Open access peer-reviewed chapter

Genetic Variation for Weed Competition and Allelopathy in Rapeseed (Brassica napus L.)

Written By

Harsh Raman, Nawar Shamaya and James Pratley

Submitted: November 1st, 2017 Reviewed: June 18th, 2018 Published: November 5th, 2018

DOI: 10.5772/intechopen.79599

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Abstract

Rapeseed (canola, Brassica napus L.) is the second major oilseed crop of the world and provides a source of healthy oil for human consumption, meal for stock markets and several other by-products. Several weed species afflict the sustainable production and quality of canola. Various agronomic practices such as crop rotation, stubble management (e.g. burning), minimum tillage, application of herbicides and cultivation of herbicide resistant varieties have been deployed to minimise yield losses. There is no doubt that herbicide-tolerant cultivars enable management of weeds which are difficult to control otherwise. However, widespread usage increases the risk of herbicide resistance. This is becoming a major impediment in sustaining high crop productivity. Allelopathic and weed competitive varieties are potential tools to reduce the dependence on herbicides and could be grown to suppress weed growth in commercial canola. Genetic variation and ‘proxy’ traits involved in both crop competition as well as allelopathy have been reported. Further research is required to link genetic variation in weed competition and allelopathy, and genetic/genomic marker technologies to unravel effective alleles to expand breeding activity for weed interference in canola.

Keywords

  • canola
  • allelopathy
  • weed competition
  • genetic variation
  • QTL mapping
  • genome wide association analysis

1. Introduction

Rapeseed (canola, Brassica napusL, 2n = 4X = 38) belongs to the family Brassicaceae,which is widely distributed across subtropical to temperate regions. It is thought to be originated as a result of natural hybridisation event between Brassica rapa(2n = 2X = 20, genome AA) and Brassica oleracea(2n = 2X =18, genome CC) [1]. Rapeseed is a close relative of Arabidopsis thaliana, a weed species widely distributed in the Northern hemisphere that diverged from Brassica∼20 million year ago [2]. Although rapeseed was domesticated approximately 400 years ago, it has become, in recent decades, the leading oilseed crop worldwide [3], providing about 13% of the world’s edible oil supply [4]. In Australia, canola was commercially grown for the first time in 1969 [5]. During the last four decades, the rapeseed industry has expanded exponentially with the development and cultivation of canola quality varieties having less than 2% erucic acid and less than 40 micromoles/g meal glucosinolates as well as resistance to blackleg disease, caused by the fungus, Leptosphaeria maculans.Higher grain prices and deployment of high yielding and herbicide tolerant hybrid varieties have further played major roles in its expansion. Currently, canola is the third largest broad-acre crop in Australia and is grown on more than 2.3 million ha [6] in a range of environments (i.e.<200 mm to >800 mm rainfall) [5]. Canola is usually sown in rotation with cereal crops such as wheat and barley to manage weeds and diseases of both crop types. Research has shown that canola can increase yields of wheat by up to15% [7].

Several weed species such as wild radish (Raphanus raphanistrum), shepherd’s purse (Capsella bursa-pastoris), capeweed (Arctotheca calendula), Indian hedge mustard (Sisymbrium orientale), annual ryegrass (Lolium rigidum) and Paterson’s curse (Echium plantagineum) afflict the production of canola. Weeds compete with the canola crop for water and nutrient uptake, and for solar radiation. This results in a reduction in the grain yield as well as in grain quality. Up to 90% reduction in grain yield of canola has been reported under high infestation of wild radish [8]. Improved agronomic practices such as stubble burning, minimal tillage, crop rotation, and application of herbicides provide valuable tools in managing weed populations. The option of manual weeding is not cost-effective for broad-acre crops such as canola. Various herbicide groups (A, B, C, D, I, K, M, and N) are currently used to control weeds in canola [9] . In addition, crop rotations provide the opportunity to rotate herbicide groups and delay the evolution of herbicide-resistant weed populations.

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2. Development of herbicide resistant varieties

Several herbicide-tolerant canola varieties marketed as Clearfield™ (CL), Roundup Ready™ (RR), and Triazine Tolerant™ (TT) are currently cultivated to widen the herbicide spectrum for control of weeds in canola and other crops. This strategy has played a major role in transforming the canola industry in Australia. The first TT variety of canola, ‘Siren’, was developed in 1993. Since then, there has been a continuous supply of open-pollinated as well as hybrid TT varieties for commercial cultivation. Although TT varieties had a 10–15% yield penalty [10] and lower oil content, these varieties have been popular among growers particularly where wild radish has been a problem, accounting for 70% of the cropped area in some states of Australia. These varieties have enabled an effective and cost effective management of common weeds, particularly wild radish, and those which are resistant to Group A and B herbicides. The other herbicide tolerant varieties, RR and CL, do not impose yield penalties.

Canola seems to be particularly vulnerable to competition from broad-leaf weeds as there are limited commercial herbicide options available. The canola industry is thus becoming more and more reliant on the herbicide tolerant varieties to provide control options for these major weeds. Analysis of weed resistance status indicates that key canola weeds in Australia are well known for their multiple herbicide sites of action resistances (Figure 1) and so existing herbicide options are either compromised or are likely to be. In recent decades, the heavy reliance on herbicides has led to herbicide resistance in numerous weed species such as annual ryegrass and wild radish with major concern being the increased incidence in particular, to Group M herbicide, glyphosate (Roundup®). Many farmers use glyphosate as a pre-planting herbicide to provide a weed-free seedbed. The advent of Roundup Ready (RR) crop varieties has transformed the use of glyphosate into an in-crop broad spectrum, selective herbicide. As a result, it has become the last herbicide used in the season and so any escapes from that use help to build glyphosate-resistant weed populations in subsequent seasons [11].

Figure 1.

Weed species resistance to multiple sites of actions [12].

Evaluation of the herbicides with the highest number of species for which herbicide resistance has been recorded (Figure 2) shows that of the 15 herbicides listed, eight are likely to be utilised in canola production, including Imazamox and Imazethapyr for CL canola, glyphosate for RR canola and atrazine and simazine for TT lines. With the development and commercial cultivation of genetically modified (GM) canola, there is now more flexibility to control a broad-spectrum of weeds through stacking of herbicide tolerant traits. For example, farmers now have access to hybrid varieties which have tolerance to glyphosate and triazines, providing pre-emergence as well as in-crop selective herbicide capability. Unfortunately, this gene stacking strategy for herbicide tolerance has further increased herbicide dependency [13] and is likely to lead to quicker herbicide resistance which in turn unfortunately will reduce weed control options.

