Quantifying trade-os between pest
sampling time and precision in commercial
IPM sampling programs
E.M. Cullen
a,*, F.G. Zalom
a, M.L. Flint
a, E.E. Zilbert
b aDepartment of Entomology, University of California, One Shields Avenue, Davis, CA 95616, USAbDepartment of Agronomy and Range Science, University of California, Davis, CA 95616, USA
Received 13 January 2000; received in revised form 30 July 2000; accepted 7 August 2000
Abstract
A key component of Integrated Pest Management (IPM) in agricultural systems is ®eld monitoring for pests prior to reaching a management decision. The study objective was to quantify a common design constraint of commercial IPM sampling programs: the trade-o between sampling time and accuracy of the resulting pest management decision. Using con-sperse stink bug (Euschistus conspersus Uhler) in processing tomatoes (Lycopersicon escu-lentumMiller), this paper develops a sample size model based on pest spatial distribution and response from a pest control advisor survey. Results identify a gap between pest sampling programs developed by University researchers and sampling methods adopted at the com-mercial ®eld level. The sample size model permits variation in pest treatment thresholds and treatment decision accuracy. Conclusions support use of this model to satisfy commercial time constraints while maintaining a reliable level of sampling precision. This study introduces a novel approach to transferring IPM sampling programs from University research to com-mercial ®eld adoption.#2000 Elsevier Science Ltd. All rights reserved.
Keywords:Integrated Pest Management; Pest sampling; Sample size; Decision making
0308-521X/00/$ - see front matter#2000 Elsevier Science Ltd. All rights reserved. P I I : S 0 3 0 8 - 5 2 1 X ( 0 0 ) 0 0 0 3 8 - X
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1. Introduction
A key component of Integrated Pest Management (IPM) in production agri-culture systems is ®eld monitoring for pests prior to reaching a management decision (Stern et al., 1959; Flint and van den Bosch, 1981; Kogan, 1988, 1998; Prokopy et al., 1994). Insect pest sampling programs quantify pest population abundance. Only after an insect pest is known to be present and an actual or potential economic threat should a decision be made to apply insecticide(s).
Pest ®eld distribution is an important determinant of the number of samples required for a precise population estimate. An insect pest with uniform ®eld dis-tribution lends itself to a random sampling program because every sample will have an equal chance of recovering the pest. For this reason, uniformly distributed pests require a smaller sample size than pests with an aggregate distribution. An aggre-gated pest population is better sampled using a strati®ed random sampling program (Dent, 1991), where the ®eld is divided into sections based on previous knowledge of pest distribution (e.g. particular locations on plants or areas within a ®eld). Strati-®ed random sampling can only be properly implemented when proportions of the pest population in dierent ®eld sections are known (Dent, 1991).
The number of samples drawn from an insect pest population relates directly to the precision of the population estimate. Too few samples will reduce the reliability of the estimate; however, more samples will increase the cost of the sampling pro-gram, where cost is measured in terms of time, labor, equipment or ®nancial outlay (Dent, 1991; Scheaer et al., 1986). Choosing the number of sample sites within a ®eld involves some risk. If a decision is made to apply insecticide, and treatment is not economically necessary, ®nancial and biological costs of insecticide application will be incurred. Growers must also accept a level of risk when a decision not to apply insecticide is made that treatment will be necessary and economic crop loss due to pest damage will occur.
Agricultural scientists have two options: (1) they can develop pest monitoring pro-grams using sampling patterns and techniques appropriate for commercial use; or (2) after developing a research monitoring program, they can modify it for commercial use (Dent, 1991). IPM sampling programs developed by a combination of basic and applied University research are reliable in yielding accurate pest-management decisions. The problem, with regard to IPM implementation, is that University pest-sampling protocols are often too time consuming to be practical under commercial ®eld conditions. For example, the need to monitor thousands of hectares and coor-dinate IPM sampling and treatment timing with equally important crop production priorities (e.g. irrigation and cultivation schedules) means that growers and pest control advisors are often left to adapt University monitoring programs to meet their commercial time constraints (Cullen, 1999).
population estimate; and (3) introduce a novel approach to transferring IPM sam-pling programs from University research to commercial ®eld adoption.
