In 2004 RIMpro-Cydia was developed as a dynamic population model that simulates the

within-year biology of a local codling moth population. The model is meant to be used by

growers and advisors to optimize the control of codling moth populations in organic and

integrated managed orchards. The model is based on literature data and unpublished

research data. Fractional boxcar trains are used to mimic the dispersion in the

developmental processes. The model is run in real time on the data input of local weather

stations, starting on 1 January. The output of the model was compared with the results of

field observations in three years in an untreated orchard. In the years 2005 to 2007 the

progress in egg deposition as predicted by the model was in general agreement with the

field data. The start of the egg deposition period was predicted well. The end of the egg

deposition period was predicted when in the field about 10% of the eggs was still to be

laid. There was no consistency in the relation between cumulated pheromone trap catches

and the cumulative egg deposition as calculated from the field data

Helsen, H.

Polfliet, M.

Trapman, M.

English

Organic Eprints

247Development of a dynamic population model as a decision support system for Codling Moth (Cydia pomonella L) management M. Trapman1, H. Helsen2, M. Polfliet3AbstractIn 2004 RIMpro-Cydia was developed as a dynamic population model that simulates the within-year biology of a local codling moth population. The model is meant to be used by growers and advisors to optimize the control of codling moth populations in organic and integrated  managed  orchards.  The  model  is  based  on  literature  data  and  unpublished research  data.  Fractional  boxcar  trains  are  used  to  mimic  the  dispersion  in  the developmental processes. The model is run in real time on the data input of local weather stations, starting on 1 January. The output of the model was compared with the results of field observations in three years in an untreated orchard. In the years 2005 to 2007 the progress in egg deposition as predicted by the model was in general agreement with the field data. The start of the egg deposition period was predicted well. The end of the egg deposition period was predicted when in the field about 10% of the eggs was still to be laid. There was no consistency in the relation between cumulated pheromone trap catches and the cumulative egg deposition as calculated from the field data.  Keywords Cydia pomonella, codling moth, simulation model IntroductionThe biology of the codling moth (Cydia pomonella L) is strongly adapted to its primary hosts apple and pear. Codling moth is a key pest in almost all regions where apples are grown. Throughout Europe codling moth damage has increased over the last two decades. Effective codling moth control is essential for both integrated and organic fruit growers. Besides  information  on  the  mode  of  action  and  efficacy  of  the  available  insecticides, accurate data on critical time-points of codling moth development are essential to plan and implement a management strategy. For the timing of control treatments, most fruit growers and advisors in Europe rely on pheromone trap catches and temperature sums or models based on temperature sums, and evaluation of evening temperatures. More sophisticated simulation models like the Bugoff 2 model (Blago et al., 1990) are hardly used by practical advisory  services,  with  the  exception  of  the  SOPRA  model  developed  by  Graf  in Switzerland (Graf et al., 2003). The aim in the development of RIMpro-Cydia was to create a decision support system for fruit growers and advisors, based on a dynamic simulation model for the codling moth population. Structuring detailed pieces of knowledge in a simulation model is an efficient and convenient way to combine available knowledge for practical decision making. The outline of the model The biology of the codling moth was divided into life stages and developmental processes (Trapman, 2006) Details on life stages, average development time, relative dispersion in the process, and other parameters are given in Table 1. These data have been taken from literature, unpublished research work and practical experiences.1 Marc Trapman, Bio Fruit Advies, Netherlands, marc.trapman@biofruitadvies.nl 2 Herman Helsen, Wageningen UR, Applied Plant Research, Netherlands, herman.helsen@wur.nl 3 Matty Polfliet, Fruit Consult, Belgium, matty@fruitconsult.com Archived at http://orgprints.org/13703/248Developmental time is given in heat units (HU). Heat units are not calculated from a linear relation  as  temperature  sums,  but  using  a  Logan  curve  with  a  lower  developmental threshold of 10 °C, maximum development speed at 28 °C, and an upper threshold of 31 °C (Figs. 1 and 2). Effective HU are calculated from temperature readings at a 30 