PRINT ISSN 2285-5718, CD-ROM ISSN 2285-5726, ISSN ONLINE 2286-0126, ISSN-L 2285-5718


Published in AgroLife Scientific Journal, Volume 7, Number 2
Written by Maria BĂLA, Cristina NAN, Olimpia IORDĂNESCU, Robert DRIENOVSKY, Florin SALA

The study followed the flowering dynamics over a 84-days vegetation period, at four genotypes of the species Lisianthus exaltatum Salisb. The biological material was represented by the Twinkles Dark Blue (TDB), Arena Series Rose (ASRose), Arena Series Red (ASRed) and Heidi Salmon (HS) genotypes. In relation to the biology of the analyzed genotypes, the vegetation period (VP) under study was of 84 days during which, five flower-counting moments were delineated at 14-days intervals, VP28, VP42, VP56, VP70 and VP84. Based on the average number of flowers open at the time of determination, the highest number of flowers was found in the TDB genotype, followed by genotypes HS, ASRose and ASRed. On the basis of the univariate statistics analysis, the highest variance was found for genotypes TDB (10.6194) and ASRose (10.24407) and a lower variance in HS genotypes (5.60978) and ASRed (5.0022), respectively. The coefficient of variation (CV) had the highest value for the ASRose genotype (CV = 111.4428), followed by the ASRed genotype (CV = 86.0215), then TDB (CV = 66.5049) and HS (CV = 62.4178), respectively. The statistical regression analysis facilitated the development of a model of a grade 3 polynomial equation and smoothing spline models (for the ASRose, ASRed and HS genotypes), models that most accurately described the flowering dynamics in relation to the vegetation period. Thus, a model of a grade 3 polynomial equation facilitated the estimation of flowering over the study period to the TDB genotype under R2 = 0.996, F = 90.681, p = 0.0770. In the other three genotypes smoothing spline models described the most accurate growth dynamics during the vegetation period under conditions of ɛi = 0.2098 at the ASRose genotype, ɛi = 0.0593 at the ASRed genotype and ɛi = 0.0607 in the HS hybrid. Clustering analysis has facilitated the classification and grouping of observational moments from the study period into two distinct, statistically safe clusters, Coph. corr = 0.899.

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