Loading...
Kerala Agricultural University, Thrissur
The history of agricultural education in Kerala can be traced back to the year 1896 when a scheme was evolved in the erstwhile Travancore State to train a few young men in scientific agriculture at the Demonstration Farm, Karamana, Thiruvananthapuram, presently, the Cropping Systems Research Centre under Kerala Agricultural University. Agriculture was introduced as an optional subject in the middle school classes in the State in 1922 when an Agricultural Middle School was started at Aluva, Ernakulam District. The popularity and usefulness of this school led to the starting of similar institutions at Kottarakkara and Konni in 1928 and 1931 respectively.
Agriculture was later introduced as an optional subject for Intermediate Course in 1953. In 1955, the erstwhile Government of Travancore-Cochin started the Agricultural College and Research Institute at Vellayani, Thiruvananthapuram and the College of Veterinary and Animal Sciences at Mannuthy, Thrissur for imparting higher education in agricultural and veterinary sciences, respectively. These institutions were brought under the direct administrative control of the Department of Agriculture and the Department of Animal Husbandry, respectively. With the formation of Kerala State in 1956, these two colleges were affiliated to the University of Kerala. The post-graduate programmes leading to M.Sc. (Ag), M.V.Sc. and Ph.D. degrees were started in 1961, 1962 and 1965 respectively.
On the recommendation of the Second National Education Commission (1964-66) headed by Dr. D.S. Kothari, the then Chairman of the University Grants Commission, one Agricultural University in each State was established. The State Agricultural Universities (SAUs) were established in India as an integral part of the National Agricultural Research System to give the much needed impetus to Agriculture Education and Research in the Country. As a result the Kerala Agricultural University (KAU) was established on 24th February 1971 by virtue of the Act 33 of 1971 and started functioning on 1st February 1972. The Kerala Agricultural University is the 15th in the series of the SAUs.
In accordance with the provisions of KAU Act of 1971, the Agricultural College and Research Institute at Vellayani, and the College of Veterinary and Animal Sciences, Mannuthy, were brought under the Kerala Agricultural University. In addition, twenty one agricultural and animal husbandry research stations were also transferred to the KAU for taking up research and extension programmes on various crops, animals, birds, etc.
During 2011, Kerala Agricultural University was trifurcated into Kerala Veterinary and Animal Sciences University (KVASU), Kerala University of Fisheries and Ocean Studies (KUFOS) and Kerala Agricultural University (KAU).
Now the University has seven colleges (four Agriculture, one Agricultural Engineering, one Forestry, one Co-operation Banking & Management), six RARSs, seven KVKs, 15 Research Stations and 16 Research and Extension Units under the faculties of Agriculture, Agricultural Engineering and Forestry. In addition, one Academy on Climate Change Adaptation and one Institute of Agricultural Technology offering M.Sc. (Integrated) Climate Change Adaptation and Diploma in Agricultural Sciences respectively are also functioning in Kerala Agricultural University.
