Evaluating the climatic impacts on dryland crop growth using multi-source datasets across Australia

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The rainfed cropland belt in Australia is of great importance to the world grain market but has the highest climate variability of all such regions globally. This thesis aims to quantify the spatial temporal climatic impacts on crop productivity, crop phenology and cropland photosynthesis activities across the Australian rainfed cropland belts using multiple source of observed datasets. The literature review on climate-crop growth relationship called for a future agenda on integrated climate driving factor employment, crop phenology and photosynthesis response focus, multiple source of datasets engagement, and bottom-up approaches for agricultural adaptation. Consistent findings from the three empirical studies in this thesis, which focused on different broad angles of the crop response, indicate that: (1) August and September are the optimum trigger months to spatially predict agricultural annual yield across the rainfed cropland belts in Australia; (2) two critical 8-day periods, beginning on day of the year (DoY) 257 (in September) and 289 (in October), were identified as the key ‘windows’ of crop growth variation that arose from the variability in climate and land surface temperature. (3) there was a seasonal hysteresis of crop photosynthesis activities in response to surface temperature change throughout the winter crop growing season in Australia. The optimum surface temperature range for satellite observed photosynthesis activity were identified as16.6-17.6 °C during August. This thesis systematically assessed the climatic impacts on crop growth across the Australian rainfed cropland belts. Practically, it provides new opportunities for large-scale cropland heat and water stress detection and can serve as an early warning system for agricultural adaptation in broad-acre rainfed cropping practices. Theoretically, it offers a fresh understanding for analyses of the climate-crop growth relationship across diverse spatial-temporal scales.
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