Statistical Characterization of the Root System Architecture Model CRootBox

The connection between the parametrization of three-dimensional (3D) root architecture models and characteristic measures of the simulated root systems is often not obvious. We used statistical methods to analyze the simulation outcome of the root architecture model CRootBox and built meta-models that determine the dependency of root system measures on model input parameters. Starting with a reference parameter set, we varied selected input parameters one at a time and used CRootBox to compute 1000 root system realizations as well as their root system measures. The obtained data sets were then statistically analyzed with regard to dependencies between input parameters, as well as distributions and correlations between different root system measures. While absolute root system measures (e.g., total root length) were approximately normally distributed, distributions of ratios of root system measures (e.g., root tip density) were highly asymmetric and could be approximated with inverse gamma distributions. We derived regression models (meta-models) that link significant model parameters to 18 widely used root system measures and determined correlations between different root system measures. Statistical analysis of 3D root architecture models helps to understand the impact of input parametrization on specific root architectural measures. Our developed meta-models can be used to determine the effect of parameter variations on the distribution of root system measures without running a full simulation. Model intercomparison and benchmarking of root architecture models is still missing. Our approach provides a means to compare different models with each other and with experimental data.

[1]  E. Newman,et al.  RESISTANCE TO WATER FLOW IN SOIL AND PLANT , 1969 .

[2]  Jan Vanderborght,et al.  A new model for optimizing the water acquisition of root hydraulic architectures over full crop cycles , 2016, 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA).

[3]  Jonathan P Lynch,et al.  The importance of root gravitropism for inter-root competition and phosphorus acquisition efficiency: results from a geometric simulation model , 2004, Plant and Soil.

[4]  Jan Vanderborght,et al.  Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models , 2018, Plant and Soil.

[5]  K. Moder,et al.  A statistical approach to root system classification , 2013, Front. Plant Sci..

[6]  Michelle Watt,et al.  OpenSimRoot: widening the scope and application of root architectural models , 2017, The New phytologist.

[7]  J. Lynch,et al.  Topsoil foraging – an architectural adaptation of plants to low phosphorus availability , 2001, Plant and Soil.

[8]  A. Diggle,et al.  Modelling the interactions between water and nutrient uptake and root growth , 2002, Plant and Soil.

[9]  K. Nagel,et al.  Simultaneous effects of leaf irradiance and soil moisture on growth and root system architecture of novel wheat genotypes: implications for phenotyping , 2015, Journal of experimental botany.

[10]  X. Draye,et al.  Dynamic aspects of soil water availability for isohydric plants: Focus on root hydraulic resistances , 2014 .

[11]  Mengzhen Kang,et al.  Analysis and modeling of the root system architecture of winter wheat seedling , 2003 .

[12]  Jean Dickinson Gibbons,et al.  Nonparametric Statistical Inference , 1972, International Encyclopedia of Statistical Science.

[13]  Peter J. Gregory.  Roots, rhizosphere and soil: the route to a better understanding of soil science? , 2006 .

[14]  Michael P. Pound,et al.  Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat , 2015, Journal of experimental botany.

[15]  Loïc Pagès,et al.  Water Uptake by Plant Roots: II – Modelling of Water Transfer in the Soil Root-system with Explicit Account of Flow within the Root System – Comparison with Experiments , 2006, Plant and Soil.

[16]  Loïc Pagès,et al.  Root Typ: a generic model to depict and analyse the root system architecture , 2004, Plant and Soil.

[17]  H. Kaul,et al.  Management of crop water under drought: a review , 2015, Agronomy for Sustainable Development.

[18]  Daniel Leitner,et al.  The algorithmic beauty of plant roots – an L-System model for dynamic root growth simulation , 2010 .

[19]  J. Vanderborght,et al.  Towards quantitative root hydraulic phenotyping: novel mathematical functions to calculate plant-scale hydraulic parameters from root system functional and structural traits , 2017, Journal of Mathematical Biology.

[20]  Peter J. Gregory,et al.  The effects of dwarfing genes on seedling root growth of wheat , 2009, Journal of experimental botany.

[21]  Fabio Fiorani,et al.  Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. , 2016, The New phytologist.

[22]  V. Vadez.  Root hydraulics: The forgotten side of roots in drought adaptation , 2014 .

[23]  Jan Vanderborght,et al.  CRootBox: A structural-functional modelling framework for root systems , 2017, bioRxiv.

[24]  Mathieu Javaux,et al.  Impact of contrasted maize root traits at flowering on water stress tolerance - A simulation study , 2014 .

[25]  M. Noordwijk,et al.  Sampling schemes for estimating root density distribution in cropped fields , 1985 .

[26]  D. W. Scott.  On optimal and data based histograms , 1979 .

[27]  Lianhai Wu,et al.  Simulation of wheat growth using the 3D root architecture model SPACSYS: Validation and sensitivity analysis , 2011 .

[28]  Jonathan P Lynch,et al.  Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. , 2013, Annals of botany.

[29]  Jonathan P. Lynch,et al.  Roots of the Second Green Revolution , 2007 .

[30]  Feng Jie,et al.  A Generalized Function of Wheat's Root Length Density Distributions , 2004 .

[31]  Jan Vanderborght,et al.  Parameterizing a Dynamic Architectural Model of the Root System of Spring Barley from Minirhizotron Data , 2012 .

[32]  E. Newman.  RESISTANCE TO WATER FLOW IN SOIL AND PLANT I. SOIL RESISTANCE IN RELATION TO AMOUNTS OF ROOT: THEORETICAL , 1969 .

[33]  K. Siddique,et al.  Characterising root trait variability in chickpea (Cicer arietinum L.) germplasm , 2016, Journal of experimental botany.

[34]  R. Richards,et al.  Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. , 2012, Journal of experimental botany.

[35]  L. Pagès,et al.  Calibration and evaluation of ArchiSimple, a simple model of root system architecture , 2014 .

[36]  Jan Vanderborght,et al.  A simple three-dimensional macroscopic root water uptake model based on the hydraulic architecture approach , 2012 .

[37]  D. Hinkley.  On the ratio of two correlated normal random variables , 1969 .

[38]  Jan Vanderborght,et al.  A hybrid analytical-numerical method for solving water flow equations in root hydraulic architectures , 2017 .

[39]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[40]  Loïc Pagès,et al.  Links between root developmental traits and foraging performance. , 2011, Plant, cell & environment.

[41]  Johannes Auke Postma,et al.  Root Cortical Aerenchyma Enhances the Growth of Maize on Soils with Suboptimal Availability of Nitrogen, Phosphorus, and Potassium1[W][OA] , 2011, Plant Physiology.