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J. Crop Prot. 2015, 4 (Supplementary): 605-615___________________________________________
605
Research Article
Three years analysis of Lobesia botrana (Lepidoptera:
Tortricidae) flight activity in a quarantined area
Guillermo Heit1, 2*, Walter Sione3 and Pablo Cortese1, 2
1. Department of Plant Protection, Faculty of Agronomy, University of Buenos Aires, Buenos Aires, Argentina.
2. Bureau of Surveillance and Monitoring, National Animal Health and Agri-food Quality Service, Av. Paseo Colón 315,
Ciudad Autónoma de Buenos Aires, Argentina.
3. Autonomous University of Entre Ríos, Regional Center for Geomatics, Matteri y España s/n, Diamante, Entre Ríos, Argentina.
Abstract: Lobesia botrana (Denis & Schiffermüller) (Lepidoptera:
Tortricidae), is an important vineyard-pest in the European and
Mediterranean areas and it was recently described in Argentina and Chile.
Since knowledge on the L. botrana phenology on Argentina is still limited,
the objective of this study was to develop a phenological model to predict
voltinism of L. botrana in Argentina through a regional
approach.Voltinism of L. botrana males was simulated based on
occurrence of four non-overlapping flights. Nonlinear regression models
were constructed using the weekly average trap catches from the
agricultural seasons 2011-2012 to 2013-2014 and amount of degree-days
accumulation. Weibull equation showed, on average for the four annual
flights, the best estimate of the observed variability in the percentage of
adult catches in relation to degree-day accumulation. It can be expected
that 50% of male adult emergence for the first flight occurs at 443.9 DD; in
the second flight at 1211.7 DD; while in the third and the fourth flights, the
accumulation of degree days reaches values of 2077.8 DD and 2905 DD,
respectively. The regional approach adopted in this work could explain the
variation found in field data and has a reasonable predictive and
explicative capability as a component in the ongoing prospective analysis
of the activity of L. botrana in Argentina.
Keywords: Lobesia botrana, surveillance system, voltinism
Introduction12
The European grapevine moth, Lobesia botrana
(Lepidoptera: Tortricidae), an endemic pest in
the Palearctic Region, widespread in all wine-
growing areas, is one of the most noxious
vineyard-pests in the European and
Mediterranean areas (Delbac et al., 2010).
Handling Editor: Saeid Moharramipour
________________________________
* Corresponding author, e-mail: gheit@agro.uba.ar
Received: 08 October 2014, Accepted: 16 July 2015
Published online: 05 October 2015
L. botrana is described as polyphagous
species and its presence in grapes is relatively
recent, its importance as a pest in vineyards
has been reported at the beginning of the
twentieth century (Thiéry and Moreau, 2005).
This species was considered a quarantine pest
absent in South America until 2008, when it
was found in Chile and subsequently, in
2010, in Mendoza Province, Argentina
(González, 2010).
This pest recently introduced into
Argentina, is under official control through
the National Program for Prevention and
Eradication of L. botrana. One of the
Three years analysis of Lobesia botrana______________________________________________ J. Crop Prot.
606
objectives of the program is to develop
predictive models of the population
dynamics of L. botrana, as phytosanitary
warning tool at regional level, in order to
estimate the behavior of the species in other
wine regions of the country at risk of being
invaded.
Potential damage of L. botrana to
grapevines varies during the grape growing
season; the later generations are the most
harmful, they can seriously affect the mature
grape berry harvest directly through larval
feeding and indirectly by predisposing the
crop to fungal infection by Botrytis cinerea
(Armendáriz et al., 2009; DallaMonta et al.,
2007). The number of generations per year
of L. botrana on Vitis vinifera differs
geographically and this variability is
determined by several factors including
photoperiod, temperature, relative humidity,
latitude and host phenology (Sciarretta et al.,
2008).For example, in the Palearctic region,
L. botrana voltinism ranges from one to five
flights (Pavan et al., 2006). In
Mediterranean areas it is usually trivoltine
although in the warmest years a fourth
partial generation has been reported (Ioriatti
et al., 2011).
In applied entomology, various empirical
approaches have been used to estimate the
population dynamics of insects, mainly based
on the study of patterns of temporal
distribution of different insect developmental
stages, for example, the distribution of
emergence periods of one or more
developmental stages (Moravie et al., 2006).
