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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 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 STATISTICAL ANALYSIS OF AREA, PRODUCTION AND PRODUCTIVITY OF COCONUT CROP IN DISTRICTS OF KARNATAKA(University of Agricultural Sciences, GKVK, 2013-06-01) PADMA PRIYADARSHINI, G; SURENDRA, H SIn India, Karnataka stands second in area (419 thousand hectare) and third in production (1492 thousand million nuts) of coconut. In Karnataka, Tumkur is the largest producer of coconut with the production of 9945.66 lakh nuts (2010). An attempt is made to study the area, production and productivity of coconut crop in districts of Karnataka. The analysis is based on secondary data taken from Directorate of Economics and Statistics, Karnataka for the period 1982-2009. The results establish an increasing shift of coconut cropped area (130.14%), production (203.65%) and productivity (112.71%) for the period 1982-2009. Three models viz., Linear, power and exponential were fitted for comparison based on R2 value reveals that 5 districts indicating power model and 9 districts showing exponential model for area. Three districts with power model and 6 districts with exponential model as best fit for coconut production. Three indices have been used, viz., Herfindhal-Hirschman’s index, Instability index and sustainability index of area and production of coconut crop. The Hirschman’s index showed highest in Tumkur followed by Hassan and Chitradurga district revealing the higher specialization for the period 1982-2009. Instability index was found to be the least in Tumkur as compared to Chitradurga and Hassan district. Highest sustainability noticed in Tumkur followed by Chitradurga and Hassan district during 1982-2009 in both area and production. To study the temporal variability, regression analysis with maximum R2 noticed in Tumkur, Hassan and Chitradurga districts for the periods in both area and production.ThesisItem Open Access STATISTICAL METHODS FOR STUDYING THE EFFECT OF MULTIPLE OUTLIERS IN DESIGNED EXPERIMENTS(University of Agricultural Sciences, GKVK, 2013-08-05) DINESH, S INAMADAR; Venugopalan, RDesign of experiments is the backbone of agricultural research experiments. Adopting RCBD, with an aim to statistically test the significance of several treatments, a given treatment is replicated ‘r’ times to assess its power of repeatability for a trait. However, it so happens that replicated values may not follow a normal pattern but have some outliers/aberrant data, leading to non-significant results in ANOVA. It is also not advised to delete them as the basic principle of randomization will be violated and every observation may carry some useful information for crop scientists to exploit. This calls for employing a robust analysis approach, which gives suitable weights to those outliers based on observed pattern of replications, extracts some information and ensures statistical adequacy. Foregoing thoughts were elucidated by adopting robust ANOVA techniques for comparing various pollination methods (treatments) on seed yield and related traits of Brinjal crop. Cook’s distance measure was computed to identify the outliers in the experimental data. Robust analysis, across eight traits, based on Huber’s and Andrew’s M-estimation methods resulted decreased error mean square as high as 90.03 per cent coupled with 97.17 per cent decrease in Probability of Type 1 error and 85.02 per cent decrease in error mean square coupled with 86.01 per cent decrease in Probability of Type 1 error, respectively. It was observed that by adopting suitable Mestimation procedure, a researcher, without removing an outlier could arrive at required inference about the treatmental differences without violating basic principles of experimental designs.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 STATISTICAL ANALYSIS OF COST OF CULTIVATION OF MAJOR CROPS IN KARNATAKA(University of Agricultural Sciences GKVK, Bangalore, 2013-10-21) NAVEEN, N C; SURENDRA, H SIn Karnataka, agriculture continues to be the major source of livelihood for roughly two third of its population. Study on farm income and welfare of farmers is essential to monitor or assess welfare of overall development of the economy. The present study attempts to know status of farmers in cultivation of crops like Paddy, Maize, Tur, Bengalgram, Groundnut and Sunflower of Karnataka state. The study is based on secondary data on area, production, productivity and factor wise cost of cultivation data of Rice, Maize, Tur, Bengalgram, Sunflower