Advance Research in Agriculture and Veterinary Science

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Journal's Information

:: Publisher: Academy for Environment and Life Sciences
:: EISSN: 2348-5353
:: Publication Dates: January, March, May, July, September, November
:: Scientific Journal Impact Factor (SJIF): 3.774

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Advance Research in Agriculture and Veterinary Science

Volume 2, No. 5 & 6 September- Nov. 2015


Table of Content




Deepmala Verma and Rajesh K . Yadav

Department of Environment Science, S S Jain Subodh PG College, Jaipur, Rajasthan


ABSTRACT: We have selected two different sites for the investigation: Agricultural Area and A natural field. During this investigation, Total 110 Species have been collected from Etah regions and also done collembolan extraction done by Modified Tullgren’s funnel apparatus. We have found that Collembolans found highly frequent in entire community of soil collembolan, comprising up to 67.78% absolute frequency in natural site whereas 65.7% in Agricultural site.

KeyWords: Collembola, Quantitative analysis, Absolute Frequency

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2.     Impact of Weather Parameters on Economics of Pearl Millet (Pennisetum glaucum L.) Varieties in Allahabad

S. K. Maurya, S. Nath, S.S. Patra and *S.Rout

School of Forestry & Environment

Sam Higginbottom Institute of Agriculture Technology and Sciences,

Allahabad-211007 (Uttar Pradesh), INDIA.


         ABSTRACT: The paper has evaluated the economics of each treatment, based on the existing market prices of inputs and output of the pearl millets production in Allahabad with different date of sowing on different varieties. The highest benefit cost ratio (1.94) was obtained treatment with 23rd July + Ganga kaveri-22.The study has suggested that the pearl millet should be sown on date 23rd July of variety Ganga kaveri-22 to ensured of getting high return.

Keywords: Pearl millet, Varieties, economics.

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Sonuwara Begum1*, Gakul Chandra Hazarika1, Swaraj Rajkhowa2

1Department of  Veterinary Public Health, College of Veterinary Science, Khanapara, Guwahati –22

2 National Research Centre on Pig (Indian Council of Agricultural Research), Rani, Guwahati – 31


ABSTRACT:  Shiga toxin producing Escherichia coli (STEC) has been considered as one of the emerging zoonotic food borne pathogens with great public health importance. In this study an attempt has been made to study the virulence characteristics as well as different serotypes of STEC isolates from cattle and pigs. A total of 260 faecal samples, comprising 110 from cattle and 150 from pigs were examined for the presence of E.coli by using conventional culture method and biochemical test. Out of which 74  and 89 faecal samples from cattle and pig  was found to be positive for E.coli. The presence of STEC in pigs was found to be 6.74% (6 out of 89 isolates) and in cattle it was 16.21% (12 out of 74). The serogroup involved were O173, O55, O78, O84, O79, O110, Untypeable, O71, O43 for both stx1 and stx2 genes and O100 and O56 for stx1 genes and serogroup O152 for stx2 gene in cattle isolates and in case of pig the virulence gene profile reported that six isolates were positive for stx1 gene only, whereas all the isolates were found negative for rest of the genes (i.e stx2, eaeA and hlyA genes) tested. It has also been observed that the stx1 positive isolates belonged to serogroup O116, O138, O56, O8, O22 and O60.

Keywords:  predominant, virulence, shiga toxin producing Escherichia coli, screened

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Adewumi I.O*1, Orisaremi K2, Ajisegiri G.O2, Oladipo O.O1, Kosemani B.S1, and Adegbulugbe T.A1

1Department of Agricultural Engineering, Federal College of Agriculture, Ibadan, Nigeria

2Department of Industrial and Production Engineering, Faculty of Technology, University of Ibadan, Nigeria


