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Crop Science 42:842-847 (2002)
© 2002 Crop Science Society of America

TURFGRASS SCIENCE

Cultivar Composition and Spatial Patterns in Kentucky Bluegrass Blends

Darin W. Lickfeldt*,a, Thomas B. Voigtb and Andrew M. Hamblinb

a Dow AgroSciences L.L.C., 9330 Zionville Road, Indianapolis, IN 46268
b Dep. Natural Resources and Environmental Sciences, Univ. of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801

* Corresponding author (DWLickfeldt{at}dowagro.com)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Turfgrass managers make informed decisions when choosing cultivars, but they do not know the cultivar composition or spatial patterns of a blended Kentucky bluegrass (Poa pratensis L.) stand after establishment. This study was conducted to determine the cultivar composition of blends and determine whether cultivars form distinct clumps or a random distribution. Percentages of ‘Blacksburg’, ‘Midnight’, and ‘Unique’ in blends were determined using random amplified polymorphic DNA markers. The two different locations were a fairway (Aspen Ridge Golf Course, Bourbonnais, IL) and a rough (Alpine Hills Golf Course, Rockford, IL). Indices of dispersion were used to determine spatial patterns. Even though the two stands had different management strategies and were 157 km apart, the two blends had similar cultivar compositions in October 2000. The fairway was comprised of 14% Blacksburg, 46% Midnight, and 40% (w/w) Unique 37 mo after seeding, while the rough composition was 14% Blacksburg, 47% Midnight, and 39% (w/w) Unique 15 mo after seeding. Chi-square analyses indicated with more than 99% confidence that the cultivar compositions were different than the intended planting. Comparing expected (first sampling) with observed (second sampling) values at both sites, chi-square analyses determined that none of the mean percentages for the three cultivars had changed from the first sampling date to the second at either location. Since the two stands were not the same age, they may have achieved an equilibrated composition dictated by the competitve advantages of the component cultivars. Indices of dispersion determined the cultivars were distributed randomly rather than clumped. This study suggests management and location do not dictate the composition of a blend. Rather, the competitive advantages of the cultivars in the blend will determine composition.

Abbreviations: µ, true population mean • bp, base pairs • GI, Green's Index • ID, index of dispersion • PCR, polymerase chain reaction • RAPD, random amplified polymorphic DNA


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
THE THEORY OF BLENDING indicates mixing cultivars is useful for capitalizing on characteristics from diverse genetic backgrounds (Golembiewski, 1999). The success of blending cultivars, however, has not been proven (Vargas, 1994). Furthermore, little is known about the ecological competition of turfgrass cultivars that vary in aggressiveness and how cultivar composition of blends change with time (Golembiewski, 1999). Golembiewski warned that the use of unsuitable cultivars and competition between cultivars may sometimes reduce any benefits gained by blending. There is, however, very little data to determine the proper cultivars for blending or competitive factors that are most important to achieve uniform stands. DNA fingerprinting should be useful for quantifying Kentucky bluegrass cultivars in blended stands and for determining their spatial patterns.

Vargas (1994)(p. 177) explained that common type Kentucky bluegrasses are susceptible to disease and they perform well with lower levels of maintenance, but including improved cultivars with a susceptible cultivar may eventually lead to the elimination of the susceptible cultivar. Vargas concluded if the elimination of a cultivar is to occur, it should probably not be included in the blend. Using only improved cultivars should produce a higher quality turf over both the short- and long-term. Still, determining the cultivar composition of a blended stand should be more efficient and precise using DNA fingerprinting by molecular markers. Other researchers have explored this topic with varying success (Wehner et al., 1976; Bell et al., 1995a, 1995b). The planting of blends in the past has included the assumption that the intended composition (that created by mixing several cultivars together by weight) was achieved in the stand.

