Improvement of the Methodology for the Assessment of the Agro-Resource Potential of Agricultural Landscapes

Abstract

The purpose of this study was the scientific justification of the concept of assessment of the agro-resource potential of agricultural landscapes and the improvement of the methodology for such assessment, on the basis of knowledge integration principles that allow for the combining of various fields of science in order to create an integrated methodological approach to addressing scientific and practical problems of environmental management. Based on the analysis of modern worldviews and natural scientific ideas on the mechanisms of biomass production in natural systems, we propose a methodology for the assessment of the agro-resource potential of agricultural landscapes that is an integral function of four key components (groups of factors)—agroclimatic resources (ACR), soil–land resources (SLR), agrobiological resources (ABR) and water resources (WR)—and that is based on the laws of nature and the principles of agricultural nature management. The proposed algorithm for predicting the natural state of agricultural landscapes based on agroclimatic, agrochemical and agrobiological integrated indexes allowed us to develop a unified integrated approach to the methodology for the assessment of the agro-resource potential of agricultural landscapes that makes it possible to determine the logical sequence of the trend of changes in the natural process, fully characterizing its state in the space–time scale.

1. Introduction

Currently, the use of the agro-resource potential of agricultural landscapes is based on “strict” management of nature–economic systems, taking into account global climate change, which is moving into the active stage of “succession” since a consistent, natural change in the structure and types of landscapes, due to internal natural and anthropogenic factors in the development of ecosystems, has become not only a scientific hypothesis, but also an axiom leading to a decrease in their agricultural productivity.

The results of studying climate changes using long-term climate indexes for 1941–2020 from 16 weather stations located in various natural and climatic zones of the Turkestan region showed that the average annual air temperature increased by 15–20% and annual precipitation decreased by 10–20% [1], which is a serious signal that calls for improvement of the natural scientific understanding of modern ecological and landscape mechanisms for the assessment of the agro-resource potential of landscapes in the conditions of a changing climate.

The qualitative and quantitative state of the agro-resource potential of agricultural landscapes is associated with the development of negative natural and anthropogenic processes, and their complex impact is difficult to predict using existing methodological approaches developed to address particular problems using individual integrated indexes that are not related to each other and that interact with the key environment formation factors. In this regard, there is a well-known contradiction between priority values in the methods of assessment of agro-resource potential used in connection with the agroclimatic, soil–land, biological and water resources of agricultural landscapes, a contradiction which necessitates improvement of the existing methodological frameworks based on the laws and principles of agricultural nature management.

The purpose of this study was the scientific justification of the concept of assessment of the agro-resource potential of agricultural landscapes and the improvement of the methodology for such assessment.

Improvement of the methodology for the assessment of the agro-resource potential of agricultural landscapes was based on an interdisciplinary synthesis of methodological approaches, ideas and concepts developed within the framework of the geographical, environmental and agricultural sciences, in which the following authors were highlighted: A.V. Yakushev [2], who analyzed the term “natural agro-potential” as a fundamental concept of agricultural nature management; P.A. Sukhanov [3], who “introduced” the concept of “agro-resource potential” as the overall integral productivity of agricultural land (lands) with inherent natural and climatic conditions; and A.P. Demin [4], who showed that the best results when determining integral assessments of natural resources can be achieved using methods based on the calculation of normalized deviations.

In their scientific works, D. Begg, Xu Fisher and R. Dornbusch [5] express their belief that the key factor determining resource potential is that of agricultural production. In the work of A. Sagaidak [6], resource potential is interpreted as a set of objective natural and economic conditions that affect the course of the reproduction process in agriculture. S. Galchenko, A. Varlamov and O. Bogdanova [7] consider natural resource potential as a complex cybernetic system that plays an important role in agricultural production.

One of the fundamental areas of research related to the assessment of the agro-resource potential of agricultural landscapes is the determination of agro-climatic resources based on complex factors. In the literature, we therefore find the hydrothermal coefficient of G.T. Selyaninov (HTC) [8]; N.N. Ivanov’s moisture index [9]; D.A. Pedia’s aridity index [10]; Ketch-Byram’s aridity index [11]; W.C. Palmer’s crop moisture index [12]; the drought reconnaissance index (DRI) of G. Tsakiris and H. Vangelis [13]; the Palmer drought severity index (PDSI) developed by the researchers W.M. Alley [14] and W.C. Palmer [15]; and the agricultural reference index for drought (ARID) of P. Woli, J.W. Jones, K.T. Ingram and C.W. Fraisse [16], all of which are directly related to climate indexes, including air temperature and precipitation.

For the assessment of soil and land resources of agricultural landscapes, the following have been proposed: the Storie index [17]; the soil productivity index [18]; the maximum limitation method based on the laws of maximum, minimum and optimum (restricting or limiting) [19]; and the Storie index rating (SIR) [20] and soil ecological index (SEI) [21], which together are used to perform comprehensive monitoring of soil fertility of agricultural lands.

The assessment of agrobiological resources of agricultural landscapes developed by V.A. Zhukov [22]; A.N. Polevaya and L.V. Florya [23]; J.S. Mustafayev, A.T. Kozykeyeva and G.E. Zhidekulova [24] and C.G. Tooming [25] is based on the concept of reference crop yields and is concerned with modeling crop productivity based on the agroclimatic and soil resources of agricultural landscapes.

The study of the influence of climatic factors on agrobiological resources of agricultural landscapes is analyzed in the works of S. M. Howden and H. Meinke [26]; Ju et al. [27]; Y. Kang, S. Khan and X. Ma [28]; J. Gornall, R. Betts, E. Burke, R. Clark, J. Camp et al. [29]; S. Chowdhury, M. Al-Zahrani and A. Abbas [30]; M.A. Otitoju and T.A. Enete [31], T. Barrett et al. [32], C. Song et al. [33] and N. Čerkasova [34], all of which are related to assessing the efficiency of agricultural production.

In assessing the water supply from river basins for the territory, population and economy as the spatial basis of population and agricultural nature management, the following are used: the sustainability index [35,36] and the reliability index [37] as a measure of the ability of the aquatic ecosystem to adapt to changing natural conditions; the environmental index [38]; the environmental stress index [39]; the environmental sustainability index [40]; and the multi-attribute environmental index [41], which is an indexed factor of water supply obtained using physical and statistical models of the formation of water resources in river basins.

The analysis of existing methodological approaches for the assessment of the agro-resource potential of agricultural landscapes, including indexes for agro-climatic, soil–land, agrobiological and water resources, showed that they lack the principle of integrity, which characterizes the correlation between and interdependence of the natural system components, allowing them to be combined into a single concept, namely the agro-resource potential of landscapes.

