International Journal of Agricultural Engineering

S. K. Jain ¹, D. M. Mahale ², N. J. Thakor

Affiliations
1 Department of Farm Structures, College of Agricultural Engineering and Technology, Dr. B.S. Konkan Krishi Vidyapeeth, Dapoli, Ratnagiri M.S., IN
2 College of Agricultural Engineering and Technology, Dr. B.S. Konkan Krishi Vidyapeeth, Dapoli, Ratnagiri M.S., IN

Abstract

In the hot and humid climate of Konkan region issue of cow comfort is ignored and hence has serious implications for barn profitability. Twelve cows were selected for study of comfort on concrete floor and rubber mat floor. The average lying down time of cows was increased on rubber mat floor from 2.00 to 4.28 h. The time required to sit and to stand the cow on rubber mat floor was less as compared to concrete floor. The average maximum number of slippage on concrete floor was observed 4.9 and on the rubber mat floor was 4.0. The average minimum number of slippage on concrete floor was 4.4 and on the rubber mat floor was 2.6. The milk production was increased by 30.4 per cent when cows were housed on rubber mat floor as compare to concrete floor due to increase in comfort.

Keywords

Rubber Mat Floor, Concrete Floor, Slippages, Time to Sit and to Stand, Milk Production

Full Text

     

S. K. Kothe ¹, B. L. Ayare ¹, H. N. Bhange ¹, S. B. Nandgude ¹, D. M. Mahale ¹

Affiliations
1 Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), IN

Abstract

The use of artificial neural network is becoming increasingly common in the analysis of hydrology and water resource problems. In present study, the observed rainfall and runoff data of four years (2010, 2011, 2013 and 2014) were used as input data. In ANN, input data was divided in three segment 70 per cent, 15 per cent and 15 per cent for training, validation and testing purpose, respectively. Rainfall-runoff relationship is an important component in water resource evaluation and therefore, the predicted runoff of 70 numbers of different types of model was tested statistically with observed runoff using statistical parameter, i.e. ischolar_main mean square error (RMSE), mean absolute error (MAE), co-efficient of determination (R²) and correlation (r). This study showed that out of 70 ANN architectures, ANN architectures 1-48-1 could be adopted to estimate runoff from ungauged watershed with rainfall as input.

Keywords

ANN, Rainfall-Runoff Modeling, Co-Efficient of Determination, Correlation.

Full Text