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 &#34;basic&#34; 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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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

Universidad de Cartagena UDEC

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JoVE Journal

Environment

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

Analysis of Fatty Acid Content and Composition in Microalgae

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of microalgal biomass. These fatty acids can be present in different acyl-lipid classes. Especially the fatty acids present in triacylglycerol (TAG) are of commercial interest, because they can be used for production of transportation fuels, bulk chemicals, nutraceuticals (&omega;-3 fatty acids), and food commodities. To develop commercial applications, reliable analytical methods for quantification of fatty acid content and composition are needed. Microalgae are single cells surrounded by a rigid cell wall. A fatty acid analysis method should provide sufficient cell disruption to liberate all acyl lipids and the extraction procedure used should be able to extract all acyl lipid classes.
With the method presented here all fatty acids present in microalgae can be accurately and reproducibly identified and quantified using small amounts of sample (5 mg) independent of their chain length, degree of unsaturation, or the lipid class they are part of.
This method does not provide information about the relative abundance of different lipid classes, but can be extended to separate lipid classes from each other.
The method is based on a sequence of mechanical cell disruption, solvent based lipid extraction, transesterification of fatty acids to fatty acid methyl esters (FAMEs), and quantification and identification of FAMEs using gas chromatography (GC-FID). A TAG internal standard (tripentadecanoin) is added prior to the analytical procedure to correct for losses during extraction and incomplete transesterification.

A method for the determination of fatty acid content and composition in microalgae based on mechanical cell disruption, solvent based lipid extraction, transesterification, and quantification and identification of fatty acids using gas chromatography is described. A tripentadecanoin internal standard is used to compensate for the possible losses during extraction and incomplete transesterification.

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of ...

Use of Chironomidae (Diptera) Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. Unfortunately, traditional benthic larval sampling methods, such as the dip-net, can be time-consuming and expensive. An alternative protocol involves collection of Chironomidae surface-floating pupal exuviae (SFPE). Chironomidae is a species-rich family of flies (Diptera) whose immature stages typically occur in aquatic habitats. Adult chironomids emerge from the water, leaving their pupal skins, or exuviae, floating on the water&#8217;s surface. Exuviae often accumulate along banks or behind obstructions by action of the wind or water current, where they can be collected to assess chironomid diversity and richness. Chironomids can be used as important biological indicators, since some species are more tolerant to pollution than others. Therefore, the relative abundance and species composition of collected SFPE reflect changes in water quality. Here, methods associated with field collection, laboratory processing, slide mounting, and identification of chironomid SFPE are described in detail. Advantages of the SFPE method include minimal disturbance at a sampling area, efficient and economical sample collection and laboratory processing, ease of identification, applicability in nearly all aquatic environments, and a potentially more sensitive measure of ecosystem stress. Limitations include the inability to determine larval microhabitat use and inability to identify pupal exuviae to species if they have not been associated with adult males.

Use of Chironomidae Diptera Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrates are often used to monitor and assess water quality. An efficient protocol involves collections of Chironomidae surface-floating pupal exuviae (SFPE). Here, techniques for field collection, laboratory processing, slide mounting, and identification of Chironomidae SFPE are described.

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. ...

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate the environment. This radiocarbon signal &#8212; or rather the absence of signal &#8212; is evenly distributed throughout a contaminant source pool (unlike an added tracer) and is not impacted by biological, chemical or physical processes (e.g., the 14C radioactive decay rate is immutable). If the fossil-derived contaminant is fully degraded to CO2, a harmless end-product, that CO2 will contain no radiocarbon. CO2 derived from natural organic matter (NOM) degradation will reflect the NOM radiocarbon content (usually &#60;30,000 years old). Given a known radiocarbon content for NOM (a site background), a two end-member mixing model can be used to determine the CO2 derived from a fossil source in a given soil gas or groundwater sample. Coupling the percent CO2 derived from the contaminant with the CO2 respiration rate provides an estimate for the total amount of contaminant degraded per unit time. Finally, determining a zone of influence (ZOI) representing the volume from which site CO2 is collected allows determining the contaminant degradation per unit time and volume. Along with estimates for total contaminant mass, this can ultimately be used to calculate time-to-remediate or otherwise used by site managers for decision-making.

