Advertisement

Regional Environmental Change

, Volume 18, Issue 2, pp 521–532 | Cite as

To produce or not to produce: an analysis of bioenergy and crop production decisions based on farmer typologies in Brandenburg, Germany

  • Sandra Venghaus
  • Lilibeth Acosta
Original Article

Abstract

The future course of the political regulation of bioenergy will have a significant sustainability impact on many levels. Understanding the specific effects of different political governance strategies on the agricultural system is essential for developing a stable and economically, ecologically as well as socially sustainable market for bioenergy. This paper contributes to this objective by providing an analysis of different decision patterns of farmers in the production of energy crops. For this purpose, an empirical analysis was conducted among farmers in the federal state of Brandenburg in northern Germany. A cluster analysis of structural factors resulted in a typology of farmers that differ in their energy crop production decisions. Six cluster typologies are identified for each of which a cluster-specific conjoint analysis helped to identify decision preferences in order to understand how and to what degree structural farm characteristics as well as respective production “traditions” influence the willingness to produce crops for energy use.

Keywords

Energy crops Farmer typologies Bioenergy Cluster analysis Conjoint analysis 

Notes

Funding information

This research was funded by the German Federal Ministry of Education and Research (BMBF) as part of its FONA-program in social-ecological research (FKZ 01UU0901A).

