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Animals including camel (Camelus dromedaries), have been domesticated by man since ancient times especially in Rajasthan. Due to harsh climatic conditions there is often scarcity of grazing pastures in this area and therefore during summers camels are fed roughages and concentrates, as there remains no option to the local farmers. As a result, the proportion of the concentrate and roughage in the complete ration is expected to change the microbial population in the rumen, which in turn is expected to affect their capacity to colonize feed particles and may influence the nutrient utilization from the feed. Cluster bean (guar) belonging to family Leguminosae is one of the most suitable feed in arid areas. Besides, camels are also fed some millet flour or barley flour and gur (molasses) 1g/kg body weight. If this molasses is given in excess amount, it causes gastro-intestinal disorders. Due to introduction of new feed resources, this study was an attempt to investigate certain blood and serum parameters of camels maintained on different diets. An experimental trial was conducted in three groups of camels aged 3-4 years, each group comprising of 4 camels. Three experimental groups were framed Group 1 camels  were fed  guar phalgati (Cyamopsis tetragonaloba) and ground nut (Arachis hypogaea) chara in 1:1 ratio. Group 2 camels were provided ground nut chara alone while, to Group 3 camels jaggery 50% w/v was administrated orally at the dose 15 g/kg body weight apart from feeding of ground nut chara. The blood samples were collected for hematological, serum biochemical and enzymatic variations. Based on biochemical and enzymatic profile comparison, in the Group 3 camels a significant change in the digestive pattern leading towards acid indigestion was noted. There was an insignificant variation among the enzymatic and biochemical profile of Group 1 and Group 2. From the above blood and serum biochemical and enzymatic changes noticed in different groups fed with different diets, it can be envisaged that there exists a significant role of plan of nutrition on digestive pattern.

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Weather being a random phenomenon its prediction has been always a challenge for the meteorologist all over the world. There are number of approaches for predicting this weather based on atmospheric data collected. Rain forecasting is a puzzling, composite, vigorous and mind-boggling task. Rain forecasting pretenses right from the primeval times as a challenging task, because it be influenced by numerous parameters like temperature, wind speed and direction, rainfall, humidity, station level pressure, mean sea-level pressure, dry bulb temperature, dew point temperature and vapour pressure. Various data mining techniques were implemented for rain forecasting. With compared to orthodox methods predicting rainfall rate, the methods that were applying chronological records and data mining technology shows improvement in computing accurate results with more accuracy. Many researchers have done excellent works to construct forecasting models with data mining methods;but in them most just test the predicting accuracy at one particular geographical area. In this paper, we analyzed the performance of k-NN, Random Forest, C5.0 and AdaBoost algorithms on different locations and compared the performance using precision, recall, f-measure and classification accuracy. The daily surface data was collected from India Meteorological Department (IMD), Pune of 3 stations form the period 2005 to 2015. The k-NN algorithm perform better accuracy 98.02 % on Jodhpur dataset with compare to other datasets, the ratio of 90:10 of training and testing records and the value of K is 10. The highest accuracy is 99.270 % of AdaBoost algorithm.

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