Toward a functional-structural model of oil palm : evaluation of genetic differences between progenies for architecture and radiation interception efficiency

HAL Id: hal-01837342

https://hal.archives-ouvertes.fr/hal-01837342

Submitted on 3 Jun 2020

HAL is a multi-disciplinary open access

archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Toward a functional-structural model of oil palm : evaluation of genetic differences between progenies for

architecture and radiation interception efficiency Raphael Perez, Jean Dauzat, Benoit Pallas, Hervé Rey, Gilles Le Moguedec,

Sebastien Griffon, Jean-Pierre Caliman, Evelyne Costes

To cite this version:

Raphael Perez, Jean Dauzat, Benoit Pallas, Hervé Rey, Gilles Le Moguedec, et al.. Toward a functional-structural model of oil palm : evaluation of genetic differences between progenies for ar-chitecture and radiation interception efficiency. 5. International Conference on Oil Palm and Envi-ronment (ICOPE), Mar 2016, Bali, Indonesia. 2016, Sustainable Palm Oil and Climate Change: The Way Forward Through Mitigation and Adaptation. �hal-01837342�

R. Perez ¹², J. Dauzat 1, B. Pallas 2, H. Rey 1, G. Le Moguédec ³, 1 4 2

Find more sustainable and productive systems is a major challenge to fulfil increasing vegetable oil demand, including palm oil. Tackling climate changes requires bold and swift ac-tions such as breeding of well suited plant material and implementation of innovative growing practices. But, to this end, we need sound bases of what ideotypes must be for the future and what the proper practices should consist in. For addressing these questions, functional-structural modelling approach (FSPM) enables to explore the relationships between 3D structure of plants with their physiological functioning in relation to weather conditions, with the possibility to simulate virtual management practices such as clearing and pruning. The main assumption underlying this project is the possibility to enhance potential oil palm production optimizing plant architecture in relation to radiation use efficiency. The present study investigates two aspects of a FSPM study applied to oil palm: i) characterize architectural variability and reconstruct three-dimensional (3D) mock-ups of oil palm and ii) esti-mate light interception efficiency of different oil palm progenies from virtual stands.

Context & Objectives

Context & Objectives

Material & Methods

Reference Progeny Origin Characteristics

DA1 Deli x Avros South East Asia Large vegetative development DL7 Deli x La Mé Africa Low vegetative development

High yield

DS Deli x (La Mé x Sibiti) Africa Medium vegetative development Medium yield

DU Deli x Unkown Africa Medium vegetative development Drought tolerance

DY4 Deli x Yangambi Africa Medium vegetative development Medium yield A C z₀ x₀ y₀ n’ y_n x_{n x} n’ y_n’ n y_n z_n x_n H D L_rac L_p L W C A n z_{n zn’} B H_C H_A φ

Comparison of progenies in respect to light inter-ception over seasons

Simulation of photosynthetically Active Radiation (PAR) intercepted by palms and canopy (virtual plot of 20 palm mock-ups)

Results

Studied progenies exhibit significantly different architectu-ral traits

Model correctly renders inter and intra progeny architectu-ral variability

Virtual experiments highlight contrasting light interception efficiency between the studied progenies

Perspectives

Identify key architectural traits affecting light interception efficiency

Interface the calculation of light interception with photo-synthesis and stomatal regulation

Define varietal ideotype and propose new phenotypic traits for breeding trials

Perform in silico experiments to test new agronomic prac-tices

Conclusions

Dauzat, J., Clouvel, P., Luquet, D., and Martin, P. (2008). Using virtual plants to analyse the light-foraging efficiency of a low-density cotton crop. Annals of Botany, 101(8):1153–1166.

Griffon, S. and de Coligny, F. (2014). Amap-studio: An editing and simulation software suite for plants architecture modelling. Ecological Modelling, 290:3–10. Special Issue of the 4th Inter- natio-nal Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA’12 ) Special Issue of PMA’12.

Pallas B., Soulie J.-C, Aguilar G., Rouan L., and Luquet D. X-palm, a functional structural plant model for analysing temporal, genotypic and inter-tree variability of oil palm growth and yield. 7th International Conference on Functional-Structural Plant Models. Saariselk a, Finland, June 2013.

