The authors of this research would like to thank the field management staff at the experimental stations of Colmenar de Oreja (Aranjuez) of the Instituto Nacional de Investigaci&#243;n y Tecnolog&#237;a Agraria y Alimentaria (INIA) and Zamadue&#241;as (Valladolid) of the Instituto de Tecnolog&#237;a Agraria de Castilla y Le&#243;n (ITACyL) for their field support of the research study crops used. This study was supported by the research project AGL2016-76527-R from MINECO, Spain and part of a collaboration project with Syngenta, Spain. The BPIN 2013000100103 fellowship from the &#34;Formaci&#243;n de Talento Humano de Alto Nivel, Gobernaci&#243;n del Tolima - Universidad del Tolima, Colombia&#34; was the sole funding support for the first author Jose Armando Fernandez-Gallego. The primary funding source of the corresponding author, Shawn C. Kefauver, came from the ICREA Academia program through a grant awarded to Prof. Jose Luis Araus.

1. Prefield crop growth stage and environmental conditions
<ol>
	<li>Make sure that the crop growth stage is approximately between grain filling and near crop maturity, with ears that are still green even if the leaves are senescent (which corresponds in the case of wheat to the range 60-87 of Zadoks&#39; scale16). Some yellowing of the leaves is acceptable but not necessary.</li>
	<li>Prepare a sampling plan for image capture with various replicates (pictures per plot) in order to capture plot/area variability; the image-processing algorithm will count the number of ears in the image and convert that to ears per square meter (ears/m2) based on the camera specifications.</li>
	<li>Plan the field excursions to capture the images within two hours of solar noon or, alternatively, on an overcast day in diffuse lighting conditions in order to avoid the negative effects of ear shadowing on the ear counting algorithm.</li>
	<li>Once in the field, check the top of the crop canopy to make sure that it is dry in order to avoid specular light reflection from moisture. 
	NOTE: In considering the objectives of this protocol, it is important to first consider whether the growth stage of the crop is suitable for applying ear counts. Capturing images outside of the recommended growth stage will either result in suboptimal or in meaningless results (if ears are not present or fully emerged). Image quality also has a considerable impact on processing the results, including resolution and sensor size, and some environmental conditions, such as time of day and cloud cover, need to be carefully considered before proceeding with image capture.</li>
</ol>
2. Image capture in field conditions with natural light
<ol>
	<li>Prepare a &#34;phenopole&#34; as shown in Figure 1 or a similar acquisition system (even handheld) to capture images quickly and yet in a standardized and consistent manner at each plot or target location.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58695/58695fig1v2.jpg" /> 
Figure 1: Ear counting system. Ear counting system using the &#34;phenopole&#34; shown in the field on the left, with a remotely controlled natural color (RGB) large sensor and high-resolution digital camera system with camera tilt and height, indicating the necessary parameters for adjusting the image-processing algorithm. The sensor and image resolution are detected automatically by the image properties, while the user should input the specifics for the lens focal length and the distance from the canopy. These are necessary to adjust the algorithm for the estimated number of pixels per ear and also the conversion of the image-based total ear count to ear density (ears/m2). For that reason, it is recommended to use the same camera and lens focal length for all field images. <a href="https://www.jove.com/files/ftp_upload/58695/58695fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Position the camera on a suitable monopod or &#34;selfie&#34; pole so that it may be maintained level, either using level bubbles or an in-camera stabilization system, to obtain zenithal images.</li>
	<li>Use a mobile phone, tablet, or another device to connect the camera for both remote control image capture and image visualization for the best results with correctly focused images. Program the camera for autofocus in order to reduce any errors in case the user is not familiar enough with their camera or photography techniques to set a correct manual focus, as demonstrated by the examples of zenithal images with correct focus and exposure in Figure 2.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58695/58695fig2.jpg" /> 
Figure 2: Zenithal crop images. Durum wheat and barley ear zenithal images for ear counting data set examples with an acceptable stage of growth and senescence from approximately 61 to 87 according to Zadoks&#39; scale. (Left) Durum wheat zenithal image data set example. (Right) Barley zenithal image data set example. <a href="https://www.jove.com/files/ftp_upload/58695/58695fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="4">
	<li>Take note of the image number prior to image capture in order to match the images correctly with the field plots. Record one image of the general field area at the start and one image of the ground/field between blocks for postprocessing controls.</li>
	<li>Position the camera at approximately 80 cm above the top of the crop canopy, using a ruler or measurement string to periodically check the camera height above the canopy. Ensure that the camera is level and capture the image. This technique may require 1-2 researcher(s).</li>
	<li>If additional field ear count validation is desired apart from a manual image count validation, install an extension arm to the frame (e.g., a small circle) and position it in the middle of the image in order to conduct manual field counts of a precise image subset; this technique may require 2-3 people to implement. 
	NOTE: Three major considerations in selecting a camera, therefore, include: (1) camera specifications; in this case, the sensor&#39;s physical size; (2) focal length of the image lens; (3) distance between the canopy and the camera: smaller distances or greater zoom lenses will capture a smaller area while images captured from a greater distance will capture a bigger crop area. See Figure 1 for the details on the relevant camera specifications.</li>
</ol>
3. Algorithm implementation and adjustments
NOTE: Here we present algorithm implementation and adjustments for different camera specifications (sensor size, megapixels, focal length, distance to crop) and crop (durum wheat or barley) for automatic ear counting. An overview of the algorithm is presented graphically in Figure 3.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58695/58695fig3.jpg" /> 
