Name: measuring stolons and rhizomes of turfgrasses using a digital image analysis system
Uploaded: 2019-02-19T15:00:00.000+00:00
Duration: 6 min 2 s
Description: Watch this Scientific Journal Video about Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System at JoVE.com

1. Collection of Biomass Samples
<ol>
	<li>Collect samples including the aboveground biomass and a soil layer with an appropriate depth depending on species (for turf species, a 15 cm depth is generally sufficient) to ensure collection of both stolons and rhizomes. 
	Note: Overall plot size must be considered prior to initiating the study, since destructive samples will be taken. In general, the longer the experiment is conducted, the larger the required plot size.</li>
	<li>Check soil conditions before sample collection: if the soil is too dry, especially in heavy soils, it may be difficult to collect samples. In this case, irrigate the plots before collection to soften sample layers.</li>
	<li>Collect samples using a soil core sampler (&#8805; 8 cm diameter) or define the surface area to collect with a frame (&#8805; 10 x 10 cm), and collect the samples with a spade. Label each sample with laboratory tape.</li>
	<li>Collect several random samples per plot so they are representative of the plant population.</li>
	<li>Use the same sampler for the entire experiment and record the area that each sample represents to calculate stolon and rhizome density. 
	Note: The protocol can be paused here, and the samples can be stored in plastic bags and preserved at a temperature less than -18 &#176;C.</li>
</ol>
2. Cleaning the Biomass Samples
<ol>
	<li>Place the sample in a large sieve with 0.5-1.5 mm openings depending on stolon or rhizome size. The openings should be small enough to retain all stolons and rhizomes, but large enough to allow soil particles to be removed. For sandy soils, two sieves with different openings, placed one on top of the other, may allow for better precision and efficiency.</li>
	<li>Clean the samples with a stream of water with enough power to remove soil particles without damaging the plants.</li>
	<li>Retrieve the cleaned samples and place them in a tray with paper towels, taking care to label the trays appropriately. 
	Note: The protocol can be paused here, and the samples can be stored in plastic bags and preserved at a temperature less than -18 &#176;C.</li>
	<li>Further clean the samples by removing roots and leaves with scissors. During this process, separate stolons and rhizomes, if needed, and record additional information such as the numbers of plants, tillers, and stolons per plant. 
	Note: Removing all root and leaf tissue from stolons and rhizomes will improve precision. Fine roots are difficult to remove; however, through the digital image analysis, it is possible to omit them from the analysis using a software application that excludes organs with a diameter less than a chosen value (see step 5.1), which is defined fairly accurately based on observations of images reproduced onscreen.</li>
	<li>Place stolons and rhizomes in paper labeled bags. 
	Note: The protocol can be paused here, and the samples can be stored in plastic bags and preserved at a temperature less than -18 &#176;C.</li>
</ol>
3. Scanning and Image Analysis of Samples
<ol>
	<li>Place the sample on a transparent plastic tray of the WinRHIZO standard scanning equipment. Manually place the stolons and rhizomes using laboratory forceps to minimize overlapping. Large samples may need to be split into subsamples.</li>
	<li>Do not add water to the tray (as recommended for roots), because stolons and rhizomes have sufficient rigidity to avoid excessive proximity of organs which may cause reading errors, which usually happens with fine roots.</li>
	<li>Place the tray on the scanner surface.</li>
	<li>Turn on the scanner and start running the program.</li>
	<li>Check image dpi in the Image menu, command Image acquisition parameter, for a possible further control in the saved image.</li>
	<li>Check threshold in Analysis, command Root &#38; Background Distinction, for good classification of pixel belonging to the scanned organs.</li>
	<li>Check that the whole tray surface will be scanned in the Image menu, command Image acquisition parameter.</li>
	<li>Check diameter class displayed for organs distribution per diameter, in the graphic area above the scanned image. Select 20 equal-width classes with 0.1 mm intervals by clicking on the horizontal axis of the graph. This function allows exclusion of data belonging to roots or small organs, when stolons or rhizomes were not perfectly cleaned. The literature reports that most roots of turf species have diameters lower than 0.2 mm. 
	Note: Width and number of classes can be modified while taking into account the average diameter of stolons and rhizomes for the analyzed samples and variability around this mean. A control should be conducted in some samples to determine the minimum diameter to be excluded.</li>
	<li>Run the first sample scanning and check that the edit allows for a good analysis.</li>
	<li>Follow the software instructions for saving the image and processed analysis. Label the image and analysis with the sample label.</li>
	<li>Proceed with the scanning of all samples. 
	Note: The protocol can be paused here, and the samples can be stored in plastic bags and preserved at a temperature less than -18 &#176;C.</li>
</ol>
4. Measurement of Dry Weight
<ol>
	<li>Using a precise electronic balance, place the scanned samples in a tared aluminum tray.</li>
	<li>Repeat step 4.1 for all scanned samples.</li>
	<li>Insert all the samples into an oven set to 105 &#176;C and dry them for 24 h.</li>
	<li>Remove the samples and wait until the tissue weight has stabilized.</li>
	<li>Weigh all the samples with their tare.</li>
	<li>Subtract the tare from the recorded weight to obtain the net weight of each sample.</li>
</ol>
5. Correction of Data and Calculation of Length and Weight Density
<ol>
	<li>Correction of length and averaged diameter
	<ol>
		<li>Convert the .txt file resulting from the analysis with WinRHIZO to a .csv file.</li>
		<li>Use the results grouped for diameter classes to exclude data of organs smaller than 0.2 mm (roots, part of leaves, or scratches on the tray).</li>
		<li>For each WinRHIZO reading (rows of .txt file) sum all the lengths recorded for diameter classes greater than 0.2 mm. The length calculated with this correction is the effective length to be used for further data processing.</li>
		<li>For each WinRHIZO reading, sum the projection areas recorded for diameter classes more than 0.2 mm. The proportion between length and projection area gives the average diameter corrected for exclusion of organs with diameters less than 0.2 mm.</li>
	</ol>
	</li>
	<li>If the sample has been split into subsamples, calculate the final length as the sum of all subsample lengths, and calculate the final average diameter as the proportion between the sum of all subsample lengths and sum of all subsample projection areas.</li>
	<li>When necessary, calculate the length and weight density per unit surface area based on sample size.</li>
	<li>Use the data obtained for statistical analysis.</li>
</ol>