2. Materials and methods
2.1. Case study: consperse stink bug in processing tomatoes
Consperse stink bug (Euschistus conspersusUhler) is a key economic insect pest of processing tomatoes (Lycopersicon esculentumMiller) in the Sacramento Valley and Delta areas of California where about 50% of the state's crop is produced (Uni-versity of California, 1998). Stink bugs feed with piercing-sucking mouthparts, injecting a toxin that lique®es fruit tissue to aid digestion. Feeding scars appear as yellow to white areas on the red fruit surface, with a white corky mass of tissue beneath the peel. Stink bugs are capable of transmitting a pathogenic yeast ( Nema-tospora sp.) between infected and uninfected fruit if the pathogen is carried on the bug's mouthparts when feeding. Insecticide treatments are routinely applied for stink bug control in processing tomatoes (Homan et al., 1987).
Several aspects of the stink bug pest problem in California processing tomatoes qualify this system as a case study quantifying the dynamic relationship between sampling eort and precision in commercial production systems:
1. Stink bug sampling methods are established for processing tomatoes. Growers and their advisors have adopted a canopy shake sample method for stink bug (University of California, 1998) and indicated interest in utilizing a commer-cially available pheromone trap as a monitoring tool (Cullen, 1999).
2. Research has established a correlation between stink bug population density in processing tomato ®elds and fruit damage at harvest (Zalom et al., 1997b). However, stink bug treatment decision making is complicated by lack of a ®rm economic injury level because treatment thresholds vary with crop end use (e.g. whole peel vs. paste). Although processing companies are known to have low tolerance for stink bug feeding damage on tomatoes, none have established an ocial tolerance level.
3. California pest control advisors (PCAs) are a group with valuable experience implementing University-developed pest sampling programs at the commercial ®eld level. Licensed by the state of California, PCAs are the most signi®cant source of pest control information for processing tomato growers (Flint and Klonsky, 1985). PCAs monitor ®elds, obtain pest population estimates, deter-mine treatment thresholds and provide growers with written recommendations for pesticide use.
2.2. Survey of PCAs
commercial level. The mailing list was compiled from the California Agricultural Production Consultants Association (CAPCA) membership list. A Yolo County Cooperative Extension farm advisor reviewed the CAPCA membership list, added non-member PCAs and eliminated those not working in processing tomatoes. Sur-veyed PCAs were employed by farm supply companies or as independent con-sultants. Respondents voluntarily answered the questionnaire entitled, ``Developing a stink bug monitoring program acceptable to processing tomato PCAs''.
The survey method was adapted from the Total Design Method (Dillman, 1978). In March 1998 a cover letter, questionnaire and self-addressed, stamped return envelope were mailed to all 42 Yolo County PCAs identi®ed as working in process-ing tomatoes. Two weeks later, a follow-up cover letter, replacement questionnaire and self-addressed stamped return envelope were mailed to non-respondents only. Two questionnaires were eliminated from the initial PCA population of 42 after these individuals responded that they no longer worked in processing tomatoes, thus reducing the total survey population to 40 individuals.
Overall survey response rate was tabulated as the percentage of completed and returned questionnaires out of the total number of questionnaires mailed. Response rate for each option per multiple choice question was tabulated as a percentage of the total number of respondents answering that particular question.
Respondents were asked to identify how much time they spend per ®eld sampling for all pests (insects, weeds and pathogens) and for stink bugs in particular; factors leading them to spend more, or less time sampling; preferred stink bug sampling time intervals throughout the season; and outcomes that would motivate them to spend additional time sampling for stink bug.
Assuming that no one can be 100% accurate at insecticide treatment decision making, and taking into account individual grower expectations, PCAs were asked to indicate the level of accuracy they expect from a stink bug sampling program in terms of whether or not an insecticide treatment is economically justi®ed. Respondents were instructed to rate their preferred level of certainty that a decision to treat is correct and crop damage prevented. On a separate rating scale, respondents indicated their preferred level of certainty that a decision not to treat was correct and insecticide treatment costs saved. Respective rating scales ranged from 50 to 95% certainty. The higher the percentage indicated, the more certain a respondent expected to be that a decision to treat, or not to treat, for stink bug is economically justi®ed. PCAs were then asked to indicate how much time they are willing to spend sampling for stink bugs on an individual ®eld basis to meet their expected treatment decision certainty.