Browse
6 results
Search Results
ThesisItem Open Access Comparison of statistical methods for control of error in long term experiments in rice (Oryza sativa L.)(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2017) Vishnu, B R; KAU; Vijayaraghava KumarThe present study entitled “Comparison of statistical methods for control of error in long term experiments in rice (Oryza sativa L.)” was conducted at College of Agriculture, Vellayani during 2015 - 17. Objective of the study is to compare different parametric and non-parametric statistical approaches in the analysis of field experiments over years and seasons in long term experiments in rice and to identify the most suitable method. Data on a field experiment on rice (var. Aiswarya) viz. ‘Permanent plot experiments on integrated nutrient supply system for a cereal based crop sequence’ conducted at Integrated Farming System Research Station (IFSRS), Karamana for the period from 1985 - 86 to 2013 - 14 were used for the study. The field experiment consisted of 12 different treatments on modified fertilizer doses based on the recommended dose including a control T1 (no fertilizers and no organic manures) and T12 (farmer’s practice). Randomized block design (RBD) with four replications was used for kharif and rabi seasons for all these years. The main observations collected were grain yield, straw yield, plant height, total number of tillers, number of productive tillers, dry matter production and harvest index. The descriptive statistics and the usual RBD analysis of variance (ANOVA) were carried out for all the biometric characters and detailed study were made on grain yield data of (kharif, rabi and yearly data) by different approaches. Pooled analysis of raw and transformed (square root and logarithmic) grain yield data indicated highly heterogeneous estimates of error variances, ie. mean sum of squares for error (MSE), (5.22 to 35.7 for kharif, 5.74 to 32.04 for rabi and 12.25 to 90.06 for yearly data). Weighted analysis was then attempted which produced non-significant year × treatment interactions which indicated that more refined statistical procedures are needed for effective conclusions. So exploratory statistical analysis was attempted. The data were subjected to univariate normality tests and those years with more than ten outliers were discarded and hence 21 years data were used for further study. The statistical procedures ordinary pooled analysis, split plot type of analysis, analysis of covariance (ANCOVA), time series (serial correlations) regression analysis and a non-parametric method (Friedman’s test) were conducted. Ordinary pooled analysis of the data indicated homogeneity of error variances with a pooled error of 8.42, 8.92 and 20.16 for kharif, rabi and yearly data respectively and year × treatment interactions were found to be significant. The treatment T6 [50% RDN of NPK through fertilizers + 50% through FYM for kharif, 100% RDN of NPK through fertilizers for rabi and (50% RDN of NPK through fertilizers + 50% through FYM + 100% RDN of NPK through fertilizers for yearly data)] was obtained as highest yield during many of the years or seasons. Then the data were subjected to Split plot type of analysis, the treatments were taken in main plot and years or seasons in subplots. In this, the sub plot error variances obtained were 10.23, 9.65 and 23.09 for kharif, rabi and yearly data respectively, which were higher than that of ordinary pooled analysis. A correlation study was conducted with grain yield and the other characters, to identify those characters having high correlation with grain yield and treated them as covariates for ANCOVA. It is observed that, as the number of covariates increased there was not much changes in the error variances but there is a declining tendency for treatment variances. So it is inferred that the variable having high correlation with grain yield (viz. straw yield) can be taken for covariance analysis. Time series regression analysis and serial correlations were attempted for specific treatments. It was found that neither serial correlations nor partial regression coefficients were found to be significant for kharif, rabi as well as yearly data. Non parametric analysis is one of the best methods for non normal data. The treatment means were ranked for each year and subjected to Friedman’s test for two way classified data. Significant treatment differences were obtained and treatment T6 obtained best score. Hence it is concluded that treatment T6 maintained the highest yield over the years and seasons. Ordinary pooled analysis of data was found to be the best under the exploratory data analysis. Analysis of covariance with one covariate was found to be equally good with adjusted MSE almost equal to that of MSE of ordinary pooled analysis.ThesisItem Open Access Modified statistical methods on estimation of optimum plot size in cassava (Manihot esculenta crantz)(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2017) Rakhi, T; KAU; Vijayaraghava KumarA study entitled “Modified statistical methods on estimation of optimum plot size in cassava (Manihot esculenta Crantz)” has been carried out at Department of Agricultural Statistics, College of Agriculture, Vellayani, Thiruvananthapuramduring 2015-2017.Modified statistical