Estimating the Economic Cost of One of the World's Major Insect Pests, Plutella xylostella (Lepidoptera: Plutellidae): Just How Long Is a Piece of String?

Abstract

Since 1993, the annual worldwide cost of diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), control has been routinely quoted to be US$1 billion. This estimate requires updating and incorporation of yield losses to reflect current total costs of the pest to the world economy. We present an analysis that estimates what the present costs are likely to be based on a set of necessary, but reasoned, assumptions. We use an existing climate driven model for diamondback moth distribution and abundance, the Food and Agriculture Organization country Brassica crop production data and various management scenarios to bracket the cost estimates. The "length of the string" is somewhere between US$1.3 billion and US$2.3 billion based on management costs. However, if residual pest damage is included then the cost estimates will be even higher; a conservative estimate of 5% diamondback moth-induced yield loss to all crops adds another US$2.7 billion to the total costs associated with the pest. A conservative estimate of total costs associated with diamondback moth management is thus US$4 billion-US$5 billion. The lower bound represents rational decision making by pest managers based on diamondback moth abundance driven by climate only. The upper estimate is due to the more normal practice of weekly insecticide application to vegetable crops and the assumption that canola (Brassica napus L.) is treated with insecticide at least once during the crop cycle. Readers can decide for themselves what the real cost is likely to be because we provide country data for further interpretation. Our analysis suggests that greater efforts at implementation of even basic integrated pest management would reduce insecticide inputs considerably, reducing negative environmental impacts and saving many hundreds of millions of dollars annually.

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Estimating the Economic Cost of One of the World's Major Insect
Pests, Plutella xylostella (Lepidoptera: Plutellidae): Just How Long
is a Piece of String?
Author(s): Myron P. Zalucki, Asad Shabbir, Rehan Silva, David Adamson, Liu Shu-
Sheng, and Michael J. Furlong
Source: Journal of Economic Entomology, 105(4):1115-1129. 2012.
Published By: Entomological Society of America
DOI: http://dx.doi.org/10.1603/EC12107
URL: http://www.bioone.org/doi/full/10.1603/EC12107
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FORUM
Estimating the Economic Cost of One of the World’s Major Insect
Pests, Plutella xylostella (Lepidoptera: Plutellidae): Just How Long
Is a Piece of String?
MYRON P. ZALUCKI,
1
ASAD SHABBIR,
2
REHAN SILVA,
1
DAVID ADAMSON,
3
LIU SHU-SHENG,
4
AND MICHAEL J. FURLONG
1,5
J. Econ. Entomol. 105(4): 1115Ð1129 (2012); DOI: http://dx.doi.org/10.1603/EC12107
ABSTRACT Since 1993, the annual worldwide cost of diamondback moth, Plutella xylostella (L.)
(Lepidoptera: Plutellidae), control has been routinely quoted to be US$1 billion. This estimate
requires updating and incorporation of yield losses to reßect current total costs of the pest to the world
economy. We present an analysis that estimates what the present costs are likely to be based on a set
of necessary, but reasoned, assumptions. We use an existing climate driven model for diamondback
moth distribution and abundance, the Food and Agriculture Organization country Brassica crop
production data and various management scenarios to bracket the cost estimates. The “length of the
string”is somewhere between US$1.3 billion and US$2.3 billion based on management costs. However,
if residual pest damage is included then the cost estimates will be even higher; a conservative estimate
of 5% diamondback mothÐinduced yield loss to all crops adds another US$2.7 billion to the total costs
associated with the pest. A conservative estimate of total costs associated with diamondback moth
management is thus US$4 billionÐUS$5 billion. The lower bound represents rational decision making
by pest managers based on diamondback moth abundance driven by climate only. The upper estimate
is due to the more normal practice of weekly insecticide application to vegetable crops and the
assumption that canola (Brassica napus L.) is treated with insecticide at least once during the crop
cycle. Readers can decide for themselves what the real cost is likely to be because we provide country
data for further interpretation. Our analysis suggests that greater efforts at implementation of even
basic integrated pest management would reduce insecticide inputs considerably, reducing negative
environmental impacts and saving many hundreds of millions of dollars annually.
KEY WORDS bioclimatic modeling, cost of management, pest control, diamondback moth, inte-
grated pest management
Entomologists are notorious for conjuring monetary
values to ascribe to pest problems when emphasizing
the importance of a given pest in a scientiÞc paper or
justifying grant applications to sanction research to
address the issue. The diamondback moth, Plutella
xylostella (L.) (Lepidoptera: Plutellidae) is, at Þrst
glance, different. Ever since Talekar and Shelton
(1993) published their landmark review, the annual
cost of diamondback moth control to the world econ-
omy has been cited as US$1 billion by pretty much
everyone writing a paper on diamondback moth, and
no doubt when applying for grant funds. Interestingly
the Þgure has not changed since 1993, even with in-
ßation and increased production of Brassica hosts,
particularly canola (Brassica napus L.). Perhaps pest
management has become more efÞcient after all or
maybe the original estimate was hyperbole? The Þg-
ure itself was not calculated by Talekar and Shelton
(1993) but was taken from a short foreword to the
Proceedings of the Second International Workshop on
Diamondback Moth and other Crucifer Pests (Talekar
1992) in which Javier (1992) suggested that “control
could cost approximately U.S. $1 billion annually,”but
provided no justiÞcation for the estimate.
Here, we present a detailed basis for estimating the
worldwide control costs of diamondback moth. Our
aim is to outline a transparent methodology for arriv-
ing at a justiÞable value that can be readily updated
over time and that could be applied to other cropÐpest
systems. The need for such a monetary value can be
justiÞed on a number of dry economic rationalist
grounds quite apart from its necessity for grant appli-
cations and the introductions to scientiÞc manu-
scripts. Ultimately, applied research needs to redress
major constraints to food security and so, of the myriad
1
School of Biological Sciences, The University of Queensland,
Brisbane, Australia, 4072.
2
School of Agriculture and Food Sciences, The University of
Queensland, Brisbane, Australia, 4072.
3
Risk and Sustainable Management Group, School of Economics,
The University of Queensland, Brisbane, Australia, 4072.
4
Institute of Insect Sciences, College of Agriculture and Biotech-
nology, Zhejiang University, Hangzhou, PeopleÕs Republic of China.
5
Corresponding author, e-mail: m.furlong@uq.edu.au.
0022-0493/12/1115Ð1129$04.00/0 䉷2012 Entomological Society of America

