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Developing a generic sampling system for monitoringthe key arthropod pests of table grapes, Vitis viniferaL.Marelize de Villiers a & Ken L. Pringle aa Department of Conservation Ecology and Entomology, Faculty of AgriSciences,University of Stellenbosch, Matieland, South AfricaPublished online: 27 Jun 2008.
To cite this article: Marelize de Villiers & Ken L. Pringle (2008) Developing a generic sampling system for monitoringthe key arthropod pests of table grapes, Vitis vinifera L., International Journal of Pest Management, 54:3, 207-217, DOI:10.1080/09670870801968872
To link to this article: http://dx.doi.org/10.1080/09670870801968872
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Developing a generic sampling system for monitoring the key arthropod pests of table grapes,
Vitis vinifera L.
Marelize de Villiers* and Ken L. Pringle
Department of Conservation Ecology and Entomology, Faculty of AgriSciences, University of Stellenbosch, Matieland,South Africa
(Received 2 August 2007; final version received 6 February 2008)
We describe a system developed for monitoring arthropod pests of table grapes in the Hex River Valley, SouthAfrica. The system has wide applicability for monitoring pests at grape-producing localities in the country. SixVitis vinifera cv. Barlinka and six V. vinifera cv. Dauphine vineyards were used as sampling sites. In eachvineyard, 100 vines were inspected bi-weekly, to record damage or the presence of arthropod pests on the cordon,one bunch and one leaf per vine. Sampling precision and operating characteristic (OC) curves were estimated foreach of the pests. Using previously determined economic thresholds together with the OC curves, actionthresholds were determined for Tetranychus urticaeKoch (Prostigmata: Tetranychidae) (11–29% leaf infestation),Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae) (2% cordon infestation), Epichoristodes acerbella(Walker) (Lepidoptera: Tortricidae) and Phlyctinus callosus Boh. (Coleoptera: Curculionidae) (1% bunchdamage). This novel system can be used for monitoring arthropod pest population levels in all types of SouthAfrican vineyards, providing a basis for the development of integrated pest management (IPM) systems in thesevineyards.
Keywords: monitoring; thresholds; decision-making; table grapes; pests
1. Introduction
Concern by consumers over insecticide and acaricideresidues on fresh fruit, as well as the negative impactthat residues can have on the environment, requiresproducers of table grapes, among others, to restrictthe use of insecticide and acaricide sprays to aminimum. Reducing the frequency of chemicalsprays, while minimizing the risk of damage to thecrop, can only be achieved by effective monitoring ofpest numbers. Additionally, a good monitoringsystem will improve the efficiency of pesticideapplications, resulting in less money being spent bythe producer, as well as improved biological controland a reduction in the possibility of arthropod pestsdeveloping resistance to insecticides and acaricides.
When pest populations in a crop are monitored, theactual density in the field is compared with theacceptable threshold, enabling management decisionsto be made (Binns et al. 2000). To make these decisionsrequires a monitoring system to be implemented. Sucha system can prevent both over-spraying, which is anunnecessary expense and can result in unnecessaryenvironmental contamination, and under-spraying,which can result in crop losses.
Table grapes are prone to attack by variousarthropods. Damage can occur directly to fruit,
making it unmarketable. Damage can also beindirect, via weakening of the plant, which in turnadversely affects production. The grapevine mealybugPlanococcus ficus (Signoret) (Hemiptera: Pseudococ-cidae) is a key pest in South African vineyards (DeKlerk 1981; Myburgh et al. 1986; Walton and Pringle2004). The grapes become infested with mealybugsand are contaminated by wax secretions, egg sacs andhoneydew, causing blemishes and resulting in un-marketable fruit, and ultimately in a general weak-ening and shortened lifespan of the vine (De Klerk1981; Nel 1983; Myburgh et al. 1986; Walton andPringle 2004). Another economic pest of table grapesis the Western flower thrips Frankliniella occidentalis(Pergande) (Thysanoptera: Thripidae), which causeshalo spot damage. The latter comprises small darkscars surrounded by whitish tissue, which makes thefruit of certain white cultivars unsightly and unmar-ketable (Weaver 1976; Flaherty and Wilson 1988;Jensen et al. 1992).
