Environmental studies professor Greg Gilbert and Center director Carol Shennan served as faculty advisors to the project, with Leap, garden manager Christof Bernau, and members of the apprenticeship course offering additional advice and information. Research for and development of the web site was supported by funds from the Center’s competitive research grants program .Blueberries offer small-scale growers a potentially profitable “niche” crop that can be developed as a U-pick operation or incorporated into other marketing activities. Although the plants need several years to get established and require careful soil preparation and fertility management, a successful blueberry crop can generate $30,000 to $50,000 per acre . To learn more about the best-performing varietal options for organic growers on California’s central coast, the Center initiated a variety trial of mostly low-chill, highbush blueberries at the UCSC Farm in the fall of 2003. This project is being conducted in collaboration with Aziz Baameur, Small Farm Program Advisor for Santa Clara County’s UC Cooperative Extension office, and Mark Bolda, UCCE’s central coast Strawberry and Caneberry Advisor. Blueberries need well-drained, acidic soil in order to thrive. In November 2003, UCSC Farm manager Jim Leap applied sulfur to the trial site at a rate of approximately 2,000 pounds per acre as well as 3–4 inches of acidic mulch, then created raised beds for the plants. With the help of second-year apprentices Aaron Blyth, Carissa Chiniaeff, Allegra Foley, Estrella Phegan, Ratoya Pilgrim, and Matthew Sutton, round pot the research team planted out 17 varieties of blueberries in January 2004. The trial includes 4 replicates of each variety planted on 3-foot plant in-row spacing with 5 feet between rows.

Peat was applied in the planting hole to further lower the pH. Varieties being tested are: Biloxi, Bluecrop, Duke, Emerald, Jewel, Jubilee, Misty, Oneal, Ozarkblue, Millennia, Santa Fe, Sapphire, Sharpblue, Southern Belle, Southmoon, Star, and Windsor. After planting, the beds were mulched with several more inches of acidic bark, and drip tape was laid on top of the mulch. Plants are irrigated weekly with the drip tape, and during each irrigation vinegar is injected into the irrigation water to maintain a low pH. Phytamin, a liquid nitrogen fertilizer, is being applied through the drip lines monthly during the summer to maintain adequate nitrogen levels and get the plants off to a strong start. Over the next several years, the research group will evaluate a variety of factors, including overall plant vigor, disease and pest resistance, and eventually, harvest dates, fruit taste and quality, and fruit production. Although the first harvest is still 12 to 18 months away, Leap is excited about the trial. “Blueberries offer a great marketing opportunity for small scale organic growers,” he says, adding that, “this project has also created great opportunities for interactions between the Center and our local UCCE advisors.” A blueberry field day organized by the Center, UCCE, and the Community Alliance with Family Farmers was held in early June, bringing farmers and gardeners to the UCSC Farm for a look at the new plantings. Speakers included Baameur, Leap, and Bolda, as well as UCCE researchers Richard Smith, who discussed organic weed management, and Laura Tourte, who talked about blueberry economics and marketing.As an environmental scientist, Center faculty affiliate Deborah Letourneau believes policy decisions should be based on the best information available at the time. That’s why she’s trying to fill an information gap with her latest research on genetically modified plants.

As insect-resistance is bred into major crops, Letourneau wonders how those crops’ wild relatives might be affected if they pick up the new traits. “There’s been a lot of research on crop-to-crop movement,” said Letourneau, referring to the contamination of organic corn grown adjacent to genetically modified corn. “But we don’t know that much about the biology of wild crop relatives. If genes transferred, would it make them more weedy, more hardy, more invasive?” To address these questions, Letourneau, a professor of environmental studies at UCSC, along with doctoral candidate Joy Hagen and Ingrid Parker, an associate professor of biology, have begun a three-year study to see what the consequences would be if GM genes transferred from Brassica plants through cross-pollination to their wild relatives. Plants in the Brassica, or cole, family include many vegetable crops, such as broccoli, Brussels sprouts, cabbage, cauliflower, and kohlrabi, as well as common weeds like wild radish and wild mustard. “Weed problems translate into economic problems for farmers,” said Letourneau, noting that 75 percent of cole crop production in the United States is concentrated on the Central Coast of California. Stubborn weeds require more herbicide applications, with accompanying higher labor costs and environmental impacts, she said, adding that highly invasive weeds can threaten native species on non-agricultural lands, too. Letourneau is a leading authority on the genetic modi- fication of plants. A member of the National Academy of Sciences’ 12-member panel investigating the environmental consequences of GM plants, she also coedited the 2002 book, Genetically Engineered Organisms: Assessing Environmental and Human Health Effects. Parker’s background is in applying mathematical models to ecological risk assessment for GM crops. A growing number of crops are being genetically modified to increase insect resistance. More than 25 percent of corn grown in the United States has been genetically engineered to contain the toxin of the Bacillus thuringiensis soil bacterium, which disrupts the digestive system of a caterpillar.

Transgenic cotton and potatoes also produce Bt toxin. Little is known about the role Bt-susceptible herbivores, round plastic planter including caterpillars, play in regulating the health and spread of wild crop relatives. In their research project, Letourneau and Hagen are protecting wild relatives from caterpillar damage to see what could happen if modified genes moved from Brassica crops to their wild relatives. The simulation is necessary because the research is being conducted in open fields—not inside greenhouses—where risks of contamination by GM plants would be high, said Letourneau. To mimic an effect of gene transfer, the UCSC researchers are spraying Bt on wild radish and wild mustard growing adjacent to commercial cole crops, and they will use models to evaluate the subsequent fitness, weediness, and invasiveness of the weedy relatives, said Letourneau. “We can’t use real transgenic crops, but we wanted to conduct this work where wild relatives live side-by-side with commercial crops,” said Letourneau. Research sites include the Center’s on-campus Farm and agricultural parcels adjacent to natural ecosystems from Wilder State Park to Elkhorn Slough Reserve. Genetic links between crops and weeds are remarkably common, and cole crops are no exception, noted Parker. “In the past, the evolution of many weeds has been driven by genes coming from crops,” she said. “Now those genes will be specially engineered by humans.” Research on consequences for wild relatives is overdue, said Letourneau, noting that field-testing of GM cole crops for California has been under way since 1999. “This kind of research is important now, during the process of risk assessment, to know whether new modified crops should be deregulated or not,” she said. “There are a lot of Bt crops in the pipeline. Anything we can find out now can be used by regulators to make more informed decisions.” Letourneau takes nothing for granted as the research gets under way. The project will use a large number of sample plants on varied research sites, and the experiments will be replicated over three years. Hazards of GM corn, including allergenicity and contamination of adjacent fields, were identified during extensive testing that was required because it is a food. Because similar tests are not required on nonfood plants, it’s harder to know what the hazards might be, and what the probability is that they’ll occur, said Letourneau. “It might be that transgene movement to wild relatives would be no problem at all,” she said. “If we don’t detect any problems or hazards, we’ll feel we’ve tried to provide the data needed for risk assessment.” The three-year project is funded by a $335,000 grant from the U.S. Department of Agriculture.Botta’s pocket gopher , the smallest gopher in the U.S. at approximately 6 inches long, is the dominant species in central California. The pocket gopher is named for the external cheek pouches it uses to carry food and nesting materials down into tunnel storage areas. They feed on a wide variety of vegetation, but generally prefer herbaceous plants, shrubs, bulbs, and trees. Gophers can bear two to four litters per year of up to ten pups each , so populations can climb quickly under ideal conditions. Once weaned, the young disperse immediately, traveling on the surface to search for new, unoccupied territory. Except during the breeding season, pocket gophers are solitary and territorial. Population densities average approximately 30–40 per acre, although up to 200 per acre have been observed where food is plentiful and other conditions are favorable. As they dig their burrows, gophers push soil to the surface, creating mounds of loose soil adjacent to the plugged burrow entrance. A gopher usually creates one to three new mounds per day, excavating and constantly enlarging and moving its main feeding burrow. Gopher numbers are often overestimated due to this activity, and to the mistaken belief that gophers live in colonies. Because they are quick to repopulate empty burrow systems it may appear that the burrows are populated communally, when in fact gophers will fight to the death to protect their territories.By thinking of a gopher infestation as a pest problem that has similar attributes to, for example, an insect pest problem, cultural practices can be adjusted to create conditions that discourage the presence of gophers. As with other pests, gopher populations increase when food is abundant. Leaving overwintering corn trash or other culls that do not decompose rapidly in the field will boost the gopher population. Weeds that gophers prefer to feed on, such as malva , dock, clovers and dandelions, will also help maintain a higher wintering population. Artichokes and other crops with large crowns are especially susceptible, and some growers have begun to grow these crops as annuals in part to avoid building up gopher populations in the winter season. Young orchard trees seem to provide the most winter-time food for gophers; however, mature orchards and vineyards also harbor gophers through the winter months.Some cover crops can both benefit your crop rotation or winter fallow and help limit gopher populations. Research has shown that gophers much prefer clover cover crops over small grains such as barley, oats and Sudan grass. And although most clovers attract gophers there is a sour clover that appears to discourage them. This can be used as a winter cover combined with a small grain to move populations out of the fields to areas where they can be trapped. I’ve also observed that gopher populations move to farm road edges and other border areas when a winter cover crop of bell beans or fava beans are planted. A focused trapping effort in these areas during winter will help limit breeding numbers. Be aware, though, that many studies have shown gophers to be extremely adaptable in their feeding habits, so no cover crop will guarantee a gopher-free field. When considering rotations on diverse farms, include gophers in the equation. If you follow a crop that attracts gophers, such as potatoes, with another that they feed on, like onions, you will exacerbate gopher problems by providing a continual food source. However, if you follow potatoes with a sour clover or small grain, populations are less likely to rise.Farmers and gardeners have tried all manner of barriers to discourage gophers. These include wire mesh, gravel, trenches filled with glass and rocks, corrugated roofing, even trenches with buried buckets that act as pitfall traps—anything that presents an obstacle for persistent gophers. These all have some effect on slowing invasions. The most promising approaches are those that create both an above- and below-ground barrier. One of the most successful is fencing made of steel corrugated roofing. Not only is it impenetrable, but gophers cannot climb the exposed portion. Because gophers can scale a welded wire fence, above-ground wire barriers must have the wire bent outward at the top or a wooden or metal rim installed. I’m currently experimenting with a material called “Root Guard,” a thirty-six inch wide plastic sheeting seventy mils thick used by landscapers to keep bamboo roots from spreading.

Root dry weight of inoculated plants was highest after using oxathiapiprolin at either rate or fluopicolide at the high rate in both experiments, mandipropamid at the high rate in the second experiment, or fluopicolide at the low rate in the first experiment. Increases as compared to the control ranged from 192.8% to 306.5% . Root dry weight was not significantly different as compared with the control after potassium phosphite treatment in the first experiment.In this study, the four new Oomycota-targeting fungicides ethaboxam, fluopicolide, mandipropamid, and oxathiapiprolin demonstrated high in vitro toxicity with relatively low mean EC50 values to the avocado root rot pathogen P. cinnamomi. The in vitro sensitivities for each of these compounds displayed a unimodal distribution and a narrow range of EC50 values for mycelial growth inhibition of 71 isolates representing the current P. cinnamomi population in major avocado growing areas in California. The narrow ranges in sensitivities among isolates with no distinct less sensitive outliers in the distribution may suggest a reduced potential for selection of resistance with the proper use of these fungicides. Because P. cinnamomi isolates were never previously exposed to ethaboxam, fluopicolide, mandipropamid, and oxathiapiprolin, the sensitivity ranges reported herein can be referred to as baseline distributions that can be used as references in future monitoring for fungicide resistance in populations of the pathogen.In our study, 10 liter drainage pot oxathiapiprolin had the lowest EC50 values for all isolates among the new fungicides evaluated ranging from 0.0002 to 0.0007 µg/ml. This fungicide also was shown to be highly inhibitory to other Phytophthora spp. from a wide range of hosts by others with mean EC50 values of less than 0.001 µg/ml .