Figure 3: Image-processing pipeline for two-row barley ear counting. Image-processing pipeline for two-row barley ear counting as implemented using specific computer code or using the CerealScanner software, both of which operate within Fiji (ImageJ). Panel 1 shows the original image. Panel 2 shows the results of the applications of the Laplacian filter. Panel 3 shows the application of the median filter, and Panel 4 shows the results of the final Find Maxima and segmentation for producing the final ear count. Then, the calculations are made to convert the image count to ear density, as shown in Figure 1. These images are an example taken from the Arazuri field site (Northeastern Spain, 42&#176;48&#39;33.9&#34;N 1&#176;43&#39;37.9&#34;W) in diffuse light conditions. <a href="https://www.jove.com/files/ftp_upload/58695/58695fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Download and install Fiji, Java 8, and the processing code or the University of Barcelona&#8217;s proprietary CerealScanner plugin (https://fiji.sc/, https://www.java.com/en/download/, and https://integrativecropecophysiology.com/software-development/cerealscanner/ [information] or https://gitlab/sckefauver/CerealScanner [code repository]); contact the corresponding authors for access permissions. The plugin is installed within Fiji by simply copying it into the plugins folder.</li>
	<li>Open the plugin from the top menu through Plugins &#62; CerealScanner &#62; Open Cereal Scanner. 
	NOTE: Apart from the work presented here, the CerealScanner plugin includes several different RGB-based vegetation indices related to crop vigor, stress, or chlorophyll17,18. The specific CerealScanner portion includes specific algorithms for Early Vigor (Fernandez-Gallego, In review), Ear Counting14, and Crop Senescence19, as shown in Figure 4.</li>
	<li>Enter the adjustments of the camera specifications and image capture details if they are different from the default values (see Figure 1 and Figure 4 for details).
	<ol>
		<li>Adjust the algorithm parameter for the camera focal length.</li>
		<li>Adjust the algorithm parameter for the distance from the crop canopy.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58695/58695fig4.jpg" /> 
Figure 4: The CerealScanner 2.12 Beta central tab on both levels, marking the Ear Counting function within the CerealScanner algorithm collection. The user must select the &#8230; button to the right of Batch Inputs to select the folder where the image files are stored, change the default values of the H Distance (distance from the camera to the top of the crop canopy) and Focal Length, if different from the default values, and then select the &#8230; button to the right of Results File to choose the name and location of the final results file. The other tabs of the CerealScanner provide algorithms for trait-based phenotyping for Early Vigor and onset of Maturity as part of the CerealScanner code suite. Under the Biomass tab, there are several algorithms for estimations of more general crop vigor and biomass calculations, also for RGB digital images. The example refers to two-row barley, as it was demonstrated in detail in&#160;Figure 3.<a href="https://www.jove.com/files/ftp_upload/58695/58695fig4large.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/58695/58695fig5.jpg" /> 
Figure 5: Algorithm adjustments. Adjustments required in the image-processing pipeline in order to successfully count both wheat and barley ears using the same algorithm are managed automatically as part of the camera-specific adjustments of H Distance (distance between the camera and the crop canopy) and Focal Length and serve to ensure that the number of pixels per ear remains more or less constant between different applications. <a href="https://www.jove.com/files/ftp_upload/58695/58695fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="4">
	<li>Select the center tab CerealScanner and the subsequent central tab Ear Counting in order to calculate the number of ears in each image of a field data set.
	<ol>
		<li>Under Options, enter, in Batch Inputs, the location of the photos to analyze.</li>
		<li>In the Results Files, select where to save the results file. The results file will include two columns with the image file name and the ear counting results.</li>
		<li>Finally, click on Process, and the results file with the ear density in square meters (ears/m2), using a simple ratio using the camera settings and the distance between canopy and camera to convert the image area to an actual canopy area in square meters following Figure 1, will be automatically produced in a few minutes, depending on the computer speed.</li>
	</ol>
	</li>
	<li>Conduct a post-processing validation after the data collection by manually counting the number of wheat or barley ears in the image and then converting this to the number of ears per square meters (ears/m2), as is shown in Figure 1, to compare the results with those based on the algorithm values.
	<ol>
		<li>Use the simple point placement tool built within Fiji, which provides easy support for this process, and the Fiji Analyze Particles function for producing the counts automatically; this is shown graphically in Figure 6.</li>
		<li>Optionally, conduct a validation using a small area circle during field data acquisition as described in step 2.6; manual counts in the field and manual image counts in the laboratory can, then, be used for algorithm validation as shown in Figure 7.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58695/58695fig6.jpg" /> 
Figure 6: Algorithm validation using manual in-image ear counts. Manual in-image ear counts for (left) durum wheat and (right) barley. The small dots were created using the Fiji Point Tool and then counted using the Analyze Particles Function with a 0.90-1.00 Circularity constraint after applying a Color Threshold from the Hue Saturation Intensity color space for the color specified by the Point Tool. This method ensures more accurate image-based manual ear counts. <a href="https://www.jove.com/files/ftp_upload/58695/58695fig6large.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/58695/58695fig7.jpg" /> 
Figure 7: Algorithm validation using manual counts in the field and manual in-image ear counts of wheat and barley, using a circle. (Left) Wheat image count validation example using a circle. (Right) Barley image count validation example using a circle. The subset counts of the ears within the white circle were counted using the same technique as described in Figure 6 with the Point Tool, Color Threshold, and then, Analyze Particles Function with Circularity constraints and color selection using Hue. <a href="https://www.jove.com/files/ftp_upload/58695/58695fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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The authors have nothing to disclose.