of competing interests, how do we objectively com-
pare which pests are major constraints and justify
research effort and allied expenditure? So that valid
comparisons can be made, the inevitable assumptions
must be justiÞed and the methodology that is applied
needs to be transparent.
To estimate the total cost of diamondback moth to
Brassica crop production and management, we re-
quire some knowledge of how much is spent per ha of
diamondback moth host plant (⫽Brassica crop) pro-
duction on diamondback moth management and how
much production is lost despite this management.
These costs will depend in part on where diamond-
back moth is found (abundance varies geographi-
cally), how abundance is distributed relative to agri-
cultural host plants and how much economic damage
diamondback moth feeding inßicts on crops. None of
these questions is easy to answer. Despite being such
a major pest of crucifers (since 1985 six international
workshops or conferences have been devoted to the
pest (Talekar and Griggs 1986, Talekar 1992, Sivapra-
gasam et al. 1997, Endersby and Ridland 2004, Shelton
et al. 2008, Srinivasan et al. 2011) and being, perhaps,
the most widely distributed lepidopteran species, our
understanding of the geographic distribution and
abundance of diamondback moth is limited to course
scale and probably incorrect maps. The original Com-
monwealth Institute of Entomology map for diamond-
back moth (Commonwealth Institute of Entomology
1967) is an amalgam of incomplete distribution records
and imagination (see Zalucki and Furlong 2008),
whereas more recent CAB maps just show a dot in a
whole country if the species is recorded (CAB 2012)!
First, we present a way to estimate, albeit crudely, the
distribution and abundance of diamondback moth glob-
ally. Next, we estimate a relationship between the suit-
ability of a location for diamondback moth and the at-
tendant pest management costs and, assuming the world
is rational, insecticides are only applied when pest levels
warrant control. We then use the production data for
each country to bracket the likely costs given certain
assumptions. The brackets will necessarily be wide and
the “piece of string”is of variable length between these
brackets!
Methods
Estimating the Distribution Of Diamondback Moth
Abundance. Zalucki and Furlong (2008, 2011) devel-
oped a CLIMEX model (Sutherst and Maywald 2004)
for diamondback moth that can be used to predict its
geographic distribution and relative abundance, its
seasonal phenology across its distribution range, and
even long-term variations in abundance; see Zalucki
and Furlong (2005) for details of the approach. The
rationale under pinning CLIMEX has been described
many times (Yonow and Sutherst 1998, Zalucki and
Furlong 2008, Li et al. 2012), but we outline the ap-
proach brießy for readers not yet familiar with the
methodology.
CLIMEX calculates a growth index (GI), analogous
to population growth rate, that describes the potential
of the population to increase at a location. The growth
index, calculated weekly, is a product of temperature
(TI) and moisture (MI) indices. For calculation of
both TI and MI, there is a range of conditions of
temperature and moisture over which growth is max-
imal. Either side of the optimum range growth rate
decreases. Above some upper threshold and below a
lower threshold growth ceases. These thresholds de-
Þne the biological parameters for a species and need
to be estimated (see, e.g., Zalucki and van Klinken
2006).
A measure of the relative suitability of a location for
a species to persist is summarized in a single annual
eco-climatic index (EI), scaled to 100: EI ⫽100 冱
GI/52 ⫻SI ⫻SX, where 52 is the number of weeks in
a year; SI are four stress indices, describing the species
response to the extremes of cold (CS, cold stress), heat
(HS), dry (DS), and wet (WS) conditions; and SX, if
needed, are interactions between extreme conditions;
cold-dry (CDS), cold-wet (CWS), hot-dry (HDS),
and hot-wet (HWS) stresses. The stress indices (SI
and SX) are accumulated at a speciÞed rate whenever
conditions exceed a speciÞed threshold.
Indices are calculated weekly for each location us-
ing standard meteorological data; a locationÕs long-
term monthly average maximum and minimum tem-
peratures, rainfall, and humidity. Areas with positive
values for EI are suitable for species persistence; the
larger the value of EI, the more suitable the location.
Generally, an EI of ⬍15Ð20 would be considered mar-
ginal for a speciesÕ long-term persistence at a location.
The values for parameters that describe the TI and MI
components of growth, and the various stress indices
(SI) and their interactions (SX), can be estimated
from laboratory or Þeld studies (Zalucki and van
Klinken 2006).
Unknown or poorly measured values are estimated
by an iterative procedure that involves comparing the
predicted geographic distribution with the actual dis-
tribution and adjusting parameter values. Comparing
the predicted and observed species distribution in an
area (e.g., a continent) not used for the procedure can
test the parameter tuning; see Maywald and Sutherst
(1991) for details. This is one independent test of the
model and can be contrasted with the more commonly
used procedure of “randomly”subsampling from a
current geographic distribution to both Þt and test
various empirical GIS models (e.g., as is done with
MAXENT or similar), but of course comparisons
across continents is only applicable if a species is
widespread.
The CLIMEX model for diamondback moth in Za-
lucki and Furlong (2008) was initially developed using
the anecdotal distribution data in Commonwealth In-
stitute of Entomology maps (Commonwealth Institute
of Entomology 1967), the seasonal prevalence of di-
amondback moth in the Cameron Highlands of Ma-
laysia and published values for various parameters
(Zalucki and Furlong 2008, 2011). There is no distinc-
tion between the growth and development responses
of temperate and tropical diamondback moth popu-
lations to different temperatures (Shirai 2000), and it
1116 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 105, no. 4

was assumed that all diamondback moth populations
have uniform responses to climatic variables. Most of
the temperature related parameters were derived
from Liu et al. (2002). Zalucki and Furlong (2011)
reÞned the initial model by adjusting the moisture-
related parameters, essentially making the species
more dry tolerant, requiring concomitant adjustments
in the dry and wet stress thresholds and similar
changes for the degree-day accumulation of cold and
heat stress parameters (Zalucki and Furlong 2008,
2011). The model was subsequently tested using dia-
mondback moth damage data from China and areas
predicted to support persistent diamondback moth
populations correlated well with regions reporting the
most severe pest damage (Li et al. 2012).
We present further independent tests of the dia-
mondback moth CLIMEX model by generating sea-
sonal phenologies of the pest at several locations.
These were chosen as data were available, principal
sources were the proceedings of the diamondback
moth and other crucifer pest workshops (Talekar and
Griggs 1986, Talekar 1992, Sivapragasam et al. 1997,
Endersby and Ridland 2004, Shelton et al. 2008, Srini-
vasan et al. 2011). The seasonal phenologies were
derived from data read off graphs or taken from tables
that estimate the seasonal prevalence of diamondback
moth based on counts of immatures in Þelds or adults
in pheromone or light traps (see sources in Fig. 2). We
have expressed the data as the percentage that oc-
curred at each sampling date over a year. These sea-
sonal phenologies use data that were not used to con-
struct the model and therefore represent independent
validation. The predictions are based on the standard
CLIMEX output of weekly GI that indicates the suit-
ability of a time period for diamondback moth popu-
lations, again expressed as a percentage of the total GI.
We are only interested in the general visual concur-
rence of these graphs, as we are not attempting to
make speciÞc predictions of abundance. We present
these tests to further justify the use of the diamond-
back moth-CLIMEX model as a reasonable approxi-
mation to the distribution and abundance of diamond-
back moth, an essential component of the method that
we have developed to estimate how much it costs to
manage this pest.
Estimating the Costs of Diamondback Moth Con-
trol. We establish a relationship between GI for a site
and the application of insecticides for diamondback
moth management by using data from several loca-
tions in the Changjiang River Valley, China, in 2000
and 2001. At each location diamondback moth abun-
dance was assessed over one to two cropping seasons
in experimental cabbage (Brassica oleracea Capitata),
caulißower (B. oleracea Botrytis), and broccoli (B.
oleracea Italica) crops. Within these crops, the num-
ber of insecticide applications required using farmer
practice (FP) or a threshold-based integrated pest
management (IPM) approach was recorded (see Ta-
ble 1 for details). We pool the data to establish the
following broad relationships for insecticide applica-
tions under each management practice.
For FP plots or Þelds, no. of applications ⫽5.6 ⫻
GI/wk ⫺0.82 (F
1, 9
⫽16.25; P⫽0.003; r
2
adj
⫽0.6). For
IPM plots or Þelds, no. of applications ⫽2.5 ⫻GI/
wk ⫺0.06 (F
1, 9
⫽9.84; P⫽0.012; r
2
adj
⫽0.45), where
GI/wk is the average GI over the period the crop was
grown at each location, using climate data from a
nearby location.
In one set of calculations, we assume spray decision
making for Brassica vegetable crops is based on GI as
detailed above. For canola, we assume that crops are
either treated with insecticide once per season or that
crops are treated with insecticide after application of
the same GI-based decision making processes that are
applied to vegetable crops. In tropical countries, it is
not uncommon for Brassica crops to be sprayed
weekly or biweekly (Rauf et al. 2004, Sandur 2004,
Mazlan and Mumford 2005); in our calculations, we
use a single insecticide application per week as the
worst-practice scenario.
We use annual world production statistics
(FAOSTAT 2012) as the basis of calculations. Data are
available for three categories of Brassica crops: cab-
bage and other brassicas (mainly leafy Chinese green
vegetables), caulißower and broccoli, and canola or
oilseed rape. The production area represents the total
Table 1. Field trials used to determine the no. of insecticide applications used in IPM or FP control strategies
Location Crop Date GI/wk
a
No. insecticide
applications
Planting Harvest FP IPM
Song-jiang Cabbage 2 Aug. 2000 8 Nov. 2000 0.49 2 1
Whenzou Cabbage 16 Aug. 2000 25 Oct. 2000 0.85 4 2
Xiao-shan Cabbage 7 Sept. 2000 20 Nov. 2000 0.63 3 1.8
Qiao-si Cabbage 14 Sept. 2000 28 Nov. 2000 0.64 2 1.33
Ningbo Cabbage 17 Sept. 2000 1 Dec. 2000 0.8 4 2
Song-jiang Cabbage 1 Jan. 2000 1 May 2000 0.47 2 0.67
Song-jiang Caulißower 24 Aug. 2001 4 Nov. 2001 0.58 2.3 1
Whenzou Caulißower 30 Aug. 2001 15 Nov. 2001 0.57 2.3 1.67
Xiao-shan Broccoli 7 Sept. 2001 20 Nov. 2001 0.58 2 1.67
Qiao-si Broccoli 16 Sept. 2001 28 Nov. 2001 0.52 1.3 1.33
Song-jiang Caulißower 17 Sept. 2001 1 Dec. 2001 0.5 3 1.67
a
For each trial the diamondback moth GI accumulated each week during the period in which the crop was growing was computed for use
in regressions (see Materials and Methods).
August 2012 ZALUCKI ET AL.: ECONOMIC COST OF P. xylostella 1117

