Journal of Bioeconomics

, Volume 20, Issue 2, pp 183–211 | Cite as

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

  • Luz Maria CastroEmail author
  • Fabian Härtl
  • Santiago Ochoa
  • Baltazar Calvas
  • Leonardo Izquierdo
  • Thomas Knoke


Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.


Optimization Uncertainty System dynamics Time Objective functions 

Mathematics Subject Classification




We want to express our gratitude to the Deutsche Forschungsgemeinschaft (DFG) for their financial support (KN 586/5-2, KN 586/9-1) and to the members of the research group FOR 816. The authors also wish to thank Mr. Dave Parsons and Michael Du for language editing and Dr. Patrick Hildebrandt for valuable comments on this article.


  1. Acs, S., Berentsen, P. B. M., & Huirne, R. B. M. (2007). Conversion to organic arable farming in The Netherlands: A dynamic linear programming analysis. Agricultural Systems, 94, 405–415.CrossRefGoogle Scholar
  2. Acs, S., Berentsen, P., Huirne, R., & van Asseldonk, M. (2009). Effect of yield and price risk on conversion from conventional to organic farming. The Australian Journal of Agricultural and Resource Economics, 53, 393–411.CrossRefGoogle Scholar
  3. Alary, V., Corbeels, M., Affholder, F., Alvarez, S., Soria, A., Valadares, J. H., et al. (2016). Economic assessment of conservation agriculture options in mixed crop-livestock systems in Brazil using farm modelling. Agricultural Systems, 144, 33–45.CrossRefGoogle Scholar
  4. Anderson, L., & Seijo, J. C. (2009). Bioeconomics of fisheries management. New York: Wiley.Google Scholar
  5. Barbier, B., & Bergeron, G. (1999). Impact of policy interventions on land management in Honduras: Results of a bioeconomic model. Agricultural Systems, 60, 1–16.CrossRefGoogle Scholar
  6. Bateman, I. J., Harwood, A. R., Mace, G. M., Watson, R. T., Abson, D. J., Andrews, B., et al. (2013). Bringing ecosystem services into economic decision-making: Land use in the United Kingdom. Science, 341, 45–50.CrossRefGoogle Scholar
  7. Blanco Fonseca, M., & Flichman, G., (2002). Dynamic optimization problems: different resolution methods regarding agriculture and natural resource economics. In Working Paper, Universidad Politecnica de Madrid and CIHEAM-Institut Agronomique Me’diterrane’en de Montpellier, Montpellier, p. 35.Google Scholar
  8. Blasi, E., Passeri, N., Franco, S., & Galli, A. (2016). An ecological footprint approach to environmental-economic evaluation of farm results. Agricultural Systems, 145, 76–82.CrossRefGoogle Scholar
  9. Brown, D. R. (2000). A review of bio-economic models. Cornell African Food Security and Natural Resource Management (CAFSNRM) Program (Vol. 102)Google Scholar
  10. Benitez, P., Kuosmanen, T., Olschewski, R., & van Kooten, C. (2006). Conservation payments under risk: A stochastic dominance approach. American Journal of Agricultural Economics, 88, 1–15.CrossRefGoogle Scholar
  11. Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, 88(3), 411–424.CrossRefGoogle Scholar
  12. Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization. Princeton and Oxford: Princeton University Press.CrossRefGoogle Scholar
  13. Behrendt, K., Cacho, O., Scott, J. M., & Jones, R. (2016). Using seasonal stochastic dynamic programming to identify optimal management decisions that achieve maximum economic sustainable yields from grasslands under climate risk. Agricultural Systems, 145, 13–23.CrossRefGoogle Scholar
  14. Beyer, H. G., & Sendhoff, B. (2007). Robust optimization–A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196, 3190–3218.CrossRefGoogle Scholar
  15. Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and applications of Robust Optimization. Society for Industrial and Applied Mathematics SIAM Review, 53(3), 464–501.Google Scholar
  16. Birge, J. R., & Louveaux, F. (1997). Introduction to stochastic programming. New York: Springer.Google Scholar
  17. Bradley, S., Hax, A., & Magnanti, T. (1977). Applied mathematical programming. Addison-Wesley.Google Scholar
  18. Cao, K., Huang, B. O., Wang, S., & Lin, H. (2012). Sustainable land use optimization using Boundary-based Fast Genetic Algorithm. Computers, Environment and Urban Systems, 36, 257–269.CrossRefGoogle Scholar
  19. Carter Ingram, J., Redford, K. H., & Watson, J. E. M. (2012). Applying Ecosystem Services Approaches for Biodiversity Conservation: Benefits and Challenges. Sapiens, 5(1), 6.Google Scholar
  20. Clasen, C., Griess, V. C., & Knoke, T. (2011). Financial consequences of losing admixed tree species: A new approach to value increased financial risks by ungulate browsing. Forest Policy and Economics, 13, 503–511.CrossRefGoogle Scholar
  21. Caramia, M., & Dell’Olmo, P., (2008). Multi-objective optimization. In Multi-objective management in freight logistics (pp. 11–36). Springer, New York.Google Scholar
  22. Castro, L. M., Calvas, B., Hildebrandt, P., & Knoke, T. (2013). Avoiding the loss of shade coffee plantations: How to derive conservation payments for risk-averse land-users. Agroforestry Systems, 87, 331–347.CrossRefGoogle Scholar
  23. Castro, L. M., Calvas, B., & Knoke, T. (2015). Ecuadorian banana farms should consider organic banana with low price risks in their land-use portfolios. Plos One, 10(3).
