next generation crop insect pest models for integrated ... · simon (2013): predicting climate...
TRANSCRIPT
The Insect Life Cycle Modeling (ILCYM) software and options for
integrated assessments
AG
MIP
, Feb
ruar
y, 2
015
J. KroschelM. Sporleder, P. Carhuapoma, H. Juarez, N. Mujica
DCE Crop Systems Intensification and ClimateChange (CSI-CC); Agroecology/IPM sub-program
Next generation crop insect pest models for integrated
assessments
Outline
� Introduction
• Development of ILCYM: For which purpose
• Main features and advantages
� ILCYM applications
• Pest Risk Analysis (PRA) on different scales
• Climate change and adaptation planning
• Regional assessments
• Decision support tool in pest management
�Outlook: considerations in integrated assessments
� Conclusions
1 Species distribution mapping (SDM) in support of pest risk
assessment (PRA, IPPC)
2 Climate change / adaptation planning
3 Integrated Pest Management (Examples)
- Simulation of pest life-table parameters under prevailing weather
conditions as decision support for pest management
- Identification of potential release sites for parasitoids (classical biocontrol)
- Simulation of field performance of biopesticides and application rates
Analytical tools for predicting, evaluating and understanding the dynamics of
insect populations in agroececosystems.
ILCYM: For which purpose
Potato Tuber Moth, Phthorimaea operculella
• Invasive species; reported from more than 90 countries
• Adapted to wide range of different climates and agroecologies
• Yield losses due to leaf and tuber infestation at harvest
• Storage pest in tropical and subtropical countries
Potato production areas and PTM distribution
Phthorimaea operculella
Symmetrischema tangolias
Tecia solanivora
Likelihood of entry and
establishment*Consequences of
establishment
Entry potential
Colonization potential
Spread potential
Economic impact potential
Environmental impact potential
Social impacts
1
2
3
A B
Establishment potential
Framework for Pest Risk Analysis
*under current and future climates
Insect Life Cycle Modeling (ILCYM)
• Required input: life-table data collected under
constant and fluctuating temperatures
• Model builder:
- Fitting functions for describing temperature-
driven development processes under constant
temperatures, i.e. development rate, mortality
and reproduction
• Validation and simulation module
- Uses life-table data established under
fluctuating temperature; can be used for
deterministic and stochastic simulations
• Potential population and risk mapping module
- Establishment index
- Generation index
- Activity index
Software package for developing temperature-based insect phenology models with
applications for regional and global pest risk assessments and mapping.
(www.cipotato.org/ilcym)
Pest risk mapping using ILCYM
Model validation
Temperature-based phenology
modelsMo
de
lb
uil
de
r
Development rate and time Survival rate, oviposition, mortality
Risk indices
ERI GI AI
Ris
km
ap
pin
g
ERI= ∑ ��� ����� /� �������� ��
∑ 365� ���� /��� �365GI=
log�� !"�� �
���AI=
Potential distribution at global, regional
and local level
Future climate: 2050With and without crop shapes
Current climate: 2000(1950-2000: www.worldclim.org/) Down-scaled data SRES-A1B, IPCC (2007):
http://gisweb.ciat.cgiar.org/GCMPage
(www.cipotato.org/ilcym)
Simulation of life-table
parameters
Net reproduction rate, generation time
0 10 20 30
50
100
50
100
150
0 10 403020 0 10 20 30
0.00
0.08
0.04
1.25
1.15
1.05
0.95
10 20 30 40
0
150
100
50
0 10 20 30 40
intrinsic and finite rate of increase, double time
Life-table studies
A constant and fluctuating
temperatures: oviposition,
survival time, development time,
mortality
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60
Indi
vidu
als
0
5
10
15
20
25
30
35
40
Tem
pera
ture
(°C
)
Arequipa 2do cycle, 13.01.99
Larva
Pupa
Eggs
Adult
Time (days)
(Keller 2003)
Model validation
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100 110
Indi
vidu
als
0
5
10
15
20
25
30
35
40
Tem
pera
ture
(°C
)
Huancayo 2do. cycle, 24.11.98
LarvaePupae Adults
Eggs
Time (days)
(Keller 2003)
Model validation
Establishment risk index (Survival risk index): Indicates the capacity of
invasive pest species to establish permanent populations
Index =
The index is defined as the number of time intervals with a net reproduction rate, R0, above 1 (Ii=1)
divided by the total number of time intervals within a year (II).