Figure 2.

Number of most common resistant species to individual active herbicides (adapted from Heap [12]). Herbicides for use on canola are indicated in orange.

Application of herbicides has its own limitations; the practice is expensive, there is a risk of spray drift to neighbouring crops, and weed resistance threatens the on-going efficacy of the herbicide armoury. An alternative approach is to breed new varieties with improved genetics for weed interference. This interference, which is environmentally friendly, can be of two types: high competitiveness and/or allelopathy. In either case the crop does most of the weed management and herbicides are used in a supplementary way, if at all.

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3. Alternative approaches used for weed management: Interference

Crop interference as a tactic has been explored in some crops [14, 15]. It can be defined as the crop plants interfering with weed growth through competition for environmental resources [16] or the crop modifying the growth environment chemically to the disadvantage of the weed [16, 17]. These mechanisms are distinct but seem to act collectively to control weed populations under field conditions [18]. Although allelopathy includes growth promoting, and inhibiting effects, it is usually used to describe growth inhibiting effects [19]. Management practices also can and should assist these processes: for example, growers can manipulate crop sowing times and sowing rates to disadvantage the weeds relative to the crop as well as impose practices that minimise weed seed additions to the seed bank.

3.1. Genetic variation for weed competition

Crop competition is the ability of crops to adapt to weed infestation by accessing limited resources also sought by neighbouring weeds. Traits associated with weed competition are generally related to morphology and phenology of both weed and crop species [20]. Several traits related to competitive ability include plant height, tiller number, leaf angle, canopy structure, early vigour and time to maturity [20]. A good understanding of component attributes underlying those traits would provide an opportunity to improve weed competition of crops using genetic and genomic tools.

Morphological traits related to the interception of radiation by leaves which determine competitiveness for light, including leaf size, number and leaf area index, stem elongation, upward leaf movement [21, 22, 23, 24] and leaf layer density [25], have not been studied in canola. These traits are associated with shade avoidance, enabling plants to photosynthesise and grow to improve their competitiveness [21, 22]. Height at maturity has also been reported to contribute to competitive ability [26, 27] although a negative relationship between plant height and weed infestation has been reported for canola [28] and wheat [27]. No such relationship has been found in rice [18]. This trait however tends to have a negative effect on grain yield due to a reduced harvest index.

In wheat, Coleman et al. [29] and Mokhtari et al. [30] showed the normal distribution for phenotypic variation for competitive ability traits in populations derived from crosses between competitive and non-competitive parents. This suggests that the competitive ability trait is controlled by quantitative genes which have minor and moderate effects. Competitive ability associated traits seem to have moderate to highly heritability. In bread wheat, [29] estimated narrow-sense heritabilities for different agronomic and morphological traits associated with weed competition to be: high for flowering date (0.99) and height stem elongation (0.91); low for tiller number (0.34), leaf area index during stem extension (0.18–0.31) and crop dry matter (0.18). Mokhtari et al. [30] estimated the narrow-sense heritability of percentage yield loss due to the weed competition in F2:F3 populations of wheat: 0.25 for the population derived from crossing two late flowering time parents and 0.57 for the population derived from crossing between two early flowering time parents.

In rice, broad-sense heritability of weed biomass and crop grain yield under weedy conditions was reported [31] to be high (0.64 to 0.79) for 40 upland rice cultivars grown under weed and weed-free conditions. Another study by Zhao et al. [32] also found that broad-sense heritability was high, being 0.88 for early vigour and 0.81 for crop height 4 weeks after seeding. Although heritability is an indication of phenotypic variation due to genetic effects, the estimation of broad and narrow-sense heritabilities for traits are influenced by population structure and environmental factors.

The genetic bases and extent of variation associated with competitive ability in Brassicacrops have received attention. In canola, plant height, leaf size, leaf number and leaf area index, stem elongation, upward leaf movement and leaf density are considered as the most important attributes for above ground competition for light; and plant root size and depth, relative growth rate, biomass, root density and total root surface area are the most important traits for below ground competition for space, soil nutrients and water [33]. However, only limited component traits have been studied so far to determine the extent of genetic variation in Brassicaspecies. For example, Beckie et al. [34] compared the competitive ability of canola with yellow mustard (B. juncea) against wild oats. Yellow mustard was superior in competitiveness to canola due to its rapid growth and plant height resulting in early-season crop biomass accumulation. It has also been shown that canola hybrid varieties are more competitive than open pollinated varieties due to their faster growth and biomass accumulation [35]. Harker et al. [36] confirmed the stronger competitive ability of hybrid canola varieties especially under cool and low growing degree day conditions. In an Australian study, Asaduzzaman et al.,(unpublished) compared the weed competiveness of 16 Brassica napusgenotypes representing open pollinated, F1 hybrid and TT lines against annual ryegrass and associated weeds and showed that open pollinated and hybrid genotypes reduced weed shoot biomass by 50% compared with less vigorous TT genotypes. In a recent study, Shamaya et al. [37] evaluated the competitive ability of 26 canola genotypes against annual ryegrass (Lolium rigidum) under field and glasshouse conditions to study the phenotypic traits associated with weed competition. Under both conditions, the canola biomass, mostly leaf biomass measured in the glasshouse only, was positively associated with competitive ability.

3.2. Detection of QTL for weed competitiveness

Several studies have employed the Quantitative Trait Locus (QTL) mapping approach for detecting, localising and determining the magnitude of loci affecting phenotypic variation for weed competition in plants (Table 1). The QTL mapping approach is based on the statistical association between phenotypic and molecular marker polymorphism data. Several molecular markers such as Restriction Fragment Length Polymorphism (RFLP), Single Feature Polymorphism (SFP), Diversity Arrays Technology (DArTs), Random Amplified Polymorphic DNAs (RAPDS), Simple Sequence Repeats/Microsatellites (SSRs), Amplified Fragment Length Polymorphisms (AFLPs), Cleaved Amplified Polymorphic Sequence (CAPs) and Sequence-Related Amplified Polymorphism (SRAP) have been used extensively to genotype populations for genetic analyses [38, 39, 40, 41, 42, 43, 44]. More recently, whole genome sequencing methods enabled to develop new marker systems such as genotyping by sequencing based on the complexity reduction methods including DArTseq, Single Nucleotide Polymorphisms (SNPs), restriction-site associated DNA (RAD), RNA-Seq and sequence captures that are more suitable for high-throughput analyses [45, 46, 47, 48, 49, 50].

