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ALLELOPATHY SYMPOSIUM

Barnyardgrass Growth Inhibition with Rice Using High-Performance Liquid Chromatography to Identify Rice Accession Activity

^a Dep. of Crop, Soil, and Environ. Sci., Univ. of Arkansas, Fayetteville, AR 72704
^b Agric. Statistics Lab., Univ. of Arkansas, Fayetteville, AR 72701

Corresponding author (jmattice{at}uark.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Some accessions of rice (Oryza sativa L.) have been shown to inhibit the growth of barnyardgrass (Echinochloa crus-galli (L.) Beauv.). Our objective was to determine if high-performance liquid chromatography (HPLC) chromatograms from leaf extracts of different accessions of rice correlated with weed control activity. Chromatograms of extracts consisting of 10 mg of fresh leaf tissue per milliliter of methanol (CH₃OH) were obtained from 40 accessions of rice. Cluster analysis was performed using 20 peaks from the chromatograms. Three clusters were found, with one cluster being distinctly separated from the other two. Although weed control data are not available for all the accessions, the isolated cluster contains all of the accessions that have been shown to inhibit growth of barnyardgrass and none that do not. This indicates that the assay could be used year-round to screen accessions of rice for weed control potential to determine which accessions should be further tested in the field. This could be done in a relatively short time using a small amount of space in the greenhouse. Because the assay requires only 10 mg of tissue per milliliter of methanol, it may potentially be used to test individual plants within an accession for weed control potential in a nondestructive manner.

Abbreviations: acet, acetonitrile • HPLC, high-performance liquid chromatography

	INTRODUCTION

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DILDAY ET AL. (1989, 1991) first observed the interference of rice on the growth of ducksalad [Heteranthera limosa (Sw.) Willd.] in field tests evaluating accessions of rice for tolerance to alachlor [2-chloro-2',6'-diethyl-N-(methoxymethyl)acetanilide]. Since 1987, laboratory and field tests have been performed to identify accessions that inhibit the growth of several weed species, including barnyardgrass. Growth inhibition of barnyardgrass has also been reported by Navarez and Olofsdotter (1996), Hasan et al. (1998), and Kim and Shin (1998). We have also observed it routinely in greenhouse bioassays.

Although the interference may be due to allelopathy, there is also the possibility that it may be due to competition or a mixture of competition and allelopathy. Either way, if the trait can be incorporated into agronomically useful varieties, fewer hours may be required for manual weeding, and reduced rates or fewer applications of herbicides may be required for weed control.

A useful tool for breeders would be an assay to screen accessions and individual plants within accessions for weed control activity. The assay would ideally be accomplished in a relatively short period of time, require a minimum amount of space, be relatively inexpensive, and could be done year-round in a greenhouse. We report here an HPLC procedure that is showing promise toward meeting most of these criteria.

	Materials and methods

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Rice Extraction
Approximately 15 seeds were placed in 100 g of soil sieved through a 2-mm mesh in the rice growing region of Stuttgart, AR. The samples were grown in 474-mL (16 oz) plastic cups and thinned to 10 plants cup^-1, with three replications per accession. After 10 d, the leaves from each replication were removed, cut into approximately 1-cm lengths, and placed in Erlenmeyer flasks. A volume of HPLC grade methanol was added such that the ratio of fresh plant tissue/methanol was 10 mg mL^-1. The samples were placed in a refrigerator overnight. Then equal parts of the methanol extract and deionized water were combined and analyzed by HPLC.