Generation Index: Indicates the potential infestation of a crop
Index =
The index is computed by averaging the sum of estimated generation lengths, Ti , calculated for
each Julian day, x, where 365 is the number of days per year and Ti is the predicted average
generation lengths in days during the month, i (i = 1, 2, 3, …, 12).
Activity Index: Indicates pest potential population growth, spread and severity
Index =
where λλλλ is the finite rate of increase at Julian day, x (x = 1, 2, 3, …365).
Three risk indices
∑ #$� �� ���� /365%&'(#$� �)
∑ 365�(��� /��� �12
log�� !"�� �
���
Main advantages of ILCYM modeling (1)
• Complex simulation of an entire insect life system.
• It uses process-based functions that provide a mechanistically detailed
representation of the insects life history, and hence population growth.
• Model functions are build based on statistical analysis of experimental
data that allows determining errors in particular processes.
• Full live history (development, survival, reproduction) is modeled and
the software compiles the overall model automatically (reducing
programming skills and time of users)
• Models have parameters with straightforward interpretation of the
biological system.
• Individual functions of a model can be validated and improved
• The information can be used in different modeling types; i.e. spatially
(SDM through the use of specific risk indices), or in time (in a given
area) for different inquiry (from basic ecological understanding to
prediction, management and policy-making).
Main advantages of ILCYM modeling (2)
• Since the model works with probability distributions for specific
processes it is possible to conduct both deterministic and stochastic
simulations.
• The simulation uses an cohort-updating algorithm and quantifies the
population age-stage specific.
• Input data can be real or simulated temperature at variable intervals.
• The simulation is implemented in R-language, which is open source
software.
ILCYM: Reference publications
Sporleder, M., H.E.Z. Tonnang, P. Carhuapoma, J. C. Gonzales, H. Juarez & J. Kroschel
(2013): Insect Life Cycle Modeling (ILCYM) software – a new tool for Regional and
Global Insect Pest Risk Assessments under Current and Future Climate Change
Scenarios. CABI Book publication, 412-427.
Tonnang, E.Z.H., P. Carhuapoma, H. Juarez, J.C. Gonzales, D. Mendoza, M. Sporleder, R. Simon,
J. Kroschel (2013): ILCYM - Insect Life Cycle Modeling (version 3.). User Guide. A software
package for developing temperature-based insect phenology models with applications for
regional and global analysis of insect population and mapping. International Potato Center,
Lima, Peru. pp 193.
Sporleder, M., J. Kroschel, M. R. Gutierrez Quispe & A. Lagnaoui (2004): A temperature-
based simulation model for the potato tuber worm, Phthorimaea operculella Zeller
(Lepidoptera; Gelechiidae). Environmental Entomology 33 (3): 477-486.
Kroschel, J., M. Sporleder, H.E.Z. Tonnang, H. Juarez, P. Carhuapoma, J.C. Gonzales & R.
Simon (2013): Predicting climate change caused changes in global temperature on potato
tuber moth Phthorimaea operculella (Zeller) distribution and abundance using phenology
modeling and GIS mapping. Journal of Agricultural and Forest Meteorology 170: 228-241.
ILCYM users
Number of ILCYM potential users that have visited the software webpage from
March 1, 2010 until 20 March 2014
Pest Risk Mapping and
new framework to assess
climate change impacts!
