Wild blueberry, Vaccinium angustifolium Aiton, is a native forest understory plant that is managed as a fruit crop. Over the past 51 years, experiments have been conducted to investigate its reproduction. A model was developed that predicts bloom to begin at 100° days (base 4.4 °C) after 1 April and to end at 500° days for a period of three to four weeks. Flower stigmas are only receptive to pollen deposition for eight to 10 days, and the rate of fruit set declines rapidly after four days. Placement of pollen upon receptive stigmas suggests that fruit set occurs with as little as a single pollen tetrad. Twelve tetrads result in 50% fruit set. Several years of exploratory fruit set field experiments show viable seeds per berry, which result from pollination with compatible genotype pollen, is associated with larger berry mass (g). Decomposition of the total variance in fruit set shows that stem variation explains 65% to 79% of total variance in the fruit set. To a lesser extent, the field, year, and clone also explain the percent fruit set variation. Variation between stems may be due to variation in the number of flowers. Fruit set tends to decrease as the flower density increases, possibly due to the limitation of pollinators.

A long-term investigation of D. suzukii dynamics in wild blueberry fields from 2012–2018 demonstrates relative abundance is either still increasing or exhibiting periodicity seven years after the initial invasion. Relative abundance is determined by physiological date of first detection and air temperatures the previous winter. Date of first detection of flies does not determine date of fruit infestation. The level of fruit infestation is determined by year, fly pressure, and insecticide application frequency. Frequency of insecticide application is determined by production system. Non-crop wild fruit and predation influences fly pressure; increased wild fruit abundance results in increased fly pressure. Increased predation rate reduces fly pressure, but only at high abundance of flies, or when high levels of wild fruit are present along field edges. Male sex ratio might be declining over the seven years. Action thresholds were developed from samples of 92 fields from 2012–2017 that related cumulative adult male trap capture to the following week likelihood of fruit infestation. A two-parameter gamma density function describing this probability was used to develop a risk-based gradient action threshold system. The action thresholds were validated from 2016–2018 in 35 fields and were shown to work well in two of three years (2016 and 2017).

Maine, USA is the largest producer of wild blueberries (Vaccinium angustifolium Aiton), an important native North American fruit crop. Blueberry fields are mainly distributed in coastal glacial outwash plains which might not experience the same climate change patterns as the whole region. It is important to analyze the climate change patterns of wild blueberry fields and determine how they affect crop health so fields can be managed more efficiently under climate change. Trends in the maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures, total precipitation (Ptotal), and potential evapotranspiration (PET) were evaluated for 26 wild blueberry fields in Downeast Maine during the growing season (May–September) over the past 40 years. The effects of these climate variables on the Maximum Enhanced Vegetation Index (EVImax) were evaluated using Remote Sensing products and Geographic Information System (GIS) tools. We found differences in the increase in growing season Tmax, Tmin, Tavg, and Ptotal between those fields and the overall spatial average for the region (state of Maine), as well as among the blueberry fields. The maximum, minimum, and average temperatures of the studied 26 wild blueberry fields in Downeast, Maine showed higher rates of increase than those of the entire region during the last 40 years. Fields closer to the coast showed higher rates of warming compared with the fields more distant from the coast. Consequently, PET has been also increasing in wild blueberry fields, with those at higher elevations showing lower increasing rates. Optimum climatic conditions (threshold values) during the growing season were explored based on observed significant quadratic relationships between the climate variables (Tmax and Ptotal), PET, and EVImax for those fields. An optimum Tmax and PET for EVImax at 22.4 °C and 145 mm/month suggest potential negative effects of further warming and increasing PET on crop health and productivity. These climate change patterns and associated physiological relationships, as well as threshold values, could provide important information for the planning and development of optimal management techniques for wild blueberry fields experiencing climate change.

Declines in wild and managed bee species richness and abundances have been observed throughout Europe and North America in recent decades. These declines have led to questions regarding pollination of wild and cultivated plants. In response to these concerns, efforts towards the conservation of pollinators have been initiated. Part of this conservation effort should be to provide the basic nutritional needs for bees. Nutrition plays one of the most important roles in bee growth, development, and reproduction. There is a large body of information regarding honey bee nutrition, whereas we lack nutritional information on native wild bees. Our knowledge of bumble bee nutritional needs has increased since the introduction of commercial rearing and sale of certain bumble bee species; however, there is still a lack of basic nutritional guidelines such as minimum dietary needs of proteins, amino acids, lipids, and sterols. The large difference in physiology and life history between honey bees and North American wild bees suggests that their nutritional requirements could be quite different.