References

References

5th _{International Conference on Oil Palm}

and Environment (ICOPE) Nusa Dua Bali, 16 - 18 March 2016

Rendering inter and intra-progeny variability

Inter-progeny variability

Intra-progeny variability

Evaluation of model compliance with field observa-tions

Simulated vs. observed mean and variances of architectural traits

0 10 20 30 40 50 0 10 20 30 40 50 Declination at C point (º) DA1 DL7 DS DU DY4 RMSE= 1.08 NRMSE= 0.03 Bias= 0.65 150 200 250 300 350 400 150 200 250 300 350 400 Rachis length (cm) DA1 DL7 DS DU DY4 RMSE= 6.08 NRMSE= 0.02 Bias= 3.98 40 60 80 100 120 40 60 80 100 120 Petiole length (cm) DA1 DL7 DS DU DY4 RMSE= 6.06 NRMSE= 0.07 Bias= 5.79 150 200 250 300 150 200 250 300

Number of leaflets per leaf

DA1 DL7 DS DU DY4 RMSE= 8.46 NRMSE= 0.03 Bias=-4.18 0 20 40 60 80 100 0 20 40 60 80 100

Leaflet length B point (cm)

DA1 DL7 DS DU DY4 RMSE= 1.49 NRMSE= 0.02 Bias= 1.07 0 1 2 3 4 5 6 0 1 2 3 4 5 6

Leaflet max width B point (cm)

DA1 DL7 DS DU DY4 RMSE= 0.11 NRMSE= 0.03 Bias=-0.03 Observed Observed Observed Observed Observed Observed

Simulated Simulated Simulated

Simulated Simulated Simulated

Validation at individual scale with terrestrial laser scans (TLS)

Comparison of TLS acquisitions with simulations on 3D mock-ups

Validation at plot scale with hemispherical photo-graphs (HPs)

Comparison of gap fraction from camera HPs and virtual HPs

DA1 (t) DL7 (y) DS (n) Simulations Photographs 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 DA1 DL7 DS DU DY4 G ap fr ac tio n a ab ab ab bc _bc cd d d d Photographs Simulations n=26 HPs.progeny-1

1 CIRAD, UMR AMAP, Montpellier, F-34000 France

2 INRA, UMR 1334 AGAP, 34398 Monpellier Cedex 5, France 3 INRA, UMR AMAP, Montpellier, F-34000 France

4 SMART Research Institute, Pekanbaru 28112, Indonesia Material and site

Material planted on 2010

SMARTRI experimental plots (South Sumatra) Field observations

Plant Scale Leaf scale Leaflet scale

H: Stem height L_p: Petiole length L: Leaflet length D: Stem diameter L_rac: Rachis length W: leaflet width

INPUT Progeny n Palm 1 Palm k OUTPUT 3D virtual plant PLANT RECONSTRUCTION

Random generation of parameters for each individual per progeny

Computation of organ geometry and plant topology

Parameters estimations Progeny effects (mean value) Individual effects (variance-covariance matrices)

Virtual plant simulator (Vpalm)*

*AMAPstudio software (Griffon & de Coligny, 2014).

Allometric-based approach

Modelling ontogenetic and morphogenetic gradients with temporal and spatial variables

Strategy for reconstructing 3D palm mock-ups

Integration of mixed-effect models to represent inter and intra progeny variability

33 n-33

Leaf geometry

Number of emitted leaves since planting date

( ) ONTOGENETIC GADIENT = PLANT AGE 1 n n+60 Leaf rank (Rk) 0 -60 GROWTH GRADIENT = LEAF AGE Leaflet geometry Relative position on rachis MOPHOGENETIC GRADIENT = LEAFLET POSITION 0 1 Pruned leaves Open leaves Unfolded leaves DL7 (106_19) DS (101_10) DU (91_18) Simulations TLS scans

2e5 3e5 4e5 5e5 2e5

3e5 4e5 5e5

Crown area (grey + black pixels)

TLS (nb pixels) 3D mock-up (nb pi xels) r2_{= 0.58} s= 1.04 x m RMSE= 44436.7 NRMSE= 0.13 Bias= 15830.26 2e5 3e5 4e5 5e5

Vegetation area (black pixels)

r2_{= 0.52} s= 0.91 x m RMSE= 36080.29 NRMSE= 0.15 Bias= 21085.4 TLS (nb pixels) 3D mock-up (nb pi xels)

2e5 3e5 4e5 5e5

Plot DL7 Plot DA1 Irradiation (MJ.m-2_.day-1₎ [1 4[ [4 8[ [8 12[ [12 16[ [16 20[ >20 1m DL7 (seed 1) DA1 (seed 1) n=20 plants.progeny-1 latitude -2.99º

PAR interception rate

DA1 DL7 DS DU DY4

PAR intercepted per palm (moles photons .m

-2 .day -1 ) a ab ab b c DA1 DL7 DS DU DY4 0.60 0.65 0.70 0.75 0.80 2014

jan feb mar apr may jun jul aug sep oct nov dec

PAR_incident - PAR_{incident (soil)}

PAR_incident

= PAR interception

rate

PAR intercepted per palm =

Palm leaf area PAR 8 10 12 14 8 10 12 14