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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 CORRECTIVE MEASURES FOR BIAS MINIMIZATION IN REMOTE SENSING ESTIMATES OF PADDY(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-09-23) SUNIL, GAYAKAWAD; Mallikarjuna, G. B.One of the important subject in agriculture is crop yield forecasting. Which began in the 1970s. Crop yield forecasting is key for government organizations at all levels, including NGO’s and international organization such as the United Nations as well as companies that are dependent on agricultural produce as an input. Precise agricultural statistics are essential for policymakers, administrators and scientists concerned with planning and evaluation of agricultural investments (De Groote and Traore 2005). The forecasting so important in that prediction of future events is a critical input in many types of planning and decision making process with application to areas such as operation management, marketing, finance, risk management, economics, industrial process control and demography. In the computer era, forecasting can be done with the help of sophisticated statistical software more efficiently. Their use includes monitoring of agricultural production changes, planning of agricultural interventions, development projects, and development of early warning systems and preparation of macroeconomic accounts. Poor agricultural data can lead to misallocation of scarce resources and policy formulations that fail to resolve critical development problems.ThesisItem Open Access STUDY OF TEMPORAL AND SPATIAL VARIATIONS IN AREA, PRODUCTION AND PRODUCTIVITY OF RICE CROP IN TELANGANA STATE(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-09-12) KOREM, SUNITHA; Gowda, D.M.An attempt has been made to study the temporal and spatial variations in area, production and productivity of rice crop in Telangana State. For the study 30 years of secondary data were collected on area, production and productivity of rice from the DES, Hyderabad. Linear, quadratic, logarithmic, exponential and power models were used to study the temporal variation in area, production and productivity. The best model was selected based on the root mean square error (RMSE) and R2 value. Linear model was found to be the best model for area and production while exponential model was found to be best model for productivity. The results indicated that area, production and productivity has shown an increasing trend over time. Further, an attempt has been made to study the spatial variations in area, production and productivity across districts of the State in different study period viz., period-I (1986-2001), period-II (2001-2016) and as a whole by computing coefficient of variation. The result indicated that in the State, period-II shown higher spatial variations in area and production and less spatial variation in productivity. Result of ‘Mann–Whitney U’ statistic indicated no significance difference in spatial variations between the periods-I and period-II with respect to production and productivity but there was a significant difference in area. Productivity levels of the districts remains the same during this study period except for the period-I.ThesisItem Open Access STUDY ON ANALYSIS OF PHYLOGENETIC TREE OF MULBERRY (Morus) GENOME(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-08-08) MALADEVI, H. V; PATIL, S. S.Phylogenetic tree is constructed in order to know the ancestral relationship of a set of sequences, designing more effective drugs, tracing the transmission of deadly viruses. It also plays predominant role in conservation of biodiversity, to analyze quantitative behavior of phylogenetic and effective heuristics of obtaining accurate trees. The study has been conducted to know higher accuracy from efficient algorithm to inferring phylogenetic relationship among Mulberry (Morus) species. A total of 609 Morus genome sequences were acquisition from the NCBI dataset. Morus species is the primary host of silkworms (Bombyx mori L.). The productivity and profitability in sericulture solely depends on the quality and yield of mulberry leaves in rearing of silkworms. Different algorithms like Neighborhood joining, UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and Maximum Likelihood were considered to prepare the phylogenetic tree. The Maximum composite model outperforms highest accuracy whereas NJ with Jukes-Cantor model gives the least. Computational biology of statistical results justifiable were compare the functional relationship between different models in which error percentage been reduced. The same algorithms carried out for the individual species under different models, Neighborhood joining algorithm with Tajima- Nei model gives the adverse classification among all other models, in this model Morus. indica gives the high F- measure value followed by Morus. australis. All other models Morus. rubra gives the best value followed by Morus. celtidifolia.ThesisItem Open Access PATTERN CLASSIFICATION SCHEME FOR REMOTE SENSING SATELLITE IMAGERY(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-08-01) KIRAN, K. S.; Patil, S.S.This study presents a features extracts and classification of satellite