able and subtle differences due to treatments are difficult to identify as a result of the high variances (Murphy Accurate cover estimates in turfgrass research plots are often diffiet al., 1995). The line-intersect method is commonly cult to obtain because of the time involved with traditional sampling and evaluation techniques. Subjective ratings are commonly used to used for ecological studies in which the occurrence of estimate turfgrass cover, but the data can be quite variable and difficult plants or the distribution of plant types within a plot to reproduce. New technologies and software related to digital image are required (Laycock and Canaway, 1980; Kershaw, analysis (DIA) may provide an alternative method to measure turf-1973). The line-intersect method involves setting up a grass parameters more accurately and efficiently than current techgrid system over an entire plot or a quadrat within the niques. A series of studies was conducted to determine the applicabilplot and counting the number or types of plants found at ity of DIA for turfgrass cover estimates. In the first study, plots each intersection on the grid. The number of intersects containing a range (1-16) of bermudagrass [Cynodon dactylon (L.) where the desired plant material is found is then Pers.] plugs of specific diameter (15.0 cm) were established to repremultiplied by the area of each grid section and divided sent values of turfgrass cover from 0.75 to 12%, by 0.75% increments.by the total sample area for a percentage of each species. Digital images (1280 by 960 pixels) were taken with a digital camera and processed for percent green color to a software package. Estimates
Color is a major component of the aesthetic quality of turf and often evaluated in field studies. Digital image analysis may be an improved, objective method to quantify turf color. Studies were conducted to determine if digital image analysis with SigmaScan software (SPSS, Chicago, IL) was capable of: (i) accurately determining the hue, saturation, and brightness (HSB) levels of Munsell Plant Tissue color chips, (ii) quantifying visual color differences among zoysiagrass (Zoysia japonica Steud.) and creeping bentgrass {Agrostis palustris Huds. [= A. stolonifera var. palustris (Huds.) Farw.]} plots receiving various N treatments, and (iii) quantifying genetic color differences among bermudagrass (Cynodon spp.) cultivars. Digital images of turf plots were analyzed with SigmaScan software to determine average HSB levels for each image. A dark green color index (DGCI) was created from HSB values for direct comparison with visual ratings. Digital image analysis accurately quantified the HSB levels (r2 = 0.99, 0.96, and 0.97, respectively) of Munsell color chips corresponding to turf colors. Significant HSB differences were present among N treatments in creeping bentgrass, while only significant hue differences existed in zoysiagrass. Significant hue and saturation differences were present among bermudagrass cultivars. There was strong agreement between DGCI values and visual ratings. The relative variances of the HSB and DGCI were significantly less than the variance associated with multiple raters. This evaluation technique may facilitate objective comparisons of turf color across researchers, locations, and years when images are collected under equal lighting conditions (i.e., the use of an artificial light source at night or in an enclosed system).
MATERIALS AND METHODSA SigmaScan Pro macro named "Turf Analysis" was written Techniques using digital image analysis have been recently develby the authors (in visual basic for applications language) that oped to evaluate turfgrass stands for percent green cover and average is capable of batch analyzing turf images. The macro may be color. Manually analyzing digital images may become cumbersome freely downloaded from the University of Arkansas web site: and tedious if turf field trials contain many plots or if images are http://www.uark.edu/campus-resources/turf/turfmacro; vericollected at frequent intervals for analysis. The objective of the follow-
Freshwater resources for turfgrass irrigation are becoming limited. Hence, the development of drought tolerant turf cultivars will be of great value to turf managers. The objective of the following research was to evaluate the field drought tolerance of turf‐type tall fescue (Festuca arundinacea Schreb.) entries that were selected based either on high root/shoot ratio under greenhouse conditions or under severe drought stress conditions in the field. Twelve tall fescue entries (two selected by root/shoot ratio, two selected by screening field drought tolerance, the four parents, and four standard controls) were established under a rain‐out shelter, and their green turf coverage was evaluated during drought stress (irrigation withheld) and drought recovery (irrigation reapplied) events in 2003 and 2004. In both years, entries selected for high root/shoot ratio demonstrated significantly improved drought tolerance compared to their parents, whereas improved drought tolerance for field‐selected entries was less consistent. Turf green‐up following drought conditions was correlated to the drought tolerance of each entry, in that those cultivars that were the most drought tolerant were also the first to green up on rewatering. These results validate that selecting germplasm based on high root/shoot ratio in the greenhouse is a viable method for improving the field drought tolerance of turf‐type tall fescue.
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. N itrogen is an important and costly input for nonleguminous grain crops, and producers are applying N fertilizer in large amounts to ensure high yields over a range of environmental conditions (Kyveryga et al., 2007). However, excessive N fertilization may lead to runoff , leaching, and nitrate pollution. A delayed N application and the use of remote sensing tools might allow a producer to apply a more economically benefi cial N rate to their fi elds. Scharf and Lory (2002) gave several reasons to delay N applications, including avoiding extra work during the busy planting season and lowering the in-season N loss during wet years. Th ey also suggested that diagnostic tools for plant N might increase fertilizer use effi ciency, and these tools include the SPAD meter, refl ectance measurements, and color analysis. Th e SPAD meter is used to make an optimum fertilizer N-rate decision by measuring N stress relative to an optimum N-rate strip within a fi eld (Hawkins et al., 2007). Th e SPAD meter is well documented as an accurate measure of the N status of corn at diff erent developmental stages (Piekielek and Fox, 1992; Blackmer et al., 1994; Schepers, 1994). Piekielek et al. (1995) showed that SPAD values expressed relative to SPAD values from a high-N strip (relative or normalized SPAD) could be compared over a wide range of sampling times when using a common critical value. Normalized SPAD values lessen the eff ect of diff erences in hybrid, soil type, growth stage, or environmental conditions (Piekielek et al., 1995). Scharf et al. (2006) found that the relationship between SPAD values and economically optimum N rate was much stronger when using normalized values as opposed to absolute values. Th e SPAD meter is a useful tool, but it has some potential limitations. Th e SPAD meter costs about $1500 USD, has a small sampling area (6 mm 2), is subject to operator bias, and Zhang et al. (2008) showed that SPAD meters have diffi culty in estimating chlorophyll levels when they are near or above optimum. Th eir observations indicate that increases in chlorophyll are not necessarily associated with increases in yield. Spectral refl ectance of crop leaves can be a valuable tool to estimate plant N status (Li et al., 2005). Spectral refl ectance is the refl ectance of certain plant components that are controlled by their visual properties and radiant energy exchange in a canopy (Huete, 1988). Th e refl ectance of certain wavelengths is related to diff erent amounts of chlorophyll a and b, which can be used to estimate the N status of certain crops (Huete, 1988). Th is method shows great potential because it off ers a method to deliver variable-rate N applications from a vehicle-mounted sensor (Kitchen et al., 2010). Tools for measuring refl ectance, howe...
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