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SMUT EPIDEMIOLOGY   SRDC-funded project Rob Magarey BSES Limited, Tully
Epidemiology  <ul><li>Definition </li></ul><ul><li>Study of the spread, build up and effect of an epidemic </li></ul>
Smut epidemic in Queensland <ul><li>First detection :  9 th  June 2006, Isis </li></ul><ul><ul><li>single side-shoot </li>...
Smut epidemics: what we knew! <ul><li>Smut can spread fast </li></ul><ul><li>Is affected by climate </li></ul><ul><ul><li>...
Smut: what we knew! <ul><li>Our commercial varieties were highly susceptible </li></ul><ul><li>That many of the best canes...
Smut epidemics: what we didn’t know <ul><li>When it would be detected in Northern, Burdekin, NSW etc </li></ul><ul><li>How...
Project (farmer) questions <ul><li>Immediate questions </li></ul><ul><li>How fast will it reach my farm?  (spread) </li></...
Epidemic   J curve Slow start Fast finish Epidemics
Epidemiology methods <ul><li>Two initial focus areas </li></ul><ul><li>Smut  spread  </li></ul><ul><ul><li>Monitoring a no...
Smut spread
Smut spread <ul><li>Two strategies </li></ul><ul><li>Smut-free farm network </li></ul><ul><li>Farmer reporting </li></ul><...
Predicted smut spread Bundaberg
Smut spread <ul><li>Second predictor </li></ul><ul><li>Known smut farms - farmer reporting </li></ul><ul><li>Recordings of...
Smut farms reported   December 2006 April 2008 100% infestation October 2008
Mackay November 2006 April 2008 February 2009 100% infestation
Bundaberg-Isis 100% infestation April 2009 April 2008 June 2006
 
 
 
Smut build-up
Smut build up ‘in-crop’ <ul><li>Example HS crops selected </li></ul><ul><li>Individual stools monitored (+ or – smut) </li...
Q205 Bundaberg
Q205 Bundaberg
Q205 Bundaberg
Q205 Bundaberg
Q205 Bundaberg
Q205 Bundaberg
Crop build up conclusions <ul><li>Build up rates </li></ul><ul><li>variable </li></ul><ul><ul><ul><li>depending on initial...
Smut yield losses
Q174: March 2010
Yield losses <ul><li>Our whole aim in the smut program, working in </li></ul><ul><ul><li>epidemiology </li></ul></ul><ul><...
Quantifying losses <ul><li>Strategy:  choose 7 plots in a crop with varying smut levels </li></ul><ul><ul><li>Assess yield...
Yield losses <ul><li>Selected 7 plots :  2 rows x 7m </li></ul><ul><ul><li>applied smut severity scores to all stools  </l...
Q157 Yield loss Smut severity vs cane yield
Yield losses <ul><li>Using these data   </li></ul><ul><li>Theoretical maximum yield loss: </li></ul><ul><li>score ‘0’ vs ‘...
Yield losses <ul><li>What losses did the farmer suffer in that crop (whole crop)? </li></ul><ul><li>Depends on average cro...
<ul><li>Average severity across whole crop </li></ul><ul><ul><li>20 plots  </li></ul></ul><ul><ul><ul><li>Mean severity sc...
Variety effects Field
Strong field variety effects Herbert Variety # crops Variety # crops Q158 154 Q220 3 Q174 151 Q233 3 Q157 67 Q115 3 Q204 1...
What level of resistance is needed? <ul><li>Natural spread trial planted in Mackay </li></ul><ul><ul><li>Varieties varying...
Mackay natural spread – April 2009
<ul><li>Highly susceptible canes   </li></ul><ul><ul><li>Severe smut very quickly </li></ul></ul><ul><ul><li>Disaster! </l...
Q208: a major variety! <ul><li>Some have expressed concern about disease levels </li></ul><ul><li>In Mackay – some signifi...
