A machine for determining root length

Summary A machine for determining the root length of a sample is described. The machine is basically an opto-electronic scanner. Root segments are cut and placed in water on a glass plate (375×375 mm). The interruption of a light beam moving across the root sample is detected by a photo-diode and the total root length computed. Using this machine a root sample can be measured in less than 3 minutes. Detailed calibration was only conducted up to 50 m although samples as large as several hundred metres can be measured using this machine.The machine has a high degree of accuracy comparable with or better than other reported methods for determining root length.     

Assessing the accuracy of simple field based root strength measurements

Background and aims

Root reinforcement of slopes is a key control on landslide triggering, yet remains difficult to measure. Dozens of studies have utilised a wide range of testing methods to understand the tensile strength properties to estimate root reinforcement. We present a systematic attempt to evaluate the simple and efficient field spring scale method.

Methods

This study compared different testing methods to assess the strength of Picea sitchensis roots. We tested roots in the field using a spring scale and with two different pre-treatments in the laboratory using a universal testing machine. Root strengths were assessed under different testing conditions in laboratory and field experiments using different pre-testing treatments, stress concentration at clamps, and spacer types.

Results

Tensile strengths measured in the laboratory and field, with different spacers and clamping stress concentrations were not significantly different in our testing. Roots that were incompletely rehydrated after being dried were significantly stronger than wetter roots, suggesting moisture is one of the dominant controls on root strength.

Conclusions

Our results suggest that there is a large range of natural variability in root tensile strength. The field-based, spring scale method produces results that are indistinguishable from those of more precise universal testing machines.     

Different chain length specificity among three polyphosphate quantification methods

Polyphosphate is ubiquitous among living organisms and has a variety of biochemical functions. Arbuscular mycorrhizal fungi have been known to accumulate polyphosphate as a key compound for their function. However, an enzymatic assay using polyphosphate kinase (PPK) reverse reaction, in which polyphosphate is converted to adenosine triphosphate (ATP) and quantified by luciferase assay, failed to detect accumulation of polyphosphate in some mycorrhizal root. When yeast exopolyphosphatase (PPX) was applied to these samples, a much higher polyphosphate level was detected than when the PPK assay was applied. Detailed analysis of substrate chain length specificity of these methods using polyphosphate chain length standards revealed that the PPX method was the most appropriate to detect short-chain polyphosphate. The average chain length of the shortest polyphosphate fraction that could be quantified with more than 50% efficiency was 3 for the PPX method and 38 for the PPK method. It was also suggested that the ratio of the PPK value to the PPX value may be useful as a simple and relative index to compare polyphosphate chain length distribution in different samples.     

The effect of surface soil consolidation on the early root development of pea (Pisum sativum L.)

Cores of repacked soil were consolidated with a compressive strength testing machine, after peas had been planted in the centre of the core. The number that emerged were counted and root and shoot lengths and diameters were measured. Consolidation had no effect on emergence, root length or root diameter of the peas grown in a loamy sand, whereas emergence, root length and root diameter were affected by a small increase in load in a clay loam.     

Accurate root length measurement by image analysis

Algorithms for estimating root length by image analysis should lead to results that have no systematic error (bias), be insensitive to preferential root orientation, valid across a wide range of sample sizes and adjust for overlap between roots in samples, to reduce the effort needed in spreading out root systems. We propose a new algorithm that forms a compromise between small bias and robustness (insensitivity to variation in sample size and preferential root orientation), and provide a simple way of dealing with root overlap. Image analysis was performed on a Macintosh computer using the public domain NIH Image program. The digital image of the root was processed to get the thinned image (skeleton). The numbers of orthogonally and diagonally connected pairs of pixels (N _o and N _d, respectively) in the skeleton were counted separately and used for length (L) calculation. A new length calculation equation was introduced so that the effect of orientation on length calculation was minimized; L=[N d ²+(N _d+N _o/2)²]^1/2+N _o/2. The maximum error due to orientation of a single line was evaluated for an ideal line, and the analysis revealed that the new equation was less affected by orientation than previous equations. Copper wire and rice (Oryza sativa L.) roots containing both primary and fine secondary root were measured manually and with image analysis. The two methods showed good agreement within 1.5%. The proposed image analysis method yielded length estimates with CV from 0.23 to 0.88%, which was lower than the CVs of the line-intersect method. Moreover, the lengths of overlapping samples were calculated correctly because the image analysis method distinguished an overlapping pixel from a thinned image, while the calculation with the line-intersect method showed underestimation because overlaps were not considered in that method. This revised version was published online in June 2006 with corrections to the Cover Date.     

A comparison of four methods for measuring roots of field crops in three contrasting soils

Rooting measurements have been made at different growth stages for sugar beets (1987) and for cereals (1988) on three different sites using four different root measurement techniques: (a) the core method where roots were extracted and root length is directly measured, (b) the core-break method where the visible roots were counted on the faces of a broken soil column, (c) the trench profile wall method where the number of visible roots were counted and the root length density was estimated on a profile wall, and (d) the monolith method where the roots were extracted from monoliths dug out from a profile wall. The calibration curves between the field methods and the extraction methods were not linear, and regression coefficients differed significantly between different sites, crops and between fields with different agronomic management, e.g. irrigation and liquid manure application. Differences between growth stages were comparably low compared with those found between locations. Root length densities obtained with the trench profile method were on average 10-fold lower in the sand brown earth, 6-fold lower in the vertisol and 4 times lower in the cambisol compared to data obtained with the core method. It is therefore concluded that the core-break method and the trench profile wall method deliver no reliable data for comparing rooting intensities between different soils and between different crops if they are not calibrated with an extraction method for each site and crop.     

Issues in using the WinRHIZO system to determine physical characteristics of plant fine roots

Image analysis systems have facilitated rapid measurements of fine root length (RL), diameter (RD), volume (RV), etc. The WinRHIZO system is unlike other image analysis systems in that it can detect, and make corrections for, areas of root overlap. It is designed to be capable of using both Regent’s non-statistical method (WinRHIZO method) and Tennant’s statistical method (line-intersect method), and can simultaneously output the root measurements by both methods when they are chosen at the same time. This study tested: (1), the efficacy of the overlap correction function in the WinRHIZO system; and (2), the consistency of fine root measurements between the WinRHIZO and Tennant methods with two sets of root measurement data from winter wheat (Triticum aestivium L.). The results showed that there were significant differences in RL, RD and RV between small root samples with and without stumps. The impact of root stumps outweighed the overlap correction efficacy in WinRHIZO. The values from the Tennant method are significantly different from those using the WinRHIZO method, although both results are statistically closely correlated. This indicated how critical it was to use without-stump root samples when using image analysis systems to measure RL, RD, RV, etc., and to keep in mind that a significant difference in root measurements may be methodologically related when comparing the results of various experiments from these two methods. Our research results bear important implications for the study of root ecology.     

Issues in using the WinRHIZO system to determine physical characteristics of plant fine roots☆

Image analysis systems have facilitated rapid measurements of fine root length (RL), diameter (RD), volume (RV), etc. The WinRHIZO system is unlike other image analysis systems in that it can detect, and make corrections for, areas of root overlap. It is designed to be capable of using both Regent’s non-statistical method (WinRHIZO method) and Tennant’s statistical method (line-intersect method), and can simultaneously output the root measurements by both methods when they are chosen at the same time. This study tested: (1), the efficacy of the overlap correction function in the WinRHIZO system; and (2), the consistency of fine root measurements between the WinRHIZO and Tennant methods with two sets of root measurement data from winter wheat (Triticum aestivium L.). The results showed that there were significant differences in RL, RD and RV between small root samples with and without stumps. The impact of root stumps outweighed the overlap correction efficacy in WinRHIZO. The values from the Tennant method are significantly different from those using the WinRHIZO method, although both results are statistically closely correlated. This indicated how critical it was to use without-stump root samples when using image analysis systems to measure RL, RD, RV, etc., and to keep in mind that a significant difference in root measurements may be methodologically related when comparing the results of various experiments from these two methods. Our research results bear important implications for the study of root ecology.     

