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Title:   Author  item
                                            Farnham, Markitem
                                            Stansell, Zacharyitem
                                            GRIFFITHS, P - Cornell Universityitem
                                            DAVIS, J - North Carolina State Universityitem
                                            HUTTON, M - University Of Maineitem
                                            BJORKMAN, T - Cornell University
  Submitted to: HortScience
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 7/1/2014
                                    Publication  Date: 9/1/2014
                                    Citation: Farnham, M.W., Stansell, Z.J., Griffiths, P.D., Davis, J.M., Hutton, M., Bjorkman, T. 2014. Using regional broccoli trial data to select experimental hybrids for input into advanced yield trials. HortScience. 49(9):S242-S243.Interpretive Summary: N/A
                                Technical Abstract: A large amount of phenotypic trait data are being generated in regional trials that are implemented as part of the Specialty Crop Research Initiative (SCRI) project entitled “Establishing an Eastern Broccoli Industry”.  These data are used to identify the best entries in the trials for inclusion in subsequent and more expansive evaluations.  Hybrids entered into the trialing system that are grown in the first phase and then selected for advancement to the second phase are ultimately included in about 28 separate trials.  The field tests are conducted at four eastern trial sites in South Carolina, North Carolina, New York, and Maine during different planting seasons over a 2-year period.  Experimental hybrids included in the trials are compared to a few standard check hybrids and evaluated for a number of heading characteristics including head shape, head smoothness, head color, bead size, bead uniformity, overall quality, and other attributes.  Aside from assessing days to maturity, nearly all other traits are assessed with a rating from optimal (rating=5) to unacceptable (rating=1).  The challenge in moving hybrids from one phase to the next comes in using the data sets to identify those hybrids with the best combinations of high trait ratings which exhibit those high ratings consistently over a range of favorable and unfavorable environments.  One of our goals is to develop an Additive Trait Index that is computed using the individual trait ratings and that provides a more robust means of differentiating the best hybrids for ultimate advancement to on farm trials.  The proposed Trait Indices are also being evaluated for genotype by environment effects and for use in stability analysis to assess the relative adaptation of tested hybrids in productive versus nonproductive conditions.


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