Tufted Apple Bud Moth (Lepidoptera: Tortricidae ... - BioOne

HORTICULTURAL ENTOMOLOGY

Tufted Apple Bud Moth (Lepidoptera: Tortricidae) Management Model for Processing Apples Based on Early Season Pheromone Trap Capture S. P. ROBERTSON,1 L. A. HULL,2

AND

D. D. CALVIN3

J. Econ. Entomol. 98(4): 1229Ð1235 (2005)

ABSTRACT Sixteen years of archived tufted apple bud moth, Platynota idaeusalis (Walker), trap capture data were compared with archived fruit injury data collected at the Penn State University Fruit Research and Extension Center to deÞne the relationship of trap capture to fruit injury. Pheromone trap capture until 15 June was the best predictor of fruit injury at harvest. Using the regression equation of fruit injury on early season trap capture, and other assumptions about insecticide cost and fruit yield, a management model was developed for apple growers in the Mid-Atlantic region. When the model was tested on archived trap capture and fruit injury data, the results indicated that a grower would lose money on average by always treating and save money on average by never treating. By using the model, a grower could expect to save more money than by never treating. The model showed sensitivity to fruit price, insecticide price, and fruit yield. KEY WORDS integrated pest management, apples, action thresholds, predictive model, Platynota idaeusalis

CONTROL MEASURES AGAINST THE tufted apple bud moth, Platynota idaeusalis (Walker), are generally based on established schedules or degree-day calculations to target early instars (Krawczyk et al. 2000). Growers want to avoid unnecessary insecticide applications, but they lack the time and expertise to determine whether the population pressure of the tufted apple bud moth warrants spraying in any given year. A decision model based on sex pheromone trap capture could help growers decide whether to apply control measures. Researchers have pursued this concept in the past, but few practical models have resulted. Riedl and Croft (1974) found a correlation between pheromone trap catch of male codling moth, Cydia pomonella (L.), and apple injury, but the relationship was established using only one yearÕs data. Vickers et al. (1998) found a highly signiÞcant relationship between pheromone trap catch and fruit injury for codling moth under mating disruption conditions when no insecticide was applied but not in mating disruption orchards when azinphosmethyl or fenoxycarb were applied. For the tufted apple bud moth, Knight and Hull (1989) found that early season pheromone trap catch correlated well with total season fruit injury for ÔYorkingÕ and ÔDeliciousÕ but not for ÔGolden DeliciousÕ. The 1 Plant Epidemiology and Risk Analysis Laboratory, USDAÐAPHISÐ PPQ, Raleigh, NC 27606. 2 Penn State Fruit Research and Extension Center, P.O. Box 330, Biglerville, PA 17307. 3 Department of Entomology, Penn State University, 501 ASI Bldg., University Park, PA 16802.

regression equations of fruit injury on trap capture, however, were not consistent from one year to the next. Krawczyk et al. 1998) found that moth capture in traps baited with 10 mg of tufted apple bud moth pheromone (during weeks 2 and 3 after the start of moth ßight in the spring) was a good predictor of tufted apple bud moth fruit injury in commercially sprayed orchards. The regression equations suggested by Knight and Hull (1989) and Krawczyk et al. (1998) were constructed from trials in commercial orchards, where the relationship between trap capture and fruit injury was almost certainly affected by the insecticides applied by growers during the study periods. Ideally, a management model should be based on the fruit injury that can be expected when no insecticides are used. To Þnd this relationship, we examined historical data sets from The Pennsylvania State University Fruit Research and Extension Center in Biglerville, PA, where fruit injury in untreated portions of blocks used for insecticide trials, and pheromone trap capture for the whole orchards have been recorded for the past 20 yr. This study sought to establish a relationship between pheromone trap capture and fruit injury by the tufted apple bud moth, and from this relationship, develop a simple model that growers could use to make management decisions. Within the larger objective, the study tested the relationship of degree-day (DD) accumulation to total pheromone trap catch and attempted to determine the best way to interpret trap capture for predictive modeling and pest management.

0022-0493/05/1229Ð1235$04.00/0 䉷 2005 Entomological Society of America

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Fig. 1. Schematic diagram of the major components and their relationship in the tufted apple bud moth decision model.

