Content uploaded by Anna Gorbacheva
Author content
All content in this area was uploaded by Anna Gorbacheva on Jan 26, 2025
Content may be subject to copyright.
639
ISSN 0147-6874, Moscow University Soil Science Bulletin, 2024, Vol. 79, No. 5, pp. 639–646. © Allerton Press, Inc., 2024.
Map of Potential Sequestration of Carbon by Arable Soils
in Rostov Oblast Updated Using Rosstat Data
V. A . D obrovol skayaa,*, Yu. L. Meshalkinaa,** (ORCID: 0000-0003-1513-2439), A. Yu. Gorbachevaa,***
(ORCID: 0000-0002-7097-5378), and V. A. Romanenkova,b,**** (ORCID: 0000-0002-8967-4225)
aSoil Science Faculty, Moscow State University, Moscow, 119991 Russia
bPryanishnikov Institute of Agrochemistry, Moscow, 127434 Russia
*e-mail: gafiatulina.valeriya@mail.ru
**e-mail: jlmesh@list.ru
***e-mail: buyvolova@gmail.com
****e-mail: geoset@yandex.ru
Received September 7, 2024; revised October 2, 2024; accepted October 8, 2024
Abstract—Since 2020, the project of the Global Soil Partnership of the Food and Agricultural Organization
of the United Nations has been implemented to create a Global Map of the Carbon Sequestration Potential
in the upper 0- to 30-cm layer of cropland (Global Soil Organic Carbon Sequestration Potential Map,
GSOCseq). The first version of the national Russian map has been developed by local specialists using a stan-
dardized methodology. It is based on global databases, which do not account for specific local peculiarities.
The aim of the study was to test new approaches in the pilot region with broad distribution of black soils for
estimating the organic matter entering the system to quantitatively approximate it to real values. This paper
describes the methodology for the transition of the input data block responsible for information about net pri-
mary products from global available resources to the local statistics of the Rosstat Municipal Database. The
yield for the main crops, taking into account the cultivated area for each municipality, was recalculated into
plant biomass using regression equations for determining the mass of plant residues according to the yield of
the main product and then converted to an absolutely dry mass and multiplied by the coefficient of conversion
of plant residues into carbon. After applying the new approach, differentiation was achieved at the municipal
level and an increase in the results of the redistribution of climatic potential between territories was recorded.
As part of the work, maps of the potential for sequestration of organic carbon by the upper 30-cm layer of
arable soils in Rostov oblast have been constructed for the first time using Rosstat data. Previously, Rostov
oblast was assessed as a carbon emitter according to previous methods; according to the new estimates, the
entire Rostov oblast accumulates carbon; the average carbon sequestration is 0.032 t C ha–1 per year even
under the business-as-usual scenario. At the same time, according to the indicator of absolute sequestration
rate, a significant increase in the sequestration rate has been recorded in the southern chernozem zone (Cal-
cic Chernozems) and its slight decrease in the zone of ordinary chernozems (Haplic Chernozem). When
implementing soil sustainable management scenarios, carbon can accumulate at an average rate of 0.063 to
0.161 t C ha–1 per year and the total potential for carbon fixation can be estimated at 475–1244 Kt per year.
The simulation results for Rostov oblast have shown that this approach can also be applied to federal subjects
of the Russian Federation.
Keywords: Food and Agricultural Organization “Global Soil Organic Carbon Sequestration Potential Map”
project, net primary production, soil organic carbon stock, organic carbon sequestration, sustainable land
management
DOI: 10.3103/S0147687424700 625
INTRODUCTION
One promising low-carbon development strategy is
to offset greenhouse gas emissions by carbon seques-
tration in arable soils. Improved management meth-
ods that make it possible to preserve and increase soil
carbon can significantly inf luence the national carbon
budget. The introduction of agricultural practices
aimed at sequestering organic carbon in soil may offset
two-thirds of its losses. Studies conducted on various
arable soils around the world have shown that the rates
of organic carbon sequestration in soils can reach 0.2
to 0.5 t C ha–1 per year (Chernova et al., 2020). How-
ever, the real potential for carbon sequestration in soils
in Russia has been so far insufficiently studied.
The study is consistent with the implementation of
task 6.5 of the “Long-Term Development Strategy of
the Russian Federation with Low Greenhouse Gas
Emissions until 2050,” that is “Modeling of Processes
640
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
DOBROVOLSKAYA et al.
