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Full Research Article

Land Transformation and its Impact on Soil Quality, Bonitation Scores and Cadastral Value Through Economic-Mathematical Modeling

I
Inga Beglaryan1,*
https://orcid.org/0000-0002-8443-3254
A
Anush Harutyunyan2
0009-0009-3092-5331 
1Hrant Petrosyan Scientific Center of Soil Science, Agrochemistry and Melioration Branch of Armenian National Agrarian University (ANAU) Foundation, 24 Isakov Ave., Yerevan 0004, RA.
2Institute of Economics after M. Kotanyan, The National Academy of Sciences of Armenia.

ABSTRACT

Background: Land transformation significantly impacts the physical, legal, and economic characteristics of agricultural resources. This study explores the role of economic-mathematical modeling in optimizing land use efficiency through land cover transformation in the Aghavnadzor community of Vayots Dzor, Armenia.

Methods: The research focused on a 7.5-hectare area (1450-1600m altitude) from 2021 to 2024. Characterized by a dry subtropical climate and humus-poor gray soils, the site served as a controlled environment to minimize climatic variability. The methodology integrated soil chemical analysis, bonitation (soil quality) assessment, and cadastral valuation within an economic-mathematical framework to evaluate the transition from low-value to high-value agricultural land types.

Result: Findings reveal that strategic land transformation substantially improves soil chemical composition, including organic matter and essential nutrients. Quantitative analysis showed a 20-30% increase in bonitation scores, leading to a proportional rise in cadastral value. The mathematical model successfully identified resource constraints and “bottlenecks,” providing a robust framework for sustainable land management. Notably, the transformation yielded a projected net community income of $17,000 USD, demonstrating significant local economic benefits. This integrated approach offers a scientifically grounded decision-making tool for land use planning. By linking soil properties with modern agricultural practices and economic outcomes, the study provides a scalable model for enhancing land productivity and rural financial stability in similar geographic regions.

INTRODUCTION

In the modern world, the efficient use of land resources plays a crucial role in agriculture, economics and environmental sustainability. Land is not only a fundamental means of agricultural production but also an essential spatial, ecological and economic resource. Changes in land use-whether caused by urbanization, industrial expansion, agricultural development, or natural processes-significantly alter the physical, chemical and legal characteristics of land (Zhang et al., 2023; Bykowa and Banikevich, 2025).
       
These transformations affect soil fertility, land-use suitability and ecological value, as well as land assessment indicators such as bonitation scores and cadastral value, which are vital for taxation, planning and investment decision-making.
       
In contemporary agriculture, the application of scientific and technological approaches, particularly mathematical modeling, has become increasingly important (Tegenie et al., 2022; Smith et al., 2016; FAO, 2017). Economic-mathematical modeling serves as an effective tool for analyzing and optimizing land use efficiency, evaluating resource limitations and developing strategies for sustainable development (Walangitan et al., 2012; Xin et al., 2020; Taratula et al., 2019; Ma and Wen, 2021).  Through such modeling, it is possible to identify the key limiting factors-often referred to as “bottlenecks”-that restrict efficiency and to design strategies for improving land productivity and economic performance.
       
The main objective of this study is to assess the impact of land cover transformation on soil qualitative properties, bonitation scores and cadastral values through the application of economic-mathematical modeling. This integrated approach enables the evaluation of how converting low-value land types into more productive ones can enhance soil fertility, improve agricultural potential and increase the economic and cadastral value of land (Volungevicius and Skorupskas, 2011; Zhang et al., 2023).
       
The study combines soil chemical analysis, bonitation assessment and cadastral valuation to develop a comprehensive understanding of how land transformation affects both the qualitative and economic aspects of agricultural resources. The results aim to provide a scientific and practical basis for sustainable land management policies, inform agricultural investment decisions and support the optimization of spatial and economic planning in similar geographic and climatic regions (Fig 1).

Fig 1: Flow diagram of land use changes and their effects on the soil.

MATERIALS AND METHODS

Land transformation, particularly the conversion of low-value agricultural lands into higher-value types, is of special relevance in many regions of Armenia, where natural and climatic conditions are diverse and often present challenges for intensive agricultural development. For this study, the Aghavnadzor community in the Vayots Dzor region was selected as the research area (Fig 2). The administrative area of   the community is 4,682 hectares: 466 hectares of arable land, 1,071 hectares of pasture and 379.4 hectares of orchards (Armenian Ministry of Agriculture, 2022).