Due to the great influence that temperature
exerts on insect phenology, most of the
models that describe insect development are
temperature-driven (Damos and Savopoulou-
Soultani, 2012).
Several authors have used field
observations to estimate the phenology of
insect populations in order to use these
estimations in integrated pest management or
even in pest risk analysis under climate
change scenarios (Satake et al., 2006; Martin-
Vertedor et al., 2010; Gutierrez et al., 2012).
Many researchers have used nonlinear
regression models to describe temperature
dependent processes (Damos and
Savopoulou-Soultani, 2012). Milonas et al.
(2001) used nonlinear regression to estimate
the voltinism of L. botrana in Greece. Tobin
et al. (2003), have used Logistic and
Gompertz functions to estimate adult
emergence of Endopiza viteana (Lepidoptera:
Tortricidae) based on cumulative day degree.
Milonas and Savapolous (2006) have used
Logistic and Weibull functions to estimate
the proportion of catches of Adoxophyes
orana (Lepidoptera: Tortricidae) on
pheromone traps.
The aim of this study was to develop a
simple species-specific phenological model
to predict voltinism of L. botrana in
Mendoza (Argentina) through a regional
approach.
Materials and Methods
Study area and monitoring system
The study area included the north, central
and eastern Oasis of Mendoza province,
Argentina (Fig. 1). The official
phytosanitary surveillance system in this
area included the installation of pheromone
traps for monitoring the temporal and spatial
variation of L. botrana adult population, in
order to identify quarantine areas for pest
eradication.
Flight activity of L. botrana was
monitored using Delta traps with (E, Z)-7,9-
dodecadienyl acetate as the major
component a of the synthetic pheromone.
Among thousands of traps installed by the
official surveillance program, only those
installed before the emergence of the first
adult (late August) and that remained till the
end of the season (April), were considered in
this study. Traps were installed on vineyard
at 1.3 and 1.5 m above the ground and were
checked once a week. Sticky floors were
changed frequently and pheromone
dispensers were renewed at least once a
month.
Heit et al. _____________________________________________ J. Crop Prot. (2015) Vol. 4 (Supplementary)
607
Figure 1 Distribution map of pheromone traps and selected grids in the study area.
Weather database
In order to incorporate the spatial variation of air
temperature along the quarantine area on
phenology model, a countrywide raster database of
daily temperature was generated. Daily records of
maximum and minimum air temperature (°C)
provided by 124 weather stations of the National
Weather Service (SMN) and the National Institute
of Agricultural Technology (INTA) were
interpolated at spatial resolution of 2km, according
to the methodology proposed by Blanco et al.
(2010). Digital terrain model of the Shuttle Radar
Topography Mission (SRTM) was used as external
drift variable for Kriging algorithm (Aalto et al.,
2013, Stahl et al., 2006, Dodson and Marks, 1997).
A total of 822 raster layers were generated, one by
each day from 1 July to 30 March for each
agricultural seasons included in this work, 2011-
2012 to 2013-2014. These raster layers were
validated by generalized cross validation (Haylock
et al., 2008). R software (gstat, gdal and automap
libraries) and QGIS 1.8 were used (R Core Team,
2012; Quantum GIS Development Team, 2013).
GIS analysis and model selection
Regional approach for L. botrana flight activity
was analysed by means of a homogeneous
polygon grid of two km. It was used to make
weekly statistics of monitoring traps installed in
the study area and to get temperature values
from raster layers.
Following Moravie et al. (2006) methodology,
traps with data from a single year or less than 10
catches by agricultural season were not included
in posterior analysis (n = 506 pheromone traps).
Only grids with recurrent trap catches were
used as input for nonlinear regression models (n
= 40 grid) and for these, weekly average
catches of L. botrana males and degree-day
accumulation were independently calculated for
each of the three agricultural seasons evaluated,
2011-2012 to 2013-2014.
Voltinism of L. botrana males was simulated
based of occurrence of a maximum of four non-
overlapping flights between early September and
late March. According with the approach followed
by Damos and Savopoulou-Soultani (2010) and
Three years analysis of Lobesia botrana______________________________________________ J. Crop Prot.
608
Kumral et al. (2005), the start of the first annual
flight was determined by the steady increase of
moth capture in early spring after a period of little
or no capture of adults. The start of the subsequent
flights were assumed to be when trap catches
began to rise consistently after a period of no catch
or a significant drop in moth captures. This analysis
was performed independently for each of the
selected grid and agricultural season.
