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Article

Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

1
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Area of Ministry of Education, Northwest A&F University, Xianyang 712100, China
2
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Academic Editor: Josef Tanny
Water 2021, 13(21), 3078; https://doi.org/10.3390/w13213078
Received: 2 August 2021 / Revised: 20 October 2021 / Accepted: 21 October 2021 / Published: 2 November 2021
(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)
Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation. View Full-Text
Keywords: greenhouse grapes; sap flow rate; random forest model; meteorology; soil moisture; uncertainty analysis greenhouse grapes; sap flow rate; random forest model; meteorology; soil moisture; uncertainty analysis
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MDPI and ACS Style

Peng, X.; Hu, X.; Chen, D.; Zhou, Z.; Guo, Y.; Deng, X.; Zhang, X.; Yu, T. Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models. Water 2021, 13, 3078. https://doi.org/10.3390/w13213078

AMA Style

Peng X, Hu X, Chen D, Zhou Z, Guo Y, Deng X, Zhang X, Yu T. Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models. Water. 2021; 13(21):3078. https://doi.org/10.3390/w13213078

Chicago/Turabian Style

Peng, Xuelian, Xiaotao Hu, Dianyu Chen, Zhenjiang Zhou, Yinyin Guo, Xin Deng, Xingguo Zhang, and Tinggao Yu. 2021. "Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models" Water 13, no. 21: 3078. https://doi.org/10.3390/w13213078

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