2.3. Field sampling
Six commercial processing tomato ®elds, each 32±40 ha in size, were sampled in the Sacramento Valley (two ®elds in 1996, four ®elds in 1997). Fields were sampled weekly from mid-June ¯ower initiation to harvest (August±September). Samples quanti®edE. conspersuspopulation abundance and ®eld distribution throughout the season.
Tomato plants were sampled using the canopy shake sample (Zalom et al., 1998) recommended by the University of California. One shake sample consisted of pla-cing a 45.735.6 cm2plastic cafeteria tray on the bed beneath a plant and shaking the plant ®ve times over the tray to dislodge stink bug nymphs and adults from the canopy, as well as scanning the ground under and around the tray for stink bugs. Stink bugs dislodged from the canopy were recorded as number of bugs per tray.
In 1996, there were 22 sample sites per ®eld (Fig. 1). In 1997, 16 sample sites were located in each ®eld (Fig. 1). At each site, ®ve shake samples were taken with 3 m between samples within the same and adjacent rows. Data collected from each shake sample included number of stink bugs per tray, genus and species, developmental stage, sex and presence or absence of reproductive diapause as indicated by ventral coloration (Toscano and Stern, 1980). Sampling, at the level of detail reported in this study, required an average of 4.0 min per sample site.
A more feasible sampling routine for commercial implementation was estimated at 2.5 min (one to two shake samples) per site. Although a sample size of one to two shake samples is less accurate than ®ve shake samples per site, this estimate was based on the assumption thatE. conspersuspopulation densities approaching those where control action would be taken will be similarly detectable by the more com-mercially acceptable, 2.5-min sample size.
2.4. Sample size model
Taylor's a and b coecients, taken from Taylor's Power Law (Taylor 1961), describe the relationship between variance and mean (S2a xb) for individuals
distributed in a natural population (Southwood, 1978). For each ®eld, the mean and variance of stink bugs caught in ®ve shake samples per site from all sample sites within a ®eld were determined for each weekly sampling date. Zero counts were omitted from mean and variance calculations. Taylor'saandbcoecients were cal-culated for each ®eld by ln±ln linear transformation of the mean-variance data, wherebis the slope of the transformed data, anda equals the antilog of the trans-formed intercept (Taylor, 1961; Wilson, 1985).
An equation for estimating pest sample size that includes treatment decision cer-tainty was developed by Karandinos (1976). Ruesink (1980), Wilson and Room (1982) and Wilson (1985) incorporated Taylor's Power Law into Karandinos' equation to form the sample size model used in this study:
nt2=2Dÿ2 a xbÿ2:
The model contains both variable and constant factors. The variable factors are:
1. n=sample size (sample sites per ®eld). 2. t2
=2=standard normal variate for a two-tailed con®dence interval.
Commer-cially, this is the percent certainty growers and PCAs expect from their stink bug treatment decisions.
3. x=treatment threshold; the mean number of stink bugs per sampling tray at which treatment is initiated. Zalom et al. (1997b) found that ®ve stink bugs per 2 m of row (0.33 bug per tray) results in 5% damage, the maximum damage acceptable to many growers.
This study variedxto simulate two commercial scenarios:
I. x=0.33 bug per tray treatment threshold for whole peel canning ®elds where stink bug feeding damage is readily apparent in the processed pro-duct. If growers require a highly precise stink bug population estimate to assure minimal fruit damage, the number of sample sites per ®eld increases. II.x=0.50 bug per tray treatment threshold for paste, sauce and dice ®elds where stink bug damage is less apparent in the processed product. If grow-ers can tolerate a less precise stink bug population estimate and a relative increase in fruit damage, the number of sample sites per ®eld decreases. 4. Dxÿxadj=xadj; a ®xed proportion of the treatment threshold representing a
range of accuracy approximating the true stink bug population mean. The value of xadj is the treatment threshold (x) adjusted by a percentage value. For example, choosing a 10% accuracy range around a treatment threshold of 0.33 bug per tray, x=0.33ÿ(0.330.10)=0.297. Then D=(0.33ÿ0.297)/ 0.297=0.11.
con®dence interval narrows and the number of sample sites per ®eld necessary for an accurate treatment decision increases.
Constant factors in this model are:
5. Taylor's coecients,aandb. Taylor's coecients were derived from 2 years of
E. conspersus mean and variance sampling data from six commercial ®elds. The coecients,a andb, are population statistics representing a species' ®eld distribution and degree of aggregation.