Competitive ability
SpeciesPopulation typePopulation sizeTraitSeasonChromosomeR2Reference
Wheat (Triticum aestivumL.)Doubled haploid lines derived from Cranbrook/Halberd161Yield19993A12.2Coleman et al. [29]
3B9.8
1000 – grain weight19985A11.0
2D8.4
19995A12.0
2B9.9
Wheat (Triticum aestivumL.)Recombinant inbred lines derived Opata 85/ and synthetic W7984108Early Season Vigour20055A16Reid [75]
20065A22
Days to Heading20055A21
20065A21
Day to Anthesis20055A20
20065A17
Days to Maturity20055A13
20065A19
Weed Suppression20055A14
20065A15
Allelopathy
Wheat (Triticum aestivumL.)Doubled haploid lines derived from Tasman (strongly allelopathy) Sunco (weakly allelopathy)271Reduction in annual ryegrass using the Equal-Compartment-Agar-Method [89]2B29Wu et al., [57]
Rice (Oryza sativaL.)F2 – F3 population derived from Indicaline PI312777 (strongly allelopathy) Japonicacv Rexmont (weakly allelopathy)192Reduction in lettuce root length using water-soluble extract method [116]1, 3, 5, 6, 7, 11, 129.4–16.1Ebana et al., [112]
Rice (Oryza sativaL.)Recombinant inbred lines derived from crossing cv IAC 165 (strongly allelopathy) and cv CO39 (weakly allelopathy)142Reduction in barnyard grass root length using relay seeding technique method [117]312Jensen et al., [113]
37.2
88.5
Rice (Oryza sativaL.)Doubled haploid lines derived from JaponicaJingxi17 (strongly allelopathy) IndicaZhaiyeqing 8 (weakly allelopathy)123Reduction in lettuce root length using water-soluble extract310.24Dali et al., [118]
98.24
108.27
129.79
Rice (Oryza sativaL.)Recombinant inbred lines derived from IndicacvAC1423 (strongly allelopathy)/cv. Aus196 (weakly allelopathy)150Reduction in Echinochloa crus-galliroot length using relay seeding technique method [117]411.1Jensen et al., [114]
Echinochloa crus-galliroot length from greenhouse pot set-up49.6
Echinochloa crus-galliroot biomass from greenhouse pot set-up35.0
66.9
Echinochloa crus-gallishoot length from greenhouse pot set-up35.9
87.1
Echinochloa crus-gallishoot biomass from greenhouse pot set-up85.1
125.8
Rice (Oryza sativaL.)Recombinant inbred lines derived from cv. Zhong-156 (strongly allelopathy)/cv. Gumei-2 (weakly allelopathy)147Allelopathy index determined by secondary metabolite1116.5Zhou et al. [111]

Table 1.

Genetic analysis of mapping populations for crop competitiveness and allelopathy.

Two strategies based on Quantitative Trait Locus (QTL) mapping and genome-wide association mapping (genome-wide association study, GWAS) approaches have been used to understand the genetic basis of natural variation for weed interference in various crop plants such as rice, corn, wheat, cowpea, barrel clover, peas, sorghum, sunflower and A. thaliana[51, 52, 53, 54, 55, 56, 57]. In B. napus, QTL for various traits of agronomic importance including seed germination/plant emergence, fractional ground cover (early vigour), plant biomass, flowering time, plant height, plant maturity, grain yield, resistance to various biotic and abiotic stresses and seed shattering have been mapped using traditional and GWAS [49, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]. However, no QTL associated with weed competition and/or allelopathy has been identified to date.

QTL for weed competition traits have been mapped in cereals and other crops. For example, in wheat Coleman et al. [29] utilised the genetic linkage map based on RFLP, AFLP, SSR, known genes and protein markers of doubled haploid (DH) populations derived from Cranbrook/Halberd to investigate the genetic control of various traits involved in grain yield loss and suppression of ryegrass growth. These traits included the width of the second leaf, canopy height, light interception at early stem elongation, tiller number, days to anthesis and plant height. Several consistent QTL for flag leaf area, flag leaf length, flag leaf width, height at stem elongation, and tiller number were identified in the vicinity of photo-period genes (Ppd-B1and Ppd-D1) on the group 2 chromosomes. Three QTL for plant height at anthesis were detected on chromosomes 3A, 4B and 5A. No QTL for crop yield loss in the presence of ryegrass or ryegrass dry matter suppression was identified in this population, likely due to the complex nature of this trait [29]. However this study reported that ryegrass dry matter was suppressed for DH lines of wheat with greater leaf area index, more tillers, taller plant height and later flowering. High genetic correlations between leaf area index and grain yield loss (r = −0.81) as well as suppression of ryegrass (r = −0.91) were observed indicating that traits contributing to early ground cover would be important for developing competitive wheat genotypes. Another wheat study conducted in the northern region of Canada determined a cluster of QTL associated with traits implicated in weed competition [75] using 108 recombinant inbred lines derived from a cross between Mexican wheat, Opata 85, and a synthetic wheat accession, W7984. Early vigour, day to heading, day to anthesis, day to maturity and weed suppression were mapped to the same region on chromosome 5A corresponding to the position of the vernalisation gene Vrn-A1,suggesting that flowering time may be associated with weed suppression.

In rice (Oryza sativaL), a mapping population developed from a cross between a weed-suppressive ‘indica’ rice line and a non-weed suppressive ‘japonica’ cultivar was used to study the genetic bases of variation for seedling germination, shoot length and dry matter weight. Thirteen QTL were detected and each QTL explained 5–10% of the phenotypic variation of the traits [76].

GWAS has been employed to investigate the genetic architecture of weed competition in A. thaliana, and rice [51, 55]. For example, a set of 195 accession of A. thalianagrown with the presence and absence of bluegrass, Poa annua, were analysed for trait (29 phenotypes related to phenology, resource acquisition, hoot architecture, seed dispersal, fecundity, reproductive strategy and survival)-marker association [51]. Several significant SNP associations for yield (fruit number on basal branches) with and without weed competition were identified. This study further identified a candidate gene, TSF(TWIN SISTER OF FT) which was associated with flowering time, duration of flowering, climate variation, the number of primary branches and escape strategy to competition, suggesting adaptive strategy to escape competition. However, no such study has been conducted in canola to identify genes which control weed competition and/or allelopathy.