Similarly, Gray et al. found that oxathiapiprolin had the lowest range of EC50 values of 0.0002 to 0.0015, 0.0002 to 0.0003, 0.0003 to 0.001, and <0.0003 µg/ml for P. citrophthora, P. syringae, P. nicotianae, and P. hibernalis, respectively, as compared with the other three compounds. Together, reported inhibitory values for oxathiapiprolin are generally 10- to 1000-fold lower than those for ethaboxam, fluopicolide, mandipropamid, and mefenoxam, depending on the fungicide-species combination. Thus, the in vitro toxicity of oxathiapiprolin to P. cinnamomi from avocado reported in our study is lower than for any previous fungicide evaluated against this pathogen. EC50 values for fluopicolide, mandipropamid, and ethaboxam for P. cinnamomi in our study were also within the range of values previously determined for several other Phytophthora spp. . The range of EC50 values for mefenoxam in our study was similar to that previously reported for P. cinnamomi from avocado , Fraser fir , and woody ornamentals in the United States. Thus, the current usage pattern for this fungicide to control avocado PRR in California nurseries and orchards has not resulted in mefenoxam resistance in P. cinnamomi populations.In contrast to the other fungicides, a wide range of in vitro sensitivities was detected for potassium phosphite, and there was a significant difference in mean EC50 values between isolates from the two geographical regions, confirming a previous report . The higher value for isolates from southern California production areas may be due to higher field rates or more frequent applications of potassium phosphite to manage PRR in avocado orchards. The bimodal distribution for the 71 isolates in this study separates the current pathogen population into two sensitivity groups indicating a shift in population sensitivity. A baseline for this compound, however, was never established before commercial field usage. Still, prolonged use of phosphite caused a shift toward reduced sensitivity of P. cinnamomi isolates from avocado orchards in Australia and South Africa .

Phosphonate resistance has also been reported for P. cinnamomi from Chamaecyparis lawsoniana in nurseries , downy mildew of lettuce , and recently in P. citrophthora, P. nicotianae, and P. syringae from citrus in California . With direct and indirect effects on the pathogen, the resistance potential of potassium phosphite is considered relatively low . The extensive and often sole use of this FRAC group in California avocado orchards to combat PRR , however, is expected to eventually lead to resistance. In our greenhouse studies, avocado seedlings and rootstocks were inoculated with P. cinnamomi isolates from southern avocado production areas that have been described as more virulent . A high incidence of PRR developed on untreated control plants of seedlings and both rootstocks with more than 75% of plated root pieces colonized by the pathogen. The high incidence on the Dusaâ rootstock that is considered more tolerant to PRR is likely due to our selection of discolored root pieces for plating of all samples. The four new fungicides were moderately to highly effective in reducing PRR and P. cinnamomi populations in rhizosphere soil of the avocado seedlings and rootstocks used. Overall, oxathiapiprolin was the most effective among fungicides evaluated. In experiments with Zutano seedlings, the efficacy of oxathiapiprolin at the low rate of 70 g/Ha was 2- to 33-times higher than that of the other fungicides and 2- to 4-times higher than that of mandipropamid, a CAA fungicide. In a study on managing P. capsici on peppers , the difference in effectiveness of oxathiapiprolin at 30 g/Ha as compared to the CAA dimethomorph at 262.5 g/Ha was similar to our study using the same FRAC codes of fungicides. In response to reducing PRR, avocado plants treated with oxathiapiprolin generally developed more shoot and root growth as compared with untreated plants. On the avocado seedlings and rootstocks used, fluopicolide, mandipropamid, and ethaboxam treatments also effectively reduced the incidence of PRR compared with the control. P. cinnamomi propagules in the rhizosphere soil were only significantly reduced on the Zutano seedlings and the Dusa rootstock. These latter treatments were often significantly more effective than potassium phosphite or mefenoxam; whereas fluopicolide often performed statistically similar to oxathiapiprolin. Still, the efficacy of potassium phosphite was demonstrated with significant reductions in PRR on the seedlings and rootstocks although its overall performance may have been compromisedby the use of three P. cinnamomi isolates with reduced sensitivities to the fungicide in our soil inoculations. These results also could explain why potassium phosphite is still effectively used in managing PRR in California since many growers cultivate avocado trees grafted on the Dusaâ rootstock. Thus, highly effective alternatives to mefenoxam and the phosphonates were identified by us for the management of avocado PRR. Oxathiapiprolin, fluopicolide, mandipropamid, and ethaboxam previously demonstrated high efficacy against selected foliar and root diseases of vegetable and tree crops caused by Oomycota organisms in greenhouse and field studies. Thus, the four fungicides were highly efficacious in reducing Phytophthora root rot of citrus caused by P. nicotianae and P. citrophthora . Oxathiapiprolin, fluopicolide, and mandipropamid were more effective in managing P. capsici on watermelon than mefenoxam or potassium phosphite . In other studies, oxathiapiprolin was shown to be highly effective in managing diseases of vegetable crops caused by Phytophthora species including P. capsici and P. infestans and controlled black shank of tobacco caused by P. nicotianae . Ethaboxam was shown to be an effective treatment for tomato late blight , as well as Phytophthora blight of pepper .

Based on our studies, 25 liter pot registration of oxathiapiprolin for use on avocado has been initiated through the Inter-regional Research Project No. 4 , and ethaboxam,fluopicolide, and mandipropamid are proposed for further development on avocado. Additional evaluations will have to be done under field conditions using rootstocks with different growth characteristics and susceptibilities to PRR. The availability of fungicides with new modes of action and options for rotation and mixture programs using previously registered and new fungicides will help reduce the risk of development and spread of resistance in P. cinnamomi populations in California avocado production. Growers currently rely heavily on the use of phosphonate-based fungicides, and as we demonstrated, pathogen populations are shifting towards reduced sensitivity to this fungicide class. Thus, there is an urgent need to register fungicides with new modes of action. In our greenhouse studies, overall treatment efficacy in reducing PRR and soil inoculum levels of the pathogen on the susceptible PS.54 was reduced as compared with the more tolerant Dusaâ rootstock, indicating additive effects of fungicide use and rootstock selection. In an integrated approach for a durable and effective management of PRR that allows the continued economical production of avocados in P. cinnamomi infested soils, the use of tolerant rootstocks is critical along with irrigation management and cultural practices such as using mulching and planting in areas with good soil drainage.Plant pathogenic oomycetes fall into two general categories when it comes to pathogenicity. There are Phytophthora species that can infect only one, or a few different hosts like Phytophthora infestans de Bary, and then there are species that can infect hundreds or even thousands of different plant species such as P. cinnamomi Rands . P. cinnamomi is of particular interest in California because it causes Phytophthora root rot of avocado, in fact, PRR is the most destructive disease of avocado production worldwide . PRR limits production of avocado by killing feeder roots which reduces fruit yield and can cause tree death . P. cinnamomi impacts other fruit crops such as peach, pineapple, and highbush blueberry, as well as affecting natural stands of eucalyptus, pine, and oak . Areas that have become infested with P. cinnamomi will never completely remove this pathogen from the soil. Current chemical treatments are being challenged by the emergence of isolates that are more virulent and less sensitive to potassium phosphite . The current challenges of PRR treatment of avocado necessitates a better understanding of the molecular and genetic basis of plant-P. cinnamomi interactions. Taking advantage of the wide host range of P. cinnamomi, we developed a detached leaf assay in Nicotiana benthamiana to elucidate the molecular and genetic basis of plant immunity against P. cinnamomi . The hemibiotrophic lifestyle of P. cinnamomi was confirmed in this model system through differential staining and quantitative PCR pathogen DNA quantification. The model plant, N. benthamiana , has been widely used to study the pathogenicity and virulence of similar broad range and root Phytophthora pathogens such as P. capsici , P. palmivora , and P. parasitica . Furthermore, several studies using model plants, crops, and tree crops to study pathogenicity, virulence, and fungicide efficacy of root rot pathogens such as P. sojae, P. capsici, P. parasitica, P. palmivora, P. cinnamomi, and P. ramorum have been performed using detached-leaf assays . Using the tools developed in previous studies and combining them with RNAseq analysis as well as functional assays using this model plant it becomes possible to gain a better understanding of plant defense responses against P. cinnamomi infection. Previous transcriptomic studies on avocado and model systems provides important information on plant gene expression in response to infection by P. cinnamomi. Avocado defense gene expression has been analyzed three separate times over the last eight years . Mahomed and Van den Berg used the tolerant avocado rootstock Dusaâ to study the gene expression changes after P. cinnamomi inoculation. By comparing expressed sequence tags and 454 pyrosequencing they were able to identify six defense related genes. The defense genes identified encoded: cytochrome P450-like TBP , thaumatin, PR10 , metallothionein-like protein, MLO transmembrane protein encoding gene, and a gene encoding a universal stress protein . In a follow up study, again on the resistant avocado rootstock Dusaâ , 16 additional defense genes encoding: WRKY transcription factors, phenylalanine ammonia-lyase , beta-glucanase, allene oxide synthase, allene oxide cyclase, oxophytodienoate reductase, 3-ketoacyl CoA thiolase, Fbox proteins, ethylene biosynthesis, isoflavone reductase, glutathione s-transferase, cinnamyl alcohol dehydrogenase, cinnamoyl-CoA reductase, cysteine synthase, quinone reductase, and NPR1 were differentially expressed after P. cinnamomi infection. Reeksting et al. found up-regulated transcripts corresponding to cytochrome P450, a germin-like protein , and chitinase genes after P. cinnamomi infection using microarray technology. It has been stated , that an important difference between gene expression in avocado and model systems is that the salicylic acid response is only seen in infected avocado, which is associated with a defense response to biotrophic and hemibiotrophic pathogens. It has been further asserted that P. cinnamomi infection of model plants initiates the jasmonic acid and ethylene pathways associated with necrotrophic pathogens. Although there are differences between expression patterns in avocado and the numerous model plants that have been studied to better understand plant defense to P. cinnamomi, there are also many similarities.

For example, when native pollinator populations have been reduced due to habitat fragmentation or other stressors, honey bees can “rescue” plants from reproductive failure , and, after honey bees have become naturalized, removing them may disrupt pollination of plants they would otherwise visit . However, regardless of whether honey bees are native or naturalized, dramatic increases of any species could disrupt species interactions and ecological processes , particularly when floral resources are limited. For example, in France, where honey bees are native, highly abundant managed honey bees can over-exploit limited floral resources, reducing pollen and nectar collection by wild bees . Indeed, although we studied only one plant species in a specific context, there are likely many systems for which introducing honey bees or other highly abundant generalist pollinators may indirectly reduce pollination by competitively displacing other pollinators. Several recent meta-analyses have revealed that honey bees are less effective than other bees . Furthermore, honey bees have been implicated in the extirpation of native bee species and frequently compete with other pollinators for limited pollen and nectar resources . Hive density is negatively correlated with wild bee abundance and diversity in many ecosystems and honey bees are replacing wild bees as floral visitors in some areas . Thus, indirect negative effects of honey bee introductions may be common where wild pollinator communities already effectively pollinate native plants. Conclusions – Our findings bear on ongoing discussion about permitting of honey bee hives on public lands.

Historically, the placement of managed hives in U.S. National Forests and Parks has been restricted and tightly regulated. However, vertical farm tower beekeepers have successfully lobbied to have honey bees considered a “non-consumptive” use of U.S. National Forest land . If adopted widely, such changes will likely lead to a massive increase in the number of managed honey bees in natural areas. Although honey bees are important pollinators in other systems, we show that indirect negative effects of competition can lead to overall negative effects of honey bee introductions on pollination. As such, introducing hives to sensitive ecosystems should be approached with extreme caution. More fundamentally, we show that introduced pollinators can disrupt plant-pollinator mutualisms and impair ecosystem functioning. These mutualists, although infrequently studied in the invasive species literature, broadly meet the definition of an “invasive” species despite their economic benefits to human society. Untangling direct and indirect effects allowed us to mechanistically understand the functional consequences of honey bee introductions. We recommend that future studies carefully consider indirect impacts of introduced species as biodiversity continues to decline and ecological communities become increasingly homogenous.Over 70% of plants depend to some degree on animal pollinators to successfully reproduce . Among the diversity of pollinators, taxa vary in their contributions to pollination in multiple intricate dimensions, some quantitative , others qualitative . At its core, the functional contributions of different pollinator taxa can be measured by the quantity and quality of visits to plant reproductive success . From a quantitative perspective, although biodiverse pollinator assemblages increase pollination , a few dominant species often provide the majority of floral visits . For example, the numerical dominance of honeybees as floral visitors has been hypothesized to drive their functional importance as pollinators . However, high visit frequencies can impair pollination in some contexts and we know little about whether strongly dominant visitors, such as honeybees, effectively pollinate the plants they visit.