The analysis of integrated indexes for the assessment of the agro-resource potential of the natural system showed that despite the availability of in-depth theoretical and methodological research in the field of agricultural nature management, it is necessary to develop a comprehensive methodological support for assessing the resource-producing and environment-forming functions of agricultural landscapes on the basis of the ecological–landscape approach.

The research approach we use as a scientific method includes the analysis of existing methods for the assessment of the agro-resource potential of landscapes in order to expand worldviews and natural scientific ideas in the field of environmental management, on the basis of the fundamental laws and principles of geography and ecology, which allowed us to develop new hypotheses in the study of the agro-resource potential, on the basis of the integrated resource-producing indexes implementing important environment-forming and ecological functions.

2. Materials and Methods

The research materials are based on modern worldviews and natural scientific ideas on the mechanisms of formation of natural resources and agricultural nature management, within the framework of combined use of the concept of rational and sustainable organization of agricultural land use and land management, providing the optimal correspondence between the agro-resource potential of landscapes and the expected productivity of agricultural land for the efficient agricultural production.

The framework of this study is the landscape approach, modern methods of physical geography and ecology, based on the conservation of energy and substances, the principle of integrity and predictability that facilitates the formation of a scientific concept intended for the implementation of the comprehensive agro-ecological monitoring as a tool for scientific, information and innovative support of the assessment of the agro-resource potential of agricultural landscapes.

3. Results and Discussion

3.1. Methodology for the Assessment of the Agro-Resource Potential of Agricultural Landscapes

The agro-resource potential of agricultural landscapes is the overall integral productivity of their agricultural lands with the inherent soil and climate that can be achieved with specific types of landscapes subject to certain supply and management resources [3].

Based on the proposed definition, the agro-resource potential of landscapes (${ARPL}_i$) is an integral function of four key components (groups of factors)—agroclimatic resources (${\mathrm{A}CR}_i$), soil–land resources (${SLR}_i$), agrobiological resources (${ABR}_i$), and water resources (${WR}_i$)—consequently, their functional dependence as the integral derivative of several complex components can be expressed in Formula (1):

$${\mathrm{A}\mathrm{R}\mathrm{P}\mathrm{L}}_{\mathrm{i}}=\mathrm{f}\left({\mathrm{A}\mathrm{C}\mathrm{R}}_{\mathrm{i}},{\mathrm{S}\mathrm{L}\mathrm{R}}_{\mathrm{i}},{\mathrm{A}\mathrm{B}\mathrm{R}}_{\mathrm{i}},{\mathrm{W}\mathrm{R}}_{\mathrm{i}}\right),$$

(1)

Agro-climatic resources (${ACR}_i$) as a natural factor responsible for environment-forming function in specific natural–territorial complexes (NTC) are determined by the ability to form soil and vegetation cover on agricultural landscapes.

Soil and land resources (${SLR}_i$) as the spatial basis for the formation of soil and vegetation cover of agricultural landscapes in specific natural and climatic conditions are the most important feature of the natural system, production of biomass and involving the synthesis of organic matter.

Agrobiological resources (${ABR}_i$) are characterized, first of all, by the ecological productivity of agricultural landscapes with the possibility of creating agricultural landscapes where human activity is optimized to realize their resource potential on a scientific basis in the interests of people for the purpose of food security.

Accordingly, the groups of factors we considered in various natural–territorial complexes, agroclimatic resources (${ACR}_i$), soil and land resources (${SLR}_i$) and agrobiological resources (${ABR}_i$), are the most interrelated and interdependent; they form, as such, the agricultural system, which serves as a conductor of human impact on the nature, converting soil components and solar energy into an organic matter for agricultural products.

Water resources (${WR}_i$) realizing important environment-forming or ecological function are the spatial basis for the formation of agricultural land and the territorial organization of agricultural landscapes, which open the potential for the comprehensive assessment of the water supply of the territory and its population.

In general, these four functions of the agro-resource potential of agricultural landscapes determine the scientific and practical value, basic and strategic, which necessitates the quantitative and qualitative regulation in the territorial organization of agricultural nature management of any territory.

To assess the agro-resource potential of agricultural landscapes, based on its structural and functional organization, it is necessary to use a comparative geographical method on the basis of integrated indexes and criteria characterizing the productive capacity of strategic resources of the natural and material environment.

3.2. Methodology for the Assessment of Agroclimatic Resources of Agricultural Landscapes

Agroclimatic resources of agricultural landscapes are characterized by the following natural energy resources and integrated heat and moisture supply indexes:

Average annual air temperature, which is not only one of the key thermodynamic properties of the daylight surface air, but also the most important climatic parameter characterizing the energy resources of agricultural landscapes, which is expressed in Formula (2):

$${AAT}_i={\sum }_{i=1}^n{AMT}_{\mathrm{M}i}/n,$$

(2)

where ${AMT}_{\mathrm{M}i}$—average monthly air temperature, °C; $n$—number of months per year equal to 12.

Sum of annual precipitation is one of the most important elements of the daylight surface moisture circulation, providing the intensity of the organic process in the soil and vegetation cover of landscapes, which is expressed in Formula (3):

$${AAP}_i={\sum }_{i=1}^{n}M{AP}_i$$

(3)

where $M{AP}_i$—monthly precipitation, mm.

Sum of biologically active temperatures above 10 °C is an important biological and climatic parameter characterizing the thermal (energy) resources of agricultural landscapes and the needs of agricultural crops, determined by summing up the average daily air temperatures for the period with temperatures above 10 °C and calculated using Formula (4) [42]:

$$\sum {SBCT}_{\mathrm{C}i}{C}^o={\sum }_{i=1}^n{AMT}_{\mathrm{M}}·N_i$$

(4)

where ${AMT}_{\mathrm{M}i}$—average monthly air temperatures above 10 °C; $N_i$—number of days per month; $n$—number of months; $\sum {SBCT}_{\mathrm{C}i}{C}^o$—sum of biologically active temperatures above 10 °C (< 1500 °C—very low (1 point); 1500–2000 °C—low (2 points); 2001–2800 °C—average (3 points); 2801–3200 °C—above average (4 points); 3201–4500 °C—elevated (5 points); 4501–5200—high (6 points); >5200 °C—very high (7 points).