Materials and Methods Relationship of Trap Capture to Fruit Injury: Archived Data. Tufted apple bud moth trap catch has been recorded for ⬎20 yr in various apple blocks a the Pennsylvania State University Fruit Research and Extension Center. The apple blocks were used for insecticide efÞcacy trials and in most years fruit injury due to tufted apple bud moth was estimated by sampling at harvest. For the insecticide efÞcacy trials, the blocks were divided into small treatment units (i.e., 2Ð3 or 12Ð15 trees) that were replicated several times. One of the treatments was always a “control” treatment, where no insecticide was applied. The blocks ranged in size from 1.2 to 3.0 ha, and each year tufted apple bud moth pheromone traps (wing traps baited with rubber septa containing 10 mg pheromone) were placed at the center of each block for general monitoring purposes, one or two traps based on orchard size. The traps were checked weekly and liners were replaced every 4 wk unless moth capture was ⬎50 moths in any week. The rubber septa were replaced every 4 wk. For this study, data from 1981 to 1999 were used, where trap capture and fruit injury data were taken in the same orchard blocks. By using the tufted apple bud moth fruit injury data from the check treatments, we were able to analyze the correlation between pheromone trap catch in each block and fruit injury occurring on the unsprayed trees in those blocks. The percentage of total Þrst brood moth ßight was graphed against the degree-day (base 7.2⬚C) (David et

al. 1989) accumulation from a bioÞx (Þrst sustained trap catch) for each of the study years, to determine whether accumulated degree-days reliably predicted percentage of ßight completed. Simple linear regression models (Minitab, Inc. 1998) were created of fruit injury on trap capture. A variety of trap capture interpretations were used to Þt models, including cumulative trap catch based on degree-day accumulation, trap catch until 3 wk after bioÞx, and trap catch until a speciÞc calendar date. Separate regression equations were calculated for ÔGolden DeliciousÕ and ÔYorkingÕ apples. Management Model. A management model similar to those of Pontius et al. (1984), Calvin et al. (1986), and Calvin et al. (1988) was constructed as a tool for apple growers to make pest management decisions based on the potential economic impact of tufted apple bud moth. The model structure had two major components: damage-loss estimation and economic analysis (Fig. 1). The model inputs were trap capture, yield, control cost, crop value, and proportional control. All other parameters, other than trap capture, were held constant to illustrate the behavior of the model relative to trap capture. The damage-loss estimation component was constructed as a two-step process: potential fruit injury and total estimated price loss from the fruit injury. To calculate the potential fruit injury, a relationship had to be established between trap capture and fruit injury. Using historical data from archives, linear regression models were Þtted to the total moth capture

August 2005 Table 1. class

ROBERTSON ET. AL.: TUFTED APPLE BUD MOTH MANAGEMENT MODEL

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Apple size categories, average proportion of fruit falling into each category, and the expected price received for each size

A

B

C

D

E

F

G

H

1

Apple size (cm) ⬎7 6.4 5.7

Price ($/cwt kg) 21.10 16.67 8.33 sum

Weighted Avg (prop ⫻ price) ($) 12.66 5.00 0.83 18.49

U.S. #1 ($) 0.11 0.04 0.01

U.S. #2 ($) 0.01 0.01 0.00 sum

Culls ($) 0.00 0.00 0.00 0.18

Price with injury ($)

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Proportion apples in each category 0.6 0.3 0.1

E8:G8 H8 D2:D4 D5 E2:E4 F2:F4 G2:G4 G5 H5

Formulae table Data Input variable ⫽ B2*C2 ⫽ SUM(D2:D4) ⫽ D2*$H$8*$E$8 ⫽ D2*$H$8*$F$8 ⫽ D2*$H$8*$G$8 ⫽ SUM(E2:G4) ⫽ D5-G

TABM injury classes 0.837 0.102 0.032

18.31 Injury 0.01

The sum of the weighted averages (D5) is the average price a grower could expect per cwt (kilograms) of processing apples. Given a level of injury (H8), 83.7% (E8) of the injured apples will not be downgraded, 10.2% (F8) will be downgraded to U.S. #2, and 3.25% (G8) will be culled. The price with injury (H5) is the price a grower could expect given 20% (H8) tufted apple bud moth (TABM) injury.

in pheromone traps (independent variable) and percentage fruit injury at harvest (dependent variable). Various methods of recording trap catch were evaluated (catch to a calendar date, catch to a given number of accumulated degree-days), and the statistical model that showed the best Þt was selected to estimate potential fruit injury. Using the estimated percentage of fruit injury calculated in the Þrst step of the damage-loss estimation component, the expected total price loss was then calculated. The relationship between fruit injury and expected total price loss was derived through a series of intermediate steps. The Þrst step partitioned a typical crop into size categories and then assigned a proportion of the crop and price to each category (Table 1). The average yield was estimated at 258 cwt kg/ha, based on averages for Pennsylvania from 1992 to 1999 (Pennsylvania Agricultural Statistics Service 2000). By multiplying the proportion of apples in each category by the expected price ($/cwt kg), a weighted value was assigned to each category. Fruit price was taken from the average price received for processing fruit in Adams County, Pennsylvania, between 1996 and 1998 (Harper et al. 2000). The weighted values in each category were then summed to estimate the average value of an undamaged apple crop. Based on this categorization and crop value per category, the average expected value of an undamaged apple crop was $18.49/cwt kg. See Table 1 for Excel formulas used to calculate each value in the matrix and to derive the average expected value of an undamaged apple crop. To estimate price loss, injured apples were further partitioned into injury grade categories. Injury grade categories were designated: undamaged, juice, and cull apples, and reßect inspectorsÕ ability to identify injury at processing facilities. Hull and Rajotte (1988) reported that inspectors at processing plants usually accept ⬇83.7% of tufted apple bud moth injured fruit as undamaged or U.S. #1, downgrade around 13.1% to juice apples (U.S. #2 plus cider apples), and cull

⬇3.2%. Therefore, each injured grade category was further weighted by the likelihood of an inspector accepting injured fruit or downgrading it into the juice or cull category. It was assumed that an inspector would grade injured apples into the damage categories at the same frequency regardless of the proportion of apples that were injured (see Table 1 for calculations). The calculations in Table 1 result in a relationship between injury and loss in price characterized by the formula FPL ⫽ 0.104*PFI (1), where FPL is the fraction of price loss caused by injury and PFI is the proportion of fruit injury. The predicted fraction of price loss for a given predicted proportion of fruit injury was used as the basis for the economic analysis component of the model. In the economic analysis component of the model, an insecticide application was recommended based on a comparison between the cost of control and the beneÞt expected from a control action. The model recommended control actions based on a ratio of cost/ beneÞt. The beneÞt was calculated using the formula BeneÞt ⫽ FPL*PC*MV*EY (2), where FPL is the fraction of price loss caused by injury due to tufted apple bud moth feeding, PC is the proportion of tufted apple bud moth injury expected to be prevented by the insecticide applications, MV is the expected market value of the apples ($/cwt), and EY is the expected yield (cwt/ha) in the absence of pest damage. The control cost (CC) was calculated using the formula CC ⫽ IC*NA (3), where IC is the insecticide cost ($/ha), and NA is the number of applications per hectare. A management action is determined by the cost/beneÞt ratio. A cost/beneÞt ratio ⬎1 represents a no-treatment decision, because the cost of control exceeds the expected beneÞt from an insecticide application, whereas a cost/beneÞt ratio ⬍1 represents a decision to treat. A ratio of exactly 1 is the break-even point and when expressed as the number of insects or injury per unit that resulted in the ratio, is the economic injury level (EIL).

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Table 2. Regression statistics of tufted apple bud moth fruit injury at harvest by trap catch at the Penn State University Fruit Research and Extension Center, 1981–1999 Predictor

na

Regression equation

SEM

r2

P value

MSE

10% Catch 20% Catch 30% Catch 40% Catch 50% Catch Catch through 15 May Catch through 1 June Catch through 15 June Catch through 1 July First 3 wk of catch 100 DD from bioÞx 300 DD from bioÞx 500 DD from bioÞx 700 DD from bioÞx

30 30 30 30 30 30 30 30 30 30 30 30 30 30

y ⫽ 0.34 ⫹ 0.302x y ⫽ ⫺0.23 ⫹ 0.158x y ⫽ ⫺0.43 ⫹ 0.108x y ⫽ 0.09 ⫹ 0.078x y ⫽ ⫺0.21 ⫹ 0.064x y ⫽ 9.77 ⫹ 0.280x y ⫽ 3.28 ⫹ 0.065x y ⫽ ⫺0.66 ⫹ 0.0411x y ⫽ ⫺0.14 ⫹ 0.033x y ⫽ 8.24 ⫹ 0.054x y ⫽ 14.3 ⫺ 0.011x y ⫽ 16.0 ⫺ 0.0159x y ⫽ 16.2 ⫺ 0.0096x y ⫽ 14.5 ⫺ 0.0018x

0.118 0.060 0.039 0.029 0.024 0.108 0.024 0.014 0.012 0.038 0.021 0.020 0.017 0.014

0.19 0.20 0.21 0.20 0.20 0.19 0.21 0.24 0.20 0.03 0.01 0.02 0.01 0.00

0.016 0.013 0.009 0.013 0.012 0.014 0.010 0.006 0.012 0.165 0.602 0.436 0.538 0.901

119.3 117.9 115.4 117.9 117.6 118.7 116.3 112.0 117.4 136.9 145.1 143.4 144.9 146.4

a

Number of orchard blocks and trap data pairs.

The cost of the insecticide used in the model was based on the price for the insect growth regulator methoxyfenozide, which is a preferred chemical for treatment of tufted apple bud moth, and estimated at $50/ha per application, at two applications per season (Anonymous 2002). This insecticide provides ⬇98% protection from tufted apple bud moth injury when applied twice per season (L.A.H., unpublished data). Evaluation of Management Model. Using the archived injury data, the model was tested for its usefulness in making management decisions. Three possible management strategies were considered: never treat, always treat, and treat only when the model indicates that treatment is warranted (i.e., IPM approach). The indicator variable was expected return (savings) a grower could anticipate by pursuing one of the management strategies. The amount of lost value that can be protected or prevented is the “BeneÞt” derived using formula 2. The expected return to always treating (ReturnT) is the beneÞt of treatment minus the cost of treatment or ReturnT ⫽ (FPL*PC*EY*MV) ⫺ CC (4) and the expected return to never treating (ReturnNT) is the cost saved minus the value lost without treatment or ReturnNT ⫽ CC ⫺ (EY*MV*FPL) (5), where the abbreviations are the same as in formulas 2 and 3. The ReturnT and the ReturnNT were calculated for each possible injury level (spanning all the orchards in the archived data set). The expected return to using the model (ReturnM) was calculated for each orchard, by using formula 4 when the fruit injury exceeded the EIL and formula 5 when the economic injury was not exceeded. The average return was what a grower would have realized per year, on average, from following a management strategy over the course of the years represented in the archived data set. Model Sensitivity Analysis. We tested the sensitivity of the model to changes in some of the variables, notably fruit price (MV), control cost (CC), and yield (EY). The sensitivity of the model was determined by systematically changing one variable while holding all others constant. The output used to measure the modelÕs sensitivity was ReturnM,, although the output of

economic threshold, EIL, or the total value of the crop also could have been used. Results and Discussion Relationship of Trap Capture to Fruit Injury: Archived Data. The degree-day requirement to reach 50% capture ranged from 152.8 to 354.3 DD from bioÞx, and averaged 254.8 (⫾10.7) DD. Because the degree-day accumulation from bioÞx until egg hatch is fairly predictable (L.A.H., unpublished data), it was surprising to see so much variation in the degree-days required to reach 50% male capture. BioÞx probably coincides with the beginning of male and female ßight, and therefore the period of oviposition, but subsequent pheromone trap capture may have little to do with degree-day accumulation. The overall weather conditions and population numbers in adjacent orchards may inßuence the duration and abundance of male trap capture. Thus, 50% male capture may be poorly predicted by degree-days. The way trap capture was interpreted (i.e., catch to a calendar date, catch to a given number of accumulated degree-days) had a large inßuence on the correlation between fruit injury and trap capture. Regression equations of fruit injury on trap catch are given in Table 2. In these regression equations, “y” is the predicted percentage fruit injury, and “x” is the cumulative male moth capture in the trap to the given event. The highest r2 value (0.24) was from using cumulative trap catch through 15 June as the predictor of total tufted apple bud moth fruit injury. The poorest predictors of fruit injury were the Þrst 3 wk of trap catch and degree-days past bioÞx (Table 2). Regression models for Yorking and Golden Delicious resulted in higher r2 values than models that included all cultivars (P ⫽ 0.068 for Yorking and P ⫽ 0.174 for Golden Delicious; Table 3), but because they were calculated from only seven data points, signiÞcance was low. Management Model. The best regression equation of fruit injury on trap capture was that of cumulative trap catch until 15 June, which had an r2 value of 0.24

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Table 3. Regression equations of tufted apple bud moth fruit injury at harvest on Yorking and Golden Delicious apples by trap catch until 15 June, Penn State Fruit Research and Extension Center, 1992–1999 Cultivar

na

Yorking Golden Delicious

7 7

a

Regression equation

SEM

r2

y ⫽ ⫺1.1 ⫹ 0.0831x 0.036 0.52 y ⫽ ⫺1.99 ⫹ 0.047x 0.030 0.33

P value

MSE

0.068 0.174

79.19 54.53

Number of orchards sampled.

(P ⫽ 0.006; Table 2), so it was selected to provide an estimate of fruit injury in the management model (Fig. 1). The relationship was described by the regression equation FI ⫽ 0.66 ⫹ 0.0411*TC (6), where FI is the proportion of fruit injury, and TC is the cumulative catch per trap through 15 June. The EIL, or breakeven point, was estimated to be 20.4% fruit injury: the point where formula 2 (BeneÞt) divided by formula 3 (Cost of treatment) equals 1 (with the fraction price lost [FPL] estimated at 0.104*Fruit Injury and inserted into formula 2). By setting y (proportional fruit injury) to 0.204 into formula 6 and then solving for x (cumulative moth capture to 15 June), the economic threshold was predicted to be 456 moths per trap. Using the EIL, 20.4% fruit injury, and cumulative trap capture through 15 June, the individual data points (Þeld observations) from the archived data were partitioned into four decision categories represented by the quadrants in Fig. 2: I) incorrect model decision not to treat, II) correct decision to treat, III) correct decision not to treat, and IV) incorrect decision to treat. An interpretation of the two quadrants (I and IV) where improper management decisions were made is as follows. Points falling in quadrant I indicate that the cumulative trap capture suggested no treatment was

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needed, but subsequent injury assessment indicated that the Þeld should have been treated. Points falling in quadrant IV indicated that cumulative trap captures suggested that treatment was needed but injury was below the EIL. Interpretation of the two quadrants where correct decisions were made were, in quadrant II, trap captures indicated that treatment was needed and Þeld observation conÞrmed the need for treatment, whereas in quadrant III, trap captures indicated that no treatment was needed and Þeld observations conÞrmed that injury was below the EIL. Given the assumptions and uncertainties inherent in the model, 86% of the archived test blocks represented in the data set would have been managed correctly, whereas 16% would have been managed incorrectly using the model. Although the EIL is exact, the economic threshold is only as precise as the regression model from which it is calculated. The incorrect decisions came about because trap capture does not perfectly predict fruit injury. A regression equation with a higher r2 value would result in a model with a smaller percentage of error but not necessarily fewer management mistakes. Evaluation of Management Model. The expected annual ReturnT (averaged over the years of the study) was ⫺$30.56 per hectare, and the average expected ReturnNT was $29.16 per hectare (Fig. 3). Thus, a grower would be, on average, $59.72 per hectare better off by never treating than by always treating, according to the model predictions. The model yielded an average ReturnM of $39.11, which is $9.95 per hectare more than by never spraying. This return came about by using the economic threshold of 456 moths per trap, as calculated from formula 6. If prediction of the EIL had been perfect, the cumulative trap capture of 456 moths per trap would have given the highest possible ReturnM. Because of variation, however, the

Fig. 2. Decision matrix based on cumulative tufted apple bud moth trap capture through 15 June. The EIL is 0.20, and the economic threshold is 456 moths/trap through 15 June. Quadrants II and III represent the probability of making a correct decision to apply chemical treatment based on the regression model. The upper left and lower right quadrants represent incorrect decisions.

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Fig. 5. Sensitivity analysis of the management model to insecticide price with “Return to model” as the output.

best ReturnM was actually $48.34, at an EIL of 550 moths per trap (Fig. 3). Based on these results, an EIL of 550 moths per trap would provide a better average return per hectare than 456 moths per trap. However, using 550 moths per trap would only improve the average returns for the data set used and may not hold up if more blocks were assessed. Model Sensitivity Analysis. The ReturnM changed with the yield, beginning at $51.50 per hectare when the yield was 200 cwt/ha, declining to $13.95 per hectare when the yield was 450 cwt/ha, and then climbing steadily as the yield increased (Fig. 4). For changes in insecticide price the trend was similar. ReturnM declined from $48.44 per hectare when insecticide cost was $20 per hectare and continued until the insecticide price reached $60, after which the ReturnM increased with higher insecticide cost (Fig. 5). Changing the price received for apples had the effect of lowering the EIL and trap catch threshold as the price increased, with the ReturnM declining erratically as the fruit price increased (Fig. 6). When fruit price and yield were low the best decision was not to treat, so both the model and the Never Treat scenarios gave high returns. As the value or yield of the crop increased, the model gave dimin-

ishing returns, because the cost of treating was offset by the value of the crop. In effect, treating or not treating gave similar results. As the crop yield continued to increase, however, ReturnM increased again, because the decision not to treat became more costly than the price of the insecticide. As the fruit price increased, it became better to always treat, rather than use the model, and the ReturnM declined. When all assumptions were held constant, and insecticide price was varied, ReturnM followed the same trend as for yield, but for the opposite reasons. When the insecticide was inexpensive the average expected return was high, again reducing the EIL. As the price increased, the gain from treating was offset by the price of the insecticide, and when the insecticide price reached a certain level ($60/ha in this example), the decision to treat when there was no need became costly and ReturnM increased. The model we constructed, although useful, accounted for only 24% of the variation in fruit injury predicted by trap capture. Much of the variation may be due to cultivar, larval survival (which is impacted by many factors, including natural enemies), weather conditions, and orchard management (Ferro et al. 1975, Campbell et al. 1983, Danthanarayana 1983). Even though the data we used to establish a relationship between trap capture and fruit injury were taken from orchards containing unsprayed trees, the trees surrounding the experiment trees had different histories of pesticide application, which could have inßuenced the population numbers of natural enemies that attack the tufted apple bud moth (Biddinger et al. 1994). The variability in larval survival in an unsprayed

Fig. 4. Sensitivity analysis of the management model to fruit yield with “Return to model” as the output.

Fig. 6. Sensitivity analysis of the management model to fruit price with “Return to model” as the output.

Fig. 3. Average “return to treatment” ($/ha) by using different trap capture thresholds. The threshold recommended by the model is represented by a circle, the return to always spraying is represented by a triangle, the return to never spraying is represented by a diamond, and the actual best threshold is represented by a represented by a square.

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situation highlights the possible importance of nonchemical management of the tufted apple bud moth, or management strategies that preserve natural enemies. Predicting fruit injury from trap capture is also probably affected by male movement. Knight et al. (1990) found 90% of the eggs deposited by mated females within 65 m from where they were released, but males were much more mobile (Knight and Hull 1988), and trap capture of males may not always represent the number of females in an orchard. Trap capture and fruit injury are most correlated early in the season, probably because males have not yet dispersed from where they fed as larvae. During that period of emergence, male and female density is probably similar, before males begin to move further from their eclosion sites. The management model presented in this article could prove useful to growers in Pennsylvania for making pest management decisions. As with all decision-making strategies based on EILs, the growers cannot always predict the price they will receive for their produce, nor know ahead of time how much insecticide will cost. Still, price is less volatile for processing apples than for some commodities where EILs are used, and the alternative to using an EIL is to make decisions based scant information. Researchers need to validate the models in different locations and with different cultivars to enhance the usefulness and accuracy of the models. It is important to establish consistent models, even if the r2 values are ⬍0.5. The temptation in past studies (Riedl and Croft 1974, Knight and Hull 1989, Krawczyk and Hull 1999) has been to cite the best correlation for each yearÕs data out of a host of regression equations. Although such models may Þt a certain set of data in a certain year, they are of little value to predict outcomes in the future. Models should be established from multiple years of data, and using archived information may be the best way to create them. References Cited Anonymous. 2002. Tree fruit production guide. Penn State University Cooperative Extension Service. Biddinger, D. J., C. M. Felland, and L. A. Hull. 1994. Parasitism of tufted apple bud moth (Lepidoptera: Tortricidae) in conventional insecticide and pheromone-treated Pennsylvania apple orchards. Environ. Entomol. 23: 1568Ð1579. Calvin, D. D., M. C. Knapp, K. Xingquan, F. L. Poston, and S. M. Welch. 1986. Using a decision model to optimize European corn borer (Lepidoptera: Pyralidae) egg-mass sampling. Environ. Entomol. 15: 1212Ð1219. Calvin, D. D., S. M. Welch, and F. L. Poston. 1988. Evaluation of a management model for second-generation European corn borer (Lepidoptera: Pyralidae) for use in Kansas. J. Econ. Entomol. 81: 335Ð343. Campbell, R. W., R. C. Beckwith, and T. R. Torgersen. 1983. Numerical behavior of some western spruce budworm (Lepidoptera: Tortricidae) populations in Washington and Idaho. Environ. Entomol. 12: 1360Ð1366.

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Danthanarayana, W. 1983. Population ecology of the light brown apple moth, Epiphyas postvittana (Lepidoptera: Tortricidae). J. Anim. Ecol. 52: 1Ð33. David, P. J., R. L. Horsburgh, G. I. Hortzman. 1989. Development of Platynota flavedana and P. idaeusalis (Lepidoptera: Tortricidae) at constant temperatures in the laboratory. Environ. Entomol. 18: 15Ð18. Ferro, D. N., R. R. Sluss, and T. P. Bogyo. 1975. Factors contributing to the biotic potential of the codling moth, Laspeyresia pomonella (L.), in Washington. Environ. Entomol. 4: 385Ð391. Harper, J. K., L. A. Hull, G. Krawczyk, and B. A. McPheron. 2000. Economic performance of tebufenozide, mating disruption, and conventional spray programs for tufted apple bud moth and leafroller management in processing apples. Department of Agricultural Economics and Rural Sociology, Penn. State University, Staff Paper #323. Hull, L. A., and E. G. Rajotte. 1988. Effects of tufted apple bud moth (Lepidoptera: Tortricidae) injury on quality and storageability of processing apples. J. Econ. Entomol. 81: 1732Ð1736. Knight, A. L., and L. A. Hull. 1988. Area-wide population dynamics of Platynota idaeusalis (Lepidoptera: Tortricidae) in south-central Pennsylvania pome and stone fruit. Environ. Entomol. 17: 1000 Ð1008. Knight, A. L., and L. A. Hull. 1989. Predicting seasonal apple injury by tufted apple bud moth (Lepidoptera: Tortricidae) with early-season sex pheromone trap catches and brood I fruit injury. Environ. Entomol. 18: 939 Ð944. Knight, A. L., L. A. Hull, and E. G. Rajotte. 1990. Patterns of egg mass deposition of Platynota idaeusalis (Lepidoptera: Tortricidae) within an apple orchard. Environ. Entomol. 19: 648 Ð 655. Krawczyk, G., and L. A. Hull. 1999. Understanding the relationships between pheromone trap capture, moth movement within and beyond the orchard and fruit injury for the tufted apple bud moth. Penn. Fruit News 79: 65Ð 67. Krawczyk, G., L. A. Hull, and P. A. Kirsch. 1998. Validation of pheromone trap catches for predicting tufted apple bud moth, Platynota idaeusalis (Walker), fruit infestation. Penn. Fruit News 78: 23Ð30. Krawczyk, G., C. M. Felland, L. A. Hull, and E. G. Rajotte. 2000. Diseases, pests, and natural enemies. In S. Tibbetts and N. Serotkin [eds.], 2000 Ð2001 Pennsylvania Tree Fruit Production Guide, Penn State College of Agricultural Sciences, University Park, PA. Minitab, Inc. 1998. MINITAB userÕs manual. Minitab, Inc., State College, PA. Pennsylvania Agricultural Statistics Service. 2000. Annual report. http://www.nass.usda.gov/pa/. Pontius, J. S., D. D. Calvin, S. M. Welch, and F. L. Poston. 1984. Software for a European corn borer management model in yellow Þeld corn. North Central Computer Institute Software J. 1: 1Ð104. Riedl, H., and B. A. Croft. 1974. A study of pheromone trap catches in relation to codling moth (Lepidoptera: Olethreutidae) damage. Can. Entomol. 106: 525Ð537. Vickers, R. A., W. G. Thwaite, D. G. Williams, and A. H. Nicholas. 1998. Control of codling moth in small plots by mating disruption: alone and with limited insecticide. Entomol. Exp. Appl. 86: 229 Ð239. Received 4 May 2004; accepted 14 April 2005.