Pollination effectiveness is defined as the per-visit contribution of floral visitors to pollination . A long history of studies within the botanical and evolutionary ecology literature documents variation in single visit effectiveness among plant visitors . To some extent, variation in pollination effectiveness reflects the wide range of methods used to measure it , such as single visit pollen deposition , the number of developed pollen tubes within styles , and/or fruit or seed set . Regardless, evidence for variation in SVE comes from numerous individual studies and this literature has yet to be synthesized in a way that would address whether and why particular taxa are more effective than others and whether dominant visitors are more effective pollinators of the plants they visit. Meta-analysis is a particularly valuable way to investigate such questions. An extensive literature on pollinator importance – the product of per-visit effectiveness and relative visitation rates of different pollinators – has concluded that pollinators that visit more frequently are generally more important . This conclusion suggests that numerical dominance outweighs among-species variation in SVE, but it is also possible that pollination effectiveness and visitation frequencies are correlated. First, frequent pollinators could be inherently more effective because of deep phylogenetic signals. For example, Ballantyne et al. found a positive correlation between a pollinator’s visit frequency and pollination effectiveness when comparing 23 plant species, likely because bees were both highly effective and highly frequent visitors compared to other floral visitors. Second, positive correlations between pollination effectiveness and visit frequency could occur if pollinators that visit frequently do so to the exclusion of other plant species. Such temporary fidelity or long-term fidelity would operate to minimize heterospecific pollen transfer, resulting in more effective pollination . On the other hand, high visitation rates may be the result of many quick and ineffective visits and have a negative or non-significant effect on reproductive success in many contexts . Despite their high visitation frequencies, the effectiveness of honeybees relative to other pollinators remains unclear. Bees are often the most effective pollinators of flowers and Apis mellifera is the most common flower-visiting bee species. However, there are several reasons to suspect that honeybees might be less effective than other bees. First, outside of their native range, honeybees lack the evolutionary history with endemic plants that could have selected for increased pollinator effectiveness . Furthermore, honeybees are floral generalists that visit a high proportion of available plants in ecosystems across the globe , and thus may not be particularly effective at pollinating specific flowering species. Second, honeybees sometimes ‘rob’ plants and efficiently extract and groom pollen from plants without depositing the pollen they extract or collect nectar without contacting reproductive structures . On the other hand, honeybees can be highly effective pollinators, even for plants with which they have no shared evolutionary history , suggesting that honeybees are highly adaptable and capable pollinators. Understanding pollinator effectiveness has important practical implications for safeguarding the production of pollinator-dependent crops. Highly effective non-honeybee pollinators are important for ensuring crop pollination in the face of global change and functionally diverse pollinator communities can increase crop pollination . Furthermore, pollination may differ in cultivated settings because interspecific plant competition, the spatial arrangement of flowers, and the pollinator taxa that provide pollination may vary between agricultural and natural landscapes .

We used a meta-analysis of the pollination effectiveness literature to address three key questions. First, how does the SVE of honeybees compare to that of other floral visitors? We hypothesized that honeybees would exhibit lower SVE relative to other pollinators because honeybees are broad generalists and might efficiently extract nectar and pollen without effectively pollinating plants. Second, to what extent do plant and pollinator attributes predict the comparative SVE of honeybees? Specifically, we evaluated whether pollinator taxonomic groups , crop status , and if plant species exist within the native range of honeybees predict differences in comparative SVE. We hypothesized that the SVE of honeybees would be lower compared to other bees, in crop systems, vertical plant tower and for plant species outside the native range of honeybees because previous studies have suggested such trends . Third, is there a correlation between floral visitation frequency and SVE? We evaluated this question separately for communities where honeybees were present or absent. We expected to find a positive correlation between visitation frequency and SVE that would be reduced when honeybees were present because honeybees are often highly frequent visitors and might be less consistently effective. Although previous studies have synthesized subsets of the pollination effectiveness literature , this paper is, at present, the most extensive meta-analysis to synthetize published results concerning single visit effectiveness.We performed a Web of Science search using a multiterm query designed to capture the highly variable terminology describing pollination effectiveness detailed in Ne’eman et al. . In May 2020, this search yielded 1,036 results. One of us screened the abstracts found by WoS to determine whether they potentially contained single visit effectiveness data. This yielded 388 papers. We also performed a Google Scholar search of the literature using a similar multi-term query , which yielded 116 additional papers. We found 62 papers from the reference sections of previously included papers. After removing duplicates and reading abstracts, we identified 468 papers which seemed appropriate for a more thorough screening. We followed the PRISMA protocol for collecting and screening data from the literature . To be included in our analysis, the paper had to contain empirical data on the per-visit contribution of at least one free-foraging visitor to plant reproduction. We considered pollen deposition, percent fruit set, fruit weight, and/or seed set as measures of SVE. Most studies were conducted with intact flowers, but we also included data from experiments that used the “interview stick” method . We did not include estimates of SVE based on equations or model outputs nor did we include data from trials that manipulated dead bees to deposit pollen. We extracted means, sample sizes, and measures of error directly from the text of the paper or from graphs using WebPlotDigitizer . When lower and upper error estimates were not symmetrical, we used the upper error estimate. When possible, we converted measures of error to standard deviation. When a paper did not report sample sizes, error, or other important information, we contacted the study authors. If we were unable to retrieve or estimate information on mean effectiveness and error, we excluded the paper from our analysis. We also excluded papers if we couldn’t convert other measures of error to standard deviation . After screening papers, 168 studies remained in our analytical dataset. We also extracted data on study year and location, plant species, plant family, whether the plant species was a crop-plant, pollinator taxon, pollinator group , and the native range of pollinator and plant species. We determined range status to bio-geographical realms by looking up the nativity of each taxon in the scientific literature and using occurrence records on the Global Biodiversity Information Facility website. If papers reported SVE outcomes from multiple sites or years, we extracted these data as separate outcomes and dealt with their non-independence statistically . We collected information on the visitation rates of pollinators if it was reported for the same plant species for which pollinator effectiveness data were reported. This rate could be reported as the number of visits to a focal flower or patch of flowers per unit time or the number of flowers visited per unit time and/or per unit area. We did not include data on the relative abundance of different visitors unless data were collected in a homogeneous landscape in which most visitors would have been visiting the focal plant species. If a study reported visitation data, we matched those data to the corresponding SVE data from the same study and plant species. Perfect matches required that pollinator taxa were reported to the same taxonomic resolution and that data were collected in the same year and location. When more than one measure of visit frequency was reported we preferentially used data on the number of visits to a focal flower per unit time. When more than one measure of SVE was reported, we preferentially chose whichever measure was better represented in our data, such that pollen deposition data were chosen over seed set data and seed set data were chosen over fruit set data.To address questions about the single visit effectiveness of honeybees and non-honeybees, we defined the effect size as the standardized mean difference of SVE values between honeybees and non-honeybees for each unique study, plant, site, and year combination. We chose to use Hedges’ g over other effect sizes because it is commonly used in the ecology literature for comparing two means , and it includes a correction for small sample sizes, which occurred with our data. Following Hung et al. , we calculated effect sizes for two separate comparisons: the difference between honeybees versus the most effective non-honeybee taxon and the average difference between honeybees and non-honeybee taxa . The SMD value is > 0 when other pollinators are more effective than honeybees and < 0 if the opposite occurs.

In this section of the paper, we perform a placebo test. We also investigate channels other than information through which congregational mergers might be driving fertilizer adoption, and provide evidence against these other possible explanations.It is still possible that our results are being driven by something other than a congregational merger driven information effect. Here, we explore two other possible explanations for our results. The first is the presence of agricultural extension. Agricultural extension, formally introduced in the United States by the Smith-Lever Act of 1914, plays a major role in information dissemination in agriculture. There is a large literature on the effect of agricultural extension, both in the United States and elsewhere, on agricultural productivity and technology adoption ; Huffman ; Birkhaeuser et al. ; Dercon et al.. Despite the importance of extension, we argue that it is in fact congregational mergers and not extension services that generate the results we find in this paper: because of the fixed effects strategy, in order for agricultural extension to be driving these results, we would need to see agricultural extension services changing differently over time in treatment counties than in control counties, having removed the state time trend, only over the 1959 to 1964 time period. This is potentially plausible, but seems unlikely, especially because extension funding and the number of extension agents allowed is governed by state laws, which do not change often. For example, the Minnesota statutes outlining extension were first passed in 1923, updated in 1953, and were not revised again until 1969. The law allows for “the formation of one county corporation in each county in [Minnesota]” to act as an extension agency, stackable flower pots with in most cases one extension agent and a specified budget, based on the number of townships in the county.

While county extension offices documented their activities for mandatory state reports, these reports were inconsistent across different counties and years. Also, many of the variables measured were endogenous, such as the number of phone calls received or the number of attendees at extension events. As a result, it is impossible to credibly measure the intensity and efficacy of extension efforts over our sample period. We argue in this paper that congregational mergers impact fertilizer use through information. Another plausible explanation would be that the mergers also facilitated increased access to capital. In order to provide evidence against this possibility, we estimate Equation again, this time with the number of farms with each of a variety of capital-intensive technologies as outcome variables. Table 2.8 shows the impact congregational mergers have on the number of farms with cars, trucks, tractors, bailers, and freezers. As expected, we find no statistically or economically significant effect of congregational mergers on capital-intensive inputs: the standard errors are quite wide, and the effect sizes small: the coefficient on cars, for example, is only a 0.01 percent increase relative to the control group mean, and the standard error is almost one hundred times the size of the coefficient. This suggests that congregational mergers did not substantially increase access to capital, and provides additional evidence that information is the main channel through which congregational mergers impacted technology adoption. Finally, one might worry that by only using TALC congregational mergers in our analysis, we are understating the true treatment effect. We argue above that the TALC mergers are exogenous, and, due to the heavily Lutheran populations in these regions, the mergers where we would expect to see an effect. Indeed, the congregations that are merging in these data have, on average, 492 baptized members, so seeing an additional 35 farms begin to use fertilizer is an entirely reasonable effect size. There is another major Lutheran church branch, the Lutheran Church – Missouri Synod , that was not directly involved in the TALC merger, but whose mergers could be attributed to increased discussion about merger surrounding TALC.

We collected data from Concordia Historical Institute, the LCMS seminary, on congregational mergers between LCMS churches during the sample period. There is only one merger that occurs in a non-metropolitan county during this time period, and the inclusion of said merger does not produce a statistically distinguishable result from using only the TALC mergers. Ultimately, given the range of tests that we perform, we have confidence that our results are robust and that we are correctly attributing them to the information effect of congregational mergers.Since the early 2000s, US ethanol production has exploded in response to federal policies incentivizing the production of renewable fuels. In 2005, Congress passed the Energy Policy Act introducing a Renewable Fuel Standard mandating that 2.78% of gasoline sold in the US be from renewable sources. In 2007, Congress passed the Energy Independence and Security Act setting annual renewable fuel mandates for US production with an ultimate goal of 36 billion gallons by 2022. Of these 36 billion gallons, 15 billion are to be conventional bio-fuels – corn-based ethanol in particular. The US ethanol industry has clearly responded to the Renewable Fuel Standards established in the EPAct and EISA. Between 2002 and 2014, US ethanol production has increased from just over 2 billion gallons per year to over 14 billion gallons per year . In order to produce such quantities of ethanol, the number of corn ethanol refineries in the US has increased from 62 in 2002 to 204 in 2014 . The striking increase in US corn ethanol production has raised several important questions about its unintended consequences. One strand of research has explored how increased demand for ethanol has affected land use in the US corn belt as aggregate demand for corn increases . Another strand of research has been more concerned about the environmental externalities of changing agricultural patterns, particularly focused on nitrate runoff and water pollution . In this chapter, I explore both the land use change effects and environmental effects of expanding ethanol production.

