Models for crop diseases: Overview of approaches & scales · Models for crop diseases: Overview of approaches & scales S Savary, P Esker, N McRoberts, L Willocquet, T Caffi, V Rossi,

Models for crop diseases: Overview of approaches & scales

S Savary, P Esker, N McRoberts, L Willocquet, T Caffi, V Rossi, J Yuen,

A Djurle, L Amorim, A Bergamin Filho, N Castilla, A Sparks, K Garrett

web of life

interactions

ecosystem

Background and vision (1)

• Modeling plant diseases: many different modeling approaches used, with different objectives

• Two main objectives in modeling plant disease: (1) analyze & understand epidemics and (2) analyze crop losses

• With the ultimate goal of improving disease management and reduce crop losses


• Most crops are exposed to several diseases

• Disease management often has to account for the existence of multiple diseases and pests in order to be relevant and efficient

• From a crop loss – crop performance – perspective: addressing multiple diseases (and pests) is desirable


• Much progress has been made on the modelling of the effects of harmful organisms on crops (damage mechanisms)

• As a result, it is possible to model crop losses caused by one or multiple injuries (diseases, pests) in a generic manner (i.e., any crop, any disease/pest)

• But the availability of injury functions – the time course of diseases/pests under actual field conditions – is a major obstacle


• Even for the main food crops worldwide (rice, wheat, maize, soybean, potato), there is a critical shortage of field data on observed (multiple) injuries

• The shortage of field data – not the limitation of process-knowledge – is the main obstacle in modeling crop pests and diseases and their relations to crops

• A critical step forward would be to develop a generic modeling framework for injury functions (ideotypes of injury time courses)

disease 1

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condition (c)

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condition (a)

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disease 1disease 2disease 3disease 4

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dynamic undercondition (a)

disease 1

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• representing the dynamics of injury over time in reference, key, conditions

• along with other dynamics (i.e., other disease/pest)

• These collective dynamics of injury functions representing injury scenarios – the time-course of crop health,

• which in turn could be used as drivers for crop loss models

Two main components of plant disease modeling

• Modelling the dynamics of plant disease epidemics

• Modelling crop losses – the effects of plant disease (pest) on crop growth and performance

Which lead to • A very large number of pathosystem (Host +

Pathogen) -specific disease management models

Our emphasis is on generic epidemiological and generic crop loss modelling structures

Epidemiological objectives in plant disease epidemiology

infection efficiency

R0

infection chain

epidemiological process

lesion expansion

latency period

infectious period

propagule production

sporulation

disease aggregation

spore dispersal

spore deposition

dispersal gradient

disease gradient

viruliferous vector

acquisition

transmission efficiency

understandingepidemics

predictingcrop lossesRI

RUE

damage mechanism

turgor reducer

assimilate sapper

stand reducer

light stealer

senescence accelerator

tissue consumer

photsynthesis rate reducer

yield loss

crop loss

actual yield

attainable yield

potential yield

production situation

managingepidemics

potato late blight

apple scab

apple powdey midew

barley powdery mildew

coffee rust

black sigatoka banana

rice tungro disease

rice blast

barley yellow dwarf

rice sheath blight

grapevine downy mildew

hop downy mildew

septoria tritici blotch

citrus bacterial canker

fusarium head blight wheat & barley

wheat leaf rust

wheat stem rust

Brief overview of epidemiological simulation modelling

– Types of epidemics and models (monocyclic; polycyclic; mixed monocyclic-polycyclic)

– Spatialized models (explicit, implicit spatialization)

–Primary inoculum

–Polyetic processes

–Genetic diversity of the pathogen

Epidemiological structural patterns Polycycle – Fraction Host Tissue

Mixed – Shoot or Tiller

Monocycle Fruiting Body - Panicle or Head

Seed- or soil-borne diseases

Vector-borne

Polycycle – Fraction Host Tissue

healthy sites latent sites infectious sites removed sites

primary inoculum

Mixed Shoot or Tiller

primary inoculum

healthy sites

infected sites

removed sites

Monocycle Fruiting Body - Panicle or Head

Seed- or soil-borne diseases

infected sites healthy sites

primary inoculum

Vector-borne

healthy sites infected sites

viruliferous vectors healthy vectors

overall structure of EPIRICE

SITES LATENT

RateInf

INFECTIOUS

RTransfer RRemoval

REMOVED

COFR

Diseased

Rc

AGGR

SenescedSites

Rsenesced

RRemoval

RG

RRG

SiteMax

Rc = f(Tx, Tn, rainfall, plant age)

R0 = ∫i Rc dt

A generic simulation model for diseases in rice

Savary S, Nelson A, Willocquet L, Pangga I, Aunario J. 2012. Modelling and mapping potential epidemics of rice diseases globally. Crop Protection 34: 6-17.