Degree days accumulation (DD) for each
selected grid from 1 July to 30 March, were
calculated according to the average method
developed by Baskerville and Emin (1969), by
subtracting the base temperature from the average
daily temperature. In this study minimum
temperature threshold for development of 7 °C was
considered for any of the developmental stages of L.
botrana (Del Tío et al., 2001; Gallardo et al., 2009).
Nonlinear regression models were constructed
using the percentage of accumulated trap catches as
the dependent variable (as values between 0 and 1)
and day-degrees accumulated above the minimum
temperature threshold for development as the
independent variable, for each flight period. The
following nonlinear regression models were used:
where Y is the cumulative percentage of
captured moths, DD is the sum of degree days
reached at the date of trap checking. Parameters
C1, C2, C3 were calculated by the nonlinear
regression models using Info stat Estudiantil
software (Di Rienzo et al., 2013).
Model performance comparison and validation
Model performance comparisons were based on
the adjusted coefficient of determination (R2), the
mean square error (MSE) and the number of
iterations to achieve the lowest MSE estimated by
the model. It was also taking into account that the
estimated coefficients were not highly correlated.
Furthermore were taken into account the Akaike
information criteria (AIC) and Schewatrz or
Bayesian information criterion (BIC) (Quinn and
Keough, 2002; Ranjbar Aghdam et al., 2011). Lack
to fit test was performed to compare models of two
and three parameters. Model with lower MSE, on
the average for the four flights analysed, was
considered as reference (Mc Meekin et al., 1993;
Zwietering et al., 1990).
Validation of the reference model was
performed using data from 15 additional randomly
selected grids that were not used for making the
original model (validation grid). For each estimated
flight the cross-validated correlation coefficient
(R2*), between the validation data and the
estimates of the dependent variable of the model
obtained with the original data was calculated (Dos
Santos and Porta Nova, 2007). Residual values
calculated for both data sets were compared by
means of the Kolmogorov Smirnov test.
The intrinsic variability of raster of mean
daily maximum and minimum temperature, was
evaluated through the estimation of the root
mean square error (RMSE) of each pixel in the
study area, from 1 July to 31March (Ali and
Abustan, 2014, Degaetano and Belcher, 2006).
On the basis of the reference model,
estimated voltinism of L. botrana for the last 24
growing seasons was simulated. Point based
temperature statistic of weather stations in the
study area, since 1990 to 2014, were
considered. Subsequently the percentiles of 5%
and 50% adult emergence date were calculated.
Results
Figure 2 shows the weekly evolution of trap
catches of L. botrana and degree days
accumulation in the study area, for the growing
season 2011-2012, 2012-2013 and 2013-2014.
Pooled nonlinear regression equation for
accumulated trap catches by grid versus day-
degrees accumulation for each flight is presented in
Table 1. Two models showed a very high
prediction capability, as i s indicated by the mean
square error values and the coefficient of
determination (R2). R2 is above 91% for all flights
and regression models considered. Therefore, it can
be deduced that a high proportion of the variability
observed in the cumulative percentage of male
catches of L. botrana can be explained by the
accumulation of degree days from 1 July for the
four flight periods analyzed.An increased variation
Heit et al. ________________________________________________________ J. Crop Prot. (2015) Vol. 4 (4)
609
with succeeding generations could be observed and
this deviation could be due to population sizes and
overlapping generations that varied considerably
among some of the data series.
Statistics for model performance comparison
are presented in Table 2. In this study, Weibull
equation shows, on average for the different
analyzed flights, the lowest MSE, AIC and BIC
values. Although compared with the Logistic
model, Weibull required a greater number of
iterations to achieve the best fitting. Because of
this, we consider the Weibull equation achieved
the best estimate of the observed variability in
the percentage of adult catches in the quarantine
area of Mendoza (Argentina), in relation to
degree-day accumulation.
Test the lack of fit (F) showed significant
differences with Logistic regression model in
the third and fourth flight (p < 0.05).
Differences in the AIC and BIC between the
reference model and Logistic models were very
strong for the 2nd, 3rd and 4th flight periods
(Jan Wagenmakers and Farrell, 2004).
