Method models climate change scenarios by processing vast amounts of high-resolution soil and weather data.

New Zealand’s economy is dependent on agriculture, a sector that is highly sensitive to climate change. This makes it critical to develop analysis capabilities to assess its impact and investigate possible mitigation and adaptation options. That analysis can be done with tools such as agricultural systems models. In simple terms, it involves creating a model to quantify how a specific crop behaves under certain conditions then simulating altering a few variables to see how that behavior changes. Some of the software available to do this includes CropSyst from Washington State University and the Agricultural Production Systems Simulator (APSIM) from the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia.

Historically, these models have been used primarily for small area (point-based) simulations where all the variables are well known. For large area studies (landscape scale, e.g., a whole region or national level), the soil and climate data need to be upscaled or downscaled to the resolution of interest, which means increasing uncertainty. There are two major reasons for this: 1) it is hard to create and/or obtain access to high-resolution, geo-referenced, gridded datasets; and 2) the most common installation of crop modeling software is in an end user’s desktop or workstation that’s usually running one of the supported versions of Microsoft Windows (system modelers tend to prefer the GUI capabilities of the tools to prepare and run simulations, which are then restricted to the computational power of the hardware used)…

Read more at: Opensource.com