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Journal of Applied Sciences
  Year: 2016 | Volume: 16 | Issue: 2 | Page No.: 47-53
DOI: 10.3923/jas.2016.47.53
 
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Evaluating the Performance of 16 Egyptian Wheat Varieties Using Self-Organizing Map (SOM) and Cluster Analysis

O.M. Ibrahim, M.M. Tawfik Elham, A. Badr and Asal M. Wali

Abstract:
To evaluate the performance of 16 wheat varieties based on agronomic parameters using self-organizing map and cluster analysis, a field experiment was conducted during 2013/2014 and 2014/2015 winter seasons at the farm of the National Research Center at Nubaria district, Albehira Governor ate, Egypt. In cluster analysis, all the studied characters were used to construct a distance matrix using the Euclidian coefficient and used to generate dendrogram showing dissimilarity among all the wheat varieties, distance matrix based on Euclidian coefficient for the 16 wheat varieties revealed that dissimilarity ranged from 0.62 between Gemmiza 10 and Beniswef 5-7.73 between Beniswef 6 and Sakha 93, which reveal the diversity among wheat varieties. Cluster analysis classified the 16 varieties into nine clusters whereas, self-organizing map classified the 16 varieties into 11 clusters which accounting for 95% of the variation. The clusters in SOM consist of nodes where, varieties in the same node are more similar than varieties in different nodes in the same cluster. However, varieties in the same cluster are more similar than varieties in different clusters. The results revealed that varieties with higher grain and straw yield were higher in plant height (cm), number of spikes per meter square, spike length (cm) and number of grain per spike suggesting that grain and straw yield were strongly correlated with those parameters than other parameters. Also, the results suggested that using self organizing map is helpful to classify varieties clearly and more interpretable than cluster analysis.
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How to cite this article:

O.M. Ibrahim, M.M. Tawfik Elham, A. Badr and Asal M. Wali, 2016. Evaluating the Performance of 16 Egyptian Wheat Varieties Using Self-Organizing Map (SOM) and Cluster Analysis. Journal of Applied Sciences, 16: 47-53.

DOI: 10.3923/jas.2016.47.53

URL: https://scialert.net/abstract/?doi=jas.2016.47.53

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