IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v10y2021i10p1097-d658185.html
   My bibliography  Save this article

Land Use Demands for the CLUE-S Spatiotemporal Model in an Agroforestry Perspective

Author

Listed:
  • Georgios Mamanis

    (University of Thessaly, GR-38221 Volos, Greece)

  • Michael Vrahnakis

    (Department of Forestry, Wood Sciences and Design, School of Technology, University of Thessaly, GR-43131 Karditsa, Greece)

  • Dimitrios Chouvardas

    (Department of Forestry and Natural Environment, Faculty of Agriculture Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece)

  • Stamatia Nasiakou

    (Department of Forestry, Wood Sciences and Design, School of Technology, University of Thessaly, GR-43131 Karditsa, Greece)

  • Vassiliki Kleftoyanni

    (University of Thessaly, GR-38221 Volos, Greece)

Abstract

Rural landscape evolution models are used as tools for the analysis of the causes and impact of land use changes on landscapes. The CLUE-S (the Conversion of Land Use and its Effects at Small regional extent) model was developed to simulate the changes in current land use, by using quantitative relationships between land uses and driving factors combined with a dynamic modeling of land use competition. One of the modules that build the “CLUE-S” is the non-spatial subset of the model that calculates the temporal evolution of the land use/cover under several socio-economic scenarios. The purpose of this research was to estimate the demands of land use in the area of Mouzaki, Thessaly plain, Greece with the ultimate goal of using them in the non-spatial module of the CLUE-S to predict the evolution of land uses in year 2040. These estimations are the quantitative prediction of the spatial change for all land use types at the aggregate level. Three models of forecasting the future land cover in the area were simulated, in order to obtain a clear view of the different land uses in the future. We distinguished three model-scenarios for calculating the demand-forecasts: (a) business as usual (BAU) scenario, that deals with a linear projection of the current land use demands, (b) rapid economic development (RED) scenario, and (c) ecological land protection (ELP) scenario. In the BAU scenario the land use demands for the year 2040 were calculated using linear interpolation utilizing historical data from 1960 to 2020. In the RED scenario, the demands were calculated by maximizing the economic benefit of land uses, and in the ELP scenario the demands were calculated by maximizing the environmental benefit of land uses. Furthermore, a multi-criteria analysis was performed to find the trade-offs between economic benefit maximization and environmental benefit optimization. It was found that the agricultural lands reach their maximum area under the RED scenario, while reaching their lower bound for the ELP scenario. The same goes for agroforestry systems. The grasslands reach their lower bound under the ELP scenario, while they achieve a higher value under the RED scenario. Concerning the silvopastoral woodlands, although an increase is foreseen under the BAU scenario, it appears that they reach their lower bound in the other two scenarios, RED and ELP. Forests receive intermediate values and cover a larger area under the ELP scenario compared with the RED scenario. The expected forest cover under the BAU scenario is higher. Moreover, sparse and dense shrublands receive their lower bound for both optimization scenarios, while the settlements reach the upper bound for the RED scenario and the lower one under the ELP scenario.