Increased agility, consistency, and precision are key to developing useful new phenotyping tools to assist the crop-breeding community in their efforts to increase grain yield despite negative pressures related to global climate change. Efficient and accurate assessments of cereal ear density, as a major agronomic component of yield of important staple crops, will help provide the tools needed for feeding future generations. Focusing on the improvement and support of crop-breeding efforts in field conditions helps keep t...

The world cereal utilization in 2017/2018 is reported expand by 1% from the previous year1. Based on the latest predictions for cereal production and population utilization, world cereal stocks need to increase yields at a faster rate in order to meet growing demands, while also adapting to increasing effects of climate change2. Therefore, there is an important focus on yield improvement in cereal crops through improved crop breeding techniques. Two the most important and harvested cereals in the Mediterranean region are selected as examples for this study, namely, durum wheat (Triticum aestivum L. ssp. durum [Desf.]) and barley (Hordeum vulgare L.). Durum wheat is, by extension, the most cultivated cereal in the south and east margins of the Mediterranean Basin and is the 10th most important crop worldwide, owing to its annual production of 37 million tons annually3, while barley is the fourth global grain in terms of production, with a global production at 144.6 million tons annually4.
Remote sensing and proximal image analysis techniques are increasingly key tools in the advancement of field high-throughput plant phenotyping (HTPP) as they not only provide more agile but also, often, more precise and consistent retrievals of target crop biophysiological traits, such as assessments of photosynthetic activity and biomass, preharvest yield estimates, and even improvements in trait heritability, such as efficiency in resource use and uptake5,6,7,8,9. Remote sensing has traditionally focused on multispectral, hyperspectral, and thermal imaging sensors from aerial platforms for precision agriculture at the field scale or for plant phenotyping studies at the microplot scale10. Common, commercially available digital cameras that measure only visible reflected light were often overlooked, despite their very high spatial resolution, but have recently become popular as new innovative image-processing algorithms are increasingly able to take advantage of the detailed color and spatial information that they provide. Many of the newest innovations in advanced agricultural image analyses increasingly rely on the interpretation of data provided by very high-resolution (VHR) RGB images (for their measurement of red, green, and blue visible light reflectance), including crop monitoring (vigor, phenology, disease assessments, and identification), segmentation and quantification (emergence, ear density, flower and fruit counts), and even full 3D reconstructions based on a new structure from motion workflows11.
One of the most essential points for improvement in cereal productivity is a more efficient assessment of yield, which is determined by three major components: ear density or the number of ears per square meter (ears/m2), the number of the grains per ear, and the thousand-kernel weight. Ear density can be obtained manually in the field, but this method is laborious, time-consuming, and lacking in a single standardized protocol, which together may result in a significant source of error. Incorporating the automatic counting of ears is a challenging task because of the complex crop structure, close plant spacing, high extent of overlap, background elements, and the presence of awns. Recent work has advanced in this direction by using a black background structure supported by a tripod in order to acquire suitable crop images, demonstrating fairly good results in ear counting12. In this way, excessive sunlight and shadow effects were avoided, but such a structure would be cumbersome and a major limitation in an application to field conditions. Another example is an automatic ear counting algorithm developed using a fully automated phenotyping system with a rigid motorized gantry, which was used with good accuracy for counting ear density in a panel composed of five awnless bread wheat (Triticum aestivum L.) varieties growing under different nitrogen conditions13. Recent work by