methods for estimation of optimum plot size for field experiments were attempted for branching (Vellayani Hraswa- 6 months duration) and non-branching (SreePavithra 8-10 months duration) varieties of cassava. A multivariate discriminant function is also developed for characterizing the above two varieties. The study was based on the primary data. The variety Vellayani Hraswa was grown with a spacing of 90cm x 90cm and Sree Pavithra with 75cm X 75cm in an area of 400 m2. Bimonthly observations were recorded for both varieties on growth parameters along with final yield parameters. Inter correlations among the growth parameters showed that the height and number of leaves were highly correlated with yield. Multiple linear regression analysis was carried out for both varieties using yield as dependent variable and biometric measurements as independent variables. Among the various regression equations the best model obtained for prediction of yield in Vellayani Hraswa was using height at 2 months after planting (MAP), intermodal length at 4MAP and number of leaves at 6MAP with an adjusted R2of 20% and Sree Pavithra with variables height at 2MAP and number of leaves at 2 MAP with an adjusted R2 of 40%. In Contour map, it was observed that fertility gradient ranged from -50 to 70 and maximum frequency was in the range from -10 to 30 for Sree Pavithra (34%) and -50 to -10 for Vellayani Hraswa(29%) and a minimum of 8%(< -50) for Sree Pavithra and 8% (>70) for Vellayani Hraswa. For determining optimum plot sizes the conventional methods (maximum curvature method, Fairfield smith variance method) and modified methods (length and breadth of plots, cost of cultivation ratios and covariate method) were attempted. For non-branching type the optimum plot size obtained was with 18 units in case of maximum curvature method as well as by the use of length and breadth of the plot method.In case of Modified curvature method optimum plot size obtained was 8 units. By Fairfield smith’s cost ratio method, the result obtained was about 8.5 units. By considering the shape of the plots minimum variance was obtained when length was taken as 9 units and breadth as 2 units. The R2 values were worked out in all cases and along with practical considerations maximum curvature method was found to be better with a plot size of 9x2 (10.12 m2) units. For branching type the optimum plot size obtained was with 24 units by using maximum curvature method. In case of Modified curvature method optimum plot size obtained was 12 units. By Fairfield smith cost ratio method the result obtained was also about 12 units. Minimum variance was obtained when length was taken as 8 units and breadth as 3 units. High R2 values indicated that maximum curvature method was found to be better with a plot size of 8x3(19.44 m2 ) units. A discriminant function was fitted to understand the categorical difference between the two varieties based on five variables and obtained a score ranging from -229 to 401 and an average score of 166 for both the varieties from which it can be concluded that when the score is less than 166, the variety is Sree Pavithra and if more the variety is Vellayani Hraswa. By studying different methods for the determination of optimum plot size for cassava, Maximum Curvature Method as well as Method using Covariate are found to be the most appropriates. Optimum plot size for Vellayani Hraswa was 19.44 m2 accommodating 24 plants. In case of Sree Pavithra, it was 10.125 m2 accommodating 18 plants.ThesisItem Open Access Exploratory analysis of permanent manurial trials in rice(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2017) Suvarna Shyam, K T; KAU; Krishnan, SPermanent Manurial Trials are experiments performed at fixed sites in a long run to assess the role of nutrient sources in catalyzing crop production. The experimental data from Permanent Manurial Trial in rice at RARS, Pattambi was explored in a view to identify suitable set of fertilizer treatments in optimizing grain yield.The experiment was commenced in 1973 under Kerala Agricultural University State Plan Project and is being conducted continually over years in two cropping seasons viz., Kharif and Rabi. Eight fertilizer treatments under test were either organic, inorganic or combination thereof. As treatment responses can be deliberately measured through yield, grain yield data maintained at research station for the period 1973 to 2015 formed the basis of study. Treatment-wise rice yield data was subjected to various statistical analyses, to screen superior fertilizer treatments. The influence of weather on yield responses were also assessed. Exploratory Data Analysis using graphical and non-graphical methods as a means to familiarize yield data was attempted to realize yielding behavior of treatments. Summary statistics viz., mean and median explicitly showed the superiority of treatment T1 ( Cattle manure at 18000 kg ha -1 to supply 90 kg N ha-1) followed by treatment T5 (Cattle manure at 9000 kg ha-1+ ammonium sulphate to supply 45 kg N ha-1+ superphosphate to supply 45 kg P2O5 ha-1 +45 kg K2O ha-1 as muriate of potash). Box plot of yield data remarked consistent yielding performance for the same treatments during both Kharif and Rabi seasons. Yield trend was assessed by regressing crop yield