When will the epidemic pass? Epidemic modelling
Epidemic modelling <ul><li>Based on weighted parameter </li></ul><ul><ul><li>% S, I and R crops: district x year </li></ul...
Herbert district
Epidemic modelling <ul><li>RISE   of the epidemic </li></ul><ul><ul><li>principally about smut </li></ul></ul><ul><ul><ul>...
Bundaberg-Childers
Epidemic modelling <ul><li>Bundaberg-Childers </li></ul><ul><li>Similar pattern to the Herbert </li></ul><ul><li>Peak in 2...
Smut comparison x district
Herbert – estimated losses
Yield losses <ul><li>Herbert region losses:   </li></ul><ul><li>2009 crop losses:  estimated at 250,000 tonnes </li></ul><...
Important management points <ul><li>Maintain transition to resistant varieties   </li></ul><ul><ul><li>if too slow, there ...
Conclusions <ul><li>This is ‘crunch’ time - yield loss phase </li></ul><ul><li>Losses will be significant in Herbert, Mack...
Overall conclusions <ul><li>Smut: 2-3 years to spread to all farms in district </li></ul><ul><li>Significant losses: withi...
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SRDC seminar

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Dr Robert Magarey presents his valuable research findings on smut epidemiology in the sugarcane industry.

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Transcript of "SRDC seminar"

  1. 1. SMUT EPIDEMIOLOGY SRDC-funded project Rob Magarey BSES Limited, Tully
  2. 2. Epidemiology <ul><li>Definition </li></ul><ul><li>Study of the spread, build up and effect of an epidemic </li></ul>
  3. 3. Smut epidemic in Queensland <ul><li>First detection : 9 th June 2006, Isis </li></ul><ul><ul><li>single side-shoot </li></ul></ul><ul><ul><li>nothing known of epidemic history </li></ul></ul><ul><ul><ul><li>How many farms? </li></ul></ul></ul><ul><ul><ul><li>Which parts of the district? </li></ul></ul></ul><ul><ul><ul><li>What varieties? etc </li></ul></ul></ul><ul><li>Other detections (2006) </li></ul><ul><ul><li>Mackay : 7 th November </li></ul></ul><ul><ul><li>Herbert: 15 th December </li></ul></ul>
  4. 4. Smut epidemics: what we knew! <ul><li>Smut can spread fast </li></ul><ul><li>Is affected by climate </li></ul><ul><ul><li>Wetter conditions can slow build up </li></ul></ul><ul><ul><li>Warmer temperatures favour the disease </li></ul></ul><ul><ul><li>Hot, dry (irrigated) is most suitable </li></ul></ul><ul><li>Inoculum can travel a long way </li></ul><ul><ul><li>1000s km </li></ul></ul><ul><ul><li>but heaviest inoculum pressure is within just a few metres of an infested crop </li></ul></ul><ul><li>Yield losses: </li></ul><ul><ul><li>0.6% loss for each 1% infested stalks </li></ul></ul><ul><ul><ul><li>= 62% yield loss in HS </li></ul></ul></ul>
  5. 5. Smut: what we knew! <ul><li>Our commercial varieties were highly susceptible </li></ul><ul><li>That many of the best canes were also HS </li></ul><ul><li>It would be difficult to replace crops quickly to establish resistant crops </li></ul><ul><li>Accessing disease-free plant sources was very important since smut can be planted in apparently healthy looking cane </li></ul>
  6. 6. Smut epidemics: what we didn’t know <ul><li>When it would be detected in Northern, Burdekin, NSW etc </li></ul><ul><li>How long it would take to reach each farm in affected districts </li></ul><ul><li>How quickly the disease would build up in HS crops </li></ul><ul><li>When significant yield losses would start occurring in infested crops </li></ul><ul><li>What influence climate would have </li></ul><ul><li>What level of resistance was needed in each district to restrict losses </li></ul><ul><li>How our potential replacement varieties would go in each district </li></ul>
