Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes

1 Introduction

Soil is the main part of the landscape biosphere in general, and the primary means of production in agriculture is an object of anthropogenic impact characterizing the value system for human life. In this regard, the need to study soil resources when assessing the agro-resource potential of agricultural landscapes under the conditions of “rough” anthropogenic activity is driven by the development of degradation processes in the soil, as a result of which there is a sharp reduction in the quantitative and qualitative indicators of the soil – the content of humus, basic organic, and mineral nutrients.

Assessment of the soil resources of agricultural landscapes with determining the trend of changes in their agrochemical indexes allows for a comprehensive accounting of their agro-resource potential during agricultural nature management. The developed methodological approaches for the assessment of soil resources of agricultural landscapes allow us to identify the most resistant types of landscapes to agricultural nature management in various natural zones and to evaluate their soil resources from the perspective of ecological-landscape approach that prevents the development of negative natural and anthropogenic processes.

Soils are a nonrenewable source and comprise a vital component of the world’s stock of natural capital with a prolonged forming process [1]. Soil security and soil sustainability are the basis for obtaining many of the UN 2030 Sustainable Development Goals and are needed to support the coming agricultural intensification in this century [2]. The characterization of soil landscapes is becoming increasingly important for making decisions regarding site-specific agriculture systems and soil management [3]. Therefore, soil property assessment, modeling, and mapping at various spatial and temporal scales are required for the study of diverse environments [4]. The assessment of the soil resource of any region has two parts, namely, an inventory of the kinds of soil and their distribution, and knowledge of the way each kind can be used and its performance under a range of circumstances [5]. Thus, assessment of soil quality has been recommended as a valuable tool for evaluating the sustainability of soil and land management practices [6].

Assessment of land for agricultural nature management in most countries of the world is based on the food and agriculture organization (FAO) methodology [7,8]. In some countries (countries of Eastern Europe and former republics of the Union of Soviet Socialist Republics), soil assessment methods developed by Dokuchaev Institute of Soil Science are applied [9,10,11,12,13].

In world practice, the following methods are most often used for the qualitative assessment of soil fertility of agricultural landscapes:

– Method of maximum limitation, based on the laws of maximum, minimum, and optimum (restricting or limiting) [14], where the selection of land properties is based on three main blocks: climatic, relief and soil, its essence (in relation to their assessment) is that the integrated assessment of land is determined by the factor which value deviates most from the optimum zone, since it limits (restricts) environment-forming activity, and it is called the limiting factor, i.e., the growth of the soil index directly depends on the increase of the factor that is located at a minimum and is determined using the following formula [15,16]:

where SI is the soil index, PF is the proportionality factor for the limiting factor, and LF is the limiting factor.

– Method of algebraic combinations of partial assessments of individual properties, where the integral suitability of lands is calculated from private ratings of the suitability of individual land properties (qualities), according to integrated assessment, the average score of soil properties (formula (2)) [17], or calculation of the geometric mean value from private assessment ratings, formula (3) [18]:

where Q is the score of productivity of soil–agroecological conditions for cultivating a specific agricultural crop, V is the coefficient of physical properties of a specific soil in relation to parameters of chernozem soil, T is the sum of temperature above 10^oC, W is the moisture index (adjusted for moisture supply of agricultural crops), and 1 / K is the continentality index (adjusted for crop facial differences) in the inverse ratio of assessment score.

– method of complicated multiplicative indices, where, as an example, we can mention the so-called Storie index (formula (4)) [19] or the soil productivity index, developed in California USA in 1930 (formula (5)) [20]:

where A i is the soil depth and texture, B i is the soil permeability, C i are the chemical properties of soil, D i is the drainage, surface flow, and E i is the climate).

where L i is the soil depth rating, T i is the mechanical composition, N i is the base saturation, S i is the degree of salinity, O i is the humus, A i is the cation-exchange capacity and nature of clay minerals, M i is the parent rock, D i is the training level, and H i is the humidity proposed by FAO experts.

These also include one of the most well-known indexes abroad, Storie index rating, which is calculated (in %) as the product of the corresponding coefficients representing different factors of soil formation, and determined using the following formula [21]:

where A i is the soil type, B i is the particle size composition, C i is the terrain slope, and D i are the other factors.

