The study focuses on creating an evaluation index for assessing yield loss in rice during the flowering period in Northeast China due to cold vortex type disasters, which combine light, temperature, and water stress.
By utilizing meteorological and yield data, researchers established a composite stress occurrence index and utilized BP neural networks to analyze the impact of these stress factors on rice yield structures.
The findings identified critical thresholds for stress conditions and related yield loss percentages, providing valuable insights for improving rice production and disaster management in the region.