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A methodological Markov-CA projection of the greening agricultural landscape—a case study from 2005 to 2017 in southwestern Finland

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Abstract

The aim of the reformed EU Common Agricultural Policy (CAP) 2014–2020 is to enhance greening via an ecological focus area, arable crop diversification, and the maintenance of permanent grasslands. This study tests the greening process in the case of agricultural landscape in southwestern Finland by projecting land use between 2005 and 2017. The study method integrates the quantitative results of Markov chains and spatial features of a cellular automata model. Initially, land use change was recognized by appropriate metrics. The trend of greening following the CAP policy indicated that permanent grassland patches were more persistent with forest patches than agricultural land that lost its vegetated strips to neighboring land use patches. The modeling approach was demonstrated to provide acceptable performance when used as a spatial assessment tool for observing critical patch level changes reflecting the greening agricultural policy.

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References

  • Araya, Y. H., & Cabral, P. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2, 1549–1563.

    Article  Google Scholar 

  • Arsanjani, J. J., Kainz, W., & Mousivand, A. J. (2011). Tracking dynamic land use change using spatially explicit Markov chain based on cellular automata: The case of Tehran. International Journal of Image and Data Fusion, 2, 329–345. https://doi.org/10.1080/19479832.2011.605397.

    Article  Google Scholar 

  • Benton, T. G., Vickery, J. A., & Wilson, J. D. (2003). Farmland biodiversity: Is habitat heterogeneity the key? Trends in Ecology & Evolution, 18, 182–188. https://doi.org/10.1016/S0169-5347(03)00011-9.

    Article  Google Scholar 

  • Botequilha Leitão, A., & Ahern, J. (2002). Applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning, 59, 65–93. https://doi.org/10.1016/S0169-2046(02)00005-1.

    Article  Google Scholar 

  • Braimoh, A. K., & Onishi, T. (2007). Spatial determinants of urban land use change in Lagos, Nigeria. Land Use Policy, 24, 502–515. https://doi.org/10.1016/j.landusepol.2006.09.001.

    Article  Google Scholar 

  • Chen, H., & Pontius Jr, R. G. (2010). Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecology, 25, 1319–1331.

  • Chen, H., Marter-Kenyon, J., López-Carr, D., & Liang, X. (2015). Land cover and landscape changes in Shaanxi Province during China’s grain for green program (2000–2010). Environmental Monitoring and Assessment, 187, 644–658.

    Article  Google Scholar 

  • Commission Delegated Regulation (EU) No 639/2014 of 11 March 2014 supplementing Regulation (EU) No 1307/2013 of the European Parliament and of the Council establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy and amending Annex X to that Regulation. http://eur-lex.europa.eu/legal-content/en/TXT/?uri=CELEX%3A32014R0639. Accessed on 29 June 2017.

  • Commission Staff Working Document (2017). Accompanying the document report from the Commission to the European Parliament and the Council on the implementation of the ecological focus area obligation under the direct payment scheme SWD/2017/0121 final. http://eur-lex.europa.eu/legal-content/SV/TXT/?uri=CELEX%3A52017SC0121. Accessed 14 August 2017.

  • Commission Staff Working Document (2016). Review of greening after one year, SWD (2016) 218 final. http://ec.europa.eu/agriculture/direct-support/greening_en. Accessed 29 June 2017.

  • Concepción, E. D., Díaz, M., & Baquero, R. A. (2008). Effects of landscape complexity on the ecological effectiveness of Agri-environment schemes. Landscape Ecology, 23, 135–148. https://doi.org/10.1007/s10980-007-9150-2.

    Article  Google Scholar 

  • Directive 2009/147/EC of the European Parliament and of the Council of 30 November 2009 on the conservation of wild birds. http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32009L0147. Accessed 1 July 2017.

  • Eastman, J. R. (2012a). IDRISI Selva Manual (Version 17.01), 227–228. IDRISI Selva. Worcester, MA: Clark Lab, Clark University. Software publication.

  • Eastman, J. R. (2012b). IDRISI Selva (Version 17.01). Worcester, MA: Clark Lab, Clark University. Software publication.

  • Fasona, M. J., Soneye, A. S., Ogunkunle, O. J., Adeaga, O. A., Fashae, O. A., & Abbas, I. I. (2013). Simulating land-cover and land use change in the savanna under present day and future climate scenarios – A GIS-based approach. Earth Science Research. https://doi.org/10.5539/esr.v3n1p25.

  • Haines-Young, R., & Chopping, M. (1996). Quantifying landscape structure: A review of landscape indices and their application to forested landscapes. Progress in Physical Geography, 20, 418–445. https://doi.org/10.1177/030913339602000403.

    Article  Google Scholar 

  • Hart, K., Baldock, D., & Buckwell, A. (2016). Learning the lessons of the greening of the CAP, a report for the UK land use policy group in collaboration with the European nature conservation agencies network (77 p). London: Institute for European Environmental Policy.

