Coordinated pest management decisions in the presence of management externalities: The case of greenhouse whitefly in California-grown strawberries

Agricultural Systems 104 (2011) 94–103

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Coordinated pest management decisions in the presence of management externalities: The case of greenhouse whitefly in California-grown strawberries Gregory J. McKee ⇑ Department of Agribusiness and Applied Economics, North Dakota State University, Department 7610, P.O. Box 6050, Fargo, ND 58108-6050, USA

a r t i c l e

i n f o

Article history: Received 17 December 2008 Received in revised form 19 October 2010 Accepted 20 October 2010

Keywords: Strawberry Greenhouse whitefly Externality Optimal management

a b s t r a c t Optimal pest management can be compounded by externalities associated with management decisions among adjacent crop fields managed by agricultural producers. In this paper, a bioeconomic model is used to measure the effects of management decisions on optimal pest management. The case of the greenhouse whitefly invasion of California-grown strawberries is considered. Results show that coordinated pest management decisions among host crop growers may improve returns, but only during certain parts of the strawberry growing season. Two generalizable pest management policy implications are presented. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction The effect of insect infestations on profit maximizing agricultural production management decisions can be complex. When an insect population of diffuses across a landscape, for example, decisions of agricultural producers managing one part of the landscape become externalities to agricultural producers managing other areas (McKee, 2006). In this case, optimal, or profit maximizing, pest management programs involve accounting for economic incentives for multiple agricultural producers, within a selected region, over time. Private decisions by one agricultural producer, such as using buffer zones (Parker, 2007; Ryan et al., 2003; Myers et al., 1998) may be sufficient to account for externalities related to pest diffusion among crop fields. In other cases, such as for invasive species, government policies may be required to promote optimal resource allocation (Kim et al., 2006). The objective of this paper is to determine whether conditions exist during the growing season of an annual agricultural crop when private decisions are sufficient to optimally manage a pest or whether coordination can improve returns. In this paper, the biological, regulatory, and price constraints faced by strawberry (Fragaria ananassa) producers in the Oxnard, California area of the United States, whose fields were affected in the early 2000s by an infestation of the greenhouse whitefly (Trialeurodes vaporariorum), are included in a bioeconomic model of whitefly management. The planting and harvesting decisions of growers of ⇑ Tel.: +1 701 231 8521; fax: +1 701 231 7400. E-mail address: [email protected] 0308-521X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2010.10.005

alternative greenhouse whitefly host crops affect the timing of pesticide application decisions made by fall-planted strawberry growers for greenhouse whitefly control. Although adult greenhouse whiteflies tend to remain on or near the host plant on which they were born (Benchwick, 2005; Ishida, 2005; Byrne et al., 1990), the entomological literature indicates that when the plant is removed, the adult whitefly population will migrate to nearby hosts, including commercially grown host crops, weeds, or even a bare crop field (Byrne et al., 1990). Hence, field management decisions by growers of any whitefly host crop create externalities for alterative host plant growers. The model is used to calculate the profit maximizing pesticide application timings for managing greenhouse whitefly population density. Optimal timings are calculated when the field management decisions of managers of alterative greenhouse whitefly host crops grown in adjacent fields are ignored. These are compared with optimal application timings which accounted for management decisions of growers of nearby crop fields. The effects of variations in policy and pesticide efficacy on the value of coordination are also discussed. 2. Background 2.1. Strawberry–greenhouse whitefly interaction Greenhouse whiteflies are a common pest of crops grown in greenhouses or outdoors. Greenhouse whiteflies feed on leaf tissue, which removes plant sap and stunts plant growth (McKee et al., 2007; Toscano and Zalom, 2003; Udayagiri et al., 2000; Byrne and Bellows, 1991), causing yield reductions. Populations of

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greenhouse whiteflies were first observed in strawberry plants, grown commercially in outdoor conditions, in the early 1990s. Population densities reached economically important levels on strawberries in the late 1990s. The largest observed populations occurred in the Oxnard growing district (hereafter Oxnard) (California Strawberry Commission, 2005) in Ventura County, California in 2000–2001, where 31% (approximately 3500 ha) of California strawberry production occurred that year. A smaller outbreak occurred in the Watsonville growing district (hereafter Watsonville) of Santa Cruz and Monterey counties, California in 2002–2003, where 41% of California strawberry production occurred. The addition of a summer strawberry crop in the Oxnard area and perhaps, as some experts suggest, genetic changes in the greenhouse whitefly, contributed to whitefly infestations in the region. Strawberry yield losses in the Oxnard area associated with greenhouse whitefly feeding were reported to be between 20% and 25% (California Strawberry Commission, 2003). Management of greenhouse whitefly in strawberries in California has particular economic and biological attributes which make it an interesting case. First, restrictions associated with registered pesticides for use against the whitefly create a complex management problem. Pyriproxyfen received a registration for use against greenhouse whiteflies on strawberries in 2003, but the number and timing of applications was restricted. Second, the greenhouse whitefly’s life cycle can be modeled plausibly with a single planting season of data. Since four to five generations occur during the life of a fall planting of strawberries in the Oxnard area, which lasts from early October through as late as August of the next calendar year, it is possible to estimate important biological parameters using data from a relatively short time period. The estimated model can then be used for sensitivity analysis, and to guide data collection efforts. Finally, since the whitefly can migrate across outdoor crop fields, whitefly management decisions made in one crop field affect those in other crop fields. 2.2. Greenhouse whitefly management externalities Bi et al. (2002a) observed migrations of adult greenhouse whiteflies through a sequence of alternative whitefly host crops. The sequence of preferred host crops appears to be determined by plant condition (Bi et al., 2002a). When any set of viable host crops are in the ground simultaneously, each may be growing with different amounts of vigor, based on their planting dates and life cycle. Since newer plantings are growing the most vigorously and have relatively high leaf nutrient levels available for the whitefly to feed upon, migrating populations of adult whiteflies will be attracted to these. Adult greenhouse whiteflies were first observed in fall-planted strawberries, which are in the ground from September through June. Since lima beans and tomatoes are growing vigorously at the time fall-planted strawberries are removed (in July), these were observed to become preferred greenhouse whitefly host plants. Between July and September, summer strawberry plantings are in the ground simultaneously with lima beans and tomatoes and become the preferred greenhouse whitefly host plant after the lima bean and tomato plants are removed in late September. Finally, fall-planted strawberries again become the preferred host by January, when summer plantings are removed. Additionally, celery plants are in the ground and removed year round, except for the month of July. 2.3. Production of strawberries in California Strawberries are an economically important crop in California. In 2008, total revenue from all varieties of strawberries grown in California was nearly $1.6 billion (California Department of Food