imagery. Visualization of feature space allows exploration of patterns in the imagery data to infer the knowledge. The Machine learning techniques algorithms are administrated to vital classifications. Supervised classifier is identifying the classes using trained set while in an unsupervised classification the classifier itself develops the spectral classes. Compiled classification has to be improvised using efficient algorithms with appropriate threshold values. The statistical significance of satellite image classifiers into constituent classes is of greater important in remote sensing pattern classification methods, test imagery were obtained through IRS P-6 LISS-IV on 14th November 2015 for Shettikere hobli, Chikkanayakanahalli Taluk. Maximum likelihood classification, Minimum distance to means classification, Mahalanobis distance classification, Spectral correlation mapper classification and Unsupervised classification were performed using ERDAS 2014 imagine and ArcGIS 10.1 image processing software. Accuracy of the classification of dataset and classifier was expressed using error matrix from which the overall accuracy, user’s accuracy, producer’s accuracy, F-measure value, Kappa coefficients and sample variance of Kappa coefficients were obtained. The test of significance of Kappa coefficient was performed using Z- test. Maximum likelihood classification was found to be best with highest overall accuracy of 74.16 per cent followed by minimum distance to mean 70.00 per cent, Mahalanobis distance 66.66 per cent, Spectral correlation mapper 51.66 per cent and Unsupervised classification 46.66 per cent were observed.This study help the farmers and policy makers to infer and estimate area of crop production.ThesisItem Open Access TIME SERIES ANALYSIS OF AREA, PRODUCTION AND PRODUCTIVITY OF SELECTED PLANTATION CROPS IN DAKSHINA KANNADA DISTRICT OF KARNATAKA(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-08-01) CHAITHRA, M.; Mallikarjuna, G.B.Plantation crops are high valued commercial crops which are export oriented. In addition to commercial importance these also generate huge employment opportunities. In the present study a prudent attempt was made using data of thirty five years to understand the trend in area, production and productivity of major plantation crops such as arecanut, cashewnut and coconut of Dakshina Kannada. Further, an attempt was also made to forecast the area and production of these crops. The polynomial regression models were fitted to assess the trend in area and production. Based on the model adequacy linear model was the best fit for area and production of arecanut. For the productivity of arecanut none of the fitted models were significant indicating that there was non-significant change. Further, cubic and quartic models were found to be best fit for production and productivity of coconut and cashewnut respectively. Due to the presence of autocorrelation in the data, ARIMA and exponential smoothing methods were used for forecasting. The appropriate ARIMA models were identified after removing the outliers. Using 10 per cent of data as testing set, ARIMA (1,1,1) and ARIMA (0,1,0) were found to be the best fitted model based on RMSE and MAPE values to forecast area and production of arecanut. Whereas, ARIMA (0,1,1) found to be the most suitable model to forecast area and production of coconut. However, Damped trend model and Brown’s linear trend model were the best fitted model for predicting the area and production of cashewnut.ThesisItem Open Access A STATISTICAL COMPARISION OF SEVERAL REFERENCE EVAPOTRANSPIRATION METHODS FOR GKVK BENGALURU (URBAN) DISTRICT(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-07-26) SUCHITA, P. KALEKAR; Krishnamurthy, K.N.Evapotranspiration (ET0) plays a key role in simulating hydrological effect of climate change, and a review of evapotranspiration estimation methods in hydrological models is of vital importance. FAO-56 PM method is a standard method for estimation of ET0 as this method requires large number of weather parameters which could not be easily available at meteorological stations. In addition to the use of complicated unit conversions and tediousness, the availability of reliable quality data, time consumption and difficulties in data collection present another serious limitation for this method. Keeping this in view, the present study attempts to identify alternative methods (both temperature and radiation) to precisely estimate ET0, on the basis of their performance with widely acclaimed FAO - 56 PM model. For this purpose, daily data on weather parameters for GKVK station was collected for 34 years (1983-2016). Based on the different statistical accuracy measures Romanenkos among temperature based methods and FAO-24 Radiation among radiation based methods were found to be best methods for ET0 estimation. Hence, these two methods can be recommended for use as an alternative to calculate reference evapotranspiration for GKVK station, Bengaluru Urban District with a proper calibration. Besides this, the weather parameters required for use in these methods are comparatively less than that of the standard FAO-56 PM model.ThesisItem Unknown STATISTICAL ANALYSIS OF BOVINE