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Reproductive Biology of Wild Blueberry ( Vaccinium angustifolium Aiton)

Author

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  • Frank Drummond

    (School of Biology and Ecology, University of Maine, Orono, ME 04469, USA)

Abstract

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

Suggested Citation

  • Frank Drummond, 2019. "Reproductive Biology of Wild Blueberry ( Vaccinium angustifolium Aiton)," Agriculture, MDPI, vol. 9(4), pages 1-19, March.
  • Handle: RePEc:gam:jagris:v:9:y:2019:i:4:p:69-:d:218599
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    References listed on IDEAS

    as
    1. Bajcz, Alex W. & Hiebeler, David & Drummond, Francis A., 2017. "Grid-Set-Match, an agent-based simulation model, predicts fruit set for the lowbush blueberry (Vaccinium angustifolium) agroecosystem," Ecological Modelling, Elsevier, vol. 361(C), pages 80-94.
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    Cited by:

    1. Efrem Yohannes Obsie & Hongchun Qu & Yong-Jiang Zhang & Seanna Annis & Francis Drummond, 2022. "Yolov5s-CA: An Improved Yolov5 Based on the Attention Mechanism for Mummy Berry Disease Detection," Agriculture, MDPI, vol. 13(1), pages 1-23, December.

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