– Method for calculating additive assessment indexes, where the integrated assessment of land is composed of partial assessments and is expressed as shares of their significance or as their sum, i.e., in the LESA land assessment system [22]. As integrated assessment, the so-called point system is used, in which the existing limitations of individual land properties are summed up and expressed as a percentage of all available limitations (e.g., 40% of the limitation exists due to physical properties of soil, 30% due to chemical properties, and 30% due to relief), and in a slightly modified form, such approaches were applied in land assessment [23,24], where the sum of deviations from the optimal was used as a decisive rule for obtaining integrated assessment of land from partial ones, i.e., value X = ∑ ( 100 − R i ) , where R i is the score for the suitability of a specific property.

– Method for calculating the soil–ecological index ( SEI ) developed by V.V. Dokuchaev Soil Institute [10], expressed in points, where the calculation and discussion of results are based on three components ( SEI ): climatic (7), agro-chemical (8), and soil (9), and their generalized form is represented formula (10):

where d is the soil bulk density (average for meter layer), g·cm⁻³; 2 is the maximum possible soil bulk density at their maximum compaction, g·cm⁻³; V is the coefficient taking into account the soil volume in a meter layer of different mechanical composition; D c are additionally considered soil properties; A is the final agro-chemical index; ∑ t > 10 o is the sum of average daily temperature exceeding 10°C; KY is the moisture index ( P is the adjustment for this index); KK is the continentality index; A is the final agro-chemical index; 12.5 is the number to bring a certain set of environmental conditions to 100 units of the SEI .

– Method for calculating the soil-agro-chemical index ( SACI ) [25], being one of the modifications of the multiplicative soil index (11):

where SACI ( S ) is the score for a specific crop; 12.0 is the constant multiplier; ( 2 − d ) is the total indicator calculated as the difference between the maximum possible soil compaction and the average density of a given soil in a meter layer; M is the soil particle-size composition; D is the additionally considered soil properties affecting plant ontogenesis; ∑ t > 10 ° C is the average annual sum of air temperature above 10°C; t n is the adjustment for the sum of air temperature above 10°C depending on the steepness, slope exposure, and terrain latitude; HI is the moisture index (adjusted for moisture supply of agricultural crops); P is the adjustment for moisture index; K n is the adjustment for KY value in terms of exposure and slope steepness; KK is the continentality index; KK = 90 is adjusted for crop facial differences.

The analysis of the use of methodological approaches to assessing soil resources of agricultural landscapes showed that many researchers in this assessment use the “soil index,” which includes soil, agrochemical, and climatic indexes. This approach allows us to calculate comparable soil index scores within soil varieties, using the aforementioned formulas.

Fischer et al. [26] believe, over the past 20 years, the term agro-ecological zones methodology, or AEZ, has become widely used. The AEZ methodology uses a land resources inventory to assess, for specified management conditions and levels of inputs, all feasible agricultural land-use options and to quantify anticipated production of cropping activities relevant in the specific agro-ecological context.

Karmanov et al. [27] believe that the successful use of adaptive landscape farming systems depends on the knowledge of natural conditions in specific territories, including the qualitative soil characteristics, integral quantitative expression of which is the assessment of the level of their agroecological potential based on the SEI and its modification – soil–agroclimatic index.

Depletion of soil quality is an important process in land degradation and a major constraint to improve plant growth and food security in developing countries [28]. Abdu et al. [29] conducted a study to assess the soil quality of arable lands and applied the methods used to estimate soil quality, including principal component analysis (PCA), a normalized PCA, and common soil parameters (soil texture, pH, OC, N, P, and K). The results were expressed in terms of soil quality index. In addition, the authors used a soil fertility/nutrient/index (NI) approach.

Assessment of positive or negative changes in soil fertility as a result of climate change and anthropogenic activity is necessary to identify the deficiency (negative balance) of humus reserves, reserves of mineral elements, and hydrolytic acidity in order to manage soil fertility, requires the use of a unified integrated approach to the methodology for assessing and forecasting agro-resource potential of agricultural landscapes.

The purpose of this study is to assess the soil fertility of agricultural landscapes in Turkestan region of the Republic of Kazakhstan under conditions of climate change and significant anthropogenic impact based on the integrated “soil index,” which includes the following indicators – humus reserve, reserves of mineral elements, and hydrolytic acidity.