3. Results
3.1. Survey of PCAs
The questionnaire, ``Developing a stink bug monitoring program acceptable to processing tomato PCAs'', yielded a response rate of 68%, with 27 of 40 ques-tionnaires completed and returned.
Eighty-nine percent of respondents (37 and 52%, respectively) indicated they spend 15 or 30 min in a typical (e.g. 40 ha) processing tomato ®eld per visit. One ®eld visit included a combination of insect, weed and pathogen sampling; soil ferti-lity programs; monitoring crop stand, fruit ripening and other agronomic factors as appropriate. It is important to note variation in total sampling time per ®eld as 100% of respondents adjust sampling time according to pest pressure and other commercial production variables (Table 1).
Seventy-eight percent of respondents (22 and 56%, respectively) allocate 10 or 15 min, as a proportion of total ®eld monitoring time, to stink bug sampling when necessary. However, 67% (18 of 27) of respondents do not spend an equal amount of time sampling for stink bug in each of their processing tomato ®elds. Respon-dents answering this question selected commercial production variables causing them to spend more time in some tomato ®elds than in others: a ®eld history of stink bug damage (52%); ®elds intended for whole peel processing (41%); degree of
Table 1
Factors leading pest control advisors (PCAs) to increase or decrease total sampling time per ®elda
Factors leading PCAs toincreasetotal sampling time per ®eld
Factors leading PCAs todecreasetotal sampling time per ®eld
Early season during stand establishment Interval from stand establishment to fruit set Late season during fruit development
``High'' insect pressure, especially fruitworm, lygus bug and stink bug
``Low'' insect pressure
Fruit damage apparent. Further sampling necessary to quantify pest abundance Weather conditions present disease threat
Field size 40.5 ha or larger Field size under 40.5 ha
grower tolerance to stink bug presence (37%); processing company restrictions on insecticide use (26%) and ®eld size (22%).
PCAs were asked whether they prefer one to two time-intensive stink bug samples per ®eld, or several less time-intensive stink bug samples per ®eld, throughout the growing season. Eighty-eight percent of respondents (44 and 44%, respectively) chose several 15-min stink bug samples per ®eld or multiple ``quick checks'' (<15 min) per ®eld with no intensive time requirements throughout the growing season. Twelve percent of respondents were willing to conduct two 30-min stink bug sam-ples per ®eld throughout the season. Zero respondents selected the option of a one-time 60-min stink bug sample per ®eld throughout the season. However, Table 2 shows how PCAs responded to potential outcomes as motivating factors to increase stink bug sampling time per ®eld.
Fifty-six percent of respondents rated stink bug as a major pest problem in Sacramento Valley processing tomato production, while 44% rated stink bug a minor problem.
Eighty-®ve percent of respondents are currently using some type of stink bug sampling technique(s). Table 3 presents detailed stink bug sampling data collected by these respondents (respondents could choose more than one option). Table 4 presents PCA ranking of sampling data most likely to warrant a stink bug treatment (respondents could choose more than one option).
Mean results from 25 responding PCAs were similar for both possible stink bug treatment decisions, with respondents expecting 86 and 84% certainty that a stink bug treatment or non-treatment decision is correct, respectively. Eighty percent of respondents (36 and 44%, respectively) were willing to spend 15 and 30 min per ®eld to achieve these levels of decision certainty. Sixteen percent of respondents were will-ing to spend more than 30 min per ®eld, while 4% will spend less than 15 min per ®eld.
Table 2
Number of respondents who selected the following outcomes as motivating factors to increase stink bug sampling time per ®eld (respondents could choose more than one outcome)
Outcomes as potential motivating factors to increase stink bug sampling time per ®eld
No. of respondents (n=27)
No. of pest control advisors willing to spend additional time to achieve outcome
+<15 min +15 min +30 min +>30 min
Increase certainty that stink bug
Eliminate an insecticide treatment 21 4 11 5 1 Limit treatment to part of a ®eld,
rather than treating the whole ®eld
19 6 9 3 1
None of the above would motivate me to increase stink bug sampling time
Eighty-nine percent of respondents indicated they would implement a stink bug sampling program based on pheromone trap catch if it allowed them to treat the more susceptible nymphal stages of this pest.