  24. Charnes, A., Cooper, W. W., & Ferguson, R. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138–151.CrossRefGoogle Scholar
  25. Charnes, A. (1977). Goal programming and multiple objective optimization. Part I. European Journal of Operational Research, 1, 39–54.CrossRefGoogle Scholar
  26. Chen, W., Carsjens, G. J., Zhao, L., & Li, H. (2015). A spatial optimization model for sustainable land use at regional level in China: A Case Study for Poyang Lake Region. Sustainability, 7, 35–55.
  27. Clark, C. W. (2006). Fisheries bioeconomics: Why is it so widely misunderstood? Population Ecology, 48(2), 95–98.CrossRefGoogle Scholar
  28. Cortez-Arriola, J., Groot, J., Rossing, W., Scholberg, J., Améndola Massiotti, R., & Tittonell, P. (2016). Alternative options for sustainable intensification of smallholder. Agricultural Systems, 144, 22–32.CrossRefGoogle Scholar
  29. Daily, G. C., Söderqvist, T., Aniyar, S., Arrow, K., Dasgupta, P., Ehrlich, P. R., et al. (2000). The value of nature and the nature of value. Science, 289, 395–96.CrossRefGoogle Scholar
  30. Del Prado, A., Misselbrook, T., Chadwick, D., Hopkins, A., Dewhurst, R. J., Davison, P., et al. (2011). SIMSDAIRY: A modelling framework to identify sustainable dairy farms in the UK. Framework description and test for organic systems and N fertiliser optimization. Science of the Total Environment, 409, 3993–4009.CrossRefGoogle Scholar
  31. Delmotte, S., Lopez-Ridaura, S., Barbier, J. M., & Wery, J. (2013). Prospective and participatory integrated assessment of agricultural systems from farm to regional scales: Comparison of three modelling approaches. Journal of Environmental Management, 129, 493–502.CrossRefGoogle Scholar
  32. De Wit, C. T. (1992). Resource use efficiency in agriculture. Agricultural Systems, 40, 125–151.CrossRefGoogle Scholar
  33. Di Falco, S., & Perrings, C. (2005). Crop biodiversity, risk management and the implications of agricultural assistance. Ecological Econonomics, 55, 459–466.CrossRefGoogle Scholar
  34. Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton, NJ: Princeton University Press.Google Scholar
  35. Doole, G. J., Marsh, D., & Ramilan, T. (2013). Evaluation of agri-environmental policies for reducing nitrate pollution from New Zealand dairy farms accounting for firm heterogeneity. Land Use Policy, 30, 57–66.CrossRefGoogle Scholar
  36. Estrella, R., Cattrysse, D., & Van Orshoven, J. (2014). Comparison of three ideal point-based multi-criteria decision methods for afforestation planning. Forests, 5(12), 3222–3240.CrossRefGoogle Scholar
  37. Eastman, J. R., Jiang, H., & Toledano, J. (1998). Multi-criteria and multi-objective decision making for land allocation using GIS. In E. Beinat & P. Nijkamp (Eds.), Multi-criteria analysis for land-use management (pp. 227–251). Dordrecht: Springer.CrossRefGoogle Scholar
  38. Eyvindson, K., & Kangas, A. (2014). Using a compromise programming framework to integrating spatially specific preference information for forest management problems. Journal of Multi-Criteria Decision Analysis, 22, 3–15.CrossRefGoogle Scholar
  39. Finger, R., Lazzarotto, P., & Calanca, P. (2010). Bio-economic assessment of climate change impacts on managed grassland production. Agricultural Systems, 103(9), 666–674.CrossRefGoogle Scholar
  40. Flichman, G., Louhichi, K., & Boisson, J. M. (2011). Modelling the relationship between agriculture and the environment using bio-economic models: Some conceptual issues. Bio-economic models applied to agricultural systems (pp. 3–14). Berlin: Springer.CrossRefGoogle Scholar
