New cherry fruitworm tool for IPM in blueberries

This degree day predictive model will help control cherry fruitworms in blueberries.

In the process of improving pest control in small fruit crops like blueberries, developing new tools that maximize the efficacy of currently used reduced risk pesticides is a key element for successfully implementing any reduced risk IPM program.

The elimination of organophosphate insecticides as main tools for pest control in blueberries and other small fruit crops has resulted in new challenges for growers and IPM practitioners. New reduced risk insecticides substituting insecticides such as Guthion (azinphos methyl) are pest-specific, require ingesting by the insect and are less potent than conventional organophosphate insecticides. However, improving monitoring techniques for timing pesticide applications and use of multiple tactics for pest control increase the effectiveness of reduced-risk insecticides. For example, Atanassov and others developed a program to reduce organophosphorus and carbamate insecticide use in New Jersey’s peach production and mitigate their associated risks. Their reduced-risk program provided a level of pest control equal or better than conventional peach pest management programs while using fewer organophosphorus and carbamate insecticides. The key for their success was improved monitoring and timely application of reduced-risk insecticides.

In Michigan, fruitworms are a serious pest of blueberries. When uncontrolled they may damage up to 80 percent of the blueberry crop. With the elimination of OP insecticides, timing for pesticide application has become a critical issue. Efficacy of insecticides recommended as substitutes for Guthion – Confirm and Intrepid among others – depends on synchronizing the application of the product with the time when the insect is exposed to the action of the insecticide. The critical time for controlling these insect pests is from the time the egg is deposited by the female on the fruit until just before the larva enters the fruit. Once inside the fruit, the larva is immune to the effect of reduced-risk insecticides. Therefore, for the control of fruitworms, predicting the insect phenology is critical for timing applications of reduced-risk insecticides.

To improve fruitworm management in blueberries, we developed two predictive models using the daily Growing Degree Day (GDD) accumulation. The first model for cranberry fruitworms was developed over a period of 5 years and is already part of the MSU’s Enviro-Weather web site. The second model, for cherry fruitworms has just been finished and we are expecting to load it into Enviro-Weather very soon. Following is a preview of this model.

During the past three years we successfully developed a set of equations that describe with great accuracy the cherry fruitworm adult emergence as well as the egg-laying period. Data collected from 2007 to 2009 in selected sites where weather recording devises and pheromone traps were next to each other indicted that on average cherry fruitworm adults emerge around 238 ± 30 GDD and the egg-laying period starts 432 ± 15 GDD (Base 50° F) accumulated after March 1. The relationship between cumulative percentages of both male moth captures in pheromone traps and oviposition and daily GDD accumulation was described using a three-parameter Weibull function. The three-parameter distribution allows for the introduction of a biofix value to start the calculation of the distribution. The resulting model was:

f(x) =1- exp[-((β-α)/γ)η], where f(x)=cumulative CFW at T(t), β=cumulated GDD at T(t), and α=biofix. The parameters γ and η are scale and shape parameters of the Weibull distribution.

For the adult population the resulting model was Y=1- exp ((CumGDD-Biofix)/277.02)2.504, where biofix=238 ± 30 GDD (base 50°F).

For egg-laying the model was: Y=1-exp ((CumGDD-Biofix)/277.03)2.224, where biofix=431±30 GDD (Base 50°F).

Table 1 summarizes the regression analysis to estimate parameters utilized to develop the model.

These equations were validated with field collected data from 2007 to 2009 at four blueberry farms located across West Michigan. The cumulative adult emergence and oviposition percentages were compared with the model’s predictions. Results indicated that in all cases, the difference between the observed dynamics of adult emergence and/or egg lying and the distribution predicted by those equations was not statistically significant.

This model when fully implemented at Enviro-weather will be another powerful tool to optimize the use of insecticides in blueberry production. An improved pest control of fruitworms early in the season will also have an important impact in decreasing the amount of pesticide residues that may end up in surface waters and on the preservation of beneficial insects such as pollinators and natural enemies of insect pests.

Table 1. Cherry Fruitworm Model; Regression analysis to estimate the Weibull distribution parameters for cherry fruitworm adult emergence and egg-laying.

Life stage Biofix (GDD50) Regression parameters   r²
Intercept (α)(± SE) Slop (β)(± SE)
Moths 238 ± 30 5.62 ± 0.036 0.399 ± 0.018 0.95
Eggs 431 ± 30 5.62 ± 0.024 0.449 ± 0.016 0.93

Literature referenced

Dr. Isaacs’ work is funded in part by MSU‘s AgBioResearch.

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