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The authors have nothing to disclose.

The protocol described here was developed and evaluated for the study of turfgrasses. However, it can be used over a range of stoloniferous or rhizomatous species with necessary adjustments according to their morphological characteristics, environmental conditions, and sample cleaning precision.
The average diameter estimated by means of this protocol cannot be compared to the internode diameter measured with a caliper. The digital image analysis includes nodes and internodes in the calculation of average diameter, that is the ratio between total projection surface and total length. As discussed by Pornaro et al.24, average diameter obtained for bermudagrass stolons with WinRHIZO system overestimated average diameter values measured with the caliper at the internode. Stolon diameter is typically used to describe the diameter of stolon internodes and is a common parameter used for botanical description18,25. For this reason, Pornaro et al.24 pointed out that average diameter estimated through WinRHIZO system and the manually measured internode diameter describe two different morphology aspects.
The time required to perform this protocol remains a limiting factor for routine analysis. The most time-consuming phase is the cleaning of the samples (step 2.4). Based on our experience, cleaning one turf sample with a large amount of biomass (i.e., 20 x 20 cm) requires approximately three people working for 2 to 4 hours. As described in the protocol, the cleaning process is necessary for both the digital analysis system and when using the caliper and ruler. When samples consist of a limited number of stolons/rhizomes, the time necessary to collect data with the two methods is similar. However, as sample size is increased, the software-based method does not have a subsequent time increase, as the only limiting factor is the surface area of the scanner. On the contrary, the time necessary to measure organs with the ruler and caliper increases with the number of stolons or rhizomes composing the sample.
The study of stolon and rhizome traits in mature turfgrasses has always been based on the measurement of internode length and diameter and mass dry weight7,11,26,27. Due to the large time required to process samples and the decrease in accuracy with the increase of sample size, manual measurements should be limited to a small number of stolons or rhizomes11,20,25. As such, they may only be suitable for single-plant experiments. The advantage of an image analysis system over traditional methods is that it that can measure the length of large stolon or rhizome samples and calculate both length density and specific weight (weight-length ratio).
This protocol allows for the measurement of stolon and rhizome length and calculation of length densities in samples with large biomass (for which stolon or rhizome weight is currently the only parameter used for morphology description). Stolon and/or rhizome length may be an important parameter in many studies that cannot be estimated with current techniques. Recent studies on different turf species6 have demonstrated that stolon weight and length densities are not always correlated, indicating that it may be desirable to measure multiple parameters to adequately assess the stolon and rhizome system. This method should be particularly suitable for cultivar or cultural management practices comparison.
Several steps within the protocol are critical for a successful estimation of length and average diameter of stolons and rhizomes. Because of high variability of plant morphology under different environment conditions, the number of samples (sample size) and ground area dimensions that should be sampled (sample dimension) should be carefully evaluated and be as representative as possible of the population in order to reduce data variability. Moreover, cleaning roots and leaves from the stolons before analysis is meticulous work requiring special attention to avoid overestimations. Lastly, before processing images, it is recommended to carefully select the width of diameter classes and minimum diameter using software options to exclude everything that is not a stolon or rhizome from the analysis. Each experiment requires the selection of a minimum diameter, as diameter varies with species and environmental conditions, including cultural practices.

