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Keywords:

  • crown rot;
  • deoxynivalenol;
  • scab

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Wheat crops in southeast Queensland (Qld) and northern New South Wales (NSW) were infected with fusarium head blight (FHB)-like symptoms during the 2010–11 wheat growing season. Wheat crops in this region were surveyed at soft dough or early maturity stage to determine the distribution, severity, aetiology and toxigenicity of FHB. FHB was widespread on bread wheat and durum, and Fusarium graminearum and/or F. pseudograminearum were diagnosed from 42 of the 44 sites using species-specific PCR primers directly on spikelets or from monoconidial cultures obtained from spikelets. Stem base browning due to crown rot (CR) was also evident in some samples from both states. The overall FHB and CR severity was higher for NSW than Qld. Deoxynivalenol (DON) concentration of immature grains was more than 1 mg kg−1 in samples from 11 Qld and 14 NSW sites, but only 13 of 498 mature grain samples sourced from the affected areas had more than 1 mg kg−1 DON. DON concentration in straw also exceeded 1 mg kg−1 in eight Qld and all but one NSW sites but this was not linked to DON concentration of immature grains. The proportion of spikelets with positive diagnosis for F. graminearum and/or F. pseudograminearum and weather-related factors influenced DON levels in immature grains. The average monthly rainfall for August–November during crop anthesis and maturation exceeded the long-term monthly average by 10–150%. Weather played a critical role in FHB epidemics for Qld sites but this was not apparent for the NSW sites, as weather was generally favourable at all sites.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Of the two wheat diseases in Australia caused by Fusarium species, crown rot (CR) is chronic and widespread throughout the wheat belt, but fusarium head blight (FHB) is sporadic and restricted in its geographical distribution to areas in northern New South Wales (NSW) and Queensland (Qld). Each year CR costs an estimated Aus$80 million from lost production and grain quality (Murray & Brennan, 2009). Crop losses in Australia from FHB are difficult to estimate due to its sporadic occurrence, but reliable data are available from elsewhere where severe infections have affected vast areas of the globe, with recent epidemics in Canada, China, Europe, South America and the USA. For instance, during 1998–2000 FHB inflicted an estimated US$2·7 billion loss due to reduced yield and price discounts from lowered grain quality in the northern Great Plains and central USA (Goswami & Kistler, 2004).

Both FHB and CR can lead to mycotoxin accumulation in grain (Desjardins, 2006; Mudge et al., 2006) with high levels of the trichothecenes nivalenol (NIV) and deoxynivalenol (DON) and other mycotoxins from FHB infection potentially rendering grain and grain products unsafe for human and animal consumption. In Australia, Fusarium pseudograminearum is the main CR pathogen and F. culmorum and other Fusarium species are distributed in localized agro-climatic regions (Burgess et al., 2001; Backhouse et al., 2004). FHB is predominantly caused by F. graminearum around the world (Xu & Nicholson, 2009) including in Australia (Southwell et al., 2003; Akinsanmi et al., 2004), although F. pseudograminearum has also caused FHB epidemics in Australia (Burgess et al., 1987). However, mycotoxin contamination has rarely been recorded in Australian grains (Moore et al., 1985; Blaney et al., 1987; Tobin, 1988). This may be largely because FHB epidemics are infrequent and restricted in distribution in Australia and the widespread CR pathogen F. pseudograminearum is less toxigenic than F. graminearum (Chakraborty et al., 2006).

The distinction between the two groups of F. graminearum infecting corn and wheat was originally made by Purss (1971), who suggested that a specialized form of F. graminearum was responsible for CR in wheat. However, all isolates from CR of wheat, stalk rot of maize or FHB of wheat were capable of causing FHB, although there were differences in severity between isolates. Similar findings were reported recently, where isolates of F. pseudograminearum were more aggressive for CR than F. graminearum isolates (Chakraborty et al., 2010). In surveys of wheat crops, F. pseudograminearum was more frequently isolated from the crown, whereas F. graminearum mostly originated from the head, but both species caused CR and FHB in infection assays (Akinsanmi et al., 2004).

Hot and dry weather during crop anthesis and maturation promotes CR expression in Australia (Burgess et al., 2001). This weather prevents the development of FHB epidemics, which require a combination of wet and warm climatic conditions during anthesis (McMullen et al., 1997). Nevertheless, since 1983 at least five FHB outbreaks have been recorded in Australia. Fusarium pseudograminearum caused the 1983 epidemic in northern NSW affecting an estimated 50% of heads, with 0·6–1·8 mg kg−1 DON recorded in grain (Burgess et al., 1987; Tobin, 1988). In 1999, F. graminearum affected between 2 and 100% of heads in durum crops on the Liverpool Plains region of northern NSW (Southwell et al., 2003), with no data available on DON concentration in infected grains. In 2000, 2–25% of wheat heads in 11 of 28 paddocks surveyed in southern NSW were affected by FHB, with the causal species recorded as F. graminearum, F. cerealis, F. culmorum and F. pseudograminearum, in order of declining frequency; one grain sample had a DON concentration of 19·1 mg kg−1 (Tan et al., 2004). In 2003, there was a localized FHB outbreak in the North Star region of northern NSW caused by F. graminearum which led to 4 mg kg−1 DON in grains from one infected durum paddock (S. Chakraborty et al., unpublished data). In 2004, F. graminearum and F. avenaceum were detected on wheat grains from southwest Western Australia following an isolated FHB outbreak (Loughman et al., 2004; Wright et al., 2010). Whether this outbreak was associated with mycotoxin production in grain was not established, although isolates obtained from grain samples including those of F. graminearum produced DON and other mycotoxins in culture (Tan et al., 2012).

