrophysical Research Institute solution AGROTOOL [21] has an ideologically similar development, but this model is not currently functioning.

AquaCrop [22] and DSSAT [23], which focus on modeling the production process of major crops with respect to heat and moisture availability, remain quite popular. As a rule, models at this level require a good understanding of the production process, large amounts of statistical data on all variables, and programming knowledge, all of which limit their applicability in practice.

There are several methodological approaches for soil ecological assessment and soil bonitet assessment in the Russian Federation. For example, the well-known technique of V.A. Semenov and N.L. Blagovidov [24] on the qualitative land assessment, based on the data generalization of field experiments with the main field crops in the northwestern soil climatic zone of the Russian Federation. Results were initially presented by technique authors in tabular form for practical use. Subsequently, Efimov A.E. [25] performed a statistical analysis of tables data and constructed the following regression equation:

Bon = a₀ + a₁ × Humus + a₂ × Acid + a₃ × Nutr—autoregression of 1st order,

(1)

Bon = a₀ + a₁ × Humus + a₂ × Humus² + a₃ × Nutr + a₄ × Nutr² + a₅ × Humus × Nutr + a₆ × Acid + a₇ × Acid² + a₈ × Acid × Nutr + a₉ × Acid × Humus—autoregression of 2nd order,

(2)

where Bon means soil bonitet, Humus is a humus content, %; Acid—soil pH; Nutr is the total content of P₂O₅ in the soil, mg × 100 g⁻¹; a₀, a₁, a₂ … a₉—regression coefficients (author’s designations retained).

It is soil properties that are indicators of the unequal quality of land plots in this method. Regression models of this type contain only agrochemical and agrophysical characteristics of soils and do not explicitly take into account climatic conditions. Indirectly, the weather conditions were taken into account when the data from the field experiments were obtained. Features of soil structure and productive soil properties are not directly determined by the influence of a changing climate. However, any climate change in the long term may affect soil properties and, ultimately, the productivity of agricultural land. The soil is a powerful carbon accumulator; organic residues enter it—the amount and degree of its decomposition are directly related to climatic changes in the temperature and humidity conditions of the atmosphere and the content of carbon dioxide and other gases. Climatic changes affect soil-forming processes. The productivity of agrocenoses and their species composition change in the “soil–plant” system will lead to the transformation of root-inhabited soil horizons. Soil-forming processes in agrozems occur with greater intensity [26,27,28].

Many researchers have considered the influence of climate on soil [29,30,31]. For example, I.M. Nedanchuk and I.O. Alyabina [32] assessed the influence of climatic parameters on the distribution of an alpha-humus horizon in the soils of the Russian plain. An analysis of the temperature values of the atmospheric and soil climate in this study quantitatively confirmed that the distribution of the alpha-humus horizon is shifted towards low values of temperature. Moisture characteristics analysis showed that the Al-Fe-humus horizon develops in a wide range of this parameter, which indicates its weak influence on this horizon distribution. Therefore, Nedanchuk and Alyabina [32] confirmed that any changes in climatic parameters will affect both the distribution of soil varieties and soil properties, i.e., these changes do not accumulate in the “memory” of the soil. Modern climate warming will be accompanied by a weakening of the podzolic process and a strengthening of the soddy process in forest soils of the northern and middle taiga [9,33,34]. An increase in aridization and the processes of soil salinization associated with it can be developed in light chestnut soils of dry steppes [35].

Scientific research is being conducted to prevent degradation processes occurring in soils and to mitigate the consequences of the conditions of global climate change observed in the world, as well as to take into account natural and climatic conditions when determining the influence of external factors on soil properties.

There are serious disagreements regarding the methods of accounting and allocation of evaluative parameters. Some authors propose to carry out separate accounting of soil properties. These proposals differ in detail, but they are built on the same principle. This principle is that the value of each soil property and its gradations are considered regardless of the manifestation degree of other properties. This approach was refuted by N.M. Sibirtsev [36].

Another characteristic, albeit less noticeable, drawback to taking into account the influence of soil properties on the yield was in the approach of S.S. Sobolev, who believed that the dependence of the yield on each of the soil properties is directly proportional [24].