    Google Scholar 

  • Hauck, J., Schleyer, C., Winkler, K. J., & Maes, J. (2014). Shades of greening: Reviewing the impact of the new EU agricultural policy on ecosystem services. Change and Adaptation in Socio-Ecological Systems, 1, 51–62.

    Article  Google Scholar 

  • Hersperger, A. M., & Bürgi, M. (2010). How do policies shape landscapes? Landscape change and its political driving forces in the Limnat Valley, Switzerland 1930–2000. Landscape Research, 35, 259–279. https://doi.org/10.1080/01426391003743561.

    Article  Google Scholar 

  • Jenerette, G. D., & Wu, J. (2001). Analysis and simulation of land use change in the Central Arizona–Phoenix region, USA. Landscape Ecology, 16, 611–626. https://doi.org/10.1023/A:1013170528551.

    Article  Google Scholar 

  • Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29, 435–447. https://doi.org/10.1016/j.apgeog.2008.10.002.

    Article  Google Scholar 

  • Luoto, M., Rekolainen, S., Aakkula, J., & Pykälä, J. (2003). Loss of plant species richness and habitat connectivity in grasslands associated with agricultural change in Finland. Ambio, 32, 447–452.

    Article  Google Scholar 

  • Maguire, D., Batty, M., & Goodchild, M. (2005). GIS, spatial analysis, and modelling. Redlands, CA: Esri Press.

    Google Scholar 

  • Mas, J. F., Kolb, M., Paegelow, M., Camacho Olmedo, M. T., & Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2013.09.010.

  • Matthews, A. (2013). Greening agricultural payments in the EU’s common agricultural policy. Bio-based and Applied Economics, 2, 1–27.

    Google Scholar 

  • Mattison, E. H. A., & Norris, K. (2005). Bridging the gaps between agricultural policy, land use and biodiversity. Trends in Ecology & Evolution, 20, 610–616. https://doi.org/10.1016/j.tree.2005.08.011.

    Article  Google Scholar 

  • McGarigal, K. (2014). Fragstats metrics. In FRAGSTATS Help. Amherst: University of Massachusetts. Software publication.

    Google Scholar 

  • Meiyappan, P., Dalton, M., O’Neill, B. C., & Jain, A. K. (2014). Spatial modeling of agricultural land use change at global scale. Ecological Modelling, 291, 152–174.

    Article  Google Scholar 

  • Myint, S. W., & Wang, L. (2006). Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32, 390–404.

    Article  Google Scholar 

  • Niemi, J. & Väre, M. eds. (2017). Suomen maa- ja elintarviketalous 2016/2017. Luonnonvara- ja biotalouden tutkimus 17/2017, Luonnonvarakeskus (Luke), Helsinki. https://www.luke.fi/wp-content/uploads/2017/04/luke-luobio_17_2017.pdf. Accessed 30 August 2017.

  • Poggio, S. L., Chaneton, E. L., & Ghersa, C. M. (2010). Landscape complexity differentially affects alpha, beta, and gamma diversities of plants occurring in fencerows and crop fields. Biological Conservation, 143, 2477–2486. https://doi.org/10.1016/j.biocon.2010.06.014.

    Article  Google Scholar 

  • Pontius Jr., R. G., & Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19, 243–265. https://doi.org/10.1080/13658810410001713434.

    Article  Google Scholar 

  • Pontius Jr., R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66, 1011–1016.

    Google Scholar 

  • Primdahl, J. (2014). Agricultural landscape sustainability under pressure: Policy developments and landscape change. Landscape Research, 39, 123–140. https://doi.org/10.1080/01426397.2014.891726.

    Article  Google Scholar 

  • Regulation (EU) No 1306/2013 of the European Parliament and of the Council of 17 December 2013 on the financing, management and monitoring of the common agricultural policy. http://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:32013R1306. Accessed 25 May 2017.

  • Regulation (EU) No 1307/2013 of the European Parliament and of the Council of 17 December 2013 establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy and repealing Council Regulation (EC) No 637/2008 and Council Regulation (EC) No 73/2009. http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv:OJ.L_.2013.347.01.0608.01.ENG&toc=OJ:L:2013:347:TOC. Accessed 29 June 2017.

  • Reidsma, P., Tekelenburg, T., Van den Berg, M., & Alkemade, R. (2006). Impacts of land use change on biodiversity: An assessment of agricultural biodiversity in the European Union. Agriculture, Ecosystems & Environment, 114, 86–102. https://doi.org/10.1016/j.agee.2005.11.026.

    Article  Google Scholar 

  • Riccioli, F., El Asmar, T., El Asmar, J. P., & Fratini, R. (2013). Use of cellular automata in the study of variables involved in land use changes. Environmental Monitoring and Assessment, 185, 5361–5374. https://doi.org/10.1007/s10661-012-2951-z.