Fig 2: Optimal scheme for land transformation.


       
The altitude is 1,450-1,600 meters above sea level and in the central part it is 1,530 meters high, characterized by a subtropical dry climate and humus-poor gray soils. The Aghavnadzor plateau has an ancient volcanic structure and is cut by numerous small rivers and ravines. The climate is characterized by a subtropical dry climate, with dry and hot summers and wet, moderately cold winters. The amount of precipitation varies from 350-400 mm. Autumn is long and warm, under which conditions fruits ripen, especially various types of grapes. The main branch of the village economy is agriculture, mainly engaged in fruit growing and viticulture.
       
The choice of this area ensured stable altitude and climatic conditions, minimizing external variability and allowing for more accurate modeling and assessment of land transformation impacts.
       
One of the main challenges in land management is developing efficient schemes for the transformation of agricultural land types, which allows for a transition from the current land use structure to a more effective land use system (Volkov, 2001; Walangitan et al., 2012; Dash et al., 2025; Hurni, 2000). To develop the economic-mathematical model, let  represent the area of the i-th plot of land that is transformed into -ith plot, considering different types of land. The system of constraints will be as follows (Taratula et al., 2019; Dobrovolsky and Kireeva, 2019, Klimenko et al., 2024).
 
A constraint based on the availability of land suitable for transformation:

 
A constraint based on the financial expenditure required for the transformation‰

 
A constraint based on the use of machinery and mechanisms (capacity of both in-house and contracted construction organizations)‰

 
A constraint based on the use of fertilizers.

 
A constraint based on the efficient use of labor resources:


A constraint based kn the use of irrigation water (community irrigation):


A constraint based on the capital investment costs for transformation:

 
A constraint based on the use of treatment materials:


A constraint based on the effectiveness of capital investments.
The payback period for capital investments (Tt) is determined by the following formula:


The non-negativity condition for the variables can be expressed as the objective function takes the following form:


The net income determined using the following formula‰

RESULTS AND DISCUSSION

The transformation of agricultural land should be carried out based on the condition that the economic efficiency of the economy strives for its maximum potential (Table 1,2).

Table 1: Key variables in land transformation.



Table 2: Baseline data for the problem.


       
The expanded numerical matrix of the problem is presented in Table 3, while the results of the optimization solution are presented in Table 4. The problem is a linear programming problem, which was solved using an Excel program’s Solver subroutine. 

Table 3: The expanded numerical matrix of the problem.



Table 4: The results of the problems computational solution.


       
Thus, in the Aghavnadzor community of Vayots Dzor, Armenia, it is necessary to transform 2.5 hectares were converted into a mulberry orchard, 1.2 hectares of grassland were converted into arable land, 2.6 hectares of pasture were transformed into grassland and 1.2 hectares of other land types were converted into arable land  (Table 4).  The calculations have been made in Armenian drams and the final result is presented in dollars. As a result of the conversion, the Aghavnadzor community will receive an income equivalent to 17.000 dollars in drams; in a similar solution to the problem, the proposed method is applicable to any community, providing the opportunity to transition from the current land use structure to a more efficient land management system.
       
The results of soil transformation confirm the theoretical expectations of how soil quality improvement and optimal land-use modeling can enhance agricultural and economic benefits (Gasparyan et al., 2025; Beglaryan et al., 2025; Zhang et al., 2023).
 
Improvement of soil chemical composition
 
After transformation, soil organic matter increased from 1.2% to 2.1% (+75%) and the main nutrients-nitrates, phosphorus and potassium-rose by 57%, 44% and 33%, respectively. These improvements directly influenced soil fertility and increased the bonitation score from 45 to 58, demonstrating that well-planned land transformation strategies can effectively restore or improve low- and medium-value soils (Table 5).

Table 5: Changes in soil quality and economic indicators before and after transformation.