3.3. Genetic variation for allelopathy

Allelopathy is a mechanism whereby a plant ensures itself a competitive advantage by placing phytotoxins into the adjacent environment [17]. Numerous allelochemicals that affect weed species have been identified and characterised [77]. Their existence varies with species and variety, and will almost always operate as a ‘cocktail’ of chemicals from any one source. An et al. [78], for example, showed that the allelopathic capability of Vulpiaspp. involved more than 20 separate compounds. The role of allelopathy in suppression of weed growth has been studied in a range of crops including wheat [57], rice [79, 80, 81, 82], barley [83], cotton [84], and sorghum [85].

Different laboratory based assays used to measure the allelopathy activity have been reviewed by Wu et al. [90]. These include the ‘plant-box method’ [86], the ‘relay-seeding technique’ [87], the ‘equal-compartment-agar-method’ or ECAM [88, 89, 90], and hydroponic methods [91, 92]. Generally, these assays involve growing of seedlings of the donor plants (e.g.crop species) in the presence of, or followed by, weed species for a short period of time. The allelopathic crops such as Brassica rapa, B. juncea, B. nigra, B. hirtaand B. napusexude phytotoxic compounds [93, 94, 95, 96, 97] which suppress the growth of the weed species depending on the tolerance of the receiver plants to the chemicals being exuded. In the field, it is necessary to recognise that there would be an exchange of allelopathic chemicals between crop and weed with the outcome determined by relative potency of the allelochemicals and the tolerances of the receiving plants to the chemicals received [98]. Allelopathic activity is measured as the reduction of weed root growth in the presence of allelochemicals relative to that in the absence of the donor plants.

One question often raised is whether the laboratory method reflects performance under field conditions. Seal et al. [99] for rice and Asaduzzaman et al. [88] for canola both showed high correlations between the ECAM method in the laboratory and field performance. The other question is how field performance can be attributed to allelopathy. Unfortunately, there is no simple measure. In some cases inspection of the roots of affected plants show symptoms of inhibited development, such as root pruning, thickened roots and distortions not normally seen. In most cases, it has to be assumed that if field performance matches that in the laboratory then allelopathy is at least part of the explanation. Root exudates can be collected and analysed for bioactive compounds. Such compounds can be then applied to the receiver plants to ensure that the same outcome is achieved as described in [100]. Weidenhamer [101] has shown that it is possible to measure the presence of allelochemicals in situin the rhizosphere using a sorptive coated stir bar inserted into the measurement zone for subsequent analysis by HPLC.

Phytotoxic allelochemicals have also been identified in Brassicaplant residues and exudates that are known to suppress weed infestation [19, 95, 102]. Brassicaspecies are also well known to synthesise glucosinolates which have shown allelopathic effects on pathogens due to the production of isothiocyanates. This process has been coined biofumigation [103, 104].

Genetic variation for allelopathy in canola and its related species, Sinapis alba L.has been studied [93, 105, 106]. Asaduzzaman et al. [107] investigated allelopathy among 70 diverse accessions of canola using annual ryegrass (Lolium rigidum) as the ‘test’ weed. The range of allelopathic impacts is shown in Figure 3. One B. napuscultivar of Australian origin, cv ‘Av-Opal’, was strongly allelopathic both in the laboratory and in the field whereas commercial cv. Barossa was at the other extreme in both laboratory and field. Field study showed that the allelopathic trait is independent of plant biomass and grain yield, and no consistent relationship between plant height and weed competitive ability was found among genotypes.

Figure 3.

Allelopathic effect of 70 canola genotypes on root length of annual ryegrass seedlings (lsd = 10) [107].

The greater weed suppression ability of cv. Av-Opal was confirmed in a two-year field study against annual ryegrass and other weeds (shepherd’s purse, Indian hedge mustard and barley grass) relative to cv. Barossa [28, 107]. Interestingly, Av-Opal was not exceptionally competitive as it is of short stature and poorly adapted to adverse environmental conditions [28]. In a subsequent study, Asaduzzaman et al. [108] investigated the biochemical basis of the allelopathy and detected numerous bioactive secondary metabolites including sinapyl alcohol, p-hydroxybenzoic acid and 3,5,6,7,8-pentahydroxy flavones in the root exudates. A comparison of the allelopathic capabilities between cv. Av-Opal and cv. Barossa is shown in Figure 4.

Figure 4.

A comparison of a strongly allelopathic cultivar (AV-opal, left) and a weakly allelopathic cultivar (Barossa, right) [109]. Barossa plot showing extensive growth of different weeds.

3.4. Detection of QTL for allelopathy

The genetic bases of allelopathy activity have been investigated in wheat [57, 110] and in rice [111, 112, 113, 114, 115]. For wheat, doubled haploid lines were developed from the strongly-allelopathic cultivar Tasman and the non-allelopathic cultivar Sunco. Significant differences were recorded for root growth of annual ryegrass between the doubled haploid lines [89]. Analysis of RFLP, AFLP and SSRs markers identified two major QTLs on chromosome 2B associated with wheat allelopathy.

In rice, several QTL have been detected across the rice genome and these QTL explain 5–36.6% of phenotypic variation in crop interference traits (Table 1). Jensen et al. [113] identified four major QTL on chromosomes 2, 3 and 8 which accounted for 35% of total variation of the allelopathic activity in the RIL population derived from japonica cv. IAC165 (allelopathic parent) and indica cv CO39. Ebana et al. [116] identified a major QTL on chromosome 6 accounting for 16.1% of the phenotypic variance in an F2 population of 192 lines from indica line PI312777/japonica line Rexmont. Jensen et al. [114] identified QTL for RLSWRL and GHWRL on the same genomic marker interval, confirming that major genes for weed root length may be located in this region. The most important QTL were on chromosomes 3, 5, 8 and 11 [111, 116]. This indicates that allelopathy activity in cereal is controlled by quantitative loci. The relatively low phenotypic variation for the individual QTL is explained by the difficulty in measuring the allelopathic traits at the individual genotype level.

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4. Conclusions

Herbicide resistance is a major impediment in sustaining high crop productivity. The lack of new chemical modes of action becoming available emphasises the need for novel approaches to control weeds. Crop competitiveness and allelopathy are potential tools to reduce the dependence on synthetic chemical inputs and in so doing may extend the lives of key herbicides. A challenge for researchers is to be able to separate competitiveness from allelopathy in the field. For crop producers it does not really matter whether it is one or the other or both as long it works. A further challenge for researchers is attracting funds to undertake this work to commercial outcomes.

What are the prospects of herbicide resistance evolution occurring to allelochemicals? Of course the risks exist but they are likely to be much lower for at least two reasons: firstly allelopathy relies on a mix of chemicals at any one time from a single crop; and different crops have different mixes of chemicals so that in a rotation of crops, weeds will be exposed to chemicals of different modes of action only once or twice in a rotation cycle.