area of each crop grown in a country per year, and we
calculated the mean production area of each crop for
each country for which data are available from 2000 to
2009, the most up to date data available (FAOSTAT
2012). We assume that all crops take 12 wk from
transplanting or sowing to harvest.
We also assume that if a region is suitable for dia-
mondback moth (GI ⬎15), then we can distribute the
crops proportionally to where diamondback moth is
located and calculate the insecticide costs for each
grid square. CLIMEX outputs a Þle of georeferenced
data that shows the value of selected output variables
for each x-y location or cell on a 30-min grid. We
output EI, GI, weeks GI positive, degree-days, and
number of diamondback moth generations for each
location. We ported these data to ArcView (ESRI
2011) and used country shape Þles to distribute dia-
mondback moth distribution and abundance by coun-
try. This enabled us to link country production area
data for each of the crops to CLIMEX output that
could then be linked to likely control costs based on
the adoption of FP, IPM, or weekly applications of
insecticide to various Brassica vegetable crops. Spe-
ciÞcally, we distribute the total area of Brassica crop
production to each grid location, j for example, rela-
tive to the GI value, for grid locations or cells with
GI ⬎15, to get A
j
.
Insecticide application costs can vary a great deal,
and we use US$35/ha for farmer practice and US$40
for IPM for all crops. Pesticides vary enormously in
costs depending on the product being used and ap-
plication costs (e.g., fuel, labor costs). The higher cost
for IPM reßects the additional labor costs of sampling
and probably use of more expensive products. If all of
these costs are included, then our estimates are not
unreasonable for costs encountered in China (Liu et
al. 2004), Canada (insecticide application to canola
costs US$35/ha; L. Dosdall, personal communication)
and the United States (insecticide and application
costs estimated to be US$74/ha in U.S. vegetable
crops; A. Shelton, personal communication).
Thus, the cost of management for each country will
be given by the product of: A
j
⫻SPH
j
⫻CPS
j
, summed
over each grid location j, where A
j
is the area of
production in ha for each Brassica crop type, SPH
j
is
the number of insecticide applications per hectare
either for the worst-case scenario (weekly), best FP or
threshold-based IPM, and CPS
j
is cost/spray for each
crop. For simplicity of presentation we present data
summed for each continent (Table 2); national area of
production data and estimated control costs under the
different application regimes can be found in Appen-
dix 1.
The overall cost of diamondback moth will equal the
cost of management plus the lost production even with
management, i.e., yield loss with management. This is
a measure of how ineffective management is due to
poor spray timing, poor application (coverage), in-
secticide resistance, and so forth. Estimates regarding
the yield losses caused by diamondback moth cover an
enormous range and losses in many tropical develop-
ing countries are likely to be much higher than in
temperate regions of industrialized countries. We use
production values for the crops published by the Food
and Agriculture Organization (FAO) (FAOSTAT
2012) and use these gross data and an assumed level
of 5% decrease in crop value to estimate yield losses
due to diamondback moth.
Test of CLIMEX Model
Predicted and Actual Geographic Distributions.
The predicted worldwide distribution, mapped as EI
values (Fig. 1a) and GI values (Fig. 1b) based on the
set of CLIMEX parameters described in Zalucki and
Furlong (2011) for diamondback moth agrees well
Table 2. Estimated annual costs of diamond back moth control for different crops on each continent when treated with insecticide
weekly or managed by rational FP or an IPM strategy
Brassica crop Continent Cost of insecticide application strategy (US$)
Weekly Single FP Rational FP IPM
Cabbage Africa $46,097,772 $19,202,651 $11,141,005
Australia and PaciÞc $1,169,364 $457,579 $267,545
Asia $695,435,398 $276,309,868 $161,195,709
Europe $216,137,670 $91,065,120 $52,762,140
North and Central America $42,129,738 $19,060,394 $10,952,758
South America $5,644,128 $2,231,623 $1,303,107
Global total $1,006,614,070 $408,327,234 $237,622,266
Caulißower and broccoli Africa $5,433,960 $1,773,879 $1,063,439
Australia and PaciÞc $3,447,234 $1,292,478 $759,913
Asia $291,613,130 $111,784,037 $65,524,683
Europe $58,007,430 $23,238,373 $13,547,207
North and Central America $19,788,426 $8,361,502 $4,842,898
South America $23,656,962 $12,056,249 $6,840,740
Global total $401,947,142 $158,506,518 $92,578,880
Canola Africa $2,857,157 $13,475,282 $7,874,566
Australia and PaciÞc $45,818,497 $199,996,489 $118,066,148
Asia $475,528,620 $2,184,449,699 $1,280,853,119
Europe $211,162,386 $1,108,960,398 $639,659,862
North and Central America $195,127,090 $974,026,206 $565,206,806
South America $4,127,208 $23,057,347 $13,207,633
Global total $934,620,957 $4,503,965,422 $2,624,868,134
1118 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 105, no. 4

with the “known”worldwide distribution (Common-
wealth Institute of Entomology 1967, CAB 2012). Re-
gions with values of EI below ⬇15 are probably mar-
ginal for long-term persistence of a species; essentially
such conditions are associated with the edge of the
species core range (Fig. 1a). The potential increase in
the species range when GI values are plotted (Fig. 1b)
should be noted. This area represents the maximal
range that diamondback moth can exploit if it migrates
out of its core (Fig. 1a) and colonizes these locations
at a time when prevailing seasonal conditions result in
a positive GI. Such migrations are highly variable from
year to year (Zalucki and Furlong 2005, 2008, 2011).
The Þne scaleÐpredicted distribution of diamond-
back moth in Japan (Fig. 1a and b) agrees well with
observations of the speciesÕ actual distribution in this
region. Diamondback moth does not persist in north-
ern Japan, but it does so in southern regions (Honda
1992) and makes annual reinvasions of northern Japan.
Again the geographic distribution in Japan is a pre-
diction based on our model parameter values, and it
was not used to generate the model. Similarly, the
species does not usually persist on the Korean penin-
sula but reinvades each year, most likely from China,
and populations build up when GI values are suitable
in the springÐsummer (Furlong et al. 2008). The
northern range limit of seasonal breeding in China
(Fig. 1a) seems to agree well with observations there
(Li et al. 2012).
Predicted and Actual Seasonal Phonologies.
CLIMEX can be used to predict average season phe-
nology at a site in terms of weekly growth indices. We
have done so for 11 locations around the world that
vary widely in their suitability for diamondback moth,
Fig. 1. Predicted geographic distribution of diamondback moth worldwide. (a) Values where EI is positive denote the
core of the species range and year round occupation. (b) Regions where GI is positive indicate the potential migratory seasonal
range. The “actual”distribution CAB maps can be found in the CABI Crop Protection Compendium (CABI 2012).
August 2012 ZALUCKI ET AL.: ECONOMIC COST OF P. xylostella 1119