Fernandez-Gallego14 has optimized this process for quicker and easier data capture, using VHR RGB color images followed by more advanced, yet still fully automated, image analyses. The efficient and high-quality data collection in field conditions emphasizes a simplified standardized protocol for consistency and high data capture throughput, while the image-processing algorithm employs the novel use of Laplacian and frequency domain filters to remove undesired image components before applying a segmentation for counting based on finding local maxima (as opposed to full delineation as in other previous studies, which may result in more errors with overlapping ears).
This work proposes a simple system for the automatic quantification of ear density in field conditions, using images acquired from commercially available digital cameras. This system takes advantage of natural light in field conditions and, therefore, requires consideration of some related environmental factors, such as time of day and cloud cover, but remains, in effect, simple to implement. The system has been demonstrated on examples for durum wheat and barley but should be extendable in application to bread wheat, which, besides exhibiting ears with similar morphology, are frequently awnless, but further experiments would be required in order to confirm this. In the data capture protocol presented here, zenithal images are taken by simply holding the camera by hand or using a monopod for positioning the digital camera above the crop. Validation data can be acquired by counting the ears manually for subplots in the field or during postprocessing, by counting ears in the image itself. The image-processing algorithm is composed of three processes that, first, effectively remove unwanted components of the image in a manner that, then, allows for the subsequent segmentation and counting of the individual wheat ears in the acquired images. First, a Laplacian frequency filter is used in order to detect changes in the different spatial directions of the image using the default ImageJ filter settings without window kernel size adjustments (Find Maxima segmentation technique determines the local peaks after the median spatial filter step, at which stage the pixels related with ears have higher pixel values than soil or leaves. Therefore, Find Maxima is used to segment the high values in the image, and those regions are labeled as ears, which identifies ears while also reducing overlapping ear errors. Analyze Particles is then used on the binary images to count and/or measure parameters from the regions created by the contrast between the white and black surface created by the Find Maxima step. The result is then processed to create a binary image segmentation by analyzing the nearest neighbor pixel variance around each local maximum to identify the wheat ear shapes in the filtered image. Finally, the ear density is counted using Analyze Particles, as implemented in Fiji15. Both Find Maxima and Analyze Particles are standalone functions and available as plugins in Fiji (https://imagej.nih.gov/ij/plugins/index.html). Though not presented specifically in the protocol here, preliminary results presented as supplementary material suggest that this technique may be adaptable to conducting ear count surveys from unmanned aerial vehicles (UAVs), providing that the resolution remains sufficiently high14.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ILCE-QX1 Camera</td>
			<td>Sony</td>
			<td>WW024382</td>
			<td>Compact large sensor digital camera with 23.2 x 15.4 mm sensor size.</td>
		</tr>
		<tr>
			<td>E-M10 Camera</td>
			<td>Olympus</td>
			<td>E-M10</td>
			<td>Compact large sensor digital camera with 17.3 x 13.0 mm sensor size.</td>
		</tr>
		<tr>
			<td>Multipod Monpod</td>
			<td>Sony</td>
			<td>VCT MP1</td>
			<td>&#34;Phenopole&#34; in the JoVE article</td>
		</tr>
		<tr>
			<td>Computer</td>
			<td>Any PC/Mac/Linux</td>
			<td>--</td>
			<td>Data and image analysis</td>
		</tr>
		<tr>
			<td>ImageJ/FIJI (FIJI is just Image J)</td>
			<td>NIH</td>
			<td>http://fiji.sc</td>
			<td>Plug-in and algorithms for data and image analysis</td>
		</tr>
		<tr>
			<td>Circle/Metal Ring</td>
			<td>Generic</td>
			<td>Generic</td>
			<td>Metal ring for in-field validation</td>
		</tr>
		<tr>
			<td>Crab Pliers Clip</td>
			<td>Newer</td>
			<td>90087340</td>
			<td>Circle support and extension arm</td>
		</tr>
	</tbody>
</table>