In particular, I study the geospatial effect of ethanol re- fineries’ placement on nearby land use change and use my results to estimate environmental consequences. I am specifically interested in how the location of ethanol refineries spatially affects agricultural land, and I do not attempt to identify the full general equilibrium effect of the 14 billion gallon US corn ethanol industry. Put another way, I study how the distribution of ethanol refineries differentially affects different agricultural areas net of the ethanol industry’s aggregate effect on corn prices. I find that within a population of almost 114 million acres of agricultural land in Illinois, Indiana, Iowa, and Nebraska, nearly 300,000 more acres of corn were grown in 2014 than in 2002 due merely to ethanol refinery location effects. This represents approximately 21,000 tons of nitrogen applied as fertilizer. Almost all the 300,000 acres of increased corn acreage exist within 30 miles of an ethanol refinery, suggesting that these refineries have strong local effects on land use change and nitrogen use. There is clear economic intuition for why ethanol refineries would differentially affect nearby and faraway agricultural land. When a corn-fed ethanol refinery is built, it represents a new terminal market for corn. Since refineries operate continuously, they have an inelastic demand for this input. And since transportation costs are significant for grains, one would expect an ethanol refinery to source its corn from the nearest producers. Thus, by reducing transportation costs for nearby producers , ethanol refineries essentially subsidize corn production for nearby farmers. On the margin, this subsidy incentivizes farmers to grow more corn – or grow corn more often – than they otherwise would. As corn production increases, so will nitrogen fertilizer use. Corn requires higher levels of nitrogen fertilizer than other Corn Belt crops, tower garden and particularly high levels of fertilizer when grown successively corn-after-corn. Thus, economic intuition suggests ethanol refineries would have a localized effect increasing corn production and nitrogen fertilizer use. Consequently, these refineries would also have an effect on localized nitrate runoff due to the increased nitrogen fertilizer use. Researchers have previously addressed different components of the ethanol industry’s effects on land use change and nitrate runoff. One line of research has explored whether the hypothesized local corn subsidy provided by nearby ethanol refineries actually exists. In a frequently cited paper, McNew and Griffith find that corn prices at an ethanol refinery are 12.5¢ higher than average, that the effect is slightly stronger for “upstream” refineries than for “downstream” refineries, and that price effects can be detected up to 68 miles from a refinery. However, Katchova and O’Brien both fail to find such a subsidy. Gallagher et al. highlight that locally-owned and non-locally-owned refineries have different effects on corn prices: the authors find that corn prices are increased by proximity to a non-locally-owned refinery, but not by proximity to a locally-owned refinery. Finally, Lewis finds different results in different states: ethanol refineries in Michigan and Kansas affect local corn prices, but refineries in Iowa and Indiana do not.

Fatal and Thurman use county-level data to estimate the corn acreage effect of ethanol re- fineries. They find that a typical ethanol refinery increases corn acreage in its home county by over 500 acres and has effects that can persist for up to 300 miles. Miao also uses county-level data and finds a significant effect of ethanol refineries on corn acreage, as well as a differential effect between locally-owned and non-locally-owned refineries. Turnquist et al. , in contrast to more recent studies, fail to find any significant agricultural land conversion in areas near Wisconsin ethanol refineries. Finally, Feng and Babcock explore the full general equilibrium effect of increased ethanol production and find an unambiguous increase in corn acreage. Several researchers have focused on how ethanol production affects water quality and nitrate runoff. Donner and Kucharik highlight how the aggregate impact of the EISA will likely make achieving nitrate level goals in the Mississippi impossible. Thomas et al. use hydrologic models to estimate the water quality impacts of corn production caused by increased demand due to biofuel mandates. They find significant negative results. While it is likely true that “refineries cause corn,” it is also likely true that “corn causes refineries.” Ethanol refineries are not located at random, and several researchers have explored the topic of ethanol refinery placement. A series of papers have shown, unsurprisingly, that ethanol refineries are more likely to locate near areas with large corn production, near transportation infrastructure, and not near existing ethanol refineries . This finding is important because it highlights that ethanol refinery placement cannot be treated as truly random in econometric analyses without accounting for the underlying drivers of this placement. In my analysis, I argue that field-level fixed effects appropriately account for the major determinants of refinery placement. In particular, I study how distance-to-nearest-refinery affects the probability of a field being planted to corn. Whenever a new refinery is built, its presence differentially affects fields close to it relative to fields slightly farther away.However, due to the spatial characteristics of soil quality and topology, “more-treated” and “less-treated” fields are qualitatively comparable. My project improves upon previous work by leveraging new sources of field-level land use data and exploiting a finer-scaled panel of observations than previous authors. I exploit both the Cropland Data Layer and Common Land Unit to create annual observations of field-level land use. These agricultural micro-data allow for much more nuanced econometric estimation than in previous studies. Other authors have exploited similar micro-data in agricultural research to great effect . I also highlight the locality effect of ethanol refineries rather than the general equilibrium effect, focusing on small-scale heterogeneous effects that have not been well identified in previous work. The remainder of this paper is divided into a theoretical framework , a summary of my data, an overview of my econometric methods, a discussion of my results, and a conclusion.The net increase in corn acreage of 298,718 that I find is only 0.26% of the 113,978,323 acres in my population, but it is 0.76% of all corn acreage in my population in 2014. This is a significant number given that it can be attributed to only the distance-to-nearest-refinery effect. In other words, the effect of new ethanol refineries since 2002 on lowering transportation costs can explain almost 300,000 acres of the corn grown in a subset of the fields across Illinois, Indiana, Iowa, and Nebraska. Figure 3.11 highlights that the entirety of this acreage effect is captured by fields less than 30 miles from the nearest ethanol refinery.

As shown in Figure 3.4, the expression level of CYP7A1 , a gene that controls bile acid synthesis rate from cholesterol, was increased by 2.1-folds in the HP diet, while that was reduced to 0.86-, 0.91- and 0.90- fold in LP, LE, and HE diets. HMG-CoAR and Cyp51 are two important genes in cholesterol biosynthesis. Compared with the control diet, LP, HP, LE, HE demonstrated the same up-regulating pattern in these two genes — 1.03-, 1.51-, 1.11- and 1.18-folds for HMG-CoAR , as well as 1.18-, 1.72-, 1.25- and 1.22-folds for Cyp51 , respectively. LDL receptor facilitates the hepatic LDL uptake from circulation. In the present study, LP and HP diets up-regulated LDLR expression by 1.18- and 1.38-folds , while that was slightly down-regulated by 0.95-fold in both LE and HE diets. These findings were in line with hepatic LDL cholesterol — all the reformulated diets prompted hepatic cholesterol synthesis. HP diet increased bile acid synthesis while other diets decreased it. Peel-formulated diets elevated LDL uptake and extract-formulated ones alleviated it. For peel-formulated diets, bile acid synthesis was dominating in lowering hepatic cholesterol, whereas for extract formulated diets it lowered LDL uptake. PPARα was an essential transcription factor regulating fatty acid β-oxidation. It was up-regulated in LP and HP diet-fed hamsters by 1.04- and 1.61- folds. In contrast, a lower expression of 0.90- and 0.93-fold of PPARα was observed in LE and HE diets . SREBP-1c is a gene encoding transcription factor for fatty acid synthesis . It targets SCD-1 to catalyze the synthesis of monounsaturated fatty acids, which is a substrate for TG synthesis and storage. LP diet slightly up-regulated the expression of SREBP-1c by 1.07-folds, while HP, LE, hydroponic nft gully and HE diet down regulated that by 0.74-, 0.85- and 0.92-fold . All the diets significantly reduced SCD-1 expression level by 0.42-, 0.24-, 0.73- and 0.61-fold . These results indicated that all the formulated diets induced lower uptake of fatty acids.

Peel-enhanced diet slightly promoted fatty acid β-oxidation in a dose-dependent manner, while extract-enhanced diet mitigated that, which was aligned with liver TG levels and hepatic lipid contents.To identify the microbiota-changing effects of different diets on lipid metabolism, relative abundance was assessed at the phylum level . Besides these, Proteobacteria is considered correlated for the variation of the functionality of gut microbiota . Compared to the control HF diet, HE and HP diets reduced the relative abundance of Firmicutes by 9.33% and 18.3% while increasing the RA of Bacteroidetes by 43.1% and 41.9% and nearly triplicated the RA of Proteobacteria. The corresponding ratio of Firmicutes/ Bacteroidetes dropped by 39.4% and 42.4%, with an increase of Proteobacteria/ Bacteroidetes ratio by89.6% and 105.1%, indicating that a shift of fecal microbiota towards leaner phenotypes. Verrucomicrobia was boosted to 4.0% and 8.5% for HP and HE diets, which was non-detectable for the HF diet. Similar observations of Verrucomicrobia increase were found in formulated diets with PPE , cranberry , and black raspberry , which could be attributed to the abundant polyphenol content. At the same time, Cyanobacteria was elevated from 0.3% in the HF diet to 2.3% in HE and shown of associated with improved gut health . To the best of our knowledge, no other research reported the change of Verrucomicrobia and Cyanobacteria after PPP incorporation in hypolipidemic diets. Further metagenomics studies are required to understand how Verrucomicrobia and Cyanobacteria modulate the lipid metabolism pathways.As shown in Figure 3.6, the expression of hepatic HMG-CoAR was significantly correlated with obesity-related indices, with a positive correlation with plasma concentrations of the total- and LDL-cholesterol , and a negative correlation with liver , adipose and body weight .

The expression of LDLR also exhibited a significant positive correlation with total cholesterol. This was consistent with previous research from Teh et al. , who concluded that HMG-GoAR and LDLR were the two major regulating factors in meditating hypo cholesterol effects of hamsters fed with fruit and vegetable seed meals. As for types of bacteria, total plasma cholesterol exhibited a significant positive correlation with the phylum Bacteroidetes, and a significant negative correlation with the F/B ratio in response, suggesting that the increase of Bacteroidetes attributed to the decrease of F/B ratio and played a role in elevating total cholesterol level.Pomegranate peel, a commonly underutilized by-product with high phenolic and fiber content, was incorporated into the hypolipidemic diet in the form of powder and extract to investigate its hypoalipidemic potential. PPP and PPE containing a rich mixture of phytonutrients demonstrated sufficient effects to suppress weight gain, hepatic lipid profile and ameliorate the symptoms of metabolic syndrome in Golden Lakeview Golden Syrian hamsters with HF diet-induced obesity. These observations can be at least partially explained by hepatic metabolism changes and changes in gut microbiota composition. In this study, PPP and PPE lowered Firmicutes and boosted Bacteroidetes, Verrucomicrobia, and Cyanobacteria to lower the F/B ratio, as well as increased microbiotadiversity. These indices were significantly correlated with obesity-related indices, indicating that microbiota might play an important role in the hypolipidemic effects of PPP and PPE. 2 hepatic genes were closely related to modulating the plasma and lipid profile, suggesting the ingested cholesterol and LDL uptake level were crucial metabolic changes. However, adverse plasma LDL-elevating effects were observed in a higher dose of PPP and PPE intake, which required further study on the potential toxicity.