Estimation of the intrinsic variability of input
temperature raster data, applied to calculation of
the cumulative degree-days in the quarantine area,
showed a Root-mean-square error (RMSE) that
averaged 1.82 °C for daily maximum temperature
and 2.05 °C for daily minimum temperature.
There was a good fit between the values
obtained experimentally in the validation grids
and the predicted equation for L. botrana moth
phenology for all flights. Cross-validated
correlation coefficient (R2*) obtained by 1st flight
were of 0.871; by 2ndflight: 0.809 and by the 3rd
and 4th flight of 0.795 and 0.773, respectively.
Residual values calculated with both sets of data
using the Kolmogorov-Smirnov test showed no
statistically significant differences for any of the
four flights analyzed (p > 0.05).
According to these results it can be
expected that 50% of male adult emergence
for the first flight occurs at 443.9 ± 2.3 DD;
in the second flight at 1211.7 ± 4.5 DD; while
in the third and the fourth flight, when the
accumulation of degree days reach values of
2077.8 ± 4.7 DD and 2905 ± 3 DD,
respectively (Fig. 3).Table 3 shows the
percentiles of the predicted dates for 5% and
50% cumulative male catches, according to
Weibull voltinism simulation, for the last 24
growing seasons.
0
20
40
60
80
100
120
140
160
180
200
213 287 434 638 851 1089 1312 1577 1738 2047 2336 2612 2858 3083
Cumulative trap catches (mean/grid)
Degree days
Growing season
2011/12 2012/13 2013/14
Figure 2 Evolution of L. botrana trap catches for each growing season. Average male catches by grid/week and
standard error (n = 40).
Three years analysis of Lobesia botrana______________________________________________ J. Crop Prot.
610
Table 1 Parameters of the nonlinear regression models for describing the relationship between degree-days and
the cumulative proportion of adult males of Lobesia botrana, by grid.
Equation parameters Voltinism Model
C1 C2 C3
MSE R2
Logistic 1.01 ± 0.01 128.0 ± 10.92 0.01 ± 2.2E-4 0.0029 0.97 1° flight
Weibull 496.9 ± 1.69 3.25 ± 0.05 0.0026 0.97
Logistic 0.94 ± 0.01 2.4E+7 ± 1.4E+7 0.02 ± 5.5E-4 0.0135 0.93 2° flight
Weibull 1262.7 ± 3.25 8.89 ± 0.26 0.0104 0.95
Logistic 0.97 ± 0.01 3.4E+9 ±1.7E+7 0.01 ± 3.9E-5 0.0101 0.93 3° flight
Weibull 2155.5 ± 3.31 9.99 ± 0.21 0.0043 0.96
Logistic 1.00 ± 0.01 9E+9 ± 9.7E+7 0.01 ± 1.5E-5 0.0114 0.91 4° flight
Weibull 2960.1 ± 2.1 19.80 ± 0.36 0.0031 0.97
Abbreviations: MSE = Mean squared error, C1, C2, C3= Parameters calculated by nonlinear regression models and standard error.
Table 2 Nonlinear regression models performance comparison.
Voltinism Model df AIC BIC ∆ BIC ∆ AIC F p
Logistic 1077 -1373 -1356 44 48 114.35 0.074 1° flight
Weibull 1078 -1416 -1404
Logistic 717 -575 -561 65 68 176.72 0.059 2° flight
Weibull 718 -640 -629
Logistic 837 -706 -690 220 224 692.05 0.030 3° flight
Weibull 838 -926 -914
Logistic 717 -839 -844 60 43 686.63 0.031 4° flight
Weibull 718 -898 -887
Abbreviations: AIC: Akaike information criterion, BIC: Bayesian information criterion.
∆i (AIC) = AICi-minAIC; ∆i (BIC) = BICi-minBIC.
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
0
10
20
30
40
50
60
70
80
90
100
183 314 472 640 846 1060 1298 1562 1833 2133 2394 2635 2872 3093 3175
Mea n trap c atche s (males /grid)
Cumulative trap catches (%)
Degree days
First flight Second Flight Thrid flight Fourth flight
Predicted cumulative catches Mean trap catches
Figure 3 Observed and predicted data for adult emergence of L. botrana in Mendoza.
Heit et al. ________________________________________________________ J. Crop Prot. (2015) Vol. 4 (4)
611
Table 3 Percentile distributions of predicted dates for cumulative male catches, according to Weibull nonlinear
regression, for the last 24 growing seasons in Mendoza (Argentina).