  7. 7. Project (farmer) questions <ul><li>Immediate questions </li></ul><ul><li>How fast will it reach my farm? (spread) </li></ul><ul><li>How quickly will it build up in my HS crops (build-up) </li></ul><ul><li>How fast will I need to terminate the crop before losses occur? </li></ul><ul><li>Additional questions (project extension) </li></ul><ul><li>How will the ‘I’ varieties withstand the epidemic? </li></ul><ul><li>What yield losses are / will occur? </li></ul><ul><li>When will the epidemic pass? </li></ul>
  8. 8. Epidemic J curve Slow start Fast finish Epidemics
  9. 9. Epidemiology methods <ul><li>Two initial focus areas </li></ul><ul><li>Smut spread </li></ul><ul><ul><li>Monitoring a non-diseased farm network </li></ul></ul><ul><li>Smut build up </li></ul><ul><ul><li>increase in % stools in example susceptible crops </li></ul></ul>
  10. 10. Smut spread
  11. 11. Smut spread <ul><li>Two strategies </li></ul><ul><li>Smut-free farm network </li></ul><ul><li>Farmer reporting </li></ul><ul><li>Smut-free network </li></ul><ul><li>Un-infested farms were selected </li></ul><ul><li>Networks in each of Bundaberg, Mackay, Herbert </li></ul><ul><li>Inspected regularly for smut </li></ul><ul><li>Speed of spread monitored </li></ul>
  12. 12. Predicted smut spread Bundaberg
  13. 13. Smut spread <ul><li>Second predictor </li></ul><ul><li>Known smut farms - farmer reporting </li></ul><ul><li>Recordings of all reported smut farms (not just study farms) </li></ul><ul><li>Database maintained </li></ul><ul><li>Provided ‘real district’ data </li></ul><ul><ul><li>Worked to a point </li></ul></ul><ul><ul><ul><li>when smut more commonplace, reporting ceased </li></ul></ul></ul><ul><li>Plotted data vs time </li></ul>
  14. 14. Smut farms reported December 2006 April 2008 100% infestation October 2008
  15. 15. Mackay November 2006 April 2008 February 2009 100% infestation
  16. 16. Bundaberg-Isis 100% infestation April 2009 April 2008 June 2006
  17. 20. Smut build-up
  18. 21. Smut build up ‘in-crop’ <ul><li>Example HS crops selected </li></ul><ul><li>Individual stools monitored (+ or – smut) </li></ul><ul><li>Data recorded using GPS </li></ul><ul><li>Stools mapped </li></ul><ul><li>Increase in smut calculated </li></ul><ul><li>Escalation rates determined </li></ul>
  19. 22. Q205 Bundaberg
  20. 23. Q205 Bundaberg
  21. 24. Q205 Bundaberg
  22. 25. Q205 Bundaberg
  23. 26. Q205 Bundaberg
  24. 27. Q205 Bundaberg
  25. 28. Crop build up conclusions <ul><li>Build up rates </li></ul><ul><li>variable </li></ul><ul><ul><ul><li>depending on initial smut crop levels </li></ul></ul></ul><ul><ul><ul><li>local environment </li></ul></ul></ul><ul><ul><ul><li>highest when smut is ‘planted’ </li></ul></ul></ul><ul><li>7-11 fold stool increase / year </li></ul><ul><ul><li>compares to 1-2 fold in Louisiana </li></ul></ul><ul><li>1-3 years: first detection to predicted ploughout! </li></ul><ul><ul><ul><li>5% infested stools </li></ul></ul></ul>
  26. 29. Smut yield losses
  27. 30. Q174: March 2010
