Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models

Background and aimsThe main difficulty in the use of 3D root architecture models is correct parameterization. We evaluated distributions of the root traits inter-branch distance, branching angle and axial root trajectories from contrasting experimental systems to improve model parameterization.MethodsWe analyzed 2D root images of different wheat varieties (Triticum aestivum) from three different sources using automatic root tracking. Model input parameters and common parameter patterns were identified from extracted root system coordinates. Simulation studies were used to (1) link observed axial root trajectories with model input parameters (2) evaluate errors due to the 2D (versus 3D) nature of image sources and (3) investigate the effect of model parameter distributions on root foraging performance.ResultsDistributions of inter-branch distances were approximated with lognormal functions. Branching angles showed mean values <90°. Gravitropism and tortuosity parameters were quantified in relation to downwards reorientation and segment angles of root axes. Root system projection in 2D increased the variance of branching angles. Root foraging performance was very sensitive to parameter distribution and variance.Conclusions2D image analysis can systematically and efficiently analyze root system architectures and parameterize 3D root architecture models. Effects of root system projection (2D from 3D) and deflection (at rhizotron face) on size and distribution of particular parameters are potentially significant

A new model for root growth in soil with macropores

Abstract: Background and aimsThe use of standard dynamic root architecture models to simulate root growth in soil containing macropores failed to reproduce experimentally observed root growth patterns. We thus developed a new, more mechanistic model approach for the simulation of root growth in structured soil. Methods: In our alternative modelling approach, we distinguish between, firstly, the driving force for root growth, which is determined by the orientation of the previous root segment and the influence of gravitropism and, secondly, soil mechanical resistance to root growth. The latter is expressed by its inverse, soil mechanical conductance, and treated similarly to hydraulic conductivity in Darcy’s law. At the presence of macropores, soil mechanical conductance is anisotropic, which leads to a difference between the direction of the driving force and the direction of the root tip movement. Results: The model was tested using data from the literature, at pot scale, at macropore scale, and in a series of simulations where sensitivity to gravity and macropore orientation was evaluated. Conclusions: Qualitative and quantitative comparisons between simulated and experimentally observed root systems showed good agreement, suggesting that the drawn analogy between soil water flow and root growth is a useful one

Mechanistic framework to link root growth models with weather and soil physical properties, including example applications to soybean growth in Brazil

Background and aimsRoot elongation is generally limited by a combination of mechanical impedance and water stress in most arable soils. However, dynamic changes of soil penetration resistance with soil water content are rarely included in models for predicting root growth. Better modelling frameworks are needed to understand root growth interactions between plant genotype, soil management, and climate. Aim of paper is to describe a new model of root elongation in relation to soil physical characteristics like penetration resistance, matric potential, and hypoxia.MethodsA new diagrammatic framework is proposed to illustrate the interaction between root elongation, soil management, and climatic conditions. The new model was written in Matlab®, using the root architecture model RootBox and a model that solves the 1D Richards equations for water flux in soil. Inputs: root architectural parameters for Soybean; soil hydraulic properties; root water uptake function in relation to matric flux potential; root elongation rate as a function of soil physical characteristics. Simulation scenarios: (a) compact soil layer at 16 to 20 cm; (b) test against a field experiment in Brazil during contrasting drought and normal rainfall seasons.Results(a) Soil compaction substantially slowed root growth into and below the compact layer. (b) Simulated root length density was very similar to field measurements, which was influenced greatly by drought. The main factor slowing root elongation in the simulations was evaluated using a stress reduction function.ConclusionThe proposed framework offers a way to explore the interaction between soil physical properties, weather and root growth. It may be applied to most root elongation models, and offers the potential to evaluate likely factors limiting root growth in different soils and tillage regimes

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

Climatic, edaphic factors and cropping history help predict click beetle (Coleoptera: Elateridae) (Agriotes spp.) abundance

It is assumed that the abundance of Agriotes wireworms (Coleoptera: Elateridae) is affected by agro-ecological factors such as climatic and edaphic factors and the crop/previous crop grown at the sites investigated. The aim of this study, conducted in three different geographic counties in Croatia from 2007 to 2009, was to determine the factors that influence the abundance of adult click beetle of the species Agriotes brevis Cand., Agriotes lineatus (L.), Agriotes obscurus (L.), Agriotes sputator (L.), and Agriotes ustulatus Schall. The mean annual air temperature, total rainfall, percentage of coarse and fine sand, coarse and fine silt and clay, the soil pH, and humus were investigated as potential factors that may influence abundance. Adult click beetle emergence was monitored using sex pheromone traps (YATLORf and VARb3). Exploratory data analysis was preformed via regression tree models and regional differences in Agriotes species\u27 abundance were predicted based on the agro-ecological factors measured. It was found that the best overall predictor of A. brevis abundance was the previous crop grown. Conversely, the best predictor of A. lineatus abundance was the current crop being grown and the percentage of humus. The best predictor of A. obscurus abundance was soil pH in KCl. The best predictor of A. sputator abundance was rainfall. Finally, the best predictors of A. ustulatus abundance were soil pH in KCl and humus. These results may be useful in regional pest control programs or for predicting future outbreaks of these species

Fibroblast growth factor 21 is induced upon cardiac stress and alters cardiac lipid homeostasis

Fibroblast growth factor 21 (FGF21) is a PPARα-regulated gene elucidated in the liver of PPARα-deficient mice or PPARα agonist-treated mice. Mice globally lacking adipose triglyceride lipase (ATGL) exhibit a marked defect in TG catabolism associated with impaired PPARα-activated gene expression in the heart and liver, including a drastic reduction in hepatic FGF21 mRNA expression. Here we show that FGF21 mRNA expression is markedly increased in the heart of ATGL-deficient mice accompanied by elevated expression of endoplasmic reticulum (ER) stress markers, which can be reversed by reconstitution of ATGL expression in cardiac muscle. In line with this assumption, the induction of ER stress increases FGF21 mRNA expression in H9C2 cardiomyotubes. Cardiac FGF21 expression was also induced upon fasting of healthy mice, implicating a role of FGF21 in cardiac energy metabolism. To address this question, we generated and characterized mice with cardiac-specific overexpression of FGF21 (CM-Fgf21). FGF21 was efficiently secreted from cardiomyocytes of CM-Fgf21 mice, which moderately affected cardiac TG homeostasis, indicating a role for FGF21 in cardiac energy metabolism. Together, our results show that FGF21 expression is activated upon cardiac ER stress linked to defective lipolysis and that a persistent increase in circulating FGF21 levels interferes with cardiac and whole body energy homeostasis

Call for participation: Collaborative benchmarking of functional-structural root architecture models. The case of root water uptake

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute towards improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development