References

  1. Acosta LA, Rounsevell MD, Bakker M, Van Doorn A, Gómez-Delgado M, Delgado M (2014) An agent-based assessment of land use and ecosystem changes in traditional agricultural landscape of Portugal. Intell Inf Manag 6(2):55–80.  https://doi.org/10.4236/iim.2014.62008 Google Scholar
  2. Al-Riffai P, Dimaranan B, Laborde D (2010) European Union and United States biofuel mandates: impacts on world markets. Inter-American Development Bank, Washington, D.C.Google Scholar
  3. Andrews RL, Currim IS (2003) Recovering and profiling the true segmentation structure in markets: an empirical investigation intern. J Res Market 20:177–192.  https://doi.org/10.1016/S0167-8116(03)00017-X CrossRefGoogle Scholar
  4. Basili M, Fontini F (2012) Biofuel from Jatropha curcas: environmental sustainability and option value. Ecol Econ 78:1–8.  https://doi.org/10.1016/j.ecolecon.2012.03.010 CrossRefGoogle Scholar
  5. Beneking A 2011. Genese und Wandel der deutschen Biokraftstoffpolitik. Eine akteurszentrierte Policy-Analyse der Förderung biogener Kraftstoffe in Deutschland. Fair fuels? Working paper 3, Institut für ökologiche Wirtschaftsforschung, BerlinGoogle Scholar
  6. Berkhout F, Bouwer L, Bayer JM, Bouzid, Cabeza M, Hanger S, Hof AP, Meller HL, Patt A, Pfluger BT, Reichardt RK, van Teeffelen AJA (2015) European policy responses to climate change: progress on mainstreaming emissions reduction and adaptation. Reg Environ Chang 15:949–959.  https://doi.org/10.1007/s10113-015-0801-6
  7. Blamey RK, Bennett JW, Louviere JJ, Morrison MD, Rolfe J (2000) A test of policy labels in environmental choice modelling studies. Ecol Econ 32:269–286.  https://doi.org/10.1016/S0921-8009(99)00101-9 CrossRefGoogle Scholar
  8. Bommarco R, Kleijn D, Potts SG (2013) Ecological intensification: harnessing ecosystem services for food security. Trends Ecol Evol 28(4):230–238.  https://doi.org/10.1016/j.tree.2012.10.012 CrossRefGoogle Scholar
  9. Bonzanigo L, Bojovic D, Maziotis A, Giupponi C (2016) Agricultural policy informed by farmers’ adaptation experience to climate change in Veneto, Italy. Reg Environ Chang 16:245–258.  https://doi.org/10.1007/s10113-014-0750-5
  10. Bost M, Böther T, Hirschl B, Kreuz S, Neumann A, Weiß J (2012) Erneuerbare Energien Potenziale in Brandenburg 2030. In: Erschließbare technische Potenziale sowie Wertschöpfungs- und Beschäftigungseffekte – eine szenariobasierte Analyse. IÖW, BerlinGoogle Scholar
  11. Bouët A, Dimaranan B, Valin H (2010) Modeling the global trade and environmental impacts of biofuel policies, Discussion Paper 010118. IFPRI, Washington DCGoogle Scholar
  12. Brendel F (2011) Energie im großen Stiel. Auswirkungen des Biogas-Booms auf Umwelt, Artenvielfalt und Landwirtschaft. WWF Deutschland, BerlinGoogle Scholar
  13. Breustedt G, Habermann H (2010) Einfluss der Biogaserzeugung auf landwirtschaftliche Pachtpreise in Deutschland. Universität Kiel, KielGoogle Scholar
  14. Brown C, Bakam I, Smith P, Matthews R (2016) An agent-based modelling approach to evaluate factors influencing bioenergy crop adoption in north-east Scotland. GCB Bioenergy 8:226–244.  https://doi.org/10.1111/gcbb.12261 CrossRefGoogle Scholar
  15. Carey MA, Asbury J-E (2016) Focus group research. Routledge, London and New YorkGoogle Scholar
  16. Charles C (2012) Should we be concerned about competition between food and fuel? Analysis of biofuel consumption mandates in the European Union and the United States. IISD, WinnipegGoogle Scholar
  17. Cotula L, Dyer N, Vermeulen S (2008) Fuelling exclusion? The biofuels boom and poor People’s access to land. IIED, LondonGoogle Scholar
  18. de Gorter H, Drabik D, Just DR (2011) The economics of a blender’s tax credit versus a tax exemption: the case of U.S. “Splash and Dash” biodiesel exports to the European Union. Appl Econ Perspect Policy 33(4):510–527.  https://doi.org/10.1093/aepp/ppr 024 CrossRefGoogle Scholar
  19. Desarbo WS, Ramaswamy V, Cohen SH (1995) Market segmentation with choice-based conjoint analysis. Mark Lett 6(2):137–147.  https://doi.org/10.1007/bf00994929 CrossRefGoogle Scholar
  20. Dillon B, Mukherjee S, 2006. A guide to the design and execution of segmentation studies. In: Grover R, Vriens M, editors. The handbook of marketing research: uses, misuses, and future advances. SAGE PublicationsGoogle Scholar
  21. Dolnicar S (2002) A review of data-driven market segmentation in tourism. J Travel Tour Mark 12(1):1–22.  https://doi.org/10.1300/J073v12n01_01 CrossRefGoogle Scholar
  22. Dymnicki AB, Henry DB (2011) Use of clustering methods to understand more about the case. Methodological Innovations Online 6(2):6–26.  https://doi.org/10.4256/mio.2010.0033 CrossRefGoogle Scholar
  23. Ecofys (2011) International biodiesel markets. In: Developments in production and trade. UFOP Schriften, Biodiesel & Co., BerlinGoogle Scholar
  24. Elsawah S, Guillaume JHA, Filatova T, Rook J, Jakeman AJ (2015) A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models. J Environ Manag 151:500–516.  https://doi.org/10.1016/j.jenvman.2014.11.028 CrossRefGoogle Scholar