Predicted flight activity Percentil 10 Percentil 50 Percentil 90
5% cumulative catches
1° flight 53 (Aug-22) 68 (Sep-06) 84 (Sep-22)
2° flight 127 (Nov-04) 134 (Nov-11) 143 (Nov-20)
3° flight 170 (Dec-17) 175 (Dec-22) 183 (Dec-30)
4° flight 220 (Feb-05) 225 (Feb-10) 239 (Feb-24)
50% cumulative catches
1° flight 88 (Sep-26) 97 (Oct-05) 107 (Oct-15)
2° flight 146 (Nov-23) 153 (Nov-30) 162 (Dec-09)
3° flight 195 (Jan-11) 199 (Jan-15) 209 (Jan-25)
4° flight 242 (Feb-27) 247 (Mar-04) 259 (Mar-16)
Days from July 1. Estimated calendar day in Argentina in brackets.
n = 24 growing seasons (1990 to 2014).
Discussion
The present study agreed with the results from the
preliminary analysis for the first two years of the
monitoring program of L. botrana in the
quarantine area in Mendoza, Argentina (Heit et
al., 2014). Although, L. botrana is trivoltine in
Mediterranean latitudes (Stefanos et al., 2005;
Martin-Vertedor et al., 2010), observational
evidences suggest that L. botrana displays four
annual flights in this newly invaded area.
Voltinism studies of L. botrana in Europe
have only described predictive equations for
two or three annual generations, using
different biofix and lower developmental
threshold (Del Tío et al., 2001; Milonas et
al., 2001; Armendáriz et al., 2007, 2009;
Gallardo et al., 2009).For this reason, it is
difficult to make a comparison of the
thermal constant found in this work with
prior studies of L. Botrana voltinism in its
endemic area.
For example, Amo-Salas et al. (2011),
predicted maximum flight of the first, second
and third generations of L. botrana, in Ribera
del Duero region (Spain),at 144 DD, 666 DD
and 1216 DD above a minimum threshold of
10ºC,from January 1st. Using the same biofix
and base temperature, in Italy, Caffarelli and
Vita (1988) estimated the occurrence of the first
generation flight peakat236 DD, the second 782
DD and the third when at least 1462 heat units
were accumulated.
Other authors have chosen 1 March as the date
from which to start computing the degree-days. In
two regions of Greece, Milonas et al. (2001)
estimated degree days required for the first
generation of L. botrana from 276 to 334 DD, the
second from 752 to 834 DD and the third
generation from 899 to 1197 DD (with a baseline
of 6.45 °C). Gallardo et al. (2009), estimated
degree-day accumulations corresponding to 50% of
captures for the second generation to be 902 DD,
above a minimum threshold of 7 ºC.
The high variability in the patterns of adult
emergence of L. botrana reported under
conditions of field studies, is not only limited to
differences between study areas, but also occurs
between different generations of the same year
or between different agricultural seasons
(Briere and Pacros, 1998; Del Tío et al., 2001;
Milonas et al., 2001). However, it is not a
specific attribute of L. botrana, since the
existence of variation in adult eclosion time has
been reported in other tortricid species (Rock et
al., 1993; Milonas and Savapolous, 2006).
Nutritional quality of the host, photoperiod,
microclimatic conditions, increasing overlap
between generations as grapevine phenology
progresses or even the reduction of trapping
efficiency of pheromone traps usually observed
over time, can function as sources of variability
and thus reduce the predictive power of the
phenology model (Milonas et al., 2001; Rakefet
et al., 2009; Pavan et al., 2010).However from
a practical point of view, the applications of
Three years analysis of Lobesia botrana______________________________________________ J. Crop Prot.
612
temperature driven models to the study of
temporal flight patterns to an invasive species
could help in assembling effective forecasting
systems for application in eradication programs.
Quality of the temperature data is further
limited by the variable distances between the
weather stations and vineyards being monitored
even after the altitude correction. Limited
number of official weather stations on the Cuyo
Region must be assumed as a priori structural
characteristic of the system itself; however, the
input temperatures used in this work showed an
acceptable RMSE value (Maurer and Hidalgo,
2008; Degaetano and Belcher, 2006).