Since the 21st century, society has an increasing awareness of health and is switching to healthier lifestyles and eating habits . Several sectors for product development are responsible for the change, such as food industries, researchers, health professionals, and regulatory authorities . In this context, functional foods have great potential. Functional foods represent the portion of the human diet that could provide health benefits and reduce the risk of chronic diseases beyond nutrition. Polyphenol-containing products are a common type of functional food with proven health benefits, such as protecting against certain cancers, cardiovascular diseases, type 2 diabetes, osteoporosis, pancreatitis, gastrointestinal problems, lung damage, and neurodegenerative diseases . According to Scalbert & Williamson , 1 g of daily consumption of polyphenols in long term is suggested to fulfill all the aforementioned health benefits of polyphenols. U.S. dietary guidelines recommended daily food intake to satisfy certain nutrient needs. However, polyphenols are not included and only 552 mg of polyphenol is satisfied through the recommended diet based on our calculation .Yogurt is a popular fermented dairy product known for its high nutritional value, especially the significant content of proteins and essential minerals, such as calcium. Greek Style Yogurt is a type of nutrient-dense yogurt with increasing popularity among consumers. According to Statista , from 2015 to 2020, the consumption of GSY in the U.S. significantly improved 50%, worth $3.7B and accounting for 52% of the U.S. yogurt market share. Compared to regular yogurts, GSY contains a higher solids content and is often perceived as being less acidic. The nutritional information commonly claims “twice the amount of protein as in regular yogurt” . However, they are never considered aYogurt is a popular fermented dairy product known for its high nutritional value, especially the significant content of proteins and essential minerals, such as calcium. Greek Style Yogurt is a type of nutrient-dense yogurt with increasing popularity among consumers. According to Statista , from 2015 to 2020, the consumption of GSY in the U.S. significantly improved 50%, worth $3.7B and accounting for 52% of the U.S. yogurt market share. Compared to regular yogurts, GSY contains a higher solids content and is often perceived as being less acidic. The nutritional information commonly claims “twice the amount of protein as in regular yogurt” . However, they are never considered a temperature pH at breaking, . cooling conditions and . handling of product post manufacture . Based on these considerations, this study aimed to investigate the effects of different contents of protein and PPE on the sensory, nutritional, dutch buckets for sale and functional attributes of GSY. Response surface methodology with the multi-response statistical technique was applied to optimize a yogurt formulation.To quantify the tannic acid equivalent, a 0.6 mL extract sample was mixed thoroughly with 2.5 mL of 10-fold diluted Folin-Ciocalteu reagent and 2 mL of 7.5% Na2CO3using a vortex mixer .

After 30 min of 25°C incubation of the mixed solution, the absorbance was measured at 760 nm using a UV spectrophotometer . To measure the DPPH scavenging activity, liquid extract or DI water was mixed thoroughly with 3 mL of DPPH solution in methanol using a vortex mixer and kept in a 25°C water bath for 20 min. Liquid extract was also mixed with 3 mL of methanol and used as a blank solution. Absorbance at 517 nm was noted. Three measurements were conducted for each liquid sample, and each test was replicated three times. For each liquid extract, the tests were conducted in triplicate, and the absorbance was read three times for each sample. A reference blank was prepared using the aforementioned procedure with DI water rather than liquid extract.For each response, linear, 2FI, and quadratic models were built. Models of the highest adjusted-R2 value without aliasing were selected, and all the responses could be extrapolated by linear models. Linear effects of protein content were significant on all responsevariables, while that of extract content were only related to TPC , firmness and DSA . The corresponding coefficients along with respective p-values were listed in Table 4.4. 3D response surface graphs were generated to visualize the interaction effects of protein and extract content on GSY characteristics . TPC increased with lower protein content and extract addition. This finding was in line with previous research. Trigueros et al., incorporated pomegranate juice into yogurt and observed the polyphenolprotein interaction. After formulation, their PGY contained 40% of juice and presented 241.44 mg GAE/L of TPC, which meant 85.35% of the theoretically expected. They also evaluated the TPC of PGY permeate after 1-day storage and concluded a TPC of 111.92 mg GAE/L. indicating nearly 54% of the TPC remained interacting with milk proteins. An increase in DSA was observed with higher protein and extract content. The same pattern was found in the study carried out by Jiménez et al., As expected, syneresis decreased with enhanced protein content, which led to a dense yogurt matrix microstructure and enhanced denaturation of whey protein . Usually, lower syneresis equals to longer shelf-life. However, a grainy texture should be avoided when enriching the milk base with high protein. pH and all the texture properties were positively correlated with protein content while not affected by the extracted content. S. thermophilus and L. bulgaricus in yogurt starter were able to produce exopolysaccharides during fermentation and improve yogurt texture. According to Sodini et al., , higher solid content was correlated with stronger EPS interaction with casein, therefore a stronger texture could be formed.In this study, a modified response surface methodology was applied to investigate the effects of protein and pomegranate peel extract on the physiochemical characteristics of Greek Style Yogurt, including total phenolic content, DPPH scavenger activity, pH, syneresis, firmness, cohesiveness, consistency, and viscosity. Enhanced protein content affects all the characteristics while extract content only affects TPC, DSA, and firmness. Based on product quality and visual appeals, the optimum formulation should consist of 8% of protein and 77g of extract for a 130 g yogurt in this study. Further research is needed to analyze the product cost and explore the bio-availability of polyphenol within the fortified GSY for better mass production guidance.Pectin is widely found in the middle lamella layers between plant cells , forming a primary cell wall during plant growth and development . It is a family of heterogeneous polysaccharides consisting of α-1,4-Dgalacturonic acid , L-rhamnose , D-galactose , L-arabinose , and other 13 different monosaccharides through 20 different linkages . The pectin backbone primarily consists of D-GalA residues linked at α-1,4 positions. Based on the abundance of side chains, pectin can be divided into “smooth”, homogalacturonan, and “hairy” regions, namely rhamnogalacturonan I, rhamnogalacturonan II, xylogalacturonan, and apio-galacturonan . A comparison of these regions is listed in Table 5.1 . The backbone unit, GalA, can be partially esterified with a methyl group or converted into the carboxylic acid amide with ammonia. . Based on the degree of methyl-esterification and acetylation, pectin can be divided into high-methoxyl pectin , low-methoxyl pectin , and amidated pectin as shown in Table 5.2.Besides the aforementioned significant industrial benefits, pectin has functional properties as dietary fiber, prebiotics, and fat replacer, as well as in antiglycation, antioxidant, and antibacterial.

The limited number of studies on blueberry phytochemicals and cell culture models of intestinal inflammation, the diversity of cell lines used, and parameters measured speak to the need for more studies to determine how blueberries modulate gut function and health.Because both inflammation and oxidative stress are frequently associated with the development of chronic diseases , it is important to understand how dietary factors impact these outcomes. Here, we have reviewed studies reporting the effects of blueberry phytochemicals on cell culture models of inflammation and/or oxidative stress . Blueberry phenolic compounds and more broadly, phytochemicals, exert regulatory effects including a decrease in proinflammatory gene expression/production in part through the modulation of the NF-κB pathway. A modulation of the MAPK pathway by blueberry phytochemicals is less evident with contradictory observations reported but may also play a role. Blueberry phytochemicals decreased DNA damage in cells in vitro, via the reduction of ROS production, lipid peroxidation, and an increase in antioxidant enzyme activities. Despite many in vitro studies on blueberry extracts, no specific compounds have emerged as singly responsible for the regulatory effects on inflammation and oxidative stress. Virtually all studies have focused on blueberry phenolic extracts or fractions, with a large emphasis on anthocyanins. Health effects of dietary anthocyanins have been extensively reported and discussed , and berries provide an excellent vector for anthocyanin consumption. Blueberries have a complex anthocyanin profile and both major anthocyanidin derivatives, planting gutter malvidin and delphinidin, have demonstrated a reduction of inflammatory markers in different in vitro models of intestinal inflammation and endothelial dysfunction .

Although it is highly likely that anthocyanins largely contribute to the health benefits provided by blueberries, as supported by the number of studies focusing on those compounds, it is doubtful that they are entirely responsible for the bioactivities. Several in vitro studies compared different fractions of blueberry phytochemicals, with reports of similar or better effects by other phenolic fractions and/or whole blueberry extract compared with anthocyanins . These different studies highlight that mechanisms of action of individual blueberry compounds and fractions are context and/or model specific. More studies comparing the effect on individual compounds and well-defined combinations of molecules in different systems are needed to investigate the impact of a system’s environment or system-specific regulation on the bioactivity of blueberries. Although the amplitude of the effect of individual compounds appears to be widely specific to the model studied, the use of whole fractions of the fruits seems to alleviate inflammation and/or oxidative stress more consistently across models, despite not always demonstrating the strongest effects compared with specific blueberry fractions. As the health effects of polyphenols have been extensively described, more data on other phytochemicals should be gathered as they may also exert health benefits. Other notable phytochemicals in blueberries include ascorbic acid , polysaccharides , and volatile compounds and could contribute to inflammatory or oxidative responses of cells to stimuli. A blueberry volatile extract, high in monoterpenes , modulated the inflammatory response in LPS-induced RAW 264.7 cells through inhibition of the NF-κB pathway . Phenolic compounds, although carrying anti-inflammatory and antioxidant modulatory effects, may not be solely responsible for the health benefits of blueberries. Whether the phytochemicals act in synergy or target different molecular pathways remains to be elucidated.

Although the scope of this review is limited to blueberries, the anti-inflammatory and antioxidant effects and mechanisms are likely applicable to other commonly consumed berries. Berries are generally rich in polyphenols, particularly anthocyanins, flavonols, and proanthocyanidins, but the profile of each berry species, and even within varieties, harbors differences in terms of the individual compounds present and their respective concentration . Gasparrini et al. reviewed in detail the anti-inflammatory effects of several berries in cellular models using LPSinduced inflammation, and consistently report alleviation of inflammation by berry phytochemicals through inhibition of NF-κB and MAPK pathways. Other reviews also discuss and compare the anti-inflammatory properties of berries, in preclinical and human models . Moore et al. and Gu et al. have reported similar anti-inflammatory effects of berry volatiles compared with phenolic extracts for cranberries, blackberries, blueberries, red and black raspberries, and strawberries. Notably, the bioactivities of berry polyphenol extracts do not always explain the overall anti-inflammatory effects observed with whole berries , highlighting that potential health effects of berries as a group derived from highly diverse phytomolecules. After consumption, blueberries and their phytochemicals undergo metabolism through phase II enzymatic reactions in the enterocytes and hepatocytes or microbial metabolism in the gut . Evidence of the role of blueberry metabolites in the modulation of inflammation and/or oxidative stress has also been established . Metabolites of elderberry were tested in RAW 264.7 and dendritic cells, and p-coumaric, homovanillic, 4- hydroxybenzoic, ferulic, protocatechuic, caffeic, and vanillic acids [also reported to be blueberry metabolites ], exerted a dose-response inhibitory effect on NO . Studies regarding berry catabolites are less abundant than studies on berry parent phytochemicals but have gained interest in more recent literature. These studies of microbeand host-modified phytochemicals are extremely important to fully understand the potential anti-inflammatory effects of blueberry consumption. Although most of the evidence focuses on the effect of individual compounds, it is essential to consider the potency of these metabolites in profiles similar to what occurs physiologically.

To take the compound profile and physiologically available doses into account, Rutledge et al. treated LPS-induced rat microglial cells with serum from subjects having regularly consumed blueberry, strawberry, or a placebo powder blends over 90 d. The blueberry consumption decreased NO production, TNF-α secretion, iNOS expression, and moderately modulated COX-2 protein expression in the cells . This type of design allows the integration of a more realistic profile of parent compounds and metabolites from blueberry consumption, at physiological doses, within a cell-culturebased model. The current review summarizes the extensive amount of literature available on blueberry phytochemicals and inflammation using cell-based models. This choice comes with limitations, since it can be challenging to interpret results using specific concentrations of berry-derived molecules on cells when concentrations of these metabolites at the site of the target organs may not be established. There have been major differences in concentrations used to treat the cells, ranging anywhere from tens of μg/mL to mg/mL for total polyphenols and from tens of ng/mL to ≤1.2 mg/mL for anthocyanin fractions. Some of these concentrations are much higher than the blood concentrations that would be present in the body after consumption, as bio-availability of anthocyanins in the body is estimated to be lower than 2%, and peaking at 100 nmol/L after consumption of grape/blueberry juice . The relevance of the findings of cell-culture-based studies in complex human systems needs further investigation. These studies should comprise of well-controlled clinical trials, with the relevant choice of placebo controls and inclusion criteria depending on the specific blueberry phytochemical and physiological condition investigated. Future studies should also quantify the entire suite of berry-derived molecules and derivatives in key pools such as the blood, concurrently with physiologic indices of inflammation and oxidative stress.General anti-inflammatory and antioxidant outcomes are consistently reported for blueberry extracts or derivatives across many studies. However, gutter berries results observed in diverse cell culture studies from different investigators are challenging to interpret due to the differences in protocol, treatment, cell line, and analyzed markers. More studies investigating the effects of blueberry extracts on different systems and using comparable conditions would be valuable. Cellculture-based models are not suitable to draw definitive conclusions on the effects of blueberry compounds on complex physiological processes occurring in the human body. Limitations include the compartmentalization of the observations in space and time: the compounds are only available in the form they are distributed and to the type of cells tested, outside of any regulatory processes by surrounding local tissues or on the whole-body scale, and tested on a one-time, acute, and usually high-dose treatment. Thus, precautions should be taken when drawing conclusions from simplified models, especially when using pharmacological doses of compounds. Despite the limitations, cell-culturebased studies have yielded critical information regarding mechanisms of action of blueberry phytochemicals, and have provided consistent evidence that components of blueberries have anti-inflammation and antioxidant properties, which likely contribute to health and functional benefits attributed to blueberries.The gastrointestinal tract, especially the large intestine, houses the most abundant and complex microbiota in humans. Most of intestinal bacteria belong to the phylum Firmicutes and Bacteroidetes , which make up more than 90% of known phylogenetic categories and dominate the distal gut microbiota. Other lower abundance bacteria include Actinobacteria, Fusobacteria, Proteobacteria, and Verrucomicrobia.

