Analysis of Climate Change on Agricultural Yields with Principal Component Analysis and Linear Regression Approaches
DOI:
https://doi.org/10.26418/justin.v13i3.91434Abstract
Climate Change has become a global issue that significantly impacts various sectors, including agriculture. This study aims to analyze the influence of climate variables, such as average temperature, rainfall, carbon dioxide (CO2) emissions, and extreme weather events on agricultural yields. The Principal Component Analysis (PCA) method was used to identify the main variability patterns in the data. At the same time, multiple linear regression was applied to determine the relationship between climate variables and crop yields. The analysis showed that temperature and precipitation were the main factors affecting agricultural yields, with increases in temperature being negatively correlated to crop productivity. PCA identified two principal components that explained the variability in the data, while multiple linear regression showed that temperature and extreme weather events significantly affected crop yields. The results underscore the potential of adaptation strategies in the agricultural sector, such as using climate-resilient crop varieties, water resource management efficiency, and agricultural technology innovation to increase resilience to climate change.References
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