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Dynamics of bioclimatic potential of agricultural formations of Almaty
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CONMECHYDRO – 2020
IOP Conf. Series: Materials Science and Engineering 883 (2020) 012075
IOP Publishing
doi:10.1088/1757-899X/883/1/012075
1
Dynamics of bioclimatic potential of agricultural formations
of Almaty region
I Sapaev1*, K Akhmetov2, A Serikbayev2 and B Sapaev3
1Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, Tashkent,
Uzbekistan
2Kazakh National Agrarian University
2Tashkent State Agrarian University Tashkent, Uzbekistan
mohim@inbox.ru
Abstract. The article has the new technique and formula for determining the dynamics of
assessment of the bioclimatic potential of climatic zones. Empirical models of potentially
possible grain yield of cultures of the agricultural organizations of the Almaty region were also
developed. The data on changes in temperature and precipitation received from metrological
stations of the region on Hydrometeorology and environmental monitoring, as well as data
from the website of the world meteorological network for 1998-2017, which reflect changes in
atmospheric circulation observed in the last twenty years are analyzed.
1. Introduction
Accounting and evaluation of actual and potential biological productivity have long attracted the
attention of many scientists - physiologists, soil scientists, farmers, and geographers such as
K.A.Timiryazev [1], V.V. Dokuchaev [2], V.R. Williams [3], etc. The work shows that the productive
power of the earth and its biological productivity is determined by the amount of incoming solar
energy, as well as the ratio of heat and moisture.
The basic methodology of the agro-climatic assessment of the territory was laid in the work of P.I.
Brounov [4]. Further, their ideas have received and developed in the researches of G.T. Selyaninov [5
and 6], P.I. Koloskov [7], Y.I. Chirkov [8], D.I. Shashko [9 and 10] and other scientists.
Agricultural evaluation of the climate in the mentioned works is based on two indisputable
established in the agronomic science positions - the equivalence of the factors of life and disparities in
the environmental factors. The first law states that no single life factor can be replaced by another; the
second one assumes that the life factors and environmental factors are not identical. Following this,
the main and secondary factors for these objects or processes are distinguished.
The main environmental factors determining the conditions of growth, development, yield, and
quality of agricultural products are, first of all, light, heat, and moisture.
The set of agro-climatic factors that create conditions for the formation of crop productivity
determines the agro-climatic resources of the territory. Various scientific and practical tasks of
agricultural production provide for the need to assess the bioclimatic potential since the biological
productivity of land in different areas is significantly different. Therefore, the most important factor in
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increasing the efficiency of agricultural production is the precise definition of the bioclimatic potential
of a certain territory.
Bioclimatic potential (BCP) is a set of climatic factors that determine the possible biological
productivity of the earth in a given area [9, 10 – 13]. The concept of bioclimatic potential is widely
used in agronomy, climatology, etc. In agronomy, this concept is based on an objective pattern of
changes in the productivity of plants depending on the main climatic factors - heat and moisture.
Various mathematical formulas for obtaining numerical estimates of bioclimatic potential are known.
The dependence of agricultural production on natural and climatic conditions has long been a well-
known fact and does not require justification and evidence. For example, randomly formed weather
conditions of each agricultural year and climatic characteristics on a certain background of soil
potential predetermine fluctuations in crop yields in the economy, district, and region, country [14 and
17].
Climatic conditions are reflected in the long-term variation of crop yields, and this variation is
objectively inherent in any culture and any region. The authors [12, 13, 16, 18 and 19], as a criterion
of "agricultural productivity of climate" suggest and use the yield of grain crops. Therefore, we can
use with good reason in assessing the bioclimatic potential of any territory of agriculture of Almaty
region systematic component of the annual natural climatic factor and the yield of grain crops.
The resource approach to the study of bioclimate uses multi-numerical complex meteorological
indicators and indices of different dimensions. A means of overcoming this multidimensionality was
the concept in which all the variety of indicators is reduced to a single indicator – bioclimatic
potential.
The purpose of our research is to determine the value of bioclimatic potential in all climatic zones
of the Almaty region.
The scientific novelty of the research results lies in the substantiation of the methodology and the
development of a new formula based on which the determination of the dynamic assessment of the
bioclimatic potential of climatic zones of Almaty region, as well as the development of empirical
models of the potential crop yield in the agricultural organizations of the region.