ABSTRACT: This research involves the development of an artificial neural network (ANN) model that forecasts the weekly production quantities of outputs for a typical cocoa processing company in order to reduce post-harvest losses. The artificial neural network was initially built with a single input and a single output with the aid of the Neurosolutions 5.07 software package. It was then trained, cross- validated and tested by carrying out a successful pilot test using raw production data obtained from the cocoa processing company. The data set consists of two input variables and two output variables, and the relationship between any input and output variable is complex. Input variables are the weekly quantities of cocoa bags tipped and batches of cocoa nibs roasted, while output variables are weekly quantities of cocoa butter and cocoa cake packaged in cartons. On training the networks, the parameters of specific networks found to give an acceptable mean square error (MSE) were recorded. The network was later modified using different combination types of input(s) and output(s). The model outputs were found to be satisfactory, lying within the defined error limit when compared to the actual outputs. The result shows that the network developed was able to predict the output quantities with a high accuracy, as the training and cross-validation errors at all times both lie within the target error of 0.0001 as specified by the software developers. The network’s ability in forecasting these outputs with a high degree of accuracy goes a long way in demonstrating that artificial neural networks are highly capable of forecasting in situations when there is no closed-formed mathematical relationship between input and output.

Keywords: Artificial Neural Network, Cross-Validation, Moisture Content, Postharvest loss.

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        Shajedur Hossain 1; Md. Maksudul Haque2,* and Jamilur Rahman3

       1 Assistant Plant Breeder, Research & Development (Rice), Supreme Seed Company Limited, Mymenshing, Bangladesh

       2 Scientific officer (Golden Rice), Plant Breeding Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh

       3 Professor, Department of Genetics & Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka 1207, Bangladesh


ABSTRACT: The experiment was conducted with 34 local rice genotypes with one check varieties at the Sher-e-Bangla Agricultural University experimental field to study characterization and genetic diversity of 35 local rice genotypes and to find out the association among the genetic characteristics. Regarding mean performance, the genotype G-4 took the longest period for first flowering is (115) followed by genotype G-35 (114) and the genotype G-12 had the minimum days for first flowering is (99). The highest number of filled grain per panicle was recorded in genotype G-10 was (144.33). The highest number of effective tiller (10.33) was recorded in genotype G-15. The highest 1000 grain weight (26.00) was recorded in the genotype G-15. The highest grain yield per hill was produced by genotype G-24 (35.90g). Analysis of variance showed that there were significant variations among the genotypes for 13 characters studied. Further study of this experiment is needed in different locations of Bangladesh for accuracy of the results obtained from the present experiment. Considering diversity pattern and other agronomic performance lines G-10, G-13, G-15, G-18 and G-24 could be considered suitable parents for efficient hybridization in future.

Keyword: Genetic characterization, Genetic variability, yield and Rice (Oryza Sativa L.)

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1.SwapnaTripathi  2. Leena Singh  3.Dr.Y.K.Singh 4. Poonam Singh Panwar
1.Research Scholar, MGCGVV Chitrakoot Satna
2.Ph.d research scholar, MGCGVV Chitrakoot Satna
3.Associate Professor of Transfer of technology, MGCGVV Chitrakoot Satna (M.P) 4. M.Sc. (Ag.) Agronomy


ABSTRACT: Adoption and knowledge level of farmers in different practices of lentil cultivation” was undertaken 6 village 0f Banda District of Uttar Pradesh state with sample size of 120 respondents. Who were randomly selected? The adoption and knowledge level of improved practices like-preparation of field, improved variety, seed treatment and sowing, manure and fertilizer, irrigation, plant protection. The farmers was 58.33% in high level adopter category in preparation of field,46.67% improved variety,56.67% seed treatment and sowing.,63.33%mannure and fertilizer,80% irrigation, and 88.33 % plant protection. The farmers were found to high level of knowledge in preparation of field 55%,improved variety 51.67%,seed treatment and sowing 46.67%,manure and fertilizer 43.33%,irrigation 53.33% and 63.33% farmers found to plant protection.

Keywords: Adoption, Lentil Cultivation

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