Loading...
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.
Browse
9 results
Search Results
ThesisItem Open Access STATISTICAL STUDY OF DISTRIBUTION OF NON-FARM INCOME IN CAUVERY COMMAND AREA OF KARNATAKA(University of Agricultural Sciences GKVK, Bangalore, 41163) SUNIL KUMARA, D S; GOPINATH RAO, MNon-farm activities play an increasingly important role in sustainable development, poverty reduction and employment generation in the rural area. There is need to identify the factors which are responsible in influencing the income from non-farm activity. Statistical distribution, Multivariate multiple regression, Non parametric regression, conditional distribution has been carried out to find the effect of explanatory variables on non-farm income and investment. Study shows that distributions of these variables are asymmetric and positively skewed and only log normal distribution fitted well for all the variables, Risk analysis for non-farm income showed that high risk income is for farmers with less than Rs.12,000 for upper reach, less than Rs. 14,400 for mid reach and less than Rs.16,000 for lower reach. Parametric regression analysis revealed that farm income, number of working adults, education and number of persons employed influence non-farm income. In case of investment significant influence is found by education, farm income age and numbers of persons employed. Projection pursuit regression showed that Non-farm income is influenced by Education, Number of persons employed, farm income, for investment number of persons employed, education, and family size. Relative frequency is maximum for nonfarm income of less than Rs 60,000 across all categories of all annual farm income. There is a steady decrease in relative frequency with increase in nonfarm income for all categories of farm income.ThesisItem Open Access SUPPLY-DEMAND PROJECTIONS FOR FOOD GRAINS IN KARNATAKA(41547) LAKSHMI, NARSIMHAIAH; Chandrashekar, HThe present study was undertaken to project the supply and demand for foodgrains in Karnataka during XII and XIII five year plan period by making use of district level data of 64th round consumer expenditure survey published by National-Sample-Survey-Organization during 2007-08 which facilitated to capture the regional variation in composition of food basket while carrying out supply-demand projections. The demand estimates are derived based on growth of population, per capita income and income elasticity of demand. Log– inverse, Double–log, Log-log–inverse, Linear, Quadratic and Semi-log models were used for computing expenditure elasticities. Best functional form was chosen based on high R2. Supply of foodgrains in the state was projected by projecting area and productivity of major food crops using time series data from 1990-91 to 2009-10. The log-linear regression model was fitted to estimate area. Productivity of food crops were projected considering the weather cycle of the state. Production was obtained by multiplying the projected area by productivity. Supply estimates were derived by adjusting the production to seed, feed and other industrial requirements. The estimate of supply-demand gaps with respect to rice, wheat, Jowar, ragi, gram, tur and total foodgrains was – 5.35, -9.15, 3.80, 3.48, 6.86, 0.35 and -3.87 lakh tons respectively by 2016 and by the end of 2021 it is expected to be -21.25, -12.87, 4.64, 3.02, 10.46, -0.78 and -38.14 lakh tons respectively. The study indicates supply-demand gap for total foodgrains would be widening at the end of XIII five year plan period. SignatureThesisItem Open Access APPLICATION OF MULTIVARIATE STATISTICAL TECHNIQUES FOR WATER QUALITY PARAMETERS OF LAKES IN BANGALORE(University of Agricultural Sciences GKVK, Bangalore, 41627) LEKHASHREE, S M; Krishnamurthy, K NWater is one of the most precious natural resources needed by all living beings. Lakes and surface water reservoirs are the planet’s most important fresh water resources. In the last few decades, Bangalore has observed rapid industrialization and urbanization. This has lead to excessive pollution of water bodies. Water quality parameters data of the 16 lakes of Bangalore for the period of 2008- 2010 considered to study the present condition and causes for the pollution of the lakes. A multivariate statistical technique was used to study the water quality of the lakes. The main idea behind using this technique was to accommodate all the water quality parameters including metallic and non metallic parameters. The descriptive statistics showed significant variability in the water quality parameters in different lakes over the years. In cluster analysis, squared Euclidian distance was used as a similarity measure for classification of lakes. The results showed variation among the lakes and identified three types of lakes. It provides evidence that former three clusters could be categorized as less polluted and latter as highly polluted. The Factor analysis identified three factors that are responsible for the data structure explaining 85.11% of the total variance of the data set. Factor 1 to 3 explains variance 46.37%, 22.71% and 16.62% respectively. The potential factors identified were lead, zinc, copper, iron, Dissolved oxygen, electrical conductivity, total dissolved solids, pH and suspended solids.ThesisItem Open Access ROBUST ESTIMATION AND OUTLIER DETECTION IN FARM AND NONFARM INCOME - AN EMPIRICAL STUDY(University of Agricultural Sciences GKVK, Bangalore, 41618) SHIVASHANKAR KADAM, KADAMThe present study is an effort to detect outliers in farm and nonfarm income and use them in estimation for the data obtained from the Cauvery command area of Karnataka. The study area divided into three reaches as upper, mid and lower reach has data on three hundred households for each reach and this is used to detect outliers and parameters are estimated for each reach and entire study area. Outliers detected by using robust procedure based on Mahalanobis distance, resulted in 14, 16, 16 and 51 outliers for farm income considered as dependent, whereas 8, 6, 7 and 25 outliers are detected when nonfarm income is considered as dependent variable for three reaches and for entire study area, respectively. Conventional MLR method and robust regression methods like MM and LTS methods are used for estimation and comparison is made among three methods. Dependent variable farm income has significant contribution from age, education, working adults and persons employed in upper and mid reach. In lower reach only age of persons is significant. For entire study area education, family