Given the cost of stink bug pheromone traps which can be used over multiple seasons and lures which remain attractive to male and female E. conspersus for 4 weeks ($13 and $1.20 each, respectively), respondents indicated they would spend $41.17 per ®eld (S.D. $29.78) to implement a pheromone-based sampling program in problem ®elds.
Sixty-nine percent of respondents indicated that the cost of pheromone traps and lures would be covered by the company they work for, 19% thought their grower clients would cover this monitoring cost, while 4% would pay out-of-pocket and 8% did not know who would cover the cost of pheromone traps and lures.
3.2. Field sampling
Taylor's a coecients ranged from 1.227 to 7.832 for all six ®elds, respectively, and combined. Taylor'sb coecients ranged from 1.512 to 2.053 for all six ®elds, respectively, and combined (Table 5).
Table 4
Response rates of surveyed pest control advisors regarding sampling information most likely to warrant a stink bug treatment (respondents could choose more than one option)
Sampling information most likely to lead to a stink bug treatment Percent responding (n=27)
Fruit damage observed 81
Nymphs obtained in shake samples 78
Adults obtained in shake samples 74
Stink bug egg masses observed on fruit or leaves 30
Adults caught in pheromone traps 26
First adult observed in canopy 19
First nymph observed in canopy 15
Stink bugs detected in surrounding ®elds or border vegetation 11 Table 3
Stink bug sampling data collected by 85% of respondents currently using some type of stink bug sampling technique(s) (respondents could choose more than one option)
Stink bug sampling data Percent responding
(n=27)
Number of stink bug damaged fruit 70
Stink bug(s) observed on tomato crop while sampling for other pests 63
Number of stink bugs per shake sample 59
3.3. Sample size model
Mean response showed that PCAs expect approximately 85% certainty on either side of a stink bug treatment decision: 86% certainty that a decision to treat is cor-rect and 84% certainty that a decision not to treat is corcor-rect.
To achieve 85% treatment decision certainty (t2
a/2), using a treatment threshold (x) of 0.33 bug/tray and varying the accuracy range (D) about the true stink bug population mean from 10 to 20 to 30%, required 348, 67 and 23 sample sites (n) per ®eld, respectively. Increasingxto 0.50 bug/tray and varying the accuracy range (D) from 10 to 20 to 30% required 297, 57 and 19 sample sites (n) per ®eld, respectively (Table 6).
Based on 2.5 min per sample site, sampling a processing tomato ®eld for 15 min (n=6 sample sites per ®eld) at all combinations ofx(0.33 and 0.50 bug/tray) andD
(10, 20 and 30%) provided less than 50% stink bug treatment decision certainty (t2 a/2). A 30-min ®eld sample (n=12 sample sites per ®eld) using either treatment threshold and varyingDfrom 10 to 20% provided less than 50% accuracy, while an accuracy range of 30% provided 50±60% treatment decision accuracy.
Sampling a processing tomato ®eld for 60 min (n=24 sample sites per ®eld), using either treatment threshold, and varying D from 10 to 20% provided less
Table 6
Number of sample sites (n) per ®eld required to achieve stink bug treatment decision certainty of 85%, as expected by pest control advisor mail survey respondents
t2
/2(decision certainty) D(accuracy range) x(treatment threshold) n(sample sites)
85% 0.11 0.33 bug/tray 348
85% 0.25 0.33 bug/tray 67
85% 0.43 0.33 bug/tray 23
85% 0.11 0.50 bug/tray 297
85% 0.25 0.50 bug/tray 57
85% 0.43 0.50 bug/tray 19
Table 5
Taylor's coecients forEuschistus conspersusnymphs and adults sampled from six ®elds over 2 yearsa
Field (year) a b R2 Number of observations
withx>0
Batavia (1996) 1.227 1.512 0.924 8
Pedrick (1996) 2.598 1.831 0.949 10
Davey (1997) 2.486 1.857 0.790 9
Cooper (1997) 1.916 1.534 0.924 8
Gill (1997) 2.671 1.918 0.867 11
Viguie (1997) 7.832 2.053 0.984 6
Combined (1996±1997) 1.843 1.613 0.893 52
than 50% treatment decision accuracy. Only a 60-min ®eld sample approached PCA expectations with 24 sample sites per ®eld, x=0.33 or 0.50 bug/tray and a wider con®dence interval (D=30%) providing 80±90% stink bug treatment decision certainty.