During the 2010 winter cropping season many crops in southeast Qld (Neate & McIntyre, 2011) and northern NSW were infected with FHB-like symptoms and many farmers reported severe infection in both bread wheat and durum crops. In a collaborative effort, CSIRO, Queensland Department of Employment, Economic Development and Innovation and the NSW Department of Primary Industries carried out surveys of crops to determine the extent and severity of the FHB epidemic and to determine its aetiology and toxigenicity. An additional objective was to examine links between key weather factors and FHB, because rainfall and temperature are important variables in all published FHB/DON prediction models (De Wolf et al., 2003). Only immature grains could be collected from the survey, as all crops were at the soft dough stage of maturity (Zadoks growth stage 83–85; Zadoks et al., 1974). Samples of commercial harvests from the affected areas were sourced from one of the bulk grain handlers.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Sample collection and severity assessment

A total of 44 sites representing affected areas in southeast Qld and the Liverpool Plains region of northern NSW with a history of FHB epidemics were selected. Of these, 25 sites in southeast Qld were surveyed in late October and 19 in northern NSW were sampled in early December 2010. In Qld, 10–20 samples were collected from a 100–150 m transect from each site, each sample consisting of 4–5 plants, to obtain 50–100 tillers per site. The 19 sites in NSW were sampled by collecting several plants from 10 evenly spaced locations along a 100–150 m transect at each site. This provided 50–100 tillers from each site but it was not possible to identify individual plants because of high planting density.

At each Qld site FHB incidence was recorded as the percentage of infected heads among 50–100 heads, or as the average percentage of infected heads in five 1 m rows within a crop. However, there was widespread blighting of spikelets by Botryosphaeria sp., which made identification difficult (Platz et al., 2011). Symptoms of FHB could not be clearly distinguished during sampling of the NSW sites as all spikelets had started to discolour due to crop senescence at the time of collection. Hence, the proportion of spikelets that gave positive diagnosis for F. graminearum, F. pseudograminearum or both of these species using species-specific PCR primers was used as an indirect estimate of FHB severity for all Qld and NSW sites. Samples of intact tillers/plants were brought back to the laboratory for further processing and assessment. Individual tillers were separated, leaf sheath removed from the stem base, and cleaned to remove soil. CR incidence was recorded as the percentage of infected tillers based on symptoms of stem base browning from 50 randomly selected tillers from each site. CR severity was estimated as the percentage of stem base browning by measuring the length of tiller and the total length of browning from the crown. Details of site, wheat variety, where available, and disease severity are given in Table 1.

Table 1. Details of areas surveyed and the severity of fusarium head blight (FHB) and crown rot (CR) at 44 different sites in Queensland and New South Wales, Australia, sown to bread wheat or durum in 2010
SiteStateAreaWheat typeBureau of Meteorology station and its proximity (km) to the field siteFHB severity diagnostics (%)CR incidence (% infected tiller)CR severity (% stem browning)
 1QldMoonieBreadInglewood, 881·790·5
 2QldKinkoraBreadToowoomba, 465·600·0
 3QldSpringvaleBreadDalby, 239·700·0
 4QldBungunyaBreadGoondiwindi, 865·600·0
 5QldBowenvilleBreadDalby, 275·600·0
 6QldMarmadua1BreadMiles, 1045·600·0
 7QldCondamineBreadMiles, 331·400·0
 8QldNoorindooBreadSurat, 170·000·0
 9QldMarmadua2BreadMiles, 10410·000·0
10QldGoondiwindiBreadGoondiwindi, 326·400·0
11QldHannafordBreadMiles, 811·400·0
12QldCondamine PlainsBreadOakey, 5425·000·0
13QldBulli CreekBreadInglewood, 418·300·0
14QldToobeahBreadGoondiwindi, 4619·461·5
15QldWyagaBreadInglewood, 326·900·0
16QldBlaxlandBreadDalby, 1551·400·0
17QldWarraBreadDalby, 425·6212·1
18QldKindonBreadInglewood, 295·6122·7
19QldPiltonBreadWarwick, 261·400·0
20QldLawesBreadGatton, 12·800·0
21QldPerseveranceBreadToowoomba, 2615·300·0
22QldCoolmundaBreadInglewood, 309·700·0
23QldThe HermitageBreadWarwick, 14·200·0
24QldBeaudesert1BreadBeaudesert, 15Not rated00·0
25QldBeaudesert2BreadBeaudesert, 1579·200·0
26NSWSpring Ridge1DurumQuirindi, 4213·33811·6
27NSWNombi1DurumCoonabarabran, 484·6339·1
28NSWSpring Ridge2DurumQuirindi, 4213·16610·6
29NSWNombi2BreadCoonabarabran, 482·800·0
30NSWTambar SpringsDurumCoonabarabran, 535·61912·7
31NSWSpring Ridge3DurumQuirindi, 426·9527·4
32NSWSpring Ridge4DurumQuirindi, 425·6125·9
33NSWBlackvilleDurumQuirindi, 444·2<15·3
34NSWSpring Ridge5DurumQuirindi, 4218·1249·6
35NSWCaroona1BreadQuirindi, 260·000·0
36NSWCaroona2DurumQuirindi, 2611·100·0
37NSWSpring Ridge6DurumQuirindi, 4231·04011·7
38NSWWerris CreekDurumQuirindi, 1827·600·0
39NSWCaroona3BreadQuirindi, 265·600·0
40NSWEmerald HillBreadGunnedah, 239·500·0
41NSWKelvinBreadGunnedah, 171·454·1
42NSWWillalaBreadGunnedah, 409·700·0
43NSWCaroona4DurumQuirindi, 265·656·6
44NSWParraweenaBreadQuirindi, 351·400·0