The study of plant morphology is largely addressed in all disciplines of plants science including ecology, agronomy, biology, and physiology. The plant root system is widely studied for its importance in stress tolerance, soil stability, plant growth, and productivity. Stolons and rhizomes are also widely studied for their role in plant propagation strategies, recuperative ability, and carbohydrate storage. Stolons and rhizomes are modified stems that grow horizontally, either above-ground (stolons) or below-ground (rhizomes). Stolons and rhizomes also contain regularly-spaced nodes and internodes, as well as meristematic nodes that are capable of giving rise to new roots and shoots1. There have been a wide number of studies on different topics investigating roots, stolons, and rhizomes of various plants2,3,4,5,6,7,8. Root systems, stolons, and rhizomes of turfgrasses are studied because of their importance in turf quality9, spring green-up after winter dormancy10, and wear tolerance and recuperative ability11. Furthermore, these organs are also studied in other crops, turfgrasses such as rice12, soybean4, and maize13, and pastures where lateral stems play a key role in soil erosion control5.
Root length density (root length per soil volume) and average diameter are commonly measured using scanning software3,4,5,9,14,15,16,17,18. Conversely, the length and diameter of stolons or rhizomes are usually measured with a ruler and caliper3,19,20 and require significant time and labor21,22,23,24. Hence, they are often measured in a restricted number of stolons or rhizomes11,20,25 and are often limited to the morphological characterization of spaced plants only. The study of stolon and rhizome traits in a mature canopy involves sampling a large amount of biomass so that usually only stolon and rhizome dry weight density (dry weight per unit of surface) are determined7,11,26,27. Stolon dry mass, in fact, can be more easily measured than stolon length and diameter by drying samples in an oven. However, stolon length is an important species and varietal character that is not well-related to dry mass. A recent study on creeping perennial ryegrass (Lolium perenne) demonstrated that samples with high stolon length density did not necessarily have high stolon weight density6.
Image-analysis systems make the analysis of roots faster28,29, more accurate, and less prone to human error30,21 than traditional, manual methods31,32,33. Moreover, these systems provide high &#64258;exibility and easy-to-use tools including the light, optical setup, and resolution, which are often calibrated for each scanned image34. Pornaro et al.24 demonstrated that the WinRHIZO system, an image analysis system specifically designed for measurement of washed roots, may provide an alternative method to analyze stolon and rhizome traits more completely than current methods by overcoming measurement errors caused by human mistakes. For a morphological description and quantitative information on stolon and rhizome growth, image-analysis systems can be used to analyze a large number of samples quickly, even with a large amount of biomass, allowing increased statistical precision. Therefore, root analysis software packages provide an alternative, reliable, and fast method for studying the growth and morphology of stolons and rhizomes of different plant species24.
We present an experiment carried out in northeastern Italy to study stolon and rhizome development of four cultivars of bermudagrass (Cynodon spp.). The study aimed to increase knowledge on the development of stolons and rhizomes in seeded (&#34;LaPaloma&#34; and &#34;Yukon&#34;) and vegetative (&#34;Patriot&#34; and &#34;Tifway&#34;) cultivars of bermudagrass. The experiment was established in May 2013, and turf samples were collected over three sampling dates per year, from autumn of 2013 to summer of 2015 [March (before the green up), July (full growing season), and October (before winter dormancy)]. For the description and explanation of this method, we used samples collected in the summer of the second growing season (July 2014), as the large biomass of the samples at this time justified the need for a rapid analysis. The WinRHIZO, a digital image-analysis software tool specifically designed for washed root measurements, was used to determine stolon length density and average diameter.