2. Methods
To determine the value of bioclimatic potential, data of metrological stations of the Almaty region on
Hydrometeorology and environmental monitoring for 1998-2017 were used, as well as the website:
[20], data of the world meteorological network. All meteorological stations of the region are included
in the world meteorological network. "The weather knows no bounds". Therefore, all information is
processed not only in the regional center but also in the world data center.
This, in turn, increases the level of responsibility of each observer, at any station for their work.
Data were collected and pre-processed for all administrative 16 districts and 3 cities engaged in the
production of agricultural products (hereinafter-the Object of study) of the Almaty region. For all
objects of study (20 units) formed an array of data – tables object-properties of dimension 20×13 15-
and climate indicators. In particular: the average amplitude of the air temperature at a height of 2 m,
0C; mean monthly surface soil temperature, 0C; point rosy for a height of 2 m, 0C; mean monthly
maximum air temperature at a height of 2 m, 0C; mean monthly minimum air temperature at a height
of 2 m, 0C; mean monthly air temperature at a height of 2 m, 0C; monthly average daily sludge per
day, mm; specific monthly average relative humidity, g kg-1; average relative humidity, %; the
average atmospheric pressure at the level of metrological stations of the cross, Mbar (kPa); average
monthly minimum wind speed at the height of 10 m, m/s; average monthly maximum wind speed at
the height of 10 m, m/s; the average monthly amplitude of wind speed fluctuations at the height of 10
m, m/s; average monthly descending thermal infrared (long-wave) radiation flux, kWh/m2 per day and
average monthly insolation clarity index.
To identify the close relationship between the calculated values of BCP and grain yield in the
objects of research, the method of correlation and regression analysis was used. Parameters of
regression models are estimated by the least-squares method. The modeling process is performed in
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Excel. The study was completed all stages of correlation and regression analysis – data collection and
processing, the construction of the regression equation, quality assurance models and regression
coefficients, interpretation of the results. In the study of the dynamics of agro-climatic and economic
indicators used mathematical and statistical methods. Statistical characteristics of data from the
applied model built with the help of the "data analysis" add-in of the Microsoft Office Excel
spreadsheet processor.
3. Results and discussion
In studies on the calculation of climate impact on crop productivity, almost all authors consider the
duration of the growing season [9 – 15] and limited the collection of information only for this period.
We did not take into account the annual accumulation of features of the climate regime of a particular
year of the horoscope. Attention is drawn to the work "Secrets of the harvest year" [14], where it is
noted that "the soil and all plants are natural batteries." Besides, it should be borne in mind that in
mass field farming, the productivity of grain crops is formed with a natural combination of heat,
moisture, without fertilizers, and other special techniques of agricultural technology. Therefore, when
assessing the bioclimatic potential of land for research objects, it is necessary to take into account
large amounts of data on natural laws.
To determine the most significant modes of climate indicators that form the BCP of the territory of
administrative 16 districts and 3 cities, we studied the closeness and directions of connection of all the
above climatic indicators with the yield of grain crops. With the help of the package "data Analysis"
MS Excel built a rectangular sub-matrix of correlation coefficients for all objects of study. Figure 1
shows a fragment of one of these matrices, built on the example of the Aksu district of Almaty region.
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Figure 1.
Sub-
matrix of correlation coefficients between yield and climatic indicators of
Aksu district of Almaty region.
As can be seen from figure 1 with the resulting indicator, Factors have a positive and significant
relationship: Х3, Х5, Х6, Х7, Х8, Х9, Х10, and Х14. Between some factors, there is multicollinearity and
therefore one variable should be excluded from consideration, in most cases, leave one of two
variables that has a greater correlation coefficient with the dependent variable. However, this rule is
not always feasible from a practical point of view. For example, the pair correlation coefficient
between X7 and X9 is 0.91, and their particular values
0.444
7
yx
r
and
0.475
9
yx
r
. In practice,
agriculture is essential to average daily, average monthly, and average annual rainfall. Therefore, this
indicator is left (X7), but the relative humidity is not excluded (X9) from further study.