2 Materials and methods

The agrochemical research base for assessing soil resources of agricultural landscapes was created on the basis of many years of information-analytical materials from monitoring agricultural lands in Turkestan region and laboratory soil studies by the Department of Land Register and Engineering Survey of the region, as well as the Institute of Geography and Water Security of the Republic of Kazakhstan, covering 1996–2019 years.

Figure 1 shows the study area, which reflects the degree of agricultural development of landscapes. The arid foothill landscapes of Turkestan region of the Republic of Kazakhstan are the most developed for agriculture.

Figure 1

Map of agricultural development of landscapes in Turkestan region.

In the course of assessing soil resources of agricultural landscapes in natural areas of Turkestan region, “integrated soil fertility index” was determined, including humus reserves, reserves of mineral elements, and hydrolytic acidity, which was determined using the following formula [30]:

where k i is the weighting coefficient of vegetation productivity characterizing the impact of humus reserve, reserves of mineral elements, and hydrolytic acidity of soil fertility; S g is the indicator of humus impact on the generalized soil index; S NPK is the impact of mineral elements (nitrogen, phosphorus, and potassium) on the generalized soil index; S Hg is the impact of hydrolytic acidity on the generalized soil index; f g is the function of humus indicators ( G ), which vary within 0 ≤ f g ≤ 600.0 ; f NPK is the function of mineral nutrition ( N , P , K ), which vary within 0 ≤ f NPK ≤ 1.0 ; f pH is the function of mineral nutrition ( H g ), which vary within 0 ≤ f Hg ≤ 1.0 .

The foregoing structural analysis of the “soil index,” consisting of three components, was calculated using the following formulas (13)–(15):

where 600.0 is the maximum possible humus content in reference (chernozem) soil, t/ha.

Based on a comparative assessment of soil indexes under the initial conditions ( G г н + 0.20 ∙ C ф к ) = 0 ; N = 0 ; P = 0 ; K = 0; δ = 0 ; H г = 0 and limited conditions ( G г н + 0.20 ∙ C ф к ) = 600 ; N = 1 ; P = 1 ; K = 1; δ = 1 ; H г = 1 [30], numerical values of the weighting coefficient with maximum productivity of the vegetation cover: humus ( k g ) – 6.4; mineral elements ( k NPK ) – 8.5; hydrolytic acidity ( k Hg ) – 5.1.

Figure 2 presents a flowchart of the process of assessing soil resources in agricultural landscapes

Figure 2

Flowchart of the soil resource assessment process for agricultural landscapes.

With maximum humus reserves ( G г н + 0.20 ∙ C ф к = 600 t / ha ) and in the absence of deficiency of basic mineral elements ( N = 1 , P = 1 , K = 1) and with hydrolytic acidity ( H г ) equal to 1, maximum value of the “soil index” will be: S g = 6.4 units; S NPK = 8.5 units; S Hg = 5.1 units, and generalized soil index S = 20.0 units, i.e., numerical values of the “soil index” characterizing soil fertility in natural conditions vary from 0 to 20 units (<1.0 – very low (1 point); 1.01–2.00 – 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); >15.000 – very high (7 points).

3 Results and discussion

3.1 Assessment of soil resources of agricultural landscapes

Based on monitoring observations (1996–2019) of soil fertility of agricultural landscapes (arable and pasture agricultural land) of Turkestan region, their soil resources have been assessed using the following agro-chemical indexes: G г к is the content of humate humus, t/ha or g/cm³; G ф к is the content of fulvate humus, t/ha or g/cm³; N , P 2 O 5 , K 2 O is the content of nitrogen, phosphorus, and potassium in the soil of agricultural land, kg/ha; N ont , P 2 O 5 ont , and K 2 O ont are the optimal contents of nitrogen, phosphorus, and potassium in the soil of agricultural land, kg/ha; N = N i / N ont , P 2 O 5 = P 2 O 5 i / P 2 O 5 ont , K 2 O = K 2 O i / K 2 O ont is the relative content of phosphorus and potassium in the soil of agricultural land; H г is the hydrolytic acidity, mEq/100 g of soil.

Assessment of soil resources of agricultural landscapes in Turkestan region has a general nature (algorithm) in fact for all landscape formations; however, specific quantitative indicators for each type of landscape will differ, depending on the type of soil and soil formation process. In this regard, we have presented a fragment of algorithm for assessing soil resources for steppe mountain (mid-mountain) zone with a tectonic–denudation type of landscape on gray-brown, slightly washed-out, heavy loamy soil, administratively covering the territory of Tyulkubas administrative district of Turkestan region (Table 1).