by using climatic data for an average year at each
location (locations are marked on Fig. 1). Again, the
predicted values agree well with observed values
across a range of sites (Fig. 2aÐk). This is not unex-
pected for the Cameron Highlands, Malaysia (Fig. 2a)
because these data were used to parameterize the
initial model (Zalucki and Furlong 2008). However,
many other sites across a wide range of geographic
locations(northern Luzon, Philippines [Fig. 2b],
China [Fig. 2cÐ e; in Fig. 2e, high populations recorded
in weeks 21Ð27 probably are the result of migration
event; population changes after week 28 follow a very
similar pattern to the growth indices predicted by the
model], Japan [Fig. 2f], India [Fig. 2g], Kenya [Fig.
2h], the United Kingdom [Fig. 2i], and Mexico [Fig.
2j]) show good visual agreement between observed
and predicted phenology, but there is a poor Þt for a
site in South Africa (Fig. 2k). The sites that were used
for these comparisons were sites for which diamond-
back moth population monitoring data could be iden-
tiÞed in one form or another (sampling of larvae on
plants, pheromone traps, and light traps) to estimate
the species seasonal phenology/abundance at the site.
Importantly, these abundance data reßect not only
vital population rates of birth, death, and movement as
inßuenced by climate and host plant (crop and non-
crop weedy Brassica species) but also widespread in-
tensive management, namely, insecticide application.
In general the modelÕs GI is a leading indicator of
population buildup, as one would expect as it is a
measure of potential growth rate. Outside areas where
EI is positive (Fig. 1a), the seasonal dynamics will have
a strong migration component. That our simple model
by and large captures both spatial (Fig. 1a and b) and
seasonal temporal dynamics (Fig. 2) argues that there
is nothing particularly special about the speciesÕ biol-
ogy and ecology at speciÞc locations throughout its
range. The predictions are based on one set of param-
eters and climate data for each location. We therefore
use our CLIMEX model to “predict”diamondback
moth abundance throughout its range and undertake
an analysis of possible management costs.
Estimated Cost of Diamondback Moth
Management and Losses
A few things stand out in terms of how much dia-
mondback moth control costs the world economy
(Table 2). For vegetable crops, if management is based
on weekly spray regimes, then diamondback moth
costs ⬇US$1.4 billion. If diamondback moth is only
treated with insecticide as required, when average GI
is suitable for diamondback moth population growth,
then the predicted control costs are ⬇US$566 million;
this drops to ⬇US$340 million if IPM strategies are
assumed to be universally adopted for vegetable crops
(Table 2). The latter assumes diamondback moth
abundance is entirely determined by climate. We have
not calculated a control cost based on weekly spraying
of canola because this is unlikely ever to be the case.
Spraying this crop a single time during the crop cycle
adds ⬇US$935 million to management costs if a single
spray farmer practice intervention is assumed (Table
2). If diamondback moth in canola is managed using
IPM or farmer practice regimes based on climate that
are equivalent to those in vegetable crops, then con-
trol costs are estimated to be in the region of US$2.6
billion and US$4.5 billion, respectively (Table 2).
However, such assessments must be interpreted with
caution, as in canola economic thresholds are usually
much higher than for Brassica vegetable crops.
Based on these estimates our approach predicts a
lower bound for the management costs of diamond-
back moth in all crops of ⬇US$1.27 billion (US$340
million for all vegetables, assuming the adoption of
IPM, and US$935 million for canola, assuming a single
insecticide application) and an upper bound of
⬇US$2.3 billion (US$1.4 billion for all vegetables
based on weekly insecticide application and US$935
million for canola, assuming a single insecticide ap-
plication across the global distribution of the crop).
The real value will lie somewhere between these
bounds. In 2009, the annual value of Brassica vegetable
and canola crops was estimated to be ⬎US$51 billion
(FAO STAT 2012), meaning that just 5% crop loss due
to the damage caused by diamondback moth would
add another US$2.7 billion to the total economic costs,
which can be attributed to this destructive pest. So, the
piece of string could be US$5.0 billion long!
Vegetable production is extremely high in Asia and
this continent consequently dominates loss estimates
(Table 2; Fig. 3). Examination of Fig. 3 allows calcu-
lation of estimated control costs for a given continent
based on the assumption of a continuum (0Ð100%) of
weekly insecticide applications; when crops are not
treated weekly it is assumed that there is equal adop-
tion of farmer practice and IPM-based management
strategies. Using these data it is possible to determine
a range of estimates for the worldwide costs of dia-
mondback moth management by assuming different
adoption rates of different pest management strategies
in different continents. Such an approach will more
likely lead to a more precise estimate of actual man-
agement costs than we attempt above. Similarly the
national data that were generated and used to deter-
mine the continental estimates (Appendix 1) are a
useful resource that enables diamondback moth con-
trol costs in each country to be estimated based on
assumptions regarding the local adoption rates of dif-
ferent pest management strategies.
Discussion
If we assume that the cost of diamondback moth of
US$1billion was correct in 1993, then, at a minimum,
one should allow for inßation. The net present value
of this sum is US$2.65 billion assuming 5% inßation per
annum and US$1.81 billion if an inßation rate of 3% is
applied. Furthermore, there have been dramatic in-
creases in both Brassica vegetable (39% increase) and
canola (59% increase) production areas since 1993,
and there is now an additional crop area of ⬎12 million
ha/annum available to the pest that requires protec-
tion (FAOSTAT 2012). This represents an overall in-
1120 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 105, no. 4

Fig. 2. Seasonal phenology of diamondback moth for locations throughout its range (see Fig. 1) based on various methods
to observe the species abundance (solid line, details below) plotted with the CLIMEX GI value (dashed line) based on average
climatic conditions at the location provided by CLIMEX. The x-axis varies depending on how the base data were collected
(e.g., weekly, monthly). (a) Cameron highlands, Malaysia, (4⬚28⬘N, 101⬚23⬘E; 1,470 m), larvae per 10 plants (Ooi 1992).
(b) La Trinidad, northern Luzon (8⬚56⬘N, 125⬚31⬘E), light and pheromone trap (Poelking 1992). (c) Hangzhou, China
(30⬚14⬘N, 120⬚26⬘E) (Zalucki and Furlong 2011). (d) Huadu, China (23⬚23⬘N, 114⬚28⬘E; 6.6 m) (Li et al. 2012). (e) Beijing
(40⬚28⬘N, 115⬚58⬘E), China, larvae per 100 plants (Shi et al. 2008). (f) Hiratsuka, Kanagawa (35⬚19⬘N, 139⬚21⬘E), Japan,
pheromone trap (Kuwahara et al. 1995). (g) Aligarh district (27⬚53⬘N, 78⬚5⬘E), India, larvae per plant, (Ahmad and Ansari
2010). (h) Naivasha, Nakuru district, the Rift Valley, altitude, 1,500 m (00⬚45⬘S, 036⬚26⬘E), Kenya, larvae per 20 plants
(Rossbach et al. 2008). (i) Holbeach, Lincolnshire (52⬚48⬘N, 0⬚1⬘E), United Kingdom, larvae per 20 plants (Collier and
Finch 2004). (j) Salamanca (20⬚34⬘N, 101⬚12⬘W), Mexico, pheromone trap (McCully and Araiza Salas 1992). (k) Brits (25⬚
25⬘S, 27⬚76⬘E, altitude, 1,100 m), South Africa, pheromone trap (KÞr 1997, 2004; Nofemela and KÞr 2008).
August 2012 ZALUCKI ET AL.: ECONOMIC COST OF P. xylostella 1121