The African bollworm Helicoverpa armigera(Hubner) (Lepidoptera: Noctuidae), a sporadic grapepest, can cause severe damage when epidemicnumbers are reached (De Klerk 1981). Deep roundholes are eaten into the berries and cork tissue formsover the injury sites. This then inhibits normal
*Corresponding author. Email: [email protected]
International Journal of Pest ManagementVol. 54, No. 3, July–September 2008, 207–217
ISSN 0967-0874 print/ISSN 1366-5863 online
� 2008 Taylor & Francis
DOI: 10.1080/09670870801968872
http://www.informaworld.com
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development of the berry, leading to malformation ofthe mature fruit (Blomefield et al. 1986).
In certain table grape-producing areas, there arepests, such as the two-spotted spider mite Tetranychusurticae Koch (Acari: Tetranychidae), which causesleaf damage and ultimately affects plant growth(Pringle et al. 1986) and yield (Prischmann et al.2002), without directly damaging the fruit itself.
Pests may also be of phytosanitary importance.These do not occur in importing countries; fruitconsignments contaminated by such pests arerejected by the importing countries in order toavoid the establishment of a potentially new pest. Inthe Hex River Valley, the main phytosanitary pestsof table grapes are the banded fruit weevil Phlyctinuscallosus Boheman (Coleoptera: Curculionidae) andthe pear leafroller Epichoristodes acerbella (Walker)(Lepidoptera: Tortricidae) (Pryke 2005; Pryke andSamways 2007). These pests can also directlydamage the grape bunches, although this may notalways be of economic importance. Phlyctinuscallosus attacks the leaves, shoots and bunches (DeKlerk 1981). Holes are eaten into the stems ofbunches and berries, as well as into the berriesthemselves, causing ringbarking and dying-off, re-duced bunch size, berry drop, and desiccation andwilting of the bunches (Perold 1927; De Klerk 1981;Annecke and Moran 1982; Nel 1983; Barnes et al.1986). Epichoristodes acerbella larvae can damagethe grape bunches by spinning together severalberries with silken threads and feeding on thesurfaces of the berries or boring into them (Anon-ymous 1997; Blomefield and Du Plessis 2000;Blomefield et al. 2004).
An array of sampling systems, including the use ofphysical plant inspections (Hluchy and Pospisil 1991;Walton 2003), blue sticky traps (Gaum and Giliomee1994; Chu et al. 2000; Tsitsipis et al. 2003), pheromonetraps (Nel 1983; Millar et al. 2002; Walton, Pringle,et al. 2003; Walton, Daane, et al. 2004; Blomefieldet al. 2004) and cardboard bands (Nel 1983; Nel andAddison 1993) can be used for monitoring pestpopulation levels in vineyards. However, only in thecase of the inspection system for monitoring P. ficus(Walton 2003), has the reliability of decisions regard-ing control intervention – which is a prerequisite forpest monitoring in vineyards – been determined. Thefirst step in establishing the reliability of a monitoringsystem, is estimating sampling precision. A samplingerror of 20% or less is acceptable for pest managementpurposes (Reusink and Kogan 1994). The second stepis determining operating characteristic (OC) curves,which can be used to estimate the probability that thecorrect decision is made when deciding not to inter-vene with a control measure (Binns et al. 2000). UsingOC curves, decisions regarding the application ofcontrol measures can be made even if the samplingerror exceeds 20%.
Additionally, it would be impracticable forproducers to have to use different sampling plansfor each pest. Therefore, a generic sampling plan,which covers the whole pest complex, should bedeveloped. Such a system, in which different arthro-pod pest population levels can be monitored using thesame sampling unit such as grape bunch, is lackingfor the monitoring of arthropod pests in SouthAfrican vineyards. It could also possibly be adaptedfor use in vineyards in other countries.
It is also important that a monitoring systemshould be easy to implement so that operators of thesystem can be easily trained. Farmers have agreedthat the sampling system for monitoring populationlevels of P. ficus – in which 20 evenly spaced plotsof five vines per plot in 1 ha are inspected (Walton2003) – is feasible. Therefore, the main aim of thepresent study was to determine whether or not thesampling protocol developed for monitoring P. ficuspopulation levels could be extended to include all themajor arthropod pests of table grapes in vineyards inthe Hex River Valley, Western Cape Province, SouthAfrica. Other pests specifically targeted in the presentstudy were E. acerbella, P. callosus, T. urticae andF. occidentalis. The sampling errors and precision ofdecisions regarding control intervention were deter-mined here, while information on seasonal abundanceand density of these pests has been reported elsewhere(De Villiers and Pringle 2007). As damage by H.armigera was not observed (De Villiers and Pringle2007) it had to be excluded from consideration here.