Rational for
phytosanitary
requirements
(by NPPO)
Stage IV:
Documentation
Stage IV:
Documentation
Pest identification
Pathway
Policy review
PRA area
Pest taxonomy etc.
Stage I:
Initiation
Stage I:
Initiation
Pest categorization
Probability
X
Magnitude of
econ./environ.
Impact
Stage II:
PR-Assessment
Stage II:
PR-Assessment
*(adopted from FAO-IPPC, 2007)
React
Test & evaluate
efficacy of
measures
Surveillance
Diagnostics
Stage III:
PR-Management
Stage III:
PR-Management
Applications of ILCYM: Pest Risk Analysis (PRA)
“The process of evaluating biological or other scientific and economic
evidence to determine whether an organism is a pest, whether it
should be regulated, and the strength of any phytosanitary measures
to be taken against it”
National and regional
adaptation planning!
Pest Risk Mapping
Validation of risk maps by using geo-referenced distribution maps
Example: potato tuber moth
Green points: countries with reported pest establishment
Red points: geo-referenced distribution data
Yellow points: permanent establishment not confirmed
ILCYM: Risk mapping under current and future
climates Phthorimaea operculella: ERI 2000 and 2050
A range expansion into temperate regions of the northern hemisphere and in
tropical mountainous regions
(Kroschel et al., J. Agric. Forest Meterol., 2013)
Europe:
2.73% (234,404 ha) North America:
4.21% (32,873 ha)
Asia:
8.3% (699,680 ha)
Africa:
1.01% (11,605 ha)
Oceania:
13.67% (7,314 ha)
South America:
11.9% (109,666 ha)
ILCYM: Risk mapping under current and future
climates
(Kroschel et al., J. Agric. Forest Meterol., 2013)
Phthorimaea operculella: GI 2000 and 2050Abundance and damage potential will progressively increase in all regions
where the pest already prevails today with an excessive increase in warmer
cropping regions of the tropics and subtropics
ILCYM: Risk mapping under current and future
climates
(Kroschel et al., J. Agric. Forest Meterol., 2013)
Phthorimaea operculella: GI 2000 and 2050Abundance and damage potential will progressively increase in all regions
where the pest already prevails today with an excessive increase in warmer
cropping regions of the tropics and subtropics
ILCYM: Risk mapping under current and future
climates Phthorimaea operculella: GI Change
>4 generations are developed on 30.2% (5.918.557 ha) of the total potato
production area worldwide; this can pot. increase to 42% (8.328.531 ha)
(Kroschel et al., J. Agric. Forest Meterol., 2013)
Africa:
2.87% (1,113,966 ha)
Asia:
15.29% (5,246,319 ha)Europe:
10.14% (1,115,432 ha)
North America:
15.87% (280,180 ha)
Oceania:
26.05% (42,465 ha)
South America:
13.11% (530,116 ha)
ILCYM: Risk mapping under current and future
climates P. operculella validation: observed and predicted numbers of
generations at different locations using different climate input data
Country/location
Life table
studies under
local natural
conditions
ILCYM modeling
results using local
weather station
data
ILCYM modeling
results using
WorldClim data
Monthly records
Peru: San Ramon,
800 m a.s.l.12 12 9.5
Peru: Arequipa,
1140 m a.s.l.71 6.7 7.2
Peru: Huancayo,
3250 m a.s.l.3-4 3.1 2.3
Yemen: Sana’a,
2150 m a.s.l. 82 7.1 5.4
Egypt: Giza,
10 m a.s.l.103 10 8.6
(Kroschel et al., J. Agric. Forest Meterol., 2013)
ILCYM: Risk mapping under current and future
climates Effect of climate change on the number of generations at different locations
Nu
mb
er
of
ge
ne
rati
on
s p
er
ye
ar
0
