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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 ANALYSIS OF BEHAVIOUR OF EGG AND MILK PRODUCTION IN INDIA(University of Agricultural Sciences, Bangalore, 2023-02-13) BHARAT KUMAR MARMAT; Mohan Kumar, T.L.A statistical analysis of behaviour of eggs and milk production was carried out using 60 years of data for all India and 30 years of data for different States of India. Simple Growth Rate (SGR) and Compound Growth Rate (CGR) were computed. For all India eggs production, SGR (4.93%) was found to be non-significant, and CGR (6.08%) was found to be significant, whereas, for all India milk production, both SGR (3.92%) and CGR (4.32%) were found to be significant. Among all the states, Haryana state exhibited the highest SGR (8.21%) and CGR (10.93%) in eggs production, whereas Rajasthan state revealed the highest SGR (6.21%) and CGR (6.42%) in milk production. The Mann-Kendal test was employed to study the trend in eggs and milk production. The result revealed that both eggs and milk production was found to be increasing trend at all India as well as each State level except for the Sikkim State which has no significant trend for eggs production but an increasing trend for milk production. Coefficient of Variance (CV%) calculated to study the temporal variation in the study period. During the period-III (1981-82 to 1990-91) has the highest CV (22.48%) noticed in production of eggs with an average of 16,089 million number per annum, whereas period-VI (2011-12 to 2020-21) showed the test statistic indicated a high spatial variation in eggs and milk production among different States of India. Haryana and Assam States have the highest (75.51%) and lowest (5.72%) CV in eggs production with an average production of 2861 and 486 million number eggs respectively. Rajasthan and Assam States have respectively highest (56.91%) and lowest (10.28%) CV in milk production with an average production of 11.81 and 0.77 million tonnes respectively. February,ThesisItem Open Access A STATISTICAL STUDY OF LONG-TERM FERTILIZER EXPERIMENTS IN MAIZE (Zea mays L.)(University of Agricultural Sciences, Bangalore, 2020-10-19) MEGHA, E; KRISHNAMURTHY, K NA field experiment on long term fertilizers under finger millet-maize cropping sequence has been in progress since 1986 at University of Agricultural Sciences, GKVK, Bangalore. These experiments provide an insight for scientific investigation of structural relationship that governs the variability of specified crop over a period of time. The experimental data on grain yield of maize and various soil characteristics for a period of 32 years was procured to study the performance of maize yield under different fertilizer treatments over a period of time. Profile analysis has been performed and results indicate that all the treatments differ significantly i.e., there is significant interaction between year and treatments. Available nutrients in soil such as nitrogen, phosphorous, potassium, sulphur zinc, manganese, iron as well as organic carbon were having significant effect. Only copper showed non-significant correlation with yield. Path coefficient analysis was used to study direct/indirect effect of soil parameters with yield. Balanced (NPK) nutrients were having better association (low residual effect) with yield. Higher residual effect was noticed with higher dose of balanced nutrition (150% NPK) and also with imbalanced nutrition (-K, -PK) indicating poor association. The analysis of beta-convergence showed evidence of statistical convergence in yield of different treatments.ThesisItem Open Access A STATISTICAL ANALYSIS OF ARRIVALS AND PRICES OF MANGO IN SELECTED MARKETS OF KARNATAKA(2022-03-03) BHUVANA, M.; KRISHNAMURTY, K.N.Analysis of prices and market arrivals over time is important in order to understand price fluctuations and for establishing effective methods and means for reducing price fluctuations of agricultural commodities. In the present study, trends in mango crop arrivals and prices over time from BinnyMill, Chintamani, and Srinivaspur markets of Karnataka state were chosen from a pool of all market places in Karnataka based on the highest amount of mango arrivals. Monthly secondary data on arrivals and prices of mango were obtained from the respective APMCs over the period of 18 years from 2002 to 2019. The Linear model predicted the trend in mango arrivals as the best in BinnyMill market while Cubic model predicted the best in both Chintamani and Srinivaspur markets. Further, Cubic model explained the trend in mango prices as the best in both BinnyMill and Chintamani markets while Quadratic model explained best for Srinivaspur market. Box-Jenkin's method was used to forecast mango prices in selected markets. Since, there was seasonality in the data, seasonal ARIMA model were fitted. The data's stationarity was tested using the Augmented Dickey-Fuller test, which revealed that all the markets' price series were non-stationary. From the analysis, it was revealed that SARIMA(0,1,1)(0,0,1)[3], SARIMA(2,0,0)(0,0,0)[3] and SARIMA(0,1,0)(1,0,1)[3] models were the best fitted SARIMA models for BinnyMill, Chintamani, and