Radiation balance of the active surface layer of air and soil (${RB}_i$) of agricultural landscapes is the only energy source for the autotrophic nutrition of organisms, limiting their physiological activity and determines the “limit” of the maximum possible productivity with the optimal value of all other conditions [43] and on the other hand, directly depends on the sum of biologically active temperatures above 10 °C, which allows the use of dependence (5) for its calculation [44]:

$${RB}_i=4.1868·\left[13.39+0.0079·\sum {SBCT}_{\mathrm{C}i}>{10 }^{°}\mathrm{C}\right],$$

(5)

where $\sum {SBCT}_{\mathrm{C}i}>10$ °C—sum of biologically active temperatures above 10 °C; ${RB}_i$—radiation balance of the active surface layer of air and soil, kJ/cm²; (<100.0 kJ/cm² year—very low (1 point); 100.0–120.0 kJ/cm² year—low (2 points); 121.0–145.0 kJ/cm² year—average (3 points); 146.0–162.0 kJ/cm² year—above average (4 points); 163.0–205.0—elevated (5 points); 206.0–230.0 kJ/cm² year—high (6 points); >230.0 kJ/cm² year—very high (7 points).

Total evaporation of biologically active periods of the year, a conditional value characterizing the potential (not limited by moisture reserves) evaporation in agricultural landscapes under the existing climatic conditions, determined by summing up the monthly evaporation using Formula (6) [45]:

$${\mathrm{C}\mathrm{E}}_{\mathrm{o}i}={\sum }_{i=1}^n{\mathrm{E}}_{oi},$$

(6)

where $E_{oi}={\left({AMT}_i+25\right)}^2\left(100-{AMRH}_i\right)$—evaporation (<200 mm—very low (1 point); 200–400 mm—low (2 points); 401–800 mm average (3 points); 801–1200 mm—above average (4 points); 1201–1400 mm—elevated (5 points); 1401–1600 mm—high (6 points); >1600 mm—very high (7 points); ${AMT}_i$—average monthly air temperature, °C; ${AMRH}_i—$average monthly relative humidity, %; $n$—number of months when the average monthly air temperatures are above 10 °C.

Water consumption is the volume of water spent in agricultural landscapes for the transpiration of plants and evaporation from the soil, which can be assessed on the basis of dependence (7) [43]:

$${\mathrm{E}\mathrm{T}}_{ci}=10·{RB}_i·L^{-1},$$

(7)

where $L$—latent heat of vaporization numerically equal to 2.5 kJ/cm²; ${\mathrm{E}\mathrm{T}}_{ci}$—total water consumption of agricultural land, mm (<100 mm—very low (1 point); 100–250 mm—low (2 points); 251–500 mm—average (3 points); 501–750 mm—above average (4 points); 751–1000 mm—elevated (5 points); 1001–1250 mm—high (6 points); >1250 mm—very high (7 points).

Deficit in water consumption in agricultural landscapes, is the difference between evapotranspiration (transpiration of the plant foliage and physical evaporation from the soil surface) and the algebraic sum of indicators responsible for the natural humidity of landscapes, determined using Formula (8):

$$∆{\mathrm{E}\mathrm{T}}_{ci}=10·{RB}_i·L^{-1}-{AC}_c={\mathrm{E}\mathrm{T}}_{ci}-{AC}_c,$$

(8)

Solar energy consumption for the soil-formation process in agricultural landscapes is in direct connection with the substantial energy flow entering the soil surface, that is, they directly depend on the value of the radiation balance of the active surface layer of air and soil (${RB}_i$) and the annual precipitation (${AC}_{\mathrm{c}i}$), which is determined according to dependence (9) [46]:

$${ESF}_{пi}={RB}_i·exp\left(-\alpha ·{\overline{R}}_i\right),$$

(9)

where $\alpha$—index of complete utilization of the radiation energy in the soil-formation processes, numerically equal to 0.47; ${\overline{R}}_i$—“radiation dryness index” or complex hydrothermal coefficient; ${ESF}_{пi}$—solar energy consumption for the soil-formation process (<10 kJ/cm² year—very low (1 point); 10–50 kJ/cm² year—low (2 points); 51–90 kJ/cm² year—average (3 points); 91–130 kJ/cm² year—above average (4 points); 131–160 kJ/cm² year—elevated (5 points); 161–200 kJ/cm² year—high (6 points); >200 kJ/cm² year—very high (7 points).

“Excess solar energy for the soil-formation process in agricultural landscapes” is the amount of radiation balance of the active surface layer of air and soil not involved in the soil-formation process during the biologically active period of the year, driven by the natural humidity of agricultural landscapes and determined by the dependence (10) [47]:

$$\begin{gathered} ∆{ESF}_{ni}^u={ESF}_{ni}^n-{ESF}_{ni}^u={RB}_i·exp\left(-\alpha ·{\overline{R}}_{oi}\right)-{RB}_i·exp\left(-\alpha ·{\overline{R}}_i\right) \\ ={RB}_i·exp\left\{-\left[\alpha ·\left(1-{\overline{R}}_i\right)\right]\right\}, \end{gathered}$$

(10)

where ${ESF}_{ni}^n$—potential solar energy consumption for the soil-formation process in agricultural landscapes, where the “radiation dryness index” or complex hydrothermal coefficient (${\overline{R}}_{oi}$) is equal to 1.0; ${\overline{R}}_{oi}$—optimal value of the “radiation dryness index” or complex hydrothermal coefficient; ${\overline{R}}_i$—“radiation dryness index” or complex hydrothermal coefficient under the existing climate conditions.

The natural moisture supply of the soil and vegetation cover in agricultural landscapes is assessed using the moisture index ($C_{mi}$), which allows for the heat and moisture supply of landscapes to be simultaneously taken into account and assessed and is the ratio of annual precipitation (${AC}_{\mathrm{c}}$) and the sum of evaporation (${\mathrm{E}}_{\mathrm{o}i}$), which is determined mainly by the average monthly temperature ($t$, °C) and air humidity ($\alpha ,\%$), that is [45]: $C_{mi}={AC}_{\mathrm{c}}/{\mathrm{E}}_{\mathrm{o}i}$ (>1.15—excessively humid (7 points); 1.00–1.15—humid (6 points); 0.77–1.01—semi-humid (5 points); 0.44–0.76—arid (4 points); 0.22–0.43—semi-arid (3 points); 0.12–0.21—arid (2 points) and <0.12—very arid (1 point).