2. Material and methods
2.1. Study sites
The study was conducted on three farms in the HexRiver Valley in the Western Cape Province of SouthAfrica, namely Klipheuwel (198310E, 338300S), Bo-plaas (198360E, 338300S) and De Vlei Boerdery(198410E, 338260S). At each farm two blocks of Vitisvinifera cv. Barlinka, a late-season black cultivar, andtwo blocks of V. vinifera cv. Dauphine, a late-seasonwhite cultivar, were used. Each block was approxi-mately one to 2 ha in size. In each block, 20 evenlydistributed plots, each consisting of five vines betweentwo trellising posts, was selected, giving a total of 100vines per block. Therefore, a total of 1200 vines wasused in the study. Producers were allowed to continuewith their spray programs in all the blocks. Thechemicals used are listed in De Villiers and Pringle(2007).
2.2. Monitoring
Monitoring was done at 2-weekly intervals over a3-year period, from May 2002 to mid-April 2005.Occasionally all sampling had to be postponed due torain. As described in De Villiers and Pringle (2007),
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monitoring of the different plant parts was doneaccording to the phenology of the vine. First, duringthe dormant stages of the vine, the top fork, orcordon, of each vine was examined for the presence orabsence of P. ficus to a distance of 30cm from thestem. From October (spring), about 1 month afterbud break, one leaf per vine was examined for thepresence or absence of T. urticae. After examinationin the field, the leaves were placed in brown paperbags and transported in cooler bags to the laboratory,where they were stored in a cool room prior tocounting. The leaves were brushed with a brushingmachine to dislodge the mites (Henderson andMcBurnie 1949; Sabelis 1985) and these were countedusing a stereomicroscope. The brushing machine wasused to obtain the actual mite counts on the leaves inorder to compare a system where actual counts areused to one where the mere presence or absence of themites are noted. The latter will be of more interest toproducers, since most of them will not have abrushing machine. During all seasons, leaf samplingwas only continued until the end of April, when thefruit season ended. From April onwards the qualityof the leaves rapidly decreased until leaf fall and itwas too difficult to brush the brittle leaves with thebrushing machine.
As soon as the inflorescences of the vine appeared,inspection of the latter started and continued throughflowering and berry set, up until harvest of the ripebunches. Inspection of the inflorescence will bereferred to hereon as bunch inspection. One bunchper vine was examined for the presence or absence of,as well as damage caused by, P. ficus, F. occidentalis,E. acerbella and P. callosus.
A hand-held computer with the Cybertrackersoftware (http://www.cybertracker.co.za) was usedto log the data obtained from vine inspections, trapcatches and band monitoring in the field. These datawere downloaded to a PC, which saved the time ofmanually typing in the data. The data were importedinto Microsoft1 Access 2000, a relational database(Dowling 2000). Appropriate queries were developedin order to prepare the data for analysis.
2.3. Sampling statistics
2.3.1. Counts of pests
To assess the reliability of a sampling system an indexof precision is required. The following index ofprecision was used (Iwao and Kuno 1971; Binnset al. 2000)
D ¼ 1=�x
� � ffiffiffiffiffiffiffiffiffiffiS2=N
q� �ð1Þ
where D is the level of precision, N is the number ofsample units or plots, �x is the average number ofinsects or mites per sample and S2 is the variance.
This function can only be used if the variance remainsconstant. However, in the case of clumped dispersionpatterns, as is the case with insect and mitepopulations, the variance usually increases with theaverage. The relationship between these two para-meters is given by the linear regression (Taylor 1961,1984; Binns et al. 2000):
Log S2� �
¼ Log að Þ þ bð ÞLog �xð Þ ð2Þ
where Log(a) and b are regression constants, esti-mated from linear regression. By taking the antilogsof Equation (2), the following expression can beobtained:
S2 ¼ a �xð Þb: ð3Þ
This expression is known as Taylor’s power law(Taylor 1961, 1965, 1971; Binns et al. 2000). Thevariance in Equation (1) can be substituted withEquation (3) to give the following expression:
D ¼ 1=�x
� � ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia �xð Þb
.N
r" #: ð4Þ
Equation (4) was used to determine the level ofprecision, D, for a given average number of insects ormites per sampling unit, �x (at the economic thresholdlevel) and different numbers of sampling units orplots, N. Multiplying D by 100, gave an estimate ofthe percentage sampling error (Davis 1994; Binnset al. 2000).
