Volume 11 - Issue 56
/ August 2022
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DOI: https://doi.org/10.34069/AI/2022.56.08.17
How to Cite:
Chumachenko, O., Kustovska, O., Tymoshevskyi, V., Kolhanova, I., & Kaminetska, O. (2022). Reclamation of the war affected
agricultural land in east of Ukraine. Amazonia Investiga, 11(56), 159-168. https://doi.org/10.34069/AI/2022.56.08.17
Reclamation of the war affected agricultural land in east of Ukraine
Recuperación de las tierras agrícolas afectadas por la guerra en el este de Ucrania
Received: July 26, 2022 Accepted: September 5, 2022
Written by:
Chumachenko Oleksandr75
https://orcid.org/0000-0002-1560-5518
Kustovska Oksana76
https://orcid.org/0000-0003-1469-9249
Tymoshevskyi Vladyslav77
https://orcid.org/0000-0002-3606-7229
Kolhanova Iryna78
https://orcid.org/0000-0001-7771-2696
Kaminetska Oksana79
https://orcid.org/0000-0002-1576-6477
Abstract
One of the most critical components of Ukrainian
economic complex is its agriculture. The
development within the industry is generally
determined by the current economy’s state which
is also impacted by the agricultural indicator. The
research aims to study the renewal and
reclamation of agricultural lands, which has
deteriorated during the military operations
especially in the east of Ukraine. Satellite data set
was used, which was obtained based on
Moderate Resolution Imaging Spectroradiometer
images and covers the whole Ukraine with a
spatial resolution of 232 m. The annual
information on inactive and active agricultural
land was then used to calculate the frequency of
fallow/active land at each pixel level and to
translate the subject/action series on neglect
trajectories. The factors determining reclamation
are related to the suitability of the land for
agriculture. Accessibility to major cities was also
important because most of the renewal and
reclamation occurred closer to population
centers, but this influence varied East of Ukraine.
These factors suggest that renewal and
reclamation patterns were primarily driven by
75
Ph. D. in Economics, Associate Professor National University of life and environmental sciences of Ukraine Faculty of Land
Management, Department Land Use Planning, Ukraine.
76
Ph. D. in Economics, Associate Professor National University of life and environmental sciences of Ukraine, Faculty of Land
Management, Department Land Use Planning, Ukraine.
77
Ph. D. in Economics, Associate Professor Kharkiv National Automobile and Highway University, faculty Road-Building,
Department of road design, geodesy and land management, Ukraine.
78
Associate Ph. D. in Economics Department Land Use Planning, Faculty of Land Management, National University of life and
environmental sciences of Ukraine, Ukraine.
79
Ph. D. in Economics, Associate Professor Bila Tserkva National Agrarian University Faculty of Agro-Biotechnological, Department
of Land Resources Management and Land Cadastre, Ukraine.
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factors related to land productivity, with renewal
and reclamation focused on the most promising
sites.
Keywords: agriculture, renovation, reclamation,
land improvement, farmland.
Introduction
Agricultural development determines a country's
level of food security and helps identify reserves
for future prosperity. Each country chooses its
own style of farming and type for growing
certain crops, livestock or fisheries. The question
of the appropriate type of agricultural activity in
rural settlements or even some areas of urban
areas depends on the type of land, geographic and
climatic zones, temperature regimes, and amount
of rainfall. How citizens use a country's natural
wealth determines the future stability of its
development. Harmful ways of using land,
forests, and water basins lead to catastrophic
consequences.