Suggested Citation

  • Georgios Mamanis & Michael Vrahnakis & Dimitrios Chouvardas & Stamatia Nasiakou & Vassiliki Kleftoyanni, 2021. "Land Use Demands for the CLUE-S Spatiotemporal Model in an Agroforestry Perspective," Land, MDPI, vol. 10(10), pages 1-16, October.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:10:p:1097-:d:658185
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/10/10/1097/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/10/10/1097/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Neupane, Ramji P. & Sharma, Khem R. & Thapa, Gopal B., 2002. "Adoption of agroforestry in the hills of Nepal: a logistic regression analysis," Agricultural Systems, Elsevier, vol. 72(3), pages 177-196, June.
    2. Marra, Michele & Pannell, David J. & Abadi Ghadim, Amir, 2003. "The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?," Agricultural Systems, Elsevier, vol. 75(2-3), pages 215-234.
    3. Huiran Han & Chengfeng Yang & Jinping Song, 2015. "Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    4. Costanza, Robert & d'Arge, Ralph & de Groot, Rudolf & Farber, Stephen & Grasso, Monica & Hannon, Bruce & Limburg, Karin & Naeem, Shahid & O'Neill, Robert V. & Paruelo, Jose, 1998. "The value of the world's ecosystem services and natural capital," Ecological Economics, Elsevier, vol. 25(1), pages 3-15, April.
    5. Carolina Perpina Castillo & Boyan Kavalov & Vasco Diogo & Chris Jacobs-Crisioni & Filipe Batista e Silva & Carlo Lavalle, 2018. "Agricultural land abandonment in the EU within 2015-2030," JRC Research Reports JRC113718, Joint Research Centre.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shengqiang Yang & Donglin Li & Heping Liao & Lin Zhu & Miaomiao Zhou & Zhicong Cai, 2023. "Analysis of the Balance between Supply and Demand of Arable Land in China Based on Food Security," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    2. Yang Yi & Chen Zhang & Jinqi Zhu & Yugang Zhang & Hao Sun & Hongzhang Kang, 2022. "Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    3. Dimitrios Chouvardas & Maria Karatassiou & Petros Tsioras & Ioannis Tsividis & Stefanos Palaiochorinos, 2022. "Spatiotemporal Changes (1945–2020) in a Grazed Landscape of Northern Greece, in Relation to Socioeconomic Changes," Land, MDPI, vol. 11(11), pages 1-22, November.
    4. Stamatia Nasiakou & Michael Vrahnakis & Dimitrios Chouvardas & Georgios Mamanis & Vassiliki Kleftoyanni, 2022. "Land Use Changes for Investments in Silvoarable Agriculture Projected by the CLUE-S Spatio-Temporal Model," Land, MDPI, vol. 11(5), pages 1-23, April.
    5. Xinbei Huang & Chengming Ye & Hongyu Tao & Junjie Zou & Yuzhan Zhou & Shufan Zheng, 2024. "Integrating Future Multi-Scenarios to Evaluate the Effectiveness of Ecological Restoration: A Case Study of the Yellow River Basin," Land, MDPI, vol. 13(7), pages 1-19, July.
    6. Beichen Ge & Congjin Wang & Yuhong Song, 2023. "Ecosystem Services Research in Rural Areas: A Systematic Review Based on Bibliometric Analysis," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    7. Dimitrios Chouvardas & Maria Karatassiou & Afroditi Stergiou & Garyfallia Chrysanthopoulou, 2022. "Identifying the Spatiotemporal Transitions and Future Development of a Grazed Mediterranean Landscape of South Greece," Land, MDPI, vol. 11(12), pages 1-22, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muluberhan Biedemariam & Emiru Birhane & Biadgilgn Demissie & Tewodros Tadesse & Girmay Gebresamuel & Solomon Habtu, 2022. "Ecosystem Service Values as Related to Land Use and Land Cover Changes in Ethiopia: A Review," Land, MDPI, vol. 11(12), pages 1-21, December.
    2. Asci, Serhat & Borisova, Tatiana & VanSickle, John J., 2015. "Role of economics in developing fertilizer best management practices," Agricultural Water Management, Elsevier, vol. 152(C), pages 251-261.
    3. Giuseppe Maggio & Marina Mastrorillo & Nicholas J. Sitko, 2022. "Adapting to High Temperatures: Effect of Farm Practices and Their Adoption Duration on Total Value of Crop Production in Uganda," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(1), pages 385-403, January.
    4. Greiner, Romy & Miller, Owen & Patterson, Louisa, 2008. "The role of grazier motivations and risk attitudes in the adoption of grazing best management practices," 2008 Conference (52nd), February 5-8, 2008, Canberra, Australia 6002, Australian Agricultural and Resource Economics Society.
    5. Bensch, Gunther & Grimm, Michael, 2024. "Behavioural constraints in energy technology uptake: Evidence from real-purchase offers in rural Rwanda and Senegal," Ruhr Economic Papers 1081, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Nunes, P.A.L.D. & Nijkamp, P., 2011. "Biodiversity: Economic perspectives," Serie Research Memoranda 0002, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    7. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    8. Aude Ridier & Caroline Roussy & Karim Chaib, 2021. "Adoption of crop diversification by specialized grain farmers in south-western France: evidence from a choice-modelling experiment," Review of Agricultural, Food and Environmental Studies, Springer, vol. 102(3), pages 265-283, September.
    9. Hendrawan, Dienda C P & Musshoff, Oliver, 2022. "Oil Palm Smallholder Farmers' Livelihood Resilience and Decision Making in Replanting," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322441, Agricultural and Applied Economics Association.
    10. Man-Jing Li & Jia-Xu Han & Mao Zhu & Yuan-Biao Zhang, 2019. "The True Valuation of Land Use Project in China Considering Ecosystem Services," Modern Applied Science, Canadian Center of Science and Education, vol. 13(10), pages 1-46, October.
    11. Ping Shen & Lijuan Wu & Ziwen Huo & Jiaying Zhang, 2023. "A Study on the Spatial Pattern of the Ecological Product Value of China’s County-Level Regions Based on GEP Evaluation," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    12. Senthold Asseng & David Pannell, 2013. "Adapting dryland agriculture to climate change: Farming implications and research and development needs in Western Australia," Climatic Change, Springer, vol. 118(2), pages 167-181, May.
    13. van der Hoff, Richard & Nascimento, Nathália & Fabrício-Neto, Ailton & Jaramillo-Giraldo, Carolina & Ambrosio, Geanderson & Arieira, Julia & Afonso Nobre, Carlos & Rajão, Raoni, 2022. "Policy-oriented ecosystem services research on tropical forests in South America: A systematic literature review," Ecosystem Services, Elsevier, vol. 56(C).
    14. Nasca, J.A. & Feldkamp, C.R. & Arroquy, J.I. & Colombatto, D., 2015. "Efficiency and stability in subtropical beef cattle grazing systems in the northwest of Argentina," Agricultural Systems, Elsevier, vol. 133(C), pages 85-96.
    15. Javad Torkamani & Shahrokh Shajari, 2008. "Adoption of New Irrigation Technology Under Production Risk," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(2), pages 229-237, February.
    16. Yunfeng Hu & Batu Nacun, 2018. "An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015," Sustainability, MDPI, vol. 10(11), pages 1-22, November.
    17. Jackson, T.M. & Hanjra, Munir A. & Khan, S. & Hafeez, M.M., 2011. "Building a climate resilient farm: A risk based approach for understanding water, energy and emissions in irrigated agriculture," Agricultural Systems, Elsevier, vol. 104(9), pages 729-745.
    18. Caroline Roussy & Aude Ridier & Karim Chaïb, 2014. "Adoption d’innovations par les agriculteurs : rôle des perceptions et des préférences," Post-Print hal-01123427, HAL.
    19. Evans, Nicole M. & Carrozzino-Lyon, Amy L. & Galbraith, Betsy & Noordyk, Julia & Peroff, Deidre M. & Stoll, John & Thompson, Aaron & Winden, Matthew W. & Davis, Mark A., 2019. "Integrated ecosystem service assessment for landscape conservation design in the Green Bay watershed, Wisconsin," Ecosystem Services, Elsevier, vol. 39(C).
    20. Bernard Fosu Frimpong & Frank Molkenthin, 2021. "Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana," Land, MDPI, vol. 10(1), pages 1-21, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:10:y:2021:i:10:p:1097-:d:658185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.