cereal crop ear counting in field conditions using zenithal rgb images

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

In Figure 8, the results show the determination coefficient between the ear density (number of ears per square meters) using manual counting and the ear counting algorithm for wheat and barley at three different crop growth stages. The first one is durum wheat with a Zadoks&#39; scale between 61 and 65 (R2 = 0.62). The second one is two-row barley with a Zadoks&#39; scale between 71 and 77 (R2 = 0.75), and the last one ...

Watch this Scientific Journal Video about Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images at JoVE.com

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, ...

cereal-crop-ear-counting-in-field-conditions-using-zenithal-rgb-images

Research

JoVE Journal

Environment

9.2K Views.  University of Barcelona. We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

Video: Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Potentially toxic chemicals are routinely applied to land to meet growing demands on waste management and food production, but the fate of these chemicals is often not well understood. Here we demonstrate an integrated field lysimetry and porewater sampling method for evaluating the mobility of chemicals applied to soils and established vegetation. Lysimeters, open columns made of metal or plastic, are driven into bareground or vegetated soils. Porewater samplers, which are commercially available and use vacuum to collect percolating soil water, are installed at predetermined depths within the lysimeters. At prearranged times following chemical application to experimental plots, porewater is collected, and lysimeters, containing soil and vegetation, are exhumed. By analyzing chemical concentrations in the lysimeter soil, vegetation, and porewater, downward leaching rates, soil retention capacities, and plant uptake for the chemical of interest may be quantified. Because field lysimetry and porewater sampling are conducted under natural environmental conditions and with minimal soil disturbance, derived results project real-case scenarios and provide valuable information for chemical management. As chemicals are increasingly applied to land worldwide, the described techniques may be utilized to determine whether applied chemicals pose adverse effects to human health or the environment.

