#multivariate analysis

The study was conducted to assess genetic divergence in wheat (Triticum aestivum L.) using yield traits. The experiment was planted at Mountain Agricultural Research Center, Juglot, Pakistan during 2014-15 following randomized complete block design with three replications. Plant material for the study comprised of 16 lines and four commercial cultivars of bread wheat. The data recorded were days to 50% heading, days to 50% maturity, plant height (cm), Number of grains spike-1, grain yield per plot, straw yield per plot, 1000 grain weight and yield per hectare. On the basis of multivariate analysis, genotypes 110, 119, 128, 143 and Pirsabak-2013 were showing maximum divergence from other genotypes. This study has shown the existence of considerable genetic variation among the genotypes considered with may help for further selection and breeding. Parents may be selected from clusters which had significant genetic distance for crossing in order to obtain genetic recombination and transgressive segregation in the subsequent generations. However further study across location and years needs to be done in order to corroborate the results obtained in the present investigation.

The present investigation describes the structure and vegetation composition of the forest located in Murree Hills, Punjab, Pakistan. The study area is a part of Himalayans moist temperate forest. The vegetation zone entirely consists of shrubs or medium size trees. The plants give the appearance of a vast flower bed, composed principally of herbaceous species. These species are adapted to withstand the extremes of cold and desiccation. Study area range in altitude from 2100m-2300 m (A.S.L.). A total of 65 species, belonging to 62 genera and 39 families were recorded from 40 stands. Angiosperms contributed a major share while Pteridophytes contributed little to the floristic richness of the area. Data were analyzed by multivariate statistics including Cluster Analysis, Detrended Correspondence Analysis (DCA) and correlation co-efficient to detect the relations between altitudinal and some environmental factors with composition and structure of the plant communities. DCA axis 1 and axis 2 were used to interpret the data. Four vegetation types were delineated by Cluster Analysis which was then plotted on the first two axes a scattered diagram. The outcome of the cluster was confirmed by using DCA. There were significant differences in the flora composition as well as the edaphic factors along the altitudinal gradient. The results of the present investigation suggest a direct altitudinal and soil chemical factors pH, EC, cations and anions on the vegetation variation. Topography predicts species composition of the study area.