Phenotyping traits associated with allelopathic activity, such as reduction of weed growth in the laboratory and field, with high-throughput genotyping technology such as sequencing and mapping populations, allow researchers to detect QTL and genes associated with allelopathy and weed competition. It is an open question whether weed competition and allelopathy are distinct traits, but if this is the case, both traits could be pyramided in a single variety. In addition to genetic and phenotypic information, functional ‘omic’ data, such as identification of secondary metabolites, can be integrated in the QTL analysis leading to the detection of genes and pathways responsible for allelopathy activity. This would enable the development of novel alleles to expand breeding activity for weed interference in canola.

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Acknowledgments

Authors thank NSW Department of Primary Industries, Charles Sturt University and E.H. Graham Centre for Agricultural Innovation for supporting crop interference research.

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Conflict of interest

Authors do not have conflict of interest to declare.

References

  1. 1. Nagaharu U. Genome analysis in Brassica with special reference to the experimental formation ofB. napusand peculiar mode of fertilization. Journal of Japanese Botany. 1935;7:389-452
  2. 2. Yang Y-W, Lai K-N, Tai P-Y, Li W-H. Rates of nucleotide substitution in angiosperm mitochondrial DNA sequences and dates of divergence betweenBrassicaand other angiosperm lineages. Journal of Molecular Evolution. 1999;48(5):597-604
  3. 3. Gómez-Campo C, Prakash S. Origin and domestication. In: Gómez-Campo C, editor. Biology of Brassica Coenospecies. Netherlands: Elsevier; 1999. pp. 33-58
  4. 4. Raymer PL. Canola: An emerging oilseed crop. In: Janick J, Whipkey A, editors. Trends in New Crops and New Uses. Alexandria, VA: ASHS Press; 2002. pp. 122-126
  5. 5. Colton B, Potter T, editors. History: The Organising Commitee of the 10th International Rapeseed Congress1999. pp. 1-4
  6. 6. AOF. Australian Oilseeds Federation. Crop Report. AOF July 2017http://www.australianoilseeds.com/__data/assets/pdf_file/0012/11190/AOF_Crop_Report_July_2017.pdf. 2017
  7. 7. Kirkegaard JA, Sprague SJ, Dove H, Kelman WM, Marcroft SJ, Lieschke A, et al. Dual-purpose canola - a new opportunity in mixed farming systems. Australian Journal of Agricultural Research. 2008;59(4):291-302
  8. 8. Blackshaw RE, Lemerle D, Mailer R, Young KR. Influence of wild radish on yield and quality of canola. Weed Science. 2002;50:344-349. DOI: 101614/0043-1745
  9. 9. Brooke G, McMaster C. Weed Control in Winter Crops. NSW Department of Primary Industries, Orange Australia ISSN 0812-907X. 2015
  10. 10. OGTR Office of the Gene technology Regulator. The Biology ofBrassica napusL. (Canola) andBrassica juncea(L.) Czern. &Coss. (Indian Mustard). Australian Government Department of Health; 2017.http:/www.ogtr.gov.au
  11. 11. Pratley J, Broster J, Stanton R. Weed management. In: Principles of Field Crop Production. Wagga Wagga,www.csu.edu.au/__data/assets/pdf_file/0006/2805567/Chapter9_PratleyBrosterStanton.pdf: Graham Centre for Agricultural Innovation, Charles Sturt University; 2018
  12. 12. Heap I. The International Survey of Herbicide Resistant Weeds – Online.www.weedscience.org[Accessed: 28 July 2017]; 2017
  13. 13. Gressel J, Gassman AJ, Owen MDK. How well will stacked transgenic pest/herbicide resistances delay pests from evolving resistance? Pest Management Science. 2017;73:22-34
  14. 14. Lemerle DV, Verbeek B, Coombes NE. Losses in grain yield of winter crops fromLolium rigidum(gaud.) depend on crop species, cultivar and season. Weed Research. 1995;35:503-509
  15. 15. Seavers GP, Wright KJ. Crop canopy development and structure influence weed suppression. Weed Research. 1999;39(4):319-328
  16. 16. Donald CM. Competition among crop and pasture plants. In: Norman AG, editor. Advances in Agronomy. Vol. 15. New York, London: Academic Press; 1963. pp. 1-118
  17. 17. Pratley JE. Allelopathy in annual grasses. Plant Protection Quartely. 1996;11:213-214
  18. 18. Olofsdotter N, Rebulanan S. Weed-suppressing rice cultivars – Does allelopathy play a role? Weed Research. 1999;39(6):441-454
  19. 19. Olofsdotter M, Jensen L, Courtois B. Improving crop competitive ability using allelopathy—An example from rice. Plant Breeding. 2002;121:1-9
  20. 20. Worthington M, Reberg-Horton C. Breeding cereal crops for enhanced weed suppression: Optimizing allelopathy and competitive ability. Journal of Chemical Ecology. 2013;39:213-231
  21. 21. Pierik R, Mommer L, Voesnek LA. Molecular mechanimsm of plant competition: Neighbour detection and response strategies. Functional Ecology. 2013;27:841-853
  22. 22. Franklin KA. Shade avoidance. The New Phytologist. 2008;179:930-944
  23. 23. Blackshaw RE. Differential competitive ability of winter wheat cultivars against downy brome. Agronomy Journal. 1994;86:649-654
  24. 24. Morgan DC, Smith H. Linear relationship between phytochrome photoequilibrium and growth in plants under simulated natural irradiation. Nature. 1976;262:210-212
  25. 25. Goodall J, Witkowski E, Ammann S, Reinhardt C. Does Allelopathy Explain the Invasiveness ofCampuloclinium macrocephalumin the South African grassland Biological Invasion. 2010;12:3497-3512
  26. 26. Challaiah OC, Burnside WGA, Johnson VA. Competition between winter wheat (Triticum aestivum) cultivars and downy brome (Bromus tectorum). Weed Science. 1986;34(5):689-693
  27. 27. Lemerle D, Verbeek B, Cousens RD, Coombes NE. The potential for selecting wheat varieties strongly competitive against weeds. Weed Research. 1996;36(6):505-513