min interval. Fractional boxcar trains where used to mimic the dispersion in the processes (De Wit et al., 1974). The model was coded in Visual Basic 6 as an extension to the apple scab program RIMpro. The output is presented in a self-explaining graphical format for direct use by advisors and fruit growers, as well  as  in  a  detailed  tabular form for evaluation purposes.Table 1. Details on stages, processes and parameters used in the RIMpro-Cydia programStage Process  Average time in HU RD  Survival  Parameters and remarks Diapause  Diapause termination  13 April  0.2  1  Value for north-western Europe. Depending on day-length and with that on geographical position. Process calculation in days, not in Heat Units. Pupa  Pupation  140  0.15  1  Average set lower than actual process time as in spring the measured temperature is lower than received by the larvae. Virgin female  Pre-oviposition  75  0.1  1   Lifetime  200  0.15  1   Number eggs/female  50  0.15     Mated female Egg deposition        The rate of egg deposition is depending upon the temperature around sunset. (Figure 1) Egg  Embryonic development 88  0.1  0.8 1. Larval instar  Larval development  60  0.1  0.4 2. Larval instar  Larval development  45  0.1  0.9 3. Larval instar  Larval development  45  0.1  0.9 4. Larval instar  Larval development  45  0.1  0.9 5. Larval instar  Larval development  125  0.1  0.9 For the calculation of the progress in all processes Heat Units (HU) per 30 minutes time interval are calculated according to the function in figure 2. Diapause induction  1 august  7    Depending on day-length  Polyvoltine fraction of the population  0.1      Estimated value for Netherlands and Belgium Pupa    160  0.1  0.9   EvaluationThe  output  of  the  model  was  compared  with  field  observations  in  three  years  in  an untreated orchard at Vogelwaarde, Southwest Netherlands. At regular intervals all codling moth damaged fruits where collected from marked plots. The age of the individual larvae was determined from their length and the width of the head capsule. For the individual larvae  their  approximate  date  of  egg  deposition  was  back-calculated  from  temperature records. These data reflect only a sub-set of eggs, i.e. those eggs from which larvae have hatched and infected fruits. This effective egg deposition represents the population the grower  and  advisor  have  to  deal  with  in  practice (Helsen,  Polfliet  &  Trapman,  in preparation). Pheromone trap records for 2005 were taken form a regional registration system  of  39  traps  in  10  orchards.  In  2006  and  2007  pheromone  trap  catches  where recorded in the trial orchard. The explanatory value of the model was assessed by comparing the cumulative number of eggs deposited in the field with the value calculated by the model. In the same way, the cumulative egg deposition in the field was compared with the cumulated pheromone trap catches.Stage Process  Average time in HU RD  Survival  Parameters and remarks Diapause  Diapause termination  13 April  0.2  1  Value for north-western Europe. Depending on day-length and with that on geographical position. Process calculation in days, not in Heat Units. Pupa  Pupation  140  0.15  1  Average set lower than actual process time as in spring the measured temperature is lower than received by the larvae. Virgin female  Pre-oviposition  75  0.1  1   Lifetime  200  0.15  1   Number eggs/female  50  0.15     Mated female Egg deposition        The rate of egg deposition is depending upon the temperature around sunset. (Figure 1) Egg  Embryonic development 88  0.1  0.8 1. Larval instar  Larval development  60  0.1  0.4 2. Larval instar  Larval development  45  0.1  0.9 3. Larval instar  Larval development  45  0.1  0.9 4. Larval instar  Larval development  45  0.1  0.9 5. Larval instar  Larval development  125  0.1  0.9 For the calculation of the progress in all processes Heat Units (HU) per 30 minutes time interval are calculated according to the function in figure 2. Diapause induction  1 august  7    Depending on day-length  Polyvoltine fraction of the population  0.1      Estimated value for Netherlands and Belgium Pupa    160  0.1  0.9   Archived at http://orgprints.org/13703/249Relative flight- and egg deposition activity of Codling Moth  in RIMpro-Cydia0.00.10.20.30.40.50.60.70.80.91.00 5 10 15 20 25 30Temperature °CRelative activityFlight Egg depositionDevelopmental speed of Codling Moth in RIMpro-Cydia 05101520250 5 10 15 20 25 30 35 40Temperature °CHU per time intervalLogan curve in RIMpro Linear = Tsum-10Figure 1 and 2. Relative flight activity, egg deposition and developmental speed of codling moth in Rimpro-Cydia. Results and discussion In Figs 3, 4 and 5 the simulated egg deposition is compared to the observed effective egg deposition. In these diagrams, the diagonal line y=x represents the perfect fit. Calculated