    Article  Google Scholar 

  • Rounsevell, M. D. A., Ewert, F., Reginster, I., Leemans, R., & Carter, T. R. (2005). Future scenarios of European agricultural land use: II. Projecting changes in cropland and grassland. Agriculture, Ecosystems & Environment, https://doi.org/10.1016/j.agee.2004.12.002, 107, 117, 135

  • Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54, 938–943. https://doi.org/10.1016/j.mcm.2010.11.019.

    Article  Google Scholar 

  • Schulz, N., Breustedt, G., & Latacz-Lohmann, U. (2014). Assessing farmers’ willingness to accept “greening”: Insights from a discrete choice experiment in Germany. Journal of Agricultural Economics, 65, 26–48. https://doi.org/10.1111/1477-9552.12044.

    Article  Google Scholar 

  • Serra, A. & Duncan, J. 2016. European farmers and the “greening” of the CAP: A critical discourse analysis. Colloquium paper no. 13. International Institute of Social Studies (ISS) Kortenaerkade 12, 2518AX, the Hague, the Netherlands.

  • Sharma, T., Carmichael, J., & Klinkenberg, B. (2006). Integrated modeling for exploring sustainable agriculture futures. Futures, 38, 93–113. https://doi.org/10.1016/j.futures.2005.04.006.

    Article  Google Scholar 

  • Subedi, P., Subedi, K., & Thapa, B. (2013). Application of a hybrid cellular automaton–Markov (CA-Markov) model in land use change prediction: A case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 1, 126–132. https://doi.org/10.12691/aees-1-6-5.

    Article  Google Scholar 

  • Theobald, D. M., Miller, J. R., & Hobbs, N. T. (1997). Estimating the cumulative effects of development on wildlife habitat. Landscape and Urban Planning, 39, 25–36. https://doi.org/10.1016/S0169-2046(97)00041-8.

    Article  Google Scholar 

  • van Delden, H., Stuczynski, T., Ciaian, P., Paracchini, M. L., Hurkens, J., Lopatka, A., & Vanhout, R. (2010). Integrated assessment of agricultural policies with dynamic land use change modelling. Ecological Modelling, 221(18), 2153–2166.

    Article  Google Scholar 

  • Veldkamp, A., & Verburg, P. H. (2004). Modelling land use change and environmental impact. Journal of Environmental Management, 72, 1), 1–1), 3.

    Article  Google Scholar 

  • Wang, S., Zhang, Z., & Wang, X. (2014, March). Land use change and prediction in the Baimahe Basin using GIS and CA-Markov model. In IOP Conference Series: Earth and Environmental Science 17(1), p. 012074. IOP Publishing.

  • Wang, F., & Marceau, D. J. (2013). A patch based cellular automaton for simulating land use changes at fine spatial resolution. Transactions in GIS, 17, 828–846. https://doi.org/10.1111/tgis.12009.

    Article  Google Scholar 

  • Weibull, A. C., Bengtsson, J., & Nohlgren, E. (2000). Diversity of butterflies in the agricultural landscape: The role of farming system and landscape heterogeneity. Ecography, 23, 743–750. https://doi.org/10.1111/j.1600-0587.2000.tb00317.x.

    Article  Google Scholar 

  • Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64, 273–284. https://doi.org/10.1006/jema.2001.0509.

    Article  Google Scholar 

  • Westhoek, H., van Zeijts, H., Witmer, M., van den Berg, M., Overmars, K., van der Esch, S. & van der Bilt, W. (2012). Greening the CAP-an analysis of the effects of the European Commission’s proposals for the common agricultural policy 2014–2020. Netherlands Environmental Assessment Agency. https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/7893.pdf. Accessed on 1 August 2017.

  • Whittingham, M. J. (2007). Will Agri-environment schemes deliver substantial biodiversity gain, and if not why not? Journal of Applied Ecology, 44, 1–5. https://doi.org/10.1111/j.1365-2664.2006.01263.x.

    Article  Google Scholar 

  • Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 16, 795–818. https://doi.org/10.1080/13658810210157769.

    Article  Google Scholar 

  • Wu, Q., Li, H. Q., Wang, R. S., Paulussen, J., He, Y., Wang, M., & Wang, Z. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78, 322–333. https://doi.org/10.1016/j.landurbplan.2005.10.002.

    Article  Google Scholar 

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Acknowledgements

We gratefully acknowledge Prof. Maohua Ma of the Chinese Academy of Sciences, who provided useful comments on this research. In addition, we would like to express our thanks to Anneli Palo, PhD, University of Tartu, Estonia, for the kind response to our query.

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Roose, M., Hietala, R. A methodological Markov-CA projection of the greening agricultural landscape—a case study from 2005 to 2017 in southwestern Finland. Environ Monit Assess 190, 411 (2018). https://doi.org/10.1007/s10661-018-6796-y

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