Table 1

Algorithm for assessing soil resources in agricultural landscapes of Turkestan region based on the agrochemical soil index (1996–2019)

Index	Years
	1996	2008	2019
Steppe mountain (mid-mountain) zone with a tectonic-denudation type of landscape on gray-brown, slightly washed-out, heavy loamy soils, Tyulkubas administrative district
Content of humate humus ( G г к ), t/ha	48.96	38.84	40.25
Content of fulvate humus ( G ф к ), t/ha	30.04	23.83	24.70
Content of nitrogen ( N i ), kg/ha	2970.0	2530.0	2816.0
Content of phosphorus ( P 2 O 5 i ), kg/ha	26.62	15.62	35.64
Content of potassium ( K 2 O i ), kg/ha	352.0	528.0	1752.96
Soil hydrolytic acidity ( H г )	6.95	7.55	7.01
Impact of humus on the soil index ( S gi )	0.384	0.305	0.316
Impact of NPK on the soil index ( S NPK i )	0.677	0.615	1.253
Impact of H г on the soil index ( S H г i )	1.152	0.992	1.135
Soil index ( S i ), units	2.214	1.912	2.704

The dynamics of changes in the “soil index” according to agrochemical indicators for all landscape formations and covering 14 administrative districts of Turkestan region (according to Figure 1) is presented in the form of diagrams in Microsoft Excel and shown in Figures 3–6.

Figure 3

Changes in the “soil index” in terms of humus content in agricultural landscapes in the context of administrative districts of Turkestan region, indicating the number (type) of the landscape for 1996–2019 (ordinate – “soil index”; abscissa – years).

Figure 4

Changes in the “soil index” in terms of the content of mineral elements (NPK) in agricultural landscapes in the context of administrative districts of Turkestan region, indicating the number (type) of landscapes for 1996–2019 (ordinate – “soil index”; abscissa – years).

Figure 5

Changes in the “soil index” in terms of hydrolytic acidity in agricultural landscapes of Turkestan region in the context of administrative districts, indicating the number (type) of landscapes for 1996–2019 (ordinate – “soil index”; abscissa – years).

Figure 6

Changes in generalized “soil index” in agricultural landscapes of Turkestan region in the context of administrative districts, indicating the number (type) of landscapes for 1996–2019 (ordinate – “soil index”; abscissa – years).

The humus content in the soil of agricultural landscapes was taken according to actual data, ratio of humic acids to fulvic acids according to D.S. Orlov (dark chestnut soil – 1.65; chestnut soil – 1.63, light chestnut soil – 1.25, brown arid-steppe soil – 0.6, gray soil – 0.53; meadow-gray soil – 0.60) [31]; the content of fertilizer elements in the soil was calculated according to Pegov and Khomyakov [30], where the maximum content of nitrogen, phosphorus, and potassium in the soil is 22227.0, 558.0, and 4432.0 kg/ha, respectively.

Assessment of the natural state of soil fertility in the agricultural landscapes of Turkestan region in the context of administrative districts was determined by the key agrochemical indicators (humus reserves, mineral elements, and hydrolytic acidity) for 1996–2019, given the regional nature of the study, the “soil index” scale was adapted to the soil and climatic conditions of Turkestan region (Table 1 and Figure 1), which allowed to establish that:

1. in the steppe mountain–mid-mountain zone in tectonic-denudation landscapes (on gray-brown slightly washed-out heavy loamy soil) of Tyulkubas district, generalized “soil index” increases from 2.214 to 2.704;

2. in semi-desert mountain–foothill zone in formation landscapes (on gray-brown slightly washed-out heavy loamy soil) of Tolebi district, generalized “soil index” decreases from 2.530 to 2.180;

3. in semi-arid mountain–foothill zone in alluvial-proluvial landscapes (on gray-earth ordinary slightly washed-out soil) of Sairam district, generalized “soil index” increases from 2.700 to 4.060;

4. in semi-arid mountain–foothill zone in hummocky (on gray-earth ordinary heavy loamy soil) of Kazygurt district, generalized “soil index” increases from 2.930 to 3.140;

5. in semi-arid mountain–foothill zone in alluvial-proluvial landscapes (on gray-brown light loamy soil) of Sozak district, generalized “soil index” increases from 1.953 to 2.663;