POPULATION AND MILK PRODUCTION OF KARNATAKA(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-07-25) BISHVAJIT, BAKSHI; Manjunath, V.Livestock production plays a major economic and cultural role in rural community. It provides indirect insurance against risk of crop failure due to natural calamities like drought and flood. In the present study an attempt was made to evaluate the regional disparity in different bovine population, its growth and factors influencing the growth. Different statistical diversity indices were used in order to evaluate the regional disparity in bovine population. The Indices showed that the bovine population in the state was highly diversified during the beginning year. However, as the time elapsed the diversified population had moved towards specialization. Kendall’s coefficient of concordance indicates that there is significant change in the structure of bovine population within and between different agro-climatic zones over year. Further, the test for independence showed that there was no significant association of agro-climatic zones and time period on structural change of bovine population. Growth rate analysis showed that bovine population in the state had increased. The increasing growth in bovine population in the state may be attributed to the changes in the net irrigated area, net sown area, area under pulse crop, area under cereals and total number of cultivars. In order to assess the trend in milk production in different districts the polynomial regression models were fitted. The appropriate models were determined based on the significance of the model adequacy of the fitted models. A single model could not be identified for all the districts indicating the different trends in milk production which was substantiated by compound annual growth values.ThesisItem Unknown EVALUATION OF STATISTICAL CORRECTIVE METHODS TO MINIMIZE BIAS WITH RESPECT TO OBSERVATORY DATA IN MODELLED CLIMATIC PARAMETERS(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-07-24) SRUTHI, U.; Mallikarjuna, G.B.Climate change refers to any systematic change in the long-term weather parameters. The study aims at reducing the bias in the climatic parameter values obtained from the satellite models by comparing with the data obtained from the observatories. Nine years day wise data for the climatic parameters such as Rainfall, Maximum and Minimum temperature are collected from the AICRP on Agro meteorology UAS GKVK Bengaluru, for the study. The day wise data was converted to, standard meteorological weekly and monthly data for the period wise analysis. Climatic parameter data obtain from the observatories are always more accurate than those from the satellite model. Bias correction methods such as difference method (DM) and modified difference method (MDM) were attempted to minimize bias of the satellite model data compared to observatories data. Best bias correction method is identified based on the coefficient of variation. Results revealed that, among them MDM was better for all the three periods of rainfall. Similarly, MDM is an ideal correction measure for daily maximum and minimum temperature, for weekly and monthly maximum and minimum temperature data DM was found to be an ideal measure. Probability distribution functions were attempted for the climatic factors considered and best fit of them were identified using chi square test. The best fitted probability distribution for the different periods were identified as Gamma and Weibull distribution as most suitable for the rainfall data. For Maximum and minimum temperature, no generalize single model was found as best fit.ThesisItem Open Access STATISTICAL ANALYSIS OF AREA AND PRODUCTIVITY OF SUGARCANE IN TAMIL NADU(UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU, 2017-07-24) DINESH KUMAR, P.; Manjunath, V.Climatic change is most challenging issue which has a serious impact on the final outcomes of the agricultural sector. To study the changing trend in area, production and the impact of precipitation over productivity of sugarcane in Tamil Nadu, the secondary data for a period of 30 years from 1985 to 2014 and district wise precipitation data of Tamil Nadu for the corresponding years were collected from the Department of Economics and Statistics, Government of Tamil Nadu. The intrinsically nonlinear Logistic and Gaussian models were found appropriate to visualize the temporal trend of area and production of sugarcane, respectively in Tamil Nadu. Principal component regression analysis was used to study the impact of precipitation on the sugarcane productivity by considering the Standardized Precipitation Indices (SPI) of four seasons along with time as regressor variables. The study indicated that the productivity of sugarcane in Cauvery Delta zone and Southern Zone mainly depended upon the precipitation during Cold Weather and Hot weather periods, respectively. However, the productivity of sugarcane in North East, North West and Western zones were mostly dependent on the precipitation of North East monsoon when compared to all other seasons. It was also seen that there is a steady decline in precipitation values during the study period in all the agro climatic zones of Tamil Nadu.