American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2018, 6(6), 232-238
DOI: 10.12691/ajams-6-6-3
Open AccessArticle

Selection of Potent Isolates from a Population of Alternaria Alternata, a Leaf Spot Pathogen of Poplar

Kartik Uniyal1, , Girish Chandra2, R.U. Khan3 and Y.P. Singh1

1Forest Pathology Division, Forest Research Institute, Dehra Dun 240 006, India

2Division of Forest Statistics, Indian Council of Forestry Research and Education, Dehra Dun 248 006, India

3Department of Plant Protection, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh-202 002, India

Pub. Date: November 09, 2018

Cite this paper:
Kartik Uniyal, Girish Chandra, R.U. Khan and Y.P. Singh. Selection of Potent Isolates from a Population of Alternaria Alternata, a Leaf Spot Pathogen of Poplar. American Journal of Applied Mathematics and Statistics. 2018; 6(6):232-238. doi: 10.12691/ajams-6-6-3


Poplar, an important tree in the agri-silvicultural system, is propagated mainly through cuttings to maintain genetic purity. Monocultures of poplar clones are amenable to many diseases as they have a narrow genetic base. Pathogenic populations have variability in terms of pathogenicity and virulence which are governed by its genetic makeup. Mapping the variability and selection of potential pathogenic isolates for breeding disease resistance remains a challenge. During the survey conducted in poplar nurseries located at different geographical sites, altogether 72 isolates of Alternaria alternata, were collected from four commercial clones of P. deltoides. Three selection methods were attempted to select fifteen potent A. alternata isolates based on growth rate, sporulation and spore size (maximum length and maximum breadth). Initially, Rough Gauging Method which is simply based upon index of sum of the character’s values and Equal Class Interval Method which depends upon the index of class interval scores were applied. To overcome the limitations of the above two methods, Unequal Class Interval Method was proposed based on Coefficient of Variation for each character assessed. The index was constructed using the geometric rather than arithmetic mean as the former normalizes the range, so that, no range dominates the scores assigned to the characters. The proposed method is recommended for the situations when the criterion variable depends upon various growth characters having inherent significant variation among each other.

agri-silviculture class interval clones coefficient of variation geometric mean order statistic

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