6. in arid mountain–foothill and plain (low-land and high-land) zones, covering the river valley of Syrdarya River, generalized “soil index” on the meadow-gray heavy loamy soil of Otyrar district decreases from 2.640 to 2.480, on the ancient alluvial meadow saline loamy soil of Shardara district, there is also a decrease from 2.860 to 2.350; on the meadow-gray soil of Maktaaral district, it increases from 3.480 to 3.490; on the meadow-gray soil of Zhetysai district, it increases from 2.810 to 2.960; on ordinary slightly washed-out heavy loamy gray soil of Keless district, it increases from 2.230 to 2.400 and is generally characterized by a very low level of soil fertility. The generalized “soil index” for the period under consideration on gray-earth ordinary loamy soil located in Baidibek district increases from 2.860 to 3.330; on gray-earth ordinary loamy soil of Ordabasinsk district, decreases from 2.380 to 1.810; on gray-brown light loamy soil of Sozak district, decreases from 2.660 to 1.950 and, in general, is characterized by a very low level of soil fertility; and on gray-earth ordinary, slightly washed-out soil of Sairam district, their quantitative values increase from 2.700 to 4.060.

7. in valley plain landscapes on meadow-gray and gray-earth light loamy soil (located in the territory of Arys, Sauran, and Sozak district), generalized “soil index” for the period under consideration increases from 2.700 to 4.060.

Based on the research, a map “Soil fertility index in agricultural landscapes of Turkestan region” was created on a scale of 1:1,000,000. When mapping the soil fertility index in agricultural landscapes of the region, a point scale was developed based on the data obtained, following which four gradations of soil fertility index were allocated: low is less than 2.00 (2 points); decreased – 2.00–3.00; average – 3.01–4.00 (4 points); elevated – more than 4.00 (Figure 7).

Figure 7

Map of soil fertility index in agricultural landscapes of Turkestan region.

Analysis of the natural state of agricultural land according to the integrated soil index in Turkestan region for 1996–2019 showed that it ranges from 1.086 to 4.800 units; thus, in comparison with the maximum values of this index in the soil (20.000 units), there is a pronounced humus deficiency, indicating very low soil fertility.

Increase of soil fertility in agricultural landscapes of Turkestan region can be achieved through a set of agrochemical measures that increase soil fertility, and the key measures are as follows:

1. increase in the humus content in the soil ( S g ), which can be achieved through additional intake of soil biomass, i.e., by-products;

2. increase in the value of mineral elements ( S NPK ), which can be achieved through increasing the humus content and inclusion of organic and mineral fertilizers into the soil;

3. increase of S Hg value through chemical reclamation to obtain a neutral soil reaction.

3.2 Assessment of solar energy consumption for soil formation in agricultural landscapes

In general, soil fertility in agricultural landscapes of Turkestan region should be increased consistently using adaptive landscape farming system based on the regulation and management of the soil formation process, which quantitatively reflects the radiation balance of the earth surface, heat, and moisture supply of agricultural land.

Radiation balance of the earth surface ( R i , kJ/cm²) is an objective and physically defined value that allows us to more strictly judge the conditions of soil formation in agricultural landscapes, which is determined by solar energy consumption for soil formation process.

Solar energy consumption for the soil formation process ( ESF п i ) in agricultural landscapes directly depends on the material–energy flow entering the soil surface, i.e., it directly depends on the value of the radiation balance of the active surface layer of air and soil ( R i , kJ/cm²) and radiation dryness index ( R ¯ i = R i / L ⋅ O c , where L is the latent heat of vaporization, numerically equal to 2.5 kJ/cm²; O c is the annual precipitation, mm), representing the heat and moisture supply of agricultural landscapes, which are determined according to dependence 16 [32]:

(16) ESF п i = R i · exp ( − α · R ¯ i ) ,

where α is the index of complete utilization of radiation energy in soil formation processes, numerically equal to 0.47; R ¯ i is the “radiation dryness index” or complex hydrothermal coefficient; ESF п i is the solar energy consumption for the soil formation process, kJ/cm² (< 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,” i.e., the amount of radiation balance of the active surface layer of air and soil not used in the soil formation process during the biologically active period of the year, due to the natural humidity of agricultural landscapes is determined by the dependence 10 [33]:

(17) ∆ ESF ni u = ESF ni n − ESF ni u = R i · exp ( − α · R ̅ oi ) − R i · exp ( − α · R ̅ i ) = R i · exp { − [ α · ( 1 − R ¯ i ) ] } ,

where ESF ni n is the potential solar energy consumption for the soil formation process in agricultural landscapes, corresponding to optimal value of “radiation dryness index” or complex hydrothermal coefficient ( R ̅ oi ), equal to 1,0; R ̅ oi is the optimal value of “radiation dryness index” or complex hydrothermal coefficient; R ̅ i is the “radiation dryness index” or complex hydrothermal coefficient under the existing climate conditions.

