Keywords
integrated assessment, watershed, climate change impact, farming system
Start Date
1-7-2010 12:00 AM
Abstract
For agricultural production, climate change will have the greatest impact on water availability.At the same time, rural farming communities are at the heart of poverty reduction strategies. Furthermore,healthy rural societies must be maintained to contain urbanization and associated sprawl. Thus, sustainableadaptation strategies must take into account the complexity of societal responses. However, scientific toolsto assess such interactions are lacking. A promising approach is the integration of data and models acrossscientific disciplines and in collaboration with local stakeholders. Empirical, process-oriented models caneven quantify these interactions and feedback. As a contribution to this challenge, the project ‘Integratinggovernance and modeling’ combined the agricultural economics multi-agent farm decision model MPMASand the hydrological model WASIM-ETH dynamically. Models were calibrated empirically, withincreasing level of detail and interactions. The stepwise and iterative integration/calibration of these coupledmodels allowed for sensitivity assessment across disciplines but it also pointed to the relevance ofknowledge gaps along disciplinary divides: production risk at multiple decision horizons, the unequal susceptibilityof different marketing venues in case of production failures, and farmers’ unequal access towater under fluctuating supply.
Farm decisions under dynamic meteorology and the curse of complexity
For agricultural production, climate change will have the greatest impact on water availability.At the same time, rural farming communities are at the heart of poverty reduction strategies. Furthermore,healthy rural societies must be maintained to contain urbanization and associated sprawl. Thus, sustainableadaptation strategies must take into account the complexity of societal responses. However, scientific toolsto assess such interactions are lacking. A promising approach is the integration of data and models acrossscientific disciplines and in collaboration with local stakeholders. Empirical, process-oriented models caneven quantify these interactions and feedback. As a contribution to this challenge, the project ‘Integratinggovernance and modeling’ combined the agricultural economics multi-agent farm decision model MPMASand the hydrological model WASIM-ETH dynamically. Models were calibrated empirically, withincreasing level of detail and interactions. The stepwise and iterative integration/calibration of these coupledmodels allowed for sensitivity assessment across disciplines but it also pointed to the relevance ofknowledge gaps along disciplinary divides: production risk at multiple decision horizons, the unequal susceptibilityof different marketing venues in case of production failures, and farmers’ unequal access towater under fluctuating supply.