Analyzing and eliminating multicollinearity of the same submatrix of other objects of study and
taking into account the research work performed on this problem, we concluded that the most
significant among the possible climatic indicators are: X6 is the average monthly air temperature at an
altitude of 2 m, X7 is monthly average daily precipitation, mm per day (X9 is average monthly relative
humidity, %) and X14 is average monthly descending thermal infrared radiation flux, kWh/m2 per day.
It should be noted that the climate indicators we have reasonably chosen correspond to the
traditionally accepted indicators.
According to the metrological stations of the Almaty region and the world meteorological network,
the average annual air temperature has decreased over the past 20 years (by an average of 0.4°C/20
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years) (Figure 2). The warmest for the study period were: 2002, 2007, 2013, 2015 and the coldest were
2003, 2012 and 2014 [21, 23].
Figure 2. The average annual air temperature in the Almaty region for the period 1998 – 2017.
The consequence of the decrease in temperature over the past twenty years in the study area was a
decrease in the duration of the growing season. At the same time, the last decade has seen an increase
in the amounts of active temperatures compared to the previous decade (Fig. 3). At the end of the XX
century, due to the increase in the total duration of blocking processes, there was a tendency to
increase the annual amplitude of the average monthly thermal infrared radiation flux. This thermal
regime, since 2012, tends to increase according to the law of the second-order polynomial (Fig. 3).
The obtained data clearly show the improvement of the heat supply of agricultural crops. One of the
reasons for the observed changes in the temperature regime is changes like atmospheric circulation.
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Figure 3.
The average daily downward thermal infrared radiation flux in the territory of
Almaty region for the period 1998-2017.
An analysis of the dynamics of precipitation data showed that there has been a slight increase in
precipitation in recent years (Fig. 4), which leads to an increase in evaporation with an increase in the
average annual temperature [22]. During the study period, the most humidified was 2016 (500 mm),
and 2014 was marked as the driest, since the annual rainfall this year was 2.5 times less.
Table 1 shows the average monthly precipitation for the different averaging periods. Attention is
drawn to the decrease in precipitation in August and September in all study periods; this is a
consequence of the fact that at a high-temperature background of these months there is an increase in
evaporation. The increase in precipitation in May and June does not compensate for the decrease in the
supply of productive moisture in the arable layer since the evaporation values exceed the amount of
precipitation in these months by almost half.
Thus, the changes observed in the last twenty years are as follows: the increase in the number of
active temperatures for August–September determines the increase in evaporation, which is not
compensated by precipitation; August and September differ in particular aridity.
The limiting factor for the optimal development of a large number of crops in the study area is
humidification resources, as evidenced by the values of the average monthly relative humidity
(AMRH) for different averaging periods, given in Table 3. The value of AMRH for the period from
June to August ranges from 27.26 % to 35.59 % and characterizes the conditions of moisture as
insufficient, therefore, the conditions become arider. This is a consequence of the location of the
regional center (c. Taldykorgan) in the middle mountain zone and the prevalence of anticyclonic
weather.
Table 1. Precipitation weather stations in Taldykorgan, averaged over different periods,
mm.
Months
1998-2002 у
2003-2007 у
2008-2012y
2013-2017 y
January
17.88
16.86
15.06
20.58
February
19.22
21.27
25.23
15.13
March
18.35
21.76
25.02
14.26
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April
27.06
22.68
22.08
29.16
May
23.62
29.92
21.7
32.05
June
29.64
24.84
16.92
21.6
July
20.03
24.43
14.94
12.83
August
7.874
9.672
9.486
11.16
September
15.00
10.85
18.17
11.97
October
36.02
16.18
24.18
38.07
November
24.72
33.18
26.28
34.50
December
25.54
34.29
22.88
21.95
In a year
267.10
255.19
244.03
267.10
Figure 4. Annual precipitation in the Almaty region for the period 1998 – 2017.
Now let's move from the analysis of individual indicators to a comprehensive assessment of agro-
climatic conditions in the region over the past twenty years.
The bioclimatic potential is the most important indicator of assessing the natural conditions of the
territories, synthesizing the influence on the biological productivity of the main factors – heat and
moisture.
