SMUT EPIDEMIOLOGY SRDC-funded project

Rob MagareyBSES Limited, Tully

Epidemiology

Definition

Study of the spread, build up and effect of an epidemic

Smut epidemic in Queensland

• First detection: 9th June 2006, Isis

– single side-shoot– nothing known of epidemic

history• How many farms?• Which parts of the district?• What varieties? etc

• Other detections (2006)– Mackay: 7th November – Herbert: 15th December

Smut epidemics: what we knew!

• Smut can spread fast

• Is affected by climate– Wetter conditions can slow build up– Warmer temperatures favour the

disease– Hot, dry (irrigated) is most suitable

• Inoculum can travel a long way– 1000s km– but heaviest inoculum pressure is

within just a few metres of an infested crop

• Yield losses: – 0.6% loss for each 1% infested

stalks = 62% yield loss in HS

Smut: what we knew!

• Our commercial varieties were highly susceptible

• That many of the best canes were also HS

• It would be difficult to replace crops quickly to establish resistant crops

• Accessing disease-free plant sources was very important since smut can be planted in apparently healthy looking cane

Smut epidemics: what we didn’t know

• When it would be detected in Northern, Burdekin, NSW etc

• How long it would take to reach each farm in affected districts

• How quickly the disease would build up in HS crops

• When significant yield losses would start occurring in infested crops

• What influence climate would have

• What level of resistance was needed in each district to restrict losses

• How our potential replacement varieties would go in each district

Project (farmer) questions

Immediate questions

• How fast will it reach my farm? (spread)

• How quickly will it build up in my HS crops (build-up)

• How fast will I need to terminate the crop

before losses occur?

Additional questions (project extension)

• How will the ‘I’ varieties withstand the epidemic?

• What yield losses are / will occur?

• When will the epidemic pass?

Epidemic J curve

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Time

Num

ber

of fa

rms

Slow start

Fast finish

Epidemics

Epidemiology methods

Two initial focus areas

• Smut spread Monitoring a non-diseased farm

network

• Smut build up increase in % stools in example

susceptible crops

Smut spread

Smut spread

Two strategies2. Smut-free farm network

3. Farmer reporting

Smut-free network• Un-infested farms were

selected• Networks in each of

Bundaberg, Mackay, Herbert• Inspected regularly for smut• Speed of spread monitored

Predicted smut spread

Bundaberg

y = 0.1183x - 0.4421

R2 = 0.99

-20

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Days

% f

arm

s in

fes

ted

Predicted 100% farms = April 2009

Smut spread

Second predictorKnown smut farms - farmer

reporting

• Recordings of all reported smut farms (not just study farms)

• Database maintained• Provided ‘real district’ data

– Worked to a point• when smut more commonplace,

reporting ceased

• Plotted data vs time

Smut farms reported

Herbert: predicted 100% farms infested

y = 6.924e0.0074x

R2 = 0.9756

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Days (since December 2006)

Nu

mb

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rms

December 2006

April 2008

100% infestation

October 2008

Mackay

Mackay: predicted 100% farms infested

y = 4.0697e0.007x

R2 = 0.9662

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Days (after December 2006)

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November 2006

April 2008

February 2009

100% infestation

Bundaberg-Isis

Bundaberg-Isis predicted 100% farms infested

y = 29.213e0.0031x

R2 = 0.9644

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Days (since June 2006)

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mb

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infe

sted 100% infestation

April 2009April 2008

June 2006

Smut build-up

Smut build up ‘in-crop’

• Example HS crops selected

• Individual stools monitored (+ or – smut)

• Data recorded using GPS• Stools mapped

• Increase in smut calculated

• Escalation rates determined

Q205 Bundaberg

Q205 Bundaberg

Q205 Bundaberg

Q205 Bundaberg

Q205 Bundaberg

Q205 Bundaberg

Crop build up conclusions

Build up rates• variable

• depending on initial smut crop levels• local environment• highest when smut is ‘planted’

• 7-11 fold stool increase / year– compares to 1-2 fold in Louisiana

• 1-3 years: first detection to predicted ploughout!• 5% infested stools

Smut yield losses

Q174: March 2010

Yield losses

Our whole aim in the smut program, working in– epidemiology– variety resistance screening– spore trapping– extension

was to avoid high smut incidence in HS varieties

• and the high associated yield losses!

