Performing Department
(N/A)
Non Technical Summary
To improve current management strategies of plum curculio, an integrated pest and pollinator management strategy will be investigated. Plum curuclio is a persistent pest of blueberries in the mid-Atlantic and damage fruit through a scar left from oviposition and feeding damage and premature fruit drop from larvae. Management can be conducted post-bloom, but pesticides cannot be applied while honey bees are present. Because plum curculio adults arrive in blueberries around peak bloom, growers cannot adequately respond to increasing populations. Honey bee hives are currently kept in blueberries until the end of bloom, but whether this contributes significantly to yields is not known. The benefits of removing hives early to increase yields and better mange plum curculio may outweigh any effects on late-season fruit production. This projects goal is to use plum curculio managmeent as a model for effective creation and implementation of integrated pest and pollinator managementstrategies in pollinator-dependent crops. The specific objectives of this multi-disciplinary project are: 1) create a bloom phenology model as a predictor of plum curculio arrival; 2) determine how yeild is effected by earlier plum curuclio management and hive removal and use a cost-benefit analysis to demonstrate economic impact and; 3) deliver and disseminate information among stakeholders. Our project creates an innovative, ecologically-based, sustainable integrated pest managementstrategy for plum curculio and enhances its adoption.
Animal Health Component
0%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Goals / Objectives
The goal of this proposal is to develop predictive phenological models for blueberry bloom, aimed at improving insect pest management, while optimizing pollination services.Objective 1. (Research). Determine plum curculio management and yield effects of late bloom pollination removal, assess bee community activity throughout bloom, and develop cost-benefit analysis, and determine optimal pollination duration.Objective 2. (Research). Create a bloom phenology model as a predictor for plum curculio activity.Objective 3. (Extension). Deliver information among stakeholders.
Project Methods
Objective1. Determine plum curculio management and yield effects of late bloom pollination removal, assess bee community activity throughout bloom, and develop a cost-benefit analysis. Effect of plum curculio management and pollination from early removal of honey bees on blueberry yield. We will use two varieties of highbush blueberry (var. 'Duke' and 'Draper') on farms located in the main blueberry growing region of New Jersey. At each farm, bushes from the edge and interior rows will be selected for sampling for each variety. Surveys of plant phenology will be conducted weekly during the bud phase until bloom cessation to determine bloom progression. Once fruit begins to mature, successful berry set will be counted, and ripe berries will be harvested from the clusters following typical harvest schedules. Once harvest has ceased, remaining berries will be collected. For all berry samples data will be collected on fruit size and weight, seed weight, and evidence of plum curculio damage or larval presence in the berry. Plum curculio adult activity will be measured using either 1 grandisoic acid/benzaldehyde-baited pyramid trap per field or on-bush sampling of 10 bushes using beating sheets (edge and interior). In addition, we will utilize historical data on plum curculio captures from the Rutgers Fruit IPM Program together with data generated from this project to create a degree-day model for plum curculio adult activity in relation to bloom.To better understand the effects of bee removal time on plum curculio damage and fruit yield, we will conduct a manipulative exclusion study. We will use pollinator exclusion bags to replicate removal of honey bee hives at 35, 50, 65, 80, and 95% bloom. Bagging of flower clusters will also serve to estimate damage reduction from earlier management of plum curculio. After bloom cessation and honey bee removal, bags will be re-opened so the cluster is fully exposed to field management practices. For all berry samples, we will measure fruit size and weight, seed weight, and evidence of plum curculio damage and larval presence in the berry.Assessment of bee community composition during bloom. In the same farms used previously two sets of paired 50m transects (n=4) will be established in blueberry plots and will run on interior and exterior rows separate from the pollinator exclusion experiments to avoid disturbance. All bee sampling will be conducted during optimal hours and weather conditions to standardize between days. Collection will occur by either hand or net-collecting any bee observed foraging on the blueberry flowers along the transects. Community composition and amount present will be quantified.Cost-benefit analysis. We will conduct a cost-benefit analysis (CBA) to determine the benefits and costs