crease of 57% to the global Brassica cropping area
since 1993, and whatever the merits of the previous
US$1 billion estimate it is clearly outdated, even with-
out consideration of the increased intensiÞcation of
practices that have accompanied this change.
Our analysis suggests that the management of dia-
mondback moth in Brassica vegetable crops alone
costs US$1.4 billion when based on the conservative
assumption of a management regime of one insecti-
cide application per week (Table 2). When the cost of
managing diamondback moth in canola is included the
total cost estimate for management increases to
US$2.3 billion. These estimates are at the extreme
range and represent the worst-case scenario of a single
spray to every canola crop and weekly spraying for
vegetables (Table 2). The latter is not uncommon for
diamondback moth on vegetable crops in many parts
of the speciesÕ range, particularly in the tropics where
insecticide application rates can be even higher (Ma-
zlan and Mumford 2005, Williamson 2005, Atumuri-
rava and Furlong 2011). As has been suggested by
many studies, diamondback moth is an induced pest
that can, to a large extent, be controlled by natural
enemies (KÞr 2004, Furlong et al. 2008, Li et al. 2012).
Our work in China (Liu et al. 2005), Democratic
PeopleÕs Republic of Korea (Furlong et al. 2008), and
Australia (Furlong et al. 2004a,b) indicates that it is
possible to greatly reduce management costs by mov-
ing away from weekly insecticide application and the
adoption of threshold-based IPM (Zalucki et al. 2009).
If management was based on diamondback moth pop-
ulations requiring control only when climatic condi-
tions were suitable, then even rational farmer practice
would reduce costs to US$560 million in vegetables
and adoption of IPM would reduce these costs by a
further 42% (Table 2). As has been found in Australia
(Furlong et al. 2004a,b); China (Li et al. 2012); the
Cameron Highlands, Malaysia (Ooi 1992); New Zea-
land (Walker et al. 2004); and South Africa (KÞr 1997,
2004), if natural enemies are not disrupted by broad-
spectrum insecticides and allowed to exert population
suppressive forces on the pest, then costs associated
with the management diamondback moth would be
even less.
The cost estimate is based on a CLIMEX prediction
of population size based on GI and decision making as
in our trials in China (Table 2). So, how reasonable is
our model and approach? CLIMEX has been used
extensively in biological control programs and pest
risk analysis to predict potential distributions of var-
ious species (for brief reviews, see Zalucki and Fur-
long 2005, van Klinken et al. 2009, Lawson et al. 2010).
However, this method has not yet been used widely to
model population changes of a species (but see Za-
lucki and Furlong 2005; Zalucki and van Klinken 2006;
Zalucki and Furlong 2008, 2011; Li et al. 2012). The
CLIMEX parameter set integrates data on species dis-
tribution into a single model. This approach may be
used to generate a climate driven null model for the
abundance of a species at a site over time. Such models
that enable one to infer likely species abundance
based on climate are critical to testing the effective-
ness of management strategies such as areawide man-
agement and the planting of transgenic crops (Carri-
e`re et al. 2003, Zalucki and Furlong 2005), as well as
predicting temporal dynamics and outbreaks of pests
(e.g., Maelzer et al. 1996). Historical climate data can
be used to predict both the expected changes in range
and variation in the seasonal and annual temporal
dynamics of the pest and therefore of likely pest status.
In our estimates of management costs, we use our
CLIMEX model to generate diamondback moth spa-
tial abundance and the scale of the problem that it
represents. The validity of the model rests on how well
the predictions match reality. Geographically, the pre-
dictions accord well with notions about the distribu-
tion of diamondback moth, but surprisingly, there is no
real data to test against per se. Predictions of seasonal
Fig. 3. Estimated costs of diamondback moth control in
vegetable Brassica crops for each continent (cabbages [and
leafy Chinese greens], caulißower and broccoli) along a
continuum of insecticide interventions: 1, weekly application
of insecticide by 100% of farmers in a given region; 0, weekly
application of insecticide by 0% of farmers in a given region.
When crops are not treated with insecticide weekly, equal
adoption of FP- and IPM-based insecticide applications are
assumed.
1122 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 105, no. 4

phenology (Fig. 2) provide good approximations to
Þeld data for almost all locations tested. Zalucki and
Furlong (2008, 2011) and Li et al. (2012) took this one
step further and showed that the model was even
useful for long-term population data sets.
Our other assumption is that Brassica crops are the
same as diamondback moth, and we have distributed
Brassica production areas accordingly. In practice, ar-
eas of agriculture are more concentrated depending
on soil, topography, water availability, and other fac-
tors that affect land use. The distribution of host plants
also will impact on populations of insects (e.g., Zalucki
and Lammers 2010) and of course their pest status
(Schellhorn et al. 2008). These matters are beyond the
scope of this paper, but they are unlikely to change key
Þndings substantially. We might expect areas of high
production to lead to higher levels of pest abun-
danceÑthe resource concentration hypothesis on a
landscape scale.
Our country estimates are close to local experts
opinions. In the United States, Anthony Shelton (per-
sonal communication) estimates that management
cost associated with Brassica vegetables are of the
order of US$220 million; our equivalent estimate is
⬇US$46 million. The difference can be explained due
to different estimates of the area of production; the
FAO estimates that the area of production is ⬇71,000
ha (FAOSTAT 2012), whereas Shelton used USDA
estimates of 107,000 ha; and differences in assumed
management costs per ha; we use US$35Ð40/ha but
Shelton uses more than double this Þgure (see above).
Allowing for these differences, our equivalent esti-
mate would be ⬇US$150 million, a value that is sur-
prisingly close. Sandur (2004) has estimated that di-
amondback moth management in vegetable crops in
India costs ⬇US$168 million/annum, we estimate
costs to be US$45ÐUS$217 million, depending on the
relative degree of adoption of IPM and weekly insec-
ticide application (Appendix 1).
Our estimates are probably conservative for two
major reasons. First, our assumptions about manage-
ment costs and the frequency of insecticide use is
likely to be an underestimate of common practices in
many parts of the world where insecticides are fre-
quently applied to Brassica vegetable crops more than
two times per week (Mazlan and Mumford 2005, Wil-
liamson 2005, Atumurirava and Furlong 2011). If this
were universally the case, then the upper cost for
vegetables would be double our current estimate. Sec-
ond, the ofÞcial FAO statistics that we have used as the
basis of our cost calculations (FAOSTAT 2012) un-
derestimate the area of Brassica crops grown. This is
well illustrated by the example the United States dis-
cussed above, and Appendix 1 shows that the produc-
tion area data that we used (FAOSTAT 2012) indicate
that many countries, even some in which diamond-
back moth is a renowned and signiÞcant pest, e.g.,
Taiwan, Myanmar, Laos, and Papua New Guinea, pro-
duce no Brassica crops at all! Indeed, the database
returns zero production data for 35 of the 168 coun-
tries for which data could be retrieved. Although the
effect of such discrepancies is in itself signiÞcant, an
even greater source of error is likely to derive from the
fact that in many developing countries signiÞcant
quantities of Brassica crops are grown in the subsis-
tence sector. Although these crops are frequently in-
tensively managed with insecticides, they often con-
tribute only to local rural economies, and as such
production data do not enter ofÞcial statistics.
In conclusion, diamondback moth is an enormous
economic problem, and certainly it now costs much
⬎US$1 billion/annum to manage. Management costs
and lost production probably make diamondback
moth a US$4ÐUS$5 billion problem. Current manage-
ment relies heavily on insecticide applications, some-
times under the guise of threshold based IPM. As has
happened repeatedly, insecticide resistance develops
rapidly (e.g., Li et al. 2012) and reliance on insecti-
cides is not sustainable. Our experience and analysis
suggest that worldwide adoption of even basic IPM
would dramatically reduce costs and allow natural
enemies to contribute to pest population suppression.
To achieve more efÞcient and long-term control,
adoption of improved management must be facili-
tated; this is “de´ja`vu all over again”(Shelton 2004).
Acknowledgments
We thank the many colleagues who have worked to im-
prove management of diamondback moth and provided es-
timates of how much it costs to manage this pest. We thank
Australian Centre for International Agricultural Research for
funding of projects on diamondback moth in China (CS2/
1998/089, Democratic PeopleÕs Republic of Korea (HORT/
2004/062), and the South PaciÞc (PC/2004/063 and PC/
2010/090).
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Received 12 March 2012; accepted 18 May 2012.
August 2012 ZALUCKI ET AL.: ECONOMIC COST OF P. xylostella 1125