To identify the statistical significance of changes in solar energy consumption for the soil formation process in agricultural landscapes of Turkestan region for 1941–2020, comparative analysis was performed with the base periods (1941–1960) with interval of 20 years and for the past 20 years (2001–2020) based on the date of 18 weather stations located in various natural zones of Turkestan region (Table 2 and Figure 8).

Table 2

Fragment of the calculation of solar energy consumption for the soil formation process in agricultural landscapes of Turkestan region in the context of weather stations based on moisture supply in the natural environment

Observation period	Weather stations
	Suyldak	T. Ryskulov	Shymkent		Shardara	Sholak-kurgan	Shayan
Radiation balance for biologically active period of year ( R i ), kJ/cm 2
Average 1941–1960	161.0	190.2	194.3		201.7	183.4	198.3
Average 2001–2020	131.5	193.8	203.4		213.8	195.2	208.0
Mean difference	−29,5	3.6	9.1		12.1	11.8	9.7
Annual precipitation ( O c i ), mm
Average 1941–1960	602.0	855.0	640.0		450.0	180.0	349.0
Average 2001–2020	601.0	786.0	615.0		450.0	203.0	362.0
Mean difference	−0.1	−69.0	−25.0		0.0	23.0	13.0
M.I. Budyko Radiation dryness index ( R ¯ i )
Average 1941–1960	1.07	0.89	1.21		1.79	2.27	4.08
Average 2001–2020	0.88	0.99	1.32		1.90	2.30	3.85
Mean difference	−0.19	0.1	0.11		0.11	0.03	−0.23
Potential solar energy consumption for the soil formation ( ESF ni n ), kJ/cm 2
Average 1941–1960	100.6	118.9	121.4		126.1	114.6	123.9
Average 2001–2020	82.2	121.1		127.1	133.6	122.0	130.0
Mean difference	18.4	−2.2		−5.7	−7.5	−7.4	−6.1
Solar energy consumption for the soil formation ( ESF п i ),
Average 1941–1960	97.4	125.2		110.0	86.9	62.9	54.2
Average 2001–2020	87.0	121.7		109.4	87.4	66.2	51.4
Mean difference	10.4	3.5		0.6	0.5	3.3	−2.8
Excess solar energy for the soil formation ( ∆ ESF ni u ), kJ/cm 2
Average 1941–1960	3.3	−6.3		11.4	39.2	51.7	69.7
Average 2001–2020	−4.8	−0.6		17.8	43.2	55.8	78.6
Mean difference	8.1	−5.7		−6.4	4.0	4.1	8.9

Figure 8

Solar energy consumption for the soil formation process in agricultural landscapes of Turkestan region in the context of weather stations located in various natural zones (1 – potential energy consumption for soil-formation for 1941–1960; 2 – potential energy consumption for soil formation for 2001–2020; 3 – natural energy consumption for soil formation for 1941–1960; 4 – natural energy consumption for soil formation for 1941–1960 (ordinate – energy consumption for soil formation, kJ/cm²; abscissa – weather station).

It should be noted that natural consumption of solar energy for the soil formation ( ESF п i ) and “excess” (unused) solar energy for the soil formation ( ∆ ESF ni u = PSF п i − ESF п i , where PSF п i is the potential solar energy consumption for the soil-formation) under the same conditions of the radiation balance of the soil surface ( R i ) directly depend on the complex hydrothermal indexes (“dryness index”) ( R ¯ i ), which is reflected in estimates within natural zones (according to 18 weather stations) of Turkestan region for 1941–1960 and 2001–2020 (Figure 7):

1. in steppe mountain (mid-mountain) zone in the area where Shuyldak weather station is located for 1941–2020, natural solar energy consumption for soil formation decreased from 97.4 to 87.0 kJ/cm² and “excess” (unused) solar energy for soil formation from 3.3 to 4.8 kJ/cm²;

