Skip to content
RDL
Network
Ekosistem
Uygulama değiştir
EN
Hakkımızda
SSS
Giriş yap
Başla
Faculty Opinions recommendation of A theoretical and empirical assessment of stomatal optimization modeling. — Dennis Baldocchi | RDL Network
Back
Cite
Save
Save for later
Share
Home
Publications
Faculty Opinions recommendation of A theoretical and empirical assessment of stomatal optimization modeling.
Shared by
Dennis Baldocchi
University of California, Berkeley
Faculty Opinions recommendation of A theoretical and empirical assessment of stomatal optimization modeling.
Dataset
en
Authors
Dennis Baldocchi
University of California, Berkeley
Abstract
1 min read
Optimal stomatal control models have shown great potential in predicting stomatal behavior and improving carbon cycle modeling. Basic stomatal optimality theory posits that stomatal regulation maximizes the carbon gain relative to a penalty of stomatal opening. All models take a similar approach to calculate instantaneous carbon gain from stomatal opening (the gain function). Where the models diverge is in how they calculate the corresponding penalty (the penalty function). In this review, we compare and evaluate 10 different optimization models in how they quantify the penalty and how well they predict stomatal responses to the environment. We evaluate models in two ways. First, we compare their penalty functions against seven criteria that ensure a unique and qualitatively realistic solution. Second, we quantitatively test model against multiple leaf gas-exchange datasets. The optimization models with better predictive skills have penalty functions that meet our seven criteria and use fitting parameters that are both few in number and physiology based. The most skilled models are those with a penalty function based on stress-induced hydraulic failure. We conclude by proposing a new model that has a hydraulics-based penalty function that meets all seven criteria and demonstrates a highly predictive skill against our test datasets.© 2020 The Authors. New Phytologist © 2020 New Phytologist Trust. PMID: 32248532 Funding information This work was supported by: U.S. Department of Agriculture, Grant ID: 2018-67019-27850 U.S. Department of Agriculture, Grant ID: 2018-67012-28020 National Science Foundation, Grant ID: 1802880 National Science Foundation, Grant ID: 1714972 National Science Foundation, Grant ID: 1450650
Discussion
(0)
Sign in
to like and join the discussion.
No comments yet. Be the first to comment.
Related publications
Dataset
Faculty Opinions recommendation of The histone H3-H4 tetramer is a copper reductase enzyme.
Christopher J Chang
Dataset
Faculty Opinions recommendation of Sex differences in the genetic architecture of obsessive-compulsive disorder.
Dan Joseph Stein
Dataset
Faculty Opinions recommendation of A lineage of myeloid cells independent of Myb and hematopoietic stem cells.
Alberto Mantovani
Dataset
Faculty Opinions recommendation of Docosahexaenoic acid protects from dendritic pathology in an Alzheimer's disease mouse model.
George Perry
Dataset
Faculty Opinions recommendation of Global change biology: A primer.
Dennis Baldocchi
Discussion(0)
No comments yet. Be the first to comment.