Generating high incomes from harvesting,
raising livestock or fishing is the basis of food
security. Problems in the agricultural sector can
lead to malnutrition, hunger, and disease among
children and adults. Another aspect, the
effectiveness of agricultural activities in the
country indicates the level of food supply, access
to foreign markets, and availability of raw
materials, and resources for other industries
(Czyżewski & Matuszczak, 2018; Maertens &
Vande Velde, 2017). Trends and prospects for
the development of the industry are determined
by the general state of the national economy,
which in turn is influenced by the dynamics of
the main agricultural indicators. The agricultural
sector of Ukraine affects the development of the
world food market, especially certain sectors, in
particular grain and oil, and fat (Khan & Ashfaq,
2018; Sinha, 2019). In this context, there is
inevitably a contradiction between the resource
potential of agricultural production in Ukraine,
the needs of the domestic market, and the
dynamics of world demand for food. This is
especially relevant due to the fact that the current
successful development of agriculture, especially
in comparison with other sectors of the Ukrainian
economy, has not eliminated a number of
important systemic problems.
These are structural imbalances in the industry,
low efficiency and provision of financial
resources (especially in comparison with
developed countries), Miller et al., (2018)
incomplete land reform, insufficient skills of
workers, and the presence of significant
environmental problems. Moreover, all this
occurs against the background of a high degree
of involvement of the domestic agricultural
sector in the global agro-food system, the
corresponding increase in their interaction in the
framework of increasing global competition
(Weiss, 2021). Delaying the process of solving
these problems can lead to the gradual
degradation of production, loss of export
positions, and even a threat to national food
security in the long term.
The military conflict in Donbas was a turning
point for many Ukrainians, and many families
who had lived and lived peacefully in the East of
Ukraine were forced to migrate from their settled
homes. It is painful to see the number of civilian
casualties, 2,000 of which have been recorded
throughout the hostilities. With the outbreak of
hostilities, the recruitment of a large number of
soldiers to the AFU (Armed Forces of Ukraine)
began. This represents approximately 250,000
persons. Most of them have gone to defend
Ukraine’s territorial integrity in the East; the
tragic fate has befallen many. Namely, some
24,000 people were wounded and about 10,000
people died.
During the fighting, a large number of protected
areas were damaged or contaminated, which
were affected by fires caused by obstetric and
arson to create smoke screens.
Private-rental relations, that concerns a
substantial part of land relations in the country,
impose a number of special features on the
system of land ownership and use, especially in
Eastern Ukraine. Thus, the development of
approaches for assessing the state of land and
developing approaches for the effective use of
land that has been damaged by the hostilities in
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the region is a relevant scientific and practical
task.
Theoretical Framework or Literature Review
The various approaches in agricultural
development research can be divided into several
groups:
1. Approach devoted to the efficiency of
agricultural production in general. Mosavi et
al. (2020), see the efficiency of agriculture
as an increase in agricultural production or
income at minimum material and financial
costs, but with multiple use of land, labor,
material, and technical resources (Mosavi et
al., 2020). The authors study such indicators
as the area of agricultural land and the
productivity of various types of crops, and
vegetables, the results of dairy and poultry
farms, and the dynamics of their
profitability.
2. A comparative analysis of the effectiveness
of public administration of agriculture in
Ukraine and the European Union (EU)
(Miller et al., 2018). Miller et al. (2018)
investigated trends in financial support,
sources of transfers to Ukraine and the EU,
the growth rate of public services, the
structure of spending on public services, and
trends in customer support. The authors
point out the differences in agricultural
support that exist between Ukraine and the
EU.
3. Presentations of quantitative analysis. For
example, some foreign researchers
investigated how farmers' readiness,
advantages, specifics of their activity, size of
farmland, remoteness from urban
settlements, and the number of family
members of the farmer influence the
efficiency of cooperation and income
growth in agriculture (Weiss, 2021).
4. Econometric models. Mosavi et al. (2020)
used econometric models to study the
profitability of small and medium
enterprises over a long period (Mosavi et al.,
2020). They analyzed accounts receivable,
accounts payable, inventories, cash
conversion cycle, and firm profitability, and
then constructed a multivariate regression
model. They presented the results of a
correlation analysis between working capital
and profitability of countless Spanish
manufacturing firms.
A similar approach is demonstrated by Van Es &
Woodard (2017) who considered the problem of
the lack of technology that could ensure both
environmental safety and agricultural growth at
the same time (Van Es & Woodard, 2017).
