Presenter/Author Information

Subana Shanmuganathan
Philip Sallis
Ajit Narayanan

Keywords

daily air maximum, minimum and soil (grass minimum) temperature

Start Date

1-7-2010 12:00 AM

Description

Modelling the effects of climate change on vegetation and agriculture isarguably one of the most challenging issues the scientific community has to deal with inrecent times. Grapevine being among the world’s old and most expensive cultivated cropswith winemaking consisting of a rich history of centuries-old traditions makescontemporary research into modelling climate effects on viticulture of significant interest.Novel approaches are explored for gaining more scientific knowledge on the phenomenonclimate change, in particular its potential impact on grapevine growth stages, phenologicalevents and wine quality. In this context, the paper looks at literature on recent  analysisbased approach to establishing associations between daily extreme weather conditions andsome perennial crop yield at larger spatiotemporal scales, i.e., yield comparisons amongwine regions/ national annual yield of apples, walnuts, oranges, almonds and avocadoswith three decade old data. Consequently, recent novel approaches investigated at theGeoinformatics Research Centre (GRC) to studying the effects of daily maximumtemperature on grapevine yield using data at a different spatiotemporal scale along withresults obtained are outlined. The paper then details on extending the approaches to otherdaily extreme weather data; minimum air and soil (grass) temperatures with a) a singlevineyard’s yield over a period of 12 years (1997-2009) and b) weather conditions, recordedat a nearby weather monitoring station belonging to the National Institute of Water andAtmosphere (NIWA), extracted via NIWA’s web portal. The results show interestingnexuses between daily extreme weather conditions, the independent variables andgrapevine yield, the dependent variable at spatiotemporal scales not previously ascertainedi.e., at a vineyard (micro scale) but using macro climate data. The approach provides ameans to gaining precise information relating to climate effects on viticulture, useful fortraining grapevines appropriately and thereby improving the quality of grapevine yield/vintage.

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Jul 1st, 12:00 AM

Modelling the effects of daily extreme weather on grapevine and wine quality

Modelling the effects of climate change on vegetation and agriculture isarguably one of the most challenging issues the scientific community has to deal with inrecent times. Grapevine being among the world’s old and most expensive cultivated cropswith winemaking consisting of a rich history of centuries-old traditions makescontemporary research into modelling climate effects on viticulture of significant interest.Novel approaches are explored for gaining more scientific knowledge on the phenomenonclimate change, in particular its potential impact on grapevine growth stages, phenologicalevents and wine quality. In this context, the paper looks at literature on recent  analysisbased approach to establishing associations between daily extreme weather conditions andsome perennial crop yield at larger spatiotemporal scales, i.e., yield comparisons amongwine regions/ national annual yield of apples, walnuts, oranges, almonds and avocadoswith three decade old data. Consequently, recent novel approaches investigated at theGeoinformatics Research Centre (GRC) to studying the effects of daily maximumtemperature on grapevine yield using data at a different spatiotemporal scale along withresults obtained are outlined. The paper then details on extending the approaches to otherdaily extreme weather data; minimum air and soil (grass) temperatures with a) a singlevineyard’s yield over a period of 12 years (1997-2009) and b) weather conditions, recordedat a nearby weather monitoring station belonging to the National Institute of Water andAtmosphere (NIWA), extracted via NIWA’s web portal. The results show interestingnexuses between daily extreme weather conditions, the independent variables andgrapevine yield, the dependent variable at spatiotemporal scales not previously ascertainedi.e., at a vineyard (micro scale) but using macro climate data. The approach provides ameans to gaining precise information relating to climate effects on viticulture, useful fortraining grapevines appropriately and thereby improving the quality of grapevine yield/vintage.