egg deposition is well correlated to observed effective egg deposition in all three years, and the start of the effective egg deposition is predicted correctly by the model in each year. The termination of egg deposition, however, was predicted when in the field about 10% of the eggs still had to be laid. This was confirmed in other orchards in 2007 (data not shown). A closer analysis of the behaviour of the model is necessary to reveal the origin of these differences. From 10 to 15 June 2006 the model predicted a massive egg deposition during a series of warm evenings at the beginning of the oviposition period (Fig. 4). Many male moths where captured during these evenings. Against all expectations, from this period only a limited number of larvae were found in the samples. This discrepancy between calculated and observed data caused the shift in the regression line in Fig. 4. A possible explanation for this case is that in our method the egg deposition data, calculated backwards from the recorded size of living larvae, only reflected the eggs of successful larvae, i.e. eggs of larvae that did not survive until sampling would have been absent from this method. As there is mortality, especially during the embryonic and first larval stage, and this mortality is probably not constant during the growing season, an absolute fit between simulation and field data is not to be expected. Beginning and end of the pheromone trap catches did not correspond with the period of effective egg deposition. At the start of the effective egg deposition already 9%, 45% and 41% of the total number of moths in the first generation was captured in 2005, 2006 and 2007 respectively. In 2005 and 2006 21% and 9% of the moths were captured after the effective  egg  deposition  in  the  field  had  terminated.  In  the  three  years  there  was  no consistency  in  the  relation  between  cumulative  flight  and  cumulative  effective  egg deposition.Archived at http://orgprints.org/13703/250Orchard Vogelwaarde 2005Cumulated egg deposition 1st generation Cydia pomonellaR2 = 0.98890%10%20%30%40%50%60%70%80%90%100%0 10 20 30 40 50 60 70 80 90 100% Effective egg deposition back-calculated form larvae samples / noitisoped gge detalumiSsehctac parTSimulation Expected Trap Lineair (Simulation)Figure 3. Simulated egg deposition and relative pheromone trap catches compared to the observed effective egg deposition (based on a sample of 163 larvae) in an untreated orchard in 2005.Orchard Vogelwaarde 2006Cumulated egg deposition 1st generation Cydia pomonellaR2 = 0.93560%10%20%30%40%50%60%70%80%90%100%0 10 20 30 40 50 60 70 80 90 100% Effective egg deposition back-calculated form larvae samples / noitisoped gge detalumiSsehctac parTSimulated Expexted Traps Lineair (Simulated)Figure 4. Simulated egg deposition and pheromone trap catches compared to the observed effective egg deposition (based on a sample of 120 larvae) in an untreated orchard in 2006. Archived at http://orgprints.org/13703/251Orchard Vogelwaarde 2007Cumulated egg deposition 1st generation Cydia pomonellaR2 = 0.99970%10%20%30%40%50%60%70%80%90%100%0 10 20 30 40 50 60 70 80 90 100% Effective egg deposition back-calculated form larvae samples / noitisoped gge detalumiSsehctac parTSimulation Expected Traps Lineair (Simulation)Figure 5. Simulated egg deposition and pheromone trap catches compared to the observed effective egg deposition (based on a sample of 98 larvae) in an untreated orchard in 2007. For the given years and location, the model outcomes provided a better description of the effective egg deposition than the cumulated pheromone trap catches.Both  at  the  beginning  and  in  the  second  half  of  the  flight  period  as  registered  with pheromone  traps  we  frequently  captured  high  numbers  of  moths  during  evenings  with suitable temperatures for egg deposition that did not lead to effective egg deposition.References Beyers, T. (2007) Bestrijding van de fruitmot (Cydia pomonella L.) op appel (Malus x domestica Borkh.) in Roemenië. Thesis Katholieke Hogeschool Kempen, België. Blago, N. & Dickler, E. (1990). Effectiveness of the Californian prognosis model BUGOFF 2 for Cydia Pomonella under central European conditions. Acta Hort. (ISHS) 276:53-62 Graf, B., Höpli, & Höhn, H. (2003). Optimizing insect pest management in apple orchards with SOPRA. Bulletin IOBC/SROP, Vol.26 No.11:43-48 Trapman,M. (2006) RIMpro-Cydia optimaliseert fruitmotbestrijding. Fruitteelt 22:12-13 Trapman, M., Helsen, H., & Polfliet, M. (2007) Beter bestrijdingsresultaat fruitmot door stapelen van technieken. Fruitteelt 8:12-13 Trapman,M. & Helsen, H., (2007) Nieuwe inzichten in de fruitmotbestrijding succes voor teler en milieu. Fruitteelt 47:12-13 Wit, de, C.T. & Goudriaan, J., (1974). Simulation of ecological Processes. PUDOC, Wageningen, ISBN 90 220 0496 1, 159 p. Archived at http://orgprints.org/13703/