Quinton et al. (2018) believe that depressions in
agriculture lead to threats to a country's economic
sustainability and food security (Quinton et al.,
2018).
Thus, in various scientific conclusions, they
pointed to the need to turn “traditional agriculture
towards agroecology,” which can prevent not
only food shortages or economic crisis, but also
ensure environmental sustainability (Menne,
2017). This approach changes the understanding
of the goals of agriculture. The term agroecology
means that agriculture should not only be for the
consumption of financial transfers or the use of
its products by other industries but also should
not be harmful to the environment. This is the
mainstream in recent research abroad.
The purpose of the study is to examine the
renewal and reclamation of agricultural land, the
condition of which has deteriorated during the
hostilities in East Ukraine.
Methodology
Maps of recultivation
Satellite dataset 10, was obtained from MODIS.
This satellite is used for covers and pictures that
are obtained from Ukraine with a resolution of
230 m. The “MODIS Normalized Difference
Vegetation Index (NDVI)” time series was
classified into active agricultural lands (i.e., with
active vegetation layer) for each year from 2010
to 2021 with a 90% accuracy, estimated from
independent verification data. Annual inactive
and active farmland information was then used to
calculate the frequency of fallow/active land at
each pixel level and to translate the subject/active
series into neglect and reclamation trajectories
(Abdulfatah et al., 2017). Data collected from the
following mods: MCD12Q2 Version 6 (Gray et
al., 2019). (From where you collected satellite
data); MCD12Q2 Version 6.1 (Gray et al., 2020).
Visual analysis of this dataset showed sites with
no evidence of management over 12 years
(consistently fallow (Abdulfatah et al., 2017).
Some of this land was used during the Soviet era,
such as for cattle grazing. However, the
conversion of meadows along rivers to cropland
is highly unlikely, and so we excluded these areas
from our analysis. The final data set consisted of
462420 hectares of agricultural land.
The available land use/land cover dataset covers
a period of 12 years. We took the period of 2017-
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2021 for analysis, since at that time reclamation
became the dominant process of land change in
Ukraine. In order to clarify the effect of
reclamation, an analysis of images over several
previous years is necessary (Abdulfatah et al.,
2017). Given that any of the crop rotation
systems in Ukraine provide for a fallow period of
more than 5 years, we defined these plots as
“neglected” during 2010-2016. Three binary
datasets were created, one for each reclamation
definition, which were subsequently used as
dependent variables in our models.
Explanatory variables
The highly emphasized level shows that there is
reliable arithmetical data in Ukraine, especially
at the district level. There are 490 districts in
Ukraine. In this paper, we analyzed the Eastern
region of Ukraine, where the hostilities are taking
place. Eastern Ukraine belongs to the steppe zone
(Figure 1, III).
Figure 1. Ukraine Geographical Regions (The continuous line highlights the limitations in zone) (Obtained
form: (National atlas of Ukraine), by L. Rudenko, 2007). I—"Mixed Coniferous zone with forests with
broad leaves”, II—"the forest step zone”, III—"steppe zone”, IV—the “Ukrainian Carpathians”, V—
"Crimean Mountains”
To relate district-level statistics to other datasets,
we used Euro-geographics (EuroGeographics,
2022a) district boundaries, which were manually
verified and, if necessary, enhanced through the
Ukrainian boundaries that are present and viewed
from the Ukraine map (State Service of Ukraine
for geodesy, cartography and cadastre (n.d.)).
The raster format’s variables were highlighted
with their separate resolution that is visible on the
maps. The variables that were biophysically
related to the terrain’s slope and elevation were
received from the 4rh version of the SRTM or the
Shuttle Radar Topography Mission (Maertens &
Vande Velde, 2017). The model consisted of two
variables that allowed for the reflection of
patterns of climate which helped in the testing of
average daily temperatures that were found to be
above 5 degrees. These were obtained at a 1 km
resection calculated from the global climate data.