Some researchers believe that indicators of the chemical and agrochemical soil properties should serve as criteria for assessing soils. The fallacy of such a view was noted by P.A. Kostychev [36], who recommended paying attention to “the soil origin and genesis, the nature of cultivated vegetation growing there, the location, soil thickness and the quality of a subsoil”.

Many scientists focused directly on soil properties [37,38,39,40,41,42,43,44,45] when assessing soils, and diagnostic properties were different for soils of various types. Three groups of properties were first distinguished: main, derivative, and autonomous. The first includes the particle size distribution and humus content; the second is chemical, physicochemical, physical, biological, and water properties; the third is waterlogging, salinity, erosion, and rockiness. For example, F.Ya. Gavrilyuk [40] relied mainly on the thickness of humus horizons and humus reserves in the soil profile. K.V. Dyakonova also suggested evaluating soils by the content and quality of humus [46].

Long-term average crop yields were taken into account in some grading scales, for example, for the forest-steppe zone of the Bashkortostan Republic [47,48,49], and agroclimatic components were reflected in the form of correction factors. The absence of this type of correction factor is typical, for example, for the appraisal method for Ukrainian lands [50].

Some researchers believe that land assessment should be carried out not on the basis of natural soil properties or productivity but on economic indicators—net income, gross output, etc. [51,52,53,54,55].

The soil bonitet assessment method developed by I.I. Karmanov [26] is based on the calculation of the soil’s ecological index (SEI), which includes soil, agrochemical, and climatic components. In contrast to the FAO land suitability rating method [11,12,13], which practically does not take into account agrochemical indicators and does not consider climatic characteristics at all, Karmanov methodology allows the determining of the soil’s ecological indicators and the bonitet quality scores of arable soils, perennial plantations, hayfields and pastures for any scale level (from a specific field area to the region and other administrative and geographical entities).

SEI refers to multiplicative (multiplying) indices that are widely used in the world’s practice of land valuation. Compared to additive (summative) assessment systems, multiplicative indices make it possible to fully identify the influence of the worst factors that limit the quality of the land as a whole. The best known are PI (Productivity Index) [56], CI (Capability Index) [57], and SIR (Storie Index Rating) [58]. The soil component has been strengthened, and a climate indicator has been added to the SEI index in comparison with foreign analogs, which significantly increases the efficiency of assessing the agroecological land potential. The agricultural territory of Russia refers mainly to the area of low biological activity, and moreover, the crop yield depends on weather conditions by 10–30% in the non-black earth region of the European part of Russia. Furthermore, the non-black earth zone is characterized by a complex moraine hilly-ridged relief, where small areas of arable land and meadows alternate with taiga vegetation and wetlands. Therefore, an integrated approach to assessing soil fertility is especially important for such conditions, which makes it possible to purposefully increase the soil fertility of each specific land plot of farms at the lowest cost.

The bonitet assessment developed by I.I. Karmanov [26], compared to other methods of soil ecological assessment, makes it possible to obtain comparable scores for the entire territory of Russia using certain formulas. This method allows for determining soil’s environmental indicators and soil quality scores of arable land, perennial plantations, hayfields, and pastures not only for individual farms but also for agroecosystem territories of any scale—from a specific site or field to a region or republic within the soil climatic zone. The evaluation parameters of this assessment include climatic factors of the appropriate averaging scale. Therefore, it is possible to change the estimated indicators of soil fertility and productivity of agroecological systems with any spatial and temporal resolution, including for forecasting purposes, by conducting regular agroecological land monitoring [26].

According to Karmanov’s methodology, each soil that forms under specific environmental conditions corresponds to a certain soil’s ecological index, and its value determines the level of soil fertility and bonitet for each cultivated crop. Soil ecological assessment is carried out based on soil properties, climatic indicators, and some other territory features.

In order to assess changes in the productivity of agroecological systems under projected climatic conditions, SEI allows the evaluation of productivity dynamics for the period of forecasts issued using global climate change models.

2. Materials and Methods

A comparison between Semenov–Blagovidov’s method of quality land estimation and Karmanov’s method of appraising soils is made, and the value of bonitet points for individual crops is calculated using both methods. The current studies were carried out using system analysis of a complex of agroclimatic and soil indexes.