Separating DNA fragments by molecular weight and visualizing by gel electrophoresis for the purpose of identifying an individual is referred to as DNA fingerprinting (Weising et al., 1995). When two individuals do not have the same corresponding band, this is referred to as a polymorphism; polymorphisms allow for distinguishing between genetically dissimilar individuals. Random amplified polymorphic DNA markers (Welsh and McClelland, 1990; Williams et al., 1990) have been used to distinguish between individual plants of Agrostis palustris Huds. [= A. stolonifera var. palustris (Huds.) Farw.] (Golembiewski et al., 1997), Lolium perenne L. (Sweeney and Danneberger, 1994; Forbes, 2000) and Poa annua L. (Sweeney and Danneberger, 1995). The fingerprinting of Kentucky bluegrass cultivars has received little attention (Wehner et al., 1976; Freeman and Yoder, 1991; Barcaccia et al., 1997).

Ecologists often utilize a belt transect when determining the composition of a plant community (Ludwig and Reynolds, 1988), but there are several factors that determine the best sampling method and the number of samples required (sample size). These transects are sampled at regular intervals called quadrats. Contiguous or point quadrats have been used to estimate populations in pastures, grasslands (Wiegert, 1962; de Pablo et al., 1982; Edwards et al., 1996), and arctic moss-turf communities (Usher, 1983).

The method for estimating the sample size needed for transect data was described by Krebs (1989)(Chapter 5). Once initial data has been collected, the equation [n = can be used to estimate the required sample size to achieve a desired level of precision. For this equation, n is the sample size needed for estimating the proportion p, while p is a ratio of the individuals of a given cultivar over all individuals. The t{alpha} value is the Student's t-distribution, and q, which equals 1 - p, is the proportion of individuals that are not the given cultivar over all individuals. Finally, d equals the desired margin of error (i.e., ±0.05 for ±5 percentage points).

Determining spatial patterns in a vegetative community would provide information regarding how turfgrasses grow and interact. Furthermore, if a stand were to become aggregated clumps of individual cultivars, the location of these clumps may reveal a lot about a cultivar's preferred or adapted environment. Uniform, random, and clumped plant communities can be estimated with mean and variance data from a transect (Fig. 1) .



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  		Fig. 1. Spatial pattern analysis is used to determine whether individuals are disributed uniformly, randomly, or in clumps. Values for each of the 10 quadrats on the transect are compared with variance values to derive indices of dispersion.

 
Other methods of studying spatial patterns have been developed. Ludwig and Reynolds (1988) suggested using an index of dispersion (ID) when N < 30 (N is the number of quadrats). The ID is the variance divided by the sample mean, and a large number of variants of this ratio have been used to measure the degree of clumping in ecosystems. An ID value equal to 1.0 indicates a random (Poisson) distribution. Chi-square analyses can be performed to determine if a given ID value equals 1.0, in which case the population can be declared randomly distributed. Maximum clumping (negative binomial distribution) would be evident when ID equals n (where n equals the total number of individuals for a given cultivar that were identified in all quadrats), and an ID equal to zero would indicate maximum uniformity.

With ID comparisons, the degree of clumping is largely dependent upon the n value. Ludwig and Reynolds (1998) suggest using Green's Index (GI) (Green, 1966) to eliminate dependence on the value of n. The GI can be calculated by GI = [(s2/mean) - 1]/(n - 1). Values for GI can range from -1/(n - 1) with maximum uniformity to 1.0 with maximum clumping. A GI value equal to 0.0 would indicate a random distribution.

This research addressed the issues of blend composition and spatial distribution. The objectives were: (i) to determine the cultivar composition on two distinctly different Kentucky bluegrass stands; and (ii) to determine whether cultivars aggregate into distinct clumps or maintain a random distribution as a stand matures.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
The fairways at Aspen Ridge Golf Course (Bourbonnais, IL) were seeded on 5 Sept. 1997 to a blend of 29% Blacksburg, 39% Midnight, and 29% (w/w) Unique (Summit Seed Lot: 7-SC-NFFB-0903; 3% (w/w) inert matter and other crop; 87% germination). The rough at Alpine Hills Golf Course (Rockford, IL) was seeded in Aug. 1999 to a blend of 25% Blacksburg, 50% Midnight, and 25% (w/w) Unique (Summit Seed Lot: 9-SC-AH3D-0914; <1% (w/w) inert matter and other crop; 99% germination). All seed was treated with Apron [N-(2,6-dimethylphenyl)-N-(methoxyacetyl) alanine methyl ester] fungicide to promote disease-free establishment. Both courses were seeded at 100 kg seed ha-1 and both blends were formulated at Summit Seeds, Inc. (Manteno, IL) by proportioning seed by weight.