  28. 28. Asaduzzaman M, Luckett D, Cowley R, An M, Pratley J. Canola cultivar performance in weed-infested field plots confirms allelopathy ranking fromin vitrotesting. Biocontrol Science and Technology. 2014;24:1394-1411
  29. 29. Coleman R, Gill G, Rebetzke G. Identification of quantitative trait loci for traits conferring weed competitiveness in wheat (Triticum aestivumL.). Australian Journal of Agricultural Research. 2001;52:1235-1246
  30. 30. Mokhtari S, Galwey N, Cousens R, Thurling N. The genetic basis of variation among wheat F3 lines in tolerance to competition by ryegrass (Lolium rigidum). Euphytica. 2002;124:355-364
  31. 31. Zhao DL, Atlin GN, Bastiaans L, Spiertz JHJ. Cultivar weed-competitiveness in aerobic rice: Heritability, correlated traits, and the potential for indirect selection in weed-free environments. Crop Science. 2006;46:372-380
  32. 32. Zhao G, Atlin N, Bastiaans L, Spiertz J. Developing selection protocols for weed competitiveness in aerobic rice. Field Crops Research. 2006;97:272-285
  33. 33. Asaduzzaman M, Pratley JE, Min A, Luckett DJ, Lemerle D. Canola interference for weed control. Springer Science Reviews. 2014;2:63-74
  34. 34. Beckie H, Johnson E, Blackshaw R, Gan Y. Weed suppression by canola and mustard cultivars. Weed Technology. 2008;22:182-185
  35. 35. Daugovish O, Thill D, Shafii B. Competition between wild oat (Avena fatua) and yellow mustard (Sinapis alba) or canola (Brassica napus). Weed Science. 2002;50:587-594
  36. 36. Harker N, O’Donovan J, Blackshaw R, Johnson E, Holm F, Clayton G. Environmental effects on the relative competitive ability of canola and small-grain cereals in a direct-seeded system. Weed Science. 2011;59:404-415
  37. 37. Shamaya N, Raman H, Rohan M, Pratley J, Wu H. Natural variation for interference traits against annual ryegrass in canola. Proceedings of AusCanola Conference, Perth; 2018
  38. 38. Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, et al. Diversity arrays technology (DArT) for whole-genome profiling of barley. Proceedings of National Academy of Sciences of the USA. 2004;101:9915-9920
  39. 39. Wenzl P, Li H, Carling J, Zhou M, Raman H, Paul E, et al. A high-density consensus map of barley linking DArT markers to SSR, RFLP and STS loci and agricultural traits. BMC Genomics. 2006;7:206.https://doi.org/10.1186/1471-2164-7-206
  40. 40. Williams J, Kubelik A, Livak K, Rafalski J, Tingey S. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research. 1990;18:6531-6535
  41. 41. Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M, et al. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research. 1995;23(21):4407-4414
  42. 42. Lander ES, Botstein D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989;121(1):185-199
  43. 43. Röder MS, Korzun V, Wendehake K, Plaschke J, Tixier M, Leroy P, et al. A microsatellite map of wheat. Genetics. 1998;149:2007-2023
  44. 44. Li G, Quiros CF. Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: Its application to mapping and gene tagging in Brassica. Theoretical and Applied Genetics. 2001;103(2):455-461
  45. 45. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One. 2008;3(10):e3376
  46. 46. Trick M, Long Y, Meng J, Bancroft I. Single nucleotide polymorphism (SNP) discovery in the polyploidBrassica napususing Solexa transcriptome sequencing. Plant Biotechnology Journal. 2009;7:334-346
  47. 47. Ganal MW, Altmann T, Röder MS. SNP identification in crop plants. Current Opinion in Plant Biology. 2009;12(2):211-217
  48. 48. Gupta PK, Rustgi S, Mir RR. Array-based high-throughput DNA markers for crop improvement. Heredity. 2008;101(1):5-18
  49. 49. Raman H, Raman R, Kilian A, Detering F, Carling J, Coombes N, et al. Genome-wide delineation of natural variation for pod shatter resistance inBrassica napus. PLoS One. 2014;9(7):e101673
  50. 50. Poland JA, Brown PJ, Sorrells ME, Jannink J-L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One. 2012;7(2):e32253
  51. 51. Frachon L, Libourel C, Villoutreix R, et al. Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nat Ecology and Evolution. 2017;1:1551-1561
  52. 52. Bartoli C, Roux F. Genome-wide association studies in plant pathosystems: Toward an ecological genomics approach. Frontiers in Plant Science. 2017;8:763. DOI: 10.3389/fpls.2017.00763
  53. 53. Botto JF, Coluccio MP. Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations ofArabidopsis thaliana. Plant, Cell and Environment. 2007;30(11):1465-1479
  54. 54. Mutic JJ, Wolf JB. Indirect genetic effects from ecological interactions inArabidopsis thaliana. Molecular Ecology. 2007;16(11):2371-2381
  55. 55. Baron E, Richirt J, Villoutreix R, Amsellem L, Roux F. The genetics of intra- and interspecific competitive response and effect in a local population of an annual plant species. Functional Ecology. 2015;29(10):1361-1370
  56. 56. Kikuchi S, Bheemanahalli R, Jagadish KSV, et al. Genome-wide association mapping for phenotypic plasticity in rice. Plant, Cell and Environment. 2017;40(8):1565-1575
  57. 57. Wu H, Pratley J, Ma W, Haig T. Quantitative trait loci and molecular markers associated with wheat allelopathy. Theoretical and Applied Genetics. 2003;107:1477-1481
  58. 58. Raman R, Taylor B, Marcroft S, Eckermann P, Rehman A, Lindbeck K, et al. Genetic map construction and localisation of qualitative and quantitative loci for blackleg resistance in canola (Brassica napusl.). 17th Crucifer Genetics Workshop, 5–8 Sept, Saskatoon, Canada; 2010
  59. 59. Luckett DJ, Cowley R, Moroni S, Raman H. Improving water-use efficiency and drought tolerance in canola - potential contribution from improved carbon isotope discrimination (CID). Proceedings of the 13th International Rapeseed Congress, Prague; 2011
  60. 60. Hou J, Long Y, Raman H, Zou X, Wang J, Dai S, et al. A tourist-like MITE insertion in the upstream region of theBnFLC.A10gene is associated with vernalization requirement in rapeseed (Brassica napusL.). BMC Plant Biology. 2012;12(1):238
