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<p>Rice production in Vietnam, particularly in the central provinces, is increasingly affected by climatic variability, yet the independent impact of climate change remains poorly understood due to concurrent agronomic improvements. This study examined the impact of key climatic factors, including temperature, rainfall, sunshine duration, and humidity, on rice yield in Quang Nam Province over a 29-year period (1995–2023). A linear detrending approach was applied to remove non-climatic influences from yield data, followed by multivariate regression modeling for the Winter–Spring (WS) and Summer–Autumn (SA) cropping seasons. Principal Component Analysis (PCA) was also used to identify dominant climatic variables. The regression model for the WS season was statistically significant (<i>p</i> &lt; 0.05), with rainfall exerting the most substantial negative impact on yield, followed by temperature (negative) and humidity (positive). In contrast, the SA season model was not statistically valid due to multicollinearity and seasonal climatic disturbances such as storms and pest outbreaks. PCA results confirmed that the selected climatic variables explained over 70% of the total variance and revealed distinct seasonal patterns in their influence on yield. These findings highlight the season-specific nature of climate–yield relationships and demonstrate the value of combining detrending, regression analysis, and PCA to improve the understanding of climatic impacts on rice production. The study offers valuable insights for enhancing yield forecasting and developing targeted, climate-resilient agricultural strategies in Central Vietnam.</p>

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Assessing the impact of climate factors on rice yield in central vietnam: a 29-year study using detrending and regression models

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Toan Nguyen-Sy,
  • Huynh Hai,
  • Do Hong Hanh,
  • Tran Thi Phu,
  • Huynh Thi Diem Uyen,
  • Tran Thi Hoan,
  • Phan Chi Uyen

摘要

Rice production in Vietnam, particularly in the central provinces, is increasingly affected by climatic variability, yet the independent impact of climate change remains poorly understood due to concurrent agronomic improvements. This study examined the impact of key climatic factors, including temperature, rainfall, sunshine duration, and humidity, on rice yield in Quang Nam Province over a 29-year period (1995–2023). A linear detrending approach was applied to remove non-climatic influences from yield data, followed by multivariate regression modeling for the Winter–Spring (WS) and Summer–Autumn (SA) cropping seasons. Principal Component Analysis (PCA) was also used to identify dominant climatic variables. The regression model for the WS season was statistically significant (p < 0.05), with rainfall exerting the most substantial negative impact on yield, followed by temperature (negative) and humidity (positive). In contrast, the SA season model was not statistically valid due to multicollinearity and seasonal climatic disturbances such as storms and pest outbreaks. PCA results confirmed that the selected climatic variables explained over 70% of the total variance and revealed distinct seasonal patterns in their influence on yield. These findings highlight the season-specific nature of climate–yield relationships and demonstrate the value of combining detrending, regression analysis, and PCA to improve the understanding of climatic impacts on rice production. The study offers valuable insights for enhancing yield forecasting and developing targeted, climate-resilient agricultural strategies in Central Vietnam.