on time factor and no significant yield trend was observed. Analysis of variance was done for each of the experiments during both seasons and the post hoc test was effected through DMRT. All the experimented seasons showed significant fertilizer effect on grain yield. The treatments subgroups as formed through DMRT for each year was further used to score treatments. Lowest scores speaking of better treatment performance was recorded for T1 in both the seasons followed by T5. Analysis of groups of experiments was further necessitated to generalize on the yielding behavior of each treatment as results followed from analysis of variance for individual experiments do not give a confirmatory account in this regard; owing to its inclusion of seasonal fluctuations (season/ year) in experimental error. The results showed superiority of treatment T1 and T5 during both seasons. Influence of weather on grain yield was studied for three growth stages of rice viz., early tillering to panicle initiation, panicle initiation to flowering and flowering to milk stage. The results followed from correlation and regression analysis pointed to minimal role of weather in affecting grain yield, attributed to the crop management practices followed in the research station. Regression models for treatment-wise yield on weather indices formulated through principal component analysis were found poor fit as observed through R2 value. Consistency of treatments were assessed through two consistency measures and treatments T1 and T5 were observed to have average consistency. Sustainability Yield Index (SYI) index was further used to identify treatments that have yielded in a sustainable manner in comparison with the maximum yield reckoned for each of the treatments over a longer period. Maximum SYI was recorded for treatments T1 followed by T5 during both seasons. Combinatorial treatments of organic and inorganic fertilizers had higher index values compared to purely inorganic treatments. Statistically optimum fertilizer requirement was obtained by compiling the results followed from the aforementioned analyses. Treatment T1 (Cattle manure at 18000 kg ha ammonium -1 to supply 90 kg N ha-1) and T5 (Cattle manure at 9000 kg ha-1+ sulphate to supply 45 kg N ha-1 + superphosphate to supply 45 kg P2O5 ha-1 + 45 kg K2O ha-1 as muriate of potash) were chosen as the optimal fertilizer schedules for rice.ThesisItem Open Access Time series analysis and forecasting of the prices of Indian natural rubber(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2017) Velpula Jhansi Rani; KAU; Krishnan, SThe study entitled “Time series analysis and forecasting of the prices of Indian Natural Rubber” is primarily intended to forecast the prices for Indian Natural Rubber (NR). For forecasting the prices, firstly, domestic NR price was decomposed it into time series components. Evaluation of growth, instability and relationship between the domestic and international prices in the pre WTO and post WTO periods were carried out in this study. For decomposition of domestic NR prices into time series components, additive decomposition was tried. The data were decomposed into trend, seasonal and cyclic components. The trend values proved that there was quadratic trend over the years. Seasonality indices revealed that the highest price was in June and lowest price in December. Cyclic components showed three cycles over a period of time under investigation. For evaluation of growth and instability, volatility and instability analyses were carried out for pre-WTO, post-WTO and overall periods in terms of rupees as well as dollars. Two types of volatility i.e., intra-annual volatility (within year dispersion) and inter annual volatility (between year dispersion) were calculated. Intra-annual and inter annual volatility were higher in post- WTO for international and domestic NR price series and the crude oil price showed higher volatility in pre-WTO period in terms of rupees as also in dollars. GARCH (1,1) model gave an additional evidence for persistence of volatility. It proved that the volatility persisted in the overall period in terms of rupees and dollars for domestic and international NR price. Instability analysis showed that the price instability in post-WTO period was almost double than that of pre- WTO period and it tripled in the overall period in terms of rupees. In terms of dollars, the instability in post-WTO and overall period was almost triple than pre-WTO period for domestic and international NR prices and crude oil prices showed almost double instability than pre-WTO period. iii The relationship between domestic and international NR prices were analysed through cointergration analysis and Vector error correction model (VECM). The direction of relation was drawn by Granger Causality test. Cointegration and Granger Causality test proved that there was at least unidirectional relationship among the variables. VECM analysis proved that there was long run relationship between domestic NR price, international NR price and crude oil price. It revealed that, a speed rate of adjustment 14.3 per cent was required for domestic NR price series to correct its previous period. There were many general factors affecting the prices of domestic NR like synthetic rubber production, crude