Since knowledge on the L. botrana
phenology in Argentina is still limited, this
work presents an analysis of field monitoring
data from three successive years and proposes a
series of equations that describe the flight
patterns of adults of L. botrana in the
quarantine area of Mendoza, where this species
is under official control.
The regional approach adopted in this work
could explain a large proportion of the variation
found in field data and has reasonable predictive
and explicative capabilities as a component in the
ongoing prospective analysis of the invasive
potential of L. botrana in Argentina.
Acknowledgments
We thank the National Programme for
Prevention and Eradication of L. botrana for
providing access to the database of the
surveillance system and the National Animal
Health and Agri-food Quality Service
(Senasa) for support of this study. We also
thank the National Weather Service (SMN)
and the National Institute of Agricultural
Technology (INTA) for providing the
national weather statistics.
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ﻠﺤﺗ و ﻪﻳﺰﺠﺗﻪﺷﻮﺧ مﺮﻛ زاوﺮﭘ ﺖﻴﻟﺎﻌﻓ لﺎﺳ ﻪﺳ ﻞﻴرﻮﮕﻧا راﻮﺧLobesia botrana (Lepidoptera:
Tortricidae( ﻪﻨﻴﻄﻧﺮﻗ ﻪﻘﻄﻨﻣ ﻚﻳ رد
ﺖﻴﻫ ﻮﻣﺮﻟﻮﮔ
1 ،2*
نﻮﻴﺳ ﺮﺘﻟاو ،
3
ﺰﺗرﻮﻛ ﻮﻠﺑﺎﭘ و
1 ،2
1 - ﺖﻋارز هﺪﻜﺸﻧاد ،ﻲﻜﺷﺰﭙﻫﺎﻴﮔ هوﺮﮔﺎﮕﺸﻧاد ، هﻮﺑﺋ ﺲﻨسﺮﻳآﻮﺑ ،ﺋﻦﻴﺘﻧاژرآ ،سﺮﻳآ ﺲﻨ.