and Agriculture, 2009). California accounted for nearly 85% of US fresh strawberry production in 2007 (USDA National Agricultural Statistics Service, 2009). This is due to both greater area and greater per hectare yield than other states. Planting occurs annually in over 95% of crop fields (California Strawberry Commission, 2005) used for commercial strawberry production in California (Daugovish, Takele, Klonsky, DeMoura, 2004). The market price for fresh strawberries varies over the course of the year. Between 1988 and 2003, during the period when relatively few strawberries are harvested (between November and February; see Fig. 1) a kilogram of fresh strawberries sold for an average real ‘‘free on board’’ (FOB) price of $3.12 (2004 $US) (USDA-Agricultural Marketing Service). During the period when most strawberries are harvested (March–October) a kilogram of fresh strawberries sold for an average real FOB price of $1.76.

2.4. Chemical management of greenhouse whiteflies Cultural and biological control techniques alone have been ineffective against outdoor infestations of the greenhouse whitefly in strawberries (Philips et al., 1999; Bi et al., 2002a; Toscano and Zalom, 2003). Chemical insecticides, therefore, are an important part of an effective whitefly control program. Two chemicals have been registered for use in California on greenhouse whiteflies on strawberries and are relatively effective at controlling them. Pyriproxyfen is an insect growth regulator marketed by Valent Corporation as Esteem. The chemical works principally by killing the eggs and nymph greenhouse whiteflies (Bi et al., 2002b,c; Ishaaya and Horowitz, 1989); has a limited direct effect on the adults (Ishaaya et al., 1994); and the effect of a single application can last from 4 to 9 weeks on whiteflies on strawberry plants (Bi et al., 2002b,c). Conversations with anonymous pest control applicators indicate that a typical foliar application costs $201 per hectare, in 2003, at the application rate of 730 mL/ha. The second chemical, imidacloprid, a systemic neonicotinoid insecticide marketed by Bayer Crop Protection as Admire, is also relatively effective on whiteflies, and has a different mode of action than pyriproxyfen. It is effective against nymph and adult greenhouse whiteflies (Ishaaya and Horowitz, 1989; Bi et al., 2002b), and the effect of a single application can last up to 8 weeks (Bi et al., 2002b). In contrast to pyriproxyfen, imidacloprid has a 14 day withdrawal period. This makes it economically infeasible for use on strawberries once harvests commence, since harvest typically occurs every 3–7 days. Conversations with anonymous pest control applicators indicate that a typical drip line application costs $648 per hectare at the application rate of 2338 mL/ha, in 2003.

120 100 80 60 40 20 0

Jan Feb Mar Apr May Jun

Jul

Aug Sep Oct Nov Dec

Fig. 1. Average monthly mass of strawberries harvested in California, 1988–2003.

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In 2003, the California Department of Pesticide Regulation issued regulations to manage use of pyriproxyfen in order to encourage development of alternatives to it and to delay development of greenhouse whitefly resistance to it (Zalom and Toscano, 2003). Since this decision, the registration label for the Esteem formulation of pyriproxyfen (California Department of Pesticide Regulation, 2003) includes regulations which restrict strawberry growers to a limit of two applications per unit area per year, the same as other plants and crops Esteem is registered for use on. Strawberry growers are required to apply pyriproxyfen as soon as whiteflies appear. This regulation makes the timing of the pyriproxyfen applications a management decision. 3. Materials and methods 3.1. Data sources 3.1.1. Fresh and processed strawberries Daily fresh market FOB strawberry price data for 1988 through 2003 were obtained from the ‘‘National Berry Report’’ (USDA-Agricultural Marketing Service). Weekly processed strawberry wholesale price and quantity data for the same period were obtained from the California Processing Strawberry Advisory Board (California Processing Strawberry Advisory Board, 2003). Prices were deflated to 2003 US dollars using a strawberry-specific producer price index (Bureau of Labor Statistics, 2003). Deflated prices were then averaged to calculate a weekly average real wholesale price so as to smooth out any year-specific market conditions, leaving a representative price for the analysis. 3.1.2. Weather Weather data were used as inputs to simulate development of the whitefly population densities on strawberry plants in outdoor field conditions. Weather data for the Oxnard area, including temperature and precipitation, were obtained online from observations made at a California Irrigation Management Information System weather station in Oxnard (University of California Integrated Pest Management Program, 2003). 3.1.3. Greenhouse whitefly population Observations between 1 February 2001 and 19 April 2001of greenhouse whitefly eggs, nymphs, and adults on strawberry plants in the Oxnard area (Bi et al., 2002c) were used to calibrate a whitefly population density simulation model based on McKee and Zalom (2009). Bi et al. (2002c) describe the study site as commercial strawberry fields in a major agricultural area near Oxnard and Ventura, CA. Diverse crops were grown in the region, with major alternative whitefly host crops including lima bean, pepper, tomato, and cucumber. Six strawberry fields were observed, three with plantings done in the fall (September or October), Fields A, B, and C, and three in the summer (July or August), fields D, E, and F. The fields were between 3 and 5 ha and maintained with standard commercial practices. Fields A, B, and C were in areas of relatively high, moderate, and low whitefly population levels, based on preliminary scouting data. Field A was about 3 km from B, and C was about 10 km away from A and B. Field A was close to Field D, B was close to E, and C was close to F. Populations of whitefly were scouted in tomato plants near field B and in lima beans near fields A and C. Adult, eggs, and nymph population densities were counted weekly. 3.1.4. Impact of pesticides on greenhouse whitefly population density The population density model calculates daily egg, nymph, and adult levels over the course of one fall-planted strawberry plant growing season in the Oxnard area. Parameter values used in the