The fairways at Aspen Ridge received 200 kg N ha-1 yr-1 in four to six applications, and no fungicides or insecticides were ever used even though a rust (Puccinia spp.) outbreak occurred in July 1998 and leaf spot (Helminthosporium spp.) in May 1999. Weed control consisted of Barricade [2,4-dinitro-N3,N3-dipropyl-6-(trifluoromethyl)-1,3-benzenediamine] in late March and Trimec Classic [2,4-dichlorophenoxyacetic acid, 2-(2-methyl-4-chlorophenoxy) propionic acid, and 3,6-dichloro-2-methoxybenzoic acid] in late June. The fairways were mowed three times per week at 2.2 cm in 1998 and 1999 and 1.6 cm in 2000. They were also core aerified (1.9-cm hollow tine) on 10-cm spacing in September 1998 and 1999. The soil at Aspen Ridge is an Elliott silt loam (fine, illitic, mesic, Aquic Argiudolls). Soil samples were collected (October 1999 at Aspen Ridge and May 2000 at Alpine Hills) to determine the percentage of organic matter, pH, cation exchange capacity, and concentrations of P, K, Ca, and Mg. Four soil samples were collected from each quadrat then mixed for homogenization and analyzed by A&L Great Lakes Laboratories, Inc. (Fort Wayne, IN).

The rough at Alpine Hills received 50 kg N ha-1 yr-1 split into 14 applications applied by injection into the irrigation system. No pesticides were applied. The rough was mowed at a 5.1-cm height twice a week and was core aerified (1.9-cm hollow tine) on 7.6-cm spacing in early April and October 2000. The soil at Alpine Hills is a Warsaw silt loam (fine-loamy over sandy, mixed, superactive, mesic Typic Argiudolls).

Samples were collected from the no. 6 fairway at Aspen Ridge on 21 Oct. 1999 and 17 Oct. 2000 (25 and 37 mo after seeding), and from the rough adjacent to the tees on hole no. 3 at Alpine Hills on 5 May and 31 Oct. 1999 (10 and 15 mo after seeding). The transect at Aspen Ridge was 274.3 m long running east to west along the center of the fairway with 15 samples collected from each of 10 quadrats every 27.4 m along the transect. A GPS system (Model XL-1000, Magellan Corp., Santa Clara, CA) was used to identify the location of each quadrat (within ±1 m) and ensure sampling from the location on later sampling dates. The Aspen Ridge transect ran from 41°11'07'' N and 87°50'26'' W (Quadrat 1) to 41°11'11'' N and 87°50'61'' W (Quadrat 10). The transect at Alpine Hills was 61 m long running east to west with 15 samples collected from each of 10 quadrats every 6.1 m along the transect. The Alpine Hills transect ran from 42°15'81'' N and 89°02'00'' W (Quadrat 1) to 42°15'81'' N and 89°02'10'' W (Quadrat 10).

Plant samples were collected with a 1.9-cm diameter soil probe to a 3-cm depth. Initial sampling revealed a 1.9-cm probe removed one intact Kentucky bluegrass plant near the center of the plug. Plants at the plug perimeter were trimmed below the crown to ensure only one plant per sample. The samples were collected using a template, which coordinated sampling in three columns x five rows spaced 5.1 cm apart. The 15 plants were transplanted individually into 133-cm3 cells of greenhouse flats, grown to at least 7.6 cm in a greenhouse with mist irrigation applied every 20 min for 20 s, at 20 ± 2°C without supplemental lighting.

Total genomic DNA was extracted and purified using DNeasy Plant Mini Kits (Qiagen Inc., Valencia, CA) for samples collected on the first sampling date. On the second sampling date, a more efficient and less costly method was used, which utilized an isolation buffer called Plant DNAzol Reagent (GIBCO BRL, Grand Island, NY; formerly Life Technologies, Inc.). Lin and Kuo (1998) described the original method and its adaptation to Kentucky bluegrass was described by Lickfeldt (2001) along with the DNA quantification, polymerase chain reaction (PCR), and visualization procedures. Primers OPA-10 (5'-GTGATCGCAG-3') and OPB-10 (5'-CTGCTGGGAC-3') (Operon Technology, Alameda, CA) were used to distinguish between the three cultivars. To ensure accurate identification of individuals, two positive polymorphic bands (one per primer) were required to declare the identity of plants.