2
4
6
8
10
12
14
16
18
Peru - San Ramon (800 m) Peru - Huancayo (3250 m) Peru - Arequipa (1440) Yemen - Sana’a (2250) Egypt - Giza (10 m)
12.5
12
+1.5
+2.5
+3.9+4.6
2.9+0.4
+0.7+1.0
+1.2
6.7+0.8
+1.4+2.4
+2.8
3.0
7.0
8.0
7.0
+0.9+1.3
+2.2+2.7 10 10
+1.2+2.0
+3.7+3.1
PRESENT PREDICTION PRESENT PREDICTION PRESENT PREDICTION PRESENT PREDICTIONPRESENT PREDICTION
Leafminer fly, Liriomyza huidobrensis
• Center of origin in the neotropics; reported from more than 66 countries
• Very polyphagous; in Peru pest in >27 horticultural crops
• Yield loss without management up to 70% in potato
Green points: countries with reported pest establishment
Yellow points: reported in protected crops
Red points: geo-referenced distribution data
ILCYM: Risk mapping under current and future
climates Liriomyza huidobrensis: Regional maps
ERI
GI
2000 2050Index change:
2000-2050
2000 2050 Change
Act
ivit
y i
nd
ex
Ge
ne
rati
on
in
de
x
ILCYM: Risk mapping under current and future
climates Liriomyza huidobrensis: country maps for Uganda
L. huidobrensis will potentially increase its risks in the northern, eastern and
southern provinces due the high values of GI>12 and AI>17
Publication Project – Pest Risk Atlas
Pest Distribution and Risk Atlas for AfricaPotential global and regional distribution and abundance of
agricultural and horticultural pests and associated biocontrol agents under current and future climates
Potato pests Potato tuber moth, Phthorimaea operculella (Kroschel et al.)Guatemalan potato tuber moth, Tecia solanivora (Schaub et al.)Andean potato tuber moth, Symmetrischema tangolias (Sporleder et al.)
Sweetpotato pestsSweetpotato weevil, Cylas puncticollis (Okonya et al.)Sweetpotato weevil, Cylas brunneus (Paul Musana et al.)Sweetpotato butterfly, Acraea acerata (Okonya et al.)Sweetpotato white fly, Bemisia tabaci Type B (Gamarra et al.)White fly, Bemisia afer (Gamarra et al.)
Vegetable pestsSerpentine leafminer fly, Liriomyza huidobrensis (Mujica et al.)Vegetable leafminer, Liriomyza sativae (Mujica et al.)American serpentine leafminer, Liriomyza trifolii (Le Ru et al.)Greenhouse whitefly, Trialeurodes vaporarium (Gamarra et al.)
3.4 Maize pestsSpotted stemborer, Chilo partellus (Khadioli et al.)Maize stalk borer, Busseola fusca (Khadioli et al.)African pink stem borer, Sesamia calamistis (Khadioli et al.)
3.5 Cassava pestsCassava mealybug, Phenacoccus manihoti (Hanna et al.)Cassava green mite, Mononychellus tanajoa (Hanna et al.)
3.6 Fruit pestsOriental fruit fly, Bactrocera invadens (Hanna et al.)Banana aphid, Pentalonia nigronevosa (Hanna et al.)
Publication Project – Pest Risk Atlas
Reducing crop vulnerability by adaptation to new and emerging pests
+ Adaptation (of IPM)
Resilient systems, new technologies,
tolerant and resistant varieties reduce pest
caused losses by 15%
Exposure
Temperature
increase: 2 °C
Vulnerability
Actual impact on crop yields by pests increase by 5%
Mitigation Reduction of CO2 emissions
(Adapted from IPCC, 2001, 2007)
Crop yields Crop pests
Sensitivity
Pot. impacts on crop yields based on pest
expansion/increased abundance: Higher
infestations reduce yields by 20%
Climate change / adaptation planning
ObjectiveTo inform stakeholders in plant health,plant quarantine and pest managementabout future pest risks on global,regional and local scales and to usethis information for crop-specificadaptation planning and to definepriorities in pest management.
