To determine if any of the available techniques for estimating stability in different environments are useful in blueberry (Vaccinium ashei Reade and V. corymbosum L.), 14 clones were evaluated in nine environments for ripening date and yield. Type 1 and 2 stability statistics, plots for each genotype mean versus its coefficient of variation (cv) across environments (genotype grouping), environmental index regression, and cluster analyses were compared. The highest yielding rabbiteye and southern highbush clones across locations were not deemed stable by Type 1 and Type 2 stability statistics, genotype grouping, or environmental regression technique. No evidence of curvilinear response was found. The nonparametric cluster analysis with known cultivars included appears to be most useful compared to other methods of estimating stability used in this study.
Creighton Gupton, John Clark, David Creech, Arlie Powell and Susan Rooks
Youping Sun, Genhua Niu, Christina Perez, H. Brent Pemberton and James Altland
used as salt tolerance indices for hierarchical cluster analysis ( Zeng et al., 2002 ). The dendrogram and clustering of the eight marigold cultivars were obtained based on the Ward linkage method and squared Euclidean distance on the means of the salt
Naomi R. Smith, Robert N. Trigiano, Mark T. Windham, Kurt H. Lamour, Ledare S. Finley, Xinwang Wang and Timothy A. Rinehart
genetic distance or similarity between all the samples. A cluster analysis was performed using the unweighted pair group cluster analysis using arithmetic means method with the Jaccard coefficient and was visualized with a genetic distance tree. Bootstrap
Nnadozie C. Oraguzie, Sue E. Gardiner, Heather C.M. Basset, Mirko Stefanati, Rod D. Ball, Vincent G.M. Bus and Allan G. White
Four subsets of apple (Malus Mill.) germplasm representing modern and old cultivars from the repository and apple genetics population of the Horticulture and Food Research Institute of New Zealand Limited were used in this study. A total of 155 genotypes randomly chosen from the four subsets were analyzed for random amplified polymorphic DNA (RAPD) variation. Nine decamer primers generated a total of 43 fragments, 42 of which were polymorphic across the 155 genotypes. Pairwise distances were calculated between germplasm subsets using the distance metric algorithm in S-PLUS, and used to examine intra-and inter-subset variance components by analysis of molecular variation (AMOVAR). A phenogram based on unweighted pair group method with arithmetic average (UPGMA) cluster analysis was constructed from the pairwise distances and a scatter plot was generated from principal coordinate analysis. The AMOVAR showed that most of the variation in the germplasm (94.6%) was found within subsets, suggesting that there is significant variation among the germplasm. The grouping of genotypes based on the phenogram and scatter plot generally did not reflect the pedigree or provenance of the genotypes. It is possible that more RAPD markers are needed for determining genetic relationships in apple germplasm. Nevertheless, the variation observed in the study suggests that the current practice of sublining populations in the first generation to control inbreeding may not be necessary in subsequent generations. If these results are confirmed by fully informative molecular markers, germplasm managers should reassess the structure of their genetics populations. There may be a need to combine sublines in order to capture the maximum genetic diversity available and to streamline breeding efforts.
James C. Sellmer, Kathleen M. Kelley, Susan Barton and David J. Suchanic
Attendees at the 2001 Philadelphia Flower Show participated in an interactive-quiz-formatted survey on touch-screen computers to determine their knowledge and use of plant health care (PHC) and integrated pest management (IPM) practices. Participants answered 15 questions in three categories: 1) PHC practices (criteria for proper plant selection, correct planting practices, and reasons for mulching and pruning); 2) IPM practices (insect identification, plant and pest monitoring, and maintenance of records on pests found and treatments applied to their landscape plants); and 3) demographic and sociographic questions to aid in characterizing the survey population. Over half of the participants (58%) were interested in gardening and a majority (77%) were interested in protecting the environment. Most participants (66%) were between 36 and 60 years of age with a mean age of 47 years, 76% lived in and owned a single-family home, and greater than half (56%) had never purchased professional landscape services. Most recognized PHC criteria for proper site selection, although not all environmental site characteristics were recognized as being equally important. Nearly half (49%) identified the correct planting practice among the choices offered; while an equal number of participants chose among the several improper practices listed. Although reasons for mulching were properly identified by the respondents, excess mulching around trees was considered a proper planting practice by over 39% of the participants. When questioned about IPM practices, a majority reported that they identify pests prior to treating them (71%) and that they scouted their landscapes (82%). However, only 21% kept records of the pests that they had found and the treatments that they applied for those pests. Participants' responses were further examined using cluster analysis in order to characterize the participants and identify meaningful consumer knowledge segments for targeting future extension programming. Three distinct segments were identified: 1) horticulturally savvy (69% of the participants), 2) part-time gardener (25% of the participants), and 3) horticulturally challenged (6%). At least 47% of the horticulturally savvy and part-time gardeners correctly answered plant health care questions (44% of the total survey participants). These two segments included more individuals who were interested in gardening and protecting the environment and are potential targets for future PHC and IPM extension education programs. In contrast the horticulturally challenged recorded no interest in or opinion on gardening or protecting the environment. It is apparent that a majority of consumers are learning and employing PHC and IPM concepts. Proper site selection, planting practices, and mulching along with record keep- ing and pest identification proficiency remain key educational areas to be developed. Although not all gardeners are well versed in all subject matter, a basic knowledge of PHC and IPM is being demonstrated.