High-Performance Liquid Chromatography Conditions
Analyses were performed using a 25-cm by 4.6-mm Phenomenex Prodigy C18 column. The HPLC system consisted of a Hitachi L-7450A diode array detector, L-7200 autosampler, L-7100 pump, and the Hitachi HSM software for data processing. Solvent was degassed with an ERC model 3415 degasser, and the column was held at 35°C with an Eppendorf TC-45 heater. The gradient used 1% acetic acid (vol./vol.) and HPLC grade acetonitrile (acet). The program was 10% acet (vol./vol.) at 1.5 mL min^-1 for 3 min, increased to 50% acet (vol./vol.) over 27 min at 1.5 mL min^-1, increased to 80% acet (vol./vol.) at 2 mL min^-1 over 0.1 min and held for 1.9 min, decreased to 10% acet (vol./vol.) over 0.1 min and held for 7.9 min, and decreased to 1.5 mL min^-1 over 0.1 min. The total run time was 40 min, and data were collected for the first 30 min. The first and last portions of the chromatogram contained only peaks that were essentially background. The injection volume was 30 µL and quantitation was at 320 nm.

Cluster Analysis
The peaks that were considered to be above background were used for data analysis. This resulted in 20 peaks being used. The chromatograms from some accessions contained all 20 peaks; for other accessions, some peaks were absent.

The set of peak heights from each sample was considered as a point in 20-dimensional space. The peak height data were subjected to K-means clustering (Hand, 1981, p. 174) for K = 2 to 7 clusters. K-means clustering is a nonhierarchical iterative clustering method in which the centroids of the K initial clusters are determined. If any point within a cluster is determined to be closer to the centroid of a different cluster, then that point is reassigned to the different cluster. The cluster centroids are then recalculated, and the procedure is repeated until there are no changes in the clusters. The K-means procedure minimizes the sum of squared distances of the observations from their assigned cluster centroids and is analogous to the minimization of the sum of squared errors in an analysis of variance by the least-squares estimators.

Using the clusters defined by the K-means procedure, the first two canonical variables were calculated and plotted to show the separation among the clusters as clearly as possible in two dimensions (Krzanowski and Marriott, 1994, p. 91). All analyses were carried out using SAS (Version 7, SAS Inst., Cary, NC).

Rice Accessions Used
The rice accessions that were used, and information regarding the pedigree, clustering, and weed control activity, when known, are listed in Table 1.

	Results and discussion

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View larger version (21K): [in this window] [in a new window]   Fig. 1 High-performance liquid chromatography (HPLC) chromatograms of methanol extracts of rice leaf tissue from PI 312777 and Rexmont from 11.8 to 15.8 min

View larger version (12K): [in this window] [in a new window]   Fig. 2 High-performance liquid chromatography (HPLC) chromatograms of methanol extracts of rice leaf tissue from PI 312777 and Rexmont from 15.0 to 15.8 min

One analytical approach is to compare each peak from the set showing activity with the same peak from the inactive set. If a peak were found to be significantly higher in the allelopathic set, it might be related to the effect. The problem with this approach is that there may be numerous peaks in the chromatograms that need to be compared. At a level of significance of , the risk of falsely finding a significant difference when there is none is approximately one minus (0.95)ⁿ, where n is the number of peaks being compared. If, as in our case, 20 peaks were being compared, we would falsely find significant differences 64% of the time even if there were no difference in the size of any of the pairs of peaks. To avoid this problem, our approach has been to use all 20 peaks in the chromatogram to determine a point in 20-dimensional space, and then use cluster analysis to see if the points are in different clusters.

K-means clustering for two clusters did not separate accessions showing weed control activity from those that did not. The results for clusters are shown in Fig. 3 where the isolated Cluster 3 contains those accessions that so far have shown activity. The other two clusters represent more of a division of a cloud of data points rather than two well-separated groups. The results for , 5, and 6 clusters showed further division of the latter into smaller, relatively nondistinct groups. The isolated Cluster 3 containing the accessions showing activity remained intact through clusters. For clusters, the cluster split, but the two newly formed clusters were not well-separated. Hence, three clusters appear to be sufficient to separate these 40 accessions into those showing activity and those that do not.