Data on climate, soil, and agrochemical parameters for a number of farms (Figure 1) in the Leningrad Region were used for calculations. The soil map was used as an information base: data about soil type and particle size distribution according to soil name were used from the descriptions and maps provided in the manuscript describing the soil diversity of the Leningrad region based on modern research [59]. The work was based on a digital medium-scale soil map and database of the Leningrad region, created at the Central Museum of Soil Science, named in honor of V.V. Dokuchaev. Agrochemical parameters of farms in the Leningrad Region—such as pH, contents of soil organic matter, phosphorus, and potassium—were provided by the staffs at the Agrophysical Research Institute (Saint Petersburg, Russia) and based on many years of farm soil surveys (1980–2023) and some publications [60,61,62]. The values of soil indicators were formerly determined in the laboratory for all soil types of the farm (by land) for ordinary arable land, perennial plantations, hayfields, and pastures. Soil indicators for each soil type of the farm (by site) are multiplied by the area of this type, then the results are summed up, and the total is divided by the total area of the given site (e.g., common arable land, etc.). In this way, an average weighted soil index is obtained for the given site, which was taken into account in further calculations. The source of climate data, such as average temperatures and precipitation sums, is the agroclimatic zoning of the Leningrad region that is based on the sufficiency of the territory with heat during the growing season and the characteristics of the winter period over the last 50 years, which determines the overwintering of crops. Based on the assessment of agroclimatic features, the Leningrad Region can be subdivided into five agroclimatic districts [63,64,65,66,67]. Statistical data processing and analysis were carried out using standard methods in the software packages MS Excel 2016, Past (version 3.20; Microsoft Corp., Redmond, WA, USA), and Statistica 64 (version 10; StatSoft Inc., St. Tulsa, OK, USA).

Based on the study purpose, the rating of land suitability for agricultural use and estimation of bonitets variability for agricultural crops was calculated according to the present time and climate forecasts for 2030 and 2050 (

Table 1. Forecasts of average annual climate changes according to the ensemble scenario of the Main Geophysical Observatory named after A.I. Voeikova.

Parameter	2030		2050
	A1F1 Scenario	B2 Scenario	A1F1 Scenario	B2 Scenario
∑t > 100	+769	+724	+1451	+1120
Precipitation total, mm	+88	+91	+143	+169
t0 of January	+3.9	+3.2	+5.6	+4.5
t0 of July	+4.1	+3.0	+7.2	+5.7

) according to two scenarios: arid (A1F1) and humid (B2) [68,69,70,71,72].

3. Results and Discussion

3.1. Assessment of Spatial and Temporal Variability of the Soil’s Ecological Index (SEI) and Soil Bonitets (B)

The analysis of generalized data of soil characteristics of farm territories in the Leningrad region with a wide range of initial conditions was carried out in order to qualitatively and quantitatively assess the influence of climatic factors on the spatial distribution of the main characteristics of the soil cover (

Table 2. Soil and agrochemical parameters of farms in the Leningrad Region.