<table><tbody><tr><td>laboratory tape</td><td>Any</td><td>NA</td><td>Tags may be used to label samples</td></tr><tr><td>plastic bags</td><td>Any</td><td>NA</td><td>Any plastic bag can be used to keep samples until they have been cleened</td></tr><tr><td>paper bags</td><td>Any</td><td>NA</td><td>Any paper bag can be used to keep cleaned samples to avoid mold formation</td></tr><tr><td>paper towels</td><td>Any</td><td>NA</td><td>After samples have been washed with water and before to clean them with scissors it is helpful to put them on a paper towel to absorb water</td></tr><tr><td>scissor</td><td>Any</td><td>NA</td><td>Any scissor with fine tips</td></tr><tr><td>aluminium box</td><td>Any</td><td>NA</td><td>Any aluminium box large enough to contain the sample</td></tr><tr><td>trays</td><td>Any</td><td>NA</td><td>It is helpful to use plastic tray to hold samples during the cleaning process</td></tr><tr><td>sieve with 0.5-1.5 mm openings</td><td>Any</td><td>NA</td><td>Any sieve</td></tr><tr><td>soil core sampler</td><td>Any</td><td>NA</td><td>We use core sampler for soil collection with diameter of at least 8 cm</td></tr><tr><td>squared frame</td><td>Any</td><td>NA</td><td>To collect large samples we use squared frame (10 x 10 cm, or 15 x 15 cm, or 20 x 20 cm)</td></tr><tr><td>spade</td><td>Any</td><td>NA</td><td>We use spade to pull out samples delimited with squared frame</td></tr><tr><td>precision electronic balance</td><td>Any</td><td>NA</td><td>Any precision electronic balance</td></tr><tr><td>laboratory oven</td><td>Any</td><td>NA</td><td>Any laboratory oven</td></tr><tr><td>freezer</td><td>Any</td><td>NA</td><td>Any freezer</td></tr><tr><td>WinRHIZO software</td><td>Regent Instruments Inc., Quebec</td><td>NA</td><td>Excluded the &quot;basic&quot; version</td></tr><tr><td>WinRHIZO scanner</td><td>Regent Instruments Inc., Quebec</td><td>NA</td><td>WinRHIZO system includes a scanner calibrated for the software</td></tr><tr><td>WinRHIZO scanner accessories</td><td>Regent Instruments Inc., Quebec</td><td>NA</td><td>WinRHIZO system includes accessories, as plastic tray and positioner, to be used with the scanner</td></tr></tbody></table>

measuring stolons and rhizomes of turfgrasses using a digital image analysis system

Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often used on a limited number of stolons or rhizomes. For this reason, these traits are limited in their use for morphological characterization of plants. The use of digital image analysis software technology may overcome measurement errors due to human mistakes, which tend to increase as the number and size of samples also increase. The protocol can be used for any kind of crop but is particularly suitable for forage or grasses, where plants are small and numerous. Turf samples consist of aboveground biomass and an upper soil layer to the depth of maximum rhizome development, depending on the species of interest. In studies, samples are washed from the soil, and stolons/rhizomes are cleaned by hand before analysis by digital image analysis software. The samples are further dried in a laboratory heating oven to measure dry weight; therefore, for each sample, the resultant data are total length, total dry weight, and average diameter. Scanned images can be corrected before analysis by excluding visible extraneous parts, such as remaining roots or leaves not removed with the cleaning process. Indeed, these fragments normally have much smaller diameters than stolons or rhizomes, so they can be easily excluded from analysis by fixing the minimum diameter below which objects are not considered. Stolon or rhizome density per unit area can then be calculated based on sample size. The advantage of this method is quick and efficient measurement of the length and average diameter of large sample numbers of stolons or rhizomes.

