Scholarlink

<p>To identify early indicators of short‑term drought in tea plantations driven by climatic and environmental changes, this study develops a LASSO–Cox–nomogram predictive model to achieve accurate prediction of short term and localized drought variation in tea plantations. Corresponding variability quantification indices were designed for multisource climatic data collected by Internet of Things devices. Limma differential analysis was used to examine climatic variables under different drought severities. Combined with univariable Cox regression, this approach systematically screened key climatic factors significantly associated with drought severity and showing clear variation patterns across drought stages. A nomogram was then constructed using LASSO for variable selection and Cox regression for multivariate analysis to assess the impact of climatic changes on drought conditions. LASSO regression was used to screen modeling factors, and fivefold cross‑validation together with multivariate Cox analysis was applied to establish the model. A nomogram was then constructed, and a visual prediction system was developed using Shiny and DynNOM. The prediction model achieved AUC values of 0.776, 0.762, and 0.777 for soil moisture content changes exceeding − 5%, 0%, and 5%, respectively, in the training set. In the validation set, corresponding AUC values were 0.742, 0.799, and 0.710. The model demonstrates strong discriminative ability and effectively captures differences in soil moisture across distinct variation intervals. The calibration curves closely matched the ideal reference lines, and the temporal hold-out testing demonstrated an accuracy of 78.57%. The developed drought prediction system enables accurate forecasting of short-term, localized drought variations in tea plantations. It offers high precision with low computational demand, thereby providing a foundation for improving the yield and quality of Yunnan tea.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of short‑term drought variation in tea plantations using a LASSO-COX-NOMOGRAM approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

查看全文
  • Baijuan Wang,
  • Wenxia Yuan,
  • Jihong Zhou,
  • Yang Li,
  • Chun Wang,
  • Weihao Liu,
  • Shihao Zhang,
  • Yuefei Wang

摘要

To identify early indicators of short‑term drought in tea plantations driven by climatic and environmental changes, this study develops a LASSO–Cox–nomogram predictive model to achieve accurate prediction of short term and localized drought variation in tea plantations. Corresponding variability quantification indices were designed for multisource climatic data collected by Internet of Things devices. Limma differential analysis was used to examine climatic variables under different drought severities. Combined with univariable Cox regression, this approach systematically screened key climatic factors significantly associated with drought severity and showing clear variation patterns across drought stages. A nomogram was then constructed using LASSO for variable selection and Cox regression for multivariate analysis to assess the impact of climatic changes on drought conditions. LASSO regression was used to screen modeling factors, and fivefold cross‑validation together with multivariate Cox analysis was applied to establish the model. A nomogram was then constructed, and a visual prediction system was developed using Shiny and DynNOM. The prediction model achieved AUC values of 0.776, 0.762, and 0.777 for soil moisture content changes exceeding − 5%, 0%, and 5%, respectively, in the training set. In the validation set, corresponding AUC values were 0.742, 0.799, and 0.710. The model demonstrates strong discriminative ability and effectively captures differences in soil moisture across distinct variation intervals. The calibration curves closely matched the ideal reference lines, and the temporal hold-out testing demonstrated an accuracy of 78.57%. The developed drought prediction system enables accurate forecasting of short-term, localized drought variations in tea plantations. It offers high precision with low computational demand, thereby providing a foundation for improving the yield and quality of Yunnan tea.