Browsing by Subject "Fertilizer"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Prevalence of Eggs of Three Trematode Genera (Fasciolaspp,Dicrocoeliumspp and schistosoma spp) In Buffaloes of Satungal Slaughter House, Kathmandu(Department of Zoology, 2007) Gurung, BharateeA studyon the prevalance of trematodes in buffalo was conductedin Satungal, Kathmandu during the period of December 2006-January2007. A total of 210 stool samples were collected during the study periodand examined employing sedimentation method. The overall prevalenceof helminth parasite was found 61.90%. Significant difference was foundin theprevalence ofthree genera oftrematode infection among buffaloes.The parasitic infections of Fasciolaspp was 38.57%,Dicrocoeliumspp18.10% and ofSchistosomaspp 28.10%.Single infection (infection withone species) wereobserved among 8.57%. Mixed infections of differentgenera of trematodes (Fasciolaspp,Dicrocoeliumspp andSchistosomaspp) were also observed and was found in 14.76%. It was noticed that ahigher infection rate was recorded in buffaloes above 2 years (71.65%)than buffaloes below 2years (46.99%). Most of the buffaloes examinedduring the present survey had low to moderateFasciola, SchistosomaandDicrocoeliumegg counts suggesting that the infections were usually sub-clinical. Pseudoparasites were also observed among 23 (10.95%) positivesamples. No work regarding these pseudo parasites was found.Item Recurrent Neural Network Based Forecasting of Crop Production in Nepal(Pulchowk Campus, 2018-11) Joshi, SurendraIn this study, Gated Recurrent Unit (GRU) model was used for predicting Rice crop production in Nepal using climatic and fertilizer variables. The climatic variables used were Maximum Temperature, Minimum Temperature, Morning Humidity, Evening Humidity and Rainfall and Ecological Regions and fertilizer variables were Nitrogen, Phosphorous, Potassium and Compost. When the model was trained on 70% of data and tested on 30% of the data, the accuracy of the model was 81% for predicting the production. When tested on year 2016, accuracy of the model was 81.33% and for year 2017, the accuracy of the model was 73.33%. While GRU was compared with baseline Artificial Neural Network (ANN) with same architecture for Siraha district, it performed better than baseline ANN. But when input variables were increased, it performed even better. This proved that GRU can be used for optimal prediction of Rice crop in Nepal.