Expressed sequenced tagged-polymerase chain reaction (EST-PCR) molecular markers were used to evaluate the genetic diversity of lowbush blueberry across its geographic range and to compare diversity among four paired managed/non-managed populations. Seventeen populations were sampled in a north–south transect throughout the eastern United States with 27 km to 1600 km separating populations. The majority of genetic variation was found within populations (75%) with each population genetically unique (p ≤ 0.0001) with the exception of the Jonesboro, ME, and Lubec, ME, populations. The effects of management for commercial fruit harvesting on genetic diversity were investigated in four locations in Maine with paired managed and non-managed populations. Significant differences were found between the populations indicating that commercial management for fruit production influences the diversity of lowbush blueberries in the landscape, even though planting does not occur. Forests are harvested and the existing understory blueberry plants become established.

Maine is the largest producer of wild blueberry (Vaccinium angustifolium Aiton) in the United States. Pollination comes from combinations of honey bees (Apis mellifera (L.)), commercial bumble bees (Bombus impatiens Cresson), and wild bees. This study addresses (1) previous research addressing wild-blueberry pollination, (2) effects of wild-bee and honey-bee activity densities on fruit set, yield, and crop value, (3) the economic value of wild-bee communities, and (4) economic consequences of pollinator loss. Bee communities were sampled in 40 fields over three years (2010–2012) and bee activity densities were estimated for bumble bees, honey bees, and other wild bees. These data were applied to an economic model to estimate the value of bee taxa. Bumble bees and honey bees predicted fruit set and reduced its spatial heterogeneity. Other wild bees were not significant predictors of fruit set. Yield was predicted by fruit set and field size, but not pest management tactics. Our analysis showed that disruption in supply of honey bees would result in nearly a 30% decrease in crop yield, buffered in part by wild bees that provide “background” levels of pollination. Honey-bee stocking density and, thus, the activity density of honey bees was greater in larger fields, but not for wild bees. Therefore, a decrease in crop yield would be greater than 30% for large fields due to the proportionally greater investment in honey bees in large fields and a relatively lower contribution by wild bees.

Wild blueberry is a native plant managed as a crop in northeastern North America. Fruit drop in wild blueberry, Vaccinium angustifolium Aiton, was studied over 6 years (1996–1999, 2008 and 2011). Three hypotheses were tested regarding fruit drop mechanisms from plants (clones) sampled in the field: (1) proportional fruit drop; (2) constant or uniform fruit drop; and (3) pollen or resource limitation. In addition, the temporal rate of fruit drop was measured between the end of pollination (petal fall) and fruit maturity just prior to harvest (2008 and 2011). The dynamics of fruit drop were different in the two years. The role of pollination intensity in fruit drop was also assessed. Evidence for both proportional fruit drop and resource limitation at the clone level was found but only proportional drop at the field level. Low levels of seed set are associated with fruit drop. In a four-year study (1996–1999), fruit drop ranged from 23.3 to 49.4% of the total set fruit. Partitioning the variance in fruit drop between years, fields and clones (plant genotypes) showed that the highest variation is between stems within clones (37.4%) and clones (plant genotype) within fields (35.4%), followed by between fields (19.8%) and between years (7.4%).

Data deficiency prevents the development of reliable machine learning models for many agroecosystems, especially those characterized by a dearth of knowledge derived from field data. However, other similar agroecosystems with extensive data resources can be of use. We propose a new predictive modeling approach based upon the concept of transfer learning to solve the problem of data deficiency in predicting productivity of agroecosystems, where productivity is a nonlinear function of various interacting biotic and abiotic factors. We describe the process of building metamodels (machine learning models built and trained on simulation data) from simulations built for one agroecosystem (US wild blueberry) as the source domain, where the data resource is abundant. Metamodels are evaluated and the best metamodel representing the system dynamics is selected. The best metamodel is re-parameterized and calibrated to another agroecosystem (Northeast China bog blueberry) as the target domain where field collected data are lacking. Experimental results showed that our metamodel developed for wild blueberry achieved 78% accuracy in fruit-set prediction for bog blueberry. To demonstrate its usefulness, we applied this calibrated metamodel to investigate the response of bog blueberry to various weather conditions. We found that an 8% reduction in fruit-set of bog blueberry is likely to happen if weather becomes warmer and wetter as predicted by climate models. In addition, southern and eastern production regions will suffer more severe fruit-set decline than the other growing regions. Predictions also suggest that increasing commercially available honeybee densities to 18 bees/m2/min, or bumble bee densities to 0.6 bees/m2/min, is a viable way to compensate for the predicted 8% climate induced fruit-set decline in the future.