Stakeholder workshop
Climate change / adaptation planning
Climate data and map resolution
• For estimating pest risk for specific locations, max. and min.
temperature data in a 1 hr time step length are used to
consider the within-day temperature variability that may
affect insects.
• For global and regional pest risk mapping, currently
available temperature surfaces use monthly average
aggregated climate data from 1960 to 2000
(www.worldclim.org).
• Aggregation and interpolation of temperature data has its
limitations especially in mountainous regions due to local
temperature variability (topographic effects) over short
distances.
• Higher accuracy of risk maps and predictions of potential
pest distribution could be achieved through locally collected
daily weather data processed and interpolated in ILCYM.
0
5
10
15
20
25
30
35
0 4 8 12 16 20 24
Time steps
Tem
pera
ture
Min
Min
Max
� Development of a tool which allows regional assessments of pest risks in
mountainous regions at high resolution.
ILCYM: regional assessments
18 x 18km (1x)
Map resolutions: from global to regional and local scales
ILCYM: regional assessments
18 x 18km (1x)9x9km (4x respect to 10’)
Map resolutions: from global to regional and local scales
ILCYM: regional assessments
18 x 18km (1x)9x9km (4x respect to 10’)4.5x4.5km (16x respect to 10’)
Map resolutions: from global to regional and local scales
ILCYM: regional assessments
18 x 18km (1x)9x9km (4x respect to 10’)4.5x4.5km (16x respect to 10’)0.9x0.9km (400x respect to 10’)
Map resolutions: from global to regional and local scales
ILCYM: regional assessments
18 x 18km (1x)9x9km (4x respect to 10’)4.5x4.5km (16x respect to 10’)0.9x0.9km (400x respect to 10’)
90m x90m (40,000x respect to 10’)
Map resolutions: from global to regional and local scales
ILCYM: regional assessments
21 weather stations
were installed in the
Mantaro Valley, Peru
Data collection
ILCYM: regional assessments
Climateinterpolator(thin plate smothing spline)
[Daily]Tmax n=365, Tmin n=365
[Mapping Index: GI, ERI, AI]
[Calculating theIndices (GI, ERI, AI) per weatherstation]
GI, ERI, AI
IndexInterpolatortool(thin platesmothing spline)
ANUSPLIN 4.1 (Australia)Data processing:
30 days versus 5 min.
ILCYM: regional assessments
Local risk maps for leaf miner fly, Mantaro Valley, Peru
ERI GI
Use of ILCYM’s Index Interpolator
ILCYM: regional assessments
Local risk maps for leaf miner fly, Mantaro Valley, Peru
ERI+1 °C GI+1 °C
Use of ILCYM’s Index Interpolator
ILCYM: regional assessments
Local risk maps for leaf miner fly, Mantaro Valley, Peru
ERI+2 °C GI+2 °C
Use of ILCYM’s Index Interpolator
ILCYM: regional assessments
Local risk maps for leaf miner fly, Mantaro Valley, Peru
ERI+3 °C GI+3 °C
Use of ILCYM’s Index Interpolator
ILCYM: regional assessments
RTB-Pest Risk Assessment project
Impact of climate change on the
livelihood of farmers
Household models
RTB yield
models
Pest and crop models
(yield gaps caused by insect pest)
Modeling climate change
New entry
pests (PRA)
Current
pests
ILCYM: regional assessments
(Framework of RTB Pest Risk Analysis (PRA) project)
Burundi Rwanda
0
500
1000
1500
2000
2500
3000A
ltitu
de (
m a
sl)
Weather stations
Action sites of RTB PRA project:
Burundi, Rusizi valley: 900 to 2600 m asl
Rwanda, Ruhengeri: 1400 to 2600 m asl
- All RTB crops
- Harmonization and standardization
of household and field survey
methodologies and collection of climatic
information along an altitudinal gradient
ILCYM: regional assessments
Pest management (1)
1. Use of calendar date: Pest status assessed at
a specific date or crop growth stage and
pesticides are applied if the pests economic
threshold is reached.