Random amplified polymorphic DNA homogeneity within a cultivar was verified by collecting 10 individual plants from field plots of the three cultivars then comparing the individuals with a bulk sample of leaf tissue collected from the same plots. The DNeasy extraction procedure was conducted on all 33 samples, and subsequent visualization after amplification and separation verified that each cultivar had reproducible banding patterns for all individual plants and the bulk samples. This was expected, since Kentucky bluegrass is an apomictic species (Tinney, 1940; Huff, 1992). Furthermore, aberrant banding patterns only occurred when the quantity of DNA going into the PCR was insufficient or buffer was lost during the PCR by evaporation. In such cases, the banding patterns were inconsistent, and repeating the amplification of the sample in question corrected the problem.

The cultivar composition (percentage of each of the three cultivars) was calculated for each quadrat before means and standard deviation values were calculated for each transect. Chi-square values were calculated using observed (second sampling) and expected (first sampling) means for each transect to detect a change in cultivar composition over time.

The ID and GI were calculated for each cultivar at each location on each sampling date. Percentages were converted to number of individuals of each cultivar identified in the sample of 150 plants for the calculation of ID. Chi-square analyses were performed to test whether ID = 1.0 (random distribution). When the calculated value for {chi}2 fell between the {chi}2 table values with N - 1 degrees of freedom at 0.975 and 0.025 probability levels (P < 0.05), the distribution was accepted as a Poisson (random) distribution. Calculated {chi}2 values less than the 0.975 probability level would suggest a more uniform pattern (with s2 < mean). Calculated {chi}2 values greater than the 0.025 probability level would suggest a clumped pattern (s2 > mean). In this study, the {chi}2 table values with nine degrees of freedom were 2.70 and 19.02, for the 0.975 and 0.025 probability levels, respectively.


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Primers OPA-10 and OPB-10 revealed distinct polymorphisms for Blacksburg, Midnight, and Unique Kentucky bluegrass, which allowed for identification of the individual cultivars (Fig. 2) . Positive DNA bands were present using OPA-10 for Blacksburg [1103 base pairs (bp)], Midnight (893 bp), and Unique (575 bp). The OPB-10 primer also revealed polymorphisms for Blacksburg (1050 bp), Midnight (850 bp), and Unique (647 bp). Band intensity varied as the quantity of extracted DNA varied. When inconsistencies occurred, the PCR was repeated on a larger (2 µL rather than 1 µL) aliquot of genomic DNA. If DNA quantities were still inadequate, a new extraction of genomic DNA was conducted on the original plant to correct the problem. Sexually produced plants with banding patterns distinctly different then the known standards were not detected. Apparently, sexually produced individuals are genetically similar to their asexually produced siblings and were indistinguishable under these conditions and methods.



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  		Fig. 2. ‘Blacksburg’, ‘Midnight’, and ‘Unique’ Kentucky bluegrass banding patterns with Primers OPA-10 and OPB-10. Polymorphisms are marked with white arrows.

 
After 15 mo at Alpine Hills and 37 mo at Aspen Ridge, blends of Blacksburg, Midnight, and Unique Kentucky bluegrass had achieved nearly identical cultivar compositions (Table 1). Because samples of the original seedlots were not available, changes in composition from the originally seeded blend could not be verified. The blend present at the time of initial sampling may simply have been what was seeded. If it is assumed that the actual blending percentages at time of seeding were the same as the intended blends (w/w, Blacksburg:Midnight:Unique) (30%:40%:30% at Aspen Ridge and 25%:50%:25% at Alpine Hills), chi-square analyses indicated with >99% confidence that the cultivar compositions were different than the intended planting. Comparing expected (first sampling) with observed (second sampling) values at both sites, chi-square analyses determined that none of the mean percentages for the three cultivars had changed from the first sampling date to the second at either location.