Diet is one of the important factors contributing to the gut microbial composition that ultimately affects human health. Obesity and associated metabolic diseases, including type 2 diabetes, are intimately linked to diet . A number of recent in vitro, in vivo, and human studies showed that polyphenols or polyphenol-rich dietary sources, particularly tea, wine, cocoa, fruits, and fruit juices, influence the relative abundance of different bacterial groups within the gut microbiota byreducing the numbers of potential pathogens and certain gramnegative Bacteroides spp. and enhance beneficial bifidobacteria and lactobacilli . Spices are derived from bark, fruit, seeds, or leaves of plants and often contain spice-specific phytochemicals. Spices have been used not only for seasoning of foods but also for medicinal purposes, and have a number of demonstrated disease preventive functions such as antimicrobial, antiinflammatory, antimutagenic activities, and are known to reduce the risk of cancer, heart disease, and diabetes . They are best known for their strong antioxidant properties that exceed most foods. It was reported that of the 50 food products highest in antioxidant concentrations among 1113 U.S. food samples, 13 were spices. Among them, oregano, ginger, cinnamon, and turmeric ranked #2, 3, 4, and 5, respectively . Previous research from our group reported that consumption of hamburger meat with spice mix added prior to cooking resulted in a reduction in the concentration of malondialdehyde, a lipid peroxidation marker, in the meat and in plasma and urine of healthy volunteers, and improved postprandial endothelial dysfunction in men with Type 2 diabetes . Subsequent study reported that commercial spices in dry or fresh form exhibited significant antioxidant capacity that correlated with total phenolic content butnot with the concentration of chemical biomarker . There is limited amount of information regarding the activity of culinary spice extracts against clinical isolated intestinal bacteria, and a limited number of bacterial strains have been assessed for their susceptibility or antimicrobial activity against spices. Gunes and colleagues reported that minimum inhibitory concentration of curcumin against 7 standard bacterial strains is in the range of 129 to 293 µg/mL . Cinnamaldehyde, a bio-active component of cinnamon, was shown to exhibit more potent in vitro antibacterial properties against 5 common foodborne pathogenic bacteria with MIC being 125 to 500 µg/mL as compared to crude cinnamon stick extract , but cinnamaldehyde did not modulate the population of selected Lactobacillus and Bifidobacterium counts in mouse cecal content . Supplementation of rosemary extract was reported to increase Bacteroides/Prevotella groups and reduce the Lactobacillus/Leuconostoc/Pediococcus group in the caecum of both obese and lean rats . Based on potential health benefits demonstrated from our group, this study investigated major chemical constituents, antioxidant activity, and in vitro effect of 7 spice extracts on the growth of 33 beneficial Bifidobacterium spp. and Lactobacillus spp., and established their antimicrobial activity against 88 intestinal, pathogenic, and toxigenic bacterial strains.Plants are some of the greatest chemists on our planet. They offer a vast, barely tapped repository of potentially bio-active compounds, with current estimates predicting over 200,000 unique specialized metabolites across the plant kingdom . Many of these metabolites act as therapeutic phytochemicals and essential nutrients in humans, making plants an invaluable source of bio-active compounds. However, barriers, such as the lack of access to healthy foods, limit the availability of these essential nutrients for human consumption . Plants also produce a wealth of therapeutic phytochemicals, both pharmaceuticals and nutraceuticals , that are difficult to chemically synthesize, leaving consumption of medicinal plants or plant extracts as the sole source of these important chemicals . Additionally, many important phytochemicals are expressed in plants that are difficult to cultivate or produce insignificant amounts of the desired phytochemical .

Protein sequences from Arabidopsis thaliana, Actinidia chinensis, and UniprotKB plant database were also used as evidence for genome annotation. We predicted a total of 128,559 protein-coding genes. Benchmarking Universal SingleCopy Orthologs analysis v.3 was performed to assess the completeness of the assembly and qual-ity of the genome annotation. The annotated gene set contains 1,394 out of 1,440 BUSCO genes . Functional annotation was assigned using Basic Local Alignment Search Tool 2GO to reference pathways in the Kyoto Encyclopedia of Genes and Genomes database. Comparative genomic analyses assigned genes to 16,909 orthogroups shared by six phylogenetically diverse plant species including five eudicots , each with distinct fruit types, and Zea mays as the outgroup. Transposable elements , both Class I and II, were identified and classified in the genome using the protocol described by Campbell et al.. Overall, 44.3% of the blueberry genome is composed of TEs . Consistent with previous reports, the most abundant Class I TEs were long terminal repeat retrotransposons , specifically the superfamily LTR/Gypsy followed by LTR/Copia, while for Class II transposons, the miniature inverted repeat superfamily hAT was the most abundant. The quality of the genome was further assessed by examining the assembly continuity of repeat space using the LTR Assembly Index deployed in the LTR retriever package. The adjusted LAI score of this blueberry genome is 14, and based on the LAI classification, dutch buckets system this score is within the range of ”reference” quality . Estimation of the regional LAI in 3 Mb sliding windows also showed that assembly continuity is uniform and of high quality across the entire genome.

The origin of highbush blueberry from either a single or multiple diploid progenitor species is a long-standing question. Previous reports have suggested that highbush blueberry may be an autotetraploid based on the segregation ratios of certain traits. However, an analysis of chromosome pairing among different cultivars revealed largely bivalent pairing during metaphase I, similar to patterns observed in known allopolyploids. To gain further insights into the polyploid history of highbush blueberry, we calculated sequence similarity and synonymous substitution rates between genes in homoeologous regions across the genome. The average sequence similarity is ∼96.3% among syntenic homoeologous genes. The average Ks divergence between syntenic homoeologous genes is ∼0.036 per synonymous site. The average Ks divergence between homoeologous genes can be used to not only identify polyploid events but also to estimate the divergence of the diploid progenitors from their most recent common ancestor. The Ks divergence between homoeologs in highbush blueberry is six times higher than that between orthologs of two A. thaliana lines that diverged roughly 200,000 years ago. Based on the relatively high Ks rate between homoeologous regions across the genome, this suggests that tetraploid blueberry is unlikely an autopolyploid that was formed from somatic doubling or failure during meiosis involving a single individual . Furthermore, comparative genomics revealed that homoeologous regions are highly collinear, except a few notable chromosome-level translocations . Rapid changes among homoeologous chromosomes is known to occur in newly formed allopolyploids. We also assessed the level of similarity and content of LTR transposable elements among the four haplotypes.

As the most prevalent transposable elements in plants, LTR-RTs undergo continual ”bloat and purge” cycles within most plant genomes, resulting in a unique signature that may distinguish subgenomes in an allopolyploid. To examine the evolutionary history of LTR-RTs in the highbush blueberry genome, we calculated the mean sequence identity of LTR sequences among each of the four haplotypes . This analysis revealed that the majority of more recent LTRs are subgenome specific in highbush blueberry. In other words, the data suggest that LTRs proliferated independently in the genomes of each diploid progenitor , following the divergence from their MRCA, but prior to polyploidy. The pair-wise LTR difference of the two ancestors is 2.4%–2.6%. With Jukes-Cantor correction and synonymous substitution rate of , the estimated time of divergence for the diploid progenitors from their MRCA is between 0.94 to 1.02 million years ago. These date estimates and the average speciation rate for temperate angiosperms suggests that highbush blueberry is either an allopolyploid derived from two closely related species or an autopolyploid derived from the hybridization of two highly divergent populations of a single species. To date the most recent polyploid event in highbush blueberry, we analyzed the unique LTR insertions present in each haplotype. Based on the pair-wise LTR difference between the four haplotypes, which is of 0.81%–0.89%, the polyploid event occurred approximately 313 to 344 thousand years ago. The substitution rate of LTR sequences is likely different from that of protein coding genes. Thus, more accurate date estimates will be possible once the LTR substition rate in highbush blueberry becomes available from future studies. After allopolyploidization, one of the parental genomes often emerges with significantly greater gene content and a greater number of more highly expressed genes.

The emergence of a dominant subgenome in an allopolyploid is hypothesized to resolve genetic and epigenetic conflicts that may arise from the merger of highly divergent subgenomes into a single nucleus. However, classic autopolyploids, formed by somatic doubling, are not expected to face these challenges or exhibit subgenome dominance since all genomic copies were contributed by a single parent. This was recently supported by genome-wide analyses of a putative ancient autopolyploid . It’s important to note that subgenome expression dominance could still be observed in intraspecific hybrids and autopolyploids formed by parents with highly differentiated genomes. To explore this in highbush blueberry, we compared gene content and expression-level patterns between homoeologous chromosomes . While gene content levels were largely similar among homoeologous chromosomes, with a few notable exceptions , gene expression levels were highest for one of the four chromosome copies in the majority of gene expression libraries . Noteworthy, in the three fruit libraries, the most dominantly expressed often became the least expressed among the four homoeologous chromosomes or among the two lowest expressed copies . The most dominantly expressed in other tissues remained so in developing fruit for only two of the chromosomes . These homoeologous chromosome sets have undergone the most structural variation, which may have modified gene expression patterns . These analyses are based on a single biological replicate from a plant grown in a growth chamber. Thus, the findings reported here should be considered as preliminary. Future studies should further explore subgenome expression dominance in highbush blueberry, including at the individual homoeolog level, with additional biological replicates and across multiple environments.The progression of fruit development in blueberry is marked with visible external and internal morphological changes including in size and color . We profiled gene expression in fruit across seven developmental stages from the earliest stage through the final stage to identify genes differentially expressed during fruit development. Distinctive transitions in gene expression were observed between early fruit growth to start of color development and complete color change to ripened fruit. We found that the majority of genes upregulated during early fruit development were involved in phenylpropanoid biosynthesis, nitrogen metabolism, as well as cutin, suberin, and wax biosynthesis . In contrast, genes involved in starch and sugar metabolism were highly expressed at the onset of and during fruit ripening . Moreover, principal component analysis showed the first two components accounted for 84% of the variation and separated the developmental stages into three groups: early developmental stages, petal fall and small green fruit; middle developmental stages, expanding green and pink fruit; and ,late developmental stages, complete fruit color change, unripe and ripe fruit . Genes associated with cell division, cell wall synthesis, and transport were found to be expressed the highest during the earliest developmental stages , which is consistent with previous work on other fruit species. In addition to genes regulating cell proliferation, defense response-related genes were also highly upregulated during the earliest developmental stages. During the middle developmental stages, genes regulating cell expansion, seed development, and secondary metabolite biosynthesis were highly expressed. During late developmental stages and as the berry transitions to ripening, late embryogenesis, transmembrane transport, defense, secondary metabolite biosynthesis, and abscisic acidrelated genes were highly over represented. Blueberry is considered a climacteric fruit; however,unlike the ethylene-driven fruit ripening in other climacteric species, dutch buckets abscisic acid has been demonstrated to regulate fruit ripening in blueberry. In summary, global gene expression patterns mirror the morphological and physiological changes observed during blueberry development .

