An experiment was carried out at Horticultural Research Centre, H.N.B. Garhwal University, Chauras Campus (Uttarakhand), India during winter season, 2016-2017 with knol-khol cv. White Vienna to study the correlation coefficient between various traits. Association of yield with other characters revealed that, the knob yield per plot was highly positively significant correlation with fresh weight of knob, whole plant weight, fresh weight of leaves, plant height at harvest, knob length, Knob diameter, dry weight of leaves and days taken to complete harvest at both genotypic and phenotypic levels, where as significantly negative correlation at genotypic and phenotypic levels was observed in days taken to knob initiation. However, the association among yield components had found that the plant height at harvest showed significantly positive correlation with knob diameter, knob length, fresh weight of knob, fresh weight of leaves, whole plant weight, dry weight of leaves and dry weight of knob. The days taken to knob initiation showed highly significant positive correlation at genotypic and phenotypic levels with days taken to first harvest only, whereas knob diameter, knob length and dry weight of leaves showed negatively significant correlation with days taken to knob imitation at genotypic and phenotypic levels. The days taken to complete harvest showed positive correlation at genotypic and phenotypic levels with Knob diameter, knob length, fresh weight of knob, fresh weight of leaves and dry weight of knob. The Fresh weight of knob showed positive significant correlation at phenotypic and genotypic levels with fresh weight of leaves, whole plant weight and dry weight of leaves.

Foliar nitrogen (N) application is considered an important factor affecting phenology, growth, yield, and yield components of dryland wheat (Triticum aestivum L.). A field experiment was conducted to study effects of foliar N on dryland wheat (cv. Prisabk-2004) at the Agronomy Research Farm, The University of Agriculture Peshawar, Pakistan, during winter 2010-2011. The experiment was laid out in randomized complete block design using four replications. A plot size of 3 m by 4 m, having 8 rows, 4 m long and 30 cm apart was used. A total of 80 kg N ha -1 in the form of urea was applied. Out of 80 kg N ha -1 , 70 kg N ha -1 was applied to the soil at sowing time, and the remaining 10 kg N ha -1 was applied in the form of foliar spray (2% N). The required foliar N was applied in various combinations (splits) at different growth stages viz. 30, 60, 90 and 120 days after emergence (DAE). Phenological development (days to anthesis and physiological maturity) was delayed, yield components and yield increased significantly (p ≤ 0.05) with foliar N over control (water spray without N). Wheat grain yield increased to the highest level (4427 kg ha -1 ) when 100 % foliar N was applied (no split) at 90 DAE, followed by 4050 kg ha -1 at 120 DAE, while the control (no foliar N) resulted in the lowest grain yield (2573 kg ha -1 ). We concluded from this study that 2 % foliar N application in a single split either at 90 or 120 DAE could improve wheat productivity under dryland condition.

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River daily discharge estimation and modeling considers an important step for scheduling and planning different water resources for sustainable socio-economic development. In the current work, four techniques of Gaussian processes regression (GPR): Polynomial Kernel, Radial Basis Function Kernel, Normalized Polynomial Kernel, and PUK Kernel, were used to model the daily discharge. Hydrological-datasets containing daily-stage (m) and discharge (m3/sec) were gathered over the period from 2004-2013. The datasets were divided into two sections: (i) models training containing 70% (2004-2010) of the total data and (ii) remaining 30% (2011- 2013) were for testing. Comparing all the four developed models, our findings show that the superlative model was the PUK-Kernel model with a correlation coefficient (r) of 0.96, MAE of 36.70 m3/s, RMSE of 90.92 m3/s, RAE of 17.50 %, RRSE of 26.05 % in the training period. Whereas, it performed equally well in the testing period with r = 0.97, MAE = 44.84 m3/s, RMSE = 95.05 m3/s, RAE = 17.98 %, RRSE = 24.94 % in the testing period. Our findings can be included that GPR-PUK was more accurate and stable than other models, and can be used to help water-users, decision-makers, development-planners for managing water resources and achieving sustainable development.

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