Appendix 1. Mean production data and associated cost estimates for diamondback moth management under different management practices and scenarios for all countries for which data were available
(FAOSTAT 2012)
Continent Country
Mean production
area (Ha) 2000Ð2009
Estimated costs (US$) based
on farmer practice (FP)
Estimated costs (US$) based
on IPM adoption
Estimated costs (US$)
based on weekly
application of
insecticide (FP)
Estimated costs based
of single application
of insecticide to canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli FP IPM
Africa Algeria 2,580 4,175 15,199 $297,585 $481,594 $1,753,243 $183,413 $296,825 $1,080,592 $1,083,516 $1,753,500 $531,969 $607,964
Africa Angola 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Benin 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Botswana 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Burkina Faso 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Burundi 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Cameroon 2,874 0 0 $529,622 $0 $0 $305,397 $0 $0 $1,206,954 $0 $0 $0
Africa Central African
Republic
00 0$0$0$0 $0 $0$0$0$0$0$0
Africa Chad 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Congo 91 0 0 $19,432 $0 $0 $11,030 $0 $0 $38,262 $0 $0 $0
Africa Coˆte dÕIvoire 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Democratic Republic
of the Congo
1,516 0 0 $309,090 $0 $0 $176,258 $0 $0 $636,678 $0 $0 $0
Africa Egypt 20,199 4,798 0 $2,745,866 $652,294 $0 $1,648,247 $391,549 $0 $8,483,622 $2,015,328 $0 $0
Africa Equatorial Guinea 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Eritrea 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Ethiopia 21,308 0 24,150 $3,871,402 $0 $4,387,795 $2,236,076 $0 $2,534,338 $8,949,360 $0 $845,257 $966,008
Africa Gabon 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Ghana 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Guinea 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Kenya 32,824 92 0 $6,685,930 $18,679 $0 $3,813,042 $10,653 $0 $13,785,870 $38,514 $0 $0
Africa Lesotho 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Liberia 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Madagascar 788 53 0 $177,216 $11,982 $0 $100,067 $6,766 $0 $331,086 $22,386 $0 $0
Africa Malawi 3,418 0 0 $631,586 $0 $0 $364,078 $0 $0 $1,435,350 $0 $0 $0
Africa Mali 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Mauritania 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Morocco 1,462 2,066 940 $225,901 $319,346 $145,284 $133,150 $188,228 $85,633 $613,872 $867,804 $32,900 $37,600
Africa Mozambique 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Namibia 145 0 0 $17,736 $0 $0 $10,824 $0 $0 $60,900 $0 $0 $0
Africa Niger 9,192 0 0 $920,872 $0 $0 $582,367 $0 $0 $3,860,556 $0 $0 $0
Africa Nigeria 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Rwanda 4,259 0 0 $1,221,522 $0 $0 $675,370 $0 $0 $1,788,864 $0 $0 $0
Africa Senegal 1,642 0 0 $162,644 $0 $0 $103,085 $0 $0 $689,682 $0 $0 $0
Africa Sierra Leone 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Somalia 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa South Africa 2,785 1,178 36,964 $496,851 $210,148 $6,594,660 $287,590 $121,639 $3,817,164 $1,169,658 $494,718 $1,293,731 $1,478,550
Africa Sudan 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Swaziland 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Togo 44 0 0 $6,494 $0 $0 $3,856 $0 $0 $18,606 $0 $0 $0
Africa Tunisia 1,556 544 4,380 $211,167 $73,813 $594,301 $126,792 $44,320 $356,838 $653,646 $228,480 $153,300 $175,200
Africa Uganda 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa United Republic of
Tanzania
3,047 0 0 $666,535 $0 $0 $377,377 $0 $0 $1,279,866 $0 $0 $0
1126 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 105, no. 4

Appendix 1. Continued
Continent Country
Mean production
area (Ha) 2000Ð2009
Estimated costs (US$) based
on farmer practice (FP)
Estimated costs (US$) based
on IPM adoption
Estimated costs (US$)
based on weekly
application of
insecticide (FP)
Estimated costs based
of single application
of insecticide to canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli FP IPM
Africa Western Sahara 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Zambia 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Africa Zimbabwe 27 32 0 $5,202 $6,024 $0 $2,987 $3,459 $0 $11,424 $13,230 $0 $0
Asia Afghanistan 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Asia Azerbaijan 6,286 0 0 $512,233 $0 $0 $338,307 $0 $0 $2,640,288 $0 $0 $0
Asia Bangladesh 13,606 13,049 266,274 $2,525,335 $2,421,901 $49,420,247 $1,455,018 $1,395,422 $28,474,369 $5,714,688 $5,480,622 $9,319,597 $10,650,968
Asia Bhutan 698 158 0 $105,119 $23,811 $0 $62,180 $14,084 $0 $293,230 $66,420 $0 $0
Asia Cambodia 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Asia China 1,007,443 343,496 6,900,183 $184,176,369 $62,796,470 $1,261,461,219 $106,301,520 $36,244,390 $728,080,617 $423,126,186 $144,268,404 $241,506,405 $276,007,320
Asia India 261,720 281,790 5,906,150 $36,148,477 $38,920,523 $815,750,900 $21,647,301 $23,307,324 $488,507,589 $109,922,400 $118,351,800 $206,715,250 $236,246,000
Asia Indonesia 68,055 7,250 0 $8,339,115 $888,317 $0 $5,087,838 $541,978 $0 $28,583,100 $3,044,790 $0 $0
Asia Iran 11,279 1,144 105,800 $1,278,847 $129,682 $11,996,457 $790,554 $80,166 $7,415,936 $4,736,970 $480,354 $3,703,000 $4,232,000
Asia Iraq 1,275 2,370 0 $153,611 $285,536 $0 $93,983 $174,697 $0 $535,500 $995,400 $0 $0
Asia Israel 1,974 1,474 0 $397,616 $296,863 $0 $227,033 $169,504 $0 $829,080 $618,996 $0 $0
Asia Japan 46,630 12,041 728 $7,546,034 $1,948,570 $117,875 $4,420,902 $1,141,584 $69,058 $19,584,600 $5,057,220 $25,494 $29,136
Asia Jordon 710 2,390 0 $130,474 $439,109 $0 $75,261 $253,290 $0 $298,200 $1,003,590 $0 $0
Asia Kazakhstan 14,865 23 72,826 $1,441,387 $2,230 $7,061,737 $917,387 $1,419 $4,494,521 $6,243,132 $9,660 $2,548,896 $2,913,024
Asia Korea North 34,474 0 0 $6,124,644 $0 $0 $3,546,879 $0 $0 $14,479,080 $0 $0 $0
Asia Korea South 48,404 0 1,045 $8,433,009 $0 $182,008 $4,895,164 $0 $105,651 $20,329,680 $0 $36,575 $41,800
Asia Kuwait 209 232 0 $0 $0 $0 $0 $0 $0 $87,906 $97,524 $0 $0
Asia Kyrgyzstan 5,017 117 486 $423,294 $9,872 $41,041 $277,385 $6,469 $26,894 $2,107,014 $49,140 $17,024 $19,456
Asia Laos 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Asia Lebanon 1,563 1,015 0 $278,047 $180,502 $0 $160,995 $104,514 $0 $656,418 $426,132 $0 $0
Asia Malaysia 1,508 0 0 $216,141 $0 $0 $128,738 $0 $0 $633,360 $0 $0 $0
Asia Mongolia 1,652 0 0 $189,773 $0 $0 $117,051 $0 $0 $693,924 $0 $0 $0
Asia Myanmar 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Asia Nepal 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Asia Oman 296 132 0 $11,633 $5,175 $0 $9,559 $4,252 $0 $124,320 $55,300 $0 $0
Asia Pakistan 4,431 11,389 322,738 $512,343 $1,316,988 $37,321,360 $315,641 $811,363 $22,992,742 $1,860,810 $4,783,254 $11,295,820 $12,909,508
Asia Philippines 7,899 1,065 0 $1,440,777 $194,286 $0 $831,797 $112,166 $0 $3,317,580 $447,370 $0 $0
Asia Qatar 97 90 0 $0 $0 $0 $0 $0 $0 $40,740 $37,926 $0 $0
Asia Saudi Arabia 2,798 0 0 $120,073 $0 $0 $95,516 $0 $0 $1,175,118 $0 $0 $0
Asia Sri Lanka 4,003 0 0 $742,953 $0 $0 $428,066 $0 $0 $1,681,260 $0 $0 $0
Asia Syrian Arab Republic 2,010 1,654 0 $242,378 $199,399 $0 $148,269 $121,978 $0 $844,158 $694,470 $0 $0
Asia Taiwan 0 0 0 $0 $0 $0 $0 $0 $0
Asia Tajikistan 1,697 0 432 $149,624 $0 $38,050 $97,113 $0 $24,696 $712,656 $0 $15,103 $17,260
Asia Thailand 27,331 5,251 0 $3,549,605 $682,011 $0 $2,145,627 $412,254 $0 $11,478,810 $2,205,504 $0 $0
Asia Turkey 29,434 5,979 8,213 $3,314,594 $673,335 $924,830 $2,051,475 $416,742 $572,397 $12,362,280 $2,511,306 $287,441 $328,504
Asia Turkmenistan 3,030 0 0 $0 $0 $0 $0 $0 $0 $1,272,600 $0 $0 $0
Asia United Arab Emirates 782 365 0 $0 $0 $0 $0 $0 $0 $328,566 $153,132 $0 $0
Asia Uzbekistan 8,983 0 1,658 $726,052 $0 $133,976 $480,412 $0 $88,649 $3,772,860 $0 $58,016 $66,304
Asia Viet Nam 35,169 1,845 0 $7,043,179 $369,457 $0 $4,024,021 $211,084 $0 $14,770,770 $774,816 $0 $0
Asia Yemen 472 0 0 $37,130 $0 $0 $24,719 $0 $0 $198,114 $0 $0 $0
Australia & PaciÞc Australia 1,929 7,105 1,307,171 $294,555 $1,085,090 $199,633,716 $173,896 $640,603 $117,857,444 $810,054 $2,984,100 $45,750,985 $52,286,840
Australia & PaciÞc Fiji 70 0 0 $15,255 $0 $0 $8,638 $0 $0 $29,316 $0 $0 $0
Australia & PaciÞc New Caledonia 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Australia & PaciÞc New Zealand 786 1,103 1,929 $147,769 $207,388 $362,774 $85,011 $119,310 $208,704 $329,994 $463,134 $67,512 $77,156
Australia & PaciÞc Papua New Guinea 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Australia & PaciÞc Solomon Islands 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Australia & PaciÞc Vanuatu 0 0 0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
August 2012 ZALUCKI ET AL.: ECONOMIC COST OF P. xylostella 1127

