Tannery sludge and urban waste compost in the production of Solanum melongena L. through regression and multivariate analysis

There are many commercial substrates available in the market of vegetables nowadays. However, a growing pressure turned for sustainability in farming, promotes a line of utilization of waste with agricultural potential, such as the use of urban waste compost and tannery sludge, which, when available, can be used as sources of compost and alternative organic matter. The objective of this study was to evaluate the potential of tannery sludge associated with the urban waste compost in the substrate composition of sweet pepper seedlings, especially regarding emergence, development, and quality of seedlings. The experimental design was a randomized block design with six replications and eight treatments. The treatments consisted of mixtures of the residue of dehydrated tannery sludge and urban waste compost, varying in the proportions of 10%, 30%, 50%, 70%, 90% and 100% of each, as well as the use of a commercial substrate as a conventional treatment for the comparisons. Graphs were performed through linear regression analysis for the treatment of statistical data. The percentage of emergence, development, and quality of seedlings were evaluated 54 days after planting. The alternative substrates showed high potential in the production of seedlings, in which all the combinations used in the study were superior to the conventional treatment, except the germination, which did not present difference. The range for the use of tannery sludge in the preparation of substrates for sweet pepper seedlings is between 32.7 and 48.2% in a mixture with urban waste compost. The plants presented better quality with the use of 46.0% of tannery sludge and 54.0% of urban compost in the preparation of the substrate.

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Genetic divergence in eggplant (Solanum melongena L.) genotypes

Bangladesh Journal of Agricultural Research ◽

10.3329/bjar.v41i3.29715 ◽

2016 ◽

Vol 41 (3) ◽

pp. 433-439

Author(s):

MR Karim ◽

MM Rahman ◽

AKM Quamruzzaman

Keyword(s):

Genetic Diversity ◽

Cluster Analysis ◽

Multivariate Analysis ◽

Genetic Distance ◽

Genetic Divergence ◽

Solanum Melongena ◽

The Other ◽

Agronomic Performance ◽

Cluster Distance ◽

And Cluster Analysis

Multivariate analysis of twenty six genotypes of eggplant were done to estimate the genetic diversity and to select the potential parents for a successful hybridization program. As per PCA, D2 and cluster analysis, the genotypes were grouped into five clusters. The highest inter-cluster distance was between Cluster II and Cluster III (37.82) and the lowest between Cluster I and Cluster III (4.39). Cluster III showed the maximum intra-cluster distance (1.58), whereas Cluster II showed the lowest intra-cluster distance (0.48). Considering the magnitude of genetic distance and agronomic performance, the genotypes SM 208 and SM 209 from Cluster II and SM 201, SM 218 and SM 227 from Cluster III might be suitable for efficient hybridization program. On the other hand the genotypes of Cluster I (SM 206, SM 210, SM 211, SM 212, SM 213, SM 215, SM 216, SM 217, SM 221, SM 224, SM 225 and SM 226) possess all the superior characters in respect of yield and yield related component. Thus the genotypes SM 206, SM 216, SM 217, SM 224 and SM 225 from this Cluster could be selected to develop high yielding eggplant varieties.Bangladesh J. Agril. Res. 41(3): 433-439, September 2016

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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