Use of dynamic models for disease management

in viticulture

Tito Caffi

Vittorio Rossi

Piacenza, 14 June 2013

The  ques(on  should  not  be  if  we  have  to  use  PPP  or  not  but  

how  we  can  use  them  reasonably    

33%  with  insec(cides    99%  with  fungicides    

3.5  mln  ha  of  vineyards  

~  70000  tons  of  fungicides  (ac(ve  substances!)  are  sprayed  each  year  

97%  of  the  cul(vated  areas  is  treated  at  least  once/season    

Integrated  Pest  Management  (IPM)  Integrated  produc(on  /  farming  Organic  farming  

-­‐   Dynamic  concepts    -­‐   High  level  of  organiza(on  -­‐   Decisional  skills  (both  strategic  and  tac(c)  -­‐   Constant  upgrade  (research  >  prac(ce)  -­‐   Specific  advices  

A  model  represents  a  simplified  simula(on  of  reality  

Modelling  must  be  based    on  a  deep  knowledge    

of  reality    

weather   host  

pathogen  

Models  can:  -­‐   increase  efficacy  and  speed  of  the  decision-­‐making  process;  -­‐   help  in  understanding  epidemic  processes  and  elabora(ng  protec(on  strategies  

 Ø   Fundamental:    explain  the  behavior  of  a  system  on  the  basis  of  the  cause-­‐effect  rela@onship  with  the  influencing  variables;  Ø   Dynamic:  the  state  of  the  system  in  every  moment  can  be  quan@ta@vely  defined  and  the  changes  described  mathema@cally;  Ø   Weather-­‐driven:  the  weather  variables  are  the  main  inputs  of  the  model;  Ø   Simula@ons  and  predic@ons  tools:  used  for  extrapola(on  beyond  measured  (mes  and  spaces  

Many  modeling  approaches…      

our  approach  is:  

Grape  Downy  mildew  is  a  disease  of  major  importance  worldwide  and  fungicide  applica(ons  

are  necessary  to  avoid  yield  losses  

With  calendar-­‐based  schedule  there  is  a  huge  amount  of  input  (some(me  useless)    A  key  role  in  the  epidemic  is  played  by  the  primary  infec(ons    

Fungicide  applica(ons  begin  in  late  April  14-­‐18  applica(ons/year  

penetra(on  

invasion  

sporula(on  

inocula(on  

dispersal  (splash)  

Winter  

Sprin

g  –  early

 summer  

Spring-­‐summer  

oosphere  

oogonium  

anteridium  

oospores  

dispersal  (air)  

zoosporangia  

macrosporangia  

Late  summer  -­‐  autumn  

zoospores  

-  pathosystem analysis -  drawing of the relational diagram -  collection of necessary information/data -  mathematical relationships/algorithms -  dynamic simulation

A new modelling approach

Time  t   Ungerminated  spores  

Time  t  +1   Spores  germinated  

 Germina(on  

T  

LW  

LLM

MMO

PMO

GEO

OLL

MMR

DOR

GER

t

T

R

T

VPD

RH

ZLL

R

ZGL

ZCI

LW

T

ISS

RHINC

T

RHSUR

T

T

LW

INF

LW

LW

LLM

MMO

PMO

GEO

OLLOLL

MMR

DOR

GER

t

T

RR

T

VPDVPD

RH

ZLL

R

ZGL

ZCI

LW

T

ISS

RHINC

T

RHSUR

T

RHSUR

T

T

LW

T

LW

INF

LW

LW

rainfall

January February March April May June July

Primary inoculum season

rainfall

January February March April May June July

Primary inoculum season

1/4

8/4

15/4

22

/4

29/4

6/

5 13

/5

20/5

27

/5

3/6

10/6

17

/6

24/6

1/

7 8/

7 15

/7

22/7

29

/7 0

10

20

30

40

50

60

70

R (m

m)

Oospore germination

Zoospore release

Zoospore dispersal

Infection

End of incubation

Dynamic simulation

2004 – 2007 15 vineyards 1999 – 2004

19 vineyards

1998 – 2002 5 vineyards

1996 – 2004 7 vineyards

1995 – 2007 45 vineyards

2004 – 2007 9 vineyards

2004 – 2007 5 vineyards

The  model  valida(on  was  performed  in  different  epidemiological  condi(ons  (locali(es  x  years):  