of early honey bee hive removal on damage by plum curculio and blueberry pollination (yield). Direct and indirect costs and benefits associated with implementing the proposed management strategies will be determined and quantified. Direct costs are related to management of plum curculio as well as potential yield loss in later varieties while benefits include increased yields in early varieties, increased hive health, and more effective plum curculio management. Indirect costs and benefits will examine broader societal and environmental impacts of altered management strategies.The Net Present Value (economic feasibility) and Benefit-Cost Ratio (beneficial economic outcome) will be determined based on predictions for changing market prices over several time spans to provide the best indicator of short and long-term effects of the management strategy. Based on the analysis, we will decide on the implementation of the strategies, considering the financial feasibility, environmental sustainability, and broader societal implications and provide a numerical representation of the costs and benefits associated with the proposed project.Objective 2. Create a bloom phenology model as a predictor for plum curculio activityWeather/climate data acquisition. We will collect weather/climate data from highbush blueberry fields to monitor the bloom period. These fields will be in and around Hammonton, NJ, the largest blueberry growing location in New Jersey. Weather and climate data for Hammonton will be obtained from the Office of the New Jersey State Climatologist (ONJSC) weather station in Hammonton (latitude: 39.635, longitude: -74.758). Shope and Oudemans have previously retrieved and archived weather station data from 2012 to 2022. Recent data corresponding to project years will be obtained from the ONJSC at an hourly resolution.Chilling and growing degree day models. We will use the weather/climate data to develop chilling and growing degree-day models. Highbush blueberry bloom development and duration is both a function of winter accumulated chilling time and accumulated growing degree-days in the spring. We will calculate the accumulated hourly chill time initially using a biofix of Jan 1. Chilling time will be evaluated using four separate models, and the model with the best fit to observation data will be used in reporting and tool development.Growing degree days (GDD) with a base of 45°F will be calculated using a biofix of January 1. GDDs will be calculated by first averaging the hourly timeseries into daily average temperature and accumulating the degree days above 45°F.Bloom phenology data and phenological modeling approach. GDD and chilling models will be processed from daily values into cumulative functions across the early growing season to capture the start and end of bloom each year. In each case, the chilling models and the GDD biofixes will be calibrated using field measurements. Shope and Oudemans (unpublished) have utilized a Julian day 1 (January 1) biofix for chilling accumulation and a Julian day 60 biofix for GDD accumulation to develop bloom onset predictive models. For this project, however, multiple biofixes will be tested to establish the best model fit thresholds, staring with January 1 for both chilling and GDD.Bloom percentage data will be averaged at each observation day each year. These percentages will be converted into a cumulative function from 0 to 100% of buds having bloomed each year. This approach will be used establish the start and end of the bloom stage whereby the start of bloom when 5% of the total blooms have occurred for a year and the end of bloom when 90% have occurred for a year. These cutoffs help reduce the influence of outliers that may bloom very early or persist until much later in the season All year's bloom percentage values throughout the season will be related to chilling, GDDs, and a combination of the two. Where appropriate, the curves will be fit to a representative sigmoidal model to aid in data synthesis, interpretations, and application for an online tool.Objective 3. Deliver information among stakeholders. Extension/Outreach activities. Phenology model will be made available as a calculator through the P.E. Marucci website to be used in conjunction with the existing degree day calculator and chilling hour calculator (https://benedick.sebs.rutgers.edu/BlueberryWeather/). Researchers with extension responsibilities (Drs. Rodriguez-Saona, Oudemans, Shope, and Ferguson) will disseminate information through traditional extension outlets like grower meetings, beekeeper meetings, newsletter articles (Rutgers Blueberry bulletin), blogs (Plant & Pest Advisory), websites (https://pemaruccicenter.rutgers.edu/), and fact sheets. At least two presentations at grower meetings and one presentation at beekeeper meeting will be performed annually by PIs. Surveys of both grower and beekeeper knowledge and willingness to adopt our proposed management strategy will be conducted in Year 1 (pre-assessment) and Year 2 (post-assessment).