Correlation Analysis for Development and Validation of Climate Exposure Indicators for Climate Impact Vulnerability Assessment of Cool-Season Grasses

초록

This study aimed to develop and verify climate exposure indicators to evaluate the vulnerability of cool-season grasses to climate change in South Korea. A correlation analysis was conducted between climate factors and productivity of cool-season grasses using nationwide productivity data from 1993 to 2024 collected through literature search and the National Institute of Animal Science (n=542), along with productivity data from field experiments conducted in Jinju and Jangheung over two years (n=15; total n=557). The cool-season grass species included in this analysis were primarily orchardgrass (Dactylis glomerata L.), tall fescue (Festuca arundinacea Schreb.), perennial ryegrass (Lolium perenne L.) and timothy (Phleum pratense L.). The correlation analysis results indicated that productivity of cool-season grasses had a significant positive correlation with the mean temperature, mean minimum temperature, and mean maximum temperature during the winter and spring seasons, as well as the growing degree days from November to March. To validate the correlation analysis between temperature and yield, we compared the results with field experiments. The findings from the field trials were consistent with the correlation analysis, reinforcing the reliability of the indicators. In contrast, the number of rainy days in July exhibited a significant negative correlation with productivity. This suggests that waterlogging during the rainy season reduces the growth and yield of cool-season grasses. Therefore, we propose that winter and spring temperatures, growing degree days, and summer precipitation serve as key climate exposure indicators that explain variations in cool-season grass productivity. Although these climate exposure indicators can be utilized to evaluate climate vulnerability, continuous data accumulation and analysis are necessary to further strengthen the explanatory and predictive power of these variables in future agricultural applications.

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