Field lysimetry and porewater sampling allow researchers to evaluate the fate of chemicals applied to soils and established vegetation. The goal of this protocol is to demonstrate how to install required instrumentation and collect samples for chemical analysis during integrated field lysimetry and porewater sampling experiments.

Potentially toxic chemicals are routinely applied to land to meet growing demands on waste management and food production, but the fate of these chemicals ...

Measuring Rates of Herbicide Metabolism in Dicot Weeds with an Excised Leaf Assay

In order to isolate and accurately determine rates of herbicide metabolism in an obligate-outcrossing dicot weed, waterhemp (Amaranthus tuberculatus), we developed an excised leaf assay combined with a vegetative cloning strategy to normalize herbicide uptake and remove translocation as contributing factors in herbicide-resistant (R) and &#8211;sensitive (S) waterhemp populations. Biokinetic analyses of organic pesticides in plants typically include the determination of uptake, translocation (delivery to the target site), metabolic fate, and interactions with the target site. Herbicide metabolism is an important parameter to measure in herbicide-resistant weeds and herbicide-tolerant crops, and is typically accomplished with whole-plant tests using radiolabeled herbicides. However, one difficulty with interpreting biokinetic parameters derived from whole-plant methods is that translocation is often affected by rates of herbicide metabolism, since polar metabolites are usually not mobile within the plant following herbicide detoxification reactions. Advantages of the protocol described in this manuscript include reproducible, accurate, and rapid determination of herbicide degradation rates in R and S populations, a substantial decrease in the amount of radiolabeled herbicide consumed, a large reduction in radiolabeled plant materials requiring further handling and disposal, and the ability to perform radiolabeled herbicide experiments in the lab or growth chamber instead of a greenhouse. As herbicide resistance continues to develop and spread in dicot weed populations worldwide, the excised leaf assay method developed and described herein will provide an invaluable technique for investigating non-target site-based resistance due to enhanced rates of herbicide metabolism and detoxification.

This manuscript describes how herbicide metabolism rates can be effectively quantified with excised leaves from a dicot weed, thereby reducing variability and removing any possible confounding effects of herbicide uptake or translocation typically observed in whole-plant assays.

In order to isolate and accurately determine rates of herbicide metabolism in an obligate-outcrossing dicot weed, waterhemp (Amaranthus tuberculatus), we ...

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Agricultural ammonia (NH3) emissions (90% of total EU emissions) are responsible for about 45% airborne eutrophication, 31% soil acidification and 12% fine dust formation within the EU15. But NH3 emissions also mean a considerable loss of nutrients. Many studies on NH3 emission from organic and mineral fertilizer application have been performed in recent decades. Nevertheless, research related to NH3 emissions after application fertilizers is still limited in particular with respect to relationships to emissions, fertilizer type, site conditions and crop growth. Due to the variable response of crops to treatments, effects can only be validated in experimental designs including field replication for statistical testing. The dominating ammonia loss methods yielding quantitative emissions require large field areas, expensive equipment or current supply, which restricts their application in replicated field trials. This protocol describes a new methodology for the measurement of NH3 emissions on many plots linking a simple semi-quantitative measuring method used in all plots, with a quantitative method by simultaneous measurements using both methods on selected plots. As a semi-quantitative measurement method passive samplers are used. The second method is a dynamic chamber method (Dynamic Tube Method) to obtain a transfer quotient, which converts the semi-quantitative losses of the passive sampler to quantitative losses (kg nitrogen ha-1). The principle underlying this approach is that passive samplers placed in a homogeneous experimental field have the same NH3 absorption behavior under identical environmental conditions. Therefore, a transfer co-efficient obtained from single passive samplers can be used to scale the values of all passive samplers used in the same field trial. The method proved valid under a wide range of experimental conditions and is recommended to be used under conditions with bare soil or small canopies (&#60;0.3 m). Results obtained from experiments with taller plants should be treated more carefully.