Tillage and N fertilization effects on maize root growth and root:shoot ratio

Two methods for estimating the size of the maize (Zea mays l.) root system from soil cores taken in the field were compared. The spatially weighed block method of estimation accounted for variation in root density by using 18 samples per plant which varied in distance from plant and soil depth. This method was compared to an estimation which averaged all of the 18 samples together. Both methods gave surprisingly similar estimates for total root growth. Increased root growth in the surface soil layers, due to tillage and N fertilization, did not impact on the estimation of total root growth. Total root length remained unchanged or increased with N fertilization, while root weight remained the same or decreased. Root mass per length decreased with N fertilization. The estimated size of the root system was used to calculate root:shoot weight ratios. The largest root:shoot ratio was found in the vegetative stage and decreased throughout the rest of the season. In this field experiment, the estimated size of the root system at 8 weeks after planting was not significantly different from the size at silking or harvest. Nitrogen fertilization significantly decreased the root:shoot weight ratio. However, tillage did not significantly change the ratio.     

A microcomputer based method for the rapid and detailed measurement of seedling root systems

Summary A simple method for making detailed measurements of seedling root systems is described. Photocopies of root systems are traced over by an operator using a digitizing system attached to a microcomputer. The computer calculates and prints the lengths of axis, laterals and sublaterals for each root system. Accurate measurements can be achieved with a degree of speed and detail unobtainable by other methods.     

Recording,processing and analysis of grass root images from a rhizotron

To develop and test a system for computer-assisted image analysis, repeated video recordings of reed canary-grass roots (Phalaris arundinacea L.) were made in an 18-window rhizotron. The images were digitized and processed using a Unix computer and the Khoros software development environment.Two image sizes, 126×95 mm and 61×46 mm, both comprising 650 × 490 pixels, were compared. Among image processing techniques used were median filtering, segmentation and skeletonization. Root area and length in both the topsoil and subsoil were estimated using the two image sizes. The resolution (image size) strongly affected the calculated root lengths. The results were compared with root length measurements obtained manually.Statistically significant differences in root length and area in the topsoil were detected between the sampling dates using the computer-assisted methods. Possible sources of error and methods for reducing them are discussed.     

Errors in estimating total root length by sub-samples method

A new method has been proposed to estimate the total length in a sample, and it assumes that roots are cylinders with a given bulk density. The technique is based on the measurement of the length and diameter of small pieces of roots, and on the measurement of the bulk density of root sub-samples. An exact formulation is presented of the error distribution in estimating total root length. This leads to a simple formula which relates directly the variance of the root length estimates to both the number of roots used in estimating the root characteristics, and the number of sub-samples used in estimating the mean bulk density of the samples. This enables analyses of experimental designs with respect to sample size and accuracy.     

Root length density and water uptake distributions of winter wheat under sub-irrigation

As the critical information to study flow transport in soil–plant systems, root distributions and root-water-uptake (RWU) patterns have been studied extensively. However, most root distribution data in the past were collected under surface irrigation. Less research has been conducted to characterize root distributions under sub-irrigation. The objectives of this study were to (1) test if the generalized function of normalized root length density (NRLD) in the literature was applicable to root distributions of winter wheat under natural sub-irrigation, which provides water from subsurface by capillary rise from the water table, and (2) estimate RWU distributions of winter wheat under natural sub-irrigation. Column experiments were conducted to study the distributions of root length density (RLD) and RWU of winter wheat (Triticum aestivum L. cv. Nongda 189) during a growing period of 57 days from planting to tillering stages under surface irrigation and natural sub-irrigation. Data of root distributions and soil water content were collected in the experiments with different treatments of irrigation levels. Results showed that the RLD distributions of winter wheat under both surface irrigation and natural sub-irrigation were of similar patterns. The NRLD distributions under sub-irrigation were adequately characterized by the generalized function. An inverse method was employed to estimate the average RWU rate distributions of winter wheat. In addition, based on the potential RWU coefficient and the NRLD function, a simple approach was developed to predict RWU rates at different depths. The predicted RWU rates had a good agreement with the estimated RWU rate distributions using the inverse method.Section editor: R. E. Munns     