As the analysis of the works of the authors [11, 12, 13 and 15], engaged in the study of the problem
of agrometeorology, almost all of them in their studies on the calculation of climate impact on crop
products use the method of D. I. Shashko [9 and 10], developed in 1967 to assess the bioclimatic
potential (BCP). In this technique, the author considers a comparative interregional assessment of
land, through the relative values of bioclimatic potential, synthesizing the impact on the biological
productivity of the main factors of climate – heat, and moisture. At the same time, the BCP was
estimated in comparison with the basic productivity at the border of possible mass field farming (at a
base temperature of 1000°C) for the period of 1961-2000 yy.
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Table 2. Average Monthly relative humidity of Taldykorgan weather station, averaged
over different periods, %
Months
1998-2002 y
2003-2007 y
2008-2012 y
2013-2017 y
January
70.76
72.00
70.09
71.08
February
64.65
65.43
75.60
68.11
March
52.00
55.84
57.84
56.45
Аpril
44.29
46.61
46.72
48.35
Мay
37.13
40.26
37.72
38.54
June
33.23
33.97
33.97
35.59
July
32.00
34.89
31.39
30.42
Аugust
27.26
29.61
29.37
31.20
September
30.35
30.49
36.10
33.74
Оctober
47.46
43.56
50.81
51.19
November
58.28
62.07
62.11
63.75
Деcember
69.47
69.07
69.50
67.96
In a year
47.16
48.58
49.98
49.60
Linking bioclimatic potential (BCP) with yield and extending the area of the territory to: high,
high, medium, and low in percentage terms, respectively, according to the value of BCP gradation in
1967, D.I. Shashko using the usual principle of proportion, developed a table of biological
productivity gradation.
D.I. Shashko believes that the comparative assessment of soil fertility should be carried out in close
connection with climatic conditions. The author proposes a technique that is based on objective
indicators of biological productivity and is expressed by the relative values of soil-bioclimatic
potential (SBP). The comparative assessment of biological productivity of the earth is made in points
where for 100 points the value of SBP corresponding to average productivity at the given technical
and economic level of development of the economy is accepted. The average yield of grain crops on
the value of SBP is approximately 1.9. This value is taken as 100 points at this technical and economic
level of development of the economy. To obtain points of the remaining territories of the SBP,
calculations are carried out through proportions.
Of course, the author proposed an original simple technique, and of course, when information
technology was still not developed, he focused on the availability and the ability to simply perform
computational operations.
Based on its methodology for the calculation of bioclimatic potential, the following formula is
used:
()
()
ak
pau
ak baz
t
BCP K t
(1)
Where:
BCP are the relative values of bioclimatic potential;
Kp(au) is the growth coefficient for the annual indicator of atmospheric moisture;
ak
t
is the sum of the average daily air temperatures for the period of active vegetation in a given
place;
()ak baz
t
is the basic sum of the average daily air temperatures for the period of active vegetation.
In the formula used, the coefficient of growth of Kp(au) is equal to the ratio of yield in these
conditions of moisture to the maximum yield in conditions of optimal moisture supply. Next, a
formula is proposed to determine its calculated value, which takes into account the coefficient of
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doi:10.1088/1757-899X/883/1/012075
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annual atmospheric moisture, equal to the ratio of precipitation to the sum of the average daily values
of the air humidity deficit. It should be noted that in our opinion all these data are collected in
limited conditions, as a result of practical experience and experiment in a certain area, which does not
characterize the scale of the entire object of study. We can replace the formula of the growth
coefficient with the ratio of each grain yield to its average value of the General population in the
region as a whole for the period under study (1998-2017).
Based on formula (1) and considering that bioclimatic potential is an integral assessment of a set of
climatic factors determining the possible biological productivity of land in a given territory or
otherwise, a bioclimate of integral estimates of its basic regimes, it is proposed to determine BCP for
all object of research of Almaty region by the new formula developed by us:
ij ij ij ij
ij
cp cp cp cp
Yt B U
BСP Yt B U
(2)
where Yij is the yield of grain crops in the i – the entity (district, city, and oblast) for the j – year (from
1998 to 2017), 100kg/ha;
tij is the average annual air temperature at a height of 2 m in the i – object at the j – year, 0C;
Bij is the average of sediment in the i – object at the j – year, mm;
Uij is the average the downward thermal infrared radiation, the heat flux in the i – object at the j –
year, kWh/m2;
11
/
mn
cp ij
ij
YYmn
is the average grain yield (mathematical expectation) of the whole
population;
11
/
mn
cp ij
ij
ttmn
is the average annual air temperature at a height of 2 m of the whole
population;
11
/
mn
cp ij
ij
BBmn
is the average annual precipitation of the whole population;
11
/
mn
cp ij
ij
UUmn
is the average annual descending thermal infrared radiation heat flow of
the whole population;
m is the number of objects of study (in our case m=20) and n is the number of years in the period
under consideration (in our case n = 20).