Pathogen diagnostics and chemotype

Fusarium species associated with FHB infection were determined using two different methods: direct diagnosis using species-specific PCR primers, and isolation and subsequent identification of monoconidial cultures using PCR and other techniques. For direct diagnostics of the Fusarium species, three spikelets each from 24–40 heads per site were analysed with species-specific PCR primers for F. graminearum and F. pseudograminearum (Table 2). The total number of spikelets processed was 3309, consisting of between 72 and 120 spikelets per site. As it was not possible to differentiate infected from uninfected spikelets, the top, middle and bottom spikelets from each head were processed. DNA was directly extracted from the entire spikelet tissue using QuickExtract™ Plant DNA Extraction Solution (EPICENTRE) according to manufacturer’s instructions. Amplification reactions were performed in 10 μL volumes containing PCR reaction buffer (67 mm Tris-HCl, pH 8·8; 16·6 mm (NH4)2SO4; 0·45% w/v Triton X-100; 0·2 mg mL−1 gelatin), 3·0 mm MgCl2, 240 nm of each primer, 200 mm dNTPs, 1·5 units Taq DNA polymerase (Biotech Int.) and 1 μL of DNA extract. For F. graminearum Fg16F/Fg16R primer pairs (Table 2), amplifications consisted of an initial denaturation at 95°C for 60 s, followed by 40 cycles of denaturation at 95°C for 30 s, annealing at 62°C for 30 s and extension at 72°C for 40 s, with a final extension at 72°C for 10 min. The cycling parameters for F. pseudograminearum primer pairs (Fp1-1/Fp1-2) were as previously described (Scott & Chakraborty, 2006). Amplicons were separated by electrophoresis in 1% agarose gels in TAE (45 mm Tris-acetate; 1 mm EDTA), containing 0·1 μL mL−1 GelRed dye and visualized under UV light.

Table 2. Polymerase chain reaction primers used for species identification, chemotyping and gene sequencing
Target species/Gene regionPrimer nameaDirectionPrimer sequence (5–3)Annealing temperature (°C)
  1. aPrimer references: Fp1-1/Fp1-2 (Aoki & O’Donnell, 1999); Fg16F/Fg16R (Nicholson et al., 1998); EF1/EF2 (O’Donnell et al., 2000); 3CON, 3NA, 3D15A and 3D3A (Starkey et al., 2007).

Fusarium pseudograminearum Fp1-1ForwardCGGGGTAGTTTCACATTTCCG57
Fp1-2ReverseGAGAATGTGATGACGACAATA
Fusarium graminearum Fg16FForwardCTCCGGATATGTTGCGTCAA62
Fg16RReverseGGTAGGTATCCGACATGGCAA
Translation elongation factor-1α (TEF)EF1ForwardATGGGTAAGGA(A/G)GACAAGAC53
EF2ReverseGGA(G/A)GTACCAGT(G/C)ATCAT
Tri33CONForwardTGGCAAAGACTGGTTCAC52
3NAReverseGTGCACAGAATATACGAGC
3D15AReverseACTGACCCAAGCTGCCATC
3D3AReverseCGCATTGGCTAACACATG

To obtain monoconidial cultures the top, middle and bottom spikelets from each of 3–7 heads were surface sterilized in 1% available chlorine solution for 3 min, rinsed three times in sterile distilled water and plated on ¼ strength potato dextrose agar (PDA) containing 10 μg mL−1 of tetracycline hydrochloride and 100 μg mL−1 of streptomycin sulphate. Following 3–5 days incubation at 25°C, a spore suspension from each colony was streaked onto water agar plates and single macroconidia were picked up under a stereoscope using a sterile needle and subcultured onto full-strength PDA and then incubated at 25°C for 5–7 days. A total of 362 monoconidial isolates were obtained from these sites.

