A physiological model of softwood growth
Department of Forest Sciences, University of Helsinki, Helsinki, Finland.Tree Physiology (Impact Factor: 3.66). 10/2010; 30(10):1235-52. DOI: 10.1093/treephys/tpq068
Cambial growth was modelled as a function of detailed levelled physiological processes for cell enlargement and water and sugar transport to the cambium. Cambial growth was described at the cell level where local sugar concentration and turgor pressure induce irreversible cell expansion and cell wall synthesis. It was demonstrated how transpiration and photosynthesis rates, metabolic and physiological processes and structural features of a tree mediate their effects directly on the local water and sugar status and influence cambial growth. Large trees were predicted to be less sensitive to changes in the transient water and sugar status, compared with smaller ones, as they have more water and sugar storage and were, therefore, less coupled to short-term changes in the environment. Modelling the cambial dynamics at the individual cell level turned out to be a complex task as the radial short-distance transport of water and sugars and control signals determining cell division and cessation of cell enlargement and cell wall synthesis had to be described simultaneously.
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- "Catesson and Roland 1981; Drew and Pammenter 2007; Lachaud 1989; Ridoutt and Sands 1994). This has informed the development of a range of models designed to simulate cambial activity and ultimately wood property variation (Deckmyn et al. 2006; Deleuze and Houllier 1998; Drew et al. 2010; Fritts et al. 1999; Hölttä et al. 2010; Kramer 2002; Meicenheimer and Larson 1983; Vaganov et al. 2006; Wilson 1964; Wilson and Howard 1968). Nevertheless, there is much about wood formation that is not understood, with room for improvement in our models. "
ABSTRACT: Key message A model of wood formation processes in pines predicted 80 % of mean wood density variation from inputs of carbohydrate allocation and tree water status from several varied sites. Abstract Numerous factors determine how wood properties vary as a tree grows. In order to model wood formation, a framework that considers the various xylogenetic processes is required. We describe a new model of xylem development and wood formation in pines (parameterised for the commercially important species, Pinus radiata D. Don). In this paper, we use as inputs simulated daily data from the CaBala stand growth model which, in turn, takes into account site and daily weather conditions, and silviculture. It incorporates a first attempt at predicting microfibril angle (the angle of cellulose microfibrils relative to the vertical axis of the cell, MFA) based on metrics of cambial vigour and carbohydrate allocation. It also predicts tracheid dimensions and wall thickness, and from these data, wood density. Pith-to-bark and intra-annual variation in predicted wood properties was realistic across a wide range of site types, although juvenile wood properties were weakly predicted. The model was able to explain 50 % of the variation in outerwood MFA and 70-80 % of the variation in outerwood and mean sample wood density respectively, from 17 study sites. The model, early results from which are very promising, provides a useful framework for testing concepts of how formation occurs, and to provide insights into areas where further research is needed.Trees 10/2015; 29(5). DOI:10.1007/s00468-015-1216-1 · 1.65 Impact Factor
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- "(4)). More recently, this model was coupled with a detailed model that predicts cambial softwood growth ( Hölttä et al. 2010). Aiming to better understand whole-tree transport processes, "
ABSTRACT: High-resolution stem diameter variations (SDV) are widely recognized as a useful drought stress indicator and have therefore been used in many irrigation scheduling studies. More recently, SDV have been used in combination with other plant measurements and biophysical modelling to study fundamental mechanisms underlying whole-plant functioning and growth. The present review aims to scrutinize the important insights emerging from these more recent SDV applications to identify trends in ongoing fundamental research. The main mechanism underlying SDV is variation in water content in stem tissues, originating from reversible shrinkage and swelling of dead and living tissues, and irreversible growth. The contribution of different stem tissues to the overall SDV signal is currently under debate and shows variation with species and plant age, but can be investigated by combining SDV with state-of-the-art technology like magnetic resonance imaging. Various physiological mechanisms, such as water and carbon transport, and mechanical properties influence the SDV pattern, making it an extensive source of information on dynamic plant behaviour. To unravel these dynamics and to extract information on plant physiology or plant biophysics from SDV, mechanistic modelling has proved to be valuable. Biophysical models integrate different mechanisms underlying SDV, and help us to explain the resulting SDV signal. Using an elementary modelling approach, we demonstrate the application of SDV as a tool to examine plant water relations, plant hydraulics, plant carbon relations, plant nutrition, freezing effects, plant phenology and dendroclimatology. In the ever-expanding SDV knowledge base we identified two principal research tracks. First, in detailed short-term experiments, SDV measurements are combined with other plant measurements and modelling to discover patterns in phloem turgor, phloem osmotic concentrations, root pressure and plant endogenous control. Second, long-term SDV time series covering many different species, regions and climates provide an expanding amount of phenotypic data of growth, phenology and survival in relation to microclimate, soil water availability, species or genotype, which can be coupled with genetic information to support ecological and breeding research under on-going global change. This under-exploited source of information has now encouraged research groups to set up coordinated initiatives to explore this data pool via global analysis techniques and data-mining.Tree Physiology 09/2015; DOI:10.1093/treephys/tpv080 · 3.66 Impact Factor
Frontiers in Plant Science 09/2015; 6:Article 730. DOI:10.3389/fpls.2015.00730 · 3.95 Impact Factor
- "Cells are created in the wood formation process, which can be divided into five consecutive developmental stages: cell division, cell enlargement, cell wall thickening, lignification and programmed cell death (e.g., Rossi et al., 2014). The process depends on genetic signaling, availability of resources, temperature, tree water and nutrient status and the stage of ontogenetic development (Hölttä et al., 2010). Thus, to understand the mechanisms and the dynamics of wood formation in relation to climatic or physiological factors better, analyses on a shorter temporal scale are required (Rossi et al., 2014). "
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