Lydia E. Wahba, Nor Hazlina, A. Fadelah and Wickneswari Ratnam
Jaccard (1908) . This data set was used as input for cluster analysis using the UPGMA to generate a dendrogram using PAST Version 1.92 software ( Hammer et al., 2001 ). Correlation coefficients between the combined data with morphological data and between
Kathleen M. Kelley, Bridget K. Behe, John A. Biernbaum and Kenneth L. Poff
Two surveys were conducted to determine characteristics important in containerized edible flowers that could be sold in retail outlets. Self-selected participants at Bloomfest at Cobo Hall, Detroit, were assigned to one group that rated the importance of attributes such as color of pansy (Viola ×wittrockiana Gams. `Accord Banner Clear Mixture'), color combinations, container size, and price. Participants assigned to a second group rated color, color combinations, and container size. Flower color was allocated the most points in the purchasing decision (63% for the first group and 95% for the second), with a mixture of all three colors (blue, yellow, and orange) being the most desirable. Responses were subjected to Cluster Analysis (SPSS Inc., Chicago), which resulted in the formation of three distinct groups. The groups were labeled “Likely Buyer” (those who had eaten and purchased edible flowers before and rated characteristics of edible flowers favorably); “Unlikely Consumer” (those who had eaten edible flowers before and had rated characteristics of edible flowers unfavorably); and “Persuadable Garnishers” (those who had not eaten edible flowers before, but were very likely to purchase edible flowers for a meal's garnish).
Jose A. Oliveira, Ana B. Monteagudo, Suleiman S. Bughrara, Jose L. Martínez, Ana Salas, Esther Novo-Uzal and Federico Pomar
10.83% and 7.91%, respectively) provided further differentiation. Principal coordinate analysis with the same data set was generally consistent with results from the cluster analysis. Comparison between an agronomic matrix (Euclidean dissimilarity
Zhen-Xiang Lu, G.L. Reighard, W.V. Baird, A.G. Abbott and S. Rajapakse
Univ., Clemson, S.C.) for assistance with the cluster analysis. The cost of publishing this paper was defrayed in part by the payment of page charges. Under postal regulations, this paper therefore must be hereby marked advertisement solely to
P. Martínez-Gómez, S. Arulsekar, D. Potter and T.M. Gradziel
The genetic relationships among peach [Prunus persica (L.) Batsch], almond [P. dulcis (Mill.) D.A. Webb or P. amygdalus (L.) Batsch] and 10 related Prunus species within the subgenus Amygdalus were investigated using simple sequence repeat (SSR) markers. P. glandulosa Pall. was included as an outgroup. Polymorphic alleles were scored as present or absent for each accession. The number of alleles revealed by the SSR analysis in peach and almond cultivars ranged from one to three whereas related Prunus species showed a range of one to 10 alleles. Results demonstrated an extensive genetic variability within this readily intercrossed germplasm as well as the value of SSR markers developed in one species of Prunus for the characterization of related species. Mean character difference distances were calculated for all pairwise comparisons and were used to construct an unrooted dendogram depicting the phenetic relationships among species. Four main groups were distinguished. Peach cultivars clustered with accessions of P. davidiana (Carr.) Franch. and P. mira Koehne. The second group contained almond cultivars. A third group included accessions of P. argentea (Lam) Rehd., P. bucharica Korschinsky, P. kuramica Korschinsky, P. pedunculata Pall, P. petunikowii Lits., P. tangutica (Spach) Batal., and P. webbii (Spach) Vieh.. P. glandulosa and P. scoparia Batal. were included in a fourth group.