DNA from each monoconidial culture was extracted from 2 mg fresh weight of fungal mycelia scraped from PDA plates by adding QuickExtract™ Plant DNA Extraction Solution following the manufacturer’s protocol, except that 50 μL of the extraction solution was added per sample instead of 100 μL. The fungal mycelium and the extraction solution were placed in a well of a 96-well plate and heated at 65°C for 6 min then at 98°C for 2 min to complete the protocol. One microlitre of DNA from each isolate was initially amplified using the F. pseudograminearum and F. graminearum species-specific primers using conditions outlined above. For isolates that did not belong to either of these species, the translation elongation factor-1α gene (TEF) was amplified by PCR using a gene specific primer (Table 2). Amplifications were performed at the annealing temperature as previously described (Scott & Chakraborty, 2006). Following amplification, amplicons were cleaned using QIAquick PCR Purification Kit (QIAGEN) according to manufacturer’s instructions and following published methods (Obanor et al., 2010). The concentration of the amplicon was determined with a NanoDrop 3300 (Thermo Scientific) and sequenced using BigDye Terminator v. 3.1 chemistry in a 96-capillary automated DNA sequencer (ABI 3730xl, Applied Biosystems) in both forward and reverse directions.

The trichothecene chemotype of each isolate was determined by multiplex PCR that targeted the Tri3 gene sequence (Starkey et al., 2007). This included a primer common to all chemotypes, 3CON, and three chemotype-specific primers: 3NA, 3D15A and 3D3A (Table 2). This multiplex produced amplicons of 243, 610 and 840 bp for 3ADON, 15ADON and NIV chemotypes, respectively. The multiplex reaction was performed in 10 μL volumes with PCR reaction buffer (67 mm Tris-HCl, pH 8·8; 16·6 mm (NH4)2SO4; 0·45% w/v Triton X-100; 0·2 mg mL−1 gelatin), 3·0 mm MgCl2, 240 nm of each primer, 200 mm dNTPs, 1·5 units Taq DNA polymerase (Biotech Int.) and 1 μL of DNA extract. Amplification conditions were an initial denaturation at 94°C for 2 min, followed by 30 cycles of 30 s at 94°C, 30 s at 52°C and 1 min at 72°C, and a final extension of 7 min at 72°C.

Mycotoxin determination

Immature grain and straw samples comprising 10–15 cm from crown upwards of the stem base from 10 tillers were pooled for each site to determine mycotoxin content. Tillers from all 44 sites only had immature grains because of the time of sampling. DON content of grain and straw samples was determined using a direct competitive quantitative enzyme-linked immunosorbent assay (ELISA). Mycotoxins were extracted in 85% acetonitrile (Merck) in water from 1–2 g ground straw or 4–5 g ground grain using Accelerated Solvent Extraction equipment (ASE 2000; DIONEX Corporation). For immature grains, mycotoxins from two replicate subsamples were extracted and assessed for each site. The extraction protocol had three cycles, each consisting of heating for 5 min at 40°C under 10·3 MPa, 5 min static phase and a flush phase. Acetonitrile was evaporated under nitrogen in a fume hood and contents resuspended in milli-Q water before mycotoxin assays. All samples were assayed using a 96-well ‘AgraQuant’ ELISA kit (Romer Labs Singapore Pty, Ltd) for DON, with a 0·25–5·0 mg kg−1 quantitation range and 0·2 mg kg−1 detection limit, following manufacturer’s instructions. The DON content was estimated from absorbance at 450 nm and 630 nm using a spectrophotometer (iEMS reader MF, Labsystems). When DON content of samples exceeded the highest standard (>5·0 mg kg−1) of the ELISA kit, extracts were diluted with milli-Q water and reanalysed to keep within the quantification range of the test kit.

In addition to ELISA, samples of immature grains were analysed for DON and NIV using Gas Chromatography-Mass Spectrometry (GC-MS, Perkin Elmer AutoSystem XL with GC and Turbomass Upgrade MS) following the method of Mirocha et al. (1998) with modifications. The MS was tuned using perfluorotributylamine according to manufacturer’s recommendations. The injector port temperature was maintained at 300°C, the transfer line at 280°C and the ion source at 280°C. The oven temperature was initially set at 80°C for 1 min, then ramped at 25°C min−1 to 280°C and maintained at 280°C for 6 min. The carrier gas helium was used at 1 mL min−1 flow rate. A portion of the mycotoxin extract was filtered through a column and 1 mL of the filtrate was dried under nitrogen and derivatized with 100 μL TMSI/TMCS 100:1 (Mirocha et al., 1998) and 1 μL of the organic phase was injected into the GC-MS to determine DON and NIV concentrations in the split-less mode of the machine.

GrainCorp routinely collects samples at grain receiving stations for quality control. These samples are directly collected from truckloads using three vertical probes and bulked for each truckload. DON content was determined from 498 of these samples. A 100 g subsample was ground and DON was extracted in water and assayed using ELISA as above.

