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A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes

The conversion of supplemental greenhouse light energy into biomass is not always optimal. Recent trends in global energy prices and discussions on climate change highlight the need to reduce our energy footprint associated with the use of supplemental light in greenhouse crop production. This can b...

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Autores principales: Joubert, Dominique, Zhang, Ningyi, Berman, Sarah.R., Kaiser, Elias, Molenaar, Jaap, Stigter, J.D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019686/
https://www.ncbi.nlm.nih.gov/pubmed/36927993
http://dx.doi.org/10.1371/journal.pone.0275047
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author Joubert, Dominique
Zhang, Ningyi
Berman, Sarah.R.
Kaiser, Elias
Molenaar, Jaap
Stigter, J.D.
author_facet Joubert, Dominique
Zhang, Ningyi
Berman, Sarah.R.
Kaiser, Elias
Molenaar, Jaap
Stigter, J.D.
author_sort Joubert, Dominique
collection PubMed
description The conversion of supplemental greenhouse light energy into biomass is not always optimal. Recent trends in global energy prices and discussions on climate change highlight the need to reduce our energy footprint associated with the use of supplemental light in greenhouse crop production. This can be achieved by implementing “smart” lighting regimens which in turn rely on a good understanding of how fluctuating light influences photosynthetic physiology. Here, a simple fit-for-purpose dynamic model is presented. It accurately predicts net leaf photosynthesis under natural fluctuating light. It comprises two ordinary differential equations predicting: 1) the total stomatal conductance to CO(2) diffusion and 2) the CO(2) concentration inside a leaf. It contains elements of the Farquhar-von Caemmerer-Berry model and the successful incorporation of this model suggests that for tomato (Solanum lycopersicum L.), it is sufficient to assume that Rubisco remains activated despite rapid fluctuations in irradiance. Furthermore, predictions of the net photosynthetic rate under both 400ppm and enriched 800ppm ambient CO(2) concentrations indicate a strong correlation between the dynamic rate of photosynthesis and the rate of electron transport. Finally, we are able to indicate whether dynamic photosynthesis is Rubisco or electron transport rate limited.
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spelling pubmed-100196862023-03-17 A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes Joubert, Dominique Zhang, Ningyi Berman, Sarah.R. Kaiser, Elias Molenaar, Jaap Stigter, J.D. PLoS One Research Article The conversion of supplemental greenhouse light energy into biomass is not always optimal. Recent trends in global energy prices and discussions on climate change highlight the need to reduce our energy footprint associated with the use of supplemental light in greenhouse crop production. This can be achieved by implementing “smart” lighting regimens which in turn rely on a good understanding of how fluctuating light influences photosynthetic physiology. Here, a simple fit-for-purpose dynamic model is presented. It accurately predicts net leaf photosynthesis under natural fluctuating light. It comprises two ordinary differential equations predicting: 1) the total stomatal conductance to CO(2) diffusion and 2) the CO(2) concentration inside a leaf. It contains elements of the Farquhar-von Caemmerer-Berry model and the successful incorporation of this model suggests that for tomato (Solanum lycopersicum L.), it is sufficient to assume that Rubisco remains activated despite rapid fluctuations in irradiance. Furthermore, predictions of the net photosynthetic rate under both 400ppm and enriched 800ppm ambient CO(2) concentrations indicate a strong correlation between the dynamic rate of photosynthesis and the rate of electron transport. Finally, we are able to indicate whether dynamic photosynthesis is Rubisco or electron transport rate limited. Public Library of Science 2023-03-16 /pmc/articles/PMC10019686/ /pubmed/36927993 http://dx.doi.org/10.1371/journal.pone.0275047 Text en © 2023 Joubert et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Joubert, Dominique
Zhang, Ningyi
Berman, Sarah.R.
Kaiser, Elias
Molenaar, Jaap
Stigter, J.D.
A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
title A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
title_full A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
title_fullStr A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
title_full_unstemmed A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
title_short A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
title_sort small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019686/
https://www.ncbi.nlm.nih.gov/pubmed/36927993
http://dx.doi.org/10.1371/journal.pone.0275047
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