The natural heat supply and water supply of the soil and vegetation cover in agricultural landscapes characterize the intensity of the cycle of substances and the biological productivity of agricultural land in landscapes, determined by the ratio of heat and moisture resources and also reflecting the radiation dryness index proposed by M.I. Budyko [43]: ${\overline{R}}_i={RB}_i/L\cdot O_c$ (<0.80—excessive moisture (7 points); 0.80–1.00—optimal moisture (6 points); 1.01–1.20—moderate moisture (5 points); 1.21–1.80—moderately deficient moisture (4 points); 1.81–2.30—deficient moisture (3 points); 2.31–3.00—very deficient moisture (2 points); >3.00—extremely deficient moisture (1 point).

The hydrothermal conditions of the soil and vegetation cover in agricultural landscapes characterize the state and development of land ecosystems, namely, the ratio of heat and moisture resources (quantitative climate indicators) are assessed by G. T. Selyaninov’s hydrothermal coefficient (${HTC}_i$) [48,49], using Formula (11):

$${HTC}_i={AAP}_{i>10}/0.10·\sum {SBCTt}_{\mathrm{C}i},$$

(11)

where $\sum {SBCT}_{\mathrm{C}i}$—the sum of average daily air temperatures for the period with average daily air temperatures above 10 °C; ${AC}_{\mathrm{C}i>10}$—the amount of precipitation for the period with an average daily air temperatures above 10 °C, mm; ${HTC}_i$—hydrothermal coefficient (<0.20—very severe drought (1 point); 0.20–0.40—severe drought (2 points); 0.41–0.70—average drought (3 points); 0.71–1.00—deficient moisture (4 points); 1.01–1.40—optimal moisture (5 points); 1.41–1.60—increased moisture (6 points); >1.60—excess moisture (7 points).

Bioclimatic potential (${BCP}_i$) is the climatic index that synthesizes the influence on the biological productivity of agricultural landscapes, heat and moisture under existing climatic conditions, calculated using Formula (12) [50]:

$${BCP}_i=C_{mi}·\left(\sum {SBCT}_{\mathrm{C}i}/1000\right),$$

(12)

where $\sum {SBCT}_{\mathrm{C}i}>$10 °C—the sum of biologically active temperatures above 10 °C; 1000 °C—the sum of biologically active temperatures above 10 °C, corresponding to the modern border of field agriculture; $C_{mi}$—moisture index; ${BCP}_i$—relative value of the bioclimatic climate potential (<1.60—very low (1 point); 1.60–2.10—low (2 points); 2.11–2.60—decreased (3 points); 2.61–3.00—average (4 points); 3.01–3.40—elevated (5 points); 3.41–3.80—high (6 points); >3.80—very high (7 points).

The effective moisture index of the soil and vegetation cover in agricultural landscapes (${HF}_i$) is an integrated climate index that reflects the quantitative relationship between average annual air temperature and the annual precipitation, characterizing the conditions for the formation of different types of soil, proposed by V.R. Volobuyev [51] and determined using Formula (13):

$${HF}_i=43.2·lg{O}_{ci}-{AAT}_{ci},$$

(13)

where ${AAT}_{ci}$—average annual air temperature, °C; ${HF}_i$—effective moisture index <120.0—very high (7 points); 111.0–120.0—high (6 points); 101.0–110.0—elevated (5 points); 91.0–100.0—average (4 points); 81.0–90.0—decreased (3 points); 70.0–80.0—low (2 points); <70.0—very low (1 point).

The climate favorable index (${CL}_i$) is an integrated index characterizing the heat and moisture supply of the soil and vegetation cover in agricultural landscapes, taking into account the limiting impact of the most unfavorable factors, which can be both heat and moisture, determined using Formula (14) [52]:

$${CL}_i=\sqrt{\left\{arctg\left[\left({AAT}_i-6\right)/4\right]+1.57\right\}·\left\{arctg\left[\left({HF}_i-112\right)/4\right]+1.57\right\}}$$

(14)

where ${AAT}_i$—average annual air temperature (°C); ${CL}_i$—climate favorable index <1.20—very high (7 points); 1.01–1.20—high (6 points); 0.96–1.00—elevated (5 points); 0.76–0.95—average (4 points); 0.66–0.75—decreased (3 points); 0.50–0.65—low (2 points); <0.50—very low (1 point);

De Martonne’s aridity index (${IA}_i$) represents a factor characterizing the degree of climate aridity in landscapes and allows us to distinguish between two types of climate that are directly opposite in terms of the degree of moisture—humid (${IA}_i$ ≥ 15) and arid (${IA}_i$ < 15)—determined using Formula (15) [53]:

$${IA}_i={AC}_{\mathrm{C}i}/\left({AAT}_{ci}+10\right),$$

(15)

where ${AAP}_i$—annual amount of precipitation, mm; ${AAT}_i$—average annual air temperature in degrees; ${IA}_i$—aridity index >40.0—hyper humid (7 points); 30.1–40.0—super humid (6 points); 20.1–30.0—average humid (5 points); 15.1–20.0—low humid (4 points); 10.1–15.0—arid (3 points); 6.0–10.0—very arid (2 points); <6.0—extremely arid (1 point).

The bioclimatic index of aridity (${BIA}_i$) [54], expressed by the ratio of the annual amount of precipitation (${AAP}_i$) and evaporation ($E_{oi}$), determined by the sum of average monthly positive air temperatures during the warm period ($\sum {SBCT}_i{,C}^o)$, characterizing the inverse relationship between the growth of aridity and increase in the calculated index as such, and determined using Formula (16):

$${BIA}_i=\left({AAP}_i/E_{oi}\right)=\left[{SBCT}_i/\left(5.12·\sum {SBCT}_i,C^o+306.0\right)\right],$$

(16)

where $\sum {SBCT}_{\mathrm{C}}{C}^o$—sum of positive temperatures (°C); ${AAP}_i$—annual amount of precipitation (mm); ${BIA}_i$—bioclimatic index of aridity <0.15—extremely arid (1 point); 0.15–0.28—very arid (2 points); 0.29–0.43—arid (3 points); 0.44–0.60—sub-arid (4 points); 0.61–0.75—moderately arid (5 points); 0.76–0.90—slightly arid (6 points); >0.90—periodically arid (7 points).

The normalized index of aridity ($N{IA}_i$) [54], obtained on the basis of the bioclimatic index of aridity (${BIA}_i$), is determined using Formula (17):

$$N{IA}_i=1-{BIA}_i,$$

(17)

where $N{IA}_i$—normalized index of aridity >0.86—extremely arid (1 point); 0.76–0.86—very arid zone (2 points); 0.60–0.75—average arid zone (3 points); 0.46–0.59—sub-arid zone (4 points); 0.32–0.45—moderately arid zone (5 points); 0.18–0.31—slightly arid zone (6 points); <0.18—periodically arid zone (7 points).

