LAUNCH OF AIRCCA: A NEW MODEL FOR THE ASSESSMENT OF IMPACTS AND RISKS OF CLIMATE CHANGE ON AGRICULTURE
A new software application AIRCCA assesses the impacts of risks and climate change for rainfed maize, wheat and rice yields on a global scale.
09/11/2020 | 11:14 AM
This model has been developed by Francisco Estrada, Wouter Botzen, and Oscar Calderon-Bustamante and was recently published with an article in Journal of Spatial Economic Analysis.
One of the main channels through which climate change is expected to affect the economy is the agricultural sector. However, the large spatial variability in these impacts and the high levels of uncertainty in climate change projections create methodological challenges for assessing the consequences this sector could face.
This study presents a reduced form emulators model for global rain-fed maize, wheat and rice yields that can closely reproduce the results of a more complex leading, spatially explicit (0.5ºx0.5º), biophysical crop model. This type of emulators offers two main advantages over other assessment methods. First, their simplicity allows them to be suitable for inclusion in integrated assessment models of climate change and the economy. Second, due to their low computational costs they can simulate the spatial variation of yields under many climate change scenarios, and offer the possibility of conducting probabilistic risk assessments to support decision-making about adaptation policies.
The authors developed a stand-alone software application based on reduced form crop emulators called AIRCCA, which is a model for the Assessment of Impacts and Risks of Climate Change on Agriculture. The user-friendly software AIRCCA allows stakeholders to make a rapid assessments of the effects of climate change on maize, wheat and rice yields in their regions of interest. These assessments can produce user-defined outputs under a large number of climate models’ simulations that use the four main climate change emissions scenarios of the IPCC.
The AIRCCA model can be downloaded here.
The article can be downloaded here.
The model and article are based on a collaboration between IVM and the Center of Atmospheric Sciences, UNAM.