Mechanical interactions between neighbouring roots during pullout tests

Background and Aims

The quantification of root reinforcement function is important for landscape managers and engineers. The estimation of root mechanical reinforcement is often based on models that do not consider the potential interaction between neighbouring roots. Root-soil mechanical interactions related to the root spacing and bundle geometry remain unclear including potential effects on the reliability of the current models. The objective of this study is to quantify the mechanical interactions among neighbouring roots or roots networks using modelling approaches and pullout laboratory experiments.

Methods

Based on simple geometrical characterization of individual root geometry, we calculated dissipation patterns of frictional root-soil interfacial stresses in radial and longitudinal directions. Considering simple superposition of shear stresses within the soil matrix, we quantified characteristic root densities at which the radial mechanical interactions influence global pullout behaviour of the root bundle both for branched and unbranched roots. Laboratory pullout tests on root bundles were carried out at root spacings of 15, 35 and 105 mm. In addition, we tested effects of non-parallel (crossing) root bundle geometry.

Results

We found no significant statistical differences in root pullout force for the different root spacing in parallel alignment of roots. Branches increase pullout force by 1.5 times. Moreover, the mean displacement at the pullout peak-force was 7.2 % of length for unbranched roots and about 4.1 % of length for branched roots. The model shows its potential comparing it with empirical results concerning the holes leaved by roots, according with the branch pattern.

Conclusion

The study quantifies the influence of root spacing and arrangement geometry within a root bundle on its mechanical behaviour. The assumption of “non-interacting” neighbouring roots in root reinforcement methods is no longer valid for root spacing less than 15 mm and root reinforcement methods. Moreover crossing roots shown a statistical difference. This information is important for improved understanding root reinforcement mechanisms in steep hill slope and the interplay between anchoring /failure and root bundle pullout vs root breakage.     

微根管在细根研究中的应用

细根（直径≤2 mm）的周转在植物生态系统碳分配过程中具有重要意义.已往细根周转研究主要采用根钻法、分室模型法和内生长法等.这些方法由于不能直接观测到细根生长动态,导致细根周转估计不准确.微根管法是一种非破坏性野外观察细根动态的方法.本文从微根管的发展、功能、安装步骤、图像采集、参数计算、影响观测因素和存在问题等方面逐一进行介绍,并通过水曲柳和落叶松微根管细根观测实例介绍在细根周转过程研究中的应用. 结果表明,微根管可以比较精确地估计出细根长度、单位面积上根长密度、单位体积上根长密度、细根生长量、细根死亡量和细根周转等.微根管是一个观察细根生长、衰老、死亡和分解过程的有效工具.微根管观测精度主要取决于微根管安装的质量和数量、微根管取样间隔期和取样数量、微根管图像分析技术等.此外,土壤质地、石砾多少、微根管材料选择、减少光系统对根系的干扰等也是影响微根管测定精度的因素.如何提高微根管测定精度将成为今后微根管在细根研究中的主要问题.     

An integrated method for quantifying root architecture of field-grown maize

Background and Aims

A number of techniques have recently been developed for studying the root system architecture (RSA) of seedlings grown in various media. In contrast, methods for sampling and analysis of the RSA of field-grown plants, particularly for details of the lateral root components, are generally inadequate.