Example: potato tuber moth
Control threshold: 0.5 mines/plant
Critical infestation period: GS 60
2. Use of degree-days (DD): The temperature
above the pest’s specific temperature
minimum is summed up (degree-day
summation) and the pest status assessed
when the pest specific temperature sum is
reached (improvement to 1).
Example: Evolution in decision support tools
Determination of pesticide application date
Mines / plant
Example:
Daily pest degree
day calculator of
the Illinois State
Water Survey,
University of
Illinois
3. Use of non-linear models for insect development: (daily development rates are
summed up until reaching a value of 1 (further improvement to 2, specially
when temperature is in the non-linear range for development).
Example: Evolution in decision support tools
Determination of pesticide application date
Pest management (2)
4. Process-based climatic response model (ILCYM phenology model): Temperature
variability in autumn influences emerging time of insects in spring because the
overwintering stage already past some development in autumn (Example: mountain pine
beetle, Dendroctonus ponderosae (Hicke et al. 2006).
Huancayo, Peru (3250 masl) Hermiston, Oregon, USA (144 masl)
Example using ILCYM: Potato tuber moth: Rate of population increase within one year
Example: Evolution in decision support tools
Determination of pesticide application date
Pest management (3)
Highland agroecology (3250 masl)
Simulation of pest life-table parameters under prevailing weather conditions
Leafminer fly, Liriomyza huidobrensis, under Peruvian lowland and highland conditions
00.020.040.060.080.1
0.120.140.160.18
0 30 60 90 120
150
180
210
240
270
300
330
360
intr
insi
c ra
te o
f in
crea
se
Julian days
11.021.041.061.081.1
1.121.141.161.181.2
0 30 60 90 120
150
180
210
240
270
300
330
360
finite
ra
te o
f in
crea
se
Julian days
0
5
10
15
20
25
30
35
0 30 60 90 120
150
180
210
240
270
300
330
360
net
rep
rod
uct
ion
ra
te
Julian days
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 30 60 90 120
150
180
210
240
270
300
330
360
imm
atu
re s
urv
iva
l
Julian days
0102030405060708090
0 30 60 90 120
150
180
210
240
270
300
330
360
gen
era
tion
tim
e (d
ays
)
Julian days
0102030405060708090
0 30 60 90 120
150
180
210
240
270
300
330
360
Dou
blin
g tim
e (d
ays
)
Julian days
0
5
10
15
20
25
30
35
0 100 200 300
Tem
per
atu
re (
°C)
Julian days-5
0
5
10
15
20
25
0 100 200 300
Tem
per
atu
re (
°C)
Julian days
Lowland agroecology (350 masl)
max.
min.
Lowland highland
Pest management (4)
(Mujica et al.,
in preparation)
Potential establishment (ER) and efficacy (number of generations, GI) of Orgilus
lepidus (Hymenoptera: Braconidae), parasitoid for classical biocontrol of the
potato tuber moth, Phthorimaea operculella
Pest management (4)
ERI GI
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N
nat. increase
r m = 0.1297A
24ºC
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N r m = 0.0622
B18ºC
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 10 20 30 40 50
age (days)
dist
ribut
ion
rate
Egg
Larva
Pupa
Adult
Simulating PhopGV field performance
Pest management (5)
(Sporleder et al. Agriculture and Forest Entomology 2007)
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N
nat. increase
1. Appl.
r m = 0.1297
applications
A24ºC
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N r m = 0.0622
applications
B18ºC
Application of
1.2×1013 granules/hectare
≈≈≈≈ 4,000 LE/hectare
≈≈≈≈ 80% mortality (neonate larvae)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 10 20 30 40 50
age (days)
dist
ribut
ion
rate
Egg
Larva
Pupa
Adult
Simulating PhopGV field performance
Pest management (5)
(Sporleder et al. Agriculture and Forest Entomology 2007)
