Recipient Organization
UNIVERSITY OF MAINE
(N/A)
ORONO,ME 04469
Performing Department
(N/A)
Non Technical Summary
Wild blueberry agroecosystems provide valuable ecosystem services including water regulation, nutrient cycling, carbon sequestration/storage, pollination, biodiversity, and provisioning services. Climate change is altering these agroecosystems, though the effects of these changes on ecosystem services and the wild blueberry industry are unclear. Our first goal is to investigate tradeoffs in ecosystem services under probable future climate scenarios, improving our understanding of ecosystem services in temperate perennial crops. Our second goal is to work together as an interdisciplinary team to deliver actionable recommendations to the wild blueberry industry, informing climate smart agricultural management strategies that conserve natural resources. Our team, supported by graduate and undergraduate students, will conduct a climate manipulation experiment in wild blueberries at a new, long-term research trial site at the University of Maine. In this field-based study, we will manipulate temperature and precipitation, and assess soil-water dynamics, cropphenology, symbiotic relationships with ericoid mycorrhizae and pollinators, and disease pressure. We will use our results to validate a new crop wild blueberry model, and project several futures based on plausible temperature and precipitation scenarios. Maps producedthrough this effort will be shared with the community of wild blueberry growers and processors. In addition, we will hold grower focus groups where qualitative and quantitative data will be collected to identify perceptions of production and financial risks, and theprotective benefit of a variety of climate risk mitigation strategies, including supplemental irrigation. Project results will support resilient and sustainable wild blueberry management within socioeconomically challenged, rural communities impacted by climate change.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The project described in this proposal examines ES tradeoffs across three dimensions: (a) at the field level in a factorial block design trial, (b) in a new crop model, applied spatially across the state of Maine, and (c) in the context of wild blueberry grower decision making, explored through focus groups and decision "gamification". Therefore, the long-term goal of this project is to investigate tradeoffs in dynamic ESs under probable future climate scenarios, and bring these findings to bear on management choices wild blueberry growers must make in the context of climate change. Under this broad goal, we have three project-specific goals:Goal 1. Investigate and describe how ecosystem services affect and are affected by wild blueberries in the context of a changing climate.Goal 2: Assess probable future growing conditions for wild blueberries, and associated ecosystem services.Goal 3: Assess farm-scale economics relevant to climate risk mitigation, and identify barriers to adoption of mitigation strategies.
Project Methods
Field-based research trial: We will test three heating regimes and three precipitation scenarios. We will identify 14 wild blueberry parent plants (64 sq. ft minimum) with unique genotypes from the Wyman's production acres in Deblois, Maine. The following variables will be recorded in situ: total plant size, average chlorophyll content, leaf and stem biomass, number of flower buds, stem density, and nutrient concentrations (N, P, and K) in leaves. We will also collect root samples to measure initial ericoid mycorrhizal abundance and diversity. Each parent plant will then be divided into four transplants, which will be brought to the Wyman's Center at the University of Maine and installed in the experiment. Because wild blueberries fruit every other year, the entire planting (community and isolation plots) will be doubled so that half of the plots will be in production in any given season. Each transplant will be planted in either a community plot (a 12'x 12', 4 transplants each) or an isolation plot (a 6'x 6' plot, one transplant each). There will be a total of 32 community plots planted (64 transplants, 4 per plot). There will be a total of 54 isolation plots planted (54 transplants, 1 per plot). Working with key collaborator Hall, PI-Schattman and Co-PI Zhang will be responsible for plant identification, initial measurements, and installation of the experiment.Following methods developed by Co-PIs Zhang and Calderwood, and described in Tasnim et al. (2020) and Zhang et al. (2021), we will construct 110 open top chambers or domes to cover 2.6m2. Open-top chambers will be built from 3 mm polycarbonate sheets (Figure 3), and installed over transplants. Fifty-five actively heated chambers will consistently maintain air temperatures 7-9ºF (3-5ºC) higher than ambient air temperatures. To heat the chambers, we will use a waterproof silicone heating tape (240 W) coiled around a metal tube and fixed inside the chambers. In 55 passively heated chambers, no heating coils will be installed, but we expecttemperatures will be 3-5ºF (2-3ºC) than ambient temperatures during the day. No chambers will be installed over control transplants. Weather stations will be installed in the center of each chamber or control to record temperature, relative humidity, soil temperature, and soil volumetric water content. Co-PI Zhang will lead this portion of the experiment. Rain exclusion shelters will be installed 1.5 m above transplants. Covers will be removed for winter to allow for normal snow accumulation and melt. When plots are established, we will line them (approximately 1 meter deep) with impervious barriers around each plot to reduce lateral water movement through the soil and limit rhizome spread. This approach has been used in studies looking at drought responses of different tree species (Clark & D'Amato, 2019), but has not been applied to small fruit. Pan lysimeters installed beneath the plots (following methods described in Zotarelli et al. (2007)) will allow us to extract leachate; leachate total amounts and nitrate concentrations will be measured 2x each year. We will then simulate precipitation over each transplant using a metered, handheld sprayer.Three precipitation simulation schedules have been developed by PI-Schattman and Co-PI Birkel with input from key collaborator Hall, following an approach developed by our team (Schattman et al. 2022). Preliminary treatment schedules have been built upon daily precipitation observations for the warm season (May 1st - October 30th) for the years 2001 and 2006, from the Jonesboro, Maine (44.6454°N, 67.6495°W; elevation 194 ft; record period 1991-2020) obtained from the PRISM dataset.After plot installation is complete and treatment design has been finalized, we will then measure a series of variables:Water regulationNutrient cyclingCarbon sequestration and storagePollinationAgroecological diversityFood provisioningWild blueberry crop model and risk maps: Our team will develop a parsimonious crop model for wild blueberries. We are currently parameterizing a model for wild blueberries, on a 10km x 10km resolution in a small sub-region of Maine. We will build upon this initial model in the following ways: (1) We will validate phenology measures with those collected in the field experiment; (2) We will apply the model spatially (statewide in Maine), using an expanded set of plausible future climate scenarios that will allow wild blueberry growers to better understand how their industry may change in space and over time; (3) we will calculate the amount of irrigation needed by wild blueberries, and apply this calculation spatially to show what growing regions will require future infrastructure/equipment investments. We will use daily temperature and precipitation values from the PRISM data set (described above) as a historical baseline to create a gridded model. We will then develop up to five plausible future scenarios, using multimodalmeans from CMIP6 to ensure that forecasted weather anomalies are present (including extreme heat and rainfall). By doing so, our models will have realistic daily variability in temperature and precipitation. Model outputs will include multiple ESs and their spatiotemporal variation (i.e. food provisioning, carbon storage, and water regulation). This will enable examining which ESs are likely to be robust, now and in future decades, across regions that produce wild blueberries in Maine.Economic assessment, focus groups, and gamification: We will update the costs of installing supplemental irrigation in the context of wild blueberry operations. While these costs have been rigorously detailed in wild blueberries (Dalton et al., 2003) prior guidance documents are 20-years old and do not represent the dramatically different costs of irrigation equipment and supplies. Our updated analysis will include clear financial break-even points identified for production at different scales (i.e., 5-, 10-, 25-, 50-, 100-, and 500-acres) and many types of irrigation systems (i.e., hand-set sprinkler guns, hose-reel systems, moveable small sprinklers, and permanent set sprinklers). Additionally, past estimates were created without precise information on the protective yield impact of supplemental irrigation or the temporal distribution of events requiring use of supplemental irrigation. These critical inputs in the cost-benefit calculation will be used to generate more realistic, location-specific estimates of the cost effectiveness of irrigation for growers of various scales.To study the financial and nonfinancial constraints to adoption of climate risk management strategies (including but not limited to irrigation), we will lead a series of focus groups with wild blueberry growers (6 focus groups, between 10-20 growers per group). These focus groups will allow us to identify perceptions of production and financial risks, and the protective benefit of a variety of climate risk mitigation strategies, including supplemental irrigation.Through the focus groups, insight will be generated into grower perceptions of both the risks posed by extreme weather events and the capacity of mitigation strategies to ameliorate those risks.