 
Increase in bonitation and cadastral value
 
The bonitation score rose by 28.9% and the cadastral value increased by 36%, highlighting the close link between soil quality and economic valuation (Bykowa and Banikevich, 2025; Volkov, 2001). The increase in cadastral value indicates that the land has become not only more fertile but also economically more valuable, which is important for community investment and tax planning (Table 6).

Table 6: Bonitation scores and cadastral values.


 
Efficiency of the economic-mathematical model
 
The model identified key resource constraints (bottlenecks) and suggested optimal adjustments. The calculated net income, Zmax = 17,000 USD, confirms that the model accurately predicts real economic benefits and can serve as a basis for community land management strategies.
 
Comparison of pre- and post-transformation conditions
 
Analysis shows that all key indicators-from soil chemical composition to financial profit-improved significantly, proving that transforming low-value lands into high-value lands has strong agricultural and socio-economic potential.
 
Practical and applicable recommendations
 
Modern mathematical models can be used to plan efficient and sustainable land-use strategies for different land types.
Soil restoration and improvement efforts should be combined with economic modeling to maximize income. The model is extendable to other similar regions with comparable climate and soil conditions.

CONCLUSION

Land transformation combined with economic-mathematical modeling demonstrated that low-value agricultural lands can be efficiently converted into high-value lands. Transfor-mation resulted in improved soil chemical composition, a 20-30% increase in bonitation score and a significant rise in cadastral value.
       
The model effectively identified key constraints and suggested optimal land-use strategies, enhancing productivity, economic efficiency and community financial well-being (net income Zmax = 17,000 USD).
       
The results highlight the strong connection between soil properties, modern agricultural practices and mathematical modeling, showing that integrated approaches can ensure sustainable and efficient land management in the studied area and other regions with similar climatic and soil conditions.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

REFERENCES


  1. Armenian Ministry of Agriculture (2022). Land valuation and bonitation guidelines. Yerevan: Government of Armenia.

  2. Beglaryan, I., Eloyan, A., Daveyan, S., Jhangiryan, T., Yeritsyan, S. and Gasparyan, G. (2025). Effectiveness of pumpkin cultivation in crop rotation on forest brown soil. Bioactive Compounds in Health and Disease. 8(1): 1-10. https://doi.org/10. 31989/ bchd.8i1.1548.

  3. Beglaryan, I., Gasparyan, G., Khachatryan, S., Yeritsyan, S., Eloyan, A. and Jhangiryan, T. (2025). The change in functional components in mulberry fruits through the development of an optimal land transformation scheme for different land types. Bioactive Compounds in Health and Disease. 8(3). https://doi.org/10.31989/bchd.v8i3.1583.

  4. Bykowa, I. and Banikevich, T. (2025). Relationship between the integral indicator of soil quality and the cadastral value of agricultural lands. Land. 14(5): 941. https://doi.org/10.3390/land 14050941.

  5. Dash, R.K., Krivins, A. and Kaze, V. (2025). Economic evaluation of land in agribusiness: Soil fertility factor. Access to Science, Business, Innovation in Digital Economy. 6(1): 25-45. https:// doi.org/10.46656/access.2025.6.1(2).

  6. Dobrovolsky, G. and Kireeva, I. (2019). Economic-mathematical modeling in agricultural land use optimization. Agricultural Systems. 172: 45-58.

  7. FAO (2017). The state of the world’s land and water resources for food and agriculture. Rome: Food and Agriculture Organization.

  8. Gasparyan, G., Eloyan, A., Jhangiryan, T., Markosyan, A., Beglaryan, I. and Barseghyan, M. (2025). Wheat production manage- ment in saline soils through the use of vinasse. Functional Food Science. 5(1): 20-29. https://doi.org/10.31989/ ffs.v5i1.1539.

  9. Hurni, H. (2000). Soil degradation, land use and sustainable develop- ment in mountain regions. Mountain Research and Development. 20(4): 324-329.

  10. Klimenko, K., Melnichuk, A. and Popovich, V. (2024). Geoinformation modeling of agricultural land transformation. BIO Web of Conferences. 141: 02026. https://doi.org/10.1051/bioconf/ 202414102026.

  11. Ma, S. and Wen, Z. (2021). Optimization of land use structure to balance economic benefits and ecosystem services under uncertainties: A case study in Wuhan, China. Journal of Cleaner Production. 15: 127537. https://doi.org/10.1016/j.jclepro.2021.127537.