Based on the collected materials for each indicator, a sub-matrix with the size: 20×20, the average
annual values of these indicators, and according to this formula, the dynamics of the BCP of
agriculture of the Almaty region was calculated, which is shown in table 3.
Оn the basis of these calculations (Table 3), it can be concluded that the biological productivity in
the whole region has increased significantly in recent years, in the Eastern-Northern part of the Almaty
region is reduced, which is associated with insufficient moisture. Analyzing the annual data of the
bioclimatic potential of the table. 3, we see that from 2009 to 2017 distinguished by increased levels of
BCP compared to other years, and the high yield of grain crops in these years is in good agreement
with the data obtained. According to the obtained values of the BCP, it is impossible to obtain high
yields of agricultural crops without agrotechnical measures in the territory of the Almaty region
regularly.
To assess whether the characterized calculated values BCP-cally, the level of grain crop yield, we
carried out correlation and regression calculation over all the objects of the study r. The calculation
results are shown in Table 4.
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As can be seen from this table in all the studied objects, the yield of grain crops is close, it is
associated with the calculated value of the BCP, which shows the calculated values of the correlation
coefficient, ranging from R = 0.890 ÷ 0.995. At the same time, in all the studied objects the calculated
value of the Student's criterion: tcal= 74.17 ÷ 42.34 > ttab = 2.1 is greater than its table value, which
shows the reliability of all the calculated values of the correlation coefficients.
The form of communication, built in the form of a linear empirical model, was also reliable. Since
in all cases the significance of the Fisher F-test (5.92∙E-11 ÷ 1.45∙E-07) is much lower than the
accepted significance level (α = 0.05). It should be noted that given in the work [9] developed by us by
the method of astrological modeling, predicting the above-considered modes of climatic parameters,
we can calculate the prospective yield of grain crops for all objects of study of the Almaty region.
The gradations of bioclimatic potential values reflect the natural zonality and zonality, as the sums
of temperatures according to the natural scale of heat supply classification are used for their
determination [157]. Therefore, the climate orientation of agriculture is relevant, aimed at regulating