A field experiment was established in autumn 2013 to compare stolon and rhizome development of four bermudagrass cultivars, including two seeded types (&#34;LaPaloma&#34; and &#34;Yukon&#34;) and two sterile vegetative hybrids (&#34;Patriot&#34; and &#34;Tifway&#34;). The experimental design was a randomized complete block with three replications, for a total of 12 plots (2 x 2 m).
Fourteen stolons and fourteen rhizomes from each turf-type cultivar and the wild bermudagrass were collected randomly in the plots, as well as from wild bermudagrass plants growing near the plots, for a total of 70 stolons and 70 rhizomes. All stolons and rhizomes were cleaned as described in the protocol (step 2) before further measurement. Internode diameter and length were measured with a caliper and ruler, respectively, and the number of nodes was counted for each stolon or rhizome. The times necessary to clean and measure stolon and rhizome samples with the ruler and caliper were also recorded. Stolon and rhizome diameters were calculated as the means of all internode diameters measured. Total stolon and total rhizome lengths were calculated as the sum of all internode lengths. Furthermore, total scanned lengths and scanned diameters of each stolon and rhizome were measured using a digital image analysis system, as described in steps 3 and 5. The times necessary to measure stolon and rhizome traits by the digital analysis system were recorded. Each stolon and rhizome were then cut with scissors to separate internodes from nodes, and the internodes were used to estimate the scanned internode diameter as described in steps 3 and 5. Pearson&#39;s correlation coefficients were calculated for stolons and rhizomes (n = 70 stolons, n = 70 rhizomes) between measured and scanned lengths, measured and scanned diameters, number of nodes and the absolute value of the difference between measured and scanned diameters, and measured diameters and scanned internode diameters. The lengths measured with the ruler were used to calibrate the lengths estimated through the digital image analysis system.
The regression analysis indicated a high correlation between stolon scanned length and measured length (Figure 1a), with a slope of 1.03 and intercept of -4.22, as well as between rhizome scanned length and measured length (Figure 1b), with a slope of 1.03 and intercept of 4.22. Cleaning by hand, 14 stolons and 14 rhizomes took an average time of 21 min and 24 s and 11 min and 12 s, respectively. The average time to measure length and diameter with a ruler and caliper was 14 min and 6 s for stolons and 13 min and 35 s for rhizomes. The scanning and software analysis of samples using WinRHIZO took an average of 11 min for stolons and 12 min and 4 s for rhizomes.
Measured and scanned diameters were also significantly correlated in both stolons and rhizomes. The relationships between measured and scanned diameter were close to 1:1, indicating a good fit of the data (Figure 2a and 2b). However, the intercept indicated that digital image analysis system overestimated measured diameter, especially for lower values, and that higher values of rhizome diameter were underestimated. This overestimation may be due to stolon nodes that are scanned by the software, affecting the total projection surface that is used for calculating diameter (ratio between total projection surface and total length), and instead are excluded when measurements are made with the caliper. The correlation between the number of nodes and difference between diameter values obtained by both methods (measured and scanned) was significant only in stolons (Figure 3a); also, variations in the number of nodes explained only a small part of the variation of this difference (R2&#160;= 14%). The significant correlation found between scanned internode diameter and measured diameter (slopes of 1.01 and 0.98 for stolons and rhizomes, respectively; intercepts of nearly zero) (Figure&#160;4a and 4b) demonstrates that internode diameter can be accurately estimated through the digital image analysis system as long as nodes are removed. Therefore, total stolon length and average diameter of samples composed by numerous stolons or rhizomes can be easily and accurately quantified using the digital image analysis system.
As part of an ongoing experiment, one turf sample (20 x 20 x 15 cm depth) was collected in each plot seasonally from autumn 2013 to summer 2015 and was handled as described in the protocol. The stolon and rhizome length per unit surface area (length density) and weight per unit surface area (weight density) of samples collected in July 2014 are presented in Figure 5. Differences in stolon length density were observed between the vegetatively propagated cultivars (&#34;Patriot&#34; and &#34;Tifway&#34;) and seeded ones (&#34;La Paloma&#34; and &#34;Yukon&#34;). &#34;Patriot&#34; displayed the highest rhizome length density, followed by &#34;Tifway&#34; and the seeded cultivars. The stolon weight density was different for all cultivars, with &#34;Patriot&#34; showing the highest value followed by &#34;Tifway&#34;, &#34;La Paloma&#34; and &#34;Yukon&#34;. The vegetatively propagated cultivars also displayed higher rhizome weight densities than the seeded cultivars. The development of stolon and rhizome length per unit surface area (length density) and weight per unit surface area (weight density) of cultivar Patriot throughout the study period are reported in Figure 6. Stolon length density displayed an increase from March 2014 to July 2014, and it did not vary from July 2014 to July 2015. Only a few rhizomes were found in samples collected in October 2013 and March 2014. Rhizome length density increased in July 2104, reaching its highest values, but decreased again in October 2014. Stolon weight density slightly increased from March to July 2014; however, a more rapid increase was observed from July to October 2014, with a subsequent decrease in March 2015. Rhizome weight density had a similar trend to rhizome length density, with its highest value in July 2014.