  25. Fairley P (2011) Next generation biofuels. Introduction Nature 474:S2–S5CrossRefGoogle Scholar
  26. [FNR] Fachagentur Nachwachsende Rohstoffe 2014. Entwicklung Biodiesel-Produktion und -Absatz in Deutschland. http://mediathek.fnr.de/entwicklung-biodiesel-in-deutschland.html. Cited August 22, 2014
  27. [FNR] Fachagentur Nachwachsende Rohstoffe 2015. Primärenergieverbruach 2015. https://mediathek.fnr.de/primaerenergieverbrauch.html. Cited October 22, 2016
  28. German L, Schoneveld GC, Pacheco P (2011) The social and environmental impacts of biofuel feedstock cultivation: evidence from multi-site research in the forest frontier. Ecol Soc 16(3), Art.):24.  https://doi.org/10.5751/ES-04309-160324 CrossRefGoogle Scholar
  29. Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. J Mark 54:3–19.  https://doi.org/10.2307/1251756 CrossRefGoogle Scholar
  30. Green PE, Krieger AM, Wind Y (2001) Thirty years of conjoint analysis: reflections and prospects. Interfaces 31(3):S56–S73.  https://doi.org/10.1287/inte.31.3s.56.9676 CrossRefGoogle Scholar
  31. Hagedorn K (2011) Die Landwirtschaft in Brandenburg unter dem Einfluss der Globalisierung. In: Diskussionspaper 13. BBAW, IAG Globaler Wandel-Regionale Entwicklung, BerlinGoogle Scholar
  32. Hair JF, Anderson RE, Tatham RL, Black WL (1995) Multivariate data analysis. Prentice-Hall, New JerseyGoogle Scholar
  33. Hoek J, Gendall P, Esslemont D (1996) Market segmentation a search for the holy grail? J Mark Pract: Appl Mark Sci 2(1):25–34.  https://doi.org/10.1108/EUM0000000000005 CrossRefGoogle Scholar
  34. Howley, P., Hynes, S., Donoghue, O C. 2012. Explaining the non-economic behaviour of farm foresters: The effect of productivist and lifestyle motivations. Working Paper 12-WP-RE-03. Available at: http://t-stor.teagasc.ie/handle/11019/683 (29 July 2017)
  35. [IPCC] Intergovernmental Panel on Climate Change (2007) Summary for policy makers. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds) Climate Change 2007: Mitigation. Contribution of working group III to the fourth assessment report on the intergovernmental panel on climate change. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  36. Kaphengst T, Wunder S, Timeus K (2012) The social dimension of EU biofuel policy. Ecologic Briefs, BerlinGoogle Scholar
  37. Kim H, Kim S, Dale BE (2009) Biofuels, land use change, and greenhouse gas emissions: some unexplored variables. Environ Sci Technol 43(3):961–967.  https://doi.org/10.1021/es 802681k CrossRefGoogle Scholar
  38. Kok M, Lüdeke M, Lucas P, Sterzel T, Walther C, Janssen P, Sietz D, Soysa Indra de (2016) A new method for analysing socio-ecological patterns of vulnerability. Reg Environ Chang 16:229–243.  https://doi.org/10.1007/s10113-014-0746-1
  39. Lasch P, Kollas C, Rock J, Suckow F (2010) Potentials and impacts of short-rotation coppice plantation with aspen in Eastern Germany under conditions of climate change. Reg Environ Chang 10:83–94.  https://doi.org/10.1007/s10113-009-0095-7
  40. Lee KC, Choi H, Lee DH, Kang S (2006) Quantitative measurement of quality attribute preferences using conjoint analysis. In: Gilroy SW, Harrison MD (eds) Interactive systems: lecture notes in computer science. Springer-Verlag, Berlin, pp 213–224Google Scholar
  41. Lim-Camacho L, Ariyawardana A, Lewis GK, Crimp S, Somogyi S, Ridoutt B, Howden M (2017) Climate adaptation of food value chains: the implications of varying consumer acceptance. Reg Environ Chang 17(1):93–103.  https://doi.org/10.1007/s10113-016-0976-5
  42. Lotze-Campen H, von Lampe M, Kyle P, Fujimori S, Havlik P, van Meijl H, Hasegawa T, Popp A, Schmitz C, Tabeau A, Valin H, Willenbockel D, Wise M (2013) Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agric Econ 45(1):103–116.  https://doi.org/10.1111/agec.12092
  43. [LUA] Landesumweltamt Brandenburg (Eds.), 2009. Umweltdaten Brandenburg 2008/09, Hellograph, PotsdamGoogle Scholar
  44. Luderer G, Bosetti V, Jakob M, Leimbach M, Steckel J, Waisman H, Edenhofer O (2012) The economics of decarbonizing the energy system: results and insights from the RECIPE model intercomparison. Clim Chang 114:9–37.  https://doi.org/10.1007/s10584-011-0105-x CrossRefGoogle Scholar
  45. [LUGV] Landesamt für Umwelt, Gesundheit und Verbraucherschutz des Landes Brandenburg, 2014. Leitfaden zur Renaturierung von Feuchtgebieten in Brandenburg. LUA Band 50 Ökologie, PotsdamGoogle Scholar
  46. Meller L, van Vuuren DP, Cabeza M (2015) Quantifying biodiversity impacts of climate change and bioenergy: the role of integrated global scenarios. Reg Environ Chang 15:961–971.  https://doi.org/10.1007/s10113-013-0504-9 CrossRefGoogle Scholar
  47. [MIL] Ministerium für Infrasturktur und Landwirtschaft des Landes Brandenburg 2012. Agrarbericht 2011/2012, PotsdamGoogle Scholar
  48. Moran D, Mcvittie A, Allcroft DJ, Elston DA (2007) Quantifying public preferences for agri-environmental policy in Scotland: a comparison of methods. Ecol Econ 63:42–53.  https://doi.org/10.1016/j.ecolecon.2006.09.018 CrossRefGoogle Scholar