soil formation processes, as well as the management of factors of accumulation of the studied indicator
during the year and plant development in terms of variability of agricultural meteorological
parameters. The results of the analysis are also presented in Table 4.
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11
Table 3. Results of assessment of bioclimatic potential of the agricultural territory of Almaty region.
The name of the district
and the city
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Oblast
1.8
2.0
2.0
2.2
3.3
3.2
2.9
2.6
2.8
3.0
2.0
3.5
3.2
3.1
2.8
3.3
2.6
3.7
4.3
3.8
Aksu
1.3
1.2
1.7
1.6
2.4
2.4
1.9
1.7
1.9
1.9
0.8
2.1
1.6
1.4
1.3
1.5
1.2
1.9
2.5
2.1
Alakol
1.0
1.2
1.6
1.5
2.7
2.5
1.8
1.7
1.9
2.1
1.2
2.9
2.3
2.1
2.2
2.5
2.1
2.9
2.9
2.8
Balkhash
1.1
2.9
3.1
3.1
3.8
3.3
3.5
3.3
4.1
3.9
3.6
4.2
4.4
4.0
3.8
4.5
3.9
4.5
5.0
4.6
Embekshikazakh
2.8
3.0
3.6
3.5
4.7
5.0
4.8
4.3
4.9
4.8
3.8
5.2
5.4
5.6
5.1
6.3
5.0
6.8
8.7
7.4
Еskeldi
2.0
2.0
1.9
1.8
2.8
2.6
2.4
2.1
2.4
2.5
1.7
2.8
2.9
2.8
2.5
2.9
2.0
2.9
3.3
2.7
Zhambyl
2.0
2.5
1.0
1.2
3.0
2.9
1.8
1.8
1.9
2.2
0.6
3.0
3.0
3.0
2.2
2.8
2.4
3.4
4.0
3.3
Ili
1.4
1.8
0.9
1.5
3.4
3.6
2.2
2.1
2.9
2.7
1.8
3.6
2.9
2.7
2.5
3.2
2.0
3.3
4.3
4.1
Каrasai
2.0
2.4
3.5
3.0
5.0
5.5
2.3
2.5
3.7
3.5
1.9
3.8
3.8
3.5
2.7
3.3
2.2
3.3
4.2
3.4
Каratal
3.5
2.6
2.6
2.6
3.3
2.9
3.0
3.0
3.5
3.3
2.9
3.8
3.4
3.4
3.2
3.8
3.2
4.0
4.6
4.3
Кеrbulak
1.7
1.8
1.6
1.7
3.0
2.8
2.3
2.3
2.1
2.3
0.8
3.0
2.3
2.5
1.9
2.5
1.9
2.7
3.3
2.8
Кокsu
2.4
1.6
1.7
1.9
3.0
2.8
2.9
2.1
2.3
2.8
1.6
2.9
3.2
3.1
2.4
2.8
2.4
3.3
3.7
3.5
Panfilov
2.2
2.5
2.7
3.2
4.1
4.4
4.6
3.6
4.7
5.4
4.7
5.7
6.6
5.9
5.7
6.1
5.8
7.1
7.8
6.7
Rayimbek
1.3
1.2
1.6
1.6
2.3
2.3
2.2
1.7
2.0
2.2
1.3
2.7
2.7
2.5
1.9
2.2
1.1
2.4
2.8
2.4
Sarkand
1.8
1.4
1.8
1.8
2.7
2.6
2.1
2.1
2.3
2.4
0.9
2.6
2.3
2.1
1.9
2.1
1.3
2.4
2.9
2.4
Таlgar
2.2
3.3
2.2
2.8
3.6
3.8
3.5
3.0
3.4
3.3
2.3
4.0
3.9
3.8
3.2
4.0
3.4
4.6
6.2
5.8
Uigur
2.0
1.6
2.8
3.3
4.2
4.3
4.3
4.3
4.7
4.9
4.7
5.8
6.3
6.0
5.9
6.9
6.5
8.0
8.8
7.5
Каpchagai
2.2
2.4
2.2
3.5
4.0
3.8
4.7
3.9
4.2
4.0
1.4
3.4
3.7
2.6
1.5
1.9
1.7
2.6
4.5
3.9
Таldikorgan
1.6
1.8
1.8
1.8
2.9
2.6
2.8
2.5
2.8
2.7
2.0
2.4
2.4
2.5
2.2
2.7
2.2
2.8
3.3
2.9
Текeli
1.5
0.8
1.1
1.0
2.4
2.5
2.5
2.3
2.5
2.4
1.9
2.5
2.9
2.9
2.8
3.2
2.0
3.5
3.4
2.9
CONMECHYDRO – 2020
IOP Conf. Series: Materials Science and Engineering 883 (2020) 012075
IOP Publishing
doi:10.1088/1757-899X/883/1/012075
12
Table 4. Variability and closeness of connection with the yield of grain crops BCP soil and climatic zones
of Almaty region for 1998-2017 yy.