No.	Farm Name	N, °	E, °	Soil Type	Particle Size Distribution	pH	Ctotal, %	P, mg × kg−1	K, mg × kg−1
1	ZAO “Lyuban”	59.37	31.26	podzolic-peat	loam and sandy loam	5.4	4.3	243	178
2	ZAO “Agrotechnika”	59.34	31.20	podzolic-peat	loam and sandy loam	5.6	4.9	167	152
3	“Agricultural enterprise” “Voskhod” LLC	59.28	31.43	podzolic-peat	loam and sandy loam	5.6	4.5	274	173
4	ZAO “Stud farm” “Rabititsy”	59.41	29.44	soddy weakly and medium podzolic soils	loamy sand	5.5	3.2	199	110
5	ZAO “Sumino”	59.47	29.48	soddy weakly and medium podzolic soils	loamy sand	5.9	3.2	137	174
6	Public limited company “Agricultural enterprise” “Syaglitsy”	59.40	29.25	soddy weakly and medium podzolic soils	loamy sand	5.9	3.0	264	162
7	Public limited company “Trud”	59.56	29.15	soddy weakly and medium podzolic soils	loamy sand	6.0	2.4	235	107
8	Public limited company “Udarnik”	59.35	29.51	soddy weakly and medium podzolic soils	loamy sand	5.5	4.9	309	111
9	ZAO “Prinevskoye”	59.87	30.50	podzolic-peat	loam and sandy loam	6.3	4.7	519	209.4
10	ZAO “Prigorodny”	60.07	30.24	podzolic-peat	loam and sandy loam	5.9	5.5	353.1	178.5
11	Agricultural artel “Kolkhoz” “Yanino”	59.95	30.56	podzolic-peat	loam and sandy loam	5.8	4.1	251.4	134.2
12	ZAO “Vartemyaki”	60.18	30.33	soddy strongly podzolic soils	loam and sandy loam	5.3	5.1	211.2	66.9
13	ZAO “Ruch’i”	60.00	30.44	podzolic-peat	loam and sandy loam	5.6	5.5	341.9	124.2
14	ZAO “Bugry”	60.07	30.40	podzolic-peat	loam and sandy loam	5.9	5.5	279.5	79.9
15	ZAO “Shcheglovo”	60.03	30.76	soddy strongly podzolic soils	loam and sandy loam	5.3	6.3	255.2	103.6
16	ZAO State farm “Romanovka”	60.05	30.71	soddy strongly podzolic soils	loam and sandy loam	5.4	nd	288	112.3
17	ZAO “Karelsky”	60.05	30.22	podzolic-peat	loam and sandy loam	6.7	5.9	250	250
18	“Agricultural enterprise “Smena” LLC	59.81	30.07	soddy weakly and medium podzolic soils	loamy sand	5.5	4.4	240.2	120.1
19	Public limited company “Vsevolozhskoe”	59.90	30.67	podzolic-peat	loam and sandy loam	4.2	4.2	258.9	108.7

).

Soil’s ecological indexes are calculated according to the following basic formula:

$$\mathrm{SEI}=12.5\times \left(2-\mathrm{V}\right)\times \mathrm{P}\times \mathrm{Dc}\times {\displaystyle \frac{\sum \mathrm{t}>10°\times (\mathrm{KY}-\mathrm{R})}{\mathrm{KK}+100}}\times \mathrm{A},$$

(3)

where SEI is the soil’s ecological index; V is the soil bulk density (the average for a meter layer), g∙cm⁻³; 2 is the maximum possible soil bulk density at their ultimate compaction, g∙cm⁻³; P is a coefficient taking into account the soil volume in a meter layer of various particle size distribution; Dc is soil properties additionally taken into account; ∑t > 10° is the sum of average daily temperatures exceeding 10 °C; KY is moisture coefficient (R is a correction to this coefficient); KK is coefficient of continentality; A is the final agrochemical indicator. The value of 12.5 is introduced into the formula in order to make a certain set of environmental conditions 100 units of the soil’s ecological index.

The value of bonitet points for individual crops is calculated according to empirical formulas:

$$\mathrm{for}\mathrm{grain}\mathrm{crops}:\mathrm{B}=8.2\times \mathrm{V}\times {\displaystyle \frac{\sum \mathrm{t}>10°\times \mathrm{KY}}{\mathrm{KK}+70}},$$

(4)

$$\mathrm{for}\mathrm{perennial}\mathrm{grasses}:\mathrm{B}=5.9\times {\mathrm{V}}^{″}\times {\displaystyle \frac{(\sum \mathrm{t}>10°+2000)\times (\mathrm{KY}-0.1)}{\mathrm{KK}+100}},$$

(5)

where V is the total indicator of soil properties; ${\mathrm{V}}^{″}={\displaystyle \frac{(\mathrm{V}+1)}{2}}$; $\sum \mathrm{t}>10°$ is the sum of average daily temperatures exceeding 10 °C during the vegetation period; KY is the moisture coefficient; KK is the coefficient of continentality.

The minimum values of 0–10 points correspond to low-yielding lands, and the maximum value can reach 80–100. For example, Chernozems (ordinary weakly washed black soil) in the southern regions of the Russian Federation corresponds to 60 points. Bonitet points for this soil type for different agricultural crops will be 54–64.

Moreover, soil bonitets were calculated for the same agricultural crops using the formula of Semenov (2), as well as a crop yield (Y, t∙ha⁻¹) was determined at the soil productivity score (

Table 3. Soil productivity score (t × ha⁻¹) of soil bonitet quality for the main field crops in the northwest of Russia, according to Semenov ¹.