  41. Flichman, G., & Allen, T. (2015). Bio-economic modelling: State-of-the-art and key priorities. Food and Agriculture Organization, United Nations. Accessed January 10, 2017.Google Scholar
  42. Ford, A. (1999). Modelling the environment: An introduction to system dynamics models of environmental systems. Washington: Island Press.Google Scholar
  43. Gentle, J., Härdle, W. K., & Mori, Y. (2012). Handbook of computational statistics: Concepts and methods. Berlin: Springer.CrossRefGoogle Scholar
  44. Griess, V., & Knoke, T. (2013). Bioeconomic modelling of mixed Norway spruce–European beech stands: Economic consequences of considering ecological effects. European Journal of Forest Research, 132, 511–522.CrossRefGoogle Scholar
  45. Grigalunas, T., Opaluch, J. J., & Luo, M. (2001). The economic costs to fisheries from marine sediment disposal: Case study of providence. USA. Ecological Economics, 38(1), 47–58.CrossRefGoogle Scholar
  46. Groot, J. C. J., Rossing, W. A. H., Jellema, A., Stobbelaar, D. J., Renting, H., & Van Ittersum, M. K. (2007). Exploring multi-scale trade-offs between nature conservation, agricultural profits and landscape quality–A methodology to support discussions on land-use perspectives. Agriculture, Ecosystems and Environment, 120, 58–69.CrossRefGoogle Scholar
  47. Hadar, J., & Russell, W. R. (1969). Stochastic dominance and diversification. Journal of Economic Theory, 3, 288–305.CrossRefGoogle Scholar
  48. Härtl, F., Hahn, A., & Knoke, T. (2013). Risk-sensitive planning support for forest enterprises: The YAFO model. Computers and Electronics in Agriculture, 94, 58–70.CrossRefGoogle Scholar
  49. Haque, A., & Asami, Y. (2014). Optimizing urban land use allocation for planners and real estate developers. Computers, Environment and Urban Systems, 46, 57–69.CrossRefGoogle Scholar
  50. Hazell, P., & Norton, G. (1986). Mathematical programming for economic analysis in agriculture. London: Macmillan Publishing Company.Google Scholar
  51. Heerink, N., Kuiper, M., & van Keulen, H. (2001). Economic policy and sustainable land use. Recent advances in quantitative analysis for developing countries (p. 376). Berlin: Springer.Google Scholar
  52. Herrero, M., Fawcett, R. H., & Dent, J. B. (1999). Bio-economic evaluation of dairy farm management scenarios using integrated simulation and multiple-criteria models. Agricultural Systems, 62, 169–188.CrossRefGoogle Scholar
  53. Herzig, A., Ausseil, A. G. E., & Dymond, J. R. (2013). Spatial optimisation of ecosystem services. In J. R. Dymond (Ed.), Ecosystem services in New Zealand–conditions and trends. Manaaki Whenua Press: Lincoln.Google Scholar
  54. Hirshleifer, J., & Riley, J. G. (2002). The analytics of uncertainty and information. Cambridge surveys of economic literature. Cambridge: Cambridge University Press.Google Scholar
  55. Hildebrandt, P., & Knoke, T. (2009). Optimizing the shares of native tree species in forest plantations with biased financial parameters. Ecological Economics, 68, 2825–2833.CrossRefGoogle Scholar
  56. Hildebrandt, P., & Knoke, T. (2011). Investment decisions under uncertainty–A methodological review on forest science studies. Forest Policy and Economics, 13, 1–15.CrossRefGoogle Scholar
  57. Holden, S., Shiferaw, B., & Pender, J. (2004). Non-farm income, household welfare, and sustainable land management in a less-favoured area in the Ethiopian highlands. Food Policy, 29, 369–392.CrossRefGoogle Scholar
  58. Homans, F. R., & Wilen, J. E. (2005). Markets and rent dissipation in regulated open access fisheries. Journal of Environmental Economics and Management, 49(2), 381–404.CrossRefGoogle Scholar
  59. Ignizio, J. P. (1976). Goal programming and extensions. Lexington, MA: Heath Lexington Books.Google Scholar