Appendix 1. Continued
Continent Country
Mean production
area (Ha) 2000Ð2009
Estimated costs (US$) based
on farmer practice (FP)
Estimated costs (US$) based
on IPM adoption
Estimated costs (US$)
based on weekly
application of
insecticide (FP)
Estimated costs based
of single application
of insecticide to canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli FP IPM
Europe Albania 1,291 161 0 $203,491 $25,419 $0 $104,676 $13,075 $0 $542,346 $67,746 $0 $0
Europe Armenia 3,328 247 0 $313,272 $23,235 $0 $200,571 $14,876 $0 $1,397,550 $103,656 $0 $0
Europe Austria 1,826 296 48,189 $351,379 $57,033 $9,271,598 $201,634 $32,727 $5,320,382 $767,046 $124,500 $1,686,626 $1,927,572
Europe Belarus 21,225 0 154,093 $4,447,894 $0 $32,291,256 $2,529,191 $0 $18,361,667 $8,914,584 $0 $5,393,248 $6,163,712
Europe Belgium 3,711 4,035 6,956 $759,397 $825,736 $1,423,399 $432,883 $470,699 $811,388 $1,558,704 $1,694,868 $243,467 $278,248
Europe Bosnia and
Herzegovina
6,569 0 811 $1,359,386 $0 $167,766 $773,988 $0 $95,520 $2,758,980 $0 $28,375 $32,428
Europe Bulgaria 4,401 334 33,926 $581,787 $44,211 $4,485,270 $350,705 $26,651 $2,703,748 $1,848,210 $140,448 $1,187,393 $1,357,020
Europe Croatia 5,771 224 15,880 $1,208,497 $46,949 $3,325,303 $687,234 $26,699 $1,890,994 $2,423,820 $94,164 $555,783 $635,180
Europe Cyprus 124 77 0 $22,078 $13,645 $0 $12,783 $7,901 $0 $52,122 $32,214 $0 $0
Europe Czech Republic 3,150 972 309,816 $641,590 $198,063 $63,111,117 $365,902 $112,956 $35,992,581 $1,322,832 $408,366 $10,843,557 $12,392,636
Europe Denmark 828 608 124,219 $173,546 $127,534 $26,039,115 $98,680 $72,517 $14,806,062 $347,718 $255,528 $4,347,676 $4,968,772
Europe Estonia 819 62 52,878 $167,102 $12,583 $10,783,603 $95,288 $7,175 $6,149,221 $344,148 $25,914 $1,850,744 $2,115,136
Europe Finland 1,015 517 75,410 $155,276 $79,083 $11,535,150 $91,650 $46,678 $6,808,515 $426,342 $217,140 $2,639,350 $3,016,400
Europe France 9,793 26,864 1,267,009 $1,706,918 $4,682,328 $220,834,869 $990,773 $2,717,838 $128,182,662 $4,113,144 $11,282,964 $44,345,315 $50,680,360
Europe Georgia 10,660 0 0 $1,982,314 $0 $0 $1,141,894 $0 $0 $4,477,200 $0 $0 $0
Europe Germany 15,065 6,560 1,322,457 $3,016,672 $1,313,528 $264,815,506 $1,723,556 $750,475 $151,300,604 $6,327,258 $2,755,032 $46,285,995 $52,898,280
Europe Greece 8,417 4,381 3,750 $1,117,811 $581,834 $498,021 $673,359 $350,491 $300,003 $3,535,098 $1,840,062 $131,250 $150,000
Europe Hungary 5,067 1,266 152,780 $752,301 $187,949 $22,685,084 $445,857 $111,389 $13,444,481 $2,127,972 $531,636 $5,347,286 $6,111,184
Europe Ireland 899 909 4,200 $120,351 $121,690 $562,449 $72,406 $73,212 $338,384 $377,454 $381,654 $147,000 $168,000
Europe Italy 20,232 20,817 13,533 $3,632,788 $3,737,831 $2,430,005 $2,101,156 $2,161,911 $1,405,482 $8,497,272 $8,742,972 $473,659 $541,324
Europe Latvia 3,108 227 54,360 $679,212 $49,692 $11,878,886 $384,590 $28,137 $6,726,178 $1,305,444 $95,508 $1,902,600 $2,174,400
Europe Lithuania 5,238 353 112,150 $1,134,463 $76,370 $24,290,745 $642,933 $43,281 $13,766,274 $2,199,876 $148,092 $3,925,250 $4,486,000
Europe Netherlands 7,189 3,850 1,836 $1,428,425 $765,011 $364,861 $816,799 $437,447 $208,634 $3,019,254 $1,617,000 $64,267 $73,448
Europe Norway 1,029 947 7,053 $156,170 $143,640 $1,070,022 $80,746 $74,267 $553,243 $432,180 $397,740 $246,855 $282,120
Europe Poland 37,501 13,493 583,532 $7,490,246 $2,695,071 $116,551,114 $4,280,674 $1,540,233 $66,608,948 $15,750,462 $5,667,186 $20,423,606 $23,341,264
Europe Portugal 9,353 2,071 0 $1,739,614 $385,110 $0 $1,002,069 $221,835 $0 $3,928,386 $869,652 $0 $0
Europe Republic of Moldova 4,458 118 16,302 $620,152 $16,414 $2,267,548 $370,987 $9,819 $1,356,489 $1,872,486 $49,560 $570,553 $652,060
Europe Romania 44,900 1,536 153,127 $7,869,272 $269,235 $26,837,204 $4,564,644 $156,172 $15,567,168 $18,858,168 $645,204 $5,359,459 $6,125,096
Europe Russian Federation 147,208 1,267 322,734 $25,638,954 $220,723 $56,210,003 $14,883,345 $128,129 $32,629,759 $61,827,360 $532,266 $11,295,690 $12,909,360
Europe Serbia and
Montenegro
23,636 0 3,461 $4,323,296 $0 $633,040 $2,495,129 $0 $365,350 $9,926,910 $0 $121,129 $138,433
Europe Slovakia 6,457 1,090 117,629 $1,242,109 $209,660 $22,627,814 $712,781 $120,313 $12,984,916 $2,711,940 $457,758 $4,117,008 $4,705,152
Europe Slovenia 836 89 2,690 $168,805 $18,040 $543,345 $96,355 $10,297 $310,145 $350,952 $37,506 $94,136 $107,584
Europe Spain 8,474 24,660 12,555 $1,086,725 $3,162,352 $1,610,024 $658,198 $1,915,347 $975,146 $3,559,122 $10,356,990 $439,415 $502,188
Europe Sweden 414 418 73,456 $69,549 $70,136 $12,328,141 $40,558 $40,900 $7,189,179 $174,048 $175,518 $2,570,960 $2,938,240
Europe Switzerland 1,081 658 17,308 $198,458 $120,816 $3,178,325 $114,485 $69,695 $1,833,488 $453,894 $276,318 $605,763 $692,300
Europe The former Yugoslav
Republic of
Macedonia
3,523 24 698 $423,947 $2,840 $84,029 $259,426 $1,738 $51,420 $1,479,492 $9,912 $24,437 $27,928
Europe Ukraine 74,930 1,770 427,170 $12,366,548 $292,123 $70,500,710 $7,226,820 $170,712 $41,199,531 $31,470,600 $743,400 $14,950,950 $17,086,800
Europe United Kingdom 11,089 17,212 541,245 $1,715,323 $2,662,488 $83,725,078 $1,010,923 $1,569,134 $49,343,266 $4,657,296 $7,228,956 $18,943,589 $21,649,816
North & Central
America
Belize 60 0 0 $11,500 $0 $0 $6,601 $0 $0 $25,158 $0 $0 $0
North & Central
America
Canada 8,647 2,312 5,116,870 $1,494,696 $399,699 $884,528,363 $868,459 $232,236 $513,935,089 $3,631,572 $971,124 $179,090,450 $204,674,800
1128 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 105, no. 4