Agricultural Crop	Agricultural Technology Level
	Medium	Enhanced	High
Secale cereale L. (winter-annual)	0.089	0.039	0.052
Spring corn	0.029	0.038	0.047
Solanum tuberosum L.	0.22	0.28	0.36
Perennial grasses (velours)	0.04	0.065	0.09
Root vegetable	0.3	0.5	0.7
Brassica oleracea L. (late variety)	0.32	0.32	0.6
Grass-and-legume mix for silage	0.25	0.32	0.4

¹ Soil productivity score was calculated by V.A. Semenov according to the actual yield [24].

and

Table 4. Soil productivity score (t × ha⁻¹) for the main field crops in the northwest of Russia, according to Karmanov.

Agricultural Crop	Agricultural Technology Level
	Low	High
Cereal crops	0.025	0.06
Beta vulgaris L.	0.25	0.55
Helianthus L.	0.018	0.03
Perennial grasses	0.025	0.08
Annual grasses	0.025	0.06

) according to the formula:

$$\mathrm{Crop}\mathrm{yield}\left(\mathrm{Y}\right),\mathrm{t}\times {\mathrm{h}\mathrm{a}}^{-1}=\mathrm{B}\times \mathrm{S}\mathrm{p}$$

(6)

where B is the soil bonitet quality for agricultural crops, and S_p is a soil productivity score, t × ha⁻¹ (Table 3 and Table 4). The agricultural technology level is accepted as high.

The results of soil bonitets for agricultural crops, according to Semenov and Blagovidov [24], are summarized in Figure 2. They indicate that the soil bonitet quality for cereals turned out to be much higher than for other crops. Moreover, the bonitet of loamy sand soils for these crops turned out to be lower than the bonitet of loamy soils. The formula used relies only on soil and agrochemical properties and does not explicitly take into account climatic conditions (weather conditions for the generalization period are indirectly taken into account). In this regard, there are good reasons to assess which soil parameter affects the bonitet quality level to a greater extent.

First of all, the pH value affects the bonitet in these soils the most. For example, the value of soil bonitet quality for the perennial grasses of ZAO “Stud farm” “Rabititsy” (Figure 2) is 54.7 scores, and of ZAO “Sumino” is 57.2 scores. Humus content is at the same level (3.2%). The phosphorus content is higher in soils of ZAO “Stud farm” “Rabititsy” (19.9 mg × 100 g⁻¹ of soil) compared to soils of ZAO “Sumino” (13.7 mg × 100 g⁻¹ of soil). pH, on the contrary, is 5.5 in ZAO “Stud farm “Rabititsy”, compared to 5.9 in ZAO “Sumino”. Similar patterns can be traced when comparing other farms.

3.2. Estimation of Soil’s Ecological Index (SEI) and Soil Bonitets (B) under Climate Change Scenarios

Statistical modeling methods based on generalizations (tables) of Semenov [24] can ensure the use of estimated indicators within the northwestern soil climatic zone of the Russian Federation, but not in case of a changed climate. However, the ecological and climatic components are taken into account in a hidden form by processing long-term data obtained in emerging weather situations. Equations (1) and (2) are not correct enough for modern climatic conditions.

The results of calculations according to Karmanov [Equations (3)–(5)] are summarized in Figure 3 (for cereals as an example). The increase in soil bonitet quality for cereal crops and the relative redistribution of the yield potential across the territory can clearly be seen (Figure 3). Therefore, the spatial heterogeneity of bonitets can increase everywhere over the years, both during aridization and humidization of the climate in the future. The increase in SEI values is more intensive (for example, 48.7 scores for the present, 94.6 scores for 2030, 124.1 scores for 2050) than soil bonitet quality scores for agricultural crops. Moreover, the range of SEI change is wider than the range of soil bonitet quality change: for example, according to the A1F1 arid forecast scenario, the limits of SEI change in 2030 are from 67.0 to 108.0 scores, and the limits of bonitet variation are from 60.4 to 85.4 scores. This indicates that the spatial heterogeneity of soil’s ecological indexes is expected to be greater than the change in bonitet quality. A comparison of the data (Figure 3) shows that the soil factor, to a certain extent, plays a greater role in the soil’s ecological spatial heterogeneity.