Tetranychus urticae Koch was the only pest forwhich actual counts of pests were obtained, as thenumber of mites on the vine leaves. Dummy variableregression (Gujarati 1970a, 1970b) was used todetermine whether or not there were differences inthe regressions between the three seasons andbetween the two cultivars.
Operating characteristic (OC) curves were calcu-lated to determine the probability of making thecorrect decision when deciding not to intervene(Binns et al. 2000):
z ¼�x� ETffiffiffiffi
S2
N
q : ð5Þ
As can be seen, m, which is usually used inestimating OC curves, has been replaced by theeconomic threshold value (ET) as suggested byBinns et al. (2000). Equation (3) was substituted intoEquation (5) and the ET was substituted for �x in (3) toobtain the following expression (Binns et al. 2000):
z ¼�x� ETffiffiffiffiffiffiffiffiffiffiffi
a ETð ÞbN
q : ð6Þ
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Values for z were determined for a range ofpopulation levels, �x. The probability levels formaking a correct decision not to intervene at variouspopulation levels were obtained from one-tailednormal probability tables (Binns et al. 2000).
2.3.2. Presence-absence sampling
Instead of counting all the mites on the leaves, theleaves can simply be classified as infested oruninfested, therefore saving time. This is calledpresence-absence sampling. This type of samplinghas been developed for several pests, includingcitrus red mite, spider mites on cotton and almondsand aphids on brussels sprouts (Flaherty et al.1992). This sampling system was also being de-veloped for sampling spider mites and predaceousmites on grapes (Flaherty et al. 1992). Binns et al.(2000) gives the following expression for the relation-ship between actual counts and presence-absencedata:
ln � lnð1� pÞ½ � ¼ aþ b lnð�xÞ ð7Þ
where ln is the base of the natural logarithm, p is theproportion of infested leaves, a and b are regressionconstants and �x is the average number of mites persampling unit. Linear regression was used, with ln(�x)as the independent variable and ln[–ln(1–p)] as thedependent variable.
Binns et al. (2000) showed that the proportion ofinfested samples, p, can be determined for any valueof �x, using the following expression:
p ¼ 1� exp f�½eað�xÞb�g: ð8Þ
In order to link the threshold value with thepresence-absence data, �x was substituted with the ET.
Presence-absence sampling was used for T. urticaeon the vine leaves. Dummy variable regression wasagain used to determine whether or not there weredifferences in regressions between the three seasonsand between the two cultivars.
OC curves can be used to evaluate the presence-absence sampling plan. The general expression forestimating OC curves is given by Equation (5). Tosolve this, Equation (8) was used to provide anestimate of the range of proportions of infested unitsand ET was replaced by an estimate of the proportionof infested units at the ET in the numerator. In thedenominator an estimate of the standard error of theaverage is required. The following expression wasused for the standard error (SE) of the average in thebinomial distribution (Binns et al. 2000):
SEð�xÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffipð1� pÞ
N
r: ð9Þ
In Equation (9) p was substituted with Equation(8) and �x was substituted with ET, giving thefollowing expression for z:
z ¼ f1� exp ½�ðeað�xÞbÞ�g � f1� exp ½�ðeaðETÞbÞ�gffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffif1�exp ½�ðeaðETÞbÞ�gfexp ½�ðeaðETÞbÞ�g
N
q :
ð10Þ
Again, values for z were obtained for a range ofpopulation levels and the probability levels of makinga correct decision not to intervene were obtainedfrom one-tailed normal probability tables (Binnset al. 2000).
2.3.3. Presence-absence cluster sampling
The initial sampling can also be conducted byclassifying the unit, such as fruit or bunch of grapes,as infested or uninfested. In cases like these, it isconvenient to inspect a number of secondary unitsfrom a primary unit, like bunches (secondary units)from a plot of five vines (primary unit) in the presentstudy. This type of sampling, or presence-absencecluster sampling (Binns et al. 2000), was used forsampling pests on the cordons and bunches of vines.Such data are described by the beta-nomial distribu-tion (Binns et al. 2000). The binomial distributiondescribes presence-absence data when there is noclustering. The proportion, p, of infested units in thebinomial distribution is the equivalent of the averagein count data. The variance of the proportion for thebinomial distribution is given by (Binns et al. 2000):
VarðBinÞ ¼ pð1� pÞn
¼ S2B ð11Þ
where n is the total number of units examined.In cluster sampling, the proportion of infested
units, p, is first estimated using (Madden et al. 1996;Madden and Hughes 1999):
p ¼Pn
i¼1PN
j¼1 Xij
nNð12Þ
whereN are the primary units, for example a plot of fivevines, and n the secondary units, for example bunches.Thus, p is the total number of infested bunches dividedby the total number of bunches inspected.