(Are et al., 2018). The ratio of annual
precipitation to evapotranspiration helped in
identifying the global rigidity index through the
process of soil data being obtained from 1 km
resolution which represented the topsoil pH
under 30cm of layer (Borelli et al., 2019, Carter
et al., 2017).
Multiple variables for accessibility were used to
assess and determine the regional and local
markets along with expenses of facilities and
costs of transportation. The lack of official
information about networks of roads and
boundaries of settlements lead the researchers to
extract data from multiple web resources such as
Euro geographics (EuroGeographics, 2022b).
The Euclidean distance was then calculated
between each avocation and its nearest
settlement. The largest city had a population of
over 50,000 and had paved roads. The distance
calculated from the forest makes to the closes
edge was attained through GlobCORINE
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landscape map (Catacutan et al., 2018).
Therefore, this value was then used as an
indication of the marginality of ecology
pertained to the locale of agriculture and its
relative accessibility. Furthermore, the value is
also a reflection of used land history as the forests
were converted to agricultural areas recently
which changed the landscape.
To be used as a reflection of demographical
conditions, the rural population’s average density
was used as well as the changes in population
between 2010 and 2016. This excluded larger
cities with over 50,000 population. The ratio of
dependency along with the portion of the
employed and registered population where the
individual was over 65 years of age or under 15
was then included in the assessment of the
availability of labor (Smaliychuk et al., 2016).
The effect of management of agriculture was
assessed on district level data and states which
were collected from 2001 to 2006 in yields of
grain. Other indicators were the usage of
fertilizers, and mechanization levels such as the
number of harvesters and tractors (Smaliychuk et
al., 2016).
The statistics about Ukrainian agriculture are
rather lacking when it comes to farms and the
only official data is held by registered
agricultural unties which comprises societies,
operations, joint stock companies, and farms.
The number of farms and the area cultivated have
seen no change as they only occupy 15% of the
study area (Smaliychuk et al., 2016). Several
covariates were inserted into the stream to reduce
the model’s complexity and enhance its
readability. The act of determining the final
variables requires multicollinearity and statistical
analysis. The variables were estimated using a
Pearson correlation value and if any greater than
0.5 were retained then the verifiable showed that
there was a higher level of correlation with the
dependent value. These descriptive statistics
allow for influencing the variables and provide a
wide array of information which can also be
found in Table 2 as per the analysis.
Regression Setup and Design of Sampling
The process involved the evaluation of different
models as per the reclamation definition (Table
1). Furthermore, the model estimation of Ukraine
was kept separate from the models of Eastern
Ukraine. Therefore, six models of regression
were estimated along with a further 3 for separate
ecological areas. Three models of Ukraine were
also used and observations were made only
regarding the agricultural land that was unused
between the time of 2010 and 2016 (Abdulfatah
et al. (2017). The reclamation that took place
from 2017 to 2021 was considered regarding the
observations that we used as code 1 and the
farmland that was wot used between 2017 and
2021 was code 0.
To lessen the impact of autocorrelation, the
observations were made using 500m of spacing
thus resulting in a 16% reduction in the accuracy
of the dataset. These steps ultimately lessened the
observation number to 169387. Then finally the
sampled observations were put in the ecological
areas and global models were selected in
correlation to the reclamation of ecological areas.
Regression Trees Boost
BRTS or boosted regression trees are often made
use of within a framework of regression allowing
for a nonparametric method (Chausson et al.,
2020). This method can view the relationships
which are complex and not linear between
predicting and relevant variables (Cohen-
Shacham et al., 2019). The general thought
behind the boosting processes is the combination
of various weak models into an entity that will
enhance performance (Are et al., 2018). BRTs
involve decision trees that highlight the different
variances of the values by dividing the space in a
fashion reminiscence of binary numbers. The
minimization of enhancement allows for the
functional loss in the decision trees by adding
new decision trees and the existing trees remain
unmodified. This allows the values to be
estimated. The loss observed is the highest in the
first tree while the other trees that come next
focus on the previous model to fit themselves
thereby resulting in an enhancement of the
prediction variables. (Are et al., 2018).
Boosted trees are not used to rebuild data rather
they create links between the missing
information and the predictors that are used in
relationships and interactions (Corwin &
Scudiero, 2019). Making sure the BRT works
perfectly, requires various parameters that are
used in calibration. They include tree
complexity, package fraction, a large number of
trees as well as the learning rate. The
determination of the batch fraction comes from
the samples that fit each model (Are et al., 2018).
A fraction of 0.5 was made use of in the division
to get observations for 10000 models as well as
the test and training samples. The test also led to
the testing of other parameters such as the cross-
evaluation to assess the parameter settings as
well as different measures. The interaction level,
therefore, was chosen at a rate of 0.01 followed
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by a level of 4. The Para termers are then used to
evaluate the number of trees required for the best
prediction in the dismo package (Diop et al.,
2018).
Results and Discussion
The overall areas for the study are differentiated
based on the concept of renovation provided in
Table 1 as well as the 170,000 ha from the
exclusive definition of cultivation that has been
going on for the last 5 years. The 445100 ha for
the last 6 years of cultivation is the intermediate
definition while the comprehensive one is
987800 ha. The place of the farmland that was
abandoned also majorly varies as they are
between different ecological zones. The rates of
reclamation were found to be the highest in the
steep zone which was around 52%.
The linearity analysis of the variables that
explain the phenomenon highlights that a major
relation of 0.5 Pearson is present. The Drought
Index along with the population, change, the
yield of grain, and other variables acted as the
final values that predict the results of the study.
The reclamation methods and their drivers
highlighted the variables of accessibility that
allowed for the overall annual amount of above
5-degree temperatures which contributes to the
performance of the model. The management of
agriculture is another set of explanatory
variables. The most explanatory power in the
models goes towards availability and
temperature. Other variables such as the
mechanization level, employment, as well as slop
have major impacts on the steppe only.
Dependency ratios along with topsoil pH and
organic and mineral fertilizer application did not
see a major contribution to the overall variable
and thus these variables had fewer significances
and varied less than they were expected to. In the
steppe model as well as the global model the
overall distance contribution from the edge sees
a decline from inclusive reclamation. The decline
is from 23% to 16% however the temperature’s
influence and impact increased from 9.7% to
10.3%. Other variables that factor influence
involve unemployment rate, slop, and the nearest
town is all seen to be stable in all the different
definitions of reclamation. The AUC and
accuracy of prediction in the global models show
an increase to 0.83 from 0.76 and 0.96 from 0.86
respectively for exceptional reclamation. The
performance increase is observed in the steep
model and they were far less emphasized than the
global ones. But these models also showed the
highest amount of positive prediction as visible
in Table 4. All the models are similar such that
their distance to the edge effect lies on the
reclamation of probability. The treatment
probability saw a sharp rise as the distance from
the edge function increased however it came to a
halt at the 5km mark which is also the 10km mark
in the steppe model. The distance to the edge
effect was the most profound in the steep model
and then the global model followed by forest-
steppe and mixed forest respectively.
Other variables about accessibility had
significantly less power of explanation such as
distance to population centers. The nearest major
towns and cities variables had little to no impact
on the reclamation in the models. Furthermore,
the high probability observed of reclamation
within the distance to identified areas within
Ukrainian agricultural land shows that the paved
roads and settlements are crucially important in
the steppe models. The daily temperature
increases above 5 degrees also impacted
reclamation in these models. The abandoned
agricultural areas and their reclamation can serve
as a viable opportunity to expand the farm area to
enhance the ecosystem (Frątczak et al., 2019).
The problem however remains that the
determinants of relation such as spatial patterns
are not understood by any. The overall evaluation
showcases that reclamation and renewal is a
major trend in Ukrainian rural areas due to the
absurd prices of products and agricultural foods
in the last decade. The high prices have led to a
major investment in agriculture in Ukraine.
Eastern Ukraine had seen a large amount of
reclamation of up to 50% of all agricultural land.
The reclamation patterns in this study mainly
showcase the factors and reasons that contribute
to product-based agriculture. The reclamation
probability highlights that the projections
showcase the high potential for further expansion
in Eastern Ukraine in agriculture. The red zone
areas of reclamation are land that is free for use
and due to the ideal conditions, it is becoming
less and less available (Daryanto et al., 2018).
The agricultural areas that are abandoned are
being reclaimed at a high speed as it leads to a
major increase in the production of agriculture.
This research can act as a starting point to
understanding their tradeoffs within the
environment.
The unused agricultural land and its plots have
led to the attraction of foreign and domestic
interest in agriculture. The holdings of
agriculture emerged in the early 2000s when
there was a reform after the post-Soviet control
of Ukraine. The agricultural lands and the sectors
allowed for the possibility to distribute and
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divide these plots of land to farm owners and
leased them to generate profit from the
production of agriculture due to the reduction in
costs of raw materials. Other aspects included
access to loans as well as tax and farming
incentives that the government implemented to
encourage the cultivation and reclamation of
agricultural lands (Gunawardana et al., 2018).
The assessment highlights that Ukraine’s eastern
regions do not have enough quality for
abandoned agricultural and reclamation and land
areas can be found elsewhere.
During the evaluation of the reclaimed, area it is
vital to note that the reclamation hotspots and the
spatial structure varied greatly. The analysis
highlighted large urban centers in the steppe area
where reclamation spots were found. These
hotspots can be explained by four different
factors. The first is the growing demand and need
for agricultural products which require an urban
population for storage and transportation
(Dorondel & Serban, 2022). The second is the
presence of necessary people and personnel for
the maintenance of agricultural holdings along
with land (Gatto et al., 2019).
The regression models showcase the patterns of
spatial differentiation where reclamation is
assessed by the agroecological conditions as well
as their accessibility of them. The distance and
temperature to the closest jungle places have a
considerable impact on the reclamation probably
as they happen more often in places where
temperatures are high. The overall suitability of
a given area is emphasized by the ground
available for agriculture, as well as the forest
distance to the nearest place of settlement which
is also connected to higher reclamation
possibility (Frątczak et al., 2019). The same can
be said for cities that are near the steppe zone.
The steppe has recorded high temperatures that
are connected with the change in the climate.
This leads to moisture in the soil and the
requirement for more water for the crops
(Alarcón & Arias, 2019). A suggestion can be
made that the balance of water in the steppe is
necessary for the reclamation of the area. The
results showcase that remote areas are much
more feasible for raw material purchase and
selling agricultural products. There are far better
opportunities and chances to make a profit and a
return on investment for decision-makers.
Therefore, it leads to reason that such policies
about agriculture need to be targeted towards
areas that have positive conditions for sustaining
agriculture. These policies can thus promote the
sustainability of the environment through
afforestation and other farmland cultivation.
The results match up with the factors that were
evaluated for the abandonment of agriculture in
the Soviet Union and eastern Europe. These
agroecological conditions, even though are
marginal, yet they are very unfavorable for the
key determinants that highlight similar neglect
patterns (van Asseldonk et al., 2018). The results
highlighted that the best lands out of the available
ones were those that were reclaimed earlier.
However, the level of neglect among the land
divided by regions and countries was also a
critical factor along with the agroecological
conditions that are often overlooked by the more
institutional and macroeconomic effects. These
include land reforms, reorganization of lands as
well as economic state and support. Other
miscellaneous variables include farmer skills and
farming structure (Blazquez et al., 2018).
By unitizing the global models that aim to
highlight reclamation, the steppe zone has the
vastest expansion of cropland along with better
soils that take precedence over the neglected
parts. Even though there are frequent droughts
within the last few years, the models suggest that
the infrastructure is improved and the steppe
zone has vital constraints that are used to farming
the region, therefore, enhancing the investment
opportunities there. A large scale of forestation
has already taken place in Northern Ukrainian
areas thus it would be costly to reclaim those
areas (Eriksen et al., 2021). Haymaking and
cattle grazing are two ways in which reclaiming
takes place while there are other viable options as
well that surpass the effectiveness of traditional
agriculture (Lin, 2022).
