Abstract

Media summary

Key Words

Introduction

Alternative technologies for weed control are necessary to combat the rising cost of labor and herbicides. Weed management tools that can reduce chemical inputs will also decrease environmental degradation caused by intensive herbicide spraying. Enhancement of weed competitive ability of rice through breeding is one of the roads that can be followed to achieve the goal of sustainable weed management (Rice 1995; Bhowmik and Inderjit 2003). Allelopathy is the direct influence of a chemical released from one plant on development and growth of another plant. Over the last decade several studies have shown that some crop cultivars are allelopathic and that their inhibitory effects on weeds do play a role under field conditions (Dilday et al, 1991; Olofsdotter and Navarez 1996; Olofsdotter et al. 1999; Fujii 1993; Wu et al. 1999). Recently, crop allelopathy research also includes identification of the responsible chemicals for the observed weed suppression (Rimando et al. 2000; Kato-Noguchi et al. 2003; Wu et al. 2000), and identification of genetics underlying allelopathy in rice (Jensen et al. 2001, Ebana 2001) and wheat (Wu et al. 2003).

Methods

Bioassay of allelopathic characteristics

One rice plant was established per pot and the pot size was minimized to 2.5 x 2.5cm square at seedling nursing tray. The pots were irrigated through the bottom of the tray. F₂ and F₃ segregates of crossed Nonganbyeo and Sathi were cut stem 3-4 cm above ground at 30 days after seeding. Echinochloa crus-galli growth inhibition in soil condition was bioassayed by ratoon seeding method (Lee et al. 2003). Twelve Echinochola crus-galli soaked seeds were seeded around the rice stalk at 1 cm distance on the cut date. Weed shoot growth was measured 10 days after seeding.

SSR markers and statistical analysis

Genomic DNA was isolated from young seedlings of the same plant materials used in allelopathy bioassay. Leaf and stem tissues of 30 days old F2, F3 individual plants was processed according to the method of Causse et al. (1994). The 385 simple sequence repeats molecular markers used and 208 markers showed polymophism and covered all 12 rice chromosomes (Table 1). The original sources of and motifs for the markers based on Gramene database addressed to http://www.gramene.org. The procedures used for PCR and SSR assay were as described by Chen et al. (1997) and Panaud et al. (1996), respectively. The analysis QTLs associated with growth suppression of Echinochloa crus-galli was evaluated using ANOVA one way analysis of variance from SAS PROC GLM (ver 8.3). The Mapmaker QTL (ver. 2.0) was used for detect QTLs and also obtain estimates of the percentage of phenotype variance explained by each QTLs.

Results and Discussion

Among the used 335 SSR markers, 208 markers showed polymophic bands and the average percentage of polymophism was 54% (Table 1). Phenotypic difference of Echinochloa crus-galli growth by F₂ of Nonganbyeo crossed Sathi, 210 segregates showed one single recessive gene may control allelopathy effect (Fig. 1). Nine QTLs controlling allelopathic effects of rice on Echinochloa crus-galli were identified on chromosome no. 1, 2, 3, 4, 5, 8, 9 and 12 (Fig. 2). QTLs on chromosome no. 1 and 5 showed the largest on the allelopathic effect and explain 36.5% of total phenotypic variation (Table 2). The QTLs in this study showed differed from Ebana et al. (2001) and Jensen et al. (2001). Parental difference of examined rice populations, difference of bioassayed species and methods may effects the results. And also specific difference of allelopathy effect could explain the different QTLs.

Table 1. Polymorphism survey of used molecular markers within parents

Fig. 1. Frequency distribution of growth inhibition of Echinochloa crus-galli bioassayed with ratoon seeding method.

Fig 2. Putative QTLs for growth inhibition effect of allelopathic characteristics of rice : Chromosome regions for the detected QTLs

Table 2. Detected QTLs of rice allelopathy effect against Echinochloa crusgalli in the Nonganbyeo x Sathi  F₃ population

1) Phenotypic variation explained by each QTL.
2) **Association between the marker and phenotype is significant at the 1% level in SAS GLM PROC.

Conclusion

The allelopathic characteristic of rice controlled by quantitative gene effects and the molecular markers located rice chromosome no. 1 and 5 could be used major allelopathic effect.

References

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