Using participatory modeling processes to identify sources of climate risk in West Africa

Participatory modeling has been widely recognized in recent years as a powerful tool for dealing with risk and uncertainty. By incorporating multiple perspectives into the structure of a model, we hypothesize that sources of risk can be identified and analyzed more comprehensively compared to traditional ‘expert-driven’ models. However, one of the weaknesses of a participatory modeling process is that it is typically not feasible to involve more than a few dozen people in model creation, and valuable perspectives on sources of risk may therefore be absent. We sought to address this weakness by conducting parallel participatory modeling processes in three countries in West Africa with similar climates and smallholder agricultural systems, but widely differing political and cultural contexts. Stakeholders involved in the agricultural sector in Ghana, Mali, and Nigeria participated in either a scenario planning process or a causal loop diagramming process, in which they were asked about drivers of agricultural productivity and food security, and sources of risk, including climate risk, between the present and mid-century (2035–2050). Participants in all three workshops identified both direct and indirect sources of climate risk, as they interact with other critical drivers of agricultural systems change, such as water availability, political investment in agriculture, and land availability. We conclude that participatory systems methods are a valuable addition to the suite of methodologies for analyzing climate risk and that scientists and policy-makers would do well to consider dynamic interactions between drivers of risk when assessing the resilience of agricultural systems to climate change

Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

<div><p>Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.</p></div

A framework for uncertainty and sensitivity analysis of ABMs of socioecological systems.

<p>Applying variance decomposition to simplify a stochastic model (A), and maintain its exploratory power embodied in outcome variability (B) or improving its explanatory power by reducing its outcome variability (C).</p

Results of uncertainty (A) and sensitivity (B) analysis for the output variable fallow land area.

<p>Fallow land area is reported in map units (equivalent of 30 m). Factor labels used in text: number of offers accepted by the Farm Service Agency - n, payment reduction used by the farmer agent to increase offer competitiveness - BID, FA's decision rule - OWA, fraction of farmland enrolled in CRP - LAND, FA's retirement status - RETIREMENT, FA's value of production - PRODUCTION, land tenure - TENURE, density of enrollment in the neighborhood - DE, measurement of environmental benefits - EBI, factor interactions - I (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109779#pone.0109779.e003" target="_blank">Equation 3</a>).</p

Study area in Michigan, U.S.

<p>Study area in Michigan, U.S.</p

Uncertainty and sensitivity analyses of model output.

<p>Uncertainty and sensitivity analyses of model output.</p

Agent-based model of enrollment in Conservation Reserve Program.

<p>Agent-based model of enrollment in Conservation Reserve Program.</p