Development of daily spatial heat unit mapping from monthly climatic surfaces for the Australian continent |
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| Authors: | Nicholas Coops Andrew Loughhead Philip Ryan Ron Hutton |
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| Institution: | 1. Department of Geoinformatics and Cartography , Finnish Geodetic Institute , PO Box 15, 02431 Masala, Finland juha.oksanen@fgi.fi;3. Department of Geoinformatics and Cartography , Finnish Geodetic Institute , PO Box 15, 02431 Masala, Finland |
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| Abstract: | In absence of other limitations, the growth rate of a plant is dependent upon the amount of heat it receives. Each species, whether a crop, weed or disease organism, is adapted to grow at its optimum rate within a specific temperature range. Within this range, the growing degree days (GDD) is the heat accumulation above a given base temperature for a specific time period, such as a crop's growing season or phenological stage. In this paper we detail a methodology to predict GDD for synthetically generated average growing seasons derived from long term average climate data over the Australian continent. An application of these techniques has been made using the GEODATA 9 second DEM, with temperature threshold values estimated to characterize optimum growth in citrus (Citrus sinensis (L.) Osbeck). Three major determinants of the annual growth cycle of Citrus sp. were established and predicted on a spatial basis including the starting day of the growing season, the GDD for a growing season, and the time required to accumulate an arbitrarily selected 2000 GDD from the estimated starting day. When these critical environmental factors are expressed on a spatial basis, covering the Australian continent, the combination can be used to identify locations where new crop varieties can most effectively be grown to maximize fruit quality and productivity, or to extend the harvest season. Likewise, new germplasm introduced to Australia from overseas can be horticulturally assessed at sites climatically matched to the source location. |
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| Keywords: | Digital elevation model LIDAR Accuracy assessment Error modelling geostatistics |
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