Methods

An integrated methodology was developed that includes a custom-made root-core sampling system for extracting intact root systems of individual maize plants, a combination of proprietary software and a novel program used for collecting individual RSA information, and software for visualizing the measured individual nodal root architecture.

Key Results

Example experiments show that large root cores can be sampled, and topological and geometrical structure of field-grown maize root systems can be quantified and reconstructed using this method. Second- and higher order laterals are found to contribute substantially to total root number and length. The length of laterals of distinct orders varies significantly. Abundant higher order laterals can arise from a single first-order lateral, and they concentrate in the proximal axile branching zone.

Conclusions

The new method allows more meaningful sampling than conventional methods because of its easily opened, wide corer and sampling machinery, and effective analysis of RSA using the software. This provides a novel technique for quantifying RSA of field-grown maize and also provides a unique evaluation of the contribution of lateral roots. The method also offers valuable potential for parameterization of root architectural models.     

Estimating the parameters of a 3-D root distribution function from root observations with the trench profile method: case study with simulated and field-observed root data

Background and aims

Root length density (RLD) is a parameter that is difficult to measure, but crucial to estimate water and nutrient uptake by plants. In this study a novel approach is presented to characterize the 3-D root length distribution by supplementing data of the 3-D distribution of root intersections with data of root length density from a limited number of soil cores.

Methods

The method was evaluated in a virtual experiment using the RootTyp model and a field experiment with cauliflower (Brassica oleracea L. botrytis) and leek (Allium porrum, L.).

Results

The virtual experiment shows that total root length and root length distribution can be accurately estimated using the novel approach. Implementation of the method in a field experiment was successful for characterizing the growth of the root distribution with time both for cauliflower and leek. In contrast with the virtual experiment, total root length could not be estimated based upon root intersection measurements in the field.

Conclusions

The novel method of combining root intersection data with root length density data from core samples is a powerful tool to supply root water uptake models with root system information.     

Improved methods of estimating root length using a photocopier,a light box and a bar code reader

Photocopying was found to be a rapid method of making a permanent record of a root sample. The method used produced a copy with white roots against a black background. Manual estimates of root length were made from photocopies using a light box. The number of intersections visible when laid over a copy of a white on black regular square grid was counted. Automated estimates of root length were made by scanning a photocopy with a bar code reader in place of a pen in a computer-driven graph plotter. Roots >0.2 mm diameter were resolved with precision and speed.     

Stroke Treatment Prediction Using Features Selection Methods and Machine Learning Classifiers

ObjectivesFeature selection in data sets is an important task allowing to alleviate various machine learning and data mining issues. The main objectives of a feature selection method consist on building simpler and more understandable classifier models in order to improve the data mining and processing performances. Therefore, a comparative evaluation of the Chi-square method, recursive feature elimination method, and tree-based method (using Random Forest) used on the three common machine learning methods (K-Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed to select the most relevant primitives from a large set of attributes. Furthermore, determining the most suitable couple (i.e., feature selection method-machine learning method) that provides the best performance is performed.Materials and methodsIn this paper, an overview of the most common feature selection techniques is first provided: the Chi-Square method, the Recursive Feature Elimination method (RFE) and the tree-based method (using Random Forest). A comparative evaluation of the improvement (brought by such feature selection methods) to the three common machine learning methods (K- Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed. For evaluation purposes, the following measures: micro-F1, accuracy and root mean square error are used on the stroke disease data set.ResultsThe obtained results show that the proposed approach (i.e., Tree Based Method using Random Forest, TBM-RF, decision tree classifier, DTC) provides accuracy higher than 85%, F1-score higher than 88%, thus, better than the KNN and NB using the Chi-Square, RFE and TBM-RF methods.ConclusionThis study shows that the couple - Tree Based Method using Random Forest (TBM-RF) decision tree classifier successfully and efficiently contributes to find the most relevant features and to predict and classify patient suffering of stroke disease.”