3.3. Methodology for the Assessment of the Soil and Land Resources of Agricultural Landscapes

The agro-resource potential (${\mathrm{A}\mathrm{R}\mathrm{P}\mathrm{L}}_i$) of agricultural landscapes requires the study of soil and land resources (${SLR}_i$) for the formation of integrated indexes (criteria) that makes it possible to determine the natural resource potential of landscapes during the agricultural organization of the territory:

–soil index, the integrated index of soil fertility based only on the edaphic soil properties, that is, the set of their physical and chemical properties that can affect the productivity of the soil and vegetation cover in agricultural landscapes as a habitat, and represents a mathematical model (18), constructed by aggregation (combination of homogeneous indexes and summarizing indicators) [52]:

$$S_i=\left[6.4\left(G_{\Gamma \mathrm{H}i}+0.2·{\mathrm{C}}_{\Phi \mathrm{K}i}\right)/600\right]+8.5\sqrt{NPK\left(\%\right)}+5.2exp\left[-\left(H_{\Gamma i}-1\right)/4\right],$$

(18)

where $G_{\Gamma \mathrm{H}i}$—humate humus reserves, t/ha; ${\mathrm{C}}_{\Phi \mathrm{K}i}$—sulfate humus reserves, t/ha; $NPK\left(\%\right)—$mass ratio of elements (in terms of $N$, ${\mathrm{P}}_2{\mathrm{O}}_5$, ${\mathrm{K}}_2\mathrm{O}$), found in accessible forms and the corresponding masses with the maximum possible content of available forms of a given element under existing conditions; $H_{\Gamma i}$—hydrolytic acidity, mg-Eq/100 g; $S$—soil index, integrated index of soil fertility at <1.00—very low (1 point); 1.00–2.00—low (2 points); 2.01–3.00—decreased (3 points); 3.01–4.00—average (4 points); 4.01–10.00—elevated (5 points); 10.01–15.00 high (6 points) and >15.00—very high (7 points) [55];

–the bioclimatic potential of agricultural landscapes is a set of climatic factors that determines the potential biological productivity of agricultural landscapes as an objective pattern of changes in their productivity depending on the thermal and water resources necessary for the growth and development of plants, built on D. M. Shashko’s physical and statistical model [56], and determined using Formula (19):

$$\begin{gathered} {B\mathrm{C}PL}_i=\left({HSC}_{ti}·{MC}_{vi}\right)·100 \\ =\left\{\left(\sum {SBCT}_{\mathrm{C}i}{C}^o/\sum {SBCT}_{\mathrm{C}mi}{C}^o\right)·\left[1-{\left(1-{AC}_{\mathrm{C}i}/E_{oi}\right)}^2\right]\right\}·100, \end{gathered}$$

(19)

where ${HSC}_{ti}$—heat supply coefficient; ${MC}_{vi}$—moisture supply coefficient of agricultural lands; $E_{oi}$—annual evaporation rate; ${AC}_{\mathrm{C}i}$—annual precipitation, mm; $\sum {SBCT}_{\mathrm{C}mi}{C}^o$—minimum value of the sums of biologically active temperatures above 10 °C in agricultural landscapes; ${BCPL}_i$—bioclimatic potential of agricultural landscapes <40.0—very low (1 point); 40.0–60.0—low (2 points); 61.0–85.0—decreased (3 points); 86.0–120.0—average (4 points); 121.0–155.0—elevated (5 points); 156.0–190.0—high (6 points) and >190.0—very high (7 points);

–potential productivity of the biomass of the vegetation cover of agricultural landscapes (${PN}_i$), located in certain soil and climatic conditions, is assessed using Formula (20) [52]:

$${PN}_i=S_i·{CL}_i,$$

(20)

where $S_i$—soil index; ${CL}_i$—climate favorable index;

–ecological productivity of agricultural landscapes (${EPL}_i$) as an ecological function of the soil and economic function of vegetation, is determined as the product of averaged indexes of two subsystems—plant (${PP}_i$) and soil (${PS}_i$)—using Formula (21) [57,58]:

$${EPL}_i={PP}_i·{PS}_i,$$

(21)

where ${PP}_i$—parameter characterizing the productivity of vegetation cover: ${PP}_i={\mathrm{Y}}_i/{PPAL}_i$, here, ${PPAL}_i$—potential productivity of agricultural land in landscapes based on the inflow of photosynthetic active radiation (PAR), and determined using Formula (22) [59]:

$${PPAL}_i={RB}_i·\eta ·{10}^6/4.19·C_i·100,$$

(22)

where $C_i$—calorific value of a unit of the vegetation biomass, equal to 4100 kcal/kg; $4.19$—conversion of kcal/kg into kJ/kg; $100$—conversion of data into c/ha; $\eta$—maximum possible utilization index of the photosynthetic active radiation: $\eta =\frac{k_{\Phi \mathrm{A}\mathrm{P}}}{100};$ $k_{\Phi \mathrm{A}\mathrm{P}}$—utilization factor of the active photosynthetic radiation by agricultural plants, early 1.0%; $100$—to take into account the percentage of PAR absorption; ${\mathrm{R}PAI}_i$—real productivity of agricultural land in agricultural landscapes depends not only on the potential productivity of the vegetation biomass (${PPAL}_i$), which directly depends on the value of the photosynthetic active radiation (${RB}_i$) and free energy utilization factor ($\eta$), but also on the natural moisture supply coefficient (humidity) of the territory ($C_m$), which is the ratio of long-term annual average precipitation (${\mathrm{A}\mathrm{C}}_{\mathrm{c}i}$) and evaporation value ($E_o$), that is ${\mathrm{R}\mathrm{P}\mathrm{A}\mathrm{I}}_i={PPAL}_i·K_y$; $PS$—parameter characterizing the productivity of the soil cover: $PS=\frac{{ESF}_i}{{ESF}_{ni}}$; ${ESF}_i$—total energy of the soil formation in soil covers of agricultural land (kJ/cm²): ${{ESF}_i=RB}_i·exp\left(-\alpha ·{\overline{R}}_i\right);$ $\alpha$—index of complete utilization of the photosynthetic active radiation (PAR), a constant value for natural geographic zones and varies depending on the hydrothermal coefficient, that is, equal to 0.47: ${\overline{R}}_i=\frac{{RB}_i}{L}·{AC}_{ci};$ $L$—specific heat of evaporation assumed as the constant and equal to 2.5 kJ/cm²; ${ESF}_{ni}$—maximum possible use of the radiation balance of the solar energy for soil formation, where under natural conditions, a balance of heat and moisture is provided, that is ${\overline{R}}_i={RB}_i/L·O_{ci}$ equal to one [41]: ${{ESF}_{ni}=RB}_i·exp\left(-\alpha \right)$; ${EPL}_i$—ecological productivity of landscapes >1.00—very high (7 points); 0.86–1.00—high (6 points); 0.71–0.85—elevated (5 points); 0.56–0.70—above average (4 points); 0.41–0.55—average (3 points); 0.20–0.40—low (2 points) and <0.20—very low (1 point);