oil prices, international rubber demand and supply, international transactions, exchange rates, natural factors and development of automobile industries. Stepwise regression analysis was used to sort out the factors affected in pre-WTO and post-WTO periods. In pre-WTO, domestic NR price was affected by international NR prices and in post-WTO by international NR prices and SR consumption. Domestic NR prices were forecasted with three different models like Stepwise regression method, ARIMA and SARIMA models. Stepwise regression method could be predicted when the variables like international NR prices and import value of NR were available. Among ARIMA and SARIMA models, ARIMA (4,1,4) and (4,1,4) (1,0,1) 12 was found to be best judged as per different statistical criteria for assessing the model fit and model adequacy.ThesisItem Open Access Statistical models for profit maximization of homesteads in Kerala(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2017) Muhammed Jaslam, P K; KAU; Brigit JosephThe research programme entitled „Statistical models for profit maximization of homesteads in Kerala‟ was carried out with the objectives of examining and developing statistical models for homestead farming systems in the southern and south central laterite agro-ecological units (AEU8 and AEU9) of Thiruvananthapuram district and to suggest suitable cropping/farming system models that maximize farm income by the optimal use of available resources. The study was based on the primary data. The relevant data from forty randomly selected homesteads of almost similar cropping systems and having area 0.1 ha to 0.3 ha from two panchayaths (Kulathoor and Karode) of AEU8 and same number of homesteads from two panchayaths (Anad and Vembayam) of AEU9 was collected using a well- structured pre-tested interview schedule. Statistical tools such as ratios, percentages and frequencies were applied to socio-economic variables and descriptive statistics were worked out to summarize homestead characteristics. It was found that 43.75 per cent of the respondents belonged to the middle aged category having secondary and higher secondary level of education (47.5%) with an annual income less than ₹4 lakhs (77.5%) and having median family size of 5. Only 12.5 per cent and 17.5 per cent of the respondents in AEU8 and AEU9 had agriculture as main source of income while majority had agriculture as subsidiary income in both agro-ecological units. Majority of the homesteads in AEU8 and AEU9 were semi-irrigated. The average size of homesteads was 0.18 ha and 0.21 ha in AEU8 and AEU9 respectively. The selected homesteads followed coconut based cropping system and comprised of other thirty eight enterprises falling under the groups namely tubers, commercial crops, spices and condiments, stimulants, fruits, vegetables, livestock and poultry. The selected coconut based homesteads were grouped into three on the basis of cropping/farming system existing in the homesteads (HFS), viz. system-I (S1) consisting of crops alone, system-II (S2) including crops, poultry and goat and system-III (S3) comprising of crops, poultry, goat and livestock. Economics of cultivation including operational cost, gross return, net return and benefit-cost ratio of all enterprises were worked out and the estimated total net return of the existing HFS for an average ( 45 cents) of S1, S2 and S3 was ₹27,596/-, ₹55,244/- and ₹1,72,245/- in AEU8 and ₹23,303/-, ₹34,272/-and ₹1,31,516/- in AEU9 (52.5 cents) respectively. The optimum model was developed by using linear programming (LP) technique with the linear objective function Z c1 x1 c2 x2 .... cn xn , where x1,x2,...,xn are the variables used to denote the enterprises and c1,c2,...,cn are the unit net return associated to each enterprise. The constraints included in the analysis were total area, intercropped area, investment amount and population of each enterprise. The optimum model was developed by giving more emphasis to safe to eat vegetable cultivation by at least doubling the area under vegetable cultivation over the existing plan and by providing adequate number of coconut palms based on farmer‟s preferences for this enterprise. The optimum model worked out for SI in AEU8 consisted of binding solution for almost all the enterprises except some enterprises like coconut and banana with 25.30 per cent enhancement in net return as compared to net return from the existing plan. The optimum model for S2 HFS was also similar to that of S1 with non-binding solution for coconut and poultry with 31.30 per cent increase in net return. However, the optimum model for S3 HFS had non-binding solution for coconut and banana as compared to the existing model and this provided only13.31 per cent increase in net return. The result of LP for S1, S2, S3 HFS‟s in AEU9 was in accordance to AEU8 with slight difference in the nonbinding enterprises, but the increase in net return based on the optimum model in S1, S2 and S3 was 22.83 per cent, 64.79 per cent and 44.94 per cent respectively. The result of LP indicated that intercropping area was an abundant resource in the optimal plan of all cropping systems. It was also found that even if income from livestock was high, farmers preferred to have the intercrops and allied enterprises which need less management practices and labour. Sensitivity analysis of the optimum model revealed that enhancement of net return in both agro-ecological regions could be achieved by increasing the cropping intensity in the underutilized intercropped area and changing the binding enterprises. The present study developed statistical models for the existing cropping systems in homesteads and LP model suggests that farm income could be further enhanced by growing more number of farmer preferred crops such as tapioca, banana, pepper etc., and by removing the most uneconomical and less important enterprises in the existing plan with due importance to food securityThesisItem Open Access Pre-harvest forecasting models and instability in production of cassava (Manihot esculenta Crantz.)