Steps in building EPIRICE, test it, link it to a crop establishment module, and embed it in a GIS

Limited climate data sets (e.g., NASA)

monocyclic process parameters (literature)

Development of a model structure

Operational definion of a site for

each disease

Parameterized model for

each disease

Actual, representative, reference disease

dynamics (literature)

Individual epidemics simulated

Model evaluation for each disease

Model usable to simulate

different diseases

Mapping levels of

epidemic risks

Choice of a synthetic epidemiological variable (AUPC)

Simplified model for crop establishment date

Mapping of median crop establishments

A generic simulation model for diseases in rice

Published shapes of a few rice disease epidemics

Leaf blast

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30

0 30 60 90 120

DACE

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Nakdong

Jingheung

Akibare

Brown spot

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40

0 50 100 150

DACE

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Bacterial blight

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20

30

40

0 25 50 75 100

DACE

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IR-24

Sabitri

Laxmi

Sheath blight

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25

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75

100

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DACE

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Tungro

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DACE

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disease severity (fraction leaf surface diseased)

disease severity (fraction leaf surface diseased)

disease incidence (fraction of leaves diseased)

disease incidence (fraction of tillers diseased)

disease incidence (fraction of plants diseased)

Site = fraction leaf area

Site = fraction leaf area

Site = whole leaf

Site = whole tiller Site = entire plant

Leaf blast

0

5000

10000

15000

20000

25000

0 30 60 90 120

Time (DACE)

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25

Healthy Latents Infectious

Removed Total diseased Severity

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%)

Brown spot

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40000

60000

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100000

0 30 60 90 120

Time (DACE)

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Removed Total diseased Severity

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Bacterial blight

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Removed Total diseased Incidence Leaves

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Sheath blight

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Removed Total diseased Incidence Tillers

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Tungro

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Total diseased Incidence Plants

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Rice disease epidemics simulated (black)

Avg

1997-2008

Std

1997-2008

Brown spot

Fungal disease

Sources: secondary hosts, soil residues

Site (disease unit): fraction of leaf area

Rain and wind-dispersed

Avg

1997-2008

Std

1997-2008

Bacterial blight

Bacterial disease

Sources: water, soil, rice plants

Site (disease unit): whole leaf

Water and wind-dispersed

Avg

1997-2008

Std

1997-2008

Leaf blast

Fungal disease

Sources: secondary hosts, rice

Site (disease unit): fraction of leaf area

Rain and wind-dispersed

Avg

1997-2008

Std

1997-2008

Sheath blight

Fungal disease

Source:

Soil-borne

Site (disease unit): whole tiller

Contact-dispersed

Avg

1997-2008

Std

1997-2008

Rice tungro disease

Virus

Vector-borne

Site (disease unit): whole plant

Vector-dispersed

overall structure of EPIWHEAT

Rc = f(Tx, Tn, rainfall, plant age)

R0 = ∫i Rc dt

HSIT ES

LAT INF REM

RCG

RSN

COFR

RRG

RIRT RREM

SMax

Start

Rc

Day INOCP

RcOpt

RcW

RcT

RcA

RRPSN

Agg

SEN

RRLEX

DVS~

T~

RLEX

rain~

A generic simulation model for diseases in wheat

Savary, S., Stetkiewicz, S., Brun, F., Willocquet, L., 2015. Modelling and mapping potential epidemics of wheat diseases—examples on leaf rust and Septoria tritici blotch using EPIWHEAT. European Journal of Plant Pathology 142: 771-790.

