Estimation of light interception in research environments: a joint approach using directional light sensors and 3D virtual plants applied to sunflower (Helianthus annuus) and Arabidopsis thaliana in natural and artificial conditions
Karine Chenu A B E , Hervé Rey C , Jean Dauzat C , Guilioni Lydie D and Jérémie Lecœur DA INRA, UMR 759 LEPSE, 2 place Viala, 34060 Montpellier cedex 01, France.
B Department of Primary Industries and Fisheries, APSRU, PO Box 102, Toowoomba, Qld 4350, Australia.
C CIRAD, UMR botAnique et bioinforMatique de l’Architecture des Plantes, Bd de la Lironde, F – 34398 Montpellier, France.
D SupAgro, UMR 759 LEPSE, 2 place Viala, 34060 Montpellier cedex 01, France.
E Corresponding author. Email: karine.chenu@dpi.qld.gov.au
This paper originates from a presentation at the 5th International Workshop on Functional–Structural Plant Models, Napier, New Zealand, November 2007.
Functional Plant Biology 35(10) 850-866 https://doi.org/10.1071/FP08057
Submitted: 7 March 2008 Accepted: 29 July 2008 Published: 11 November 2008
Abstract
Light interception is a major factor influencing plant development and biomass production. Several methods have been proposed to determine this variable, but its calculation remains difficult in artificial environments with heterogeneous light. We propose a method that uses 3D virtual plant modelling and directional light characterisation to estimate light interception in highly heterogeneous light environments such as growth chambers and glasshouses. Intercepted light was estimated by coupling an architectural model and a light model for different genotypes of the rosette species Arabidopsis thaliana (L.) Heynh and a sunflower crop. The model was applied to plants of contrasting architectures, cultivated in isolation or in canopy, in natural or artificial environments, and under contrasting light conditions. The model gave satisfactory results when compared with observed data and enabled calculation of light interception in situations where direct measurements or classical methods were inefficient, such as young crops, isolated plants or artificial conditions. Furthermore, the model revealed that A. thaliana increased its light interception efficiency when shaded. To conclude, the method can be used to calculate intercepted light at organ, plant and plot levels, in natural and artificial environments, and should be useful in the investigation of genotype–environment interactions for plant architecture and light interception efficiency.
Additional keywords: artificial environment, radiative model.
Acknowledgements
We thank M. Van Lijsebettens, J.L. Micol and S. Vernhettes for seeds of se-1, ron2-2 and p70S-KOR, respectively. We also thank G. Chenu and F. Painparay for their support, and S.C. Chapman and the anonymous reviewers for improving the manuscript. This work was partly funded by PROMOSOL in connection with the PRODUCTIVITE I and II projects and by the European Community Human Potential Program (HPRB-CT-2002-00267) as part of the DAGOLIGN Research Training Network.
Allen MT,
Prusinkiewicz P, DeJong TM
(2005) Using L-systems for modeling source–sink interactions, architecture and physiology of growing trees: the L-PEACH model. New Phytologist 166, 869–880.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
[Verified 8 September 2008]
Monteith JL
(1977) Climate and efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 281, 277–294.
| Crossref | GoogleScholarGoogle Scholar |
Rey H,
Dauzat J,
Chenu K,
Barczi JF,
Dosio GAA, Lecoeur J
(2008) Using a 3-D virtual sunflower to simulate light capture at organ, plant and plot levels: contribution of organ interception, impact of heliotropism and analysis of genotypic differences. Annals of Botany 101, 1139–1151.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Rinaldi M,
Lsavio N, Flagella Z
(2003) Evaluation and application of the Oilcrop-Sun model for sunflower in southern Italy. Agricultural Systems 78, 17–30.
| Crossref | GoogleScholarGoogle Scholar |
Röhrig M,
Stützel H, Alt C
(1999) A three-dimensional approach to modeling light interception in heterogeneous canopies. Agronomy Journal 91, 1024–1032.
Sadras VO, Milroy SP
(1996) Soil-water thresholds for the responses of leaf expansion and gas exchange: a review. Field Crops Research 47, 253–266.
| Crossref | GoogleScholarGoogle Scholar |
Sinoquet H,
Thanisawanyangkura S,
Mabrouk H, Kasemsap P
(1998) Characterization of the light environment in canopies using 3D digitising and image processing. Annals of Botany 82, 203–212.
| Crossref | GoogleScholarGoogle Scholar |
Sinoquet H,
Le Roux X,
Adam B,
Ameglio T, Daudet FA
(2001) RATP: a model for simulating the spatial distribution of radiation absorption, transpiration and photosynthesis within canopies: application to an isolated tree crown. Plant, Cell & Environment 24, 395–406.
| Crossref | GoogleScholarGoogle Scholar |
Soler C,
Sillion FX,
Blaise F, de Reffye P
(2003) An efficient instantiation algorithm for simulating radiant energy transfer in plant models. ACM Transactions on Graphics 22, 204–233.
| Crossref | GoogleScholarGoogle Scholar |
Tardieu F
(2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends in Plant Science 8, 9–14.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Varlet-Grancher C,
Gosse G,
Chartier M,
Sinoquet H,
Bonhomme M, Allirand JM
(1989) Mise au point: rayonnement solaire absorbé ou intercepté par un couvert végétal. Agronomie 9, 419–439.
| Crossref | GoogleScholarGoogle Scholar |
Walter A,
Scharr H,
Gilmer F,
Zierer R, Nagel KA , et al.
(2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist 174, 447–455.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Wang XP,
Guo Y,
Li BG,
Wang XY, Ma YT
(2006) Evaluating a three dimensional model of diffuse photosynthetically active radiation in maize canopies. International Journal of Biometeorology 50, 349–357.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