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  		Table 1. Cultivar composition of the Kentucky bluegrass blends on an Aspen Ridge Golf Course fairway in Bourbonnais, IL, and a golf course rough at Alpine Hills Golf Course in Rockford, IL.

 
Soil data for Aspen Ridge and Alpine Hills showed a lower pH at Aspen Ridge (6.6 vs. 6.9) and greater percentage of organic matter (5.8 vs. 1.4%, w/w). Phosphorus, K, and Mg contents were also greater at Aspen Ridge (52, 222, and 713 mg kg-1, respectively) than at Alpine hills (34, 120, and 498 mg kg-1, respectively). Conversely, the calcium concentration at Alpine Hills was greater than at Aspen Ridge (2660 vs. 2315 mg kg-1). Soil sampling did not reveal differences along either transect that may have contributed to varying levels of stress or plant nutrition leading to changes in cultivar composition. Surprisingly, even with large differences in soil fertility, the blends had similar cultivar compositions at the time of the October 2000 sampling.

Because Midnight usually had the highest variation, its mean values were used in the sample size equation of Krebs (1989) to provide a conservative estimate of the number of samples required for accurate estimations of cultivar composition. The collection of 150 samples provided an estimate of the true population mean (µ) within ±8 percentage points ({alpha} = 0.05). In other words, where Midnight was declared to be 45% (v/v) of the stand, the µ is actually between 37 and 53%, with {alpha} = 0.05. Estimating populations within ±5 or ±1 percentage points of the true mean would have required 379 or 9477 samples, respectively. Clearly, such extensive sampling to gain better precision would be costly using DNA fingerprinting to identify individuals. Still, many more samples would be needed to declare the cultivar composition of the two stands significantly different from each other.

As a p value in Krebs' equation approaches 0.5 (i.e., 50% of the stand, v/v), the estimate of required sample size is more conservative. Therefore, the calculation for Midnight, where P > 0.45, is the most conservative estimate in this study. When the sample size equation was used for determining the proportion of Blacksburg in the stands (p = 0.14), the required number of samples was much less. Collection of 150 samples determined the percentage of Blacksburg within ±6 percentage points of the true mean with {alpha} = 0.05. Determining the percentage of Blacksburg within ±5 or ±1 percentage points would require 188 and 4695 samples, respectively.

Calculating mean values for the three cultivars at two sites and two sampling dates produced 12 variance estimates (Table 1) that can be used as an estimation of spatial patterns (Fig. 1). These variance values were usually less than the mean (count) values, indicating a random or more uniform distribution (Ludwig and Reynolds, 1988). Table 1 also lists the ID with {chi}2 tests and the GI values for all cultivars. Since calculated {chi}2 values were in the range of 2.7 to 19.02, we can declare with 95% probability that the cultivars are distributed randomly (ID = 1) rather than in clumps. More importantly, GI values, which are insensitive to the number of individuals collected, were all 0.0 again, indicating Kentucky bluegrass cultivars are distributed randomly in golf course fairways and rough during the first 3 yr after establishment.


   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
Even though the golf courses had different management strategies and were 157 km apart, the two blends of Kentucky bluegrass achieved similar cultivar compositions. In addition, this finding is more surprising given the intended cultivar compositions of the stands, stand age, and different levels of soil fertility. This result suggests that management, location, or even stand maturity does not dictate the composition of a blend. Rather, the final composition may be derived from the competitive growth advantages of the cultivars included in the blend. Once an understanding of the most critical, competitive factors is obtained, turfgrass breeders and seed distributors may be able to make better informed decisions regarding the cultivars they include in their blends.