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N
nat. increase
1. Appl.
2. Appl.
r m = 0.1297
applications 1 - 2
A24ºC
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N r m = 0.0622
applications 1 - 2
B18ºC
Application of
1.2×1013 granules/hectare
≈≈≈≈ 4,000 LE/hectare
≈≈≈≈ 80% mortality (neonate larvae)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 10 20 30 40 50
age (days)
dist
ribut
ion
rate
Egg
Larva
Pupa
Adult
Simulating PhopGV field performance
Pest management (5)
(Sporleder et al. Agriculture and Forest Entomology 2007)
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N
nat. increase
1. Appl.
2. Appl.
3. Appl.
r m = 0.1297
applications 1 - 3
A24ºC
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N r m = 0.0622
applications 1 - 3
B18ºC
Application of
1.2×1013 granules/hectare
≈≈≈≈ 4,000 LE/hectare
≈≈≈≈ 80% mortality (neonate larvae)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 10 20 30 40 50
age (days)
dist
ribut
ion
rate
Egg
Larva
Pupa
Adult
Simulating PhopGV field performance
Pest management (5)
(Sporleder et al. Agriculture and Forest Entomology 2007)
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N
nat. increase
1. Appl.
2. Appl.
3. Appl.
4. Appl.
5. Appl.
6. Appl.
7. Appl.
8. Appl.
r m = 0.1297
applications 1 - 8
A24ºC
0
2
4
6
8
10
12
14
0 30 60 90 120
Time (days)
ln N r m = 0.0622
applications 1 - 8
B18ºC
Application of
1.2×1013 granules/hectare
≈≈≈≈ 4,000 LE/hectare
≈≈≈≈ 80% mortality (neonate larvae)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 10 20 30 40 50
age (days)
dist
ribut
ion
rate
Egg
Larva
Pupa
Adult
Simulating PhopGV field performance
Pest management (5)
(Sporleder et al. Agriculture and Forest Entomology 2007)
Global economic assessments/first approximation of crop losses:
Relate pest abundance (pot. no. of generations/season) to % crop loss; consider
precipitation as co-factor
Linking to crop models:
Use of same input data (temperature); daily or hourly simulation interval (e.g.,
using InfoCrop, DSSAT, EPIC).
ILCYM quantify pest populations by separating stage- and age-classes. Individuals
in the damaging age-stage can be quantified over time. These numbers need to be
expressed for a determined leaf area and further calculated into “crop damage-loss
relationships”.
Leaf miner fly: Apply foliar injury (%) crop loss functions in crop models; yields of
early varieties (Desiree) are more affected than of late varieties (Yungay).
y = 1.9501x + 0.1364R² = 0.8941
-10
0
10
20
30
40
50
60
70
0 10 20 30 40
Yie
ld lo
ss (
%)
2008-Desiree
P<.0001
y = 0.6155x - 0.0138R² = 0.7555
-10
0
10
20
30
40
50
0 10 20 30 40 50 60
Yie
ld lo
ss (
%)
Foliar injury (%)
2008-Yungay
P<.0001
Outlook: Integrated assessments
(Muijca, N. & J. Kroschel Crop Protection 2013).Foliar injury (%)
Next steps
ILCYM version 4 with a new interface fully in R-language (shiny-applications) to reduce required skills to maintain ILCYMsoftware and to increase user-friendliness and flexibility to usethe software.
Develop new sub-modules for assessing other influencing co-factors (e.g., precipitation) to improve pest predictions underclimate change on related crop losses.
Combining pest models with crop models in order to betterpredict/quantify losses. Leafminer fly as model pest tointegrate into crop models. Parametrize crop models with cropspecific data at the Peruvian coast.
Acknowledgements
Donors:
• German Federal Ministry for Economic Cooperation
and Development (BMZ/GIZ)
• World Bank
• El Fondo Regional de Tecnologia Agropecuaria (FONTAGRO)
• CRP-RTB
• CCAFS
Thank-you!