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ARS Home » Midwest Area » Madison, Wisconsin » Vegetable Crops Research » Research » Publications at this Location » Publication #306379
Title: Yield estimation in commercial cranberry systems using physiological, environmental, and genetic variables

Author
item DEVETTER, LISA - Washington State University
item COLQUHOUN, JED - University Of Wisconsin
item Zalapa, Juan
item HARBUT, REBECCA - Kwantlen Polytechnic University

Submitted to: Scientia Horticulturae
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/2015
Publication Date: 5/15/2015
Publication URL: http://handle.nal.usda.gov/10113/60975
Citation: DeVetter, L., Colquhoun, J., Zalapa, J., Harbut, R. 2015. Yield estimation in commercial cranberry systems using physiological, environmental, and genetic variables. Scientia Horticulturae. 190(1):83-93.

Interpretive Summary: Improved methods of yield prediction are essential to develop early crop pricing forecasts for the cranberry industry. However, yield is a complex trait that is influenced by multiple interacting factors involving crop genetics, plant physiology, and the environment. The fact that each factor is poorly understood and the interaction between factors complicate yield prediction. This study sought to improve the current understanding of yield by measuring the effects of genetic, physiological, and environmental variables on yield. Sixty-six variables representing several commercial cultivars were studied. Yield in cranberry was strongly influenced by fruit number and size. However, fruit traits are not usefulness for early prediction of yield. Although early crop forecasting in cranberry may be difficult, this study suggests that managing environmental and genetic variability while maintaining consistency in yield may be crucial in the development of more accurate methods of yield prediction.

Technical Abstract: This study sought to improve the current understanding of yield by measuring the effects of genetic, physiological, and environmental variables on yield. Sixty-six variables representing ‘Stevens’ and ‘Ben Lear’ cultivars were evaluated from samples collected from eight commercial cranberry marshes located in Wisconsin during the 2011 and 2012 growing cycles. Regression analysis revealed berry number alone explained 84.5% and 91.3% of the variation associated with yield of ‘Stevens’ and ‘Ben Lear’, respectively. Resultant predictive models including berry number and size had R2 exceeding 90%. The utility of these metrics are marginal for early crop forecasting purposes given that these variables are determined later in the growing cycle. Additional regressions performed with berry number revealed large amounts of unexplained variation are associated with this trait.