The proportion of infested bunches in the samplefrom each individual plot of vines is estimated toproduce pj for the jth plot. The variance of theseproportions was estimated using (Madden andHughes 1999):
VarðObsÞ ¼PN
j¼1 ðpj � pÞ2
N� 1¼ S2
O: ð13Þ
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This is the observed variance. The following linearregression was fitted (Binns et al. 2000):
lnðS2OÞ ¼ lnðaÞ þ b lnðS2
BÞ: ð14Þ
Taking the antilogs results in the followingexpression (Binns et al. 2000):
S2O ¼ aðS2
BÞb: ð15Þ
If infested bunches conform to the binomialdistribution, the proportion of infested bunches ineach plot will be the same as the proportion infestedbunches in the whole sample, because, if the binomialdistribution is assumed, infestation will occur atrandom. Every bunch will thus have an equal chanceof being infested. If the binomial variance (Equation(11)) is substituted into Equation (15), the followingexpression, similar to Taylor’s power law, is obtained(Binns et al. 2000):
S2O ¼ a
pð1� pÞn
b: ð16Þ
Here the variance, S2O, is expressed in terms of the
proportion of infested bunches, p. From (16) thesampling error can be estimated using (Binns et al.2000):
D ¼ 1
p
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia
N
pð1� pÞn
bs: ð17Þ
For bunch and cordon infestation, the samplingerror was determined for a constant infestation level(taken as the economic threshold value) and differentnumbers of primary sampling units.
Presence-absence cluster sampling was used forP. ficus bunch and cordon infestation, as well asfor F. occidentalis, E. acerbella and P. callosus bunchdamage. Dummy variable regression (Gujarati 1970a,1970b) was again used to determine whether or notthere were differences in regressions between the threeseasons and between the two cultivars. Where thenumber of observations for some of the combinationsof season and cultivar were too few, dummy variableregression was used only to determine whether or notthere were differences between the regressions of thetwo cultivars. OC curves were also calculated for aparticular ET value, using Equation (17) as thestandard error and substituting p in Equation (17)with the ET:
z ¼ p� ETffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiaN
ETð1�ETÞn
h ibr : ð18Þ
It was not meaningful to draw an OC curve for F.occidentalis, because the halo spots were formed earlyin the season. However, during scouting, this damagewas not observed until later in the season, when theberries were larger (De Villiers and Pringle 2007).Intervening with a control measure at this later stagewould therefore not prevent damage.
2.3.4. Dummy variable regression models: full modelsversus reduced models
The full models assumed separate slopes and inter-cepts for all combinations of seasons and cultivars.This was compared to reduced models, selected byinspecting the regression coefficients of the fullmodels, where common slopes and/or interceptswere assumed for some or all of these combinations(Gujarati 1970a, 1970b). The F-test was used to deter-mine whether or not the reduced models differed fromthe full model. In the case of a difference between areduced model and the full model, the reduced modelcannot be used and will therefore be rejected.
2.3.5. Economic thresholds
According to the guidelines for Integrated Productionof Wines in South Africa (IPW), expert advice shouldbe obtained when an average of more than five T.urticae per leaf is found (ARC Infruitec-Nietvoorbij2004). This is similar to the threshold of six T. urticaeper leaf used in Arkansas (Johnson et al. 2003). Amean of six T. urticae per leaf was therefore used asthe economic threshold.
For P. ficus, an infestation level of 5% cordon andbunch infestation was taken as the economic thresh-old, as farmers agreed that they would accept a bunchinfestation level of up to 5% (Walton 2003). Noeconomic thresholds have been determined for F.occidentalis, E. acerbella and P. callosus. Therefore, itwas assumed that a 5% bunch infestation level ofthese pests would also be acceptable and the sameeconomic threshold as for P. ficus was therefore used.In the case of the phytosanitary pests E. acerbella andP. callosus, this threshold of 5% damage is onlyapplicable if the grape crop is to be exported tomarkets where these insects are not of phytosanitaryconcern. Where they are of phytosanitary concernthere is zero tolerance.
3. Results
3.1. Monitoring
The data showed that the sampling plan developedfor P. ficus (Walton 2003) can be used to monitordifferent arthropod pest species, although the level ofaccuracy did vary among the pest species monitored.Below, results of the general model fit, the samplingerror and operating characteristic curves, including a
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comparison between presence–absence sampling andsampling using actual mite counts, are described.