Occurring in the Climate System, Including Modeling
of the Consequences of Different Forms of Active
Influence on These Processes at Global, Regional,
and Local Scales.” The intensive low-carbon develop-
ment scenario of the Strategy assumes an increase in
the absorption capacity of managed ecosystems,
including through the introduction of climate-ori-
ented technologies and practices, which can provide
additional carbon sequestration in agricultural soils
and reduce carbon losses (Vinogradova et al., 2022).
In 2020, the Food and Agricultural Organization
(FAO) Global Soil Partnership initiated work to create
a global map of the carbon sequestration potential
of cropland soils, (Global Soil Organic Carbon
Sequestration Potential map (GSOCseq)) (FAO,
2020). The first version of the interactive map,
along with the corresponding global statistics, was
published a year later. It includes predictions of
the annual increase in soil carbon stocks in crop-
land (0–30-cm layer, t C ha–1 per year) depending
on various land cultivation approaches.
Since the launch of the GSOCseq project, we have
taken targeted steps to update datasets and enhance
simulation processes, with the goal of delivering more
accurate and realistic results specifically for Russian
croplands. The use of simulation models based on
local data is quite productive in predicting changes in
soil carbon stocks under different cropland manage-
ment conditions (Jones, Donnelly, 2004). It is
expected that detailed modeling at the regional level
will provide better reporting materials to the Intercon-
tinental Panel on Climate Change (IPCC) (Morais
et al., 2019).
We hypothesize that incorporating localized data
on organic matter inputs into the system will lead to a
significant refinement of simulation results. The study
was based on studies of the previous stages (Romanen-
kov et al., 2023, 2024), when carbon sequestration
maps were obtained for the 0- to 30-cm layer of arable
soils across the entire Russian Federation based on
various land use strategies using global open databases
according to the FAO methodology for creating the
Global Map of Soil Carbon Sequestration in Arable
Soils in the 0- to 30-cm layer (FAO, 2020).
The objective of the study was to test new
approaches to calculating the organic matter entering
the system based on example of Rostov oblast to quan-
titatively approximate it to real values. This paper pres-
ents a methodology for the transition of the input data
block responsible for information about net primary
production (NPP) from publicly available resources to
the local statistics of the Rosstat Municipalities Data-
base. The Rostov oblast was chosen as the object of
research primarily because of the predominance of
Chernozems in the soil cover of this region; it is Cher-
nozem that seem to be most problematic in terms of
carbon sequestration, since they tend to lose, rather
than increase, initially high humus reserves (Chernova
et al., 2020; Husniev et al., 2023). Second, there are
detailed and reliable data for Rostov oblast from the
local agrochemical service (Nazarenko et al., 2014). In
addition, the choice of Rostov oblast for testing this
methodology was also determined by the high involve-
ment of its area in agricultural production.
MATERIALS AND METHODS
Taking into account the global nature of the
GSOCseq project, the methodology for creating
sequestration potential maps was unified and pub-
lished for all participants (FAO, 2020).
Input Data
Climate data were taken from the Climatic
Research Unit (CRU) TS v4.05 database (Harris
et al., 2020). Calculations required the average
monthly values of temperature, precipitation, and
evapotranspiration for the period from 1980 to 2020.
SoilGrids maps served as a source of soil texture data
(Poggio et al., 2021). The amount of organic matter
entering the system in the form of NPP was calculated
during modeling using climate data (temperature and
precipitation data) according to the MIAMI model
(Gottschalk et al, 2012; Romanenkov et al., 2024) and
correlated to the length of the growing season. The lat-
ter is also a calculated indicator and estimated as the
period with an NDVI value exceeding 0.5. These cal-
culations were performed using the GoogleEngine
tool based on the MODIS satellite images contained
in this tool. The Russia Cropland Map, developed in
2021 by national researchers using an innovative
methodology, delivers up-to-date insights into culti-
vated areas, serving as a crucial resource for advanced
modeling efforts (Krenke, 2020). It was necessary to
set the initial conditions for modeling by entering data
on the soil organic carbon stock in the 0- to 30-cm
layer for 2000. For this purpose, the corresponding
nationally developed mapping information was used
(Chernova et al., 2021).