The software includes in the analysis all objects in the scanned image. An example of a digital image analysis layout from WinRHIZO software is presented (Figure 7), where lines of different color overlay objects (stolons) of varying diameter to calculate the total length per diameter class. We can observe that the analysis takes into account fragments of roots or leaves. As described in the step 3.9, it is possible to restrict the width and number of diameter classes that are analyzed. The histogram shows the distribution of lengths into selected diameter classes (Figure 7). This histogram can be used to assess the minimum diameter classes to be excluded. A visual observation of this graph in the upper part of the screen image highlights that the length has a normal distribution around an average mean diameter class, with the exception of the first two classes that show higher values than those fitting the normal distribution. Even if samples have been carefully cleaned, including these smaller classes, data analysis may affect the results, overestimating length density and underestimating average diameter. Our results show that the length of smaller classes (diameter &#60; 0.2 mm) accounted for 13-32% of the total rhizome length values resulting from the software analysis (Table 1). Moreover, average diameter was underestimated from 2-17% (Table 1).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58042/58042fig1.jpg" /> 
Figure 1: Regression analysis of length values measured with the ruler against values estimated with the digital image analysis system of bermudagrass stolons24 (a) and) rhizomes (b). The dashed line represents a 1:1 ratio. Panel A has been modified from Pornaro et al.24. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58042/58042fig2.jpg" /> 
Figure 2: Regression analysis of diameter values measured with the caliper against values estimated with the digital image analysis system of bermudagrass stolons24 (a) and rhizomes (b). The dashed line represents a 1:1 ratio. Panel A has been modified from Pornaro et al.24. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58042/58042fig3.jpg" /> 
Figure 3: Regression analysis of number of nodes of bermudagrass stolons24 (a) and rhizomes (b) against absolute values of difference between diameter estimated with the digital image analysis system and measured with the caliper. The dashed line represents a 1:1 ratio. Panel A has been modified from Pornaro et al.24. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58042/58042fig4.jpg" /> 
Figure 4: Regression analysis of diameter values measured with the caliper against values estimated with the digital image analysis system of bermudagrass stolons24 (a) and rhizomes (b) for internodes only. The dashed line represents a 1:1 ratio. Panel A has been modified from Pornaro et al.24. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58042/58042fig5.jpg" /> 
Figure 5: Example results of length and weight density of stolons and rhizomes from a field trial comparing four turfgrass bermudagrass cultivars (Patriot, Tifway, La Paloma, Yukon). Stolon length density (a), rhizome length density (b), stolon weight density (c), and rhizome weight density (d). Vertical bars represent standard errors of six replicates. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58042/58042fig6.jpg" /> 
Figure 6: Example results of length and weight density of stolons and rhizomes from a field trial showing stolon and rhizome development of Patriot bermudagrass cultivar. Stolon length density (a), rhizome length density (b), stolon weight density (c), and rhizome weight density (d). Vertical bars represent standard errors of six replicates. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58042/58042fig7.jpg" /> 
Figure 7: Example layout of digital image analysis from WinRHIZO software. The scanned image in the foreground and the bar charts in the upper part of the screen image show the length distribution in selected diameter classes. The colored lines indicate the image analysis, and each color corresponds to colors of diameter classes reported in the bar charts. <a href="https://www.jove.com/files/ftp_upload/58042/58042fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Cultivar</td>
			<td rowspan="2">Block</td>
			<td colspan="3">Rhizome length (cm/dm2)</td>
			<td colspan="3">Average diameter (mm)</td>
		</tr>
		<tr>
			<td>&#60; 0.2 mm</td>
			<td>total</td>
			<td>ratioa</td>
			<td>&#60; 0.2 mm</td>
			<td>total</td>
			<td>ratiob</td>
		</tr>
		<tr>
			<td>Patriot</td>
			<td>1</td>
			<td>231</td>
			<td>278</td>
			<td>16.9</td>
			<td>1.637846</td>
			<td>1.5994</td>
			<td>97.7</td>
		</tr>
		<tr>
			<td>Patriot</td>
			<td>2</td>
			<td>304</td>
			<td>349</td>
			<td>12.8</td>
			<td>1.620667</td>
			<td>1.588371</td>
			<td>98.0</td>
		</tr>
		<tr>
			<td>Patriot</td>
			<td>3</td>
			<td>304</td>
			<td>366</td>
			<td>16.8</td>
			<td>1.649918</td>
			<td>1.621367</td>
			<td>98.3</td>
		</tr>
		<tr>
			<td>Tifway</td>
			<td>1</td>
			<td>184</td>
			<td>231</td>
			<td>20.6</td>
			<td>2.149745</td>
			<td>1.9951</td>
			<td>92.8</td>
		</tr>
		<tr>
			<td>Tifway</td>
			<td>2</td>
			<td>155</td>
			<td>193</td>
			<td>19.9</td>
			<td>1.866253</td>
			<td>1.76605</td>
			<td>94.6</td>
		</tr>
		<tr>
			<td>Tifway</td>
			<td>3</td>
			<td>119</td>
			<td>150</td>
			<td>20.9</td>
			<td>1.877386</td>
			<td>1.75865</td>
			<td>93.7</td>
		</tr>
		<tr>
			<td>La Paloma</td>
			<td>1</td>
			<td>17</td>
			<td>23</td>
			<td>24.4</td>
			<td>2.139019</td>
			<td>1.8904</td>
			<td>88.4</td>
		</tr>
		<tr>
			<td>La Paloma</td>
			<td>2</td>
			<td>26</td>
			<td>38</td>
			<td>31.6</td>
			<td>2.101385</td>
			<td>1.7455</td>
			<td>83.1</td>
		</tr>
		<tr>
			<td>La Paloma</td>
			<td>3</td>
			<td>34</td>
			<td>47</td>
			<td>27.5</td>
			<td>2.033729</td>
			<td>1.7354</td>
			<td>85.3</td>
		</tr>
		<tr>
			<td>Yukon</td>
			<td>1</td>
			<td>32</td>
			<td>44</td>
			<td>28.0</td>
			<td>1.700155</td>
			<td>1.4945</td>
			<td>87.9</td>
		</tr>
		<tr>
			<td>Yukon</td>
			<td>2</td>
			<td>17</td>
			<td>25</td>
			<td>33.2</td>
			<td>1.68339</td>
			<td>1.4284</td>
			<td>84.9</td>
		</tr>
		<tr>
			<td>Yukon</td>
			<td>3</td>
			<td>67</td>
			<td>87</td>
			<td>23.6</td>
			<td>1.844721</td>
			<td>1.6774</td>
			<td>90.9</td>
		</tr>
		<tr>
			<td colspan="4">a Length of classes &#8804;0.2 mm/total length</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="5">b Total diameter/diameter of classes &#8804;0.2 mm</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: Rhizome length density and rhizome average diameter with and without smaller diameter classes. Length density with and without including diameter classes smaller than 0.2 mm and their ratios (length of classes &#8804; 0.2 mm/total length); and average diameter with and without including diameter classes smaller than 0.2 mm and their ratios (diameter including classes &#60; 0.2/diameter without including diameter classes &#60; 0.2 mm).