  28. 31. Yield losses <ul><li>Our whole aim in the smut program, working in </li></ul><ul><ul><li>epidemiology </li></ul></ul><ul><ul><li>variety resistance screening </li></ul></ul><ul><ul><li>spore trapping </li></ul></ul><ul><ul><li>extension </li></ul></ul><ul><li>was to avoid high smut incidence in HS varieties </li></ul><ul><ul><ul><li>and the high associated yield losses! </li></ul></ul></ul><ul><li>We wanted to pre-warn farmers of the potential yield effects </li></ul>
  29. 32. Quantifying losses <ul><li>Strategy: choose 7 plots in a crop with varying smut levels </li></ul><ul><ul><li>Assess yield in each plot </li></ul></ul><ul><ul><li>Relate smut severity to yield </li></ul></ul><ul><li>Identified a badly-affected 2009 crop in the Herbert (Abergowrie) </li></ul><ul><li>Highly susceptible Q157 </li></ul>
  30. 33. Yield losses <ul><li>Selected 7 plots : 2 rows x 7m </li></ul><ul><ul><li>applied smut severity scores to all stools </li></ul></ul><ul><ul><li>0 = no smut </li></ul></ul><ul><ul><li>1 = a few primary whips only </li></ul></ul><ul><ul><li>2 = moderate number of whips but no grassiness </li></ul></ul><ul><ul><li>3 = 50-75% primary whips and some grassiness </li></ul></ul><ul><ul><li>4 = >75% primary whips and most of the stool grassy </li></ul></ul><ul><li>Calculated average severity / plot </li></ul><ul><li>Cut / weighed all cane in plots </li></ul><ul><ul><li>quantified weight cane / plot </li></ul></ul><ul><li>Graphed yield vs severity </li></ul>
  31. 34. Q157 Yield loss Smut severity vs cane yield
  32. 35. Yield losses <ul><li>Using these data </li></ul><ul><li>Theoretical maximum yield loss: </li></ul><ul><li>score ‘0’ vs ‘4’ </li></ul><ul><ul><ul><ul><li>62% </li></ul></ul></ul></ul><ul><li>= same as predicted at start of Childers epidemic! </li></ul>
  33. 36. Yield losses <ul><li>What losses did the farmer suffer in that crop (whole crop)? </li></ul><ul><li>Depends on average crop severity </li></ul><ul><li>Selected 20 plots scattered randomly through the crop (10m length) </li></ul><ul><li>Scored all stools in all plots </li></ul><ul><li>Found average severity </li></ul><ul><ul><li>= average whole crop severity </li></ul></ul><ul><li>Related yield loss estimate x severity </li></ul><ul><ul><li>to estimate total crop losses in that particular crop </li></ul></ul>
  34. 37. <ul><li>Average severity across whole crop </li></ul><ul><ul><li>20 plots </li></ul></ul><ul><ul><ul><li>Mean severity score = 1.6 </li></ul></ul></ul><ul><ul><ul><li>(0 to 4 severity scale) </li></ul></ul></ul><ul><li>Related to yield </li></ul><ul><ul><ul><li>using the previous graph </li></ul></ul></ul><ul><ul><li>The average smut-induced yield loss for that whole crop = 26%. </li></ul></ul>Yield losses
  35. 38. Variety effects Field
  36. 39. Strong field variety effects Herbert Variety # crops Variety # crops Q158 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
  37. 40. What level of resistance is needed? <ul><li>Natural spread trial planted in Mackay </li></ul><ul><ul><li>Varieties varying in resistance </li></ul></ul><ul><ul><li>Included important ‘replacement’ canes </li></ul></ul><ul><ul><li>Planted ‘clean’ </li></ul></ul><ul><ul><ul><li>Monitored disease buildup vs HS canes </li></ul></ul></ul><ul><ul><ul><li>Worst affected farm in Mackay </li></ul></ul></ul>
  38. 41. Mackay natural spread – April 2009