105  vineyards  in  Italy  +  43  in  Quebec  (Canada)  +  6  in  Georgia  (US)  

2008 – 2009 43 vineyards

2010  –  2012    6  vineyards  

Yes  

No  

No  

74%  

0%  

Yes  

10%  

16%  

Obs

erve

d

Expected

1 8 15 22 29 5 12 19 26 3 10 17 24 30 Aprile Maggio Giugno

Modena

0.0% 0.1% 0.9%

0.0% 0.0% 0.0%

2006

2007

3

3 6

6

2008

4 8 0.0%

0.2% 72.2%

Farm practice Model-based strategy

DM intensity on bunches at fruit set

Phytoiatric validation

-50%

-50%

-50%

The model was: detailed it follows, step by step, the infection process accurate it estimated correctly 96% of the total cases robust it estimated early and late infections and it operated well under different epidemiological situations

Model’s application within the real time warning system produced very promising result

disease control as good as farm practice

50% reduction of fungicide applications

Conclusions I

MoDeM A web-based system for real-time Monitoring and Decision Making for Integrated Vineyard Management

VineMan.org Integration of plant resistance, cropping practices, and biocontrol agents for enhancing disease management, yield, and biodiversity in organic European vineyards

PURE Pesticide Use-and-Risk reduction in European Farming systems with Integrated Pest Management

Innovine Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture

International research

A Decision Support System: vite.net®

Manager  

informa(on  

upda(ng  

advice  

decision  supports  

vite.net®

Mediterranean  vine  mealybug  

American  grapevine  leaTopper  

Grape  berry  moth  

Pests  (synte@c  risk  indicators)  

Summary

vite.net® - main dashboard

Detail Informa(on  about  how  many  oospores  overcome  dormancy  

Informa(on  about  how  (already  mature)  oospores  

are  germina(ng  

Detail  of  each  key  event  of  the  infec(on  cycle  

Informa(on  about  the  efficiency  of  each  infec(on  and  their  incuba(on  period  

vite.net® - downy mildew primary infections

Mediterranean  vine  mealybug  

American  grapevine  leaTopper  

Grape  berry  moth  

Pests  (synte@c  risk  indicators)  

vite.net® - fungicides protection

Support Before 1st leaf

unfolded

Yes No

After 1st leaf unfolded

Preventive treatment

Does the model simulates an infection?

Yes

Did you apply any fungicide?

Treatment is not justified

high low medium

Residual protection level

high low high

Infection risk

Treatment justified

No Yes

Are grapevines between flowering

and fruit set?

No

Infection risk

low Treatment suggested

vite.net® - decision tree

24  

DSS  

NT  

1   48  

Farm  

Row  

Reduction in the number of treatments

5 ha plot of cv. Barbera in north-western Italy DSS  

9

5

Farm

No.  treat.   No.  Xn  

IPM   9,4   1,9  

vite.net®   7   1,5  

8 farms - 2012

No.  treat.   No.  Xn  

IPM   10,2   1,8  

vite.net®   7,9   1,5  

Downy mildew scheduling

Powdery mildew scheduling

Pilot application in organic: vitebio.net®

21 organic farms 2011-2012

Same protection against the disease

No. of treatment / season : 9 vs 12 - 24%

Kg of Cu++ / application: 0.56 vs 0.68 - 18%

Kg of Cu++ / season: 5.1 vs 8.1 - 37%

Cost of the disease management strategy: -195 €/ha

Mechanis@c  (explanatory)  models  explain  the  rela(ons  within  a  pathosystem  and  describe  how  the  system  changes  over  @me  and  

space,  depending  from  external  (weather  variables)  

These  models  produce  simula@ons  of  plant  disease  epidemics  and  can  be  successfully  used  for  decision  making  in  plant  disease  management  at  different  scales  of  complexity,  from  territorial  

warning  systems  to  precision  farming  

A  Decision  Support  System  based  on  such  models  was  used  in  a  wide  range  of  environmental  condi(ons  and  reduced  the  number  of  

fungicides  applica@on  and  increased  the  ra@onale  and  the  sustainability  of  the  fungicide  scheduling        

Final conclusions

Thank you for your attention