–agro-ecological productivity of agricultural landscapes (${AEPL}_i$), determined as the product of averaged indexes of two subsystems characterizing the biological function of the vegetation cover and the ecological function of the soil cover, and determined using Formula (23) [58]:

$${AEPL}_i={BPAL}_i·{PS}_i,$$

(23)

where ${BPAL}_i$—biological function of the vegetation cover expressed by the relative values of the bioclimatic potential of agricultural landscapes, that is, via the climate index of biological productivity [56]: ${BPAL}_i={CIBP}_i/{PCIBP}_i$, here ${CIBP}_i$—climatic index of biological productivity of the vegetation cover of agricultural land in landscapes (relative values or points), which is determined using Formula (24):

$$\begin{gathered} {CIBP}_i=\left\{{GF}_{\left(mci\right)}·\left[\sum {SBCT}_i>{10 }^{°}\mathrm{C}/\sum {SBCT}_{bi}>10^o{C}_o\right]\right\}; \\ {CIBP}_i=\left\{{GF}_{\left(mci\right)}·\left[\sum {SBCT}_i>{10 }^{°}\mathrm{C}/\sum {SBCT}_{bi}>10^o{C}_o\right]·100\right\}, \end{gathered}$$

(24)

where $CIBP$—climatic index of biological productivity of the vegetation cover of agricultural land in landscapes (relative values or points) <20.0—very low (1 point); 20.0–35.0—low (2 points); 36.0–50.0—decreased (3 points); 51.0–64.0—average (4 points); 65.0–79.0—elevated (5 points); 80.0–95.0—high (6 points); >95.0—very high (7 points), in %; ${GF}_{\left(mci\right)}—$growth coefficient in terms of the annual atmospheric moisture, characterized by a complex function (logarithmic main and parabolic auxiliary), representing the ratio of productivity under given conditions of moisture supply and the maximum productivity under subject to the optimal moisture supply, and determined using Formula (25):

$$\begin{gathered} {GF}_{\left(mci\right)}=1.51\cdot \mathit{lg}(20\cdot {MI}_{di}); \\ {GF}_{\left(mci\right)}=1.51\cdot \mathit{lg}(20\cdot {MI}_{di})-0.21+0.63\cdot {MI}_{di}-{MI}_{di}^2, \end{gathered}$$

(25)

where ${\mathrm{M}I}_{di}={AC}_i/\sum {AAHD}_i$—moisture index of the territory (>0.60—excessive moisture (7 points); 0.45–0.60—humid (6 points); 0.35–0.44—semi-humid (5 points); 0.20–0.34—arid (4 points); 0.10–0.19—very arid (3 points); 0.05–0.09—arid (2 points); <0.05—very arid (1 point)); $O_{ci}$—precipitation, mm; $\sum {AAHD}_i$—sum of the air humidity deficit during the biologically active period of the year, mb; $\sum {SBCT}_i>{10 }^{°}\mathrm{C}$—sum of average daily air temperatures above +10 °C, reflecting the supply of solar energy and heat supply in landscapes; ${\sum {SBCT}_{bi}>10^{o}C}_o$—basic sum of average daily air temperature values during the active vegetation period, that is, the value for which, a comparative assessment is made; ${PCIBP}_i$—potential climatic index of biological productivity of the vegetation cover of agricultural land, determined at ${GF}_{\left(mci\right)}=1.0$ using Formula (26):

$${PCIBP}_i=\left[100\cdot \left(\sum {SBCT}_i>{10 }^{°}\mathrm{C}/\sum {SBCT}_i>{10}^o{C}_o\right)\right],$$

(26)

where ${PCIBP}_i$—potential climatic index of the biological productivity of agricultural land vegetation cover <40.0—very low (1 point); 40.0–60.0—low (2 points); 61.0–85.0—decreased (3 points); 86.0–120.0—average (4 points); 121.0–155.0—elevated (5 points); 156.0–190.0—high (6 points); >190.0—very high (7 points), in %;

Gradation of the agro-ecological productivity of agricultural landscapes (${AEPL}_i$): >1.10—very high (7 points); 0.96–1.10—high (6 points); 0.81–0.95—elevated (5 points); 0.66–0.80—above average (4 points); 0.46–0.65—average (3 points); 0.25–0.45—low (2 points); and <0.25—very low (1 point).

Biological productivity of the climate (${BPC}_i$), with the base sum of average daily air temperature values during the active vegetation period equal to 3400 °C, for comparison with the productivity under optimal growth conditions, which is accepted as the standard (100%) and determined using Formula (27):

$${BPC}_i=0.612·{CIBP}_i$$

(27)

Grading scale for the biological productivity of the climate (${BPC}_i$) in points and percentage: <40—very low (1 point); 40–60—low (2 points); 61–85—decreased (3 points); 86–120—average (4 points); 121–155—elevated (5 points); 156–190—high (6 points) and >190—very high (7 points).