We wanted to pre-warn farmers of the potential yield effects

Quantifying losses

Strategy: choose 7 plots in a crop with varying smut levels– Assess yield in each plot– Relate smut severity to yield

• Identified a badly-affected 2009 crop in the Herbert (Abergowrie)

• Highly susceptible Q157

Yield losses

Selected 7 plots: 2 rows x 7m– applied smut severity scores to all stools

0 = no smut1 = a few primary whips only2 = moderate number of whips but no grassiness3 = 50-75% primary whips and some grassiness4 = >75% primary whips and most of the stool grassy

Calculated average severity / plot

Cut / weighed all cane in plots– quantified weight cane / plot

Graphed yield vs severity

Q157 Yield loss

y = -15.567x + 99.657

40.0

50.0

60.0

70.0

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90.0

100.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Smut severity

Yie

ld (

ton

ne

s ca

ne

/ h

a)

Smut severity vs cane yield

Yield losses

Using these data

Theoretical maximum yield loss:

• score ‘0’ vs ‘4’

62%

= same as predicted at start of Childers epidemic!

Yield losses

What losses did the farmer suffer in that crop (whole crop)?

• Depends on average crop severity• Selected 20 plots scattered randomly

through the crop (10m length)• Scored all stools in all plots• Found average severity

= average whole crop severity

Related yield loss estimate x severity

– to estimate total crop losses in that particular crop

• Average severity across whole crop– 20 plots

• Mean severity score = 1.6 (0 to 4 severity scale)

• Related to yield • using the previous graph

The average smut-induced yield loss for that whole crop = 26%.

Yield losses

Variety effectsField

Strong field variety effects

Variety # crops Variety # cropsQ158 154 Q220 3

Q174 151 Q233 3

Q157 67 Q115 3

Q204 18 Q152 3

Q186 14 Q164 3

Q194 14 Q216 3

Q162 13 Q172 2

Q166 10 Q127 2

Q195 10 Q138 2

Q200 5 Q99 1

Argos 5 KQ228 1

Q179 5 Q219 1

Q190 3 Q183 1

Herbert

What level of resistance is needed?

• Natural spread trial planted in Mackay

– Varieties varying in resistance – Included important ‘replacement’ canes– Planted ‘clean’

• Monitored disease buildup vs HS canes• Worst affected farm in Mackay

Mackay natural spread – April 2009

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Q157 Q209 Q190 Q138 Q226 Q200 Q208 Q205ss Q205 Q170 Q171 Q135 Q183

Variety

% s

too

ls in

fest

ed

• Highly susceptible canes – Severe smut very quickly– Disaster!

• Susceptible (7-8)– Not so fast

• Intermediate canes– pretty good– especially Q183, Q135,

Q208

• Resistant canes– No problem

Field resistance

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Q157 Q209 Q190 Q138 Q226 Q200 Q208 Q205ss Q205 Q170 Q171 Q135 Q183

Variety

% s

too

ls in

fest

ed

Q208: a major variety!

• Some have expressed concern about disease levels

• In Mackay – some significant disease (around 1.5% disease)

• Herbert – one report of 9% infested stools

• But following crops have had low smut levels– this also seen in the Ord

• No problem with this variety!

When will the epidemic pass?

Epidemic modelling

Epidemic modelling

• Based on weighted parameter– % S, I and R crops: district x year– plus estimated smut severity

• Calculated parameter: ‘relative smut’ - smut indicator

• Models: guide to when the maximum smut stress on ‘I’ varieties

Herbert district

Relative smut

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2005 2006 2007 2008 2009 2010 2011 2012 2013

Year

Rel

ativ

e sm

ut

Epidemic modelling

• RISE of the epidemic– principally about smut

• spread• escalation

– in HS crops (plenty around)

• FALL of the epidemic– almost wholly to do with: -

• elimination of HS crops

Bundaberg-Childers

Relative smut

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2005 2006 2007 2008 2009 2010 2011 2012 2013

Year

Rel

ativ

e sm

ut

Epidemic modelling

Bundaberg-Childers

• Similar pattern to the Herbert• Peak in 2009 (a little earlier)

– More rapid replacement of susceptibles

Peak smaller than Herbert

Smut comparison x district

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2004 2006 2008 2010 2012 2014 2016

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Re

lati

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Herbert

Bundaberg

Burdekin

Herbert – estimated losses

Estimated yield losses from smut - Herbert

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2009 2010 2011 2012

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Yield losses

Herbert region losses: • 2009 crop losses: estimated at

250,000 tonnes

• 2010 losses: estimated at

>300,000 tonnes cane

• In 2010: > 30% of Herbert crop supplied by S varieties, and – smut likely to be severe in HS crops.

Important management points

• Maintain transition to resistant varieties – if too slow, there will be high direct

losses, and – maximum inoculum pressure on

intermediates

• Industry needs to make common sense decisions on which crops to terminate

Conclusions

• This is ‘crunch’ time - yield loss

phase• Losses will be significant in

Herbert, Mackay and Bundaberg in 2010 and 2011

• Largely confined to the HS varieties

Urgent need to transition out of HS to avoid yield losses!

Overall conclusions

• Smut: 2-3 years to spread to all farms in district

• Significant losses: within 3 years from first finding of smut in a crop (susceptible)– 10-fold stool increase each

year

• Some intermediates will be OK

• Smut losses: a little slower to occur than anticipated