1. INSECT MODEL
& SIMULATION
Kyungsan Choi

2. Future
DATA BASE
Simulation

3. Target When How Expert System of Pest management Database
- pest information
- control information
- experience data
- simulated data
Simulation
: Prediction / Estimation
- Occurrence time
- Spraying time
Target
When
How

4. Simulation in Insect model
∙ Theoretical base (Curry et al., 1978)
∙ Multi cohort simulation with a program (Wagner et al., 1985)

5. < Practical usage in Agriculture>
∙ 30 years later
1. Major researcher use Excel program
- Problem : Takes long time, difficulty in building Model
2. Some use Simulation program
- STELLA, DYMEX , etc
- hardness in comprehension, restricted in specific model, etc
3. A few make a structural program by himself
- Restricted for a model , low confidence, almost impossible to modify it
< Practical usage in Agriculture>
- Rare but in a few nation developed a system based on Degree day models. However, it does not well used because of the low accuracy

6. Why there is no platform for insect models ?
Complication and variety
in a model
and its application

7. Is there easy way to build and simulate complicated models
Program
language X
Variety
STELLA
DYMEX
Difficulty
Simple

8. X R Main C ∙ Template : STAGE MODEL - INPUT : X, R data
- Main Function and Component Function X R
Main C

9. ∙ Various Method - Cal. Method - Array variable (I, N, T) ARRAY 1
TRANSITION

10. ∙ Simple Stage linkage

11. PopModel 1.0 (2014)

12. PopModel 1.5 (2016)

13. Prediction System for Insect Pest (coming soon 2017)
☐ Forecasting :
∙ Predict the insect occurrence, damage period
∙ Suggest the timing of Spray
☐ Display structure
∙ Pest List according to crop
∙ Illustration of the occurrence and damage time
∙ Recommendation table :
- protection time List for each pest
- best insecticides and its best time to apply
☐ Management and Simulation engine
∙ Occurrence, damage, agro-chemical effective, and optimum timing Models are saved in Database as the model template of PopModel 1.5
∙ Simulation run by the engine of the PopModel system

14. How to make insect occurrence model?
☐ Rate
Distance
R(T) = D/ T
Rate =
Time
☐ Insect development rate
Insect are Poikilothermal animal
=> The development time is dependent on the temperature
if The distance = 1 then dev. rate = 1/days (median or mean day)
Temp
day
rate 16
4.9
0.203 20
2.7
0.364 24
1.8
0.545 28
1.6
0.620 32
1.3
0.799 35
0.568

15. 1) Linear model : Y= a*X +b
☐ Degree-Day
1) Linear model : Y= a*X +b
TL=10.3
DD = 27.6
Day
Temp(T)
If( T > TL, T, 0)
Sum 1
8.8 2
9.5 3
6.4 4
14.5
4.46 5
22.5
12.46
16.9 6
19.3
9.26
26.1 7
38.6 8 18
7.96
46.6
Dev. Zero
a =
b =
Dev. Zero = -b/a = 10.04
Thermal constant = 1/ a
= 27.6

16. 1. higher/lower estimation
2) Problem
1. higher/lower estimation
higher estimation over optimal temp.
Lower/higher estimation under
Dev. Zero
Dev. Zero is not a real
but an abstract value deduced from
linear model
2. No consideration on the variation of development
Median : <= Pos. at 50%
Mean : 22.2
SE : 0.15
That’s why Degree-day is not
failed in practical usage

17. ☐ Non-linear model 1) Developmental rate Lactin 2 :
Y=EXP(P0*X)-EXP(P0*P1-(P1-X)/P2)+P3
Estiamted value of parameter :
P0= , P1 = 36.97
P2= 1.13, P3=

18. 2) Developmental complete distribution
No. individual
Dev. Period (days) 30 25 20 15
There are differences in variation and sample size
from each temp treatments.
So, How can we make a distribution model with
those data?

19. Step 1. Convert them into cumulative probability
Probabilty 20 30 25 15
Dev. Period (days)
Step 2. Normalize the developmental period as dev. rate 1
dev. rate = 1/days
So. We can say that 50% individuals
complete their development
when physiological age is 1
Cumulative
Probabilty 1 2
Physiological age

20. 3) Simulation method Pc : density of the cohort
No. hatched larva
100 eggs
Today ( N)
data 1 2 3 4 5 6 7 8 9
N-1 of Px
N of Px
Pc : density of the cohort
F(px) : developmental distribution model
pxi : physiological age at ith day
Pxi-1
Pxi

21. ☐ Example : Stage emergence model
- Examine the development of the target species at different constant temperature

22. - Make dev, rate and distribution model as well as degree-day model

23. - Simulate and validate model with field occurrence

24. ☐ Example : Adult Oviposition model
- Examine adult longevity , daily egg production

25. - Make 4 models

26. Adult longevity model
Physiological age is calculated
By summing the result of the model (1)
Daily egg laid model (cumulative)
Daily adult survival model
Oviposition model of adult

27. ☐ Example : Population model

28. Adult
Larva
Egg
Pupa

29. Formula usually used in insect model
☐ Hilbert & Logan (1983)
☐ Lactin (1995)
Lactin 1 model
Lactin 2 model

30. ☐ Gaussian (Taylor, 1981)
☐ Briere (Briere et al., 1999)

31. ☐ SM model (Sharpe & DeMichele , 1977)
☐ SS model (Schoolfield, 1981)
☐ SSI model (Ikemoto, 2005)