model (Table 1) compare with those in the entomology literature for observed interactions between the whitefly and fall-planted strawberry plants in the Oxnard area (Bi et al., 2002a,b,c) and other plants (Hulspas-Jordaan and Lenteren, 1989). A figure comparing Oxnard adult greenhouse whitefly sample and simulation results is provided (Fig. 2). Degree-day thresholds for nymphs and adults were assumed to be 122 degree-days and 380.7 degree-days respectively (Osborne, 1982). Greenhouse whitefly carrying capacity for an average strawberry plant leaf was assumed to be 200 nymphs and 50 adults. Rain events greater than 2.54 cm are assumed to cause 30% adult whitefly mortality. For purposes of sensitivity analysis, the effects of relatively high and moderate efficacy rates for pyriproxyfen and imidacloprid on pyriproxyfen application dates will be considered. High and moderate values are based on observations by Bi et al. (2002a,b). Baseline efficacy values for pyriproxyfen assume 73% mortality of whitefly adults, 82% mortality for nymphs, and 65% mortality for eggs. High efficacy values for pyriproxyfen assume 73% mortality of whitefly adults, 82% mortality for nymphs, and 65% mortality for eggs. Moderate efficacy values for pyriproxyfen assume 40% mortality of whitefly adults, 45% mortality of nymphs, and 32% mortality of eggs. High efficacy values for imidacloprid assume 83% mortality for whitefly adults; moderate efficacy values assume 31% mortality.

3.2. The effect of greenhouse whitefly population density on strawberry yield Anecdotal (California Strawberry Commission, 2003) and published observations (Bi et al. 2002a,b,c) of a negative relationship between cumulative greenhouse whitefly population density and strawberry yields from commercial strawberry fields in the Oxnard area exist. These observations, however, are insufficient to statistically estimate a relationship between cumulative whitefly population density and strawberry yields. Woets and Lenteren (1976) concluded that the host plant has a major influence on the population growth rate of T. vaporariorum. Camarosa was the most common strawberry variety grown in commercial field in Oxnard between 2000 (67.5% of plantings) and 2003 (45.8% of plantings) (California Strawberry Commission, 2005). Greenhouse whitefly population development data were collected from a commercial strawberry field located west of Watsonville, Santa Cruz Co., Calif. in which strawberries (var. Camarosa) were transplanted into the field on 12 November 2002 (McKee et al., 2007). Limited sensitivity analysis will be given to determine the effect of using data from Watsonville on optimal pyriproxyfen

Table 1 Greenhouse whitefly simulation parameters, by month. Month

September October November December January February March April May June

Mortality (% of daily cohort population dying before next lifestage, or end of lifespan [adults]) Eggs

Nymphs

Adults

0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

0.50 0.50 0.50 0.50 0.50 0.38 0.23 0.65 0.80 0.80

0.30 0.30 0.30 0.35 0.50 0.50 0.65 0.91 0.95 0.99

Oviposition (eggs/ female/day)

Adult lifespan days

0.38 0.34 10.12 7.32 2.18 4.85 1.12 0.98 0.00 0.00

26 26 26 26 26 25 25 25 25

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# Adult Greenhouse Whitefly per Leaf

25 Simulated

20

Observed

15

10

5

0 21- 5-Oct 19- 2-Nov 16Sep Oct Nov

30Nov

14Dec

2000

28Dec

11Jan

25- 8-Feb 22- 8-Mar 22- 5-Apr 19Jan Feb Mar Apr

2001

Fig. 2. Simulated and observed greenhouse whitefly population in experimental plot in Oxnard growing district, California, September 2000–April 2001.

application timing in Oxnard; benchmark yield estimates are increased or decreased by 10%. 3.3. Bioeconomic model Several assumptions were made in order to simplify a model of optimal strawberry producer behavior. First, growers seek to maximize returns from a representative crop field infested with greenhouse whiteflies at planting. The biological model was scaled up from the plant level to hectare level, with the assumption that the crop field is uniformly infested with whiteflies; 72,882 plants per hectare are assumed (Daugovish et al., 2004). Constant returns to scale were assumed and analyze returns for each treatment scenario on a per-hectare basis. Finally, changes in average harvest costs as a function of insecticide use (average costs decrease when the number of higher-quality berries harvested per unit of time increases) were ignored due to a lack of data. The grower chooses the timing of pyriproxyfen treatments to maximize returns as follows: T X

pt ¼

t¼1

T t X X ðpt ÞY t WFt Ei;k t¼1

!!  C e  C e;t ; t 2 ½1; T

ð1Þ

k¼1

where pt refers to returns net of treatment and other expenses in week t, through week T. pt is the weekly weighted average real price of strawberries. WF is the cumulative whitefly population density through week t. Profit maximization is subject to the following biological and regulatory constraints.

0 6 Y t WFt

t X

!! Ei;k

6 g;

i 2 f0; 1; 2g;

t 2 ½1; T

ð2Þ

k¼1

06

T X

Ei;k 6 2; 8k; k 2 ½1; T

ð3Þ

k¼1

WFt P 0; 8t

ð4Þ Pt

The ith pyriproxyfen application in week t is Ei;t . k¼1 Ei;k is the cumulative number of applications within week t. Ce is the perhectare cost of pyriproxyfen and C e;t refers to other all expenses in week t, which are assumed to be unaffected by pyriproxyfen applications.

The model was used to determine the optimal timing for a treatment control program. For example, the numerical simulation model of the greenhouse whitefly population density was used to compute the optimal timing of two applications of pyriproxyfen by calculating the increased gross returns, relative to an untreated hectare of strawberries, for any 2 weeks in the season at least 4 weeks apart. Optimal timing of the application was calculated by combining results of the population density, yield, and economic models to determine the weeks when the estimated return to the simulated pyriproxyfen application is greatest. To determine the effect of field management externalities on optimal greenhouse whitefly management in fall-planted strawberry fields, the optimal timing for pyriproxyfen applications for a strawberry grower who only considers the effect of a whitefly infestation at planting was compared with the optimal timing for a grower who also considers a second infestation later in the season associated with an immigrating adult whitefly population from another host crop. If the timing of pyriproxyfen applications does not change as a result of considering the second infestation, then the strawberry grower’s private whitefly control decision is optimal and unaffected by planting and harvesting decisions of growers of alternative greenhouse whitefly host crops. If the timing of pyriproxyfen applications does change as a result of the second infestation, then coordination among growers of greenhouse whitefly host crops may be justified if the costs of coordination equal or exceed the benefits. To ascertain the effect of a second whitefly infestation on optimal pyriproxyfen application timing at any point in the season, 10 representative dates within the planting season were considered (Table 2). These dates were selected to assess grower response under various price (Fig. 1) or whitefly population densities (Fig. 2) and are sufficient to describe whether a fall-planted strawberry grower can make decisions alone in order to optimally manage a greenhouse whitefly population in their crop field or whether coordination can improve returns. Tests of other dates (e.g. 15 January) add no qualitative information to the conclusions. The effect of strawberry grower decisions on neighboring greenhouse whitefly host crop growers was not explicitly considered because data do not exist about the interaction between greenhouse whiteflies, alternative host crops, and human decisions. Hence, this analysis is insufficient to suggest socially optimal decisions. This analysis is sufficient, however, to demonstrate whether coordination among growers of whitefly host crops could improve whitefly management for strawberry growers.

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Table 2 Increased strawberry production returns per hectare using two optimally-timed pyriproxyfen applications. Date of second migration

Application dates when migration ignored

Increased returns relative to untreated

Optimal application dates

Additional increased returns

Coordination needed?

Benchmark pesticide efficacy 1 November 1 November and 13 January

$10,900

and 30

$200

N

1 December

1 November and 13 January

$1500

and 3

$500

N

15 December

1 November and 13 January

$3000

and 26

$700

N

1 January

1 November and 13 January

$9600

and 13

$0

N

1 February

1 November and 13 January

$6200

and 3

$500

Y

15 February

1 November and 13 January

$4700

and 13

$0

Y

1 March

1 November and 13 January

$6200

and 6

$0

Y

15 March

1 November and 13 January

$9600

and 13

$0

Y

1 April

1 November and 13 January

$11,100

and 13

$0

Y

1 May

1 November and 13 January

$11,100

1 November December 1 November February 1 November February 1 November January 1 November February 1 November January 8 November January 1 November January 1 November January 1 November January

and 27

$0

Y

6 December and 13 January 6 December and 13 January 1 November and 3 February 1 November and 3 February 1 November and 3 February 1 November and 3 February 8 November and 23 December 1 November and 6 January 1 November and 30 December 1 November and 3 February

$0

N

$1000

Y

$1000

N

$4200

N

$4200

Y

$1200

Y

$200

Y

$4000

Y

$5200

Y

$5400

Y

and 30

$0

N

and 27

$3000

N

and 3

$200

N

and 20

$0

Y

and 12

$1000

Y

and 19

$2000

Y

and 23

$0

Y

and 13

$0

N

and 20

$0

Y

and 20

$0

Y

Moderate pesticide efficacy 1 November 1 November and 13 January

$6200

1 December

1 November and 13 January

$1200

15 December

1 November and 13 January

$2500

1 January

1 November and 13 January

$4900

1 February

1 November and 13 January

$4400

15 February

1 November and 13 January

$3000

1 March

1 November and 13 January

$4000

15 March

1 November and 13 January

$4900

1 April

1 November and 13 January

$5400

1 May

1 November and 13 January

$5700

High pesticide efficacy 1 November 1 November and 13 January

$12,500

1 December

1 November and 13 January

$4200

15 December

1 November andand 13 January

$5400

1 January

1 November and 13 January

$10,100

1 February

1 November and 13 January

$8400

15 February

1 November and 13 January

$4700

1 March

1 November and 13 January

$7400

15 March

1 November and 13 January

$9400

1 April

1 November and 13 January

$11,100

1 May

1 November and 13 January

$11,600

4. Results Migrations of adult greenhouse whiteflies triggered by the decisions of managers of host crops in adjacent fields can have three possible effects on pyriproxyfen application timing decisions of fall-planted strawberry growers. First, migrations may be triggered for which a grower would find it optimal to change one or more

1 November December 1 November January 1 November February 1 November January 1 November February 1 November February 8 November December 1 November January 1 November January 1 November January

application dates, but coordination is required in order to choose dates which maximize returns from strawberry production. Second, migrations may be triggered which would find it optimal to change one or more application dates, but coordination no coordination among growers would be needed. Third, migrations which may be triggered which have no effect on optimal application dates. As shown below, the effect of a given migration date on

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application timing decisions may depend on various factors, including changes in the regulations on the number of applications allowed, uncertainty in pesticide efficacy, and crop diversity in the growing area. 4.1. Benchmark The model was used to determine the optimal timing of two pyriproxyfen applications, in combination with imidacloprid applied at planting. The returns from a treated hectare are compared with an untreated hectare. An untreated hectare is estimated to produce approximately 65,400 kg of fruit per hectare, and gross revenues of $91,000 per hectare. An optimally treated hectare produces approximately 83,000 kg of fruit and revenues of $115,300, net of application costs. Optimal applications of pyriproxyfen occur on 1 November and 13 January. 4.2. Externalities with two pyriproxyfen applications Whitefly migrations triggered near 1 March require a fallplanted strawberry grower to coordinate field management decisions with other host crop growers in order to maximize returns. In this case, the optimal timing of the first application changes from 1 November to 8 November, and the optimal timing of the second application changed from the week of 13 January to 6 January. The grower can earn an estimated $6200 in additional returns relative to an untreated hectare without coordination. If, instead, the grower accounts for the effect of the second migration they would earn still earn an estimated $6200 in additional returns, a difference of $0. The strawberry grower must coordinate with growers of alternative host crops in order to identify the optimal application dates since both pyriproxyfen applications occur before the whitefly migration is triggered, such as by a celery harvest. A second adult greenhouse whitefly migration triggered near 1 February also fits into this category (Table 2). Whitefly migrations may be triggered which may cause a fallplanted strawberry grower to change pesticide application dates, but do not require them to coordinate field management decisions with other host crop growers. This can happen when the grower can observe the immigration prior to any changes to optimal pesticide applications. Whitefly migrations triggered near 1 November, such as by a celery harvest, change the optimal applications from the weeks of 1 November and 13 January to 1 November and 30 December. Optimal timing of the second application changed because the 30 December application reduced combined nymph population density from the eggs laid by the first and second immigrations by more than the 6 January application. The grower can earn an estimated $10,900 in additional returns relative to an untreated hectare by ignoring the migration. Accounting for the migration allows the grower to earn $200 more per hectare. Coordinated field management is not required since the grower can observe development of the combined whitefly population be-

fore making a decision about when to make a second application. Migrations near 1 December and 15 December fit into this category (Table 2). Finally, whitefly migrations may be triggered which may not induce a fall-planted strawberry grower to change pesticide application dates. A migration around 1 January, triggered by removal of a summer-planted strawberry field, had no effect on the optimal weeks for two pyriproxyfen applications; these remained 1 November and 13 January, generating $9600 more in returns per hectare. In this case, this program reduced the February, earlyMarch adult whitefly population density by more than waiting to make the second application. This occurred because the pyriproxyfen application killed enough of the eggs from the original and secondary immigration of adult whiteflies to make it preferable to any other time. In addition, the cooler temperatures at this time slow egg production, making the combined effect of the 6 January application on the adult and egg population density more important than the more powerful effect of a later application on the nymph population density. Pest control advisors confirmed this interpretation of the results (Benchwick, 2005; Ishida, 2005). 15 February, 1 and 15 March, 1 April, and 1 May also fit into this category. The analysis in this section shows pest management decisions in one crop field affect returns from producing fall-planted strawberries in an adjacent field. The level of coordination required in order for growers of fall-planted strawberries to realize this changed over the course of the season. Migrations triggered early in the strawberry growing season required changes in application dates, but may or may not require coordinated field management. Migrations triggered later in the season had no effect on the optimal application dates. These results suggest the expected commercial value of the crop after the possible migration dates affects whether a grower will find it optimal to change application dates.

4.3. Three pyriproxyfen applications Changes in regulation may also improve returns from pest management and change the need for coordination among growers. In this section, the effect of second whitefly infestations on the optimal timing of three pyriproxyfen applications, done in combination with an application of imidacloprid at transplanting, is assessed for fall-planted strawberries in the Oxnard area. As in Section 4.1, triggered migration dates are discussed which cause one of three effects on optimal pyriproxyfen application dates. If a second migration does not occur, optimal pyriproxyfen applications are done during the weeks of 1 November, 30 December, and 29 January, generating additional returns of $12,700 per hectare relative to an untreated strawberry field. A second migration triggered around March 1, however, causes the third optimal application to be made around 5 February but generates no additional returns. No coordination between adjacent growers would be required prior to making the first pyriproxyfen application.

Table 3 Increased strawberry production returns per hectare using three optimally-timed pyriproxyfen applications. Date of second migration

Increased returns relative to untreated

Optimal application dates

Additional increased returns

Coordination needed?

1 November 1 December 15 December 1 January 1 February 15 February 1 March 15 March 1 April 1 May

$16,900 $2300 $3900 $10,800 $10,700 $2000 $8000 $13,000 $15,700 $15,900

1 1 1 1 1 1 1 1 1 1

$0 $300 $500 $3300 $1100 $100 $0 $0 $0 $200

N N N Y Y Y Y N N Y

November, November, November, November, November, November, November, November, November, November,

30 December, 29 January 6 January, 5 February 6 January, 26 February 6 January, 5 February 6 January, 5 February 1 December, 1 January 30 December, 5 February 30 December, 29 January 30 December, 29 January 6 January, 5 February

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However, since the grower may not be able to observe migration before having to change the timing of the third application, coordination must occur in order for the strawberry grower to maximize returns. The reason for the change in application timing is that making a later application kills nymphs that will mature later, preserving yields when plants produce berries the most rapidly (during March through May in the Oxnard area) by reducing the adult population density at that time. Since the fresh season ends after this application (most berries produced in Oxnard are sold to the processed berry market by 1 May), this result indicates that optimal timings of a three-application program emphasize protecting fresh, rather than processed, harvests. 1 January, 1 and 15 February, and 1 May also fits this category. To illustrate the second category, an immigration triggered around 1 December is examined. Added returns of $2300 per hectare are earned when making pyriproxyfen applications on 1 November, 6 January, and 5 February (Table 3). Coordination was unnecessary, as in the two-application case, but $300 more in returns are available to the grower when accounting for the second migration. 15 December also fits into this category. Infestations in the third category do not change optimal application dates as a result of the second infestation. An infestation around 1 November fits this category, earning $16,900 per hectare in additional returns. Applications are done the weeks of 1 November, 30 December, and 29 January regardless of whether infestations are considered. This final category includes migrations around 15 March and 1 May. In summary, there are still second migration weeks for which coordination increases the strawberry grower’s returns when three pyriproxyfen applications can be made. Since fewer dates require coordination, a third application weakens, but does not eliminate, the benefit of coordination. Also, an overall a pattern of reduced

need for coordination is appears in these results, but a new date, 1 May requiring coordination arises. 4.4. Uncertainty in pesticide efficacy Changes in pesticide effectiveness can come from a variety of factors, including environmental conditions, application method, and development of resistance. In this section, the effect of increased and decreased pyriproxyfen and imidacloprid efficacy are considered on the optimal pyriproxyfen treatment dates. In this section, the effect of variations in pesticide efficacy on the possibility of increased returns through coordinated whitefly management is considered. Since the objective is to determine whether variations in efficacy affect the incentives for coordination, it must be assumed that the grower can accurately predict whether the imidacloprid and pyriproxyfen rates will be relatively high or moderate. This would be the case if the grower detects the development of resistance or is aware of improved application techniques. Without this assumption, this case is equivalent to the benchmark in Section 4.1. Only two categories of effects are observed if the pesticide is unexpectedly ineffective. A second migration around 1 December changes the optimal pyriproxyfen application dates and requires the grower to coordinate with other growers. The optimal pyriproxyfen applications are done during the weeks of 6 December and 13 January, generating $2200 per hectare, with $1000 of this being the benefits of coordination (Table 2). 1 and 15 February, 1 and 15 March, 1 April, and 1 May fit into this category. Migrations on 1 November, 15 December, and 1 January require a change in optimal application dates, but without needing coordination with other growers. All three categories are observed if the pesticide is unexpectedly effective. Prospective whitefly migration dates in the first category

Table 4 Sensitivity of increased strawberry production returns per hectare to strawberry yield assumptions. Date of second migration

Optimal application dates

Additional increased returns

Coordination needed?

Benchmark yield effect assumption 1 November $10,900 1 December $1500 15 December $3000 1 January $9600 1 February $6200 15 February $4700 1 March $6200 15 March $9600 1 April $11,100 1 May $11,100

Increased returns relative to untreated

1 1 1 1 1 1 8 1 1 1

November November November November November November November November November November

$200 $500 $700 $0 $500 $0 $0 $0 $0 $0

N N N N Y Y Y Y Y Y

Low yield effect assumption 1 November 1 December 15 December 1 January 1 February 15 February 1 March 15 March 1 April 1 May

$5400 $3000 $1700 $4400 $2700 $0 $1200 $4400 $5700 $5900

1 6 1 1 1 1 8 1 1 1

November and 30 December December and 6 January November and 3 February November and 3 February November and 3 February November and 3 February November and 23 December November and 3 February November and 30 December November and 3 February

$200 $200 $500 $0 $1000 $0 $500 $0 $0 $0

N Y N N Y Y Y Y Y Y

High yield effect assumption 1 November 1 December 15 December 1 January 1 February 15 February 1 March 15 March 1 April 1 May

$17,000 $6900 $8400 $15,800 $13,800 $10,400 $12,100 $15,800 $17,500 $17,500

1 1 1 1 1 1 8 1 1 1

November November November November November November November November November November

$200 $500 $700 $0 $1000 $0 $500 $0 $0 $0

N N N Y Y Y Y Y Y Y

and and and and and and and and and and

and and and and and and and and and and

30 December 3 February 26 February 13 January 3 February 13 January 6 January 13 January 13 January 27 January

30 December 27 January 3 February 20 January 12 February 19 February 23 December 13 January 20 January 20 January

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include those which require a fall-planted strawberry grower to coordinate field management decisions with other host crop growers in order to maximize returns. These include 1 January, 1 and 15 February, 1 March, 1 April, and 1 May. Whitefly migration dates in the second category include 1 November, 1 and 15 December. Migrations on these dates change the optimal pyriproxyfen application dates, but do not require the grower to coordinate with other growers. Finally, a second migration around 15 March will not cause a change in the optimal application dates. These results show unexpected variations in pesticide efficacy can affect the need for coordination. Unexpectedly moderate pesticide efficacy tends to increase the value of coordination (Table 2), while unexpected increases in efficacy decrease additional returns from coordination, relative to the benchmark yield. This indicates improvements in pesticide application technologies or use of more effective pesticides can weaken the need for coordinated pest management. 4.5. Homogeneous crop To this point, the analysis has considered the effect of a variety of host crops on optimal pyriproxyfen application dates. In this section, the need for coordination when only strawberry plants are in adjacent fields is considered. This occurs in Oxnard when fallplanted strawberries are in crop fields adjacent to summer-planted crop fields. It also occurs in the Watsonville growing region when fall-planted crop fields are adjacent to crop fields with plants left in the ground from the previous year. In 2003, 5.6%, or 638 ha, of all strawberry-planted area was left in the ground 2 years. Anecdotal information from the California Strawberry Commission indicates most of this area is in the Watsonville growing district. These plants are less common because they tend to produce smaller or deformed (‘‘catfaced’’) berries. The effect of removing summer-planted strawberry plants in the Oxnard area, which typically occurs in early December, was already considered in Section 4.2. Adult whitefly migrations around 1 December and 15 December make it optimal for a fall-planted strawberry grower to change pyriproxyfen application dates, but coordination is not required in this case. An additional $300 to $700 can be earned in this case (Table 2). Removal of second year plants can happen just before, during, or soon after winter-planted berries are transplanted in the Watsonville area. In each of these cases, adult whiteflies can be expelled from the previous field and migrate to the new planting. According to McKee and Zalom (2009), the optimal treatment program is consists of an application of imidacloprid at planting, an application of pyriproxyfen on 1 February, and a second application of pyriproxyfen on March 8. This program results in increased returns of about $18,300 relative to an untreated hectare, or about 21% of average gross returns from strawberry production in the Watsonville area (Bolda et al., 2004). Since the migrating whiteflies arrive at planting, coordination is not required in this case. 4.6. Sensitivity of results to study assumptions In order to evaluate the relative importance of using data from a greenhouse whitefly population found in a commercial strawberry field in Watsonville instead of Oxnard, a limited sensitivity analysis was performed. The effect of a 10% increase or decrease of the strength of the negative effect of the cumulative whitefly population pressure on strawberry yields was observed (Table 4). Two key results arise. First, the size of increased returns due to coordination was observed. Second, the number of prospective adult whitefly migration dates which would fit into the three categories was observed.

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When compared with the benchmark, a 10% decrease in the effect of whitefly population pressure on strawberry yields left the size of the increased returns to coordination generally unchanged. Additional returns from coordination were unchanged for migrations on 1 November, 1 and 15 December, 1 January, 15 February, 15 March, 1 April, and 1 May (Table 3). Returns doubled for a migration on 1 February, and returns were generated for a migration on 1 March. The number of dates corresponding to each category changed, relative to the benchmark. Six of the ten dates appear in the first category (optimal application dates change, but coordination not needed), instead of three. Three dates appear in the second category (optimal application dates change, and coordination is needed) in both cases, and one prospective migration date leaves the optimal pyriproxyfen application dates unchanged instead of four. Hence, a decrease in the effect of whitefly population pressure on strawberry yield will tend to increase the number of occasions for coordination, but leave the increase in returns generally unchanged. In contrast, when compared with the benchmark, a 10% increase in the effect of whitefly population had three effects. First, two of the prospective whitefly migration dates had total yields that were sufficiently smaller than the benchmark case as to generate negative returns to planting. In these cases, growers would prefer to not plant strawberries. Returns to coordination were unchanged for whitefly migrations on 1 November, 1 January, 15 February, 15 March, 1 April, and 1 May. On two dates, returns to coordination declined, but on one date (1 February) they increased. Seven of the ten prospective adult whitefly migration dates appear in the first category instead of three. One of these, 1 December, would create incentives for the grower to not plant strawberries. Three dates, as in the benchmark case, appear in category two; none appear in category three. Hence, an increase in the effect of whitefly population pressure on strawberry yield will tend to increase the number of occasions adult greenhouse whitefly migration require coordination in order to optimally manage the population and will likely change the optimal pyriproxyfen application dates for most whitefly migration dates.

5. Discussion A relationship exists between the expected value of learning about whitefly immigration dates and incentives to coordinate management decisions. To maximize returns, growers must form an expectation about the timing of future whitefly infestations and calculate any changes these will make on the optimal timing of pyriproxyfen applications. When a strawberry grower expects a future infestation will change the optimal timing of his pyriproxyfen application, it is possible the only way to verify this is to communicate with neighboring growers of greenhouse whitefly host crops. Alternatively, if the strawberry grower expects these infestations will occur at other times of the growing season, communicating with neighboring greenhouse whitefly host crop growers is irrelevant because optimal treatment dates either do not change or the strawberry grower will calculate changes to optimal pyriproxyfen application times on his own. On six of the ten representative adult whitefly migration dates, generally midway or later in the fall-planted strawberry season, information about whitefly immigration dates would be required for adjusting pyriproxyfen application timing. However, on only one of these occasions strawberry growers would be willing to pay for the information in order to make optimally timed pesticide applications. This result holds regardless of allowing an additional pyriproxyfen applications, managing the whitefly population in a region of homogeneous strawberry plantings, or if pesticide efficacy can be increased. Hence, in the case of the greenhouse whitefly on strawberries,

