Spatializing the modelled impacts of future climate change – a case study on wheat in the Western Cape

Abstract

Analysis and interpretation of future climate change impacts on a particular crop, require a number of different models and datasets. Such datasets often operate at vastly disparate spatial scales. Mechanistic crop models, for example, classically operate at a site-specific, point location, for which soil and climate must be described in great detail. Future climate scenarios however, are obtained from various Global Climate Models (GCMs) at a very coarse resolutiontypically gridded to 300 km or more. In order to be useful at a local level they need to be downscaled to a spatial scale useful for local analysis. Weather monitoring station locations in the province are irregularly distributedmuch denser in the fruit and vine areas than in the extensive wheat areas. The Western Cape Province is a highly diverse region with regard to geology, topography, climatic influences and the resulting agricultural systems and practices. Future climate change therefore, is likely to have different impacts in different zones of the province where wheat is produced. To address this heterogeneity, the province was divided into 21 distinct response zones for modelling purposes. Geographic Information Systems (GIS) play a key role in addressing the spatial complexities-facilitating issues such as weighted average zonation, aggregation (or disaggregation) of spatial components, local parameterisation of crop models through interpolation, integration of ancillary data such as satellite imagery within the modelling framework and finally in the spatial analysis and display of modelled scenarios. This paper uses a recent climate impact study in the Western Cape to demonstrate the role of GIS in the assessment of expected climate change impacts on dryland wheat agriculture.

Modeling climate change impact on dryland wheat production for increased crop yield in the Free State, South Africa, using GCM projections and the DSSAT model

“…(BFAP, 2018;Gouws, 2018). This makes it important to look at the agroclimatic modeling of wheat to determine crop yield and production beforehand (Wallace, 2013).…”