The economic value of blueberry is largely determined by its fruit quality and nutritional value. We assessed the total antioxidant capacity in mature fruit across a blueberry diversity panel and the abundance of secondary metabolites responsible for its antioxidant activity in developing fruit. A diversity panel, composed of 71 highbush blueberry cultivars and 13 wild Vaccinium species, was evaluated for total antioxidant capacity in mature fruit using the oxygen radical absorbance capacity assay. Similar to previous reports, we observed a wide range in antioxidant capacity across cultivars, with ”Draper” having the highest levels of antioxidants . The observed variation in antioxidants among highbush blueberry, consistent with our results, were previously shown not to correlate with fruit weight or size. However, in another study, a correlation between fruit size and total anthocyanin levels was identified within a few select highbush blueberry cultivars but not across other Vaccinium species or blackberry. This inconsistency is likely due to sample size differences between studies. To further examine the antioxidant capacity in ”Draper” during fruit development, fruits from the seven aforementioned fruit developmental stages were assayed for antioxidant levels . The highest level of antioxidants was observed at the earliest ”petal fall” stage after which, the level of antioxidants declined during the middle and late developmental stages. This is consistent with previous reports on the antioxidant activity in blueberry during fruit maturation and similar to observations in blackberry and strawberry, wherein green fruit have the highest ORAC values. The antioxidant capacity in blueberry is influenced by various metabolites including anthocyanins. Using the same fruit development series, we quantified anthocyanin and flavonol aglycones in ”Draper” using liquid chromatography-mass spectrometry . Overall, as the fruit changed its exocarp color from pink to dark blue during ripening, delphinidine-type anthocyanins started to accumulate and were the most abundant compound in ripe fruit followed by cyanidin, malvidin, and petuni-din . Flavonols were also detected in all developmental stages, with quercetin glycoside being the most abundant , while myricetin glycoside and rutin were present at very low levels. Blueberry also has high levels of phenolic acids; among phenolics, chlorogenic acid was the most abundant. High levels of CGA were observed throughout fruit development, with the highest accumulation detected in young fruits . This correlates with the pattern of antioxidant capacity across different fruit stages, suggesting that CGA is one of the major metabolites contributing to high ORAC values in young developing fruit. CGA is derived from caffeic acid and quinic acid and has vicinal hydroxyl groups that are associated with scavenging reactive oxygen species. The antioxidant properties of CGA have been associated with preventing various chronic diseases.To better understand the biosynthesis of antioxidants in blueberry fruit, we identified homologs of previously characterized genes in other species involved in ascorbate, flavonols, chlorogenic acid, and anthocyanin biosynthesis. The key biosynthetic genes for these compounds exhibited a distinct developmental-specific pattern of expression . For example, genes involved in the conversion of leucoanthocyanidins to proanthocyanidins are highly expressed in the earliest and middle developmental fruit stages but not in ripening fruit . Conversely, genes involved in the conversion of leucoanthocyanidins to anthocyanins were highly expressed in mature and ripe fruit but not during early fruit developmental stages . Additionally, paralogs encoding the same anthocyanin pathway enzymes and genes involved in vacuolar localization of proanthcyanidins and aldehydes -2-hexenal, -2-hexenol, -3-hexenol. Both linalool and geraniol are associated with sweet floral flavor. However, linalool was reported to largely impart the characteristic blueberry flavor when combined with certain aldehydes. Here, we also identified and examined the expression of genes involved in the biosynthesis of linalool. Four of the linalool synthase homologs in tetraploid blueberry are highly expressed during late fruit development . This pattern of expression coincides with previous reports of linalool accumulation in ripened blueberry fruit. On the other hand, one homolog of linalool synthase, although it was expressed during fruit growth, did not show a clear fruit development-specific pattern. Investigating the underlying factors regulating these enzymes will facilitate genetic manipulations that may lead to further improving blueberry flavor in the future.

Some volatiles have been lost during domestication and breeding as a combined result of negative selection and linkage drag in tomato and watermelon . Likewise, gain and loss of terpene compounds during strawberry domestication and its genetic causes have been investigated . Recent advances in sequencing technology and analytical approaches have opened new opportunities to understand the chemistry and genetics of fruit flavor. Genome-wide association studies have revealed loci for flavor in a variety of fruit crops . Meanwhile, genomes-wide expression quantitative trait loci studies have the capability to bridge the gaps between GWAS signals and their underlying causative genes. Integration of GWAS and eQTL studies has led to discovery of a master metabolite regulator in tomato and a flesh-color-determining gene in melon . Long-read sequencing now allows assembly of genomes with high contiguity, and when coupled with parental short-read data , the two haplotypes of a heterozygous individual can be fully resolved. Phased assemblies have improved variant discovery, especially for large structural variants . The extent, diversity and impact of SVs increasingly are being studied in horticultural crops and have been shown to alter fruit flavor, nft hydroponic fruit shape and sex determination . Garden strawberry is an allo-octoploid species with highly palatable non-climacteric fruit . It increasingly has been utilized as a model for Rosaceae fruit crops genomics and flavor research as a result of its short generation time, wide cultivation and high value.

Through exploration of spatiotemporal changes in gene expression and homolog search, several flavor genes have been cloned and validated, including an alcohol dehydrogenase and several alcohol acyltransferases for esters, a nerolidol synthase 1 for terpenes and a quinone oxidoreductase for furaneol. Recently, QTL studies and transcriptome data analyses for strawberry volatiles using biparental crosses have detected QTL and causative genes for mesifurane and gamma-decalactone . Nevertheless, low mapping resolution and a lack of subgenome-specific markers have hampered further characterization of causal genes underlying other QTL. This problem recently was addressed by the development of 50K Fana SNP array using probe DNA sequences physically anchored to the octoploid ‘Camarosa’ genome . High heterozygosity combined with an allopolyploid genome presents difficulties for resolving causative genes and their haplotypes. To further the goal of discovering causative genes affecting flavor in strawberry, association studies with larger sample sizes and additional genetic resources such as eQTL and additional genomes are required. Furthermore, these resources must span the breadth of natural variation in breeding germplasm. Here we present multi-omics resources consisting of an eQTL study representing the genetic diversity of strawberry breeding programs in the US, phased genome assemblies of a highly- flavored University of Florida breeding selection, a structural variation map in octoploid strawberry and a volatile GWAS of 305 individuals. These are combined to leverage the extensive metabolomic, genomic and regulatory complexity in strawberry for the discovery of natural variation in genes affecting flavor. Ultimately, the functional alleles identified will be selected in breeding to achieve superior flavor.The eQTL population consisted of 196 genotypes including 133 newly sequenced accessions . The University of Florida genotypes were grown at GCREC and collected in the spring of 2020 and 2021.

The University of California-Davis collection of diverse selections from multiple breeding programs were grown at either Santa Maria CA or Oxnard CA, for day-neutral and short-day accessions, respectively, and collected in the spring of 2021. Four UC genotypes were collected at both sites to ensure sequencing and SNP quality. Total RNA was extracted from a bulk of three fully ripe fruits using a Spectrum™ Plant Total RNA Kit , after flash freezing in liquid nitrogen. Illumina 150-bp pair-end sequencing was performed on the Illumina NovoSeq platform by Novogene Co. . On average, 6.9 Gb of sequence data were obtained for each sample. Raw RNA-Seq data of 63 samples from previous published studies were retrieved from the NCBI SRA database . In order to quantify gene expression, short reads were trimmed for adapter sequences and low-quality reads with TRIMMOMATIC v.0.39 and aligned against the reference genome using STAR v.2.7.6a in the two-pass mode . Only unique aligned reads were scored by HTSEQ v.0.11.2 in the union mode with the ‘–nonunique none’ flag supplied with the latest Fragaria_ananassa_v1.0.a2 annotation . All count files were compiled in R and normalized with the DESEQ package . To generate the marker dataset for eQTL mapping, SNPs and InDels were called using the mpileup and call commands. Markers were further hard-filtered using BCFTOOLS with the following steps: individual calls with lower than sequencing depth of three were set to missing using + setGT plugin; marker sites with quality < 30, missing rate > 0.3, heterozygous call rate > 0.98, minor allele frequency < 0.05, or number of alternative alleles > 1 were purged; the filtered markers were imported and analyzed in R, and only markers showing more than three matched calls in four duplicated sample pairs were retained. A total of 491 896 markers passed the three stages of filtering.

The missing calls were imputed, and all calls were phased using BEAGLE v.5.2 using the default settings . The eQTL mapping was performed for 62 181 fruit expressed genes using the filtered markers. Linear mixed models implemented in GEMMA were used for association analysis . The relationship matrix was computed in GEMMA and supplied to explain relationship within populations, and the top five principal components with a total of 25.0% variance explained were imported as covariates to reduce effects from population stratifi- cation to signify the genetic variance underlying the target traits. The Bonferroni corrected 5% significance threshold was used, determined the by number of LD-pruned markers . The approach to define an eQTL was similar to that used in previous studies . Briefly, we first clustered all significant markers with distance < 100 kb and purged clusters with fewer than three markers. The lead marker with lowest P-value was used to identify the eQTL, and boundaries of eQTL were defined as the furthest flanking significant markers. Clusters in LD were merged and boundaries were updated. The longest distance between cis-eQTL boundaries and eGene boundaries was limited to 500 kb.Because a substantial number of regulatory elements were found for fruit-expressed genes, a structural variant map would greatly facilitate the identification of potential causative SVs underlying the regulatory elements. To construct an SV map, we first assembled a phased genome of an UF accession. The genome of FL 15.89-25 was assembled into 1480 and 672 phased contigs with N50 of 12.8 and 12.4 Mb, respectively , with similar contiguity to other recent high-quality octoploid strawberry genomes . A Kmer-based approach revealed 97.1% and 99.2% completeness for the haploid assemblies based on parental Illumina short reads, which were corroborated by 98.1% and 98% completeness of the BUSCO eudicots odb10 genes . Phasing quality was evaluated by parent-specific Kmers; the average switching error and hamming error were 0.19% and 0.18% for the F12 haploid assembly , comparable to phased genomes in other species . The phased contigs were scaffolded into pseudochromosomes based on alignment to the ‘Camarosa’ reference genome, with 96.0% and 92.8% of phased contigs placed on 28 pseudochromosomes for the respective F12 and Bea haploid assemblies , consistent with previous flow cytometry estimations . There were only 88 and 79 gaps in the final scaffolds, averaging 3.14 and 2.82 per chromosome for the respective F12 and Bea assemblies . Scaffolding quality was evaluated by a linkage map and public Hi-C data . High collinearity was observed between haplotypes . The FL 15.89-25 assemblies and three additional haploid assemblies were utilized to explore SV diversity in garden strawberry. These geographically and genetically diverse accessions empowered the discovery of SVs across all chromosomes except for a large portion of Chr 4B which may be under strong purifying selection . Individual haplotypes had between 31 574 and 60 453 SVs relative to the PHASE1 assembly of ‘Royal Royce’ , hydroponic gutter with the WONG haplotype harboring the most SVs, consistent with the larger genetic distance of Asian populations to North American populations .

