STRUCTURAL_ Chapter 14_ Explicit Dynamics Analysis (UP19980818)
Models for crop diseases: Overview of approaches &...
Transcript of Models for crop diseases: Overview of approaches &...
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
Background and vision (2)
• 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
Background and vision (3)
• 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
Background and vision (4)
• 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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disease 2
disease 4
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condition (a)
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disease 1disease 2disease 3disease 4
condition (b)
dynamic undercondition (a)
disease 1
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Background and vision (5)
• 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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DACE
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Nakdong
Jingheung
Akibare
Brown spot
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0 50 100 150
DACE
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Bacterial blight
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0 25 50 75 100
DACE
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IR-24
Sabitri
Laxmi
Sheath blight
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0 25 50 75 100
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
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10000
15000
20000
25000
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Time (DACE)
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25
Healthy Latents Infectious
Removed Total diseased Severity
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Brown spot
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Time (DACE)
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Healthy Latents Infectious
Removed Total diseased Severity
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Bacterial blight
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Healthy Latents Infectious
Removed Total diseased Incidence Leaves
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Sheath blight
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Healthy Latents Infectious
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
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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: 26.1%
Rainy season in rice-wheat rotation, rainfed rice
0
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BLB
SHB
BS LB N
BSHR
BPH
DEF
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WH
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Injury
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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
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
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
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ield
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(%
) RYL IP: 14.0%
RYL SII: 15.4%
A
YA: 1000 g.m-2
YL IP: 140 g.m-2
IP C, conventional
0
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6
WD
TA
K
EY
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FS
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SN
BR
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AP
H
BY
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B
Injuries
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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
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.
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
Dis
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inju
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time
Ea
E
Conceptual research framework
actual
climate
cropmanagement
HPRpesticides
E
epidemiological model
attainable
climate
cropmanagement
Ea
epidemiological model
potential
climate
Ep
epidemiological model
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