Weather conditions

To better understand which weather conditions favoured the development of FHB epidemics, data on monthly average rainfall, minimum and maximum temperature and relative humidity for August, September, October, November and December 2010 were accessed from the Bureau of Meteorology (BOM) weather stations located near the survey sites. These included Beaudesert, Dalby, Goondiwindi, Inglewood, Miles, Surat, Toowoomba and Warwick in Qld and Coonabarabran, Gunnedah, Murrurundi and Quirindi in NSW. Long-term average monthly rainfall data for these months were accessed from the same source. Data on the proportion of spikelets with positive diagnosis of F. graminearum (GPROP), F. pseudograminearum (PPROP), and/or either species (ALLPROP), proportion of spikelets with successful isolation of these two species, CR incidence and severity, DON in grain (GRDON) and stem base tissue (STDON), type of wheat and weather variables were summarized and Pearson’s correlation coefficients determined for each pair. The influence of weather and other variables on ALLPROP and GRDON was determined by multiple linear regression analysis using SAS software (SAS Institute Inc.).

Data for each state were analysed together and separately and a stepwise procedure was used with the condition for entry and retention of a variable set at  0·05.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Disease severity

The 25 Qld sites varied in FHB incidence and severity, and based on visual assessment at the time of sampling, FHB was present in all but two sites, Kinkora and The Hermitage. Fusarium species causing FHB were diagnosed from 24 of the 25 Qld sites. FHB severity ranged from 1·4 to >79% of spikelets infected, as tested using PCR. Although infected spikelets could not be differentiated from uninfected ones at the time of PCR diagnostics, all Qld sites except Noorindoo provided a positive diagnosis for F. graminearum and/or F. pseudograminearum (Table 1). Fusarium species were also diagnosed from Kinkora and The Hermitage, even though FHB symptoms were not visible at the time of sampling. Symptoms of stem base browning due to CR were present on tillers from four sites in Qld and the extent of stem browning ranged from 0·4 to 2·7%.

Of the 19 sites in NSW, PCR diagnostics indicated the presence of Fusarium species in all but one site (Caroona1), with 1·4–31% of spikelets infected, based on positive diagnosis of F. graminearum and/or F. pseudograminearum (Table 1). Stem base browning due to CR was present at 11 sites, with >10% of tillers infected with CR at eight of these sites. The severity of CR based on the extent of browning ranged from 4 to 12%. The overall FHB and CR severity was higher in NSW than Qld sites.

All Qld and seven NSW sites were planted to bread wheat varieties and the remaining 12 NSW sites had a durum crop, but there was no clear-cut association between the type of wheat and FHB or CR severity in NSW. However, CR was generally more severe on durum than on bread wheat varieties and only two of the 11 NSW sites planted to durum varieties were free of CR. The two adjacent paddocks at Beaudesert in Qld planted to bread wheat varieties, Crusader and Lang, differed greatly in FHB severity. FHB damage was extensive on Crusader with 100% spikes and spikelets infected, while infection on Lang was moderate to high with 42% of spikes infected (Fig. 1).

image

Figure 1.  Fusarium head blight epidemic at a field site in Queensland planted with two different bread wheat varieties. (a) Severe blighting occurred on susceptible variety Crusader (in the background) and moderate levels of infection on Lang (in the foreground). In Crusader all spikelets of all plants were affected (b), some with pink mycelium and sporodochia (c, arrow), but only some heads were infected in Lang (d) and not all spikelets within a head were infected (e).

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Pathogen diagnosis and chemotype

Overall, direct diagnosis using PCR resulted in a higher number of spikelets yielding F. graminearum and/or F. pseudograminearum compared with the isolation and diagnosis of monoconidial cultures. The number of sites where both species were present was also higher with direct diagnosis of spikelets (Fig. 2). In Qld both detection methods gave a higher frequency of F. graminearum than F. pseudograminearum. In NSW, F. pseudograminearum was more frequent than F. graminearum according to identification of monoconidial isolates, while the two species were isolated with nearly equal frequency by direct diagnosis of spikelets (Fig. 2). In addition, direct diagnosis showed that both species were present at seven Qld sites but the two species could only be isolated from one of these sites (Wyaga) based on identification of monoconidial isolates. Neither species was isolated from six NSW and 16 Qld sites using identification of monoconidial isolates but only three sites (one in NSW and two in Qld) gave a negative result for either Fusarium species using direct diagnosis from spikelets. Isolation and species identification of monoconidial cultures was more successful with samples collected from NSW than from Qld.

image

Figure 2.  Fusarium species identified from 44 field sites in Queensland and New South Wales from monoconidial isolates (a) or directly diagnosed from wheat spikelets (b) using species-specific primers.

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Monoconidial cultures that did not belong to either F. graminearum or F. pseudograminearum were further tested using the TEF gene sequence to assign isolates to F. cortaderiae, F. equiseti or F. poae. Of these, F. poae was the most widely detected from one site in Qld (Coolmunda) and six sites in NSW (Blackville, Caroona2, Spring Ridge3, Spring Ridge4, Spring Ridge5, Spring Ridge6). The other two Fusarium species were only detected from Qld, with F. cortaderiae isolated from Kindon and Perseverance, and F. equiseti only from Kindon. Eight other isolates, six from Qld and two from NSW, were confirmed as unidentified members of the genus Fusarium based on their TEF gene sequence.

Of the 80 isolates tested for their potential to produce 3ADON, 15ADON and NIV mycotoxins, two isolates of F. cortaderiae and two isolates of F. graminearum sensu lato were NIV chemotype; the remaining 31 F. graminearum isolates were 15ADON and all 45 isolates of F. pseudograminearum were 3ADON chemotype.