Figure 4 shows the relative increase in soil bonitet quality for cereal crops (i.e., the ratio of soil bonitet quality under predicted conditions to that calculated for the present) according to the arid (A1F1) and humid (B2) scenarios. An increase in soil bonitet quality both by 2030 and 2050 for the A1F1 scenario is characterized by almost all farms. The spatial distribution of productivity factors across the study area is not the same. Under climate change, the value of soil productivity and bonitet quality factors change significantly; for instance, bonitet is increasing. However, the nature of the relative spatial distribution of bonitet quality rates in part of the studied territory (farms No. 1 to 10) remains. This heterogeneity increases in the rest of the studied territory. There is an increase in soil bonitet quality according to the B2 scenario by 2030 in the same proportion as according to the arid scenario, but this increase is noticeably less by 2050; spatial heterogeneity is preserved.

Therefore, based on the results obtained, climate change in the studied territory in an arid scenario will create more favorable conditions for agriculture and crop production in the region.

Compared to foreign analogs, the SEI index has strengthened the soil component and added a climatic indicator, which significantly increases the efficiency of assessing the agroecological potential of land.

The yield of cereal crops (Figure 5), calculated with Karmanov’s formula, is two times lower than according to Semenov–Blagovidov’s formula. This is due to the fact that the soil bonitet score, in this case, is related to the yield of cereal crops for the soil and climatic conditions of the northwestern soil climatic zone of the Russian Federation in the 1960s of the last century. This means that it is necessary to rely only on the values of soil bonitet quality while evaluating calculations since the soil bonitet score in the future may be different due to an increase in the level of agricultural technologies and a change in the properties of cultivated crops.

Therefore, soil bonitet quality will increase proportionally to changes in the agrochemical, soil, and as well as climatic characteristics of the studied territory in accordance with both arid and humid scenarios of climate change in the Leningrad Region, which will further increase the diversity of soil bonitet quality values.

Accurate instrumental and predictive analysis of the processes occurring in the soil under the influence of various factors, including climate change, is the basis for managing soil fertility and the quality of agricultural products, including cereal crops. Moreover, using the ecosystem services apparatus, it is possible to assess the levels of soil properties changes during degradation and progradation, and this will allow reaching a new level of appraisal of agricultural soils associated with the monetization of ecosystem services.

Such assessments of soil state are necessary for the purposes of continuous monitoring of agricultural lands in the process of its transformation into farms of various forms of ownership. The objectives of agricultural land monitoring include controlling changes in soil fertility and determining the factors that determine them. It refers to control of the influence of anthropogenic and economic factors in order to determine the most effective ones affecting land productivity. It is important to emphasize that the value of yield depends on many conditions, often random and independent: drought, pests and diseases, poor-quality sowing material, violations of technology, and others. Soil properties and composition will predict in advance the coming negative phenomena and processes leading to soil degradation, reduction in soil productivity, or decrease in the quality of agricultural products.

4. Conclusions

This study analyzes approaches and methods of rating assessments of land suitability for agricultural use, as well as qualitative and quantitative assessments of the influence of climatic factors on the spatial and temporal distribution of soil’s ecological index and soil bonitet on the example of farms in the Leningrad region.

The formula proposed by Karmanov allows for comparative assessments of changes in the productivity of agricultural crops with changes in climatic parameters. Moreover, in contrast to the FAO land suitability rating method, which practically does not take into account agrochemical indicators and does not consider climatic characteristics, Karmanov’s method allows the determining of soil’s ecological indicators and appraisal scores of soils for any scale level. Unlike additive (summative) assessment systems, multiplicative indices, which include SEI, make it possible to fully identify the influence of the worst factors that limit the quality of the land as a whole.

Calculation of SEI and soil bonitets for two climate forecasts for 2030 and 2050 on the example of farms in the Leningrad region showed that spatial heterogeneity of bonitets over the years may increase everywhere, both under aridization and humidization of climate in the future. In general, climate change at the considered territory under the arid scenario will create more favorable conditions for farming and crop production in the region, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields.

It seems interesting to carry out comprehensive research on the analysis of the spatial and temporal variability of soil bonitet quality based on the world rating scales under the conditions of predicted climate change in accordance with various world scenarios for the studied region and analyzed farms.