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University of Agricultural Sciences, Bengaluru
University of Agricultural Sciences Bangalore, a premier institution of agricultural education and research in the country, began as a small agricultural research farm in 1899 on 30 acres of land donated by Her Excellency Maharani Kempa Nanjammanni Vani Vilasa Sannidhiyavaru, the Regent of Mysore and appointed Dr. Lehmann, German Scientist to initiate research on soil crop response with a Laboratory in the Directorate of Agriculture. Later under the initiative of the Dewan of Mysore Sir M. Vishweshwaraiah, the Mysore Agriculture Residential School was established in 1913 at Hebbal which offered Licentiate in Agriculture and later offered a diploma programme in agriculture during 1920. The School was upgraded to Agriculture Collegein 1946 which offered four year degree programs in Agriculture. The Government of Mysore headed by Sri. S. Nijalingappa, the then Chief Minister, established the University of Agricultural Sciences on the pattern of Land Grant College system of USA and the University of Agricultural Sciences Act No. 22 was passed in Legislative Assembly in 1963. Dr. Zakir Hussain, the Vice President of India inaugurated the University on 21st August 1964.
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ThesisItem Open Access A STATISTICAL ANALYSIS OF ARRIVALS AND PRICES OF TOMATO IN MAJOR MARKETS OF KARNATAKA(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2019-09-11) GOVINDARAJU, H. S.; Gopinath Rao, M.Analysis of agricultural commodity price and market arrivals over time is important to know the fluctuations. It helps to formulate appropriate ways and means for reducing price fluctuations. Hence, trends in arrivals and prices of tomato were studied in selected markets viz., Bengaluru-Urban, Chikkaballapura, Chinthamani, Kolar and Srinivasapura of Karnataka. The secondary monthly data pertaining to arrivals and prices of tomato in the above mentioned five markets was collected from Krishimaratavahini website for the period of 11 years i.e. from 2008 to 2018. Linear and Nonlinear models were used to analyze the trends in arrivals and prices of tomato. The co-integration between markets was analyzed using Johansen’s co-integration test and the pairwise causality between markets was analyzed using Granger causality test. Different forecasting models like Holt’s-Winter Exponential smoothing model and SARIMA models were considered to forecast and to measure the forecast accuracy among different selected models. Trend analysis carried out for arrivals and prices of tomato in different markets revealed that the different trend lines were suitable for different markets. In case of arrivals, quadratic (Bengaluru-Urban & Kolar), exponential (Chikkaballapura & Chinthamani) and cubic (Srinivasapura) models were found to be the best fitted lines. In case of market prices, power (Bengaluru-Urban, Chikkaballapura & Chinthamani) and cubic (Kolar & Srinivasapura) models were fitted best with lower RMSE and higher R2 values. Market co-integration analysis revealed that all the five selected markets were co-integrated and pair-wise co-integration exist between the markets. Different SARIMA models were found suitable for forecasting for different markets.ThesisItem Open Access BEHAVIOUR OF ARRIVALS AND PRICES OF COTTON IN SELECTED MARKETS OF KARNATAKA- A STATISTICAL ANALYSIS(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2019-08-22) NAGARAJA, Y.; Krishnamurthy, K. N.Cotton is the most important commercial crop grown in India. It is also known as ‘White Gold’. India is the highest producer of cotton in the world followed by USA and China. Karnataka has the fourth position in area and production of cotton in the country. To study the trend, seasonal variation and for forecasting, the monthly data on arrivals and prices of cotton were collected from Vijayapura, Haveri, Raichur and Ranibennur APMCs for the period of 16 years (2003-2018). Two years weekly price data was collected for studying the co-integration of cotton markets. To analyze the trend in arrivals and prices of cotton, linear and nonlinear models were fitted. RMSE and R2 values were considered to check the adequacy of the fitted models. For arrivals of cotton, in few markets linear and in other markets nonlinear models were best fitted. Whereas, for prices of cotton only linear model was best