View larger version (15K): [in this window] [in a new window]   Fig. 3 Two-dimensional representation of 3 clusters where each point is determined by 20 chromatographic peaks. Cluster 3 contains the accessions showing weed control activity

The next step in our research is to determine which peaks, and ultimately which compounds, are primarily responsible for the clustering. This step must necessarily consider peak heights as well as missing peaks.

It is important to remember that correlation does not imply causality, and we do not imply that the compounds producing the larger peaks in the PI 312777 extract would be allelochemicals; they would, however, be candidates for identification and testing. Differences in the peak size, regardless of which chromatogram it is in, may be useful in differentiating accessions according to their ability to inhibit barnyardgrass growth. Whether or not the compounds are allelochemicals, and whether or not the observed effect is allelopathy or competition, the procedure shows promise for predicting which accessions are likely to show a weed control effect toward barnyardgrass and perhaps other weed species. The procedure allows assaying of 7- to 10-d-old samples, so screening can be done on a series of samples during late summer through early spring to identify promising accessions to take to the field for further testing. This meets the objectives of being accomplished in a relatively short period of time (10 d), using a minimum amount of space (1 m² for 30 accessions), and can be done year-round. High-performance liquid chromatography is not an inexpensive technique but is widely available.

Because the procedure requires only 10 mg of tissue per milliliter of methanol, it can be done in a nondestructive manner on a rice plant. It remains to be seen if the procedure could be used to identify which plants within a cross between two accessions would be most likely to have the highest weed control activity, and would thus be the most useful to breeders.

	ACKNOWLEDGMENTS

 
We acknowledge the contribution of the USDA, Arkansas Rice Research and Promotion Board, and IRRI for support of this project.

Received for publication November 29, 1999.

	REFERENCES

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 

• Dilday, R.H., P. Nastasi, J. Lin, and R.J. Smith Jr. 1991. Allelopathic activity in rice (Oryza sativa L.) against ducksalad [Heteranthera limosa (Sw) Willd.]. p. 193–201. In J.N. Hanson, et al. (ed.) Proc. Sustainable Agric. for the Great Plains, Beltsville, MD. USDA-ARS.
• Dilday, R.H., P. Nastasi, and R.J. Smith Jr. 1989. Allelopathic observation in rice (Oryza sativa L.) to ducksalad (Heteranthera limosa). Proc. Arkansas Acad. Sci. 43:21–22.
• Hand, D.J. 1981. Discrimination and classification. John Wiley & Sons, New York.
• Hasan, S.M., I.R. Aidy, A.O. Bastawisi, and A.E. Draz. 1998. Weed management using allelopathic rice varieties in Egypt. p. 27–38. In M. Olofsdotter (ed.) Allelopathy in rice. Proc. Workshop on Allelopathy in RiceManila, Philippines. 25–27 Nov. 1996. IRRI, Makati City, Philippines.
• Kim, K.U., and D.H. Shin. 1998. Rice allelopathy research in Korea. p. 39–44. In M. Olofsdotter (ed.) Allelopathy in rice.Proc. Workshop on Allelopathy in Rice, Manila, Philippines. 25–27 Nov. 1996. IRRI, Makati City, Philippines.
• Krzanowski, W.J., and F.H.C. Marriott. 1994. Multivariate analysis. Part 1. Distributions, ordination, and inference. Edward Arnold, London.
• Navarez, D.C., and M. Olofsdotter. 1996. Relay seeding technique for screening allelopathic rice (Oryza sativa). p. 1285–1290. In Proc. Int. Weed Control Congr., 2nd, Copenhagen, Denmark. 25–28 June 1996.

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This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (12) Citing Articles via Google Scholar Google Scholar Articles by Mattice, J. D. Articles by Skulman, B. W. Search for Related Content PubMed Articles by Mattice, J. D. Articles by Skulman, B. W. Agricola Articles by Mattice, J. D. Articles by Skulman, B. W. Related Collections Weed Management Allelopathy Rice