Watch this Scientific Journal Video about Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System at JoVE.com

Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System

A software-based image analysis system provides an alternative method to study the morphology of stoloniferous and rhizomatous species. This protocol permits measurement of the length and diameter of stolons and rhizomes and can be applied to samples with a large amount of biomass and to a wide variety of species.

Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often ...

measuring-stolons-rhizomes-turfgrasses-using-digital-image-analysis

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9.8K Views.  University of Padova. A software-based image analysis system provides an alternative method to study the morphology of stoloniferous and rhizomatous species. This protocol permits measurement of the length and diameter of stolons and rhizomes and can be applied to samples with a large amount of biomass and to a wide variety of species.

Video: Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&ndash;Environment Interactions

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant&#8211;environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment&#8217;s effects on the plants&#8217; physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (&#952;) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions

This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant&#8211;environment interactions.

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

This protocol describes a process for rapid collection of images of Arabidopsis seedlings responding to a gravity stimulus using commercially-available flatbed scanners. The method allows for inexpensive, high-volume capture of high-resolution images amenable for downstream analysis algorithms.

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories ...

Biology

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

Arbuscular mycorrhizal fungi are symbionts in the roots of plants. Their role is to sustain host development and maintain the nutritional equilibrium in the ecosystems. The colonization process is dependent on several factors like soil ecology, the genetic diversity of the fungi and host, and agronomic practices. Their synchronized action leads to the development of a complex hyphal network and leads to the secondary development of vesicles and arbuscules in the root cells. The aim of this research was to analyze the efficiency of the mycorrhizal patterns (MycoPatt) method for the positioning of fungal structures in the roots of Festuca rubra and Zea mays. Another objective was to explore the fungal colonization strategy as revealed by mycorrhizal maps of each species. The acquisition and assemblage of multiple microscopic images allow mycorrhizal colonization assessment in both corn and red fescue plants to provide information on the realistic position of the developed structures. The observed mycorrhizal patterns highlight the variable efficiency of each plant in terms of developing connections with soil symbiotic fungi, caused by applied treatments and growth stage. Mycorrhizal detailed maps obtained through the MycoPatt method are useful for the early detection of plant efficiency in symbiotic acquisition from the soil.

