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Mapping the Future: An insight into Modeling
10 May 2012


Climate change is a buzz word and a major concern globally. There are many theories and predictions as to what implications climate change has on communities, especially impoverished communities in Africa. However, the future cannot be predicted with accuracy. Modeling is one method whereby researchers can arrive at a close approximation of what that future will be.

Modeling can be defined as a scientific process that is intended to mirror or simplify reality. In essence, the laboratory is replaced by a computer and experiments are replaced by simulation.

In an era where time and resources (especially financial resources) are limited, modeling offers a viable option since it allows the user to generate scenarios and to test hypotheses using input data. However, the data should be carefully monitored so that quality data will yield accurate model outputs or results.

Whilst models offer the viewer a predicted future scenario, this is by no means gives an accurate view of what the future scenario holds. The advantage of modeling is that the researcher is able to test systems and different parameters to offer multiple scenarios in a controlled manner. It also offers the exciting option of exploring new research areas and opens up many possibilities when we question the dynamics of a model or series of models.

Students at the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) training workshop hosted by the Food, Agriculture and Natural Resources Policy Analysis Network (FANRPAN) did many brainstorming sessions on data analysis and modeling. The participating students are being funded by FANRPAN's SECCAP project (http://www.fanrpan.org/projects/seccap/), in partnership with International Development Research Centre (IDRC). The training is being funded by IDRC and USAID. What was clear from the training session was that models, if used correctly, can yield reliable and useful outputs that can inform future decisions, especially by policy and decision makers. It was a conglomeration of networking, information sharing, technology transfer and valuable discussion and workshops that gave students the depth that was needed to broaden their proverbial horizons as far as modeling goes.

Since the focus of the training was on linkages between models used for climate change impact assessment and agricultural productivity in southern Africa, the workshop saw some bright young minds grappling with agro-social-economics as part of the climate change impact assessment process.

While models are invaluable in a research environment, it is important that accurate data be taken into account and updated regularly so that modeling results are not static and can offer the decision- maker or the policy maker timely information to make informed decisions at any given point in time.

It must be noted that there are challenges in developing and using models due to complex nature of systems being modeled, such as difficulty in modeling random events. Furthermore, there is the obvious challenge of spatial scale that some of the global models tend to overlook, resulting in the results being skewed or generalized and in some cases data useable for important stakeholders (such as villager residents) are left out completely.

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