Modelling Climate Change Impacts on Location Suitability and Spatial Footprint of Apple and Kiwifruit

The threats and opportunities faced by primary production industries from future climate changes can be adequately prepared for only with the guidance of model projections that can assist the development of robust policy and climate adaptation plans by governments and industries. We used continuous...

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Veröffentlicht in:Land (Basel) 2022-10, Vol.11 (10), p.1639
Hauptverfasser: Vetharaniam, Indrakumar, Timar, Levente, Stanley, C. Jill, Müller, Karin, van den Dijssel, Carlo, Clothier, Brent
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Sprache:eng
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Zusammenfassung:The threats and opportunities faced by primary production industries from future climate changes can be adequately prepared for only with the guidance of model projections that can assist the development of robust policy and climate adaptation plans by governments and industries. We used continuous suitability models capable of reflecting incremental changes to project the suitability of locations across New Zealand for cultivating apple and kiwifruit in the mid- and late-century. These projections used future weather data from climate model simulations for two contrasting greenhouse gas (GHG) pathways: stringent GHG mitigation and unabated GHG emissions. To improve the suitability of the modelled temperature data, specifically for use with biologically driven, crop suitability models, we developed new bias-variance adjustments that preserved climate change signals within the data. Preliminary projections of land use across a range of alternative primary industries were obtained from a multinomial logit model incorporating continuous suitability scores as predictors. We refined the preliminary land-use projections by providing them as inputs into a simulation model of land use incorporating other drivers and constraints. This methodology provides a means for projecting future land use and the spatial footprints of primary industries, based on biological and econometric considerations, under different modelled climate change scenarios.
ISSN:2073-445X
2073-445X
DOI:10.3390/land11101639