3.1.1. General
In most cases there were no differences between theregression lines of ln(S2
B) on ln(S2O). An exception
was for P. callosus, where there were differencesbetween the two seasons (during the 2002/2003season almost no bunch damage was recorded so itwas omitted from the analysis). In the case of T.urticae there were differences in the regressionsbetween the two cultivars for both counts on leavesand the presence–absence sampling. Therefore, forP. ficus bunch and cordon infestation and F.occidentalis and E. acerbella bunch damage, reducedmodels with common intercepts and slopes wereused. In the case of P. callosus, models with separateintercepts and slopes were used for the 2003/2004and 2004/2005 season, while for the counts of T.urticae and the presence–absence sampling, modelsusing separate intercepts and slopes for the twocultivars were constructed. The correlation coeffi-cients for all the models were high, ranging between0.79 and almost 1 (see Table 1). Descriptions ofthese models and the values of the regressioncoefficients are given in Table 1.
3.1.2. Sampling error
When N¼ 20 plots and �x¼ the economic threshold(six mites per leaf for T. urticae) were used inEquation (4) or when N¼ 20 plots, n¼ five vines orfive bunches and p¼ the economic threshold (5%infestation or damage for all the insect pests) wereused in Equation (17), the sampling error variedbetween 18.85 and 53.03% (see Table 2, Figure 1).Only in the case of actual counts of T. urticae onBarlinka leaves, was the sampling error less than theacceptable value of 20%. For P. ficus bunch andcordon infestation and F. occidentalis, E. acerbellaand P. callosus bunch damage (both the 2003/2004and 2004/2005 seasons), sampling errors were morethan 40%. In no instance was there a markedreduction in sampling error when more than 20 plotswere used (see Figure 1).
3.1.3. Operating characteristic curves
Separate operating characteristic (OC) curves weredrawn for T. urticae on the Barlinka and Dauphineleaves, using actual mite counts and using presence–absence data (see Figure 2). For the presence–absencedata, Equation (8) was used to determine theproportion infested leaves for the threshold value ofsix mites per leaf. This gave a threshold of 29%infested leaves for Barlinka and 11% infested leavesfor Dauphine. T
able
1.
Pests,sample
units,
adescriptionofthereducedmodel,theFvalues
withtheirdegrees
offreedom
forcomparingthereducedmodel
withthefullmodel,F(df),the
probabilitylevelsforacceptingthereducedmodels(P
1),thecultivars,seasons,intercepts,slopes,multiple
correlationcoeffi
cients
(R)andtheprobabilitylevelsforgoodnessoffitof
thereducedregressionmodels(P
2).
Pest
Sample
unit
Model
description
F(df)
P1
Cultivar
Season
Intercepts
Slope
RP2
Tetranychusurticae
(mite)
Counts
onleaves
Separate
slopes
andintercepts
for
BarlinkaandDauphine
1.380(8,182)
0.208
Barlinka
All
0.24
1.50
0.98
50.001
Dauphine
All
0.62
1.69
Presence–absence
data
Separate
slopes
andintercepts
forBarlinkaandDauphine
1.966(8,95)
0.059
Barlinka
All
72.06
0.56
0.79
50.001
Dauphine
All
72.90
0.42
Planococcusficus
(mealybug)
Bunch
infestation
Commonslopeandinterceptfor
allseasonsandboth
cultivars
0.075(2,57)
0.928
Both
All
0.46
1.07
0.97
50.001
Cordoninfestation
Commonslopeandinterceptfor
allseasonsandboth
cultivars
1.734(10,91)
0.085
Both
All
0.90
1.15
0.97
50.001
Frankliniellaoccidentalis
(thrips)
Bunch
damage
Commonslopeandinterceptfor
allseasonsandboth
cultivars
0.587(10,212)
0.823
Both
All
0.36
1.06
0.97
50.001
Epichoristodes
acerbella
(tortricidmoth)
Bunch
damage
Commonslopeandinterceptfor
allseasonsandboth
cultivars
0.197(2,52)
0.822
Both
All
–0.54
0.91
�1.00
50.001
Phlyctinuscallosus
(curculionid
beetle)
Bunch
damage
Separate
slopes
andintercepts
forthe
2003/2004and2004/2005seasons
1.865(4,45)
0.133
Both
03/04
1.37
1.21
0.97
50.001
Both
04/05
70.04
0.99
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When there was an average of 4.5 (Barlinka) and 3.3(Dauphine) mites per leaf (actual mite counts), thedecision not to intervene would be correct in 90% of thecases (see Figure 2). The presence–absence datawere notas reliable as using the actual counts. Both the Barlinkaand Dauphine OC curves were flatter for the presence–absence data than for the actual counts (see Figure 2).The decision not to intervene, when making use ofpresence–absence sampling, would be correct in 90% ofthe cases when an average of 1.8 mites per leaf (aninfestation of 16%) were recorded on Barlinka and 0.08mites per leaf (an infestation of 1.9%) on Dauphine.