  61. 61. Raman R, Taylor B, Lindbeck K, Coombes N, Barbulescu D, Salisbury P, et al. Molecular mapping and validation ofRlm1genes for resistance toLeptosphaeria maculansin canola (Brassica napusL). Crop & Pasture Science. 2012;63:1007-1017
  62. 62. Raman R, Taylor B, Marcroft S, Stiller J, Eckermann P, Coombes N, et al. Molecular mapping of qualitative and quantitative loci for resistance toLeptosphaeria maculans; causing blackleg disease in canola (Brassica napusL.). Theoretical and Applied Genetics. 2012;125(2):405-418
  63. 63. Tollenaere R, Hayward A, Dalton-Morgan J, Campbell E, Lee JRM, Lorenc M, et al. Identification and characterization of candidateRlm4blackleg resistance genes inBrassica napususing next-generation sequencing. Plant Biotechnology Journal. 2012;10(6):709-715
  64. 64. Zou X, Suppanz I, Raman H, Hou J, Wang J, Long Y, et al. Comparative analysis ofFLChomologues in Brassicaceae provides insight into their role in the evolution of oilseed rape. PLoS One. 2012;7(9):e45751
  65. 65. Raman H, Raman R, Eckermann P, Coombes N, Manoli S, Zou X, et al. Genetic and physical mapping of flowering time loci in canola (Brassica napusL.). Theoretical and Applied Genetics. 2013;126:119-132
  66. 66. Raman H, Dalton-Morgan J, Diffey S, Raman R, Alamery S, Edwards D, et al. SNP markers-based map construction and genome-wide linkage analysis inBrassica napus. Plant Biotechnology Journal. 2014;12(7):851-860
  67. 67. Raman H, Raman R, Luckett D, Cowley R, Diffey S, Leah D, et al. Understanding the genetic bases of phenotypic variation in drought tolerance related traits in canola (Brassica napusL.) Proceedings of the 18th Australian Research assembly on Brassicas, 29th September-2nd October, 2014. p 119-125
  68. 68. Raman R, Tanaka E, Coombes N, Diffey S, Lindbeck K, Price A, et al. Genome-wide association analyses identify novel loci for blackleg resistance inBrassica napus.2015.https://event-wizardcom/files/clients/RKYES4VI/IRC2015_ABSTRACTS_July2015-webpdf. p. 69
  69. 69. Larkan NJ, Raman H, Lydiate DJ, Robinson SJ, Yu F, Barbulescu DM, et al. Multi-environment QTL studies suggest a role for cysteine-rich protein kinase genes in quantitative resistance to blackleg disease inBrassica napus. BMC Plant Biology. 2016;16(1):1-16
  70. 70. Liu J, Wang J, Wang H, Wang W, Zhou R, Mei D, et al. Multigenic control of pod shattering resistance in chinese rapeseed germplasm revealed by genome-wide association and linkage analyses. Frontiers in Plant Science. 2016;7:1058. DOI: 10.3389/fpls.2016.01058. e
  71. 71. Raman H, Raman R, Coombes N, Song J, Diffey S, Kilian A, et al. Genome-wide association study identifies new loci for resistance toLeptosphaeria maculansin canola. Frontiers in Plant Science. 2016;7:1513. DOI: 10.3389/fpls.2016.01513
  72. 72. Raman H, Raman R, Coombes N, Song J, Prangnell R, Bandaranayake C, et al. Genome-wide association analyses reveal complex genetic architecture underlying natural variation for flowering time in canola. Plant, Cell & Environment. 2016;39(6):1228-1239
  73. 73. Raman R, Diffey S, Carling J, Cowley R, Kilian A, Luckett D, et al. Quantitative genetic analysis of yield in an AustralianBrassica napusdoubled haploid population. Crop & Pasture Science. 2016;67(4):298-307
  74. 74. Raman H, Raman R, McVittie B, Orchard B, Qiu Y, Delourme R. A major locus for manganese tolerance maps on chromosome A09 in a doubled haploid population ofBrassica napusL. Frontiers in Plant Science. 2017;8:1952. DOI: 10.3389/fpls.2017.01952
  75. 75. Reid T. The Genetics of Competitive Ability in Spring Wheat. Alberta, Canada: University of Alberta; 2010
  76. 76. Zhang H, Yu T, Huang Z, Zhu G. Mapping quantitative trait loci (QTLs) for seedling-vigor using recombinant inbred lines of rice. Field Crops Research. 2005;91:161-170
  77. 77. Lankau R. A chemical trait creates a genetic trade-off between intra- and interspecific competitive ability. Ecology. 2008;89(5):1181-1187
  78. 78. An M, Haig T, Pratley JE. Phytotoxicity of vulpia residues: II. Separation, identification, and quantitation of allelochemicals from Vulpia myuros. Journal of Chemical Ecology. 2000;26:1465-1476
  79. 79. Kong CH, Chen XH, Hu F, Zhang SZ. Breeding of commercially acceptable allelopathic rice cultivars in China. Pest Management Science. 2011;67:1100-1106
  80. 80. Dilday RH, Lin J, Yan W. Identification of allelopathy in the USDA-ARS rice germplasm collection. Australian Journal of Experimental Agriculture. 1994;34:907-910
  81. 81. Gealy DR, Wailes EJ, Estorninos LE, Chavez RSC. Rice cultivar differences in suppression of barnyardgrass (Echinochloa crus-galli) and economics of reduced propanil rates. Weed Science. 2003;51:601-609
  82. 82. Gealy DR, Yan W. Weed suppression potential of ‘rondo’ and other Indica Rice Germplasm lines. Weed Technology. 2012;26(3):517-524
  83. 83. Liu DL, Lovett JV. Biologically active secondary metabolites of barley. II. Phytotoxicity of barley allelochemicals. Journal of Chemical Ecology. 1993;19(10):2231-2244
  84. 84. Uludag A, Uremis I, Arslan M, Gozcu D. Allelopathy studies in weed science in Turkey – A review. Journal of Plant Diseases and Protection. 2006;20:419-426
  85. 85. Einhellig FA, Souza IF. Phytotoxicity of sorgoleone found in grain Sorghum root exudates. Journal of Chemical Ecology. 1992;18(1):1-11
  86. 86. Fujii Y. The potential biological control of paddy weeds with allelopathy-allelopathic effect of some rice varietie. In: Interantional Symposium Biological Control and Intreagted Management of Paddy and Aquatic Weeds in Asia. Tsukuba: National Agricultural Research Centre of Japan; 1992