  12. Smith, P., House, J.I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., West, P.C., Clark, J.M., Adhya, T., Rumpel, C., Paustian,  K., Kuikman, P., Cotrufo, M.F., Elliott, J.A., Lal, R. and Richter, R. (2016). Global change pressures on soils from land use and management. Global Change Biology. 22(3): 1008-102.

  13. Taratula, R., Kovalyshyn, O. and Ryzhok, Z. (2019). Application of mathematical modelling for optimization of land-use management. Real Estate Management and Valuation. 27(3): 59-68. https://doi.org/10.2478/remav-2019-0025.

  14. Tegenie, M., Ashebir, S. and Heliyon, S. (2022). Potential of mathe- matical model-based decision making to promote sustainable performance of agriculture in developing countries: A review. Heliyon. 8(2): e08968. https://doi.org/10.1016/j.heliyon. 2022.e08968.

  15. Volkov, S.N. (2001). Land management: Economic and mathematical methods and models. Kolos. (in Russian).

  16. Volungevicius, J. and Skorupskas, R. (2011). Classification of anthro- pogenic soil transformation. Geologija. 53(4). https:// doi.org/10.6001/geologija.v53i4.1904.

  17. Walangitan, H.D., Setiawan, B., Raharjo, B.T. and Polii, B. (2012). Optimization of land use and allocation to ensure sustainable agriculture in the catchment area of Lake Tondano, Indonesia. International Journal of Civil and Environmental Engineering (IJCEE-IJENS). 12(3): 68-75.

  18. Xin, Z., Dominique, Y., Keeney, R. and Wallace, T. (2020). Improving the way land use change is handled in economic models. Economic Modelling. 84: 13-26. https://doi.org/10.1016/ j.econmod.2019.03.003.

  19. Zhang, Z., Ghazali, S., Miceikienë, A., Zejak, D., Choobchian, S., Pietrzykowski, M. and Azadi, H. (2023). Socio-economic impacts of agricultural land conversion: A meta-analysis. Land Use Policy. 132: 106831. https://doi.org/10.1016/j. landusepol.2023.106831.
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    Full Research Article

    Land Transformation and its Impact on Soil Quality, Bonitation Scores and Cadastral Value Through Economic-Mathematical Modeling

    I
    Inga Beglaryan1,*
    https://orcid.org/0000-0002-8443-3254
    A
    Anush Harutyunyan2
    0009-0009-3092-5331 
    1Hrant Petrosyan Scientific Center of Soil Science, Agrochemistry and Melioration Branch of Armenian National Agrarian University (ANAU) Foundation, 24 Isakov Ave., Yerevan 0004, RA.
    2Institute of Economics after M. Kotanyan, The National Academy of Sciences of Armenia.

    ABSTRACT

    Background: Land transformation significantly impacts the physical, legal, and economic characteristics of agricultural resources. This study explores the role of economic-mathematical modeling in optimizing land use efficiency through land cover transformation in the Aghavnadzor community of Vayots Dzor, Armenia.

    Methods: The research focused on a 7.5-hectare area (1450-1600m altitude) from 2021 to 2024. Characterized by a dry subtropical climate and humus-poor gray soils, the site served as a controlled environment to minimize climatic variability. The methodology integrated soil chemical analysis, bonitation (soil quality) assessment, and cadastral valuation within an economic-mathematical framework to evaluate the transition from low-value to high-value agricultural land types.

    Result: Findings reveal that strategic land transformation substantially improves soil chemical composition, including organic matter and essential nutrients. Quantitative analysis showed a 20-30% increase in bonitation scores, leading to a proportional rise in cadastral value. The mathematical model successfully identified resource constraints and “bottlenecks,” providing a robust framework for sustainable land management. Notably, the transformation yielded a projected net community income of $17,000 USD, demonstrating significant local economic benefits. This integrated approach offers a scientifically grounded decision-making tool for land use planning. By linking soil properties with modern agricultural practices and economic outcomes, the study provides a scalable model for enhancing land productivity and rural financial stability in similar geographic regions.