The OC curves for P. ficus bunch and cordoninfestation were very similar (see Figure 3). At an ETof 5%, the decision not to intervene with a controlmeasure when 2% of the cordons were infested, didnot lead to under reacting in 89% of the cases,irrespective of cultivar or season (see Figure 3). In the
Table 2. Percentage sampling error (sampling precision, D, multiplied by 100) and economic threshold (ET) for Tetranychusurticae on vine leaves (N¼ 20 plots), Planococcus ficus bunch and cordon infestation, as well as Frankliniella occidentalis,Epichoristodes acerbella and Phlyctinus callosus bunch damage (N¼ 20 plots; n¼ five vines).
Pest Cultivar Season ET D (%)
T. urticae (actual counts) Barlinka All 6 mites per leaf 18.85Dauphine All 6 mites per leaf 34.59
P. ficus bunch infestation Both All 5% infestation 46.61P. ficus cordon infestation Both All 5% infestation 48.21F. occidentalis bunch damage Both All 5% infestation 45.38E. acerbella bunch damage Both All 5% infestation 41.03P. callosus bunch damage Both 03/04 5% infestation 53.03
Both 04/05 5% infestation 43.73
Figure 1. Percent sampling error for (A) Tetranychusurticae on Barlinka (—) and Dauphine (- - - -) vine leaves ata constant population level set at the economic threshold(six mites per leaf) and various numbers of sampling units,and (B) Planococcus ficus bunch (—) and cordon (- - - -)infestation, (C) Frankliniella occidentalis (—) andEpichoristodes acerbella (- - - -) bunch damage and (D)Phlyctinus callosus bunch damage during the 2003/2004season (––) and 2004/2005 season (- - - -) at a constantinfestation level of p¼ 0.05 and a range of plots of fivevines.
Figure 2. Operating characteristic curves for monitoringTetranychus urticae on vine leaves. (A) Barlinka, actualcounts (first X-axis); (B) Dauphine, actual counts (firstX-axis); (C) Barlinka, presence–absence data (first andsecond X-axis); (D) Dauphine, presence–absence data (firstand third X-axis). Threshold value¼ six mites per leaf.
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case of bunch infestation, this was similar with a 90%chance of not under reacting when deciding not tointervene at a 2% infestation level (see Figure 3).
At an ET of 5% bunch damage by E. acerbella,the decision not to intervene when 1 and 2% buncheswere damaged was correct in 97 and 93% of the cases,respectively (see Figure 3), regardless of cultivar orseason.
The OC curves for P. callosus bunch damageduring the 2003/2004 and 2004/2005 seasons weresimilar, but the level of precision was higher for datafrom the 2004/2005 season. At an ET of 5%, thedecision not to intervene with a control measure when1% of the bunches were infested, was correct in 93%of the cases for the 2003/2004 season and 97% of thecases for the 2004/2005 season (see Figure 3), for bothcultivars. Not intervening when 2% infestationoccurred was correct in 87% of the cases for the
2003/2004 season and 91% of the cases for the 2004/2005 season (see Figure 3).
4. Discussion
Despite the sampling errors being generally higherthan what is normally acceptable for pest manage-ment, the use of operating characteristic curves allowscontrol decisions to be made and therefore enablesproducers to use the sampling system developed here.This is particularly so for T. urticae, which is not adirect pest, and for which a high degree of precisionis unnecessary, since under reacting will not lead todirect crop losses. Although the level of precisionusing presence–absence sampling of the mites was lessthan when using actual counts, such sampling can stillbe used in a sampling system for monitoring mitepopulations. This would be easier and less time-consuming than taking actual counts as suggested byHluchy and Pospisil (1991) and in the IPW guidelines(ARC Infruitec-Nietvoorbij 2004). Different thresh-old values were obtained for presence–absencesampling of T. urticae on Barlinka and Dauphineleaves. In practice, it would be better to use only onethreshold value for all cultivars. Control measuresshould therefore be applied when 11–29% of theleaves are infested, depending on factors such as leafquality. It would be helpful to use a 10X hand lens toexamine the leaves (as suggested by Flaherty et al.1992), especially if this is going to be done bylabourers on the farm, since it can be difficult todistinguish between T. urticae and other smallarthropods.