2. in semi-arid mountain (foothill) zone, in the areas where Tasaryk weather station is located, natural solar energy consumption for soil formation increased from 114.9 to 113.0 kJ/cm² and “excess” (unused) solar energy for soil formation from −8.3 to −4.7 kJcm², which shows that the natural consumption of solar energy for soil formation here is above average (4 points);

3. in semi-arid mountain (low-hill terrain and foothill) zone in the areas where Ashchysay and T. Ryskulov weather stations are located for 1941–2020, natural consumption of solar energy for soil formation increased, respectively, from 94.9 to 97.9 kJ/cm² and from 87.0 to 87.5 kJ/cm², and “excess” (unused) solar energy for soil formation from 20.3 to 21.3 kJ/cm² and from 31.9 to 33.6 kJ/cm². These indicators show that the natural consumption of solar energy for soil formation is average (3 points);

4. in semi-arid mountain (foothill) zone (Shymkent weather station) for 1941–2020, natural solar energy consumption for soil formation decreased from 110.0 to 109.4 kJ/cm². There is increase in the “excess” (unused) of solar energy for soil formation from 11.4 to 13,5 kJ/cm². In the areas where Kazygurt weather station is located, over the same period of time, natural consumption of solar energy for soil formation increased from 92.4 to 99.7 kJ/cm², and the “excess” (unused) solar energy for soil formation decreased from 24.9 to 27.1 kJ/cm², which shows that the natural consumption of solar energy for soil formation is average (3 points);

5. in the arid mountain (foothill) and plain (low-land and high-land) zones (according to weather stations: Sholakkorgan, Tashkent, Syrdarya, Shayan, Shardara, Bogen, Arys, Bayrkum, Turkestan, Tasty, Kyzylkum, and Akkum) for 1941–2020, in general, natural consumption of solar energy for soil formation ranged from 26.9 to 66.2 kJ/cm², and the “excess” (unused) solar energy for soil formation ranged from 52.8 to 108.4 kJ/cm², which reflects low (2 points) natural consumption of solar energy for soil formation.

Based on the calculations made, map of solar energy consumption for the soil formation process in agricultural landscapes of Turkestan region was created ( ESF п i ) (Figure 9).

Figure 9

Map of solar energy consumption for the soil formation process of agricultural landscapes in Turkestan region.

As is clear from Figure 8, climatic zonation is the key factor of the soil formation process in agricultural landscapes of Turkestan region, since the potential energy consumption for soil formation in the region increased from the mountainous to the arid zone, and the natural consumption of solar energy for soil formation contrarily, which is due to their low natural moisture, which can be increased by various types of agricultural land reclamation.

4 Conclusions

Assessment of soil resources of agricultural landscapes of Turkestan region is based on the “integrated index of soil fertility,” proposed by Pegov and Khomyakov [30], including humus reserves, mineral elements, and hydrolytic acidity, which allows to regulate their properties based on a set of agrochemical measures.

Assessment of soil resources of agricultural landscapes in Turkestan region using the “soil index” from the perspective of ecological-landscape approach, on the basis of automated spreadsheet based on Microsoft Excel, showed that all types of soil that are part of the landscape varieties of the region are poorly supplied with humus and mineral elements and have a high content of hydrolytic acidity, i.e., they have a low level of fertility, since the maximum generalized soil index in the region is about 4.80 units, which is four times less than its optimal value (20.0 units).

Environmental significance of the soil formation energy in agricultural landscapes of Turkestan region can be judged by the formation of the type and variety of soil depending on the solar energy consumption for the soil formation process, which is strictly subject to the law of vertical zonation and changes from the mountainous zone toward the arid one. It has been found that for 1941–1960 and 2001–2020, potential energy consumption for the soil formation process in agricultural landscapes of the region increased from 82.2–101.9 to 129.4–135.5 kJ/cm². Natural energy consumption for soil formation decreased from 121.7–125.2 to 17.2–21.7 kJ/cm² due to a decrease in the relative humidity of the natural environment, and the difference in indicators between them shows the unused reserves of solar energy of agricultural land, which must first be considered when planning the territorial organization of agricultural nature management.

Accordingly, it means that there is a need to study soil resources of agricultural landscapes for the effective organization of agricultural production, taking into account economic and material resources ensuring the sustainability of agricultural landscapes and increase of soil fertility.