Modeling According to the FAO Methodology
Process-oriented dynamic models have been
widely used to simulate the carbon cycle (Nemo et al.,
2016). FAO technical guidelines recommend the
Rothamsted model (RothC) for predicting the seques-
tration potential (FAO, 2020). This model is designed
to simulate carbon flows between five soil pools (res-
ervoirs); however, it does not contain functionality for
calculating vegetation productivity (Coleman, Jenkin-
son, 1999). RothC requires separate input of data on
the amount of monthly incoming plant residues and
organic fertilizers (t C ha–1). Modeling was carried out
for each pixel of the cropland map of the Rostov oblast
at a resolution of 1 km2. The sample size for modeling
exceeded 171 thousand pixels. The absolute sequestra-
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
MAP OF POTENTIAL SEQUESTRATION OF CARBON 641
tion rate (t C ha–1 per year) is calculated as the differ-
ence between carbon stocks in 2040 and 2020, divided
by the number of years of this period.
Modeling is conducted based on the FAO method-
ology, encompassing four potential scenarios. They
differ in the input data on the incoming amount of
organic matter. The first scenario, the business-as-
usual scenario (BAU), introduces the amount of
organic matter calculated from climate and satellite
data into the model. It implies maintaining current
agricultural technologies. In the other three scenarios,
scenarios of soil sustainable management (SSM1,
SSM2, and SSM3, respectively), the amount of
organic matter entering the soil increases by 5, 10, and
20%, respectively. It is believed (Smith et al., 2005;
Jones, Donnelly, 2004; Stamati et al., 2013) that the
use of carbon-saving technologies for cultivating agri-
cultural crops influences precisely the NPP indicator:
the more intensive the technologies used, the greater
the above-mentioned proportional increase in its NPP
indicator.
Modeling Based on Rosstat Data
Since the above information is extracted by the
model from the NPP input data block, this block was
subject to revision. By default, this indicator is calcu-
lated at the stage of preparation for modeling as the
minimum amount of carbon that can enter the soil at
under current temperature and precipitation condi-
tions. In this paper, we tried to replace this approach
and base NPP calculations on actual statistical data
extracted from the Rosstat database and adapted for
input into the model. This data is detailed at the
municipal level.
Calculations were performed using regression
equations for determining the mass of plant residues
based on the yield of main products developed by
domestic researchers (Levin, 1977). Data arrays on the
cultivated area of agricultural crops, as well as on the
yield per harvested area, were extracted from the Ross-
tat Municipality Indicators Database (https://ross-
tat.gov.ru/storage/mediabank/Munst.htm). As a
result of the calculations, we found the biomass of the
underground and aboveground parts of the plants. To
calculate the incoming amount of carbon, it was nec-
essary to recalculate the amount of plant dead plant
biomass (mortmass) to absolutely dry matter. For this
purpose, it was assumed according to (Levin, 1977)
that the moisture content of the incoming plant resi-
dues was 17% for grain crops and 75% for row crops
and other crops. The conversion index from dry plant
residues to carbon is generally accepted and was 0.45.
The median value for all pixels of the estimated area
was taken as an estimate of average sequestration. The
mean and median values proved to be very close or the
same for almost all assessed contours.
Object of Study
The territory of Rostov oblast covers mainly the
steppe zone of ordinary and southern Chernozems
and the dry steppe zone of dark chestnut and chestnut
soils (Kastanozems). The state of agro-industrial pro-
duction in Rostov oblast has shown positive dynamics
in recent years. The structure of crop areas is domi-
nated by wheat (51.4%), sunflower (12.0%), barley
(10.1%), and corn (5.2%) (Oborin, 2021). Agrochem-
ical research data for the period from the 1970s to 2017
showed that the average humus content in the soils of
Rostov oblast was 3.1% (Bezuglova et al., 2020). Mod-
eling was carried out using the above-described data
for 43 municipalities of the Rostov oblast.
RESULTS
The resulting values of carbon entering the soil in
the model are characterized by the NPP indicator (t
ha–1). After updating the data, the NPP included in
the model using the traditional prediction method
were compared with the local NPP values (Fig. 1). It
can be seen that the results proved to be similar.
In some approximation, it can be assumed that the
MAIMI model characterizes the general natural
potential of the territory for NPP production. Local
data showed that the bioclimatic potential was redis-
tributed depending on the peculiarities of specific
areas. Overall, the analysis confirms a consistent trend
of declining carbon input from southwest to northeast,
with variations in intensity observed across specific
regions. Thus, it slightly decreased for the zone of
southern Chernozems (Calcic Chernozems) com-
pared to the natural potential, while it somewhat
increased for the zone of ordinary Chernozems (Hap-
lic Chernozems). In addition, data localization led to
a significant increase in the detail of the results, which
is important for predictions at the municipality level.
The difference in carbon input is observed both in the
negative direction (maximum: –1 t C ha–1 per year)
(i.e., the MIAMI model overestimates the values) and
in the positive direction (maximum: 0.64 t C ha–1
per year). In the latter case, the amount of produced car-
bon is greater in some areas than previously assumed.
Calculations of the average rate of absolute seques-
tration of soil organic carbon based on simulation
according to the RothC model in the Rostov oblast
using the traditional methodology (Fig. 2) (when the
MIAMI model was used to calculate the NPP value)
gave values close to zero (Romanenkov et al., 2023,
2024). On average, a slight emission (–0.007 t C ha–1
per year) was observed for the BAU scenario for all
pixels (more than 171 thousand pixels) related to agri-
cultural lands of the Rostov oblast. When carbon-sav-
ing technologies were used, the emission changed for
carbon accumulation at an average rate of 0.031,
0.062, and 0.127 t C ha–1 per year for SSM1, SSM2,
and SSM3, respectively.
642
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
DOBROVOLSKAYA et al.
Fig. 1. Net primary production (NPP, t C ha–1 per year) for Rostov oblast according to (a) the FAO methodology and (b) Rosstat
data.
050100
(a) (b)
NN
5.0
150 200
4.5
4.0
3.5
3.0
2.5
2.0
0 50 100 150 200
The use of NPP values calculated based on Rosstat
data in modeling made it possible to obtain more
accurate (localized) estimates of the rate of soil carbon
sequestration in arable soils in Rostov oblast (Fig. 3).
The main differences are observed in the zones of
southern Chernozems (Calcic Chernozems), where a
significant increase in the sequestration rate is
observed, while it slightly decreases in the zone of
Fig. 2. Results of calculations based on data using the MIAMI model: absolute rate of soil carbon sequestration in Rostov oblast,
t C ha–1 per year for the BAU, SSM1, SSM2 and SSM3 scenarios.
NN
NN
BAU
SSM2
SSM1
[0.25, 0.20)
[0.20, 0.15 )
[0.15, 0.10)
[0.10, 0.05)
[0.05, 0)
[0, 0.05)
[0.05, 0.10)
[0 .10, 0.15 )
[0.15, 0.20)
[0.20, 0.25]
SSM3
0 50 100150km 0 50 100150km
0 50 100150km 0 50 100150km
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
MAP OF POTENTIAL SEQUESTRATION OF CARBON 643
ordinary Chernozems (Haplic Chernozems). Overall,
a shift towards positive values was observed for the
BAU scenario; it averages 0.032 t C ha–1 per year. On
average, negative values are observed in 12 of the
43 areas of the Rostov oblast; however, all the values
are very close to zero (Table 1). When carbon-saving
technologies are used, all the areas demonstrate posi-
tive dynamics. Carbon accumulated at an average rate
of 0.063 C ha–1 per year for SSM1, 0.096 for SSM2,
and 0.161 for SSM3 for all pixels related to croplands
of Rostov oblast.
Thus, unlike the previous results for the regions of
the Russian Federation (Romanenkov et al., 2024),
the new estimates place Rostov oblast among the
regions with the largest increase in sequestration vol-
umes, given the implementation of scenarios for sus-
tainable soil resource management practices, which
can be estimated at 475–1244 Kt per year.
DISCUSSION
NPP of terrestrial ecosystems is usually estimated
based on the annual increase in plant biomass. To
determine its value over a growing season in a certain
field, all plants in the sampling sites with the known
area are removed and weighed at the end of this sea-
son. Underground biomass should also be taken into
account. For agricultural crops, it will be equal to the
total biomass of the collected commercial and non-
commercial (straw and tops) products, as well as after-
math and root residues. It is impossible to perform
aboveground measurements on all agricultural fields.
In this study, we used estimates based on crop yields
for each area and cultivated area over a period of
10 years. The use of regression equations for convert-
ing yield to crop biomass seems more correct than the
use of a model based on climatic parameters.
Although dynamic carbon models are generally used
as a tool for fundamental studies, they are increasingly
applied at national scales for soil carbon and soil
greenhouse gas inventories. This approach allows for a
transition to the third tier of estimates of changes in soil
carbon stocks according to the IPCC Guidelines for
National Inventories (IPCC, 2006), which makes it
possible to improve inventory approaches. Inclusion
of independent refining data on the carbon f lux
through incoming plant residues into the model places
this estimate at the third tier of accuracy.
Transition to more detailed input data on organic
matter input, namely, the replacement of the NPP
estimate based on climate data with estimates based on
statistical data on crop yields and crop areas for spe-
cific areas, makes it possible to obtain more accurate
data on carbon stocks and the rate of carbon seques-
tration, differentiated for every municipality. This
approach allows regional authorities to take a targeted
Fig. 3. Results of calculations based on local data: absolute rate of soil carbon sequestration in Rostov oblast, t C ha–1 per year for
the BAU, SSM1, SSM2 and SSM3 scenarios.
[0.25, 0.20)
[0.20, 0.15 )
[0.15, 0.10)
[0.10, 0.05)
[0.05, 0)
[0, 0.05)
[0.05, 0.10)
[0 .10, 0.15 )
[0.15, 0.20)
[0.20, 0.25]
NN
NN
BAU
SSM2
SSM1
SSM3
0 50 100150km 0 50 100150km
0 50 100150km 0 50 100150km
644
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
DOBROVOLSKAYA et al.
Table 1. Average absolute rate (t C ha–1 per year) soil carbon sequestration in arable soils in the 0- to 30-cm layer in admin-
istrative municipalities of Rostov oblast for different soil sustainable management scenarios
No. Name of area
Scenarios
BAU SSM1 SSM2 SSM3
1Azovsky –0.015 0.021 0.059 0.135
2 Aks aysk y 0.073 0.117 0.162 0.251
3 Bagaevsky –0. 037 –0. 00 9 0.016 0.074
4 Belokalitvinsky 0.110 0.151 0.193 0.275
5 Bokovsky 0.170 0.219 0.268 0.364
6 Verkhnedonsky 0.141 0.186 0.231 0.319
7 Veselovsky –0.017 0.014 0.0 47 0.111
8 Volgodonskoi 0.017 0.050 0.083 0.154
9 Dubovsky 0.041 0.068 0.096 0.151
10 Egorlyksky 0.063 0.106 0.149 0.234
11 Zavetinsky 0.150 0.186 0.221 0.291
12 Zernogradsky 0.000 0.034 0.068 0.136
13 Zimovnikovsky –0.024 –0.001 0.022 0.065
14 Kagalnitsky 0.001 0.035 0.069 0.138
15 Kamensky 0.088 0.131 0.173 0.258
16 Kasharsky 0.065 0.103 0.143 0.222
17 Konstantinovsky 0.016 0.047 0.082 0.152
18 Krasnosulinsky 0.115 0.161 0.207 0.300
19 Kuibyshevsky –0.005 0.029 0.0 65 0.136
20 Martynovsky 0.003 0.030 0.058 0.115
21 Matveyevo-Kurgansky 0.001 0.035 0.071 0.142
22 Millerovsky 0.075 0.112 0.150 0.225
23 Milyutinsky 0.110 0.150 0.189 0.269
24 Morozovsky 0.063 0.104 0.145 0.228
25 Myasnikovsky –0.025 0.004 0.033 0.094
26 Neklinovsky –0.027 0.005 0.039 0.110
27 Oblivsky 0.015 0.044 0.071 0.127
28 Oktyabrsky 0.086 0.129 0.171 0.256
29 Orlovsky –0.011 0.017 0.046 0.105
30 Peschanokopsky –0.025 0.006 0.038 0.101
31 Proletarsky –0.030 –0.004 0.021 0.073
32 Remontnensky 0.041 0.068 0.095 0.150
33 Rodionovo-Nesvetaisky 0.073 0.114 0.155 0.238
34 Salsky 0.018 0.054 0.090 0.165
35 Semikarakorsky –0.042 –0.017 0.006 0.055
36 Sovetsk y 0.078 0.111 0.14 4 0.210
37 Tarasovsky 0.077 0.117 0.158 0.238
38 Tatsinsky 0.037 0.067 0.098 0.158
39 Ust-Donetsky 0.072 0.109 0.145 0.217
40 Tselinsky –0.013 0.021 0.055 0.123
41 Tsimlyansky 0.022 0.049 0.076 0.130
42 Chertkovsky 0.123 0.167 0.210 0.297
43 Sholokhovsky 0.112 0.151 0.190 0.268
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
MAP OF POTENTIAL SEQUESTRATION OF CARBON 645
approach to the implementation of priority strategies
and programs for agricultural development.
CONCLUSIONS
As part of the work, maps of the organic carbon
sequestration potential in the upper 30 cm of arable
soils in Rostov oblast were made for the first time
using Rosstat data. The Rostov oblast was previously
assessed as a carbon emitter using previous methods;
according to the new estimates, the Rostov oblast gen-
erally accumulates carbon; the average carbon seques-
tration is 0.032 t C ha–1 per year even under the busi-
ness-as-usual scenario.
The results of modeling for Rostov oblast showed
that the approach to calculating the amount of organic
matter entering the soil based on the use of statistical
Rosstat data can be implemented and, consequently,
applied to other subjects of the Russian Federation.
FUNDING
This study was performed as part of the most important
innovative project of national importance “Development of
a system for ground-based and remote monitoring of car-
bon pools and greenhouse gas f luxes in the territory of the
Russian Federation, ensuring the creation of recording data
systems on the fluxes of climate-active substances and the
carbon budget in forests and other terrestrial ecological sys-
tems,” reg. no. 123030300031-6.
ETHICS APPROVAL AND CONSENT
TO PARTICIPATE
This work does not contain any studies involving human
and animal subjects.
CONFLICT OF INTEREST
The authors of this work declare that they have no con-
flicts of interest.
REFERENCES
Bezuglova, O.S., Nazarenko, O.G., and Il’inskaya, I.N.,
Land degradation dynamics in Rostov Oblast, Arid
Ecosyst., 2020, vol. 10, no. 2, pp. 93–98.
Chernova, O.V. and Ryzhova, I.M., Historical trends in
the amount and structure of organic carbon stocks in
natural and managed ecosystems in European Rus-
sia, IOP Conf. Ser.: Earth Environ. Sci., 2020, vol. 438,
p. 012005.
https://doi.org/10.1088/1755-1315/438/1/012005
Chernova, O.V., Alyabina, I.O., Bezuglova, O.S., and Lit-
vinov, Yu.A., Modern state of arable chernozems hu-
mus content at the true steppe. By the example of Ros-
tov Region, Yug Ross.: Ekol., Razvit., 2020, no. 4 (57).
https://doi.org/10.18470/1992-1098-2020-4-99-113
Chernova, O.V., Golozubov, O.M., Alyabina, I.O., et al.,
Integrated approach to spatial assessment of soil organ-
ic carbon in the Russian Federation, Eurasian Soil Sci.,
2021, vol. 54, no. 3, pp. 325–337.
https://doi.org/10.1134/S1064229321030042
Coleman, K. and Jenkinson, D., RothC-26.3-a model for
the turnover of carbon in soil. model description and
windows users guide, Proc. Evaluation of Soil Organic
Matter Models Conf., Harpenden, 1996.
FAO. Global Soil Organic Carbon Sequestration Potential
Map – SOCseq v.1.1. Technical Report, Rome, 2022.
FAO. Technical Specifications and Country Guidelines for
Global Soil Organic Carbon Sequestration Potential Map
(GSOCseq), Rome: FAO, 2020.
Gottschalk, P., Smith, J.U., Wattenbach, M., et al., How
will organic carbon stocks in mineral soils evolve under
future climate? Global projections using RothC for a
range of climate change scenarios, Biogeosciences, 2012,
vol. 9, no. 8.
https://doi.org/10.5194/bg-9-3151-2012
Harris, I., Osborn, T.J., Jones, Ph., et al., Version 4 of the
CRU TS monthly high-resolution gridded multivariate
climate dataset, Sci. Data, 2020, no. 7.
https://doi.org/10.1038/s41597-020-0453-3
Husniev, I., Romanenkov, V., Siptits, S., Pavlova, V., Pas-
ko, S., Yakimenko, O., and Krasilnikov, P., Perspec-
tives on effective long-term management of carbon
stocks in chernozem under future climate conditions,
Agriculture, 2023, vol. 13, p. 1901.
https ://d oi.org/10.339 0/agri culture13101901
IPCC Guidelines for National Greenhouse Gas Inventories,
2006.
Jones, M.B. and Donnelly, A., Carbon sequestration in
temperate grassland ecosystems and the influence of
management, climate and elevated CO2, New Phytol.,
2004, vol. 164, no. 3, pp. 423–439.
h tt ps :/ /d o i. o rg / 10 .1111/ j .14 69 - 8137 .2 0 0 4. 012 01 .x
Krenke, A.N., The way to detect agrolandscapes invariant
state by means of hierarchical factor analysis for distant
data, Printsipy Ekol., 2020, no. 3.
Levin, F.I., Floral wastes amount in field crops seeding and
the way to determine it according to the main product
crop, Agrokhimiya, 1977, no. 8.
Morais, T.G., Teixeira, R.F.M., and Domingos, T., De-
tailed global modelling of soil organic carbon in crop-
land, grassland and forest soils, PLoS One, 2019,
vol. 14, no. 9, p. e0222604.
https://doi.org/10.1371/journal.pone.022260 4
Nazarenko, O.G., Pashkovskaya, T.G., and Chebot-
nikova, E.A., Agrochemical Service of Rostov Region.
50 years on the look-out of arable soils fertility and its
strategic mission, Dostizh. Nauki Tekh. Agroprom.
Kompleksa, 2014, no. 7.
Nemo Klumpp, K., Coleman, K., Dondini, M., et al., Soil
organic carbon (SOC) equilibrium and model initial-
isation methods: an application to the rothamsted
carbon (RothC) model, Environ. Model. Assess., 2016,
vol. 22, no. 3.
https://doi.org/10.1007/s10666-016-9536-0
646
MOSCOW UNIVERSITY SOIL SCIENCE BULLETIN Vol. 79 No. 5 2024
DOBROVOLSKAYA et al.
Oborin, M.S., 2021. Strategic pre-conditions for providing
Rostov Region food security, Reg. Ekon. Yug Ross.,
2021, vol. 9, no. 3.
https://doi.org/10.15688/re.volsu.2021.3.14.
Poggio, L., De Sousa, L.M., Batjes, N.H., et al., SoilGrids 2.0:
Producing soil information for the globe with quantified
spatial uncertainty, Soil, 2021, vol. 7, no. 1.
https://doi.org/10.5194/soil-7-217-2021
Romanenkov, V.A., Meshalkina, Yu.L., Gorbacheva, A.Yu.,
et al., The way to predict carbon storage dynamics in ar-
able soils of European Russia in the frames of low car-
bon development strategy, Izv. Ross. Akad. Nauk, Ser.
Geogr., 2023, vol. 87, no. 4.
https://doi.org/10.31857/S2587556623040106
Romanenkov, V.A., Meshalkina, J.L., Gorbacheva, A.Yu.,
et al., Maps of soil organic carbon sequestration poten-
tial in the Russian croplands, Eurasian Soil Sci., 2024,
vol. 57, no. 5.
https://doi.org/10.1134/S106422932360375X
Smith, J.O., Smith, P., Wattenbach, M., et al., Projected
changes in mineral soil carbon of European croplands
and grasslands, 1990–2080, Global Change Biol., 2005,
vol. 11, no. 12, pp. 2141–2152.
htt p s : //d o i . or g/10.1111 / j.13 6 5-24 8 6 . 2005 . 0 0 1075.x
Stamati, F.E., Nikolaidis, N.P., Schnoor, J.L., Modeling
topsoil carbon sequestration in two contrasting crop
production to set-aside conversions with RothC—cali-
bration issues and uncertainty analysis, Agric., Ecosyst.
Environ., 2013, vol. 165, pp. 190–200.
https ://d oi.org/10.1016/j.agee.2012.11.010
Vinogradova, V.V., Glezer, O.B., Gracheva, R.G., et al.,
Climate change effect onto human potential, economy
and ecosystem, Dokl. k XXIII Yasinskoi (Aprel’skoi) me-
zhdunar. nauch. konf. po problemam razvitiya ekonomiki
i obshchestva (Proc. 23rd Yasinskaya (April) Int. Sci.
Conf. on Economy and Society Development Prob-
lems), Moscow, 2022.
Translated by D. Zabolotny
Publisher’s Note. Allerton Press remains neutral
with regard to jurisdictional claims in published maps
and institutional affiliations.
AI tools may have been used in the translation or
editing of this article.