Srinivaspur markets, respectively.ThesisItem Embargo RISK ESTIMATION AND PREDICTION OF BLACK QUARTER DISEASE OUTBREAK AMONG CATTLE IN KARNATAKA: A STATISTICAL APPROACH(2022-12-15) ANIL KUMAR, B. S; MALLIKARJUN B. HANJICattle suffer from various diseases, Black Quarter (BQ) disease is one among them, and it is highly fatal disease, caused by Clostridium chauvoei. Hence, a study is carried out on Risk Estimation and Prediction of BQ disease outbreak among Cattle in Karnataka: a statistical approach by collecting 11 years secondary data from 2010 to 2020. Data were analysed using appropriate statistical tools through R software. Results indicated that the Spatial endemicity reveals Hassan district has got more outbreak of BQ disease during the period. Six districts namely Tumakuru, Mandya, Mysuru, Kodagu, Chikkamagaluru and Dakshina Kannada were identified as disease hotspot through Spatial autocorrelation. Space-time cluster analysis reveals that, Hassan district has high Relative Risk of 11.02 and Vijayapura, Raichur, Koppal, Bagalakote, Belagavi, Gadag, Dharwad, Udupi, Uttara Kannada, Haveri, Chitradurga, Shivamogga, Davanagere, Ballari, Chikkamagaluru together (0.25). Factors such as EVI, LST, PET, Rain precipitation rate, Soil moisture, Surface pressure and Wind speed significantly associated with the BQ disease outbreak during Linear discriminant analysis. Among the five different models (GBM, RF, MARS, NB and SVM) adopted, RF and GBM models performed better for predicting the risk with AUC values 0.837 and 0.823, respectively. The average model (combination of GBM and RF model) risk prediction analysis, showed that the districts Mysuru, Mandya, Ramanagara, Chamarajanagar, Kolar, Bengaluru Rural, Chikkaballapura, Bengaluru Urban and Chikkamagaluru were in high heavy risk.ThesisItem Open Access STATISTICAL APPRAISAL OF AREA, PRODUCTION AND PRODUCTIVITY OF BENGAL GRAM CROP IN KALABURAGI DISTRICT OF KARNATAKA(University of Agricultural Sciences GKVK, Bangalore, 2021-12-26) VIJAYKUMAR; VIJAYKUMAR; GOPINATH RAO, M.; GOPINATH RAO, M.An attempt was made to analyse the trends in area, production, and productivity of the Bengal gram crop by considering the data of 25 years (1995-96 to 2019-20) in the Kalaburagi district of Karnataka state. The models used for trend analysis includes linear, quadratic, cubic, exponential, and log-logistic. The model with the lowest MAPE was chosen as the best fit. The cubic model was the best-fitting model for Bengal gram area and productivity, whereas the linear model found better fitted for production. The study also conducted to forecast the production of Bengal gram in the district for the next five years using the ARIMA model by considering the data of 50 years (1970-71 to 2019-20) out of which 47 years were considered for training the model and the remaining 3 years for testing purpose. The results revealed that ARIMA (0,1,1) with a drift model better fitted for the data and showed an increasing trend in the production of Bengal gram for the next 5 years. Further, an attempt was made to analyse the structural change in area and productivity of Bengal gram. Structural change was seen in 2007-08 and 2015-16 for area and productivity respectively. The difference between the pre and post-break periods established mean yields was found to be statistically significant in both area and productivity.ThesisItem Embargo PROFITABILITY OF ORGANIC AGRICULTURE IN INDIA – META ANALYSIS(2022-12-29) SURAJ SHIVALINGAPPA CHINIVAL; MALLIKARJUN. B. HANJIIndia has a rich history of organic farming and has tremendous potential to establish itself on both the domestic and international markets. Immediate effort must be taken to promote organic farming in order to increase our exports. In this regard, an attempt is made to provide a basic understanding of the benefits and profitability of organic agriculture as compared to inorganic agriculture across the country with this research project through an appropriate statistical model. Data was extracted from the selected 27 studies from the year 2008 to 2021 for the present study. Statistical analyses were carried out with the aid of STATA statistical software version 2.0. The results of meta-analysis over twenty-seven studies demonstrated significant heterogeneity leading to the pooled profit of organic agriculture (Rs.3850/acre) as compared to inorganic agriculture in India. Cumulative metaanalysis demonstrated the trend of the income of organic agriculture, compared to inorganic agriculture, less then pooled estimate in early years and further reduced over years and gradually increased and stabilized later to pooled estimate of Rs.3850/acre. The sub-group analysis results indicated that the mean estimated value for the performance of organic agriculture was highest in the South region (Rs.4332.54/acre) among different regions; in sugarcane (Rs. 8135.57/acre) among different crops; in Karnataka (Rs. 7181.75/acre) among different states; and in 2017 (Rs. 7045.46/acre) among different years. The 20th study, titled Deshmukh's on Turmeric Crop, had a greater impact on the pooled estimate in an influential meta-analysis. It was therefore