Insertions and deletions were the most common SV types, together consisting of 88.3– 94.1% of SVs. All SVs across haplotypes were then merged into a non-redundant set of SVs . In total, 56 342 deletions, 60 983 insertions, 12 016 translocations, 166 interspersed duplications, 236 tandem duplications and 137 inversions were identified. Unlike the SV composition of a tomato population in which the majority of SVs were singletons , an average of 62.6% strawberry SVs were shared by at least two haplotypes . We observed a gradually reduced number of new SVs every time a new haplotype was merged , suggesting this SV map surveys a substantial portion of SV diversity in cultivated strawberry. The majority of SVs were < 1 kb , whereas only 3.3% were > 10 kb . Structural variations were present extensively in exons , introns and promoter regions . Transposable elements were rich resources of SVs. We identified 34 379 deletions overlapped with TEs, especially inverted tandem repeats and long terminal repeats , consisting of 61.0% of total deletions, significantly higher than the genome-wide TE percentage of 38.42% . In order to investigate whether SVs were related to allele-specific expression in fruit, we performed total RNA sequencing for four biological replicates of FL 15.89-25 ripe fruit . Consistent density distributions of allelic expression ratios were observed across replicates . A total of 12 503 genes exhibited significant ASE. Extreme expression ratios were inflated, with 3415 genes showing extremely imbalanced expression in which the dominant allele contributed to > 90% of gene expression .In order to investigate the genetic control of fruit volatiles, we performed volatile phenotyping and SNP array genotyping with 49 330 markers on a panel of 305 accessions from the UF strawberry breeding program, with 59 individuals overlapped with the eQTL panel . A total of 97 volatiles including esters, terpenes, aldehydes, alcohols, acids, ketones and lactones were quantified . Based on relationships among volatiles, we identified at least five clusters belonging to the same chemical class or biosynthetic pathway, including clusters of eight aldehydes, three ethyl esters, three hexanoic acid derivatives, seven medium-chain esters and three terpenes . Generally high narrow-sense heritability was observed across volatiles , ranging from 0.212 to 0.916, with a mean of 0.660. The highest value of h2 was found for mesifurane and the lowest for octanoic acid, ethyl ester . Genome-wide association study identified 62 signals for 35 volatiles . The lead SNP effects varied from 0.27 to 2.44 , with the largest effect for methyl anthranilate . Two hotspots which contained multiple signals of volatiles belonging to the same class or pathway were found for medium chain esters and for terpenes , which also were detected to in previous studies and reflected in chemical relationships . Our GWAS results confirmed previous homoeologous group assignments for these volatile QTL and clarified their subgenome and physical positions. The SNP AX-166515537 was the lead SNP for three esters, and a 14 Mb region on Chr 6A shared signals for six medium-chain esters. An LD analysis revealed three linkage blocks . The distal region of Chr 3C was associated with six volatiles including five terpenes . This 3.1-Mb region did not display clear LD block separation . Two significant markers for medium-chain ester hotspot and methyl thiolacetate were tested for their predictability of flavor characteristics . Some abundant volatiles including: 2-hexenal, -; butanoic acid, 2-methyl-; and pentanal were associated with multiple DNA variants , suggesting polygenic inheritance. Pentanal was associated with threeloci, together explaining 30.7% of phenotypic variation in a GLM model. Significantly higher pentanal content was observed in genotypes with three doses of the alternative allele at two loci .In this study we leveraged eQTL, GWAS and haplotype-resolved genome assemblies of a heterozygous octoploid to identify allelic variation in flavor genes and their regulatory elements. Finetuning of metabolomic traits such as amylose content in rice and sugar content in wild strawberry recently were made possible via CRISPR-Cas9 gene-editing technology. Similar approaches can be taken in cultivated strawberry for flavor improvement, but not before thebiosynthetic genes responsible for metabolites production and their regulatory elements are identified. Our pipeline has proven to be effective in identification of novel causal mutations for flavor genes responsible for natural variation in volatile content and can be further applied to various metabolomic and morphological aspects of strawberry fruit such as anthocyanin biosynthesis , sugar content and fruit firmness.

Multiple diseases occur in a plant is more common in the real field condition. But the combinations of different diseases are too many to collect sufficient samples for each category from classification perspective . The current researches prefer to solve this problem by semantic segmentation. We do not cover this challenging problem due to limitations of data resources in this work. 2. Formulation of meta-learning data. The samples of PV were taken under controlled condition , which have a clean board as the unified background, the illumination is under controlled, only single leaf in per image, only single disease occurs in per leaf. The settings are simple and very different from the in-wild conditions. That is the reason many researches already achieved high accuracy by using deep learning CNNs on PV . But the samples of AFD were taken under in-wild condition, which have complex surroundings. When testing with AFD, we use PV in meta learning, mainly considering that both datasets are about plant diseases. Since we did not find any other appropriate dataset, the degree of similarity of the data used in training and test was not taken in account. According to our hypothesis, the degree of similarity of data used in meta-learning and test is higher, the adapting is easier, and the result would be better. It is demonstrated that the selection of meta-learning data is critical in this pipeline. The data used in meta-learning stage should be determined by the target. When the application scenarios cannot be predicted, how to formulate an appropriate meta-learning dataset is worthy to study. Inspired by Nuthalapati and Tunga and Li and Yang , round pot the effectiveness of a mixed dataset for meta learning will be considered. 3. Sub-class classification. For the application of plant disease recognition, it is more meaningful to distinguish the diseases belonging to the same species.

What farmers need more than anything else is a diagnostic assistant that can identify similar diseases belonging to the same plant. Although sub-class classification is difficult , it is an inescapable work in plant disease recognition and the performance is needed to be improved urgently. Fine-grained features of the lesions being the distinguishable features to solve this issue. In this direction, lesion detection and segmentation, fine-grained visual classification are involved. 4. The quality and quantity of training data. Most of the current researches of FSL deal with the configuration of data used in test, but very little work has concerned the data used in training. The common sense is that deep learning networks rely on large-scale data. However, a new direction is discussing the quality and quantity of training data recently . These works indicate that part of data can achieve at the same performance as full data. Date quality can be assessed, which can guide to establish a dataset with enough diversity data while without redundant samples. The networks of appropriate depth using good data can achieve optimal results in many traditional CNN classification tasks. In this work, we use large-scale data in base-training and meta-learning. The quantity of data follows the conventional settings for comparison purposes. The data quality assessment work is not involved in this work. For the specific topic of plant disease, the data quality is very important. We know that at different stages of development of plants and diseases, the symptom appearances are very different. How to construct a comprehensive set without redundant data to represent a disease is a valuable work in the future . 5. Cross-domain. The significance of cross-domain has been introduced in prior sections. We emphasize cross-domain again because it is common when we cannot predict the species, surroundings, and photo conditions in test. In this work, we consider it from training strategies.

There are many aspects to explore in future work, such as network architecture, feature distribution calibration etc.In response to the two problems when using FSL for plant disease recognition, we propose a network based on the MB approach that merges CMSFF and CA to obtain a richer feature representation. From experiments, we found that the CMSFF is effective to obtain richer feature representation, especially under the few-shot condition. The CA is an important compensation to the CMSFF, which helps to focus on these meaningful channels. Our method outperforms the existing related works, which indicates that our method is highly robust. The CMSFF+CA is an appropriate combination that fits for any algorithm that needs enhance the feature representation. In addition, a group of training strategies is proposed to meet requirements of different generalization situations. Many factors are discussed in this work, such as backbone networks, distance metrics etc. The limitations of this work and some new related research directions are discussed.Insect pollination is important to commercial blueberry production, and farmers usually rent honeybee hives and, when possible, also buy Bombus terrestris L. colonies and place them in the field during the flowering season. Bumblebees are very effective pollinators for this crop compared to other bees . They can work relatively early in the season when most blueberries flower, at cool temperatures unfavorable to honeybee pollination .According to a review by Garibaldi et al. the proximity of natural areas can increase bumblebee density in crop areas since it can provide floral resources and/or undisturbed nesting/overwintering habitat. Floral resources provided by crop and non-crop areas can also increase bumblebee densities , but the temporal dynamics of flowering crops alters bumblebee densities as well. Previous studies reported a ‘transient dilution effect’ in which bumblebee densities decrease with increasing area of oilseed rape fields during flowering, both in this crop area and in nearby grassland areas .

Only after the flowering events of oilseed rape bumblebee densities increased in nearby areas . Based on these observations, it is possible that bumblebee density in blueberry crops is positively influenced by natural areas and negatively by simultaneously flowering crops in the surrounding landscape. Research questions of this study were: Are there any effects of surrounding natural areas and flowering crop areas on wild bumblebee abundance associated with blueberry fields? What is the relevant spatial scale for these effects? Increasing the understanding of landscape effects on important wild pollinators can provide cues on possible management strategies to increase pollination services for blueberry production.Eight blueberry farms in the central valley of the Region La Araucanía, Chile, were studied . Samples were collected from ‘Briggita’ cultivar of highbush blueberry . Distance between farms ranged from 7.4 to 97.8 km . Blueberry fields varied in size from 0.5 to 120 ha . Commercial honeybee colonies were employed in 6 farms while 4 of those also had B. terrestris colonies. None of the surveyed farms applied pesticides during flowering. Weeds were suppressed in and around all of the fields. Sampling of pollinators was performed on two different dates within the flowering season, between October 13th and November 5 th , 2011, between 11:00 to 17:00 on days with favorable weather conditions . All sampled fields had the same plant density . Flower density was assumed to be relatively constant as blueberry variety was the same and plant age was relatively homogeneous . Depending on the field size, 4, 5 or 6 sites per farm were sampled; in one case only 1 site was sampled because of the limited field size . Sampled sites were placed at different locations within the fields including field edges and field centers, round planter pot in order to capture possible variation of pollinator densities. Each sampling event consisted of separate counts of 4 successive rows in which a person walked through 10 consecutive plants in a row for 5 minutes, recording all insects that visited flowers. All row counts were summed to produce a site-level estimate of abundance.Sampling was performed when the proportion of open flowers on 10 randomly chosen branches was ≥ 0.2. Bumblebees and honeybees were visually identified to the species level and other pollinators were recorded as other hymenopterans, syrphids, or other. During sampling wild bumblebee workers were not yet active and thus all sampled workers were assumed to be from commercial colonies placed by farmers. At the time of sampling no workers were observed in the area, except for those provided by commercial colonies.Natural forest areas and high-food-resources areas surrounding the blueberry fields were mapped in a radius of 3.50 km from the center of the fields, based on orthorectified aerial photos acquired between 2008 and 2010 depending on the location, and high resolution imagery available from Google Earth . The software ArcGIS 9.3 was used for this purpose. Natural forest areas correspond to unmanaged secondary forests and don’t include exotic pine and eucalyptus plantations. High-food resources areas were mapped using the imagery and identified via visual inspection as food resource if flowering in the mapped area at the time of the surveys. The influence of landscape context on pollinator abundance was analyzed at different spatial scales using circular neighborhoods centered on the central point of sampled sites within the farm with radii of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 3.5 km. To explore larger landscape effects circular areas of 5 and 8 km radius were used from on a land cover map of 15 m spatial resolution. This map was generated by classification of an ASTER satellite image from 2008 and was used to measure only natural forest areas at these scales. One farm at these scales of analysis was excluded because its buffer overlapped with neighboring farms. The proportion of natural forest and high-food-resources areas varied across farms and both tended to decrease with increasing spatial scale .Analysis of landscape effects focused on the relationship between the proportion of forest area and high-food-resources area with the abundance of wild B. terrestris queens. This group was the most abundant among sampled pollinators. Bumblebee abundances were averaged for each farm and across sampling dates. Linear regression models to predict farm-level bumblebee abundance were fitted as a function of the proportion of forest area and high-food-resources area, accounting for unequal number of sites per farm weighting each observation by the number of sites of that farm. Models with two covariates were tested at each scale of analysis considering also all combinations of different spatial scales. In all models loge-loge transformation of the data was used to improve linearity. Correlation between forest and high-food resources areas at each spatial scale was tested using Pearson correlation coefficient. R v.2.15.0 was used for statistical analysis. These colonies are placed in the field only during blueberry flowering and competition for food resources is unlikely given the over-abundance of this resource.Honeybees were the most abundant pollinator sampled, followed by naturally occurring B. terrestris queens and syrphids . Few individuals of B. ruderatus and B. dahlbomii, or other hymenopterans were found. In farms stocked with commercial bumblebee colonies B. terrestris workers were also observed. The abundance of wild B. terrestris queens was positively associated with the area of natural forest and negatively associated with areas of high-food resources. These associations were significant at various spatial scales and peaked at 1 and 3.5 km radii for forest and high-food resources respectively . No correlation was found between natural forest area and high-food resources area at any spatial scale. However positive correlation of the proportion of natural forest and high-food resources areas between similar spatial scales was found.Among wild pollinators, B. terrestris queens were most abundant reflecting a successful spread of this exotic species in the study area. The only native bumblebee was almost absent which is consistent by previous reports on the decline of this species after the introduction of B. ruderatus and B. terrestris in 1982-1983 and 1997-1998, respectively . The positive relationship between natural forest area and bumblebee abundance might reflect nesting suitability and/or hibernation habitat requirements of queen bumblebees. Undisturbed areas such as forests and forest edges can be suitable nesting habitat for bumblebees . These areas might be a source of continuous pollen and nectar resources throughout the foraging stage of bumblebees. While food supply in early spring can favor bumblebee colony establishment and initial growth , the reproductive success of the colony seems to be determined by late-season food availability provided by surrounding natural areas .. Late-season food supply is crucial for hibernating queens since Beekman et al. found that body weight at the start of diapause positively affects its success, while environmental temperature has no effect.