Mycotoxin in wheat tissue

Based on ELISA assays of immature grains, 11 Qld and 14 NSW sites had DON concentrations >1 mg kg−1 (Fig. 3), with the Crusader bread wheat paddock at Beaudesert having the highest DON concentration of >233 mg kg−1 (data truncated in Fig. 3). Condamine Plains and the Lang bread wheat paddock at Beaudesert, both in Qld and geographically separated by >200 km, had >30 mg kg−1 DON. Overall, DON levels were lower in NSW samples and the highest concentration of >6 mg kg−1 DON was detected at Spring Ridge1 and Caroona4. Immature grains of durum varieties generally had higher levels of DON than bread wheat varieties.

image

Figure 3.  Deoxynivalenol concentration in immature grain and straw of wheat plants from 44 field sites in Queensland and New South Wales determined using enzyme-linked immunosorbent assay.

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DON in straw exceeded 1 mg kg−1 in eight Qld and all but one site in NSW (Fig. 3). Unlike DON in immature grains, there were similar levels of DON in straw at many Qld and NSW sites. There was no link between DON in immature grains and straw, and Pearson’s correlation coefficient (= 0·15) was not significant (= 0·32).

In common with ELISA, GC-MS assays of immature grains showed >1 mg kg−1 DON in samples from the same 11 Qld and 14 NSW sites (data not shown). Overall, there was good agreement between DON levels determined using the two methods, and Pearson’s correlation coefficient (= 0·98) for the loge-transformed data was highly significant (< 0·0001). Immature grain samples from the Beaudesert1 site also had detectable levels of 11·95 mg kg−1 NIV according to the GC-MS assay.

Of the 498 mature grain samples, DON concentration was <1 mg kg−1 in 485 samples, between 1 and 5 mg kg−1 in 12 samples and >5 mg kg−1 in one sample (data not shown). Ten of the 485 samples had between 0·7 and 0·9 mg kg−1 DON.

Weather conditions

According to Pearson’s correlation coefficients, the proportion of spikelets with positive diagnosis for F. graminearum (GPROP) was positively correlated with mean rainfall in October (RO; = 0·52, < 0·001) and 9 AM temperature in August, September and November (data not shown) but negatively correlated (= –0·39, < 0·01) with rainfall in November. In contrast, the proportion of spikelets with positive diagnosis for F. pseudograminearum (PPROP) was negatively correlated (= –0·31, < 0·04) with September rainfall but there was no significant correlation with any other weather variable. In addition to several weather variables, DON concentration in immature grain (GRDON) was positively correlated (= 0·89, < 0·001) with GPROP but not PPROP (= –0·05, = 0·72). In contrast, DON concentration in straw (STDON, = 0·48, = 0·001) was positively correlated with PPROP but not GPROP (= 0·17, = 0·27). Interestingly, the type of wheat planted had the highest number of significant correlations with weather and other variables, and these may reflect the different climatic requirements for the growth and development of durum and bread wheat crops.

The two-state multiple linear regression model that best described the proportion of spikelets with positive diagnosis for F. graminearum and/or F. pseudograminearum (ALLPROP) for all sites had only one weather variable, minimum temperature for November; the other two variables were related to DON in wheat tissue (data not shown). The low R2 (0·78) of this model was contributed by data from Qld sites, and when data for each state were considered separately, the state-wide model for Qld had a lower R2 (0·64) than the NSW model (R2 = 0·9). The model explaining DON in immature grains (GRDON) accounted for a very high degree of certainty using data for both states (R2 = 0·9) or separately for Qld (R2 = 0·9) and NSW (R2 = 0·8). In addition to spikelets with positive diagnosis for F. graminearum and/or F. pseudograminearum, weather-related variables were significant terms in the GRDON model for Qld, but not for NSW, indicating that weather conditions at all NSW sites were favourable for FHB infection and DON contamination.

Long-term monthly rainfall data from 13 nearby BOM stations were examined to further explain the unusually high level of FHB in 2010. The rainfall figures for 2010 were higher than the long-term monthly average for August (40%), September (50%), October (27%), November (10%) and December (150%) across these stations (Fig. 4). Only November rainfall was lower than the long-term average for 8 of the 13 stations.

image

Figure 4.  Change in rainfall in 2010 from long-term average during the months of wheat anthesis in areas affected by FHB in 2010. Data are from Bureau of Meteorology stations nearest to the sites surveyed.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Fusarium head blight was widespread in southeast Qld and northern NSW during the 2010 winter cropping season, with spikelet samples collected from 42 of 44 sites surveyed testing positive for F. graminearum and/or F. pseudograminearum. All of the Qld and seven of the 19 NSW sites were planted to bread wheat varieties. Hence, the widespread occurrence of FHB in 2010 does not indicate a clear association with durum wheat, which is more susceptible than bread wheat (Ma et al., 2011). Stem base browning due to CR was also evident in some samples from both states. The overall FHB and CR severity was higher for NSW than Qld. DON concentration in immature grains exceeded 1 mg kg−1 in samples from 11 Qld and 14 NSW sites. However, only 15 of the 498 mature grain samples tested had >1 mg kg−1 DON. DON levels in straw were above 1 mg kg−1 in eight Qld and all but one site in NSW but this was not related to DON concentration of immature grains. The proportion of spikelets with positive diagnosis for F. graminearum and/or F. pseudograminearum and weather-related factors were important determinants of DON in immature grains, but the role of weather appeared more important for sites in Qld than in NSW. The average monthly rainfall for August–November 2010, coinciding with crop anthesis and maturation, exceeded the long-term monthly average by 10–150%, and this appears to be the most important weather factor leading to this widespread FHB epidemic in 2010.