Development of a dynamic population model as a decision support system for Codling Moth (Cydia pomonella L) management

In 2004 RIMpro-Cydia was developed as a dynamic population model that simulates the within-year biology of a local codling moth population. The model is meant to be used by growers and advisors to optimize the control of codling moth populations in organic and integrated managed orchards. The model is based on literature data and unpublished research data. Fractional boxcar trains are used to mimic the dispersion in the developmental processes. The model is run in real time on the data input of local weather stations, starting on 1 January. The output of the model was compared with the results of field observations in three years in an untreated orchard. In the years 2005 to 2007 the progress in egg deposition as predicted by the model was in general agreement with the field data. The start of the egg deposition period was predicted well. The end of the egg deposition period was predicted when in the field about 10% of the eggs was still to be laid. There was no consistency in the relation between cumulated pheromone trap catches and the cumulative egg deposition as calculated from the field data

Helsen, H.H.M.

NARCIS 

Development of a dynamic population model as a decision support system for Codling Moth (Cydia pomonella L.) management

Name not available

Wageningen Yield 

247
Development of a dynamic population model as a decision support 
system for Codling Moth (Cydia pomonella L) management 
M. Trapman1, H. Helsen2, M. Polfliet3
Abstract
In 2004 RIMpro-Cydia was developed as a dynamic population model that simulates the 
within-year biology of a local codling moth population. The model is meant to be used by 
growers and advisors to optimize the control of codling moth populations in organic and 
integrated managed orchards. The model is based on literature data and unpublished 
research data. Fractional boxcar trains are used to mimic the dispersion in the 
developmental processes. The model is run in real time on the data input of local weather 
stations, starting on 1 January. The output of the model was compared with the results of 
field observations in three years in an untreated orchard. In the years 2005 to 2007 the 
progress in egg deposition as predicted by the model was in general agreement with the 
field data. The start of the egg deposition period was predicted well. The end of the egg 
deposition period was predicted when in the field about 10% of the eggs was still to be 
laid. There was no consistency in the relation between cumulated pheromone trap catches 
and the cumulative egg deposition as calculated from the field data.  
Keywords Cydia pomonella, codling moth, simulation model 
Introduction
The biology of the codling moth (Cydia pomonella L) is strongly adapted to its primary 
hosts apple and pear. Codling moth is a key pest in almost all regions where apples are 
grown. Throughout Europe codling moth damage has increased over the last two decades. 
Effective codling moth control is essential for both integrated and organic fruit growers. 
Besides information on the mode of action and efficacy of the available insecticides, 
accurate data on critical time-points of codling moth development are essential to plan and 
implement a management strategy. For the timing of control treatments, most fruit growers 
and advisors in Europe rely on pheromone trap catches and temperature sums or models 
based on temperature sums, and evaluation of evening temperatures. More sophisticated 
simulation models like the Bugoff 2 model (Blago et al., 1990) are hardly used by practical 
advisory services, with the exception of the SOPRA model developed by Graf in 