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there is usually not a positive expected benefit of communicating for the strawberry grower. The exchange of information regarding the presence of greenhouse whiteflies in adjacent host crop fields, and the likely time of their migration, can be done via informal communication among agricultural consultants or greenhouse whitefly host crop growers. Strawberry growers typically grow more than one strawberry crop in different locations simultaneously, as well as other crops. Production activities at several locations make it uneconomical for the strawberry producer to make constant observations of whitefly population densities in his own crop fields as well as planting and harvesting decisions of managers of adjacent whitefly host crop fields. The incremental costs associated with the transfer of this information could be covered by a surcharge, which the results in Tables 2–4 show strawberry growers would be willing to pay, to their crop consultants to observe the edges of adjacent host crop fields (Benchwick, 2005; Ishida, 2005). This method assumes their consultants will be able to determine the planting and removal dates of host plants from specific crop fields. Alternatively, policymakers may enact a formal coordination requirement, administered by some type of government entity, if informal methods of data gathering and communication do not emerge due to transactions costs or other coordination problems. To estimate the costs of such a program, one could use the notification requirements associated with methyl bromide application in California as an example. The direct costs of the methyl bromide notification program are negligible. In this program, strawberry growers are required to formally notify the occupants of property within a specified range of the application area of the timing of the methyl bromide application. Carter et al. (2005) estimated the cost of a typical methyl bromide notification ranged between $1.30 and $6.53, far less than the estimated, nonzero, increase in returns shown in Tables 2–4. The time required for assembling and distributing information to notify fall-planted strawberry adjacent greenhouse whitefly host crop growers about anticipated crop planting and removal dates would be comparable to assembling and distributing methyl bromide application notices. Since notification costs are equal across growers of different types of plants, strawberry growers would be willing to compensate any grower of greenhouse whitefly host crop in adjacent fields for the costs associated with assembling and distributing information, and these growers would be at least indifferent between receiving the payment or providing information.

6. Conclusion The unique features of the greenhouse whitefly infestation of fall-planted strawberries provides information about whether the combination of biological, regulatory, and price constraints are likely to provide incentives for growers to internalize the negative pest management externalities caused by other growers. Relaxing regulations associated with pesticide use alone may be insufficient for growers to optimally respond to negative externalities. Coordination can be beneficial to strawberry growers producing in crop fields adjacent to ones with alternative host crops which generate populations of migrating adult whiteflies at certain times of the year. Hence, information exchange among growers may be needed to account for these externalities, but this will likely be done voluntarily under limited conditions. These results have two important policy implications. First, if a public goal exists which does not complement the profit maximization motive of strawberry growers, conditions exist for which it may be necessary to create a central agency for controlling the economic effects pests. In a few instances strawberry growers would pay growers of alternative greenhouse whitefly host crops

in an adjacent field to gather and communicate information about events which trigger adult whitefly migrations. As the above analysis has shown, however, if there are no benefits to receiving the information, policymakers may increase welfare by mandating grower participation in an information exchange managed by the university cooperative extension, a regulatory agency, or other government entity. Optimal design of a mandatory or voluntary program is outside the scope of this analysis. Second, analysis in this paper demonstrated a method that policymakers can use to obtain information about the likelihood of growers successfully developing voluntary coordination measures. A bioeconomic model can enable an analyst to evaluate a series of explanations for how a set of agricultural producers will behave. For example, the model generated results which explain why Oxnard area strawberry growers may not voluntarily coordinate their whitefly management strategies. An analyst can incorporate other factors identified by growers or policymakers as essential determinants of the emergence of voluntary coordination into the model and evaluate their contributions through sensitivity analysis, in order to see whether or not these are likely to affect the conclusion. In this way, the model both predicts behavior and provides a means of identifying future data collection priorities. Acknowledgements The author thanks Pat Thompson for invaluable research support and Frank Zalom, Rachael Goodhue, and anonymous reviewers for helpful comments. This research was supported by the Program of Research on the Economics of Invasive Species Management (PREISM), Economic Research Service of the USDA, under Cooperative Agreement 43-3AEM-3-80081. References Benchwick, B, 2005. Chairman, Whitefly Action Committee, Personal Communication Bi, J., Toscano, N., Ballmer, G., 2002a. Field evaluations of novel chloronicotinyls and insect growth regulators against the greenhouse whitefly on strawberry. Hortscience 37, 914–918. Bi, J., Toscano, N., Ballmer, G., 2002b. Greenhouse and field evaluation of six novel insecticides against the greenhouse whitefly Trialeurodes vaporariorum on strawberries. Crop Protection 21, 49–55. Bi, J., Toscano, N., Ballmer, G., 2002c. Seasonal population dynamics of the greenhouse whitefly Trialeurodes vaporariorum (Homoptera: Aleyrodidae) on strawberries in southern California. Journal of Economic Entomology 95, 1179– 1184. Bolda, M., Tourte, L., Klonsky, K., De Moura, R., 2004. Sample Costs to Produce Strawberries: Central Coast Region, Monterey and Santa Cruz Counties. University of California Cooperative Extension Report ST-CC-04. (accessed April 2010). Bureau of Labor Statistics. Producer Price Index. . Byrne, D., Bellows, T., 1991. Whitefly biology. Annual Review of Entomology 36, 431–457. Byrne, D., Bellows, T., Parrella, M., 1990. Whiteflies in Agricultural Systems. Intercept, Ltd., Andover, Hants, UK. pp. 227–261. California Department of Food and Agriculture, 2009. California Agricultural Resource Directory 2008. Sacramento, California Department of Food and Agriculture, CA. California Department of Pesticide Regulation, 2003. Emergency Registration Number 03-18. . California Processing Strawberry Advisory Board, 2003. Personal Correspondence. California Strawberry Commission, 2003. Request for Section 18 Emergency Exemption. Unpublished, Watsonville, Calif. California Strawberry Commission, 2005. 2004 Acreage Survey. Watsonville, CA. . Carter, C., Chalfant, J., Goodhue, R., McKee, G., 2005. Costs of 2001 methyl bromide rules estimated for California strawberry industry. California Agriculture 59, 41–46. Daugovish, O., Takele, E., Klonsky, K., De Moura, R., 2004. Sample Costs to Produce Strawberries: South Coast Region, Oxnard Plain. University of California

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