  49. Nainggolan D, Termansen M, Reed MS Cebollero ED, Hubacek K (2013) Farmer typology, future scenarios and the implications for ecosystem service provision: a case study from south-eastern Spain. Reg Environ Chang 13:601–614.  https://doi.org/10.1007/s10113-011-0261-6
  50. Nölting, B., Boeckmann, T. 2005 Struktur der ökologischen Land- und Ernährungswirtschaft in Brandenburg und Berlin–Anknüpfungspunkte für eine nachhaltige Regionalentwicklung. Diskussionspapier Nr. 18/05, Zentrum Technik und Gesellschaft, TU BerlinGoogle Scholar
  51. Orme B (2009) CBC/HB v5 software for hierarchical bayes estimation for CBC data. Sawtooth Software, Inc., Sequim (WA)Google Scholar
  52. Piroli G, Ciaian P, Kancs d’A (2012) Land use change impacts of biofuels: near-VAR evidence from the US. Ecol Econ 84:98–109.  https://doi.org/10.1016/j.ecolecon.2012.09.007 CrossRefGoogle Scholar
  53. Plevin RJ, O’Hare M, Jones AD, Torn MS, Gibbs HK (2010) Greenhouse gas emissions from biofuels’ indirect land use change are uncertain but may be much greater than previously estimated. Environ Sci Technol 44:8015–8021.  https://doi.org/10.1021/es101946t CrossRefGoogle Scholar
  54. Rajagopal D, Plevin RJ (2013) Implications of market-mediated emissions and uncertainty for biofuel policies. Energ Policy 56:75–82.  https://doi.org/10.1016/j.enpol.2012.09.076 CrossRefGoogle Scholar
  55. Reyer C, Bachinger J, Bloch R, Hattermann F, Ibisch P, Kreft S, Lasch P, Lucht W, Nowicki C, Spathelf P, Stock M, Welp M (2012) Climate change adaptation and sustainable regional development: a case study for the Federal State of Brandenburg, Germany. Reg Environ Chang 12:523–542.  https://doi.org/10.1007/s10113-011-0269-y
  56. Ribeiro BE (2013) Beyond commonplace biofuels: social aspects of ethanol. Energ Policy 57:355–362.  https://doi.org/10.1016/j.enpol.2013.02.004 CrossRefGoogle Scholar
  57. Rosillo-Calle, F., 2012. Food versus Fuel: toward a new paradigm—the need for a holistic approach. ISRN Renewable Energy 2012, Article ID 954180Google Scholar
  58. Sayadi S, Gonzalez-Roa MC, Calatrava-Requena J (2005) Ranking versus scale rating in conjoint analysis: evaluating landscapes in mountainous regions in southeastern Spain. Ecol Econ 55:539–550.  https://doi.org/10.1016/j.ecolecon.2004.12.010 CrossRefGoogle Scholar
  59. Sayadi S, Gonzalez-Roa MC, Calatrava-Requena J (2009) Public preferences for landscape features: the case of agricultural landscape in mountainous Mediterranean areas. Land Use Policy 26:334–344.  https://doi.org/10.1016/j.landusepol.2008.04.003 CrossRefGoogle Scholar
  60. Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, Fabiosa J, Tokgoz S, Hayes D, Yu Tun-Hsiang (2008) Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319(5867):1238–1240.  https://doi.org/10.1126/science.1151861
  61. Shih MY, Jheng JW, Lai LF (2010) A two-step method for clustering mixed categorical and numeric data. Tamkang J Sci Eng 13(1):11–19Google Scholar
  62. Statistics Berlin-Brandenburg 2014 www.statistik-berlin-brandenburg.de. 19 Accessed Sept 2014
  63. Stevens TH, White S, Kittredge DB, Dennis D (2002) Factors affecting NIPF landowner participation in management programs: a Massachusetts case study. J For Econ 184:169–184.  https://doi.org/10.1078/1104-6899-00012 Google Scholar
  64. Tano K, Kamuanga M, Faminow MD, Swallow B (2003) Using conjoint analysis to estimate farmer’s preferences for cattle traits in West Africa. Ecol Econ 45:393–407.  https://doi.org/10.1016/S0921-8009(03)00093-4 CrossRefGoogle Scholar
  65. Tkaczynski A (2016) Segmentation using two-step cluster analysis. In: Dietrich T, Rundle-Thiel S, Kubacki K (eds) Segmentation in social marketing-process, methods and application. Springer, SingaporeGoogle Scholar
  66. Venghaus S, Selbmann K (2014) Biofuel as social fuel: introducing socio-environmental services as a means to reduce global inequity? Ecol Econ 97:84–92.  https://doi.org/10.1016/j.ecolecon.2013.11.003
  67. [WBGU] Wissenschaftlicher Beirat der Bundesregierung für Globale Umweltveränderungen (German Advisory Council on Global Change) (2008) Future land use and sustainable bioenergy. WBGU, BerlinGoogle Scholar
  68. [WCED] World Commission on Environment and Development 1987. Our common future. Aka.‘The Brundtland Report’, Oxford University Press, OxfordGoogle Scholar
  69. Witcover J, Yeh S, Sperling D (2013) Policy options to address global land use change from biofuels. Energ Policy 56:63–74.  https://doi.org/10.1016/j.enpol.2012.08.030 CrossRefGoogle Scholar
  70. Zichy, M., Dürnberger, C., Formowitz, B., Uhl, A., 2014. Energie aus Biomasse—ein ethisches Diskussionsmodell. Springer, Wiesbaden, 2011/2014Google Scholar
  71. Zschache U, von Cramon-Taubadel S, Theuvsen L (2009) Die öffentliche Auseinandersetzung über Bioenergie in den Massenmedien. Discussion Papers, Nr. 0906. Department for Agricultural Economics and Rural Development. Georg-August University Göttingen, GöttingenGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  2. 2.Institute for Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), Forschungszentrum JülichJülichGermany

Personalised recommendations