Bayesian inference of root architectural model parameters from synthetic field data

Shehan Morandage,Eric Laloy,Harry Vereecken,Jan Vanderborght,Andrea Schnepf

doi:10.1007/s11104-021-05026-4

Shehan Morandage, Eric Laloy + Show 3 more

Open Access

https://doi.org/10.1007/s11104-021-05026-4

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Abstract

Background and aimsCharacterizing root system architectures of field-grown crops is challenging as root systems are hidden in the soil. We investigate the possibility of estimating root architecture model parameters from soil core data in a Bayesian framework.MethodsIn a synthetic experiment, we simulated wheat root systems in a virtual field plot with the stochastic CRootBox model. We virtually sampled soil cores from this plot to create synthetic measurement data. We used the Markov chain Monte Carlo (MCMC) DREAM(ZS) sampler to estimate the most sensitive root system architecture parameters. To deal with the CRootBox model stochasticity and limited computational resources, we essentially added a stochastic component to the likelihood function, thereby turning the MCMC sampling into a form of approximate Bayesian computation (ABC).ResultsA few zero-order root parameters: maximum length, elongation rate, insertion angles, and numbers of zero-order roots, with narrow posterior distributions centered around true parameter values were identifiable from soil core data. Yet other zero-order and higher-order root parameters were not identifiable showing a sizeable posterior uncertainty.ConclusionsBayesian inference of root architecture parameters from root density profiles is an effective method to extract information about sensitive parameters hidden in these profiles. Equally important, this method also identifies which information about root architecture is lost when root architecture is aggregated in root density profiles.

Highlights

Root system architecture (RSA) describes the morphology and topology of a root system, responsible for water and nutrient uptake, anchorage to soil and interacting with soil biota
The number of zero-order roots (NB) parameter in the CRootBox model is considered as a plant parameter that determines how many primary roots emerge from the seed or next to the seed
We demonstrated using a synthetic experiment that soil core sampling data may contain enough information to inversely retrieve a few parameters (maximum length of zero-order roots, number of zero-order roots (NB), elongation rate of zero-order roots (r0), insertion angle of zero-order roots) of wheat root system architectures based on a RSA model that simulates root growth in a field plot

Summary

Introduction

Root system architecture (RSA) describes the morphology and topology of a root system, responsible for water and nutrient uptake, anchorage to soil and interacting with soil biota. Plant functions, such as water and nutrient uptake, are strongly affected by root system architecture (Hochholdinger 2016). Characterizing root system architectures of field-grown crops is challenging as root systems are hidden in the soil. We investigate the possibility of estimating root architecture model parameters from soil core data in a Bayesian framework. Methods In a synthetic experiment, we simulated wheat root systems in a virtual field plot with the stochastic CRootBox model.

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Conclusion