regarded as an outlier among the chosen studies.ThesisItem Embargo PRODUCTION FORECASTING MODELS FOR SELECTED MAJOR CROPS OF KARNATAKA(2022-12-05) VIJAY, M.; SURENDRA, H. S.Sugarcane and Maize crops were grown in most of the states in India as they can be cultivated in various geographical locations. Sugarcane is one of the traditional crops grown abundantly across the state of Karnataka. Maize is cultivated for various purposes including grain, fodder, sweet corn, etc. The secondary data on area and production of sugarcane and maize along with the weather parameters of Karnataka was collected for the period of 30 years from 1991 to 2020. In this study an attempt has been made to elicit the trend in area and production of sugarcane and maize along with the forecasting of production of both the crops. Further an attempt has been made to know the impact of area and weather parameters on the production of sugarcane and maize crops. Trend analysis was performed by fitting different models such as linear, quadratic, cubic, exponential models and Generalized additive model (GAM). Based on the maximum R2 value best fitted model was selected. GAM was best fitted for area (64.7%) and production (85.1%) of sugarcane, whereas Exponential and GAM was best fitted for production (49.3%) and area (95.1%) of maize crop respectively. Production of sugarcane and maize crop was forecasted for the period of 5 years from 2021 to 2025 using GAM and Exponential model respectively. By employing Multiple Linear Regression (MLR) and Step-wise Multiple Linear Regression (SMLR) it was observed that area had significant positive influence on production of both the crops.ThesisItem Open Access STATISTICAL ANALYSIS OF AREA, PRODUCTION AND PRODUCTIVITY OF COFFEE IN SELECTED DISTRICTS OF KARNATAKA(University of Agricultural Sciences, Bangalore, 2021-12-23) RAJESHREDDY, S.; K. N. KRISHNAMURTHYCoffee is one of the commercial crops in the world. The present study was carried out to analyze the growth and trend in area, production, productivity and to forecast the production of Coffee crop in selected districts of Karnataka viz., Chikkamagaluru, Kodagu and Hassan. The secondary data pertaining to area, production and productivity of Coffee for the period of 25 years (1995-96 to 2019-20) was collected from Coffee Board of India.Linear, cubic, exponential, logistic and Gompertz models were fitted and the best-fitted model was selected based on lowest MAPE value. Results revealed that cubic model was found to be best fitted model for area under coffee in Chikkamagaluru and Hassan districts. The logistic model was found to be the best-fitted model for area under coffee in Kodagu district, while the best fitted model for production and productivity of coffee in Chikkamagaluru district was linear and Gompertz models, respectively. Gompertz model was best-fitted for production and productivity of coffee in Kodagu district and exponential model was best-fitted for production and productivity of coffee in Hassan district. The study revealed that area under coffee in three districts had an upward trend and productivity of coffee in Chikkamagaluru and Hassan had a downward trend, In Kodagu productivity of coffee is in upward trend over the study period. The forecasted values of production of coffee crop in three districts for the period of 5 years (2020-21 to 2024-25) revealed an upward trend.ThesisItem Open Access TRENDS AND DIVERSIFICATION ON AREA AND PRODUCTION OF PRINCIPAL CROPS IN KARNATAKA: A TIME SERIES ANALYSIS(University of Agricultural Sciences, Bangalore, 2021-12-23) HARSHITH K V; H. S. SURENDRAIn this study an attempt was made to assess the extent of agriculture diversificationof crops in different districts of Karnataka, using Herfindhal, Simpson and Entropy indices. Chamarajanagar district showed high diversification as compared to Yadgir and Bangalore urban districts showed very low diversification for the time period 1995-2007 and 2008- 2019. Further, to analyse the trends in area and production of selected principal crops (Paddy, Jowar, Bajra, Ragi, Tur, Groundnut, Sugarcane, Coconut, Wheat, Bengal gram, Cotton, Brinjal and Mango) of Karnataka using linear, cubic, exponential and log-logisticmodel. The best fit model was selected based on the minimum value of RMSE. Exponential model was found to be the best fit model for area and production for Bajra, Ragi, Tur, Groundnut, area and production had linear growth for Paddy and Sugarcane, Jowar had exponential growth over area and linear growth over production, Coconut showed cubic growth for area and linear growth for production for above crops during 1950 to 2019. During 1955-2019 linear model was found to be the best fit for both area and production for Bengal gram, Cotton and Mango, cubic model was found to be the best fit for area andlinear model was found to be the best fit for the production of Wheat. Further, an attemptwas made to predict the production of selected crops using MLR and SMLR with area, rainfall, temperature (maximum, minimum) and relative humidity as independent variables. SMLR was found best based on least MAPE for most of the principal crops selected.