Switzerland (Graf et al., 2003). 
The aim in the development of RIMpro-Cydia was to create a decision support system for 
fruit growers and advisors, based on a dynamic simulation model for the codling moth 
population. Structuring detailed pieces of knowledge in a simulation model is an efficient 
and convenient way to combine available knowledge for practical decision making. 
The outline of the model 
The biology of the codling moth was divided into life stages and developmental processes 
(Trapman, 2006) Details on life stages, average development time, relative dispersion in 
the process, and other parameters are given in Table 1. These data have been taken from 
literature, unpublished research work and practical experiences.
1 Marc Trapman, Bio Fruit Advies, Netherlands, marc.trapman@biofruitadvies.nl 
2 Herman Helsen, Wageningen UR, Applied Plant Research, Netherlands, herman.helsen@wur.nl 
3 Matty Polfliet, Fruit Consult, Belgium, matty@fruitconsult.com 
Archived at http://orgprints.org/13703/
248
Developmental time is given in heat units (HU). Heat units are not calculated from a linear 
relation as temperature sums, but using a Logan curve with a lower developmental 
threshold of 10 °C, maximum development speed at 28 °C, and an upper threshold of 31 
°C (Figs. 1 and 2). Effective HU are calculated from temperature readings at a 30 min 
interval. Fractional boxcar trains where used to mimic the dispersion in the processes (De 
Wit et al., 1974). The model was coded in Visual Basic 6 as an extension to the apple scab 
program RIMpro. The output is presented in a self-explaining graphical format for direct 
use by advisors and fruit growers, as well as in a detailed tabular form for evaluation 
purposes.
Table 1. Details on stages, processes and parameters used in the RIMpro-Cydia program
Stage Process Average 
time in HU 
RD Survival Parameters and remarks 
Diapause Diapause termination 13 April 0.2 1 Value for north-western Europe. Depending 
on day-length and with that on geographical 
position. Process calculation in days, not in 
Heat Units. 
Pupa Pupation 140 0.15 1 Average set lower than actual process time as 
in spring the measured temperature is lower 
than received by the larvae. 
Virgin female Pre-oviposition 75 0.1 1  
Lifetime 200 0.15 1  
Number eggs/female 50 0.15   
Mated female 
Egg deposition    The rate of egg deposition is depending upon 
the temperature around sunset. (Figure 1) 
Egg Embryonic 
development 
88 0.1 0.8 
1. Larval instar Larval development 60 0.1 0.4 
2. Larval instar Larval development 45 0.1 0.9 
3. Larval instar Larval development 45 0.1 0.9 
4. Larval instar Larval development 45 0.1 0.9 
5. Larval instar Larval development 125 0.1 0.9 
For the calculation of the progress in all 
processes Heat Units (HU) per 30 minutes 
time interval are calculated according to the 
function in figure 2. 
Diapause induction 1 august 7  Depending on day-length  
Polyvoltine fraction of the population 0.1   Estimated value for Netherlands and Belgium 
Pupa  160 0.1 0.9  
Evaluation
The output of the model was compared with field observations in three years in an 
untreated orchard at Vogelwaarde, Southwest Netherlands. At regular intervals all codling 
moth damaged fruits where collected from marked plots. The age of the individual larvae 
was determined from their length and the width of the head capsule. For the individual 
larvae their approximate date of egg deposition was back-calculated from temperature 
records. These data reflect only a sub-set of eggs, i.e. those eggs from which larvae have 
hatched and infected fruits. This effective egg deposition represents the population the 
grower and advisor have to deal with in practice (Helsen, Polfliet & Trapman, in 