size and working adults are significantly contributing to farm income. Age, family size and farm income are significantly contributing for nonfarm income as dependent variable in upper and mid reach. In lower reach only farm income is significant. For entire study area education, persons employed and farm income significantly contribute to nonfarm income in both the methods. Finally estimates by LTS method have lesser standard errors compared to MM and MLR method.ThesisItem Open Access APPLICATION OF COVARIANCE TECHNIQUE IN LONG TERM FERTILIZER EXPERIMENT(41835) DARSHAN, K H; MALLIKARJUNA, G BData generated in AICRP on LTFE for the 2007-2012 has been considered in this study. Primary nutrients such as Nitrogen, phosphorous, Potassium and Sulphur have positive significant influence with yield in addition to Zinc, Manganese, Iron and Organic Carbon. Rest have Positive non significant correlation with yield. Path coefficient analysis is used to study direct /indirect effect of soil parameters with the yield. The association is recorded after neutralizing the negative direct effect by the positive indirect effect while causing interaction effect in response. Balanced (NPK) nutrients are having better association (low residual effect) with yield. Higher residual effect noticed with higher dose of balanced nutrition (150% NPK) and also with imbalanced nutrition indicating poor association. Canonical correlation of response group (grain and straw yield) with primary nutrients group is more followed by secondary and tertiary nutrition group. This indicated primary nutrients are more responsible for productivity. First canonical covariate itself is explaining more variation in all the years and for the pooled data. Strong relation between them is observed by the higher Eigen values compared to the second canonical variate. ANCOVA revealed that majority of the significantly related variables (soil parameters) as covariates are having less error mean sum of squares than ANOVA. It can be inferred that, analysis of covariance will minimize the error component of analysis when analyzed with significantly related components. ANCOVA is relatively more efficient than ANOVA, since value of relative efficiency is more than 1 with the significant variables as covariates.ThesisItem Open Access IMPACT OF WEATHER PARAMETERS ON PRODUCTIVITY OF SELECTED CROPS IN MANDYA DISTRICT- A STATISTICAL APPROACH LAKSHMI, L. N. PALB 2167(University of Agricultural Sciences GKVK, Bangalore, 41850) L N, LAKSHMI; H S, SURENDRAThe present study attempts to know the trend of selected weather parameters and to analyse the impact of weather parameters on productivity of ragi and sugarcane in Mandya district. The secondary data of weather parameters for the study was obtained from ZARS, V. C. Farm, Mandya for the period from 1986 to 2010. Further, the available data of crop productivity was obtained for the period from 1991-2010 (25 years). The parameters consider viz., maximum temperature, minimum temperature, relative humidity, cloudiness, wind speed, sun shine hour, rainfall and ragi and sugarcane yield for the study. The trend analysis revealed that the maximum temperature showed a positive significant trend whereas relative humidity and rainfall showed a positive non significant trend over a period of time for most of the months. The cloudiness and sun shine hour showing a negative significant trend whereas minimum temperature and wind speed shown a negative non significant trend during study period for most of the months. The correlation analysis indicated that the productivity of ragi and sugarcane was positively correlated with average minimum temperature, wind speed and rainfall, however negatively correlated with maximum temperature and relative humidity. The sun shine hour contribution was higher (75.97%) towards the productivity of ragi, followed by wind speed (27.33%), cloudiness (14.67%), and rainfall (7.76%). The lowest contribution was minimum temperature (0.35%) and relative humidity (0.87%). Whereas productivity of sugarcane the maximum temperature contribution was higher (23.42%), followed by relative humidity (15.15%), minimum temperature (13.33%). The lowest contribution was sun shine hour (0.75%), followed by cloudiness (1.05%) and wind speed (1.22%).ThesisItem Open Access TEMPORAL AND SPATIAL ANALYSIS OF CROPPING PATTERN IN KERALA(University of Agricultural Sciences GKVK, Bangalore, 41838) K C, MARJANA BEEGUM; H, CHANDRASHEKARChange in cropping pattern implies a change in proportion of area under different crops. The study aimed at analyzing the changes in cropping pattern in Kerala with the help of time series data for a period of 20 years from 1993-94 to 2012-13 and for four sub periods viz. Period I (1993-94 to 1997-98), period II (1998-99 to 2002-03), period III (2003-04 to 2007-08) and period IV (2008-09 to 2012-13) over districts of the state. Data are collected from the Department of Economics and Statistics, Thiruvananthapuram. Kerala state experienced declining trend in area under food crops while the area under cash crops increased during the study period. The state experienced diversified cropping pattern during last twenty years and the inference is supported by Kendall’s coefficient of concordance and rank correlation coefficient. Extent of diversification varies considerably across the districts, while some districts have highly diversified cropping pattern. Certain other districts have shown tendency to move towards specialization. Multiple regression analysis revealed that annual rainfall, number of market, road length and land holdings were the factors responsible for the changes in cropping pattern. Though the environmental factors are more congenial to diversify crops in the state, socio-economic factors do act as barriers in adopting diversified cropping pattern. On the other hand the development of infrastructure like road and markets facilitates to move towards specialization. Ginger, tapioca and sweet potato showed higher degree of maladjustment indicating that districts with higher area under these crops have registered lower productivity.ThesisItem Open Access Genetic Diversity Analysis Of National Germplasm Collection On Little Millet (Panicum Miliare Lamk.) - A Statistical Approach(University of Agricultural Sciences; Bangalore, 1986) Gulapannavar G.B.; Venkataramu M.N.ThesisItem Open Access Statistical Evaluation Of Crop Responses To Fertilizers And Other Soil Characteristics(University Of Agricultural Sciences; Bangalore, 1984) Havaldar Y.N.; Sharma K.M.S.