Wheat - Septoria tritici blotch

Simulations on daily meteorological data from 1993 to 2012 (20 years) on a 50x50 km grid

Distribution of mean AUDPCs (quintiles) Distribution of SD of AUDPCs (quintiles)

Wheat – brown rust

Simulations on daily meteorological data from 1993 to 2012 (20 years) on a 50x50 km grid

Distribution of mean AUDPCs (quintiles) Distribution of SD of AUDPCs (quintiles)

Eriksen, L., Shaw, M. W., and Østergård, H. 2001. A model of the effect of pseudothecia on genetic recombination and epidemic development in populations of Mycosphaerella graminicola. Phytopathology 91:240-248.

A simulation model for diseases accounting for genetic variation

Brief overview of crop loss simulation modelling

– Crop (agrophysiological) growth models with damage mechanisms

– Damage mechanisms

– RI – RUE models

– multiple diseases (pests) models

Production levels

Potential

Attainable

Actual

Yield

defining factors

Yield

limiting factors

Yield

reducing factors

radiation

temperature

crop phenology

physiological properties

crop architecture

water

nitrogen

phosphorus

pests

diseases

weeds

pollutants

calamities

Production

levels:

• Rabbinge, R. 1993. The ecological background of food production. In: Crop protection and sustainable agriculture. Ciba Foundation 77. Chadwick DJ, Marsh J, Eds. John Wiley & Sons, Chichester, UK.

• Van Ittersum, M. K., and Rabbinge, R. 1997. Ecology for analysis and quantification of agricultural input-output combinations. Field Crops Res. 52:197-208.

Simulation modelling of yield losses - examples

Crop Pest Reference

Rice Leaf blast Bastiaans, 1993

Rice Multiple diseases Pinnschmidt et al, 1994

Rice Multiple pests Willocquet et al, 2000; 2002; 2004

Rice, wheat Multiple pests Aggarwal et al, 2006a; 2006b

Wheat Aphids Rossing, 1991

Wheat Leaf rust Roermund & Spitters, 1990

Wheat Multiple pests Willocquet et al, 2008

Potato Multiple pests Johnson, 1992

Damage mechanisms of crop pest injuries Damage mechanism

Physiological effect Effect in a crop growth model Examples of pests

Light stealer Reduces the intercepted radiation

Reduces the green LAI Pathogens producing lesions on leaves

Leaf senescence accelerator

Increases leaf senescence, causes defoliation

Reduces leaf biomass by increasing the rate of leaf senescence

Foliar pathogens such as leaf spotting pathogens, downy mildews

Tissue consumer Reduces the tissue biomass Outflows from biomasses of the injured organs

Defoliating insects

Stand reducer Reduces the number and biomass of plants

Reduces biomass of all organs Damping-off fungi

Photosynthetic Rate reducer

Reduces the rate of carbon uptake

Reduces the RUE Viruses, root-infecting pests, stem infecting pests, some foliar pathogens

Turgor reducer Disrupts xylem and phloem transport

Reduces the RUE, accelerates leaf senescence

Vascular, wilt pathogens

Assimilate sapper

Removes soluble assimilates from host

Outflows assimilates from the pool of assimilates

Sucking insects, e.g. aphids, some planthoppers, biotrophic fungi exporting assimilates from host cells

• Rabbinge, R., and Vereyken, P. H. 1980. The effects of diseases or pests upon host. Z. Pflanzenk. Pflanzensch. 87:409-422; • Rabbinge, R., and Rijsdijk, P. H. 1981. Disease and crop physiology: a modeler’s point of view. Pages 201-220 in: Effects of Disease

on the Physiology of the Growing Plants. P. G. Ayres, ed. Cambridge Univ. Press, Cambridge, UK; • Boote, K. J., Jones, J. W., Mishoe, J. W., and Berger, R. D. 1983. Coupling pests to crop growth simulators to predict yield

reductions. Phytopathology 73:1581-1587; • Savary S, Willocquet L. 2014. Simulation Modeling in Botanical Epidemiology and Crop Loss analysis. APSnet Education Center.

The Plant Health Instructor. DOI: 10.1094/PHI-A-2014-0314-01.

