Designing oil palm architectural ideotypes for optimal light interception and carbon assimilation through a sensitivity analysis of leaf traits

Perez, Raphaël; Dauzat, Jean; Pallas, Benoît; Lamour, Julien; Verley, Philippe; Caliman, Jean‐Pierre; Costes, Evelyne; Faivre, Robert

doi:10.1093/aob/mcx161

Cited by 37 publications

(27 citation statements)

References 73 publications

(113 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Photosynthesis is also the focal process in a study on the consequences of the management practice of branch girdling in apple (Poirier-Pocovi and Buck-Sorlin, 2018). An additional promising application of FSP models in crops is the possibility to find plant traits and whole phenotypes that optimize crop resource capture, as is shown for light absorption and photosynthesis in oil palm (Perez et al, 2018). As well as crop science, FSP models are widely used to address questions in plant ecology and ecophysiology.…”

Section: Novel Applications Of Fsp Modelsmentioning

confidence: 99%

“…To avoid this problem, many studies use a mixture of different methods to parameterize FSP models. In this special issue, most studies take values measured experimentally or derived from the literature (Garin et al, 2018;Perez et al, 2018;Robert et al, 2018), in addition to separate parameter estimation by submodels (Barczi et al, 2018;Chen et al, 2018;De Vries et al, 2018;Garin et al, 2018;Gu et al, 2018;Louarn and Faverjon, 2018;Perez et al, 2018;Poirier-Pocovi and Buck-Sorlin, 2018;Robert et al, 2018;Whitehead et al, 2018;Zhu et al, 2018), and calibration that includes variables that are outputs of the whole model (Bongers et al, 2018;Ma et al, 2018;Robert et al, 2018). This use of mixed methods for model parameterization raises questions regarding how to decide whether to obtain parameter…”

Section: Evaluation Of Fsp Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Computational botany: advancing plant science through functional–structural plant modelling

et al. 2018

View full text Add to dashboard Cite

The need to integrate the ever-expanding body of knowledge in the plant sciences has led to the development of sophisticated modelling approaches. This special issue focuses on functional-structural plant (FSP) models, which are the result of cross-fertilization between the domains of plant science, computer science and mathematics. FSP models simulate growth and morphology of individual plants that interact with their environment, from which complex plant community properties emerge. FSP models can be used for a broad range of research questions across disciplines related to plant science. This special issue presents the latest developments in FSP modelling, including the novel incorporation of plant ecophysiological concepts and the application of FSP models to address new scientific questions. Additionally, it illustrates the breadth of model evaluation approaches that are performed. FSP modelling is a very active domain of plant research which brings together a wide range of scientific disciplines. It offers the opportunity to address questions in complex plant systems that cannot be addressed by empirical approaches alone, including questions on fundamental concepts related to plant development such as regulation of morphogenesis, as well as on applied concepts such as the relationship between crop performance and plant competition for resources.

show abstract

Section: Novel Applications Of Fsp Modelsmentioning

confidence: 99%

Section: Evaluation Of Fsp Modelsmentioning

confidence: 99%

Computational botany: advancing plant science through functional–structural plant modelling

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Erect architectures avoid upper leaves receiving saturating light and allow lower leaves in the canopy to receive more light (Long, Zhu, Naidu, & Ort, ). Overall, traits involved in plant 3D architecture tend to have smaller effects on light interception than leaf area, but their effects increase with phenological stages and become appreciable at canopy closure, when self and/or mutual shading occur (Chen et al, ; Perez et al, ). Furthermore, the metabolic cost of changes in architecture is negligible compared with that of increasing leaf area in terms of nitrogen and carbon resources, so even small increases in the use of incident light obtained by optimizing plant architecture may have a beneficial effect on biomass accumulation.…”

Section: Introductionmentioning

confidence: 99%

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

Perez

Fournier

Cabrera‐Bosquet

et al. 2019

Plant Cell & Environment

View full text Add to dashboard Cite

Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.

show abstract

“…Here, the term “parameterization” refers to the estimation of developmental and functional (source and sink) parameters controlling dynamic growth processes as opposed to the reconstruction of static 3D plant structures using image-based approaches or laser scanning. Some calibrations occur at the submodel level by incorporating measured, empirical values (Garin et al, 2018 ; Perez et al, 2018 ; Robert et al, 2018 ), or by individual parameter estimation (Gu et al, 2018 ; Perez et al, 2018 ; Zhu et al, 2018 ), while some calibrations are performed at the whole-model level (Bongers et al, 2018 ; Ma et al, 2018 ; Robert et al, 2018 ). Among the submodels, photosynthesis modeling, which dates back to the classical work of Farquhar (Farquhar and Roderick, 2003 ), has been studied extensively at the leaf and canopy levels in recent years with the support of plant 3D structure (Li and Tang, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Estimating Sink Parameters of Stochastic Functional-Structural Plant Models Using Organic Series-Continuous and Rhythmic Development

et al. 2018

View full text Add to dashboard Cite

Functional-structural plant models (FSPMs) generally simulate plant development and growth at the level of individual organs (leaves, flowers, internodes, etc.). Parameters that are not directly measurable, such as the sink strength of organs, can be estimated inversely by fitting the weights of organs along an axis (organic series) with the corresponding model output. To accommodate intracanopy variability among individual plants, stochastic FSPMs have been built by introducing the randomness in plant development; this presents a challenge in comparing model output and experimental data in parameter estimation since the plant axis contains individual organs with different amounts and weights. To achieve model calibration, the interaction between plant development and growth is disentangled by first computing the occurrence probabilities of each potential site of phytomer, as defined in the developmental model (potential structure). On this basis, the mean organic series is computed analytically to fit the organ-level target data. This process is applied for plants with continuous and rhythmic development simulated with different development parameter sets. The results are verified by Monte-Carlo simulation. Calibration tests are performed both in silico and on real plants. The analytical organic series are obtained for both continuous and rhythmic cases, and they match well with the results from Monte-Carlo simulation, and vice versa. This fitting process works well for both the simulated and real data sets; thus, the proposed method can solve the source-sink functions of stochastic plant architectures through a simplified approach to plant sampling. This work presents a generic method for estimating the sink parameters of a stochastic FSPM using statistical organ-level data, and it provides a method for sampling stems. The current work breaks a bottleneck in the application of FSPMs to real plants, creating the opportunity for broad applications.

show abstract

Designing oil palm architectural ideotypes for optimal light interception and carbon assimilation through a sensitivity analysis of leaf traits

Cited by 37 publications

References 73 publications

Computational botany: advancing plant science through functional–structural plant modelling

Computational botany: advancing plant science through functional–structural plant modelling

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

Estimating Sink Parameters of Stochastic Functional-Structural Plant Models Using Organic Series-Continuous and Rhythmic Development

Contact Info

Product

Resources

About