Growth physiology of Brassica rapa var. yellow sarson under integrated nutrient management and seed soaking approaches in eastern sub-Himalayan plains

The field experiment was conducted at Uttar Banga Krishi Viswavidyalaya, West Bengal, India during rabi 2007-08 to 2008-09 to study the growth and productivity of yellow sarson under different nutrient managementi practices and pre-sowing seed soaking. The highest value of LAI (4.00), apart from the maximum plant height (129.97cm), dry matter accumulation (481.93g m-2) at 90 days, crop growth rate (12.29 g m-2 day-1) in between 45 to 60 days, root dry weight (1.645 g plant-1) with the highest average root diameter (0.932 mm) were observed in treatments receiving 75% recommended dose of chemical fertilizer with farm yard manure, Azotobacter and phosphate solubilizing bacteria as non-chemical source. Similarly at 45 days of crop age, significantly higher stomatal conductance (701.68 m mol m-2 s-1) and transpiration rate (4.55 m mol m-2 s-1) were reflected by the same treatment combination. These were attributed to the production of maximum seed yield (1374 kg ha-1), which was 39.91% higher than the recommended dose of chemical fertilizer application. On an average, seeds soaked with water before sowing reflected 9% lesser yield (1103 kg ha-1) against chemical soaking. Yellow sarson crop grown with integrated nutrient management practice consisting 75% of the recommended dose of chemical fertilizer along with farm yard manure (5t ha-1), Azotobacter (5kg ha-1) and phosphate solubilizing bacteria (5kg ha-1) coupled with seed soaking in 100ppm KH2PO4 confirms to be the best treatment combination from the treatment schedule considered during the study for the sub-Himalayan plains of West Bengal, India in terms of crop growth and productivity

Performance of Toria (Brassica campestrisvar. toria) on sulphur nutrition and soil test based nutrient management practice in farmers’ field of West-Bengal Himalayan range

An on-farm trial was conducted in the farmers’ field during 2013-14 and 2014-15 to assess the technology of application of sulphur on Toria (Brassica campestrisvar. toria)along with soil test based nutrient management practice under rain-fed condition at brown forest soil of the Himalayan range of West Bengal. The experiment was conducted at the seven villages namely Bong Busty, Charkhol, Sangsey, Bungkulung, Sakyong, Pudung, Didabling of Kalimpong district at an altitude ranged between 1210 m to 1300m . Significantly higher values of no. of primary branches (9.78); no. of siliquaplant-1 (82.12); no. of seeds siliqua-1 (35.51) as well as seed yield (1023 kg ha-1) were recorded with soil test based nutrient management practice along with soil application of sulphur (80%) @ 20kg ha-1as basal compared to the farmers’ practice. The soil application of sulphur and soil test based nutrient management practice also fetched higher return per rupee invested (1.61) compared to the other treatments. No remarkable change was observed in soil fertility status after two years of experimentation. As it was an adoptive trial with the participation of farmers, the necessity of soil test based nutrient management practice and application of sulphur have been well realized by the participating farmers

Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table

Run Out Tables (ROTs) have been used for long time in order to achieve different microstructure of steel in the industries. The microstructure of steel controlled by the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others. Achieving new steel grade thus demand a proper combination setting of all such parameters. The observed data like upper nozzle distance, lower nozzle distance and mass flow rate of coolant from the laboratory scale ROTs are used to find out the cooling rate which is important parameter for achieving desired properties in steel. An Artificial Neural Network has been used here to creating an empirical relation between the observed data and thermodynamics parameter which will determine the cooling rate and validate it

Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table

Run Out Tables (ROTs) have been used for long time in order to achieve different microstructure of steel in the industries. The microstructure of steel controlled by the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others. Achieving new steel grade thus demand a proper combination setting of all such parameters. The observed data like upper nozzle distance, lower nozzle distance and mass flow rate of coolant from the laboratory scale ROTs are used to find out the cooling rate which is important parameter for achieving desired properties in steel. An Artificial Neural Network has been used here to creating an empirical relation between the observed data and thermodynamics parameter which will determine the cooling rate and validate it