## 4 Conclusions  
In this study, we presented the potential of EL models (RF, bagging, and Ada) for precisely estimating the daily ET\({}_{\text{a}}\) of tea plantations with six scenarios of available tea plantation meteorological and evapotranspiration data collected over 12 years (2010-2021). The results suggest that the RF model exhibited superior performance, with higher values of \(R^{2}\) (0.84-0.91) and lower values of MAE (0.32-0.42 mm day\({}^{-1}\)) and RMSE (0.41-0.56 mm day\({}^{-1}\)) compared with the other state-of-the-art models for predicting the daily ET\({}_{\text{a}}\) of tea plantations. Meanwhile, RF and bagging models exhibited the highest steadiness with low RMSE values increasing (\(-\)15.3\(\sim\)+18.5%) in the validation phase over the testing phase. Considering the high prediction accuracy and dependability of the studied models, the RF and bagging models can be recommended for the daily ET\({}_{\text{a}}\) estimation of tea plantations. The importance analysis from the studied models determined that R\({}_{\text{n}}\) and T\({}_{\text{mean}}\) are the most critical influential variables that affect the daily dynamics of the observed and predicted ET\({}_{\text{a}}\) values for tea plantations. In the absence of climatic datasets, R\({}_{\text{n}}\), T\({}_{\text{mean}}\), and RH combinations (Scenario 4) achieved reasonable precision in assessing the daily ET\({}_{\text{a}}\) of tea plantations in all the models. The results of this study will contribute to the management of water resources for tea plantations.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132312961/s1](https://www.mdpi.com/article/10.3390/app132312961/s1)]([https://www.mdpi.com/article/10.3390/app132312961/s1](https://www.mdpi.com/article/10.3390/app132312961/s1)), Figure S1: The general schedule of phenology, managements and rainy/dry season for the study tea plantations. The pruning period is also the early  
Figure 9: Summary plots for KNN, SVM, MLP, Ad, bagging, and RF model using the scenario 1 dataset.  
growing season of the study tea plantations. The pictures were taken in the middle or late days of each month [6]; Figure S2: (**a**) Normal Support Vector Machine model; (**b**) Soft-margin Support Vector Machine model; Figure S3: Classic multilayer perceptron structure; Table S1: Seasonal variations of major biophysical parameters at the tea plantation.  
[PERSON] designed the research; [PERSON], [PERSON], [PERSON] and [PERSON] collected the data and performed the measurements; [PERSON] and [PERSON] wrote the manuscript. All authors were involved in the discussion and interpretation of the data as well as the revision of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation (No. 42201127, 41877513), the Science and Technology Planning Project of Yunnan Provincial Department of Science and Technology (202202 AE090034), and the Science and Technology Planning Project of NIGLAS (NIGLAS2022 GS10).  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to the funding conditions.  
The authors declare no conflict of interest.