Display options
Cite
Louarn G, Frak E, Zaka S, et al. An empirical model that uses light attenuation and plant nitrogen status to predict within-canopy nitrogen distribution and upscale photosynthesis from leaf to whole canopy. AoB Plants. 2015;7doi: 10.1093/aobpla/plv116.
Louarn, G., Frak, E., Zaka, S., Prieto, J., & Lebon, E. (2015). An empirical model that uses light attenuation and plant nitrogen status to predict within-canopy nitrogen distribution and upscale photosynthesis from leaf to whole canopy. AoB PLANTS, 7. https://doi.org/10.1093/aobpla/plv116
Louarn, Gaëtan, et al. "An empirical model that uses light attenuation and plant nitrogen status to predict within-canopy nitrogen distribution and upscale photosynthesis from leaf to whole canopy." AoB PLANTS vol. 7 (2015). doi: https://doi.org/10.1093/aobpla/plv116
Louarn G, Frak E, Zaka S, Prieto J, Lebon E. An empirical model that uses light attenuation and plant nitrogen status to predict within-canopy nitrogen distribution and upscale photosynthesis from leaf to whole canopy. AoB Plants. 2015 Oct 03;7. doi: 10.1093/aobpla/plv116. PMID: 26433705; PMCID: PMC4635319.
Copy Download .nbib Format: NLM
Share it on

AoB Plants. 2015 Oct 03;7. doi: 10.1093/aobpla/plv116.

An empirical model that uses light attenuation and plant nitrogen status to predict within-canopy nitrogen distribution and upscale photosynthesis from leaf to whole canopy.

AoB PLANTS

Gaëtan Louarn, Ela Frak, Serge Zaka, Jorge Prieto, Eric Lebon

Affiliations

  1. INRA UR4 URP3F, BP6, F86600 Lusignan, France [email protected].
  2. INRA UR4 URP3F, BP6, F86600 Lusignan, France.
  3. INTA EEA Mendoza, San Martín 3853, Luján de Cuyo (5507), Mendoza, Argentina.
  4. INRA, UMR 759, LEPSE, 2 place Viala, F34060 Montpellier, France.

PMID: 26433705 PMCID: PMC4635319 DOI: 10.1093/aobpla/plv116

Abstract

Modelling the spatial and temporal distribution of leaf nitrogen (N) is central to specify photosynthetic parameters and simulate canopy photosynthesis. Leaf photosynthetic parameters depend on both local light availability and whole-plant N status. The interaction between these two levels of integration has generally been modelled by assuming optimal canopy functioning, which is not supported by experiments. During this study, we examined how a set of empirical relationships with measurable parameters could be used instead to predict photosynthesis at the leaf and whole-canopy levels. The distribution of leaf N per unit area (Na) within the canopy was related to leaf light irradiance and to the nitrogen nutrition index (NNI), a whole-plant variable accounting for plant N status. Na was then used to determine the photosynthetic parameters of a leaf gas exchange model. The model was assessed on alfalfa canopies under contrasting N nutrition and with N2-fixing and non-fixing plants. Three experiments were carried out to parameterize the relationships between Na, leaf irradiance, NNI and photosynthetic parameters. An additional independent data set was used for model evaluation. The N distribution model showed that it was able to predict leaf N on the set of leaves tested. The Na at the top of the canopy appeared to be related linearly to the NNI, whereas the coefficient accounting for N allocation remained constant. Photosynthetic parameters were related linearly to Na irrespective of N nutrition and the N acquisition mode. Daily patterns of gas exchange were simulated accurately at the leaf scale. When integrated at the whole-canopy scale, the model predicted that raising N availability above an NNI of 1 did not result in increased net photosynthesis. Overall, the model proposed offered a solution for a dynamic coupling of leaf photosynthesis and canopy N distribution without requiring any optimal functioning hypothesis.

Published by Oxford University Press on behalf of the Annals of Botany Company.

Keywords: Light; Medicago sativa; nitrogen distribution; nitrogen nutrition index; photosynthesis; transpiration; upscaling; within-canopy variability

References

  1. Tree Physiol. 1999 Jul;19(8):481-492 - PubMed
  2. Ann Bot. 2003 Nov;92(5):679-88 - PubMed
  3. J Exp Bot. 2003 Nov;54(392):2393-401 - PubMed
  4. J Plant Res. 2004 Dec;117(6):481-94 - PubMed
  5. J Exp Bot. 2005 Mar;56(413):935-43 - PubMed
  6. Plant Physiol. 1937 Apr;12(2):285-307 - PubMed
  7. Plant Physiol. 1981 Jan;67(1):30-6 - PubMed
  8. Plant Cell Environ. 2007 Sep;30(9):1035-40 - PubMed
  9. J Exp Bot. 2010 May;61(8):2203-16 - PubMed
  10. Ann Bot. 2010 Nov;106(5):735-49 - PubMed
  11. Ann Bot. 2011 Oct;108(6):1013-24 - PubMed
  12. Plant Cell Environ. 2012 Jul;35(7):1313-28 - PubMed
  13. Plant Physiol. 2012 Nov;160(3):1479-90 - PubMed
  14. Ann Bot. 2014 Jan;113(1):145-57 - PubMed
  15. Planta. 1980 Jun;149(1):78-90 - PubMed
  16. Photosynth Res. 1994 Mar;39(3):389-400 - PubMed
  17. New Phytol. 2015 Feb;205(3):973-93 - PubMed
  18. Oecologia. 1995 Apr;101(4):504-513 - PubMed
  19. Oecologia. 1983 Feb;56(2-3):341-347 - PubMed
  20. Oecologia. 1989 Jan;78(1):9-19 - PubMed
  21. Oecologia. 1987 Jul;72(4):520-526 - PubMed
  22. Oecologia. 1994 May;97(4):451-457 - PubMed
  23. Oecologia. 1993 Feb;93(1):63-69 - PubMed
  24. Oecologia. 2002 Nov;133(3):267-279 - PubMed

Publication Types