This study has highlighted the importance of correct diagnosis of Fusarium species associated with FHB in accurately determining FHB incidence and severity. Visual observation of wheat spikes can be inconclusive, as other pathogens can mimic FHB symptoms. Blighting of spikelets by Botryosphaeria sp. (BHB) can easily be confused with symptoms of FHB. Some of the samples processed in this study that were negative for Fusarium spp. were possibly affected by BHB. Although BHB has become widespread throughout the wheat growing regions of eastern Australia, there has been no indication of any associated mycotoxin contamination (Platz et al., 2011). Even with a positive FHB diagnosis, the aetiology cannot be easily determined using traditional techniques such as macroconidia morphology or production of perithecia because these characteristics are often difficult to produce reliably enough to distinguish between pathogenic Fusarium species. Application of molecular tools for pathogen diagnostics have gained wide acceptance (Nicholson, 2001) due to their speed and accuracy. In addition, there is no need for specialists trained in fungal taxonomy, or initial selection of isolates from culturing on agar plates, where large number of competing saprophytic colonies can obscure recovery of the causal pathogen. In addition, molecular tools are the only way to delineate current species, which were previously described under the group-species F. graminearum (Starkey et al., 2007). The PCR based method used in this work offers a rapid and high throughput direct diagnostic assay. With further development, this method may be used to process large numbers of samples without the need for separate DNA extraction and/or isolation of the pathogen. Although the direct diagnostic method provided a higher percentage of positives than the recovery of the pathogen from infected tissue using monoconidial isolation, this is largely a reflection of sample size (72–120 spikelets processed by direct diagnostics per site, compared with 9–21 spikelets used to obtain monoconidial isolates). It is also possible that the direct diagnostic method may have detected propagules or mycelia of these species present on the surface of spikelets, and surface disinfection could minimize this. However, only isolation of monoconidial cultures can be used for plant infection studies necessary to establish pathogenicity applying Koch’s postulates.

The confirmed diagnosis and isolation of both F. graminearum and F. pseudograminearum from FHB infected spikelets from this and previous research (Burgess et al., 1987; Southwell et al., 2003) clearly indicates a lack of specialization for FHB among these two species in Australia. This contrasts with CR aetiology, where F. pseudograminearum is predominantly associated with CR under field conditions (Akinsanmi et al., 2004), and is more aggressive than F. graminearum (Chakraborty et al., 2010). These findings indicate that, as a species, F. pseudograminearum is specialized for CR despite both species being able to cause both diseases under artificial inoculation (Purss, 1971).

Deoxynivalenol concentration in immature grains was higher than 1 mg kg−1 at most sites, and this was linked to the proportion of spikelets yielding F. graminearum. In contrast, DON concentration of straw was closely associated with the proportion of F. pseudograminearum. With samples of straw and immature grains originating from the same plants at each site, a lack of correlation between DON in immature grains and straw suggests that different Fusarium species/isolates may be responsible for causing FHB and CR in northern NSW and southeastern Qld and the ensuing DON levels in the affected tissue. Although CR infected plants can lead to DON accumulation in grains (Mudge et al., 2006), its contribution to the overall DON concentration in grains is expected to be low in comparison to FHB infection. In a surveillance of commercial grains from 2007, 2008 and 2009 harvests, DON concentration did not exceed 1 mg kg−1 in any of >1100 samples tested from eastern Australia, including areas that have been affected by previous FHB epidemics (S. Chakraborty et al., unpublished data). DON concentration in mature grains from the 2010 harvests also did not reflect the high levels observed in immature grains. There may be several reasons for this. Weather damaged grains from the 2010 harvest were downgraded to feed grade and some growers, including at sites covered in this work, chose not to harvest their badly-affected crop, and the vast majority of mature grains tested were not feed grade. However, mature grains could not be collected from the survey sites due to time constraints and a lack of access due to continuous rain and severe flooding in some areas. FHB-affected grains lose their amber translucence to become opaque, bleached and chalky white in colour and shrivelled, and often these lightweight grains are discarded at screenings during commercial harvesting. However, apparently healthy-looking grains can contain high levels of DON, and Fusarium damaged kernels are only one indication of FHB severity. Sampling is another factor that contributes to variation in DON concentration. Nevertheless, any future model to predict DON risk must be validated using DON levels in mature grains from commercial harvests.

