Effects of meteorological factors on oil content and composition in Camellia oleifera seed kernels: a machine learning and multi-omics study.

Camellia oleifera has attracted considerable attention for its richness in unsaturated fatty acids, yet the associations between meteorological variation and oil biosynthesis remain poorly understood. In this study, machine learning models trained on 542 samples were combined with multi-omics profiling from fatty-acid synthesis to oil-body formation. The results showed that temperature, humidity, and rainfall from August to October were among the meteorological factors most strongly associated with fatty-acid synthesis and triacylglycerol assembly. Higher August humidity was associated with lower oleic acid, whereas a wider September diurnal temperature range was associated with lower stearoyl-ACP desaturase expression and higher downstream desaturase expression, thereby decreasing fatty-acid saturation and potentially affecting oxidative stability and nutritional quality. Additionally, warmer temperature and moderate humidity were associated with enhanced oil biosynthesis, higher oil yield, and lower saturation levels. This study provides a scientific basis for improving the nutritional value and yield of C. oleifera production.