For P. ficus, the decision not to intervene with acontrol measure when 2% cordon infestation wasobserved was correct in 95% of the cases in a study byWalton (2003), as opposed to the lower probabilitylevel (89%) in the present study. The main reason forthis higher precision was that the expression for zused by Walton (2003) was not the same as that givenin Equation (18). The one used by Walton (2003) was:
z ¼ p� ETffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiaN
pð1�pÞ�n
h ibr :
Using the above expression and the data from thepresent study, the decision not to intervene when 2%cordon infestation was observed produced a 98%probability of under reacting. This was similar to thatfound by Walton (2003) who recommended thatcontrol measures should be applied at 2% cordoninfestation.
A suitable sampling method for F. occidentalis stillneeds to be developed. The monitoring system used inthe present study only provided information on theinfestation status of the vineyard. Jensen et al. (1992)
Figure 3. Operating characteristic curves for monitoring(A) Planococcus ficus bunch (—) and cordon (- - - -)infestation, (B) Epichoristodes acerbella bunch damageand (C) Phlyctinus callosus bunch damage during the2003/2004 season (—) and 2004/2005 season (- - - -).Threshold value¼ 5% infestation or damage.
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suggested that thrips should be sampled by strikingbunches of the blossoms or small grapes three timesagainst the flat surface of a cardboard, 215.96279.4mm (8½611 inch). The dislodged thrips are thencounted on the cardboard. This can be very time-consuming, especially if it forms part of a genericmonitoring system, where sampling of all pests, notonly F. occidentalis is included. It can therefore onlyform part of a monitoring system if it is done onlyonce or twice during the fruit season. However, it willbe difficult to decide when this should be done and thetiming of this procedure could influence the numberof thrips recorded per bunch. Furthermore, it mayalso be difficult for farmers to distinguish betweenF. occidentalis and other thrips.
For E. acerbella and P. callosus, control measuresshould be applied at 1% bunch damage due to thehigh probability of making the correct decision at thisdamage level. This is, however, only when grapes areexported to markets where these pests are not ofphytosanitary concern. When exporting grapes tomarkets like USA or Israel where phytosanitaryrestrictions are imposed on these pests (Pryke 2005;Pryke and Samways 2007), there will be a zerotolerance. According to the IPW guidelines (ARCInfruitec-Nietvoorbij 2004), wild shoots should be lefton 20 vines per block and inspected weekly for P.callosus damage. However, in the generic monitoringsystem developed here, bunches will have to beinspected for pests besides P. callosus. Makingrecommendations for P. callosus based on bunchdamage will exclude the need to monitor shoots inaddition to bunches, thereby saving time. Addition-ally, shoot and bunch damage occurred at the sametime (De Villiers and Pringle 2007). Therefore, shootdamage cannot be used as an early warning methodfor bunch damage.
The sampling protocol developed for P. ficus(Walton 2003) has now been extended to include allthe major table grape arthropod pests (see Table 3).Although recommendations could not be made forF. occidentalis, the sampling system can still beused to gain information on the infestation status ofthis pest in vineyards. Information gained here,regarding the number of sampling units needed and
recommendations on when to apply control mea-sures, can now be used in a monitoring system for thetable grape pest complex. See also De Villiers andPringle (2007) for information on when to startmonitoring for different arthropod pests and also tocombine the control recommendations of the presentstudy with the use of traps and bands to enhance theefficiency of the monitoring system.
Acknowledgements
The financial assistance of the National Research Founda-tion (NRF) towards this research is hereby acknowledged.Opinions expressed and conclusions arrived at, are those ofthe authors and are not necessarily to be attributed to theNRF. The authors would also like to thank the owners andmanagers of the farms Klipheuwel, Boplaas and De VleiBoerdery for availability of vineyards that were used in thestudy and co-operation during the study, the DeciduousFruit Producers’ Trust (DFPT) for funding the research andthe following personnel from the Department of Conserva-tion Ecology and Entomology, University of Stellenbosch:Dr JM Heunis for help with identification of predatorymites and Juanita Liebenberg for helping with countingmites.
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