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Ammonia emissions are a major threat to the environment by eutrophication, soil acidification and fine particle formation and stem mainly from agricultural sources. This method allows ammonia loss measurements in replicated field trials enabling statistical analysis of emissions and of relationships between crop development and emissions.

Agricultural ammonia (NH3) emissions (90% of total EU emissions) are responsible for about 45% airborne eutrophication, 31% soil acidification and 12% ...

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 ...

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions

We describe techniques for approximating seed bank dynamics over time using Helianthus annuus as an example study species. Strips of permeable polyester fabric and glue can be folded and glued to construct a strip of compartments that house seeds and identifying information, while allowing contact with soil leachate, water, microorganisms, and ambient temperature. Strips may be constructed with a wide range of compartment numbers and sizes and allow the researcher to house a variety of genotypes within a single species, different species, or seeds that have experienced different treatments. As opposed to individual seed packets, strips are more easily retrieved as a unit. While replicate packets can be included within a strip, different strips can act as blocks or can be retrieved at different times for observation of seed behavior over time. We used a high temperature glue gun to delineate compartments and sealed the strips once the seed and tags identifying block and removal times were inserted. The seed strips were then buried in the field at the desired depth, with the location marked for later removal. Burrowing animal predators were effectively excluded by use of a covering of metal mesh hardware cloth on the soil surface. After the selected time interval for burial, strips were dug up and seeds were assessed for germination, dormancy and mortality. While clearly dead seeds can often be distinguished from ungerminated living ones by eye, dormant seeds were conclusively identified using a standard Tetrazolium chloride colorimetric test for seed viability.

Here we present a protocol for assessing seed survivorship, germination and dormancy under field conditions using buried, labeled seed strips and tetrazolium chloride (TZ) viability testing.

We describe techniques for approximating seed bank dynamics over time using Helianthus annuus as an example study species. Strips of permeable polyester ...

Protocols for Quantifying Transferable Pesticide Residues in Turfgrass Systems

Plant canopies in established turfgrass systems can intercept an appreciable amount of sprayed pesticides, which can be transferred through various routes onto humans. For this reason, transferable pesticide residue experiments are required for registration and re-registration by the United States Environmental Protection Agency (USEPA). Although such experiments are required, limited specificity is required pertaining to experimental approach. Experimental approaches used to assess pesticide transfer to humans including hand wiping with cotton gloves, modified California roller (moving a roller of known mass over cotton cloth) and soccer ball roll (ball wrapped with sorbent strip) over three treated turfgrass species (creeping bentgrass, hybrid bermudagrass and tall fescue maintained at 0.4, 5 and 9 cm, respectively) are presented. The modified California roller is the most extensively utilized approach to date, and is best suited for use at low mowing heights due to its reproducibility and large sampling area. The soccer ball roll is a less aggressive transfer approach; however, it mimics a very common occurrence in the most popular international sport, and has many implications for nondietary pesticide exposure from hand-to-mouth contact. Additionally, this approach may be adjusted for other athletic activities with limited modification. Hand wiping is the best approach to transfer pesticides at higher mowing heights, as roller-based approaches can lay blades over; however, it is more subjective due to more variable sampling pressure. Utility of these methods across turfgrass species is presented, and additional considerations to conduct transferable pesticide residue research in turfgrass systems are discussed.