fitted. Ratio to moving average method was used to calculate the seasonal indices of prices of cotton. All the markets showed low prices during peak period of harvest. For forecasting of cotton prices, Holt’s winter and ARIMA models were considered. The estimates of Holt Winter’s model were nonsignificant in all the markets. Different ARIMA models were found suitable in each of the markets. Cointegration test revealed that all the selected markets were co-integrated and they have one Cointegration equation. Pairwise causality also existed between the selected markets.ThesisItem Open Access EVALUATION OF STATISTICAL MODELS FOR FORECASTING OF RAINFALL IN METEOROLOGICAL SUBDIVISIONS OF KARNATAKA(UNIVERSITY OF AGRICULTURAL SCIENCES, GKVK BENGALURU, 2019-08-26) NAMRATHA, K. S.; MOHAN KUMAR, T. L.Rainfall is one of most important source of water for agriculture and livelihood. Since it varies over time and space, Indian Meteorological Department classify India into thirty-six meteorological subdivisions based on the rainfall distribution pattern, among which four are in Karnataka namely North Interior Karnataka, South Interior Karnataka, Coastal and Malnad subdivisions. The study has been carried out on the secondary data pertaining to monthly rainfall (mm) collected for each subdivision from AICRP, AgroMeteorology, Bengaluru and KSNDMC for the period from 1968 to 2018. In order to know the rainfall distribution pattern for pre-monsoon, monsoon and post-monsoon seasons in four different subdivisions, five probability distribution models viz., gamma, Weibull, normal, lognormal and Gumbel were fitted. The study revealed that the Gumbel distributions was found to be best fitted for both pre-monsoon and monsoon seasons, and gamma distributions for post-monsoon season. Further, Mann-Kendall test and Sen’s slope estimators were used to estimate the trend in rainfall occurrence, results that there is no monotonic trend in rainfall occurrence for different seasons in different subdivisions except for monsoon season of Coastal subdivision, which has downward trend. For forecasting future rainfall occurrence in different subdivisions, Holt-Winter’s exponential smoothing and SARIMA models were employed. The result showed that Holt’s-Winter exponential smoothing models were performed better than SARIMA models for all the subdivisions. Hence, Holt’s-Winter exponential smoothing models can be used for forecasting monthly rainfall in meteorological subdivisions of Karnataka.ThesisItem Open Access GROWTH AND INSTABILITY ANALYSIS OF SELECTED VEGETABLES IN KARNATAKA(UNIVERSITY OF AGRICULTURAL SCIENCES, GKVK BENGALURU, 2019-08-26) KALAISELVI, K.; Krishnamurthy, K. N.The time series data of area, production and productivity of major vegetables viz., Potato, Onion and Tomato from the period 1985-86 to 2017-18 (33 years) and minor vegetables viz., Beans, Brinjal and Cabbage from the period 1993-94 to 2017-18 (25 years) were studied. To assess the functional form of trend, models such as linear, quadratic, cubic, logarithmic and exponential models were fitted and the best fitted model was chosen based on R2 and RMSE values. Compound growth rate was calculated. Further, an attempt has been made to study variability in production through instability index and Hazell’s decomposition technique. Also, study has been carried to identify structural change in area and productivity of selected vegetables. Various models were fitted for area, production and productivity. In case of area, production and productivity cubic model was found to perform better for tomato, potato and onion respectively. Linear and quadratic model was found to perform better for potato and brinjal. Whereas exponential model was found to perform better for onion and cabbage. There was an increasing growth rate for area and production of selected vegetables, except tomato production, which has a negative growth rate. With respect to productivity, all the crops showing increasing growth rate except potato and tomato which showed decreasing growth rate. The results showed that change in average sown area and yield were the major sources of change in average production. The structural break in area and productivity of selected vegetables was observed during the post 2000 periods.ThesisItem Open Access DEVELOPMENT OF STATISTICAL MODEL FOR PREDICTING OUTBREAK OF HAEMORRHAGIC SEPTICAEMIA AMONG RUMINANTS IN KARNATAKA(UNIVERSITY OF AGRICULTURAL SCIENCES, GKVK BENGALURU, 2019-08-24) VINAY, P.; MALLIKARJUN B. HANJIIndia occupies the first position in the world’s Buffalo population and it stands second in the world Cattle and Sheep Population as per 19th Livestock Census. These Animal suffers from different kinds of infectious diseases, among which Haemorrhagic Septicaemia is lethal, which is caused by the bacteria called Pasteurella multocida. To develop a statistical model to forewarn the outbreak of Haemorrhagic Septicaemia and to identify the significant risk factors, the data on the outbreak of Haemorrhagic Septicaemia (HS) in all districts of Karnataka for the period from 2006 to 2017 was obtained from the Department of Animal Husbandry and Veterinary Services, Government of Karnataka. Zero Inflated Poisson model was found to perform better when compared Poisson, Negative Binomial and Zero Inflated Negative Binomial models with the lowest AIC value of 3260.991 and Pearson Chi-square value of 1.0028. The same model was used to identify the significant risk factors in the outbreak of Haemorrhagic Septicaemia and it was found that Land Surface Temperature, Air Temperature, Potential evapotranspiration Rainfall and Soil Moisture was found to have significant impact in the disease outbreak. Risk areas for the disease outbreak were identified based on the Standardized Mortality Rate which takes into account the bovine population of each district and it showed that Belgaum district of Karnataka was found to have a very high risk followed by Hassan and Shimoga with high risk of disease outbreakThesisItem Open Access PATTERN CLASSIFICATION OF RS AND GIS IMAGERY OF MADIKERI TALUK OF KODAGU DISTRICT OF KARNATAKA STATE(UNIVERSITY OF AGRICULTURAL SCIENCES, GKVK BENGALURU, 2019-08-24) HARSHAVARDANA, R.; Patil, S. S.This study presents a land use, land cover classification of satellite imagery. Visualization of feature space allows exploration of patterns in the imagery data. The Machine learning algorithms are utilized for pattern classifications. Test imagery was obtained through Sentinel-2 Satellite on February 2018 and February 2019 for Madikeri Taluk, Kodagu District. The second image was taken to measure the changes due to disaster happened in the study area in the month of August 2018.The supervised classifier is identifying the classes using trained set. The statistical significance of satellite image classifiers into constituent classes is of greater importance in remote sensing pattern recognition methods. Maximum Likelihood Classification, Minimum Distance to means Classification, Mahalanobis Distance Classification and Spectral Correlation Mapper Classification were performed using ERDAS imagine and ArcGIS 10.5.1 image processing Algorithms. Accuracy of the classification of data set and classifier were expressed using confusion matrix. F-measure value and Kappa coefficients were used to measure the overall accuracy, user’s accuracy, producer’s accuracy. The test of significance of the Kappa coefficient was performed using Z- test. Maximum likelihood classification was out performing with highest overall accuracy by 75.56 per cent followed by Spectral correlation mapper 71.02 per cent, Minimum distance 67.61 per cent and Mahalanobis distance 61.93 per cent were observed. The post disaster image accuracy by the Maximum likelihood classifier is 73.29 per cent. The changes in the total area among the feature after the disaster noticed was 1184 ha. This study guides the Researchers and policymakers to study the pre and post disaster.ThesisItem Open Access STATISTICAL APPRAISAL OF PERFORMANCE OF SELECTED CROPS BASED ON DROUGHT INDEX(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BANGALORE, 2019-08-06) BHAGYASHREE; Manjunath, V.Climatic change is most challenging issue which has a serious impact on the final outcomes of the agricultural produce. Hence, it is necessary to assess the impact of climatic factor on agricultural crops grown under varied climatic condition. In the present study a modest attempt has been made to assess the performance of Finger millet, Groundnut and Red gram grown under rainfed condition. In all the three crops, the study was conducted over a period with staggered dates of sowing with different varieties in each of the period. To evaluate the performance split split block technique was adopted with periods as main plot, staggered dates of sowing as sub plot and varieties