Jute is the second most important fibre crop in India after cotton. There is a problem of non-uniform productivity over 87 jute

growing districts located in varying agro-climatic regions of the country including 17 jute growing districts of West Bengal. In

West Bengal relatively higher productivity is observed in South Bengal than in North Bengal., Factors of production were identified through field survey and interaction with the farmers in a participatory mode. The experiment was conducted during 2007 to 2009 at Dakshin Dinajpur district of North Bengal with the aim of determining critical production factors of jute and also

to overcome the productivity constraints through soil mapping followed by soil fertility management. The results revealed that fertilizer management is the most critical factor of production followed by weed management. The soil resource mapping indicated that the experimental soils were having acute acidity problem followed by deficiency in some macro and micro nutrients which are certainly responsible for yield decline of jute in these areas.

Peach is a climacteric and highly perishable fruit and it losses its quality in a short time after harvesting. During storage browning of fruits deteriorates its quality. Pre-harvest sprays of putrescine (PUT) and calcium nitrate at three concentrations each viz. 1.0, 2.0, 3.0 mmol L^-1 and 0.5, 1.0 and 2.0% respectively were done 10 and 20 days before harvesting to maintain the quality during 40 days of storage. Physiologically mature peach fruits were harvested from treated plants, packed in CFB boxes and stored at 0 to

1° C and 90-95 % RH. Results revealed that all the treatments significantly effected the fruit colour development during storage. The maximum colour development was observed in control fruits, followed by calcium nitrate @ 0.5% (sprayed 20 days before harvesting) treatment. Fruits treated with putrescine @ 3 mmol L^-1 showed mean minimum values of “a” and “b”, followed by putrescine @ 2 mmol L^-1 and calcium nitrate @ 2% (sprayed 10 days before harvesting). It can be concluded that putrescine and

calcium nitrate treatments slowed the colour development process during storage.

The experiment was laid out in the pomegranate experimental block of the Department of Fruit Science, Dr. Yashwant Singh Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh. The experiment consisted of 19 treatments with three replications. The pomegranate trees under investigation were subjected to different concentrations of biostimulants viz. Vipul (TRIA) (5 ml/l, 10 ml/l, 15 ml/l), Spic cytozyme (1 ml/l, 2 ml/l, 4 ml/l), Homobrassinolides (Godrej Double) (0.5 ml/l, 1 ml/l, 1.5 ml/l),Biozyme Crop Plus (1 ml/l, 2 ml/l, 3 ml/l) and Vipul + Homobrassinolides (0.5 + 5 ml/l, 1 + 5 ml/l, 1.5 + 5 ml/l, 5 + 0.5 ml/l, 5 + 1 ml/l, 5 + 1.5 ml/l). The results revealed that the highest tree growth was recorded in trees treated with Spic cytozyme at 4 ml/l. The highest total chlorophyll content was observed with the application of Vipul 15ml/l, whereas the highest fruit set and minimum flower drop was recorded with the application of Vipul + Homobrassinolides (1.5 + 5 ml/l).

Neuro-fuzzy and regression models predicting of outflow of an on farm reservoir of 300 m³ capacity, located at Indian Agricultural Research Institute (IARI), New Delhi site of India, were developed and validated using crop water requirement, evaporation losses and farm pond inflow data. The outflow predicted by these two models was compared with each other. It is raveled that Neuro-Fuzzy model predicted the outflow with high coefficient of determination (R²) of 0.98, model efficiency (E) of 0.97 and absolute average deviation (AAD) of 0.002. The regression model was found to be inferior with R² = 0.940.78, E = 0.72 and AAD = 0.031. MATLAB software was used for development of Neuro-Fuzzy and regression model.

The present energy generating systems in developing countries depend largely on local resources: wood, straw, dung for burning, hydraulic power for water wheels and electric power generation and whatever fossil fuel supplies are locally available. A country’s energy requirements often are not fully met by these local resources and foreign-currency resources must be expended to import the needed fossil fuel. In most developing countries, the economic base and the majority of the population are still rural and machinery that requires energy (especially fossil fuel) is not heavily utilized. However, the lack of cheap and adequate energy often hampers rural development plans and retards improvement in the quality of rural life. As on March, 2007, the cumulative grid-interactive power-generating capacity from renewable energy sources was about 9372 MW (6315 MW – wind energy, 1905 MW – small hydro power, 1152 MW – bio power), contributing about 7% of the total installed power-generating capacity in the country. In addition to power generation, renewable energy is being used for a variety of other applications in the country such as cooking, heating domestic water, drying crops, heating in industrial process, and so on. Status of Renewable Energy Sources in Punjab as on March 31, 2012 was studied in details in the fields of biogas technology (1, 30,000 family size and 45 power generation plants), biomass power plants (42 projects of 375 MW capacity), 23 projects of solar energy (10 MW power generations and other applications of solar energy) and hydro power (56 projects of 72 MW capacity).

Pigeonpea (Cajanus cajan) is one of the most important food legume, making it an ideal supplement to traditional cereals, which are generally protein-deficient. So, due to its high nutritional value and enormous losses caused by insect pests, it is very important to forecast the damage caused by major insect-pests and the yield of this crop. In this paper, Artificial Neural Network (ANN) model was developed to forecast productivity (Kg/ha) and percent pod damage by a key insect pest Helicoverpa armigera of long duration pigeonpea in North East Plain Zone (NEPZ) of India. The forecasted values of percent pod damage by

this pest and productivity of Pigeonpea during 2012-13 were obtained as 26.29% and 1137.40 kg/ha, respectively. The performance of the model was assessed by values of the mean squared error, and the model was found suitable for the problem under study.