  87. 87. Navarez D, Olofsdotter M. Allelopathic rice forEchinochloa crus-gallicontrol. In: Brown H, Cussans GW, Devine MDD, Fernandez-Quintanilla CSO, Helweg A, Labrada RE, Landes M, Kudsk PS, editors. 2nd International Weed Control Congress. Denmark; 1996
  88. 88. Asaduzzaman M, An M, Pratley JE, Luckett DJ, Lemerle D. Laboratory bioassay for canola (Brassica napus) allelopathy. Journal of Crop Science and Biotechnology. 2014;17(4):267-272
  89. 89. Wu H, Pratley JE, Lemerle D, Haig T. Laboratory screening for allelopathic potential of wheat (Triticum aestivum) accessions against annual ryegrass (Lolium rigidum). Australian Journal of Agricultural Research. 2000;51:259-266
  90. 90. Wu H, Pratley JE, Lemerle D, An M, Liu DL. Autotoxicity of wheat (Triticum aestivumL.) as determined by laboratory bioassays. Plant and Soil. 2007;296:85-93
  91. 91. Belz RG, Hurle K. Dose-response-a challenge for allelopathy? Nonlinearity. 2005;3:173-211
  92. 92. Kim S, Madrid A, Park S, Yang S, Olofsdotter M. Evaluation of rice allelopathy in hydroponics. Weed Research. 2005;45:74-79
  93. 93. Brown PD, Morra MJ. Hydrolysis products of glucosinolates inBrassica napustissues as inhibitors of seed germination. Plant and Soil. 1996;181(2):307-316
  94. 94. Buchanan AL, Kolb LN, Hooks CRR. Can winter cover crops influence weed density and diversity in a reduced tillage vegetable system? Crop Protection. 2016;90:9-16
  95. 95. Haramoto ER, Gallandt ER. Brassica cover cropping: I. Effects on weed and crop establishment. Weed Science. 2005;53(5):695-701
  96. 96. Petersen J, Belz R, Walker F, Hurle K. Weed suppression by release of isothiocyanates from turnip-rape mulch. Agronomy Journal. 2001;93(1):37-43
  97. 97. Vaughn SF, Boydston RA. Volatile allelochemicals released by crucifer green manures. Journal of Chemical Ecology. 1997;23(9):2107-2116
  98. 98. Moore JR, Asaduzzaman M, Pratley JE. Dual direction allelopathy: the case of canola, wheat and annual ryegrass Building Productive, Diverse and Sustainable Landscapes: Proceedings of the 17th Australian Society of Agronomy Conference, 20–24 September 2015. Hobart, Australia; 2015
  99. 99. Seal AN, Pratley JE, Haig T. Can results from a laboratory bioassay be used as an indicator of field performance of rice cultivars with allelopathic potential againstDamasonium minus(starfruit). Australian Journal of Agricultural Research. 2008;59:183-188
  100. 100. Haig T, Haig TJ, Seal AN, Pratley JE, An M, Wu H. Lavender as a source of novel plant compounds for the development of a natural herbicide. Journal of Chemical Ecology. 2009;35:1129-1136
  101. 101. Weidenhamer JD. Biomimetic measurement of allelochemical dynamics in the rhizosphere. Journal of Chemical Ecology. 2005;31:221-236
  102. 102. Alcántara C, Pujadas A, Saavedra M. Management ofSinapis albasubsp.maireiwinter cover crop residues for summer weed control in southern Spain. Crop Protection. 2011;30(9):1239-1244
  103. 103. Kirkegaard JA, Sarwar M. Biofumigation potential of brassicas. Plant and Soil. 1998;201:71-89
  104. 104. Kirkegaard JA, Sarwar M. Glucosinolate profiles of Australian canola (Brassica napusL.) and Indian mustard (Brassica junceaL.) cultivars: Implications for biofumigation. Australian Journal of Agricultural Research. 1999;50(3):315-324
  105. 105. Kruidhof HM, Bastiaans L, Kropff MJ. Cover crop residue management for optimizing weed control. Plant and Soil. 2009;318(1–2):169-184
  106. 106. Boydston RA, Morra MJ, Borek V, Clayton L, Vaughn SF. Onion and weed response to mustard (Sinapis alba) seed meal. Weed Science. 2011;59(4):546-552
  107. 107. Asaduzzaman M, An M, Pratley JE, Luckett DJ, Lemerle D. Canola (Brassica napus) germplasm shows variable allelopathic effects against annual ryegrass (Lolium rigidum). Plant and Soil. 2014;380:47-56
  108. 108. Asaduzzaman M, Pratley JE, An M, Luckett DJ, Lemerle D. Metabolomics differentiattion of canola genotypes: Towards an understanding of canola alleolochemicals. Frontiers in Plant Science. 2015;5:765. DOI: 10.3389/fpls.2014.00765
  109. 109. Asaduzzaman M, Luckett DJ, An M, Pratley JE, Lemerle D. Management of Paterson’s curse (Echium plantagineum) through canola interference. In: Nineteenth Australasian Weeds Conference, Hobart; 2014. pp. 162-165
  110. 110. Bertholdsson NO. Breeding spring wheat for improved allelopathic potential. Weed Research. 2010;50:49-57
  111. 111. Zhou YJ, Cao CD, Zhuang JY, Zheng KL, Guo YQ, Ye M, et al. Mapping QTL associated with rice allelopathy using the rice recombinant inbred lines and specific secondary metabolite marking method. Allelopathy Journal. 2007;19:479-485
  112. 112. Ebana K, Yan W, Dilday R, Namai H, Okuno K. Analysis of QTL associated with the allelopathic effect of rice using water-soluble extracts. Breeding Science. 2001;51:47-51
  113. 113. Jensen LB, Cortois B, Shen LS, Li ZK, Olofsdotter M, Mauleon RP. Locating genes controlling allelopathic effects against barnyard grass in upland rice. Agronomy Journal. 2001;93:21-26
  114. 114. Jensen LB, Courtois B, Olofsdotter M. Quantitative trait loci analysis of allelopathy in rice. Crop Science. 2008;48:1459-1469
  115. 115. Chen XH, Hu F, Kong CH. Varietal improvement in rice allelopathy. Allelopathy Journal. 2008;22:379-384
  116. 116. Ebana K, Yan W, Dilday RH, Namai H, Okuno K. Variation in allelopathic effect of rice (Oryza sativaL.) with water-soluble extracts. Agronomy Journal. 2001;93:12-16
  117. 117. Navarez DC, Olofsdotter M, editors. Relay Seeding Technique for Screening Allelopathic Rice (Oryza sativa). Copenhagen, Denmark: The Second International Weed Control Congress; 1996
  118. 118. Dali Z, Qian Q, Sheng T, Guojun D, Fujimoto H, Yasufumi K, et al. Genetic analysis of rice allelopathy. Chinese Science Bulletin. 2003;48:265-268

Written By

Harsh Raman, Nawar Shamaya and James Pratley

Submitted: November 1st, 2017 Reviewed: June 18th, 2018 Published: November 5th, 2018