as sub sub plot treatments. In ragi and groundnut, varietal effect were non-significant, however period and dates of sowing were significant, indicating the effect of prevailing environment influence on performance of crops. In red gram all the three effects were significant, hence after correcting for varietal effect, the influence of environment was measured. To determine the influence of Sun Shine Hours and Rainfall during different stages of crop growth multiple regression analysis was carried out. Using thirty years of Rainfall data precipitation index were computed at different stages of crop growth and was used as regressors to assess the influence. Partial regression coefficients of indices revealed the influences of these effects on yield of the crops.ThesisItem Open Access EFFICIENT CLASSIFICATION OF SUGARCANE GENOMES(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BANGALORE, 2019-07-30) PRABHAT KUMAR; PATIL, Dr. S. S.A Phylogenetic tree construction in order to know to compel of the ancestral relationship of species. The genome sequences, tracing the transmission of functional and genetic classification. It plays a predominant role to analyze quantitative behaviour of phylogenetic in conservation of biodiversity and effective heuristics of obtaining accurate distribution of trees. The study to know higher accuracy from efficient algorithm to inferring phylogenetic relationship among Sugarcane (Saccharum) species. A sample of 431 Saccharum genome sequences was drawn from NCBI dataset. Efficient algorithms like Neighborhood joining (NJ), Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Maximum Likelihood (MLE) were considered to construct the phylogenetic tree. The maximum likelihood with Kimura 2-parameter model, Tamura 3- parameter model, and Neighborhood joining with P distance measure achieve outperforms highest accuracy whereas MLE with Maximum likelihood with Jukes-Cantor model and UPGMA with Maximum composite model achieve the least. Computational biology of statistically results are justifiable and compared the functional relationship between different models in which error percentage had been reduced. The same algorithms performs on individual species under different models like maximum likelihood with Kimura 2-parameter model, Tamura 3-parameter model, and Neighborhood joining with P distance measure more efficient than others to differentiate the species genomic sequences and group them to correct taxon.ThesisItem Open Access PATTERN RECOGNITION OF SATELLITE IMAGERIES OF SOMWARAPET OF KODAGU LAND USE AND MEASURE THE IMPACT OF DISASTER(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BANGALORE, 2019-07-26) VAIBHAV, CHITTORA; Patil, Dr. S. S.This study presents a land use, land cover classification of satellite imagery. Visualization of feature space allows exploration of patterns in the imagery data. The Machine learning algorithms are utilized for pattern classifications. Test imagery was obtained through Sentinel-2 Satellite on February 2018 and February 2019 for Somwarapet Taluk, Kodagu District. The second image was taken to measure the changes due to disaster happened in the study area in the month of August 2018.The supervised classifier is identifying the classes using trained set. The statistical significance of satellite image classifiers into constituent classes is of greater importance in remote sensing pattern recognition methods. Maximum Likelihood Classification, Minimum Distance to means Classification, Mahalanobis Distance Classification and Spectral Correlation Mapper Classification were performed using ERDAS imagine and ArcGIS 10.5.1 image processing Algorithms. Accuracy of the classification of data set and classifier were expressed using confusion matrix. F-measure value and Kappa coefficients were used to measure the overall accuracy, user’s accuracy, producer’s accuracy. The test of significance of the Kappa coefficient was performed using Z- test. Maximum likelihood classification was out performing with highest overall accuracy by 72.72 per cent followed by Spectral correlation mapper 64.21 per cent, Minimum distance 61.36 per cent and Mahalanobis distance 56.25 per cent were observed. The post disaster image accuracy by the Maximum likelihood classifier is 71.04 per cent. The changes in the total area among the feature after the disaster noticed was 612 ha. This study guides the Researchers and policymakers.