Agri-
cultura
l areas
The name of the
district and the city
Bioclimatic potential (BCP)
Empirical and correlation of grain yield
with BCP
Max
Min
Хср
V, %
Y=f(x)
R
I
Aksu
2.5
0.8
1.7
20.9
Y=3.505 + 7.738
Xbkp
0.960
Alakol
2.9
1.0
2.1
23.1
Y = 2.022 + 6.742
Xbkp
0.967
Sarkand
2.9
0.9
2.1
18.0
Y = 4.63 + 7.543
X
bkp
0.995
Каratal
4.6
2.6
3.3
12.1
Y = 4.732 + 6.579
X
bkp
0.890
Balkhash
5.0
1.1
3.7
15.8
Y = 2.419 + 7.093
Xbkp
0.945
II
Zhambyl
4.0
0.6
2.4
29.0
Y = 2.291 + 5.97
X
bkp
0.954
Ili
4.3
0.9
2.6
28.7
Y = 3.109 + 6.153
X
bkp
0.955
t.Kapchagai
4.7
1.4
3.1
30.6
Y = 2.365 + 5.394
X
bkp
0.983
III
Каrasai
5.5
1.9
3.3
21.8
Y = 5.066 + 5.434
X
bkp
0.955
Таlgar
6.2
2.2
3.6
19.6
Y = 5.666 + 5.8
X
bkp
0.919
IV
Еskeldi
3.3
1.7
2.4
14.9
Y = 5.052 + 6.552
X
bkp
0.911
Кеrbulak
3.3
0.8
2.3
20.3
Y = 2.41 + 6.031
Xbkp
0.966
Kоksu
3.7
1.6
2.6
19.8
Y = 2.877 + 6.793
X
bkp
0.956
t.Taldykorgan
3.3
1.6
2.4
15.3
Y = 3.804 + 6.481
X
bkp
0.924
t.Tekeli
3.5
0.8
2.3
25.0
Y = 1.423 + 8.022
Xbkp
0.968
V
Embekshikazakh
8.7
2.8
5.0
20.5
Y = 6.745 + 6.044
X
bkp
0.914
Rayimbek
2.8
1.1
2.0
22.9
Y = 2.678 + 6.793
X
bkp
0.975
Panfilov
7.8
2.2
5.0
26.2
Y = 1.64 + 8.276
X
bkp
0.97
Uigur
8.8
1.6
5.1
30.1
Y = 1.788 + 6.978
X
bkp
0.979
Total on Oblast
4,3
1.8
2.9
18.9
Y = 4.213 + 6.322
X
bkp
0.941
The calculation of the value of the bioclimatic potential index for the period from 1998 to 2017
revealed its relatively low value and significant variability. Its minimum value for the analyzed period
was measured in the territory of Zhambyl (0.6), Aksu (0.8) and Kerbulak (0.8) regions, and the
maximum – in the V-South-Eastern zone (2.8 to 8.8).
Higher minimum, maximum, and average values of bioclimatic potential are characterized by the
territory of Uighur, Enbekshikazakh, and Panfilov districts (table 4).
The tendency of increasing the average value of bioclimatic potential in the plain-steppe zone from
the North-East (1.7) to the South-West (3.7) direction is noted.
The variability of the bioclimatic potential index is strong regardless of the soil-climatic zone and
increases from the plain-steppe zone to the South-Eastern zone. The maximum variability of
bioclimatic potential is characterized in the territory of Kapchagay (30.6 %) and districts: Uighur (30.1
%), Zhambyl (29.0 %), and Ili (28.7%).
4. Conclusions
For the first time, based on complex research, the moisture and warm-power resources of the
productivity of agricultural cultures on the territory of the Almaty region have been studied. Based on
the offered new formula, a twenty-year dynamic assessment of bioclimatic potential and adequate
CONMECHYDRO – 2020
IOP Conf. Series: Materials Science and Engineering 883 (2020) 012075
IOP Publishing
doi:10.1088/1757-899X/883/1/012075
13
empirical models of potentially possible grain yield of cultures of the agricultural organizations of the
Almaty region are developed.
The proposed approach and a new formula for assessing the bioclimatic potential reflect the natural
explanation and zoning, using the sum of temperatures on the natural scale of the classification of heat
supply. It takes into account the climatic orientation of agriculture, aimed at regulating soil formation
processes, as well as management of factors of accumulation of the studied indicator during the year
and plant development in terms of variability of agricultural meteorological parameters.
The analysis of the data carried out in the course of the work testifies to the growth of average
annual and monthly values of air temperature in the territory of Almaty region for the studied period.
The increase in the total duration of blocking processes contributes to the increase in summer
temperatures, mainly in July and August, which leads to an increase in the number of active
temperatures and as a consequence – to an increase in evaporation. A slight increase in precipitation
does not compensate for increased evaporation, hence conditions become arider. Insufficient moisture
determines the reduced bioclimatic potential of the territory.