Incorporating damage mechanisms into an agrophysological model: GENEPEST

RUE

RAD

k

Pool

Leaf B StemB StorB RootB

PartS

PartL

PartSO

PartR

RG

LAI

RSenL

RTranslocAtt

Light Stealer

Leaf senescence

accelerator Leaf consumer

Photosynthetic

rate reducerTurgor reducer

Turgor reducer

Assimilate sapper

Rate of assimilate diversion

Savary S, Willocquet L. 2014. Simulation Modeling in Botanical Epidemiology and Crop Loss analysis. APSnet Education Center. The Plant Health Instructor. DOI: 10.1094/PHI-A-2014-0314-01.

A generic simulation model for yield losses caused by diseases, insects and weeds on an annual field crop

Incorporating different damage mechanisms into a crop growth model: GENEPEST

Light Stealer

Photosy nthetic

rate reducer

Leaf senescence

accelerator

Assimilate sapper

Rate of assimilate div ersion

Leaf consumer

rrsenL

Turgor reducer

Turgor reducer

RUE

RAD

k

Pool

Leaf B StemB StorB RootB

PartS

PartL

PartSO

PartR

RG

LAI

RremL

RTransloc

Savary S, Willocquet L. 2014. Simulation Modeling in Botanical Epidemiology and Crop Loss analysis. APSnet Education Center. The Plant Health Instructor. DOI: 10.1094/PHI-A-2014-0314-01.

A generic simulation model for yield losses caused by diseases, insects and weeds on an annual field crop

structure of RICEPEST

Willocquet, L., Elazegui, F. A., Castilla, N., Fernandez, L., Fischer, K. S., Peng, S., Teng, P. S., Srivastava, R. K., Singh, H. M., Zhu, D., and Savary, S. 2004. Research priorities for rice pest management in tropical Asia: a simulation analysis of yield losses and management efficiencies. Phytopathology 94: 672-682.

A simulation model for yield losses caused by diseases, insects and weeds for rice

Yield losses caused by rice pests in two key rice production situations

Willocquet, L., Elazegui, F. A., Castilla, N., Fernandez, L., Fischer, K. S., Peng, S., Teng, P. S., Srivastava, R. K., Singh, H. M., Zhu, D., and Savary, S. 2004. Research priorities for rice pest management in tropical Asia: a simulation analysis of yield losses and management efficiencies. Phytopathology 94: 672-682.

Rainy season in rice-rice rotation, irrigated rice

0

3

6

9

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15

BLB

SHB

BS LB N

BSHR

BPH

DEF

DH

WH

WEED

Injury

Re

lati

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yie

ld lo

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(%

)

Relative yield loss for the

whole injury profile: 26.1%

Rainy season in rice-wheat rotation, rainfed rice

0

3

6

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12

15

BLB

SHB

BS LB N

BSHR

BPH

DEF

DH

WH

WEED

Injury

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ld lo

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(%

)

Relative yield loss for the

whole injury profile: 35.2%

Pest and disease injuries: BLB: bacterial leaf blight SHB: sheath blight BS: brown spot LB: leaf blast NB: neck blast SHR: sheath rot BPH: (brown) plant hopper DEF: defoliating insects DH: dead hearts WH: white heads WEED: weeds

Pests and diseases included in WHEATPEST

• Diseases – brown rust – yellow rust – powdery mildew – Septoria tritici blotch – Septoria nodorum blotch – Eyespot, Sharp eyespot – Fusarium stem rot – Fusarium head blight – Take-all – BYDV

• Insects – Aphids

• Weeds

Willocquet L, Aubertot JN, Lebard S, Robert C, Lannou C, Savary S, 2008. Simulating multiple pest damage in varying winter wheat production situations. Field Crops Research 107: 12-28.

