Ecology and Society
Home | Archives | About | Login | Submissions | Subscribe | Contact | Search

 E&S Home > Vol. 16, No. 1 > Resp. 1

Copyright © 2011 by the author(s). Published here under license by The Resilience Alliance.
Go to the pdf version of this article

The following is the established format for referencing this article:
Van Noordwijk, M., B. Lusiana, G. Villamor, H. Purnomo, and S. Dewi. 2011. Feedback loops added to four conceptual models linking land change with driving forces and actors. Ecology and Society 16(1): r1. [online] URL:

Response to Hersperger et al. 2010. “Linking Land Change with Driving Forces and Actors: Four Conceptual Models.

Feedback Loops Added to Four Conceptual Models Linking Land Change with Driving Forces and Actors

Meine van Noordwijk 1, Betha Lusiana 1,2, Grace Villamor 1,3, Herry Purnomo 4 and Sonya Dewi 1

1World Agroforestry Centre (ICRAF), Bogor, Indonesia, 2Institute for Plant Production in the Tropics and Sub-Tropics, University of Hohenheim, Germany, 3Centre for Development Research (ZEF), Bonn, Germany, 4Centre for International Forestry Research (CIFOR), Bogor, Indonesia

Feedback loops that link the consequences of land change to agents and driving forces are essential in understanding the relevance of models. This aspect needs to be added to the four model types discussed by Hersperger et al. (2010)

Although we appreciate the efforts to develop a functional taxonomy of models of land use change, driving forces, and actors, we miss an important class: models with feedback from the consequences of land use change to actors, to driving forces, and/or both. Because the primary societal reason for a scientific analysis of changes in land cover is the consequences of land cover change on a wide range of stakeholder interests and the various ways stakeholders can try to modify land cover change in their favor, the utility of the conceptual models will depend strongly on the type of entry points the models provide for feedback (Fig. 1).

Four main types of feedback are:
  1. Land use, or the direct benefits that agents derive from their impact on land cover; it usually involves direct learning and relatively short response cycles, although there is ongoing debate about how much an economic lens misses of real motivations of the agents (Villamor et al. 2011).
  2. Land use planning, or the attempts by stakeholders of land cover beyond the land user, to change the rules that are part of the set of drivers influencing land users.
  3. Agent-specific modification of incentive structures that are conditional on performance, as attempted in forms of Payments for Ecosystem Services and related institutions (Tomich et al. 2004, Van Noordwijk et al. 2004, Swallow et al. 2009, Van Noordwijk and Leimona 2010).
  4. Generic changes in rules and economic incentives through policy change that is expected to enhance ecosystem services and/or economic performance at (sub)national scale, as currently discussed under the Reducing Emissions from Deforestation and Forest Degradation (REDD) umbrella where clarity on drivers and agents is needed (Blom et al. 2010).
A fifth component of the system is at the interface of numbers 1 and 5 in the form of Negotiation Support Systems (Van Noordwijk et al. 2001, Clark et al. 2010) in which multiple stakeholders, usually based on their own understanding and interpretation of the drivers-agents-change relationship, negotiate a range of options to manage the trade-offs between their respective stakes.

Regarding the claim of Hersperger et al. that most current agent-based models consider only one type of agent, that may be true numerically, but the exceptions are important and point to a way forward. Typically, agent-based models capture the ‘heterogeneity’ of a group that would be considered to be homogenous or represented by an average in other models. Brown and Robinson (2006) referred to heterogeneity in two types, namely (1) variability, which reflects continuous variation in agent characteristics across entire populations or within single agent types, and (2) categorization, introducing multiple types or groups of individuals with similar or differentiated preferences. Accordingly, heterogeneity is represented through various agent characteristics, e.g., preferences on a number of different factors that are independent and uncorrelated, thus creating complex interactions. This method of categorization was applied in the LUDAS model, a multiagent system model applied in Vietnam, of Le et al. 2008 and in follow-up models that are currently in development. In fact, agent-based models can also apply to the drivers rather than to the actors, as is done in organization centered multiagent systems (Purnomo and Guizol 2006).

Current modeling efforts that take the driver-agent-land relationship as a subsystem of a dynamic feedback description (van Noordwijk 2001, Lusiana et al. 2010, Villamor et al. 2011) are challenged by the way models can be validated (Lusiana et al. 2011). However, important aspects that emerge from these efforts are that the degree to which models can be learning tools for multiple stakeholders and act as ‘boundary objects’ (Clark et al. 2010) is at least as important as their academic ‘validation’ as conventionally quantified.