2 - هراداﺖﺒﻗاﺮﻣﺶﻴﭘ و ،ﻲﻳاﺬﻏ تﻻﻮﺼﺤﻣ ﺖﻴﻔﻴﻛ و ﻲﻣاد ﺖﺷاﺪﻬﺑ ﻲﻠﻣ ﺰﻛﺮﻣ ،ﻲﻫﺎﮔآﻮﺑﺋ ﺲﻨﻦﻴﺘﻧاژرآ ،سﺮﻳآ.
3 -هﺎﮕﺸﻧاد ﻪﻘﻄﻨﻣ ﺰﻛﺮﻣ ،سﻮﻳر ﺮﺘﻧا رﺎﺘﺨﻣدﻮﺧ ﻦﻴﺘﻧاژرآ ،سﻮﻳر ﺮﺘﻧا ،ﺖﻧﻮﻣﺎﻳد ،ﺲﻜﻴﺗﺎﻣﻮﺋژ يا.
*ﺖﺴﭘ ﻲﻜﻴﻧوﺮﺘﻜﻟا
هﺪﻨﺴﻳﻮﻧ ﻪﺒﺗﺎﻜﻣ لﻮﺌﺴﻣ: gheit@agro.uba.ar
ﺖﻓﺎﻳرد :16 ﺮﻬﻣ 1393شﺮﻳﺬﭘ ؛ :25 ﺮﻴﺗ 1394
هﺪﻴﻜﭼ:ﻪﺷﻮﺧ مﺮﻛ رﻮﮕﻧا راﻮﺧ
Lobesia botrana (Denis & Schiffermüller) (Lepidoptera:
Tortricidae),
و ﺎﭘورا رد رﻮﮕﻧا ﻢﻬﻣ تﺎﻓآ زا ﻲﻜﻳ ﻲﻣ ﻪﻧاﺮﺘﻳﺪﻣ ﻲﺣاﻮﻧ ًاﺮﻴﺧا ﻪﻛ ﺪﺷﺎﺑ و ﻦﻴﺘﻧاژرآ زا
ﻲﻠﻴﺷشراﺰﮔﺖﺳا هﺪﺷ .زا رد ﻲﻤﻛ تﺎﻋﻼﻃا ﻪﻛﺎﺠﻧآﻪﺷﻮﺧ مﺮﻛ يژﻮﻟﻮﻨﻓ ﺎﺑ طﺎﺒﺗرا رد رﻮﮕﻧا راﻮﺧ
ﻟﺎﻄﻣ ﻦﻳا دراد دﻮﺟو ﻦﻴﺘﻧاژرآﻪﺑ ﻪﻌﻜﻳژﻮﻟﻮﻨﻓ لﺪﻣ ﻚﻳ ﻪﻴﻬﺗ رﻮﻈﻨﻣ مﺮﻛ ﻞﺴﻧ داﺪﻌﺗ ﻦﻴﻤﺨﺗ ياﺮﺑ ﻲ
ﻪﺷﻮﺧا ﻦﻴﺘﻧاژرآ رد رﻮﮕﻧا راﻮﺧﺖﺳا هﺪﺷ مﺎﺠﻧ.ﻪﺷﻮﺧ مﺮﻛ ﻞﺴﻧ داﺪﻌﺗ ﺮﻧ تاﺮﺸﺣ سﺎﺳاﺮﺑ رﻮﮕﻧا راﻮﺧ
ﻲﻣ نﺎﺸﻧ ار زاوﺮﭘ ﻚﻴﭘ رﺎﻬﭼ عﻮﻗوﺪﻫد.ﺘﻣ سﺎﺳاﺮﺑ ﻲﻄﺧﺮﻴﻏ نﻮﻴﺳﺮﮔر لﺪﻣ و ﻲﮕﺘﻔﻫ رﺎﻜﺷ ﻂﺳﻮ
ﻪﺟرد زور ناﺰﻴﻣﻲﻃ ﻲﻌﻤﺠﺗ يﺎﻫﻲﻋارز لﻮﺼﻓ 2012 -2011 ﺎﺗ 2014 -2013ﺪﺷ ﻪﻴﻬﺗ .ﻟدﺎﻌﻣ ﻪ
ﻪﺑ هﺮﺸﺣ ﻦﻳا ﻪﻛ داد نﺎﺸﻧ لﻮﺒﻳو سﺎﺳاﺮﺑ ﻦﻴﻤﺨﺗ ﻦﻳﺮﺘﻬﺑ ﻪﻛ دراد لﺎﺳ رد زاوﺮﭘ رﺎﻬﭼ ﻂﺳﻮﺘﻣ رﻮﻃ
تاﺮﺸﺣ ﺪﺻردهﺪﺷ رﺎﻜﺷ ﺖﺳا ﻲﻌﻤﺠﺗ ﻪﺟرد زور ناﺰﻴﻣ و .ﻲﻣ رﻮﻬﻇ ﻪﻛ ﺖﺷاد رﺎﻈﺘﻧا ناﻮﺗ50 %
رد زاوﺮﭘ ﻦﻴﻟوا ياﺮﺑ ﺮﻧ تاﺮﺸﺣ9/443ﺮﭘ ﻦﻴﻣود ،ﻪﺟرد زور رد زاو7/1211ﻲﻣ قﺎﻔﺗا ﻪﺟرد زور -
رد ﻦﻳا ﺪﺘﻓاﻲﻟﺎﺣﻪﺑ مرﺎﻬﭼ و مﻮﺳ يﺎﻫزاوﺮﭘ ﻪﻛ ﺖﺳا ﻪﺟرد زور رد ﺐﻴﺗﺮﺗ يﺎﻫ8/2077 و 2095
ﻲﻣ هﺪﻫﺎﺸﻣدﻮﺷ .شورﻲﻣ ﺶﻫوﮋﭘ ﻦﻳا رد هﺪﺷ ذﺎﺨﺗا ﺶﻴﭘ ﻚﻳ ﺪﻧاﻮﺗ مﺮﻛ ﺖﻴﻟﺎﻌﻓ زا ﻲﻘﻄﻨﻣ ﻲﻨﻴﺑ
ﻪﺷﻮﺧﺑ ار ﻦﻴﺘﻧاژرآ رد رﻮﮕﻧا راﻮﺧﺪﻳﺎﻤﻧ نﺎﻴ .
يﺪﻴﻠﻛ نﺎﮔژاو:ﻪﺷﻮﺧ مﺮﻛ ﻞﺴﻧ داﺪﻌﺗ ،ﻲﺳرزﺎﺑ ﻢﺘﺴﻴﺳ ،رﻮﮕﻧا راﻮﺧ
