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Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China. | LitMetric

Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China.

Ying Yong Sheng Tai Xue Bao

Innovation and Opening Laboratory of Eco-Meteorology in Northeast China, China Meteorological Administration, Harbin 150030, China.

Published: March 2024

AI Article Synopsis

  • 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.

Article Abstract

The construction of a yield loss evaluation index for the cold vortex type light-temperature-water composite adversity during rice flowering period in Northeast China is important for elucidating the impacts of cold vortex type composite disasters on rice yield loss in middle and high latitude areas. Moreover, it can provide meteorological support to ensure safe production of high-quality japonica rice in China and contribute to regional disaster reduction and efficiency improvement. By combining growth period data, meteorological data, and yield data, we delineated and constructed the composite stress occurrence index of cold vortex type light-temperature-water at the flowering stage of japonica. We analyzed the relationship between factors causing disasters and yield structure, as well as the relationship between different yield structures and yield by employing BP neural network method. We further dissected the processes involved in the causation of combined disasters. Based on the K-means clustering method and historical typical disaster years, we quantified the critical thresholds and disaster grades, and established an evaluation index and model for assessing yield loss caused by combined stress from cold vortex type light-temperature-water. Finally, we examined the spatial and temporal variations of low temperature, abundant rainfall, and reduced sunlight during the flowering period in the three provinces of Northeast China. Results showed that the critical thresholds for light, temperature, and water stress index during the flowering stage of mild, moderate, and severe cold vortex types were [0, 0.21), [0.21, 0.32), and [0.32, 0.64], respectively. The rates of yield loss were [0, 0.03), [0.03, 0.08), and [0.08, 0.096], respectively. Based on the verification results of a total of 751 samples in 11 random years from 1961 to 2020, the percentage of stations for which the production reduction grade, as calculated by the composite index developed in this study, aligning with the actual production reduction grade was 63.7%, consistently exceeding 58.0% annually. Moreover, the proportion of sites with a similarity or difference level of 1 stood at 88.3%, surpassing 85.0% in each year. The index could effectively assess the extent of rice yield loss caused by cold vortex disasters in Northeast China.

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Source
http://dx.doi.org/10.13287/j.1001-9332.202403.035DOI Listing

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