  60. Ijiri, Y. (1965). Management goals and accounting for control. Amsterdam: North Holland.Google Scholar
  61. Janssen, S., & van Ittersum, M. K. (2007). Assessing farm innovations and responses to policies: A review of bio-economic farm models. Agricultural Systems, 94, 622–636.CrossRefGoogle Scholar
  62. Kall, P., & Wallace, S. (1994). Stochastic programming. Wiley.Google Scholar
  63. Kanellopoulos, A., Reidsma, P., Wolf, J., & van Ittersum, M. K. (2014). Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling. European Journal of Agronomy, 52, 69–80.CrossRefGoogle Scholar
  64. Keeler, B. L., Gourevitch, J. D., Polasky, S., Isbell, F., Tessum, C. W., Hill, J. D., et al. (2016). The social costs of nitrogen. Science Advances, 2, e1600219.CrossRefGoogle Scholar
  65. Kennedy, C. M., Hawthorne, P. L., Miteva, D. A., & Baumgarten, L. (2016). Optimizing land use decision-making to sustain Brazilian agricultural profits, biodiversity and ecosystem services. Biological Conservation, 204, 221–230.CrossRefGoogle Scholar
  66. Knoke, T., & Wurm, J. (2006). Mixed forests and a flexible harvest strategy: A problem for con-ventional risk analysis? European Journal of Forest Research, 125, 303–315.CrossRefGoogle Scholar
  67. Knoke, T., & Seifert, T. (2008). Integrating selected ecological effects of mixed European beech–Norway spruce stands in bio-economic modelling. Ecological Modelling, 210, 487–498.CrossRefGoogle Scholar
  68. Knoke, T., Paul, C., Härtl, F., Castro, L. M., Calvas, B., & Hildebrandt, P. (2015). Optimizing agricultural land-use portfolios with scarce data–A non-stochastic model. Ecological Economics, 120, 250–259.CrossRefGoogle Scholar
  69. Knoke, T., Paul, C., Hildebrandt, P., Calvas, B., Castro, L. M., Härtl, F., Döllerer, M., Hamer, U., Windhorst, D., Wiersma, Y.F., & Fernández, G. F. C. (2016). Compositional diversity of rehabilitated tropical lands supports multiple ecosystem services and buffers uncertainties. Nature Communications, 7, Article number: 11877.
  70. Komarek, A. M., Bell, L. W., Whish, J., Robertson, M. J., & Bellotti, W. (2015). Whole-farm economic, risk and resource-use trade-offs associated with integrating forages into crop–livestock systems in western China. Agricultural Systems, 133, 63–72.CrossRefGoogle Scholar
  71. Kragt, M. (2012). Bioeconomic modelling: Integrating economic and environmental systems? In International Environmental Modelling and Software Society. International Congress on Environmental Modelling and Software. Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany.Google Scholar
  72. Knowler, D. (2002). A review of selected bioeconomic models with environmental influences in fisheries. Journal of Bioeconomics, 4, 163–181.CrossRefGoogle Scholar
  73. Lalani, B., Dorward, P., Holloway, G., & Wauters, E. (2016). Smallholder farmers’ motivations for using conservation agriculture and the roles of yield, labour and soil fertility in decision making. Agricultural Systems, 146, 80–90.CrossRefGoogle Scholar
  74. Lambin, E. F., Rounsevell, M. D. A., & Geist, H. J. (2000). Are agricultural land-use models able to predict changes in land-use intensity? Agriculture, Ecosystems & Environment, 82(1), 321–331.CrossRefGoogle Scholar
  75. Landa, J. T., & Ghiselin, M. T. (1999). The emerging discipline of bioeconomics: Aims and scope of the Journal of Bioeconomics. Journal of Bioeconomics, 1(5), 5–12.
  76. Larkin, S., Alvarez, S., Sylvia, G., & Harte, M. (2011). Practical considerations in using bioeconomic modelling for rebuilding fisheries. OECD Food, Agriculture and Fisheries Papers No., 38. Accessed October 24, 2016.