Appendix 1. Continued
Continent Country
Mean production
area (Ha) 2000Ð2009
Estimated costs (US$) based
on farmer practice (FP)
Estimated costs (US$) based
on IPM adoption
Estimated costs (US$)
based on weekly
application of
insecticide (FP)
Estimated costs based
of single application
of insecticide to canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli Canola
Cabbages
& other
brassicas
Caulißower
and broccoli FP IPM
North & Central
America
Costa Rica 2,997 0 0 $425,580 $0 $0 $253,821 $0 $0 $1,258,614 $0 $0 $0
North & Central
America
Cuba 8,302 0 0 $1,677,686 $0 $0 $957,607 $0 $0 $3,487,008 $0 $0 $0
North & Central
America
Dominican republic 362 0 0 $96,235 $0 $0 $53,535 $0 $0 $152,166 $0 $0 $0
North & Central
America
El salvador 89 0 0 $9,261 $0 $0 $5,815 $0 $0 $37,380 $0 $0 $0
North & Central
America
Guatemala 3,898 5,108 0 $818,173 $1,072,222 $0 $465,152 $609,585 $0 $1,636,950 $2,145,234 $0 $0
North & Central
America
Haiti 1,710 0 0 $401,120 $0 $0 $225,586 $0 $0 $718,116 $0 $0 $0
North & Central
America
Honduras 1,837 107 0 $365,766 $21,280 $0 $209,110 $12,166 $0 $771,708 $44,898 $0 $0
North & Central
America
Jamaica 1,288 119 0 $277,852 $25,604 $0 $157,531 $14,517 $0 $541,002 $49,854 $0 $0
North & Central
America
Mexico 6,456 23,581 3,018 $1,022,702 $3,735,260 $478,071 $600,831 $2,194,443 $280,864 $2,711,688 $9,904,020 $105,634 $120,724
North & Central
America
Nicaragua 8,981 0 0 $1,577,173 $0 $0 $914,638 $0 $0 $3,772,188 $0 $0 $0
North & Central
America
Panama 305 0 0 $50,850 $0 $0 $29,683 $0 $0 $128,268 $0 $0 $0
North & Central
America
Puerto Rico 50 0 0 $11,468 $0 $0 $6,463 $0 $0 $21,000 $0 $0 $0
North & Central
America
United States of
America
55,326 15,889 455,172 $10,820,332 $3,107,438 $89,019,773 $6,197,926 $1,779,952 $50,990,852 $23,236,920 $6,673,296 $15,931,006 $18,206,864
South America Argentina 0 0 12,183 $0 $0 $2,602,942 $0 $0 $1,477,191 $0 $0 $426,419 $487,336
South America Bolivia (Plurinational
State of)
802 359 0 $120,780 $54,017 $0 $71,437 $31,949 $0 $336,672 $150,570 $0 $0
South America Brazil 0 0 47,600 $0 $0 $8,127,678 $0 $0 $4,729,535 $0 $0 $1,666,000 $1,904,000
South America Chile 2,050 1,542 13,246 $224,588 $168,885 $1,450,992 $139,686 $105,040 $902,467 $861,084 $647,514 $463,600 $529,828
South America Colombia 3,413 621 0 $566,680 $103,158 $0 $330,908 $60,238 $0 $1,433,460 $260,946 $0 $0
South America Ecuador 1,546 51,958 0 $339,958 $11,424,507 $0 $192,377 $6,464,941 $0 $649,362 $21,822,234 $0 $0
South America French Guiana 329 0 0 $47,478 $0 $0 $28,256 $0 $0 $138,348 $0 $0 $0
South America Guyana 68 0 0 $10,213 $0 $0 $6,037 $0 $0 $28,350 $0 $0 $0
South America Paraguay 0 0 35,491 $0 $0 $7,965,426 $0 $0 $4,498,506 $0 $0 $1,242,189 $1,419,645
South America Peru 2,620 1,311 0 $428,189 $214,201 $0 $250,544 $125,334 $0 $1,100,526 $550,536 $0 $0
South America Suriname 31 0 0 $4,819 $0 $0 $2,843 $0 $0 $13,188 $0 $0 $0
South America Trinidad and Tobago 58 56 0 $9,922 $9,546 $0 $5,772 $5,553 $0 $24,360 $23,436 $0 $0
South America Uruguay 352 0 9,400 $109,013 $0 $2,910,308 $59,929 $0 $1,599,934 $147,882 $0 $329,000 $376,000
South America Venezuela (Bolivarian
Republic of)
2,169 480 0 $369,983 $81,936 $0 $215,319 $47,684 $0 $910,896 $201,726 $0 $0
Global totals 2,396,700 957,017 26,703,456 $408,327,232 $158,506,517 $4,503,965,422 $237,595,774 $92,566,401 $2,624,789,098 $1,006,614,070 $401,947,142 $934,620,957 $1,068,138,236
August 2012 ZALUCKI ET AL.: ECONOMIC COST OF P. xylostella 1129