4. Discussion
Taylor's a andb values (Table 5), all greater than 1, indicate an aggregated dis-tribution of E. conspersus in processing tomato ®elds. Taylor (1961) and Taylor et al. (1978) interpreted coecientaas a computing factor dependent upon sample size and method of population variance estimation, designating coecientbas the true population statistic or ``index of aggregation'' with a continuous gradient from near regular distribution (b<1), through random (b=1) to aggregated (b>1).
Several authors dispute Taylor's interpretation, however, and provide data sup-porting bothaandbcoecients as equally representative population statistics of a species' aggregation index (Banerjee, 1976; Wilson et al., 1983, 1984; Wilson and Room, 1982, 1983). When the a coecient is equal to or greater than 1, a corre-sponding b value greater than 1 con®rms an insect's clumped ®eld distribution (Zalom et al., 1985).
Dispersion patterns of arthropod populations in agricultural systems commonly exhibit some degree of aggregation (Taylor, 1984). The aggregated distribution observed for stink bugs in processing tomatoes, especially in ®eld sides/corners adjacent to stink bug overwintering habitat, is likely due to bug immigration from these nearby alternate host habitats, and may indicate a reduced tendency for within-®eld dispersal possibly in¯uenced byE. conspersus aggregation pheromones as described by Aldrich et al. (1994; Zalom et al., 1997a).
Our result of an aggregated ®eld dispersion pattern supports implementation of a strati®ed random sampling program for stink bugs in processing tomatoes where the ®eld is divided into sampling sections based on ®eld history of stink bug population density and corresponding fruit damage at harvest. For example, in a processing tomato ®eld with a history of stink bug damage along one side adjacent to stink bug overwintering habitat, a PCA could obtain a conservative population estimate by limiting sampling eorts to this ®eld section. Time constraints may prevent sampling an entire tomato ®eld. Based on our ®eld distribution data, it is reasonable to assume that a reliable population estimate can be obtained by directing sampling eorts to ®eld sections where stink bugs historically aggregate.
Raising the treatment threshold (x) in the model from 0.33 to 0.50 bug/tray reduces the number of sample sites (n) per ®eld necessary to obtain a given level of certainty (t2
/2). A higher treatment threshold in the model represents a less con-servative action level in the ®eld. Likewise, increasing the value of Din the model reduces the number of sample sites per ®eld. A higherDvalue in the model repre-sents a wider commercial con®dence interval in the ®eld: the PCA's treatment deci-sion will still fall within a desired certainty level, but there is more risk tolerance for an inaccurate decision than at lowerDvalues.
The sample size model, by incorporating pest spatial distribution data and PCA survey response, reveals the commercial expectation of a highly precise pest popu-lation estimate with minimal sampling time as unfeasible. A processing tomato PCA who spends no more than 15 min per ®eld will sacri®ce treatment decision certainty. However, if a PCA is willing to spend more time sampling or, when possible, toler-ate more risk (e.g. increased treatment threshold and/or wider con®dence interval about the true stink bug population mean) then a reliable commercial stink bug treatment decision is possible.
Survey respondents introduced the commercial assumption that high stink bug treatment decision certainty, and a correspondingly large sample size, are not eco-nomically necessary in all processing tomato ®elds. Sixty-seven percent of respon-dents currently reduce sampling time in some ®elds to allow increased sampling time in ®elds requiring a more accurate stink bug population estimate.
Processing company end use contributes to how PCAs determine sample size. Survey respondents indicated they spend more time, with a presumably larger sam-ple size, in ®elds contracted for whole peel canning. Stink bug feeding damage to whole peel fruit is of great concern to processors. Equivalent damage to fruit con-tracted for paste processing may be of little concern to canneries since fruit damage is not apparent in the processed product. Because tolerance for stink bug damaged fruit in the whole peel market is quite low compared to the paste market, whole peel ®elds require more precise stink bug population estimates. Results of this study con®rm that a relatively large sample size is necessary to achieve a precise popula-tion estimate and make accurate treatment decisions.
Low stink bug tolerance on the part of tomato processing companies and grower apprehension that processors will reject whole peel loads if stink bug damage is too high are probably accurate perceptions on the part of PCAs. However, stink bug damage tolerance on the part of processing companies is not known. Thus, grower and PCA perceptions may lead to stink bug insecticide applications based on exag-gerated avoidance of processor rejection in lieu of a true economic injury level.