3.4. Methodology for the Assessment of the Agrobiological Resources of Agricultural Landscapes

The agrobiological resources (${ABR}_i$) providing the productivity of agricultural landscapes are assessed on the basis of complex integrated indexes reflecting various ratios of the potential productivity (${PP}_i$), climatic potential productivity (${CPP}_i$), maximum possible productivity (${MPP}_i$), real maximum possible productivity (${RMPP}_i$), real possible productivity (${RPP}_i$), possible productivity (${VP}_i$) and production–economic productivity (${PEP}_i$), and based on the concept of reference yields [27,28]:

–potential productivity (${PP}_i$), that is, the maximum yield in agricultural landscapes, which can theoretically be provided by the inflow of the photosynthetic active radiation (radiation balance) (${RB}_i$) with optimal agro-meteorological factors during the vegetation period (light, heat, water), in this case, Formula (22) should be used to determine it;

–climatic potential productivity of agricultural landscapes (${CPP}_i$) is the potential productivity that will be limited by the impact of one of the uncontrollable factors of the natural system, temperature regime of the soil and vegetation cover, determined using Formula (28) [60]:

$${CPP}_i={PP}_i\cdot {FT}_i,$$

(28)

where ${CPP}_i$—climatic potential yield; $FT$—function of temperature impact;

–maximum possible productivity of agricultural landscapes (${MPP}_i$) is calculated on the basis of the use of the photosynthetic active radiation (PAR) by soil and vegetation cover, subject to limiting energy consumption for the soil-formation process, and determined using Formula (29):

$${MPP}_i={CPP}_i\cdot F{W}_{Qi},$$

(29)

where $F{W}_{Qi}$—function of impact of energy consumption for the soil-formation process and productivity of agricultural land, which is determined using Formula (30):

$$F{W}_{Qi}=\left\{\mathit{exp}\left[-\left(1-F_{Qi}\right)\right]\right\},$$

(30)

$F_{Qi}$—function of potential use of the radiation balance (31):

$$\begin{gathered} F_{Qi}={ESF}_i/{ESF}_{ni}=\left[R_i·exp\left(-\alpha ·{\overline{R}}_i\right)\right]/\left[R_i·exp\left(-\alpha \right)\right] \\ =\left[exp\left(-\alpha ·{\overline{R}}_i\right)\right]/\left[exp\left(-\alpha \right)\right]; \end{gathered}$$

(31)

–real maximum potential productivity (${RMPP}_i$) in agricultural landscapes is based on the consumption of the photosynthetic active radiation (PAR) energy by the soil and vegetation cover, subject to limitation by agrometeorological conditions (32):

$${RMPP}_i={MPP}_i·{FW}_i={CPP}_i\cdot F{W}_{Qi}·{FW}_i,$$

(32)

where ${FW}_i$ is the function of the impact of moisture conditions on the crop productivity (moisture index), dimensionless: $W_i=1-(1-F_{Ei}{)}^2=1-{\left[1-\left(E_i/E_{opt}\right)\right]}^2$; $E_i$—total water consumption by agricultural land; $E_{opt}$—optimal total water consumption by agricultural land;

–real potential productivity (${RPP}_i$) in agricultural landscapes is limited by the degree of soil salinity (33):

$$\begin{gathered} {RPP}_i={RMPP}_i\cdot {FS}_i={RMPP}_i·\mathit{exp}\left[-k\cdot {\left(F_{si}-1\right)}^b\right] \\ ={RMPP}_i·\mathit{exp}\left[-k\cdot {\left(S_{\mathrm{H}u}/S_{дoпi}-1\right)}^b\right], \end{gathered}$$

(33)

where $F_{Si}$—value of the function of optimal salt content in the soil of agricultural landscapes; $S_{\mathrm{H}i}$—salt content in the soil; $S_{дoпi}$—maximum permissible level of the soil salinity, providing the maximum possible productivity of landscapes used for agricultural land; where $k$—parameter characterizing the response of plants to toxic salts; $b$—parameter characterizing the type of soil salinization;

–potential productivity (${VP}_i$) in agricultural landscapes is limited by the level of natural soil fertility, organic and mineral fertilizers (34):

$${VP}_i={RPP}_i\cdot F{W}_{Gum}\cdot F{W}_{\epsilon f}$$

(34)

where ${VP}_i$—potential productivity in agricultural landscapes; $F{W}_{Gumi}$—function of impact of the humus content in the soil on the landscape productivity: $F{W}_{Gumu}=\left\{\mathit{exp}\left[-\left(1-F_{Gumi}\right)\right]\right\}$; $F_{Gumi}$—ratio of the humus content in the soil and optimal value for cultivation of agricultural crops, expressed in relative units: $F_{Gum}=G_m/G_{opt}$, where $G_m$—humus content in the soil, %; $G_{opt}$—humus content in the soil, providing a high level of crop yield depending on the type of soil, %; $F{W}_{\epsilon f}$—generalized function of the efficiency of applying organic and mineral fertilizers on agricultural land, calculated according to Böhme’s principle [61], that is, according to the law of minimum in the form of Equation (35):

$$F{W}_{\epsilon f}=\mathit{min}\left[F{W}_{Org},F{W}_N,F{W}_P,F{W}_K\right],$$

(35)

where ($F{W}_N$), ($F{W}_P$), ($F{W}_K$)—the function characterizing the ratio of the content of nitrogen, phosphorus and potassium in the soil and the optimal value for agricultural land in various agricultural landscapes, expressed in relative units, which can be determined using Formulas (36)–(39) [27]:

$$F{W}_{Ni}=\left\{\mathit{exp}\left[-\left(1-F_{Ni}\right)\right]\right\},$$

(36)

$$F{W}_{Pi}=\left\{\mathit{exp}\left[-\left(1-F_{Pi}\right)\right]\right\},$$

(37)

$$F{W}_{Ki}=\left\{\mathit{exp}\left[-\left(1-F_{Ki}\right)\right]\right\},$$

(38)

$$F_{Ni}=N_{mi}/N_{opt};F_{Pi}=P_{mi}/P_{opt};F_{Ki}=K_{mi}/K_{opt}$$

(39)

where $F_{Ni}$, $F_{Pi}$, $F_{Ki}$—function of the supply of nitrogen, phosphorus and potassium for agricultural land;

–production–economic productivity of agricultural land (${PEP}_i$) in various agricultural landscapes is limited by the real level of technological risk inherent in organizational and economic activity (40):

$${PEP}_i={VP}_i·{CTR}_i,$$

(40)

where ${CTR}_i$—coefficient characterizing the level of technological risk inherent in organizational and economic activity; ${PEP}_i$—production–economic productivity of agricultural land, c/ha (>50.0—very high (7 points); 41.0–50.0—high (6 points); 32.0–40.0—elevated (5 points); 24.0–31.0—above average (4 points); 15.0–23.0—average (3 points); 6.0–14.0—low (2 points) and <6.0—very low (1 point)).

3.5. Methodology for the Assessment of the Water Resources of Agricultural Landscapes

The water resources providing environment-forming and ecological functions are the spatial basis of the nature management, including in agricultural landscapes, and necessitate the objective assessment of water supply in terms of the amount of water resources per square kilometer of territory, per capita and the economy of the region [58].