Factors such as disease resistance, stress tolerance, and aggressiveness differ significantly between cultivars (Bonos et al., 1999), and these factors may be most important in determining blend composition. Environment and management seemed to have no affect on cultivar composition. If turfgrass managers are equipped with the specific strengths and weaknesses of cultivars, and understand which cultivars are most prevalent in their blend, they may be able to make better informed decisions regarding management strategies. For instance, if a blend is comprised of a 30% (w/w) drought tolerant cultivar and 70% less drought tolerant cultivars, they may irrigate based on the needs of the less drought tolerant cultivars. Likewise, if a cultivar is included in a blend because it is resistant to a popular disease such as summer patch (Magnaporthe poae Landschoot & Jackson), but this cultivar only achieves 20% (v/v) of the blend following establishment, the turfgrass manager should not consider the stand resistant to this disease. There may be a threshold level for the resistant cultivar's prevalence, below which the stand is not resistant to disease as hypothesized by Vargas and Turgeon (1978). Once an understanding of the particular needs of individual cultivars is achieved, more control over the final composition of the stand years after establishment is possible.

Christians et al. (1979) determined that Kentucky bluegrass cultivars with smaller seed sizes, and consequently more seeds per unit weight, establish a full stand more quickly than cultivars with larger seed sizes. Unique had the smallest seed size of the three cultivars evaluated in this study (Lickfeldt, 2001), which may have caused an increase in the percentage of Unique at seeding relative to the intended percentage. Furthermore, Christians et al. (1979) determined that not only do seed sizes differ between Kentucky bluegrass cultivars, but large differences in seed size between seed lots of a cultivar also exist. Maybe blends should be created using seed size data for the specific seed lots combined with germination tests rather than blending based on seed weight in order to achieve the desired cultivar composition (Lickfeldt, 2001).

Likewise, the seeding of a new site is usually conducted with the intention of spreading seed uniformly, but the degree of uniformity has never been tested. With pest invasion, stress, and traffic, a blended stand should form clumps of the most stress tolerant, disease resistant, or more aggressive growing cultivars, but this does not appear to be the case. In this study, the blends became more uniform with time as evidenced by the decrease in ID values from the first sampling date to the second sampling date (Table 1). More research is needed to quantify changes in composition following quantifiable, replicated treatments of biotic and abiotic stress.

The transects in this study ran parallel to each fairway, which seemed to be a logical choice, but distinct clumps of bluegrass that formed aggregates of single cultivars away from the transect may have gone undetected. Although the use of belt transects has become popular in ecological studies, their validity in turf ecology is suspect. Two or three replicated transects randomly selected from all possible transects might be a more valid option. Likewise, subdividing the turfgrass stand into a 2 dimensional grid and randomly selecting points from which to sample might produce a more representative result if the inference space is the entire stand. In future studies, random or unbiased transects should be evaluated.

Future research should quantify and compare the indices of dispersion for many cultivars included in the most popular blends. The distribution of a cultivar with consistently large ID values may still be randomly distributed, but the size of clumps may be used to evaluate stress tolerance, disease resistance, or aggressiveness. The Kentucky bluegrass groupings being formed and investigated at Rutgers University (Bonos et al., 1999) will potentially generate much of the information needed about such traits, and future studies may allow for the creation of blends based on a cultivar's inclusion in a particular grouping. The results presented here suggest cultivars from a single group with similar growth habits and performance may result in a more uniform distribution and an actual composition more similar to the intended composition.


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
Random amplified polymorphic DNA markers were successfully used to identify the Kentucky bluegrass cultivars in two blended stands. Independent of management, location, time of seeding, and the intended cultivar composition, blends of Blacksburg, Midnight, and Unique Kentucky bluegrass achieved nearly identical compositions at two sites across 3 yr. Therefore, aggressiveness or competitive advantage and not environment or management may dictate the prevalence of a cultivar in a blended stand. Collecting 150 samples from transects allowed for the estimation of a cultivar's percentage within ±8 percentage points with 95% confidence. Indices of dispersion determined the three cultivars were distributed randomly rather than clumped.


   ACKNOWLEDGMENTS
 
We thank B.E. Branham, T.W. Fermanian, and T. Rocheford for professional guidance. We thank Ed Lee, Todd Zimmerman, and Rick Kroeger for their cooperation. We also thank the Illinois Turfgrass Foundation and the Illinois Agricultural Experiment Station for financial assistance.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
This paper is a portion of a thesis submitted by D.W. Lickfeldt in fulfillment of a Ph.D. at the Univ. of Illinois-Urbana.

Received for publication April 6, 2001.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 




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