Finally, military conflict in eastern Ukraine has
affected where reclamation (van Asseldonk et al.,
2018) occurs and is likely to significantly reduce
foreign investment in eastern Ukrainian
agriculture (Cristan et al., 2019).
State financial support is an important element in
the long term. It ensures intensive innovative
development of the agricultural region (Denisova
et al., 2021). One of the essential elements of
financial support is the modernization and
improvement of the state of the material and
technical base (Smaliychuk et al., 2016). The
combination of these factors (i.e., financial
support and modernization of the material and
technical base) is a necessary condition for the
reclamation of lands devastated as a result of
military operations. The study of agricultural
reclamation patterns and drivers is based on
spatial and temporal factors and a nonparametric
regression model, which is a powerful means of
explaining the most influential factors and
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predicting reclamation patterns. The study of the
process of land reclamation in Ukraine was
carried out in the past, during 2006-2016
(Smaliychuk et al., 2016). A key factor in
reclamation is the suitability of land for planting
crops. This is determined by abiotic indicators -
soil types and temperature regime. In addition,
the presence of a large settlement nearby
agricultural land is of great importance, although
this factor was not as important as the first one.
Thus, the most effective reclamation takes place
in promising areas with fertile soils, but near
large urban agglomerations.
There are however uncertain sources that need to
be highlighted. The first of which is the result of
an error during the sensing of data. Climate
fluctuations are a contributing factor that leads to
the dry year pixelation that is prominent when the
cropless has not seen a large-scale harvest. This
can lead to unmanaged and barren land.
Furthermore, the difficulty in separating the
unmanaged lands from the managed ones lead to
the exclusion of dependent lands from the study
area because they had become permanent
grasslands situated alongside rivers. However,
their omission means that the rates of reclamation
cannot be overestimated. Secondly, eastern
Ukraine’s agricultural system follows a specific
time which leads to the misclassification of the
dataset provided by the satellites due to the range
of testing that took place over 6 years ago. These
definitions showcase that field reclamation and
reliability of the analysis lie within the space of
agricultural holdings and large private farms.
Lastly, various observations were made except
for the steppe model. This leads to the conclusion
that the data for the entirety of Ukraine is not
available.
Conclusions
Land renewal and reclamation is a necessary
measure, starting with the return of land to
optimal qualities for further use in public
production. Through the restoration of areas over
a period of time it is possible to return the area to
sustainable development and peaceful life for the
population.
The key factors determining reclamation are
related to the suitability of the land for
agriculture (e.g., soil quality, temperature).
Accessibility to major cities was also important
because much of the renewal and reclamation
occurred closer to population centers, but this
influence varied east of Ukraine. Variables
related to agricultural management (fertilizer
application, mechanization) and demography
were insignificant in explaining renewal and
reclamation in the study. These factors suggest
that renewal and reclamation patterns were
primarily driven by factors related to land
productivity, with renewal and reclamation
focused on the most promising sites.
Although the study did not assess the
environmental costs of renovation and
reclamation, it was shown that they have become
the dominant land-use trend in the region since
2017 and that renovation and reclamation were
primarily associated with agricultural entities
targeting unused. land with the greatest
agricultural suitability and therefore potential
profitability. These results provide a starting
point for assessing where renewal and
reclamation might occur and, therefore, what the
production possibilities and socioeconomic and
environmental consequences of renewal might
be. Predicting where future renewal may occur in
eastern Ukraine suggests that this area will be the
focus because unused farmland there is still more
prevalent than in the most fertile steppe zone.
The resulting models also have implications for
the release of unused productive capacity,
highlighting major constraints on renewal and
reclamation, mainly affordability that can be
addressed through infrastructure investment.
Given the significant area of currently unused
agricultural land in Eastern Europe and the
former Soviet Union, our results provide
important information about the neglected
process of land change and an assessment of the
socioeconomic and environmental consequences
of renovation.
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