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Articles by Asal M. Wali
Total Records ( 2 ) for Asal M. Wali
  O.M. Ibrahim , A.A. Gaafar , Asal M. Wali , M.M. Tawfik and Marwa M. El-Nahas
  Background and Objective: Crop simulation models are used for simulating crop growth as affected by management and climate. Simulating the growth of a certain variety in a certain soil, climate and management needs specific parameters of that variety due to the genetic variations among varieties, which are called genetic coefficients. Methodology: Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model has two programs for estimating specific parameters of a variety. Genotype coefficient calculator (GenCalc) and Generalized Likelihood Uncertainty Estimation (GLUE). An experiment was conducted during winter seasons of 2012/2013 and 2013/2014 to simulate the effect of three rates of nitrogen fertilizer (75, 100 and 125 kg N/feddan) on grain yield and its components of wheat cultivar Sakha 93 and to make a comparison between GenCalc and GLUE in their ability to assess the genetic coefficient of the cultivar. Results: Results showed that GenCalc program performed better than GLUE. The results of model validation revealed that the average of the difference between the simulated and observed parameters when using GenCalc were 4.02, 3.96 and 4.14% for biological yield, grain yield and straw yield, respectively, while they were 5.47, 8.32 and 6.12% for the same aforementioned parameters when using GLUE. The GLUE has three disadvantages, first it does not provide estimation for PHINT (Interval between subsequent leaf tip appearances), 2nd it does not provide options for keeping some coefficients fixed, while others are being calibrated like GenCalc, for example in wheat crop there are spring wheats and winter wheats, in GenCalc it can set P1V (required days for vernalization) at 0 meaning that this variety is spring, while in the same time GenCalc are calibrating the other coefficients, 3rd GLUE takes a lot of time for calibration. Conclusion: However, GLUE is more easily to use than GenCalc.
  O.M. Ibrahim , M.M. Tawfik Elham , A. Badr and Asal M. Wali
  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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