  77. Levy, H. (2006). Stochastic dominance: Investment decision making under uncertainty (2nd ed.). Berlin: Springer.Google Scholar
  78. Liu, X., Lehtonen, K., Purola, T., Pavlova, Y., Rötter, R., & Palosuo, R. (2016). Dynamic economic modelling of crop rotations with farm management practices under future pest pressure. Agricultural Systems, 144, 65–76.CrossRefGoogle Scholar
  79. Louhichi, K., Flichman, G., & Zekri, S. (1999). A bio-economic model for analyzing the impact of soil and water conservation policies applied to a Tunisian farm. Economie Rurale, 252, 55–64.CrossRefGoogle Scholar
  80. Macmillan, W. D. (1992). Risk and agricultural land use: A reformulation of the Portfolio-Theoretic Approach to the analysis of a von Thünen Economy. Geographical Analysis, 24, 142–158.CrossRefGoogle Scholar
  81. Messerer, K., Pretzsch, H., & Knoke, T. (2017). A non-stochastic portfolio model for optimizing the transformation of an even-aged forest stand to continuous cover forestry when information about return fluctuation is incomplete. Annals of Forest Science, 74, 45.CrossRefGoogle Scholar
  82. Mouysset, L., Doyen, L., Jiguet, F., Allaire, G., & Leger, F. (2011). Bio economic modeling for a sustainable management of biodiversity in agricultural lands. Ecological Economics, 70, 617–626.CrossRefGoogle Scholar
  83. Pacini, C., Wossink, A., Giesen, G., & Huirne, R. (2004). Evaluation of sustainability, integrated and conventional: A farm and field scale analysis. Agriculture, Ecosystems & Environment, 102, 349–364.CrossRefGoogle Scholar
  84. Phelps, J., Roman Carrasco, L., Webb, E. L., Koh, L. P., & Pascual, U. (2013). Agricultural intensification escalates future conservation costs. PNAS, 110(19), 7601–7606.
  85. Pandey, S., & Hardaker, J. B. (1995). The role of modelling in the quest for sustainable farming systems. Agricultural Systems, 47, 439–450.CrossRefGoogle Scholar
  86. Paracchini, M. L., Bulgheroni, C., Borreani, G., Tabacco, E., Banterle, A., Bertoni, D., et al. (2015). A diagnostic system to assess sustainability at a farm level: The SOSTARE model. Agricultural Systems, 133, 35–53.CrossRefGoogle Scholar
  87. Pfister, F., Bader, H. P., Scheidegger, R., & Baccini, P. (2005). Dynamic modelling of resource management for farming systems. Agricultural Systems, 86, 1–28.CrossRefGoogle Scholar
  88. Poppy, G. M., Jepson, P. C., Pickett, J. A., & Birkett, M. A. (2014). Achieving food and environmental security: New approaches to close the gap. Philosophical Transactions of the Royal Society B, 369, 20120272.
  89. Putten, A. B., & van MacMillan, I. C. (2004). Making real options really work. Harvard Bussiness Review, 82, 134–141. PMID: 15605572.Google Scholar
  90. Rădulescu, M., Rădulescu, C., & Zbăganu, G. (2014). A portfolio theory approach to crop planning under environmental constraints. Annual of Operational Research, 219, 243–264.CrossRefGoogle Scholar
  91. Romero, C., Tamiz, M., & Jones, D. F. (1998). Goal programming, compromise programming and reference point method formulations: Linkages and utility interpretations. The Journal of the Operational Research Society, 49, 986–991.CrossRefGoogle Scholar
  92. Samuelson, P. A. (1969). Lifetime portfolio selection by dynamic stochastic programming. The Review of Economics and Statistics, 51, 239–246.CrossRefGoogle Scholar
  93. Stephens, E., Nicholson, C. F., Brown, D. R., Parsons, D., Barrett, C. B., Lehmann, J., et al. (2012). Modelling the impact of natural resource-based poverty traps on food security in Kenya: The Crops, Livestock and Soils in Smallholder Economic Systems (CLASSES) model. Food Security, 4, 423–439.CrossRefGoogle Scholar
  94. Schumpeter, J. A. (1954). History of economic analysis. London: Routledge. ISBN 0-415-10888-8.Google Scholar