and Groundnut crops in Karnataka. Data was collected for the period from 1990-91 to 2009-10 from Directorate of Economics and Statistics, Karnataka. Among growth rate of area, production and productivity of six crops Groundnut and Sunflower shown negative significant result for area and Groundnut shown negative significant result in Production. Among growth rate of inputs of seed, fertilizer, manure, human labour, animal labour and insecticides all six crops had shown positive significant result. In resource use efficiency of crops Maize (0.97) noticed highest returns to scale followed by Tur (0.93), Sunflower (0.92), Groundnut (0.87), Paddy (0.83) and Bengalgram (0.76). Among Cost concepts total cost expenditure per hectare was found in Paddy (Rs.27248) followed by Groundnut (Rs.15309), Maize (Rs.15058), Bengalgram (Rs.12236), Tur (Rs. 10998) and Sunflower (Rs.10244). Among returns per rupee Bengalgram has highest returns of Rs. 1.51 followed by Tur (Rs. 1.39), Groundnut (Rs. 1.29), Maize (Rs 1.28), Sunflower (Rs. 1.24) and Paddy (Rs. 1.18).ThesisItem Open Access STATISTICAL STUDY OF ARRIVALS AND PRICES OF MAIZE IN SELECTED MARKETS OF KARNATAKA(University of Agricultural Sciences GKVK, Bangalore, 2013-09-23) SHRUTHI, M; Krishnamurthy, K NAnalysis of prices and market arrivals over time is important to know the fluctuations in them and it helps to formulate appropriate ways and means for reducing price fluctuations of agricultural commodities. In this direction, trends in arrivals and prices of maize were studied in selected markets of Karnataka. Monthly time series data over a period of 21 years for Bagalkot and Belgaum markets indicated a raising trend. Analysis of seasonal fluctuations revealed that prices were at its peak during the months of March to May and begins to decline. During July and August, the prices were low. Different forecasting models like Trend analysis, Exponential smoothing models and Box-Jenkins models were considered to produce forecast and to measure the forecast accuracy among selected different models. In case of Bagalkot and Belgaum markets price forecast by Box-Jenkins (ARIMA) (1,1,1) model was best validated with the higher correlation coefficient and low coefficient of variation indicating the best model for forecasting both the markets. Market integration was studied between price pairs of maize in three regional markets of Karnataka using co-integration approach for daily wholesale maize prices over the period of one year from 1st January 2012 to 31st December 2012.Results showed that regional markets of maize have strong price linkages and thus are spatially integrated. Bi directional influences were seen to be exerted by Bagalkot and Davangere markets on Belgaum market.ThesisItem Open Access STATISTICAL EVALUATION OF SOIL AND YIELD PROPERTIES IN LONG TERM FERTILIZER EXPERIMENTS(University of Agricultural Sciences GKVK, Bangalore, 2014-07-25) KARTHIK KUMAR, V; MANJUNATH, VOptimum fertilizer usage is a key factor in increasing agriculture production and at the same time maintaining the soil fertility. Long term fertilizer experiments are repositories of valuable information regarding the sustainability of intensive agriculture. Hence, it was important to study the long term impact of fertilizer application on crop yield along with the influence of weather parameters. The 26 years data from 1987-2012 of soil properties and crop yield for the study were collected from AICRP on LTFE and weather data was obtained from the weather observatory center situated in the campus of GKVK, UAS, Bangalore. The data was analysed using Multivariate analysis of variance technique in order to see the differences, if any exist in the soil nutrients following the long term fertilizer application on the same plots. The principal component regression analysis was adopted as the ordinary regression analysis was found not suitable due to the presence of Multicollinearity and hence, to understand the effect of different soil properties and the influence of weather factors on the grain and straw yield in long term fertilizers application. The long term application of the same fertilizers had a significant effect on available micro nutrient in the soil. Due to change in the soil properties and available nutrients by applying fertilizers showed an impact on the grain and straw yield of the crop studied. It was also seen different weather factors during different stages of crop growth and development had different influence on the crop yield.ThesisItem 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.