  39. 42. <ul><li>Highly susceptible canes </li></ul><ul><ul><li>Severe smut very quickly </li></ul></ul><ul><ul><li>Disaster! </li></ul></ul><ul><li>Susceptible (7-8) </li></ul><ul><ul><li>Not so fast </li></ul></ul><ul><li>Intermediate canes </li></ul><ul><ul><li>pretty good </li></ul></ul><ul><ul><li>especially Q183, Q135, Q208 </li></ul></ul><ul><li>Resistant canes </li></ul><ul><ul><li>No problem </li></ul></ul>Field resistance
  40. 43. Q208: a major variety! <ul><li>Some have expressed concern about disease levels </li></ul><ul><li>In Mackay – some significant disease (around 1.5% disease) </li></ul><ul><li>Herbert – one report of 9% infested stools </li></ul><ul><li>But following crops have had low smut levels </li></ul><ul><ul><li>this also seen in the Ord </li></ul></ul><ul><li>No problem with this variety! </li></ul>
  41. 44. When will the epidemic pass? Epidemic modelling
  42. 45. Epidemic modelling <ul><li>Based on weighted parameter </li></ul><ul><ul><li>% S, I and R crops: district x year </li></ul></ul><ul><ul><li>plus estimated smut severity </li></ul></ul><ul><li>Calculated parameter: ‘relative smut’ - smut indicator </li></ul><ul><li>Models: guide to when the maximum smut stress on ‘I’ varieties </li></ul>
  43. 46. Herbert district
  44. 47. Epidemic modelling <ul><li>RISE of the epidemic </li></ul><ul><ul><li>principally about smut </li></ul></ul><ul><ul><ul><li>spread </li></ul></ul></ul><ul><ul><ul><li>escalation </li></ul></ul></ul><ul><ul><ul><ul><li>in HS crops (plenty around) </li></ul></ul></ul></ul><ul><li>FALL of the epidemic </li></ul><ul><ul><li>almost wholly to do with: - </li></ul></ul><ul><ul><ul><li>elimination of HS crops </li></ul></ul></ul>
  45. 48. Bundaberg-Childers
  46. 49. Epidemic modelling <ul><li>Bundaberg-Childers </li></ul><ul><li>Similar pattern to the Herbert </li></ul><ul><li>Peak in 2009 (a little earlier) </li></ul><ul><ul><li>More rapid replacement of susceptibles </li></ul></ul><ul><li>Peak smaller than Herbert </li></ul>
  47. 50. Smut comparison x district
  48. 51. Herbert – estimated losses
  49. 52. Yield losses <ul><li>Herbert region losses: </li></ul><ul><li>2009 crop losses: estimated at 250,000 tonnes </li></ul><ul><li>2010 losses : estimated at > 300,000 tonnes cane </li></ul><ul><li>In 2010 : > 30% of Herbert crop supplied by S varieties, and </li></ul><ul><ul><li>smut likely to be severe in HS crops. </li></ul></ul>
  50. 53. Important management points <ul><li>Maintain transition to resistant varieties </li></ul><ul><ul><li>if too slow, there will be high direct losses, and </li></ul></ul><ul><ul><li>maximum inoculum pressure on intermediates </li></ul></ul><ul><li>Industry needs to make common sense decisions on which crops to terminate </li></ul>
  51. 54. Conclusions <ul><li>This is ‘crunch’ time - yield loss phase </li></ul><ul><li>Losses will be significant in Herbert, Mackay and Bundaberg in 2010 and 2011 </li></ul><ul><li>Largely confined to the HS varieties </li></ul><ul><li>Urgent need to transition out of HS to avoid yield losses! </li></ul>
  52. 55. Overall conclusions <ul><li>Smut: 2-3 years to spread to all farms in district </li></ul><ul><li>Significant losses: within 3 years from first finding of smut in a crop (susceptible) </li></ul><ul><ul><li>10-fold stool increase each year </li></ul></ul><ul><li>Some intermediates will be OK </li></ul><ul><li>Smut losses: a little slower to occur than anticipated </li></ul>
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