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A protocol is described wherein CO2 mineralized from organic contaminant (derived from petroleum feedstocks) biodegradation is trapped, quantified, and analyzed for 14C content. A model is developed to determine CO2 capture zone's spatial extent. Spatial and temporal measurements allow integrating contaminant mineralization rates for predicting remediation extent and time.

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate ...

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies

Most of the trace metal concentrations in ambient waters obtained a few decades ago have been considered unreliable owing to the lack of contamination control. Developments of some techniques aiming to reduce trace metal contamination in the last couple of decades have resulted in concentrations reported now being orders of magnitude lower than those in the past. These low concentrations often necessitate preconcentration of water samples prior to instrumental analysis of samples. Since contamination can appear in all phases of trace metal analyses, including sample collection (and during preparation of sampling containers), storage and handling, pretreatments, and instrumental analysis, specific care needs to be taken in order to reduce contamination levels at all steps. The effort to develop and utilize "clean techniques" in trace metal studies allows scientists to investigate trace metal distributions and chemical and biological behavior in greater details. This advancement also provides the required accuracy and precision of trace metal data allowing for environmental conditions to be related to trace metal concentrations in aquatic environments.
This protocol that is presented here details needed materials for sample preparation, sample collection, sample pretreatment including preconcentration, and instrumental analysis. By reducing contamination throughout all phases mentioned above for trace metal analysis, much lower detection limits and thus accuracy can be achieved. The effectiveness of "clean techniques" is further demonstrated using low field blanks and good recoveries for standard reference material. The data quality that can be obtained thus enables the assessment of trace metal distributions and their relationships to environmental parameters.

Special care using "clean techniques" is required to properly collect and process water samples for trace metal studies in aquatic environments. A protocol for sampling, processing, and analytical procedures with the aim of obtaining reliable environmental monitoring data and results with high sensitivity for detailed trace metal studies is presented.

Most of the trace metal concentrations in ambient waters obtained a few decades ago have been considered unreliable owing to the lack of contamination ...

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropod populations in rice. When used in combination with an enclosure, application of this sampling device provides absolute estimates of the populations of arthropods as numbers per standardized sampling area. The sampling efficiency depends critically on the sampling duration. In a mature rice crop, a two-minute sampling in an enclosure of 0.13 m2 yields more than 90% of the arthropod population. The device also allows sampling of arthropods dwelling on the water surface or the soil in rice paddies, but it is not suitable for sampling fast flying insects, such as predatory Odonata or larger hymenopterous parasitoids. The modified blower-vac is simple to construct, and cheaper and easier to handle than traditional suction sampling devices, such as D-vac. The low cost makes the modified blower-vac also accessible to researchers in developing countries.

Assessment of arthropod abundance in crops is critical for investigating population dynamics and species interactions. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropods in rice.

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the ...

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence the oxidizing capacity of the atmosphere through interactions with ozone and hydroxyl radicals. The main sink of NOx is the formation and deposition of nitric acid, a component of acid rain and a bioavailable nutrient. NOx is emitted from a mixture of natural and anthropogenic sources, which vary in space and time. The collocation of multiple sources and the short lifetime of NOx make it challenging to quantitatively constrain the influence of different emission sources and their impacts on the environment. Nitrogen isotopes of NOx have been suggested to vary amongst different sources, representing a potentially powerful tool to understand the sources and transport of NOx. However, previous methods of collecting atmospheric NOx integrate over long (week to month) time spans and are not validated for the efficient collection of NOx in relevant, diverse field conditions. We report on a new, highly efficient field-based system that collects atmospheric NOx for isotope analysis at a time resolution between 30 min and 2 hr. This method collects gaseous NOx in solution as nitrate with 100% efficiency under a variety of conditions. Protocols are presented for collecting air in urban settings under both stationary and mobile conditions. We detail the advantages and limitations of the method and demonstrate its application in the field. Data from several deployments are shown to 1) evaluate field-based collection efficiency by comparisons with in situ NOx concentration measurements, 2) test the stability of stored solutions before processing, 3) quantify in situ reproducibility in a variety of urban settings, and 4) demonstrate the range of N isotopes of NOx detected in ambient urban air and on heavily traveled roadways.