preparation). Pheromone trap records for 2005 were taken form a regional registration 
system of 39 traps in 10 orchards. In 2006 and 2007 pheromone trap catches where 
recorded in the trial orchard. 
The explanatory value of the model was assessed by comparing the cumulative number of 
eggs deposited in the field with the value calculated by the model. In the same way, the 
cumulative egg deposition in the field was compared with the cumulated pheromone trap 
catches.
Stage Process r e 
ti  i   
RD Survival Paramet rs and remarks 
Diapause Diapause t r i ti   ril 0.2 1 Value for north-western Europe. Dep ndi g 
on day-length and with that on geographical 
position. Process calculation in days, not in 
Heat Units. 
Pupa Pupation 140 0.15 1 Average set lower than actual process time as 
in spring the measured temperature is lower 
than received by the larvae. 
Virgin female Pre-oviposition 75 0.1 1  
Lifetime 200 0.15 1  
Number eggs/female 50 0.15   
Mated female 
Egg deposition    The rate of egg deposition is depending upon 
the temperature around sunset. (Figure 1) 
Egg Embryonic 
development 
88 0.1 0.8 
1. Larval instar Larval development 60 0.1 0.4 
2. Larval instar Larval development 45 0.1 0.9 
3. Larval instar Larval development 45 0.1 0.9 
4. Larval instar Larval develop ent 45 0.1 0.9 
5. Larval instar Larval develop ent 125 0.1 0.9 
For the calculation of the progress in all 
processes Heat Units (HU) per 30 minutes 
time interval are calculated according to the 
function in figure 2. 
Di pause induction 1 august 7  Depending on day-length  
Polyvoltine fraction of the population 0.1   Estimated value for N therlands and Belgium 
Pupa  160 0.1 0.9  
Archived at http://orgprints.org/13703/
249
Relative flight- and egg deposition activity of 
Codling Moth  in RIMpro-Cydia
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 5 10 15 20 25 30
Temperature °C
R
el
at
iv
e 
ac
tiv
it y
Flight Egg deposition
Developmental speed of 
Codling Moth in RIMpro-Cydia 
0
5
10
15
20
25
0 5 10 15 20 25 30 35 40
Temperature °C
H
U
 p
er
 ti
m
e 
in
te
rv
al
Logan curve in RIMpro Linear = Tsum-10
Figure 1 and 2. Relative flight activity, egg deposition and developmental speed of codling moth in 
Rimpro-Cydia. 
Results and discussion 
In Figs 3, 4 and 5 the simulated egg deposition is compared to the observed effective egg 
deposition. In these diagrams, the diagonal line y=x represents the perfect fit. Calculated 
egg deposition is well correlated to observed effective egg deposition in all three years, 
and the start of the effective egg deposition is predicted correctly by the model in each 
year. The termination of egg deposition, however, was predicted when in the field about 
10% of the eggs still had to be laid. This was confirmed in other orchards in 2007 (data not 
shown). A closer analysis of the behaviour of the model is necessary to reveal the origin of 
these differences. 
From 10 to 15 June 2006 the model predicted a massive egg deposition during a series of 
warm evenings at the beginning of the oviposition period (Fig. 4). Many male moths where 
captured during these evenings. Against all expectations, from this period only a limited 
number of larvae were found in the samples. This discrepancy between calculated and 
observed data caused the shift in the regression line in Fig. 4. A possible explanation for 
this case is that in our method the egg deposition data, calculated backwards from the 
recorded size of living larvae, only reflected the eggs of successful larvae, i.e. eggs of 
larvae that did not survive until sampling would have been absent from this method. As 
there is mortality, especially during the embryonic and first larval stage, and this mortality 
is probably not constant during the growing season, an absolute fit between simulation and 
field data is not to be expected. 
Beginning and end of the pheromone trap catches did not correspond with the period of 
effective egg deposition. At the start of the effective egg deposition already 9%, 45% and 
41% of the total number of moths in the first generation was captured in 2005, 2006 and 
2007 respectively. In 2005 and 2006 21% and 9% of the moths were captured after the 
effective egg deposition in the field had terminated. In the three years there was no 
consistency in the relation between cumulative flight and cumulative effective egg 
deposition.
Archived at http://orgprints.org/13703/
250
Orchard Vogelwaarde 2005
Cumulated egg deposition 1st generation Cydia pomonella
R2 = 0.9889
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80 90 100
% Effective egg deposition back-calculated form larvae samples
 / noitisoped gge detalu
mi
S
sehctac parT
Simulation Expected Trap Lineair (Simulation)
Figure 3. Simulated egg deposition and relative pheromone trap catches compared to the 
observed effective egg deposition (based on a sample of 163 larvae) in an untreated orchard in 
2005.
Orchard Vogelwaarde 2006
Cumulated egg deposition 1st generation Cydia pomonella
R2 = 0.9356
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80 90 100
% Effective egg deposition back-calculated form larvae samples
 / noitisope d gge  detalu
mi
S
sehc ta c parT
Simulated Expexted Traps Lineair (Simulated)
Figure 4. Simulated egg deposition and pheromone trap catches compared to the observed 
effective egg deposition (based on a sample of 120 larvae) in an untreated orchard in 2006. 
Archived at http://orgprints.org/13703/
251
Orchard Vogelwaarde 2007
Cumulated egg deposition 1st generation Cydia pomonella
R2 = 0.9997
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80 90 100
% Effective egg deposition back-calculated form larvae samples
 / noitisop ed gg e de talu
mi
S
sehct ac p arT
Simulation Expected Traps Lineair (Simulation)
Figure 5. Simulated egg deposition and pheromone trap catches compared to the observed 
effective egg deposition (based on a sample of 98 larvae) in an untreated orchard in 2007. 
For the given years and location, the model outcomes provided a better description of the 
effective egg deposition than the cumulated pheromone trap catches.
Both at the beginning and in the second half of the flight period as registered with 
pheromone traps we frequently captured high numbers of moths during evenings with 
suitable temperatures for egg deposition that did not lead to effective egg deposition.
References 
Beyers, T. (2007) Bestrijding van de fruitmot (Cydia pomonella L.) op appel (Malus x domestica 
Borkh.) in Roemenië. Thesis Katholieke Hogeschool Kempen, België. 
Blago, N. & Dickler, E. (1990). Effectiveness of the Californian prognosis model BUGOFF 2 for 
Cydia Pomonella under central European conditions. Acta Hort. (ISHS) 276:53-62 
Graf, B., Höpli, & Höhn, H. (2003). Optimizing insect pest management in apple orchards with 
SOPRA. Bulletin IOBC/SROP, Vol.26 No.11:43-48 
Trapman,M. (2006) RIMpro-Cydia optimaliseert fruitmotbestrijding. Fruitteelt 22:12-13 
Trapman, M., Helsen, H., & Polfliet, M. (2007) Beter bestrijdingsresultaat fruitmot door stapelen 
van technieken. Fruitteelt 8:12-13 
Trapman,M. & Helsen, H., (2007) Nieuwe inzichten in de fruitmotbestrijding succes voor teler en 
milieu. Fruitteelt 47:12-13 
Wit, de, C.T. & Goudriaan, J., (1974). Simulation of ecological Processes. PUDOC, Wageningen, 
ISBN 90 220 0496 1, 159 p. 
Archived at http://orgprints.org/13703/


Development of a dynamic population model as a decision support system for Codling Moth (Cydia pomonella L) management

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Wageningen Yield