Eighty-nine percent of respondents indicated interest in trying a commercially available E. conspersus pheromone trap if this monitoring tool would help focus sampling eort on the more susceptible nymphal stages. Furthermore, interest in a pheromone-based stink bug sampling program targeting nymphal hatch is strong enough that respondents estimated $41.17 (S.D. $29.78) being spent per ®eld on pheromone traps and lures to achieve this goal.
early season (June) pheromone trap catch in tomato ®elds as a degree-day bio®x to detect nymphal hatch of progeny fromE. conspersus adults invading tomato ®elds earlier in the season from adjacent overwintering sites. The more labor-intensive shake samples could be delayed up to the point in the season when pheromone trap catch and the phenology model predict nymphal hatch. At that time, a sucient number of shake samples could be taken in strati®ed ®eld sections to assess popu-lation abundance with a given degree of certainty. When treatment is necessary, ecacy of reduced-risk insecticides is expected to increase against the more suscep-tible stink bug nymph stages. These `softer' insecticides are ineective against adults which are currently killed most eectively by the organophosphate, methamidophos (Monitor 4 [Spray], Valent USA Corporation, Walnut Creek, CA, USA) and (Mon-itor 4 [Liquid Insecticide], Bayer Corporation, Kansas City, MO, USA).
Although our results dier from PCA respondent expectations, this study sup-ports the IPM assumption that reliable treatment decisions at the commercial ®eld level require relatively time-consuming sample sizes. The sample size model inte-grates PCA expectations of treatment decision certainty and permits sampling pro-gram variation to mitigate the con¯ict between a cost-eective sample size and reliability of the pest population estimate.
While a pheromone-based ®eld monitoring program forE. conspersusin process-ing tomatoes can help PCAs focus samplprocess-ing eorts and treatment decisions to the more susceptible nymphal stages, such a program would not eliminate the need for shake sampling if treatment decisions are to be based on quanti®cation of popula-tion abundance. PCA expectapopula-tions to the contrary may lead them to abandon stink bug ®eld sampling prior to insecticide application if their desired treatment decision certainty cannot be met within their sampling time frame.
An overall PCA survey response rate of 68% shows that Yolo County processing tomato PCAs are eager to achieve reliable stink bug population estimates through ®eld sampling prior to making treatment decisions. In fact, respondents would be willing to spending additional time per ®eld if it allowed them to increase treatment decision certainty, eliminate economically unnecessary treatments and/or spot treat ®elds according to stink bug ®eld dispersion patterns (Table 2). Although the majority of survey respondents currently maintain a 15±30 min per ®eld time limit on stink bug sampling, they could be motivated to spend a total of 30±60 min per ®eld in a proportion of their ®elds (Table 2).
5. Conclusion
The sample size model developed forE. conspersusin processing tomatoes quan-ti®ed a trade-o between sampling time and precision common to commercial IPM sampling programs in production agriculture. In contrast to case study PCA expec-tations, the model established that one or two 45±60 min samples are required to reliably estimate stink bug population density and select treatment thresholds.
precision of the pest population estimate. For example, increased sampling time in whole peel ®elds could be o-set by decreased sampling time in paste ®elds. Pest sampling protocols incorporating this trade-o model could aord PCAs high treatment decision certainty with a more streamlined pest sampling eort.
It is feasible to apply the sample size model, as discussed in this case study, to insect pests in crops other than processing tomatoes. Integrating pest spatial dis-tribution data with expectations of commercial ®eld practitioners is a novel systems approach with the potential to improve transfer of IPM sampling programs from University research to commercial ®eld adoption.
Acknowledgements
We thank Cerrell Rivera and Marisela Cerda for their ®eld assistance; Dr. Lloyd T. Wilson for discussion of Taylor's Power Law; Gene Miyao for ®nalizing the PCA survey mailing list; and two anonymous reviewers whose constructive comments improved the ®nal manuscript. This study was supported by grants from the Cali-fornia Tomato Research Institute, the CaliCali-fornia League of Food Processors and the California Department of Pesticide Regulation. This paper was submitted in partial ful®llment of the MS degree in Plant Protection and Pest Management at the University of California, Davis.
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