A model of canopy photosynthesis incorporating protein distribution through the canopy and its acclimation to light, temperature and CO2

Johnson, I. R.; Thornley, J. H. M.; Frantz, Jonathan M.; Bugbee, Bruce

doi:10.1093/aob/mcq183

Cited by 54 publications

(45 citation statements)

References 37 publications

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“…[1.8] in Supplementary Table S1). Johnson et al (2010) developed a flexible nonlinear function for the protein (N) vertical distribution within plant canopies (Eq. [1.9] in Supplementary Table S1).…”

Section: Group I-exponentialmentioning

confidence: 99%

“…¶ Cited in Goudriaan (1979). # Goudriaan (1979) and Johnson et al (2010) used the nonrectangular hyperbola to describe the photosynthetic rate response to CO 2 . † † This is simplified version of the Farquhar model.…”

Section: Group Ii-sigmoid Curvesmentioning

confidence: 99%

“…The photosynthesis response to CO 2 has been quantified empirically using a nonrectangular hyperbola (Goudriaan, 1979;Johnson et al, 2010) and mechanistically using a biochemical model (Farquhar et al, 1980). The biochemical model is based on Michaelis-Menten kinetics for substrate-limited growth and the law of minimum between carboxylation and electron transport rates (Eq.…”

Section: Group Iii-photosynthesismentioning

confidence: 99%

“…This equation is "fragile" and requires careful parameterization (Medlyn et al, 2002;Archontoulis et al, 2012). Johnson et al (2010) argued that temperature functions based on the activation energy of chemical reactions are quite complex and difficult to apply routinely. They used a modified beta function to describe the photosynthesis response to temperature (Eq.…”

Section: Group Iv-temperature Dependencementioning

confidence: 99%

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Nonlinear Regression Models and Applications in Agricultural Research

2015

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In data analysis, we often ask the following questions:Which is the best model to describe our data? Which is the best statistical index to judge the goodness of fit? How do we choose among competing models? There are no simple answers to these questions. Here we attempt to provide agronomists with a general framework on how to approach these questions appropriately. Our specific objectives are: (i) to provide a succinct overview of nonlinear models and to develop a guideline to understand the family of functions used in agricultural applications; (ii) to indicate techniques to modify nonlinear models and how to cope with multiple nonlinear models; (iii) to discuss key methodological issues on parameter estimation, model performance, and comparison; and (iv) to demonstrate step-by-step analysis of experimental data using a nonlinear regression model. The structure follows the flow diagram in Fig. 1. We start with the definition of nonlinear regression models and discuss their main advantages and disadvantages. Then we present 77 nonlinear functions (including those in supplemental tables) with references to applications in agriculture. We offer an updated overview of methodologies to fit models, choose starting values, assess goodness of fit, select the best models, and evaluate residuals. Finally, we reanalyze experimental data on biomass growth with time (Danalatos et al., 2009). NONLINEAR REGRESSION MODELS DefinitionIn general, statistical models used in agricultural applications can be described with the following notation:where y is the response variable, f is the function or model, x are the inputs, q denotes the parameters to be estimated, and e is the error. Each parameter can be evaluated for whether it is linear or not: if the second derivative of the function with respect to a parameter is not equal to zero, then the parameter is nonlinear. Thus a given function ( f ) can have a mix of linear and nonlinear parameters. Why Should We Use Nonlinear Models?The main advantages of nonlinear models are parsimony, interpretability, and prediction (Bates and Watts, 2007). In general, nonlinear models are capable of accommodating a vast variety of mean functions, although each individual nonlinear model can be less flexible than linear models (i.e., polynomials) in terms of the variety of data they can describe; however, nonlinear models appropriate for a given application can be more parsimonious (i.e., there will be fewer parameters involved) and more easily interpretable. Interpretability comes from the fact that the parameters can be associated with a biologically meaningful process. For example, one of the most widely used nonlinear models is the logistic equation (Eq. [2.1] in Table 1). This model describes the pervasive S-shaped growth curve. The ABSTRACT Nonlinear regression models are important tools because many crop and soil processes are better represented by nonlinear than linear models. Fitting nonlinear models is not a single-step procedure but an involved process that requires careful examinati...

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Section: Group I-exponentialmentioning

confidence: 99%

Section: Group Ii-sigmoid Curvesmentioning

confidence: 99%

Section: Group Iii-photosynthesismentioning

confidence: 99%

Section: Group Iv-temperature Dependencementioning

confidence: 99%

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Nonlinear Regression Models and Applications in Agricultural Research

2015

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“…The N supply of growing leaves is a dominant target of whole-plant N metabolism. This is primarily related to the high N demand of the photosynthetic apparatus and the related metabolic machinery of new leaves (Evans, 1989;Makino and Osmond, 1991;Grindlay, 1997;Lemaire, 1997;Wright et al, 2004;Johnson et al, 2010;Maire et al, 2012). The N supply system, as defined here, is an integral part of the whole plant: it includes all N compounds that supply leaf growth.…”

mentioning

confidence: 99%

Nitrogen Stress Affects the Turnover and Size of Nitrogen Pools Supplying Leaf Growth in a Grass

2013

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The effect of nitrogen (N) stress on the pool system supplying currently assimilated and (re)mobilized N for leaf growth of a grass was explored by dynamic 15 N labeling, assessment of total and labeled N import into leaf growth zones, and compartmental analysis of the label import data. Perennial ryegrass (Lolium perenne) plants, grown with low or high levels of N fertilization, were labeled with 15 NO 3 2 / 14 NO 3 2 from 2 h to more than 20 d. In both treatments, the tracer time course in N imported into the growth zones fitted a two-pool model (r 2 . 0.99). This consisted of a "substrate pool," which received N from current uptake and supplied the growth zone, and a recycling/mobilizing "store," which exchanged with the substrate pool. N deficiency halved the leaf elongation rate, decreased N import into the growth zone, lengthened the delay between tracer uptake and its arrival in the growth zone (2.2 h versus 0.9 h), slowed the turnover of the substrate pool (half-life of 3.2 h versus 0.6 h), and increased its size (12.4 mg versus 5.9 mg). The store contained the equivalent of approximately 10 times (low N) and approximately five times (high N) the total daily N import into the growth zone. Its turnover agreed with that of protein turnover. Remarkably, the relative contribution of mobilization to leaf growth was large and similar (approximately 45%) in both treatments. We conclude that turnover and size of the substrate pool are related to the sink strength of the growth zone, whereas the contribution of the store is influenced by partitioning between sinks.This article examines the nitrogen (N) supply system of growing grass leaves, and it investigates how functional and kinetic properties of this system are affected by N stress. The N supply of growing leaves is a dominant target of whole-plant N metabolism. This is primarily related to the high N demand of the photosynthetic apparatus and the related metabolic machinery of new leaves (

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Different Morphologies and Functional Nitrogen Accumulation Results in the Different Nitrogen Use Efficiency of Tobacco Plants

Ahmed

Zhang

et al. 2023

J Plant Growth Regul

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A model of canopy photosynthesis incorporating protein distribution through the canopy and its acclimation to light, temperature and CO2

Cited by 54 publications

References 37 publications

Nonlinear Regression Models and Applications in Agricultural Research

Nonlinear Regression Models and Applications in Agricultural Research

Nitrogen Stress Affects the Turnover and Size of Nitrogen Pools Supplying Leaf Growth in a Grass

Different Morphologies and Functional Nitrogen Accumulation Results in the Different Nitrogen Use Efficiency of Tobacco Plants

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