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

The protocol here describes the methods for the assessment of the arbuscular mycorrhizal colonization patterns and strategy in roots for two species: Zea mays and Festuca rubra. The use of the MycoPatt method permits the calculation of parameters, the conversion of mycorrhizal structures into digital data, and the mapping of their real position in roots.

Arbuscular mycorrhizal fungi are symbionts in the roots of plants. Their role is to sustain host development and maintain the nutritional equilibrium in ...

A Simple Protocol for Mapping the Plant Root System Architecture Traits

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar tolerance to environmental challenges. An experimental protocol is presented for setting up the hydroponic system, plantlet growth, RSA spreading, and imaging. The approach used a magenta box-based hydroponic system containing polypropylene mesh supported by polycarbonate wedges. Experimental settings are exemplified by assessing the RSA of the plantlets under varying nutrient (phosphate [Pi]) supply. The system was established to examine the RSA of Arabidopsis, but it is readily adaptable to study other plants like Medicago sativa (Alfalfa). Arabidopsis thaliana (Col-0) plantlets are used in this investigation as an example to understand the plant RSA. Seeds are surface sterilized by treating ethanol and diluted commercial bleach, and kept at 4 &#176;C for stratification. The seeds are germinated and grown on a liquid half-MS medium on a polypropylene mesh supported by polycarbonate wedges. The plantlets are grown under standard growth conditions for the desired number days, gently picked out from the mesh, and submersed in water-containing agar plates. Each root system of the plantlets is spread gently on the water-filled plate with the help of a round art brush. These Petri plates are photographed or scanned at high resolution to document the RSA traits. The root traits, such as primary root, lateral roots, and branching zone, are measured using the freely available ImageJ software. This study provides techniques for measuring plant root characteristics in controlled environmental settings. We discuss how to (1) grow the plantlets, and collect and spread root samples, (2) obtain pictures of spread RSA samples, (3) capture the images, and (4) use image analysis software to quantify root attributes. The advantage of the present method is the versatile, easy, and efficient measurement of the RSA traits.

We use simple laboratory tools to examine the root system architecture (RSA) of Arabidopsis and Medicago. The plantlets are grown hydroponically over mesh and spread using an art brush to reveal the RSA. Images are taken using scanning or a high-resolution camera, then analyzed with ImageJ to map traits.

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar ...

Microscopical Analysis for Enhancing Photosynthetic Efficiency in C4 Crops

Assessing Structural Traits in Triticum aestivum and Zea mays for C3 and C4 Photosynthetic Differentiation Using Free-hand and Semi-thin Sections

The enhanced efficiency of C4 photosynthesis, compared to the C3 mechanism, arises from its ability to concentrate CO2 in bundle sheath cells. The effectiveness of C4 photosynthesis and intrinsic water use efficiency are directly linked to the share of mesophyll and bundle leaf cells,&#160;size and density of bundle sheaths, and size, density, and cell wall thickness of bundle sheath cells. Rapid microscopical analysis of these traits can be performed on free-hand and semi-thin sections using conventional light microscopy, providing valuable information about photosynthetic efficiency in C4 crops by means of identifying and examining specific cell types. Additionally, errors in freehand and semi-thin section preparation that affect anatomical measurements and cell type diagnoses are shown, as well as how to avoid these errors. This microscopical approach offers an efficient means of gathering insights into photosynthetic acclimation to environmental variation and aids in the rapid screening of crops for future climates.

Author Spotlight: Innovative Approaches to Understanding Plant Structure-Function Relationships for Climate-Resilient Crops

Assessing the anatomical differences between C3 and C4 leaf cross sections helps understand photosynthesis efficiency. This paper describes free-hand and semi-thin leaf cross sections preparation and examination, along with the caveats in the preparation for the crop species Triticum aestivum and Zea mays.

Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System

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Chapters in this video

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

A Simple Protocol for Mapping the Plant Root System Architecture Traits

Assessing Structural Traits in Triticum aestivum and Zea mays for C₃ and C₄ Photosynthetic Differentiation Using Free-hand and Semi-thin Sections