Characterization of Elite Upland Cotton Genotypes for Earliness and Yield Traits
Pages 508-513

Shahnaz Memon, Wajid Ali Jatoi, Nasreen Fatima Veeser, Nabila Kaleri, Samreen Khanzada, Nazia Kamboh and Lubna Rajput
DOI: https://doi.org/10.6000/1927-5129.2017.13.83

Published: 22 September 2017

Abstract: Short duration cotton genotypes increased the chances to harvest crop before cold and rainy weather conditions. However, earliness in cotton is a difficult character, which is assessed by determining many plant traits. The current experimental was conducted at Cotton Section, A.R.I. Tando Jam, during kharif season 2013. The eight cotton genotypes including CRIS-342, Sindh-I, Haridost, Malmal, Bt-121, Bt-3701, TS-501 and Shahbaz were sown in randomized complete block design with four replications. In a total, seven quantitative traits were measured such as days to first flowering, node number to set first sympodial branch, node number to set first flowering, sympodial branch length (cm), sympodial branches plant^-1, number of bolls plant^-1, seed cotton yield plant^-1 (g). The mean squares from analysis of variances showed that genotypes differed significant (P≤0.01) for all the studied traits, demonstrating a vast genetic variability in tested genotypes. The genotypes, Sindh-1, CRIS-342, Bt-121 and Bt-3701 were characterized as early maturing or short duration cotton varieties. However, the correlation between yield and earliness traits were best criteria for developing the short duration cotton yield varieties. .

Keywords: Characterization, earliness, association, yield traits, cotton genotypes.

Download Full Article

Classification Techniques in Machine Learning: Applications and Issues
Pages 459-465

Aized Amin Soofi and Arshad Awan
DOI: https://doi.org/10.6000/1927-5129.2017.13.76

Published: 29 August 2017

Abstract: Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification purpose. In this paper, we present the basic classification techniques. Later we discuss some major types of classification method including Bayesian networks, decision tree induction, k-nearest neighbor classifier and Support Vector Machines (SVM) with their strengths, weaknesses, potential applications and issues with their available solution. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning. This work will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods.

Keywords: Machine learning, classification, classification review, classification applications, classification algorithms, classification issues.

Download Full Article

Chemical Investigation of Mesua nagassarium (Burm. f.) Kosterm
Pages 124-128
Ridwan Islam, Iftekhar Ahmed, Al Amin Sikder, Mohammad Rashedul Haque, Abdullah Al-Mansur,Mansoor Ahmed, Munawar Rasheed and Mohammad A. Rashid

DOI: http://dx.doi.org/10.6000/1927-5129.2014.10.17

Published: 17 April 2014

Abstract: Repeated chromatographic separation and purification of pet-ether and carbon tetrachloride soluble fractions of a methanol extract of stem bark of Mesua nagassarium (Burm. f.) Kosterm yielded five compounds. Extensive spectroscopic studies, including high field NMR analyses was conducted to identify these compounds which resulted to be friedelin (1), 3β-friedelanol (2),lupeol (3), 3-oxo-betulin(4) and spinasterol (5). Although compounds 1-3 have been reported from various plant species, but 3-oxo-betulinand spinasterol have been discovered from M. nagassarium (Burm. f.) Kosterm for the first time.

Climate Change Impact on Flow Discharge of Kunhar River Catchment using Snowmelt Runoff Model
Pages 184-192
Syed Shahid Ali and Sayed Sanaullah Shah

DOI: http://dx.doi.org/10.6000/1927-5129.2015.11.27

Published: 05 March 2015

Abstract: Most of the runoff in Kunhar Catchment is generated from the snowmelt which makes it more sensitive to climate change. The Snowmelt-Runoff Model (SRM), a simple degree-day model, has been applied around the world under different climatic regions to evaluate the hydrological effects of climate change.

The current study encompasses the use of Snowmelt-Runoff Model (SRM) to forecast stream flows in Kunhar river catchment depicting three varying climate change scenarios i.e. a) an increase in +2°C temperature b) an increase in +2°C temperature along with 20% increase in precipitation, and c) a 20% increase in precipitation. The study area was distributed with respect to varying altitude with 500 m elevation interval. Moderate resolution Imaging Satellite (MODIS) daily snow product MOD10A1 is used to map snow cover. SRM model was simulated and later climate change run was evaluated. SRM Model was calibrated for 2003 – 2004 and validated for 2005 – 2006 with an average coefficient of 0.93 R² and average seasonal volume difference D_v of 1.46%. The stimulated results for scenario “b” show an increase of flow discharge by 27%. Whereas, there was 21% and 6% increment in discharge simulated for scenarios “a” and “c” respectively.