In the future, the assessment of the water supply of a territory, population and economy of river basins, which fulfills important environment-forming and ecological functions, should not be some isolated form of human perception of reality, but a system of views on the outside world, where, along with philosophical, scientific, political, moral, aesthetic and other values, there are also environmental values, which stipulate the urgent need to take care of nature in the interests of not only existing, but also future generations, for whom nature will remain the same source of material resources.

In this regard, there was a need to revise the existing structure of water supply indexes: M. Falkenmark’s criteria [62], the sustainability index [63], water resource utilization factor [64], water stress [65], specific water supply of the population [66], specific water supply of the territory [67] and complex index of specific water supply of the territory and population [68] with the inclusion of ecological flow of river basins (${EFRB}_i$) using point scales:

–M. Falkenmark’s criteria are determined by the ratio of water resources ${(RWR}_i-{EFRB}_i)$ and the quantity of population (${QP}_i$): ${CMF}_i={(RWR}_i-{EFRB}_i)/{QP}_i,$ where >1700 m³/person per year—no stress; 1501–1700 m³/person per year—low stress; 1301–1500 m³/person per year—average stress; 1101–1300 m³/person per year—high stress; 801–1100 m³/person per year—water deficit; 501–800 m³/person per year—chronic deficit; <500 m³/person per year—absolute water deficit;

–the sustainability index (${SI}_i$) is determined by the ratio of water intake from water resources (${WIV}_i$) and available water resources ${(RWR}_i-{EFRB}_i)$: ${SI}_i={WIV}_i/{(RWR}_i-{EFRB}_i)·100$, where >10%—very low level of water deficit; 11–20%—low level of water deficit; 21–30%—low water deficit; 31–40%—moderate water deficit; 41–50%—high level of water deficit (water stress); 51–60%—very high level of water deficit (severe water stress); and >60%—chronic water deficit (very high water stress);

–water resource utilization factor (${WRUF}_i$) equal to the percentage ratio of full water consumption (${FWC}_i$) and available water resources ${(RWR}_i-{EFRB}_i)$: ${WRUF}_i={FWC}_i/{(RWR}_i-{EFRB}_i)·100$, where <10%—minimum risk; 10–20%—moderate risk; 21–30%—average risk; 31–40%—elevated risk; 41–50%—high risk; 51–60% very high risk; >60%—catastrophic risk;

–water stress (${WS}_i$) characterizes the ratio of water intake from water sources (${WIV}_i$) and available water resources ${(RWR}_i-{EFRB}_i)$: ${WS}_i={WIV}_i/{(RWR}_i-{EFRB}_i)$, where <0.10—very low; 0.10–0.20—low; 0.21–0.30—moderate; 0.31–0.40—average; 0.41–0.50—high; 0.51–0.60—very high; and >0.60—catastrophic;

–specific water supply of the population (${SWSP}_i$) is determined by the ratio of available water resources ${(RWR}_i-{EFRB}_i)$ and population in terms of catchment area of river basins (${PS}_i$): ${SWSP}_i=\left({RWR}_i-{EFRB}_i\right)/{PS}_i$, where <1.00 thousand m³/person per year—catastrophically low; 1.01–2.00 thousand m³/person per year—very low; 2.01–5.00 thousand m³/person per year—low; 5.01–10.00 thousand m³/person per year—moderate; 10.01–15.00 thousand m³/person per year—average; 15.01–20.00 thousand m³/person per year—high; >20.00 thousand m³/person per year—very high;

–specific water supply of a territory (${SWST}_i$) is determined by the ratio of water resources ${(RWR}_i-{EFRB}_i)$and catchment area of river basins (${CARB}_i$): ${SWST}_i=({RWR}_i-{EFRB}_i)/{CARB}_i$, where <5.00 thousand m³/km²—catastrophically low; 5.01–10.00 thousand m³/km²—very low; 10.01–20.00 thousand m³/km²—low; 20.01–30.00 thousand m³/km²—moderate; 30.01–40.00 thousand m³/km²—average; 40.01–80.00 thousand m³/km²—high; >80.00 thousand m³/km²—very high;

–complex index of specific water supply of a territory and population ($CISWS$) is determined as the product of the ratio of water resources ${(RWR}_i-{EFRB}_i)$ and catchment area of river basins (${CARB}_i$) and population (${PS}_i$) derived under the square of roots, that is, specific water supply based on the territories and population: $CISWS=\sqrt{{SWP}_i·{SWST}_i}$, where ${SWP}_i=\sqrt{{\left[\right(RWR}_i-{EFRB}_i)/{PS}_i]}$ and ${SWST}_i=\sqrt{{\left[\right(RWR}_i-{EFRB}_i)/{CARB}_i]}$, <1.00 thousand m³/km²—catastrophically low; 1.00–2.25—very low; 2.26–3.35—low; 3.36–7.25—moderate; 7.26–13.70 catastrophically low—average; 13.71–32.40—high; and >32.40—very high.

The developed methodological approach to the integral assessment of the agro-resource potential of agricultural landscapes ($\mathrm{A}RPL$), based on information resources (using mathematical and statistical tools for processing massive quantities of data in Microsoft Excel 2016), and a system of indexes (resource indexes) formed on the basis of the modern system of agricultural nature management allows: firstly, to determine qualitative and quantitative changes in the agro-resource potential of agricultural landscapes in the natural agricultural systems; secondly, to model the transformation of agricultural landscapes under climate changes; thirdly, landscape-ecological zoning of the natural and agricultural systems.

4. Conclusions

Development of the methodology for the assessment of the agro-resource potential of agricultural landscapes is based on ideas and methods resulting from landscape and systems approaches that make it possible to describe, systematize and understand the set of natural processes in the context of global climate changes, where a multi-purpose study of the natural system fully meets the environmental requirements of transformative human activity, when the natural scientific worldview allows a wide range of ideas to be obtained on the targets of research.

The proposed algorithm for predicting the natural state of agricultural landscapes based on agro-climatic, agrochemical and agro-biological integrated indexes allows us to develop a unified integrated approach for the assessment of the agro-resource potential of landscapes, determine the logical sequence of the tendency of changes in the natural process, which fully characterizes their state in the space–time scale, and can be used to prepare a strategic action plan for agricultural zoning and recommendations in order to minimize the impact of unfavorable natural factors on the territorial organization of the agricultural nature management.

Improvement of the methodology for the assessment of the agro-resource potential of agricultural landscapes is based on the selection and development of new integrated assessment indexes characterizing agro-climatic, soil-land, agro-biological and water resources, which fully characterizes its state in the space–time scale. The methodology was tested in the land administration of the Turkestan region of the Republic of Kazakhstan during natural and agricultural zoning of the territory.