Testing a Convective-dispersive Model of Two-dimensional Root Growth and Proliferation in a Greenhouse Experiment with Maize Plants

Reddy, Vangimalla R.; Pachepsky, Yakov

doi:10.1006/anbo.2001.1409

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…We used a statistic proposed by Reddy and Pachepsky (2001) to test the difference between 3D fine root distribution patterns of the two tree species. This statistic compares within-species variation and between-species variation.…”

Section: Resultsmentioning

confidence: 99%

Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Mulia¹,

Dupraz

Noordwijk³

2010

Plant Soil

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Dynamic models of tree root growth and function have to reconcile the architectural rules for coarse root topology with the dynamics of fine root growth (and decay) in order to predict the strategic plus opportunistic behaviour of a tree root system in a heterogeneous soil. We present an algorithm for a 3D model based on both local (soil voxel level) and global (tree level) controls of root growth, with development of structural roots as a consequence of fine root function, rather than as driver. The suggested allocation rules of carbon to fine root growth in each rooted voxel depend on the success in water uptake in this voxel during the previous day, relative to overall supply and demand at plant level. The allocated C in each voxel is then split into proliferation (within voxel growth) and extension into neighbouring voxels (colonisation), with scaledependent thresholds and transfer coefficients. The fine root colonisation process defines a dynamic and spatially explicit demand for transport functions. C allocation to development of a coarse root infrastructure linking all rooted voxels depends on the apparent need for adjustment of root diameter to meet the topologically defined sap flow through this voxel during the previous day. The allometric properties of the coarse root system are maintained to be in line with fractal branching theory. The model can predict the dynamics of the shape and structure (fine root density, coarse root topology and biomass) of the root system either independently of soil conditions (purely genetically-driven) or including both the genetic and environmental effects of roots interacting with soil water supply and its external replenishment, linking in with existing water balance models. Sensitivity of the initial model to voxel dimensions was addressed through explicit scaling rules resulting in scale-independent parameters. The model was parameterised for two tree species: hybrid walnut (Juglans nigra × regia) and wild cherry (Prunus avium L.) using results of a pot experiment. The model satisfactorily predicted the root growth behaviour of the two species. The model is sparse in parameters and yet applicable to heterogeneous soils, and could easily be upgraded to include additional local influences on root growth (and decay) such as local success in nutrient uptake or dynamic soil physical properties.

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Section: Resultsmentioning

confidence: 99%

Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Mulia¹,

Dupraz

Noordwijk³

2010

Plant Soil

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“…These models were rarely related to meaningful growth parameters that could explain the changes taking place in the root length density profiles through time. Reddy and Pachepsky (2001), Heinen et al (2003), and Bonneu et al (2012) have proposed the use of advection-diffusion equations to describe temporal changes in root spatial distribution in soil. In each case, the methods proposed could reliably mimic the spread of the root system in soil, but the models used in these studies were very simple and could not provide information on parameters such as the branching rate or the elongation rate of lateral roots.…”

Section: Using Models To Extract Root Growth Parameters From Experimementioning

confidence: 99%

“…Since some form of density estimation is required during an optimization process to compare a model with experiments, why not simulate the root system as a density distribution directly? This was the original idea of Reddy and Pachepsky (2001), Heinen et al (2003), Bastian et al 2008, andBonneu et al (2012). However, because their density model was a generic advection-diffusion equation, it was not possible for them to obtain biologically meaningful growth parameters from their model.…”

Section: Parameter Unitsmentioning

confidence: 99%

“…For example, Bonneu et al (2012) used a combination of Nelder-Mead and simulated annealing algorithms to determine the parameters of their model. Reddy and Pachepsky (2001) used a Marquardt-Levenberg algorithm, and Heinen et al (2003) used a Powell algorithm. Garré et al (2012) applied the AMALGAM multicriteria optimization method.…”

Section: Accuracy Of the Estimated Root Growth Parametersmentioning

confidence: 99%

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Analysis of root growth from a phenotyping data set using a density-based model

Kalogiros

Adu

White

et al. 2016

EXBOTJ

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Major research efforts are targeting the improved performance of root systems for more efficient use of water and nutrients by crops. However, characterizing root system architecture (RSA) is challenging, because roots are difficult objects to observe and analyse. A model-based analysis of RSA traits from phenotyping image data is presented. The model can successfully back-calculate growth parameters without the need to measure individual roots. The mathematical model uses partial differential equations to describe root system development. Methods based on kernel estimators were used to quantify root density distributions from experimental image data, and different optimization approaches to parameterize the model were tested. The model was tested on root images of a set of 89 Brassica rapa L. individuals of the same genotype grown for 14 d after sowing on blue filter paper. Optimized root growth parameters enabled the final (modelled) length of the main root axes to be matched within 1% of their mean values observed in experiments. Parameterized values for elongation rates were within ±4% of the values measured directly on images. Future work should investigate the time dependency of growth parameters using time-lapse image data. The approach is a potentially powerful quantitative technique for identifying crop genotypes with more efficient root systems, using (even incomplete) data from high-throughput phenotyping systems.

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“…In the latter case, analytical methods can provide simple growth functions (de Willigen et al, 2002;Schnepf et al, 2008). Approximated numerical solutions can also be obtained to analyse more complex systems (Reddy and Pachepsky, 2001). However, density-based models were seldom used to model ensembles of interacting plants.…”

Section: Introductionmentioning

confidence: 98%

An algorithm for the simulation of the growth of root systems on deformable domains

Dupuy¹,

Vignes²

2012

Journal of Theoretical Biology

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Testing a Convective-dispersive Model of Two-dimensional Root Growth and Proliferation in a Greenhouse Experiment with Maize Plants

Cited by 16 publications

References 36 publications

Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Analysis of root growth from a phenotyping data set using a density-based model

An algorithm for the simulation of the growth of root systems on deformable domains

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