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Previous work suggests that the nitrogen isotopic composition of atmospheric nitrogen oxides might distinguish the influence of different sources in the environment. We report on an automated, mobile, field-based method for the high collection efficiency of atmospheric NOx for N isotopic analysis at an hourly time resolution.

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence ...

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a relatively low cost. This manuscript describes an application of particle tracking velocimetry (PTV) to a bed-load experiment with lightweight sediment. The key characteristics of the investigated sediment transport conditions were the presence of a covered flow and of a fixed rough bed above which particles were released in limited number at the flume inlet. Under the applied flow conditions, the motion of the individual bed-load particles was intermittent, with alternating movement and stillness terms. The flow pattern was preliminarily characterized by acoustic measurements of vertical profiles of the stream-wise velocity. During process visualization, a large field of view was obtained using two action-cameras placed at different locations along the flume. The experimental protocol is described in terms of channel calibration, experiment realization, image pre-processing, automatic particle tracking, and post-processing of particle track data from the two cameras. The presented proof-of-concept results include probability distributions of the particle hop length and duration. The achievements of this work are compared to those of existing literature to demonstrate the validity of the protocol.

The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a ...

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes ...

On-Site Molecular Detection of Soil-Borne Phytopathogens Using a Portable Real-Time PCR System

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management strategies that reduce the impact of the disease. Presently in many diagnostic laboratories, the polymerase chain reaction (PCR), particularly real-time PCR, is considered the most sensitive and accurate method for plant pathogen detection. However, laboratory-based PCRs typically require expensive laboratory equipment and skilled personnel. In this study, soil-borne pathogens of potato are used to demonstrate the potential for on-site molecular detection. This was achieved using a rapid and simple protocol comprising of magnetic bead-based nucleic acid extraction, portable real-time PCR (fluorogenic probe-based assay). The portable real-time PCR approach compared favorably with a laboratory-based system, detecting as few as 100 copies of DNA from Spongospora subterranea. The portable real-time PCR method developed here can serve as an alternative to laboratory-based approaches and a useful on-site tool for pathogen diagnosis.

Rapid and accurate detection of plant pathogens on-site, especially soil-borne pathogens, is crucial to prevent further inoculum production and proliferation of plant diseases in the field. The method developed here using a portable real-time PCR detection system enables on-site diagnosis under field conditions.

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management ...

Measuring the Flight Ability of the Ambrosia Beetle, Platypus Quercivorus (Murayama), Using a Low-Cost, Small, and Easily Constructed Flight Mill

The ambrosia beetle, Platypus quercivorus (Murayama), is the vector of a fungal pathogen that causes mass mortality of Fagaceae trees (Japanese oak wilt). Therefore, knowing the dispersal capacity may help inform trapping/tree removal efforts to prevent this disease more effectively. In this study, we measured the flight velocity and duration and estimated the flight distance of the beetle using a newly developed flight mill. The flight mill is low cost, small, and constructed using commonly available items. Both the flight mill arm and its vertical axis comprise a thin needle. A beetle specimen is glued to one tip of the arm using instant glue. The other tip is thick due to being covered with plastic, thus it facilitates the detection of rotations of the arm. The revolution of the arm is detected by a photo sensor mounted on an infrared LED, and is indicated by a change in the output voltage when the arm passed above the LED. The photo sensor is connected to a personal computer and the output voltage data are stored at a sampling rate of 1 kHz. By conducting experiments using this flight mill, we found that P. quercivorus can fly at least 27 km. Because our flight mill comprises cheap and small ordinary items, many flight mills can be prepared and used simultaneously in a small laboratory space. This enables experimenters to obtain a sufficient amount of data within a short period.

Measuring the Flight Ability of the Ambrosia Beetle, Platypus Quercivorus Murayama, Using a Low-Cost, Small, and Easily Constructed Flight Mill

We developed a low cost and small flight mill, constructed with commonly available items and easily used in experimentation. Using this apparatus, we measured the flight ability of an ambrosia beetle, Platypus quercivorus.

The ambrosia beetle, Platypus quercivorus (Murayama), is the vector of a fungal pathogen that causes mass mortality of Fagaceae trees (Japanese oak wilt). ...

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

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