    INTRODUCTION

    In the modern world, the efficient use of land resources plays a crucial role in agriculture, economics and environmental sustainability. Land is not only a fundamental means of agricultural production but also an essential spatial, ecological and economic resource. Changes in land use-whether caused by urbanization, industrial expansion, agricultural development, or natural processes-significantly alter the physical, chemical and legal characteristics of land (Zhang et al., 2023; Bykowa and Banikevich, 2025).
           
    These transformations affect soil fertility, land-use suitability and ecological value, as well as land assessment indicators such as bonitation scores and cadastral value, which are vital for taxation, planning and investment decision-making.
           
    In contemporary agriculture, the application of scientific and technological approaches, particularly mathematical modeling, has become increasingly important (Tegenie et al., 2022; Smith et al., 2016; FAO, 2017). Economic-mathematical modeling serves as an effective tool for analyzing and optimizing land use efficiency, evaluating resource limitations and developing strategies for sustainable development (Walangitan et al., 2012; Xin et al., 2020; Taratula et al., 2019; Ma and Wen, 2021).  Through such modeling, it is possible to identify the key limiting factors-often referred to as “bottlenecks”-that restrict efficiency and to design strategies for improving land productivity and economic performance.
           
    The main objective of this study is to assess the impact of land cover transformation on soil qualitative properties, bonitation scores and cadastral values through the application of economic-mathematical modeling. This integrated approach enables the evaluation of how converting low-value land types into more productive ones can enhance soil fertility, improve agricultural potential and increase the economic and cadastral value of land (Volungevicius and Skorupskas, 2011; Zhang et al., 2023).
           
    The study combines soil chemical analysis, bonitation assessment and cadastral valuation to develop a comprehensive understanding of how land transformation affects both the qualitative and economic aspects of agricultural resources. The results aim to provide a scientific and practical basis for sustainable land management policies, inform agricultural investment decisions and support the optimization of spatial and economic planning in similar geographic and climatic regions (Fig 1).

    Fig 1: Flow diagram of land use changes and their effects on the soil.

    MATERIALS AND METHODS

    Land transformation, particularly the conversion of low-value agricultural lands into higher-value types, is of special relevance in many regions of Armenia, where natural and climatic conditions are diverse and often present challenges for intensive agricultural development. For this study, the Aghavnadzor community in the Vayots Dzor region was selected as the research area (Fig 2). The administrative area of   the community is 4,682 hectares: 466 hectares of arable land, 1,071 hectares of pasture and 379.4 hectares of orchards (Armenian Ministry of Agriculture, 2022).

    Fig 2: Optimal scheme for land transformation.


           
    The altitude is 1,450-1,600 meters above sea level and in the central part it is 1,530 meters high, characterized by a subtropical dry climate and humus-poor gray soils. The Aghavnadzor plateau has an ancient volcanic structure and is cut by numerous small rivers and ravines. The climate is characterized by a subtropical dry climate, with dry and hot summers and wet, moderately cold winters. The amount of precipitation varies from 350-400 mm. Autumn is long and warm, under which conditions fruits ripen, especially various types of grapes. The main branch of the village economy is agriculture, mainly engaged in fruit growing and viticulture.
           
    The choice of this area ensured stable altitude and climatic conditions, minimizing external variability and allowing for more accurate modeling and assessment of land transformation impacts.
           
    One of the main challenges in land management is developing efficient schemes for the transformation of agricultural land types, which allows for a transition from the current land use structure to a more effective land use system (Volkov, 2001; Walangitan et al., 2012; Dash et al., 2025; Hurni, 2000). To develop the economic-mathematical model, let  represent the area of the i-th plot of land that is transformed into -ith plot, considering different types of land. The system of constraints will be as follows (Taratula et al., 2019; Dobrovolsky and Kireeva, 2019, Klimenko et al., 2024).
     
    A constraint based on the availability of land suitable for transformation:

     
    A constraint based on the financial expenditure required for the transformation‰

     
    A constraint based on the use of machinery and mechanisms (capacity of both in-house and contracted construction organizations)‰

     
    A constraint based on the use of fertilizers.