A simulation model for yield losses caused by diseases, insects and weeds for wheat

• Plant organs affected foliage, whole plant foliage, whole plant foliage foliage foliage base of tillers, crown base of tillers, crown heads whole plant whole plant

whole plant

entire crop stand

structure of WHEATPEST

LEAFBM STEMBM EARBM

POOL STEMP

DTEMP

RG

ROOTBM

k

RUE

DVS

RROOT RLEAF RSTEM REAR

CPRCPSTCPL CPE

RADTM IN TM AX

RSENL

RDIST

TBASE

RRSENL

FHB

RSAP

ST

RDIV

BYDV SHY EYS FST TAK WD

APH

PM

BR

YR

SN

SLA

LAI


A simulation model for yield losses caused by diseases, insects and weeds for wheat

Examples of simulated yield losses in wheat for different injury profiles

IP A, conventional

0

2

4

6

WD

TA

K

EY

S

SH

Y

FS

T

ST

SN

BR

YR

PM

AP

H

BY

DV

FH

B

InjuriesR

ela

tiv

e y

ield

lo

ss

(%

) RYL IP: 14.0%

RYL SII: 15.4%

A

YA: 1000 g.m-2

YL IP: 140 g.m-2

IP C, conventional

0

2

4

6

WD

TA

K

EY

S

SH

Y

FS

T

ST

SN

BR

YR

PM

AP

H

BY

DV

FH

B

Injuries

Re

lati

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yie

ld lo

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(%

) RYL IP: 16.5%

RYL SII: 18.0%

C

YA: 1000 g.m-2

YL IP: 165 g.m-2

Pest and disease injuries: WD: weeds TAK: take-all EYS: eyespot SHY: sharp eyespot FST: crown/foot fusarium ST: septoria tritici blotch SN: septoria nodorum blotch BR: brown (leaf) rust YR: yellow (stripe) rust PM: powdery mildew APH: aphids BYDV: barley yellow dwarf FHB: fusarium head blight


Lines of thoughts

• Crop growth models: exist potential yield (T, rad, plant genotype)

attainable yield (same, + yield limiting factors)

• New step: add yield-reducing factors to existing models: implies

driving functions for diseases (pests)

couplers = damage mechanisms

• Missing: driving functions for diseases develop a framework to model potential epidemics

Proposed definitions • Potential epidemic: occurs

in absence of any control in a given climatic context (factor: climate)

• Attainable epidemic: occurs in absence of any specific plant protection method in a given production situation (factors: climate + cropping practices)

• Actual epidemic: occurs in a specified production situation (factors: climate + cropping practices + direct disease management tools)

Ep: potential epidemic Ea: attainable epidemic E: actual epidemic

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time

Ea

E

Conceptual research framework

actual

climate

cropmanagement

HPRpesticides

E

epidemiological model

attainable

climate

cropmanagement

Ea


potential

climate

Ep


Framework of activities for a Research Group

• Focusing on crop health (multiple diseases, pests) • Generic simulation models for disease epidemics • Enabling to develop crop health scenarios • A crop health scenario = a set of injury levels

caused by different diseases, pests • Crop health scenarios: used as driving functions

for crop growth models, and model crop losses • Allows addressing (1) potential and actual crop

health risks and (2) crop losses (and gains from management) in a generic manner

Target patho- systems

Crops and Ecologies

Table to fill

Check « ecologies »

NOT too many crosses

To be discussed further:

- perennial crops

- other or different annual crops

Wheat Rice Potato Soybean Coffee

Temperate X X X Etc.

Sub-tropical X X X

Tropical dry X X X

Tropical humid X X

Tropical

mountain X X

web of life

interactions

ecosystem infection efficiency

R0

infection chain

epidemiological process

lesion expansion

latency period

infectious period

propagule production

sporulation

disease aggregation

spore dispersal

spore deposition

dispersal gradient

disease gradient

viruliferous vector

acquisition

transmission efficiency

understandingepidemics

predictingcrop lossesRI

RUE

damage mechanism

turgor reducer

assimilate sapper

stand reducer

light stealer

senescence accelerator

tissue consumer

photsynthesis rate reducer

yield loss

crop loss

actual yield

attainable yield

potential yield

production situation

managingepidemics

potato late blight

apple scab

apple powdey midew

barley powdery mildew

coffee rust

black sigatoka banana

rice tungro disease

rice blast

barley yellow dwarf

rice sheath blight

grapevine downy mildew

hop downy mildew

septoria tritici blotch

citrus bacterial canker

fusarium head blight wheat & barley

wheat leaf rust

wheat stem rust

Documents

Models for crop diseases: Overview of approaches & scales · Models for crop diseases: Overview of approaches & scales S Savary, P Esker, N McRoberts, L Willocquet, T Caffi, V Rossi,