Transferable pesticide residue experiments in turfgrass systems are integral components of human risk exposure assessments. Experimental approaches to measure transferable residues should be adjusted to the human interaction of interest and turfgrass system dynamics. Three transferable pesticide residue protocols are presented and the suitability across three turfgrass systems is discussed.

Plant canopies in established turfgrass systems can intercept an appreciable amount of sprayed pesticides, which can be transferred through various routes ...

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 ...

Procedures of Laboratory Fumigation for Pest Control with Nitric Oxide Gas

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on fresh products and procedures for residue analysis and product quality evaluation. An airtight fumigation chamber containing fresh fruit and vegetables is first flushed with nitrogen (N2) to establish an ultralow oxygen (ULO) environment followed by injection of NO. The fumigation chamber is then kept at a low temperature of 2 - 5 &#176;C for a specified time period necessary to kill a target pest to complete a fumigation treatment. At the end of a fumigation treatment, the fumigation chamber is flushed with N2 to dilute NO prior to opening the chamber to ambient air to prevent the reaction between NO and O2, which produces NO2 and may damage delicate fresh products. At different times after NO fumigation, NO2 in headspace and nitrate and nitrite in liquid samples were measured as residues. Product quality was evaluated after 2 weeks of post-treatment cold storage to determine effects of NO fumigation on product quality. Keeping&#160;O2 from reacting with NO is critical to NO fumigation and is an important part of the protocols. Measuring NO levels is challenging and a practical solution is provided. Possible protocol modifications are also suggested for measuring NO levels in the fumigation chambers as well as residues. NO fumigation has the potential to be a practical alternative to methyl bromide fumigation for postharvest pest control on fresh and stored products. This publication is intended to assist other researchers in conducting NO fumigation research for postharvest pest control and accelerating the development of NO fumigation for practical applications.

This paper describes nitric oxide (NO) fumigation protocols for postharvest pest control. Fumigation chambers are flushed with nitrogen (N2) to establish ultralow oxygen conditions before NO is injected. At the end, chambers are flushed with N2 to dilute NO before exposing products to ambient air to prevent exposure to NO2.

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on ...

A Flexible Low Cost Hydroponic System for Assessing Plant Responses to Small Molecules in Sterile Conditions

A wide range of studies in plant biology are performed using hydroponic cultures. In this work, an in vitro hydroponic growth system designed for assessing plant responses to chemicals and other substances of interest is presented. This system is highly efficient in obtaining homogeneous and healthy seedlings of the C3 and C4 model species Arabidopsis thaliana and Setaria viridis, respectively. The sterile cultivation avoids algae and microorganism contamination, which are known limiting factors for plant normal growth and development in hydroponics. In addition, this system is scalable, enabling the harvest of plant material on a large scale with minor mechanical damage, as well as the harvest of individual parts of a plant if desired. A detailed protocol demonstrating that this system has an easy and low-cost assembly, as it uses pipette racks as the main platform for growing plants, is provided. The feasibility of this system was validated using Arabidopsis seedlings to assess the effect of the drug AZD-8055, a chemical inhibitor of the target of rapamycin (TOR) kinase. TOR inhibition was efficiently detected as early as 30 min after an AZD-8055 treatment in roots and shoots. Furthermore, AZD-8055-treated plants displayed the expected starch-excess phenotype. We proposed this hydroponic system as an ideal method for plant researchers aiming to monitor the action of plant inducers or inhibitors, as well as to assess metabolic fluxes using isotope-labeling compounds which, in general, requires the use of expensive reagents.

A simple, versatile, and low-cost in vitro hydroponic system was successfully optimized, enabling large-scale experiments under sterile conditions. This system facilitates the application of chemicals in a solution and their efficient absorption by roots for molecular, biochemical, and physiological studies.

A wide range of studies in plant biology are performed using hydroponic cultures. In this work, an in vitro hydroponic growth system designed for ...

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Here, we present a microscale protocol for processing grain samples and for incorporating this microscale approach into a high-throughput analytical pipeline. This is a higher throughput adaptation of currently available protocols.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole ...