Explainable machine learning for predicting stomatal conductance across multiple plant functional types-Reference-Cited by-同舟云学术

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Explainable machine learning for predicting stomatal conductance across multiple plant functional types

Published:2024-05 Issue: Volume:350 Page:109955
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Gaur Srishti,Drewry Darren T.^ORCID

Funder

National Science Foundation

National Aeronautics and Space Administration

Publisher

Elsevier BV

Reference101 articles.

1. Woody plants optimise stomatal behaviour relative to hydraulic risk;Anderegg;Ecol. Lett.,2018

2. Plant water potential improves prediction of empirical stomatal models;Anderegg;PLOS One,2017

3. A comparison among interpretative proposals for Random Forests;Aria;Mach. Learn. Appl.,2021

4. A model predicting Stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions;Ball,1987

5. An Analysis of Stomatal Conductance;Ball,1988

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interpreting Controls of Stomatal Conductance across Different Vegetation Types via Machine Learning;Water;2024-08-09

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