The Hersperger et al. taxonomy does not really address the nature of multiple scale issues in overall system dynamics. Further work on the framework is needed before such categorization of models can help individual research projects, communication and generalizations beyond the individual project, as the paper claims.


Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.


Blom, B., T. Sunderland, D. Murdiyarso. 2010. Getting REDD to work locally: lessons learned from integrated conservation and development projects. Environmental Science & Policy 13(2):164-172.

Brown, D. G., and D. T. Robinson. 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society 11(1): 46. [online] URL:

Clark, W. C., T. P. Tomich, M. van Noordwijk, N. M. Dickson, D. Catacutan, D. Guston, and E. McNie. 2010. Toward a general theory of boundary work: insights from the CGIAR’s natural resource management programs. Harvard Kennedy School Faculty Research Working Paper Series RWP10-035, Harvard University, Boston, Massachusetts, USA.

Hersperger, A. M., M. Gennaio, P. H. Verburg, and M. Bürgi. 2010. Linking land change with driving forces and actors: four conceptual models. Ecology and Society 15(4): 1. [online] URL:

Le, Q. B., S. J. Park, P. L. G. Vlek, and A. B. Cremers. 2008. Land-use dynamic simulator (LUDAS): a multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification. Ecological Informatics 2:135-153.

Lusiana, B., N. Khususiyah, K. Hairiah, M. van Noordwijk, and G. Cadisch. 2010. Trade-off analysis of land use change, livelihoods and environmental services in the Upper Konto catchment (Indonesia): prospecting land use options with the FALLOW model. International Conference on Integrative Landscape Modelling. Montpellier, France. ISBN 978-2-7592-0859-3. [online] URL:

Lusiana, B., M. van Noordwijk, D. Suyamto, L. Joshi, and G. Cadisch. 2011. Users’ perspectives on validity of a simulation model for natural resource management. International Journal of Agricultural Sustainability 11(2), in press

Purnomo, H., and P. Guizol. 2006. Simulating forest plantation co-management with a multi-agent system. Mathematical and Computer Modelling 44:535-552.

Swallow, B. M., M. F. Kallesoe, U. A. Iftikhar, M. van Noordwijk, C. Bracer, S. J. Scherr, K. V. Raju, S. V. Poats, A. Kumar Duraiappah, B. O. Ochieng, H. Mallee, and R. Rumley. 2009. Compensation and rewards for environmental services in the developing world: framing pan-tropical analysis and comparison. Ecology and Society 14(2): 26. [online] URL:

Tomich, T. P., M. van Noordwijk, and D. E. Thomas. 2004. Environmental services and land use change in Southeast Asia: from recognition to regulation or reward? Agriculture, Ecosystems and Environment 104:229-244.

van Noordwijk, M., 2001. Understanding local action and its consequences for global concerns in a forest margin landscape: the FALLOW model as a conceptual model of transitions from shifting cultivation. ASB Lecture Notes No. 11. International Centre for Research in Agroforestry Southeast Asia, Bogor, Indonesia.

van Noordwijk, M., F. Chandler, and T. P. Tomich. 2004. An introduction to the conceptual basis of RUPES: rewarding upland poor for the environmental services they provide. International Centre for Research in Agroforestry Southeast Asia, Bogor, Indonesia.

van Noordwijk, M., and B. Leimona. 2010. Principles for fairness and efficiency in enhancing environmental services in Asia: payments, compensation, or co-investment? Ecology and Society 15(4): 17. [online] URL:

van Noordwijk, M., T. P. Tomich, and B. Verbist. 2001. Negotiation support models for integrated natural resource management in tropical forest margins. Conservation Ecology 5(2): 21. [online] URL:

Villamor, G. B., M. van Noordwijk, Q. B. Le, B. Lusiana, R. Matthews, and P. L. G. Vlek. 2011. Diversity deficits in modelled landscape mosaics. Ecological Informatics, in press. doi:10.1016/j.ecoinf.2010.08.003

Address of Correspondent:
Meine van Noordwijk
World Agroforestry Centre (ICRAF)
Bogor, Indonesia


Home | Archives | About | Login | Submissions | Subscribe | Contact | Search