Toxicology studies have shown NIV chemotypes to be 10 times more toxic than DON, and there has been a shift towards NIV chemotypes in some areas (Yli-Mattila, 2010). The chemotype structure of the Australian F. graminearum population can be useful for an assessment of potential impacts on mycotoxin. Previously Tan et al. (2004) diagnosed four NIV chemotypes from six F. graminearum sensu lato isolates examined from southern NSW. Blaney & Dodman (2002), using F. graminearum sensu lato from wheat and maize, found that isolates from northern Qld were NIV chemotypes, while most southern Qld isolates were DON chemotypes. Of the 35 F. graminearum sensu lato (33 F. graminearum sensu stricto + two F. cortaderiae) isolates examined in this study, there were only four NIV chemotypes, with all being isolated from Qld (two from Kindon and one each from Perseverance and Beaudesert1). Of these four sites, immature grain samples from Beaudesert1 had 11·95 mg kg−1 NIV. Whether the absence of NIV chemotypes among the 11 F. graminearum isolates from northern NSW is due to the small number of isolates tested from this region needs to be examined further.

In common with previous FHB epidemics in Australia, high rainfall appears to have been a major contributing factor to the 2010 outbreaks of FHB and the ensuing DON contamination of wheat tissue. Monthly average rainfall during anthesis and the post-anthesis period was 10–150% higher than the long-term averages at the 13 BOM weather stations covering the areas surveyed. Quirindi in the Liverpool Plains of NSW is one of these stations. Rainfall at Quirindi during September and October was 170 mm in the 1999 FHB outbreak and 177 mm in 2010. Analysis of long-term rainfall data from this station has shown that in the past 116 years, apart from the years 1998–2000, rainfall during September–October had never exceeded 150 mm in consecutive years (S. Dobson, Australian Food Safety Centre of Excellence, Tasmanian Institute of Agricultural Research, University of Tasmania, personal communication). Although it is not possible to accurately predict future rainfall, it was estimated that there is a 20% chance of >154 mm at this location and a 10% chance of >172 mm during anthesis in the Spring Ridge area (Manning et al., 2000).

In the current study, different multiple regression models explained FHB severity and DON levels in immature grain in Qld and NSW, indicating that factors in addition to weather are also important. The two-state model for the proportion of spikelets with positive diagnosis for F. graminearum and/or F. pseudograminearum (ALLPROP) had only one significant weather variable, with other variables being related to DON levels in wheat tissue. The state-wise model for ALLPROP did not have a single significant weather variable for either Qld or NSW. Similarly, the NSW model for GRDON did not include a weather variable, but the Qld model had three. A lack of weather-related variables in the models suggests that weather was favourable for FHB at all sites in NSW, and hence not a discriminating factor between sites. This data will be useful to better pinpoint critical factors determining FHB and DON risks by combining with contrasting weather and FHB data from other seasons.

The proportion of spikelets with positive diagnosis for F. graminearum was a positive term in all GRDON models, indicating the contribution of the more toxigenic species F. graminearum (Chakraborty et al., 2006). However, F. pseudograminearum was equally prevalent as a FHB pathogen in both states. Other studies have also demonstrated this species as the causal agent of previous FHB epidemics in Australia (Burgess et al., 1987; Tan et al., 2004). These findings highlight the importance of early diagnosis of FHB pathogens as an essential element of any future surveillance for FHB/DON risk management in Australia, as the causal Fusarium species has a significant bearing on the risk of DON contamination in grains.

The availability of inoculum is another major contributor, and any prediction of FHB/DON risk needs to account for inoculum build-up during previous season(s). A preliminary model to estimate fusarium inoculum causing CR in Australia (Backhouse, 2006) may be adapted to predict FHB inoculum. In many other countries including the USA, the aetiology, biology and epidemiology of FHB are somewhat different to the Australian situation. For instance, while ascospores are the dominant primary inoculum for FHB worldwide, in Australia macroconidia constitute the bulk of aerial FHB inoculum (Mitter et al., 2006). A comparison of Gibberella zeae, the teleomorph of F. graminearum, from Australia and the USA has shown that North American isolates more readily produced perithecia in culture than Australian isolates. Similarly, perithecia of the heterothallic G. coronicola, teleomorph of F. pseudograminearum, have rarely been seen in the field (Summerell et al., 2001). While large quantities of ascospores released from perithecia in the spring travel great distances to infect crops in the USA, the geographical distribution of FHB in Australia has been limited by weather conditions and perhaps the limited dispersal of macroconidia. These findings caution against a direct application of FHB/DON prediction models (De Wolf et al., 2003) developed overseas without first testing their usefulness under Australian conditions.

Once validated, models to predict FHB/DON risk in combination with diagnostics data can be used to target areas potentially vulnerable to FHB infection for further surveillance. This will better differentiate FHB from BHB. Currently multiple fungal problems are lumped together into white grain and pink grain rejection categories, which does not consider DON risk associated with some of these grains. Implementation of such risk intelligence in the form of early warning systems can be used for FHB/DON prevention/management, including the use of fungicide sprays, screening and gravity tables to separate pinched and light grains, and downgrading to feed grade.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Financial support for this work from the Grains Research and Development Corporation, CSIRO Plant Industry, Queensland Department of Employment, Economic Development and Innovation and NSW Department of Primary Industries are gratefully acknowledged. Mr Ross Perrott of CSIRO Plant Industry provided technical assistance, which is gratefully acknowledged.

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  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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