     
    A constraint based on the efficient use of labor resources:


    A constraint based kn the use of irrigation water (community irrigation):


    A constraint based on the capital investment costs for transformation:

     
    A constraint based on the use of treatment materials:


    A constraint based on the effectiveness of capital investments.
    The payback period for capital investments (Tt) is determined by the following formula:


    The non-negativity condition for the variables can be expressed as the objective function takes the following form:


    The net income determined using the following formula‰

    RESULTS AND DISCUSSION

    The transformation of agricultural land should be carried out based on the condition that the economic efficiency of the economy strives for its maximum potential (Table 1,2).

    Table 1: Key variables in land transformation.



    Table 2: Baseline data for the problem.


           
    The expanded numerical matrix of the problem is presented in Table 3, while the results of the optimization solution are presented in Table 4. The problem is a linear programming problem, which was solved using an Excel program’s Solver subroutine. 

    Table 3: The expanded numerical matrix of the problem.



    Table 4: The results of the problems computational solution.


           
    Thus, in the Aghavnadzor community of Vayots Dzor, Armenia, it is necessary to transform 2.5 hectares were converted into a mulberry orchard, 1.2 hectares of grassland were converted into arable land, 2.6 hectares of pasture were transformed into grassland and 1.2 hectares of other land types were converted into arable land  (Table 4).  The calculations have been made in Armenian drams and the final result is presented in dollars. As a result of the conversion, the Aghavnadzor community will receive an income equivalent to 17.000 dollars in drams; in a similar solution to the problem, the proposed method is applicable to any community, providing the opportunity to transition from the current land use structure to a more efficient land management system.
           
    The results of soil transformation confirm the theoretical expectations of how soil quality improvement and optimal land-use modeling can enhance agricultural and economic benefits (Gasparyan et al., 2025; Beglaryan et al., 2025; Zhang et al., 2023).
     
    Improvement of soil chemical composition
     
    After transformation, soil organic matter increased from 1.2% to 2.1% (+75%) and the main nutrients-nitrates, phosphorus and potassium-rose by 57%, 44% and 33%, respectively. These improvements directly influenced soil fertility and increased the bonitation score from 45 to 58, demonstrating that well-planned land transformation strategies can effectively restore or improve low- and medium-value soils (Table 5).

    Table 5: Changes in soil quality and economic indicators before and after transformation.


     
    Increase in bonitation and cadastral value
     
    The bonitation score rose by 28.9% and the cadastral value increased by 36%, highlighting the close link between soil quality and economic valuation (Bykowa and Banikevich, 2025; Volkov, 2001). The increase in cadastral value indicates that the land has become not only more fertile but also economically more valuable, which is important for community investment and tax planning (Table 6).

    Table 6: Bonitation scores and cadastral values.


     
    Efficiency of the economic-mathematical model
     
    The model identified key resource constraints (bottlenecks) and suggested optimal adjustments. The calculated net income, Zmax = 17,000 USD, confirms that the model accurately predicts real economic benefits and can serve as a basis for community land management strategies.
     
    Comparison of pre- and post-transformation conditions
     
    Analysis shows that all key indicators-from soil chemical composition to financial profit-improved significantly, proving that transforming low-value lands into high-value lands has strong agricultural and socio-economic potential.
     
    Practical and applicable recommendations
     
    Modern mathematical models can be used to plan efficient and sustainable land-use strategies for different land types.
    Soil restoration and improvement efforts should be combined with economic modeling to maximize income. The model is extendable to other similar regions with comparable climate and soil conditions.

    CONCLUSION

    Land transformation combined with economic-mathematical modeling demonstrated that low-value agricultural lands can be efficiently converted into high-value lands. Transfor-mation resulted in improved soil chemical composition, a 20-30% increase in bonitation score and a significant rise in cadastral value.
           
    The model effectively identified key constraints and suggested optimal land-use strategies, enhancing productivity, economic efficiency and community financial well-being (net income Zmax = 17,000 USD).
           
    The results highlight the strong connection between soil properties, modern agricultural practices and mathematical modeling, showing that integrated approaches can ensure sustainable and efficient land management in the studied area and other regions with similar climatic and soil conditions.

    CONFLICT OF INTEREST

    The authors declare no conflict of interest.

