If you are interesting in global patterns of agricultural change, as I am, you might find this recently published paper helpful for selecting the most appropriate global framework.
van Wart, J., van Bussel, L. G., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaarde, H., Gerberf, J., Muellerf, N. D., Claessensg, L.,
van Ittersum, M. K. & Cassman, K. G. (2013). Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research.
A particular challenge of investigating the causes of land-use change is the multi-scale nature of factors that influence land-use decisions. In an increasingly globalized world, land-use choices and livelihood strategies are linked to local AND regional to global forces. But attempts to incorporate such multi-scale causation in land change models often run into significant knowledge and data gaps – especially when trying to link incomplete and/or low quality global data to individual agents’ decision-making processes.
One way forward, which my co-author Dr. Erle Ellis and I present in this new open-access article in Transactions in GIS, is to use pattern-oriented modeling (Grimm et al., 2005) within an agent-based virtual laboratory to experimentally bound the possible values of uncertain parameters. By targeting characteristic patterns tied to important individual- and landscape-level processes – the selection of which are informed by theory, data, or both – ABMs can be designed and tested to be more realistic despite data limitations. We propose that this experimental method can help overcome significant data gaps, and help land change scientists begin to quantify some global trends in local land change processes.
Local land-use and -cover changes (LUCCs) are the result of both the decisions and actions of individual land-users, and the larger global and regional economic, political, cultural, and environmental contexts in which land-use systems are embedded. However, the dearth of detailed empirical data and knowledge of the influences of global/regional forces on local land-use decisions is a substantial challenge to formulating multi-scale agent-based models (ABMs) of land change. Pattern-oriented modeling (POM) is a means to cope with such process and parameter uncertainty, and to design process-based land change models despite a lack of detailed process knowledge or empirical data. POM was applied to a simplified agent-based model of LUCC to design and test model relationships linking global market influence to agents’ land-use decisions within an example test site. Results demonstrated that evaluating alternative model parameterizations based on their ability to simultaneously reproduce target patterns led to more realistic land-use outcomes. This framework is promising as an agent-based virtual laboratory to test hypotheses of how and under what conditions driving forces of land change differ from a generalized model representation depending on the particular land-use system and location.
Posted in Agent-Based Modeling, Land-Use Change, Livelihoods, Sustainability
Tagged agent-based modeling, cross-scale, data, global, land-use change, livelihoods, pattern-oriented modeling, virtual laboratories
Source: ESRI Story Map and the Institute of the Environment, U. of Minnesota.
I’ve used these global crop and land-use data many times, as they are excellent data sets by Foley et al (2011) and Monfreda et al. (2008). But this visualization in the form of an ESRI story map gives the data new life and power. An excellent and thought-provoking presentation, which is made all the better with its interactive qualities.
I will have to explore such a presentation for conveying the results of spatial ABMs.