References
[1] Timiryazev K A 1957 Agriculture and plant physiology: Selected works Lectures and speeches
(Moscow Selkhozgiz) p 326
[2] Dokuchaev V V 1948 Selected works Volume 1 (Moscow Selkhozgiz)
[3] Williams V R 1948 Soil science agriculture with the basics of soil science. 5th ed (Moscow
Selkhozgiz)
[4] Bronow P I 1957 Works on agricultural Metrology Issue 1 (Moscow)
[5] Selyaninov G T 1966 Agroclimatic map of the world (Selyaninov G T Leningrad:
Hydrometeoizdat) p 12
[6] Selyaninov G T 1968 On the agricultural evaluation of climate (Selyaninov G T Work on
agricultural meteorology) Issue 20 pp 165-177
[7] Koloskov P I 1962 Bioclimatic potential and its distribution on the territory of the USSR
(Colosco P I Trudy NIIAK) Issue 15 pp 90-111
[8] Chirkov Yu I 1986 Agrometeorology (Yu I Chirkov Leningrad: Hydrometeoizdat) p 296
[9] Shashko D I 1967 Agro-Climatic zoning of the USSR (Shashko D I Moscow Kolos) p 334
[10] Shashko D I 1985 Agro-Climatic resources of the USSR (Shashko D I Leningrad:
Hydrometeoizdat) p 248
[11] Konovalova N In 2006 Interpolation of climate data using GIS technologies (H In Konovalova
Korobov V B Yu L Vasiliev Meteorology and hydrology) №5 pp 46–53
[12] Gordeev A V 2006 Bioclimatic potential of Russia (Gordeev A V Kleshchenko A D
Chernyakov B A Sirotenko O D Moscow) Creativity of scientific publications of the CMK p
508
[13] Gordeev A V Kleschenko A D Chernyakov B A Sirotenko O D SIP-TCI S A Romanenko I A
Bartalev S A Savin I Yu 2012 Bioclimatic potential of Russia: productivity and rational placing
of agricultural crops in conditions of climate change (Edited by Gordeev A V Moscow): [band.]
p 202
[14] Gorban L I 2014 Secrets of the harvest year. Grain portal of the Central Chernozem region
4.07.2014. E-mail Gorban.Leonid@gmail.com
[15] Agro-climatic resources of natural zones of the USSR and their use 1970 (Ed Davitaya F F
Gidrometeoizdat) p 350
[16] Agroclimatic resources of Akmola region: a scientific and practical guide 2017(Baysholanova S
S Astana) p 133
[17] Akhmetov K A Zhylkybek T E Toktarbek E 2016 Prediction of Uro-gainotti crops with the
consideration of astrological factors Proceedings of the XXI International scientific and
practical conference "Problems and prospects of science development at the beginning of the
third Millennium in Europe and Asia" Collection of scientific papers (Pereyaslav Khmelnitsky
CONMECHYDRO – 2020
IOP Conf. Series: Materials Science and Engineering 883 (2020) 012075
IOP Publishing
doi:10.1088/1757-899X/883/1/012075
14
Ukraine: TOV "Colbr") p 379
[18] Battalov F Z 1980 Agricultural productivity of climate for spring crops (L: Gidrometeoizdat) p
112
[19] Sapozhnikova S A 1979 Experience of integrated agricultural climate assessment of the territory
of socialist countries of Europe (Sapozhnikova S A Agro-Climatic zoning of five main crops in
the territory of socialist countries of Europe Sofia. Bulgarian Academy of Sciences) pp 99-120
[20] Website: https:youtu.be/kr6_PFRu3qo https:youtu.be/kr6_PFRu3qo - the world meteorological
network
[21] Lebedeva M G Krymskaya O V 2008 Manifestation of modern climatic changes in the
Belgorod region Scientific Bulletin of BelSU Ser «Natural science»№3 (43) Issue 6 pp 188-196
[22] Assessment of evaporation from the water surface in the South of the Central black earth region
(Reshetnikova L K Lebedeva M G Petina M A Environmental monitoring 2010) 2013 №5
Bulletin of the Natural science № 7(160) Issue 24 pp 60-64
[23] Stock materials of Almaty center of Hydrometeorology (CGM), branch of RSE "Kazhydromet"
in Almaty region (CGM: Republic of Kazakhstan 040010 Almaty region Taldykorgan, Gagarin
street) 216