  95. Schönhart, M., Schauppenlehner, T., Kuttner, M., Kirchner, M., & Schmid, E. (2016). Climate change impacts on farm production, landscape appearance, and the environment: Policy scenario results from an integrated field-farm-landscape model in Austria. Agricultural Systems, 145, 39–50.CrossRefGoogle Scholar
  96. Semaan, S., Flichman, G., Scardigno, A., & Steduto, P. (2007). Analysis of nitrate pollution control policies in the irrigated agriculture of Apulia Region (Southern Italy): A bio-economic modelling approach. Agricultural Systems, 94, 357–367.CrossRefGoogle Scholar
  97. Seppelt, R., Lautenbach, S., & Volk, M. (2013). Identifying trade-offs between ecosystem services, land use, and biodiversity: A plea for combining scenario analysis and optimization on different spatial scales. Current Opinion in Environmental Sustainability, 5, 1–6.CrossRefGoogle Scholar
  98. Smith, P., Clark, H., Dong, H., Elsiddig, E. A., Haberl, H., Harper, R., House, J., Jafari, M., Masera, O., Mbow, C., & Ravindranath, N. H. (2014). Chapter 11—Agriculture, forestry and other land use (AFOLU). In Climate change 2014: Mitigation of climate change. IPCC Working Group III Contribution to AR5. Cambridge: Cambridge University Press.Google Scholar
  99. Sirén, A., & Parvinen, K. (2015). A spatial bioeconomic model of the harvest of wild plants and animals. Ecological Economics, 116, 201–210.CrossRefGoogle Scholar
  100. Tamiz, M., Jones, D., & Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research, 111, 569–581.CrossRefGoogle Scholar
  101. Tilman, D., Balzer, C., Hill, J., & Befort, L. (2011). Global food demand and the sustainable intensification of agriculture. PNAS, 108(50), 20260–20264.CrossRefGoogle Scholar
  102. Touza, J., Termansen, M., & Perrings, C. (2008). A bioeconomic approach to the Faustmann-Hartman model: Ecological interactions in managed forest. Natural Resource Modelling, 21(4), 551–581.Google Scholar
  103. Townsend, T., Ramsden, R. J., & Wilson, P. (2016). Analyzing reduced tillage practices within a bio-economic modelling framework. Agricultural Systems, 146, 91–102.CrossRefGoogle Scholar
  104. Ten Berge, H. F. M., van Ittersum, M. K., Rossing, W. A. H., van de Ven, G. W. J., & Schans, J. (2000). Farming options for The Netherlands explored by multi-objective modelling. European Journal of Agronomy, 13, 263–277.CrossRefGoogle Scholar
  105. Uhde, B., Heinrichs, S., Stiehl, C. R., Ammer, C., Müller-Using, B., & Knoke, T. (2017). Bringing ecosystem services into forest planning–Can we optimize the composition of Chilean forests based on expert knowledge? Forest Ecology and Management, 404, 126–140.CrossRefGoogle Scholar
  106. Vanclay, J. K. (1994). Modelling forest growth and yield. Applications to mixed tropical forests. Wallingford: CAB International.Google Scholar
  107. Van den Belt, M. (2004). Mediated modelling: A system dynamics approach to environmental consensus building. Washington, DC: Island Press.Google Scholar
  108. Yemshanov, D., McCarney, G. D., Hauer, G., Luckert, M., Unterschultz, J., & McKenney, D. W. (2015). A real options-net present value approach to assessing land use change: A case study of afforestation in Canada. Forest Policy and Economics, 50, 327–336.CrossRefGoogle Scholar
  109. Yu, H. X., & Jin, Li. (2012). A brief introduction to robust optimization approach. International Journal of Pure and Applied Mathematics, 74, 121–124.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Luz Maria Castro
    • 1
    • 2
    Email author
  • Fabian Härtl
    • 2
  • Santiago Ochoa
    • 1
  • Baltazar Calvas
    • 2
    • 3
  • Leonardo Izquierdo
    • 1
  • Thomas Knoke
    • 2
  1. 1.Departamento de EconomiaUniversidad Tecnica Particular de LojaLojaEcuador
  2. 2.Institute of Forest Management, Department of Ecology and Ecosystem Management, TUM School of Life Sciences WeihenstephanTechnische Universität MünchenFreisingGermany
  3. 3.Facultad de Ciencias Pecuarias, Universidad Técnica Estatal de QuevedoQuevedoEcuador

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