    REFERENCES


    1. Armenian Ministry of Agriculture (2022). Land valuation and bonitation guidelines. Yerevan: Government of Armenia.

    2. Beglaryan, I., Eloyan, A., Daveyan, S., Jhangiryan, T., Yeritsyan, S. and Gasparyan, G. (2025). Effectiveness of pumpkin cultivation in crop rotation on forest brown soil. Bioactive Compounds in Health and Disease. 8(1): 1-10. https://doi.org/10. 31989/ bchd.8i1.1548.

    3. Beglaryan, I., Gasparyan, G., Khachatryan, S., Yeritsyan, S., Eloyan, A. and Jhangiryan, T. (2025). The change in functional components in mulberry fruits through the development of an optimal land transformation scheme for different land types. Bioactive Compounds in Health and Disease. 8(3). https://doi.org/10.31989/bchd.v8i3.1583.

    4. Bykowa, I. and Banikevich, T. (2025). Relationship between the integral indicator of soil quality and the cadastral value of agricultural lands. Land. 14(5): 941. https://doi.org/10.3390/land 14050941.

    5. Dash, R.K., Krivins, A. and Kaze, V. (2025). Economic evaluation of land in agribusiness: Soil fertility factor. Access to Science, Business, Innovation in Digital Economy. 6(1): 25-45. https:// doi.org/10.46656/access.2025.6.1(2).

    6. Dobrovolsky, G. and Kireeva, I. (2019). Economic-mathematical modeling in agricultural land use optimization. Agricultural Systems. 172: 45-58.

    7. FAO (2017). The state of the world’s land and water resources for food and agriculture. Rome: Food and Agriculture Organization.

    8. Gasparyan, G., Eloyan, A., Jhangiryan, T., Markosyan, A., Beglaryan, I. and Barseghyan, M. (2025). Wheat production manage- ment in saline soils through the use of vinasse. Functional Food Science. 5(1): 20-29. https://doi.org/10.31989/ ffs.v5i1.1539.

    9. Hurni, H. (2000). Soil degradation, land use and sustainable develop- ment in mountain regions. Mountain Research and Development. 20(4): 324-329.

    10. Klimenko, K., Melnichuk, A. and Popovich, V. (2024). Geoinformation modeling of agricultural land transformation. BIO Web of Conferences. 141: 02026. https://doi.org/10.1051/bioconf/ 202414102026.

    11. Ma, S. and Wen, Z. (2021). Optimization of land use structure to balance economic benefits and ecosystem services under uncertainties: A case study in Wuhan, China. Journal of Cleaner Production. 15: 127537. https://doi.org/10.1016/j.jclepro.2021.127537.

    12. Smith, P., House, J.I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., West, P.C., Clark, J.M., Adhya, T., Rumpel, C., Paustian,  K., Kuikman, P., Cotrufo, M.F., Elliott, J.A., Lal, R. and Richter, R. (2016). Global change pressures on soils from land use and management. Global Change Biology. 22(3): 1008-102.

    13. Taratula, R., Kovalyshyn, O. and Ryzhok, Z. (2019). Application of mathematical modelling for optimization of land-use management. Real Estate Management and Valuation. 27(3): 59-68. https://doi.org/10.2478/remav-2019-0025.

    14. Tegenie, M., Ashebir, S. and Heliyon, S. (2022). Potential of mathe- matical model-based decision making to promote sustainable performance of agriculture in developing countries: A review. Heliyon. 8(2): e08968. https://doi.org/10.1016/j.heliyon. 2022.e08968.

    15. Volkov, S.N. (2001). Land management: Economic and mathematical methods and models. Kolos. (in Russian).

    16. Volungevicius, J. and Skorupskas, R. (2011). Classification of anthro- pogenic soil transformation. Geologija. 53(4). https:// doi.org/10.6001/geologija.v53i4.1904.

    17. Walangitan, H.D., Setiawan, B., Raharjo, B.T. and Polii, B. (2012). Optimization of land use and allocation to ensure sustainable agriculture in the catchment area of Lake Tondano, Indonesia. International Journal of Civil and Environmental Engineering (IJCEE-IJENS). 12(3): 68-75.

    18. Xin, Z., Dominique, Y., Keeney, R. and Wallace, T. (2020). Improving the way land use change is handled in economic models. Economic Modelling. 84: 13-26. https://doi.org/10.1016/ j.econmod.2019.03.003.

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