(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2017) Neethu, S Kumar; KAU; Brigit JosephThe study entitled “Pre-harvest forecasting models and Instability in production of cassava (Manihot esculenta Crantz.)” was conducted at Instructional Farm, College of Agriculture, Vellayani during 2015-2017 with the objectives to develop early forecasting models for yield of five major short duration varieties of cassava and also to carry out trend and instability analysis on area and production of cassava in Kerala. The study was based on both primary and secondary data. The varieties Sree Jaya, Sree Vijaya, Sree Swarna, Vellayani Hraswa and Kantharipadarppan were grown in Randomized Block Design with three replication in a spacing of 90 cm x 90 cm. Twenty five plants were randomly selected and monthly observations were recorded for all the varieties on biometric parameters. Yield and yield parameters were recorded at harvest. Secondary data on area, production and productivity over a period of twenty five years (1992-2016) were collected from published sources of Directorate of Economics and Statistics, Government of Kerala and State Department of Agriculture. In order to give an idea about the behavior of the biometric observations and yield of the plants, summary statistics including mean, standard deviation, minimum and maximum were worked out for all variety at each growth stage. Inter correlations were worked out between growth parameters and yield and the results showed that the number of primary branches, height of branching and number of functional leaves had positive and significant correlation with yield while correlation between yield and yield attributes revealed that number of tubers and average tuber weight were positively correlated with yield. Multiple linear regression and non linear regression analysis were carried out for all the varieties using yield as dependent variable and biometric observations as independent variables. Stepwise regression was performed and significantly contributing biometric characters were selected using R2, Mallow’s Cp and t-values for predicting the yield. Among various linear regression equations the best model obtained for the prediction of yield in Sree Jaya was using inter nodal length at 2 and 3 MAP and number of primary branches at 4 and 5 MAP with R2 of 50 per cent and based on non linear equations the best model obtained was using number of functional leaves at 2 MAP, number of primary branches at 4 and 5 MAP and inter nodal length at 3 MAP with R2 of 56 per cent. Best linear model obtained for the pre-harvest prediction of yield in Sree Vijaya was by using inter nodal length at 2, 4 and 5 MAP, number of functional leaves at 2 MAP and plant height at 5 MAP with R2 of 58 per cent. Non linear model obtained was using inter nodal length at 2, 3 and 5 MAP, number of functional leaves at 3 and 5 MAP with R2 of 59 per cent Best linear model obtained for prediction of yield in Sree Swarna was using inter nodal length at 2 and 3 MAP and number of functional leaves at 5 MAP with R2 of 43 per cent and with non linear functions the best model obtained was with inter nodal length at 2 and 3 MAP and number of functional leaves at 5 MAP and leaf area index with R2 of 47 per cent. Best linear model obtained for prediction of yield in Vellayani Hraswa was using number of functional leaves at 2 MAP and plant height at 4 MAP with R2 of 35 per cent and with non linear function the best model obtained was with plant height at 3 and 4 MAP and number of functional leaves at 2 MAP with R2 of 40 per cent. Best linear model obtained for prediction of yield in Kantharipadarppan was using number of functional leaves at 4 MAP and plant height at 3 MAP with R2 of 34 per cent and with non linear equations the best model obtained was using plant height at 2 MAP and number of functional leaves at 4 MAP with R2 of 33 per cent . The estimated trends in area, production and productivity of cassava using semilog function revealed that there was a significant decline in area (CAGR= -1.37 %), non significant decline in production (CAGR= -.02 %), and a significant increase in productivity (CAGR= 1.3 %). Instability in area, production, productivity and nominal price of cassava was also worked out using various measures and the results of the analysis shown that Cuddy-Della Valle index provides best estimates and instability was found to be more in productivity (4.04) followed by area (3.98) and production (.80). The present study concluded that non- linear model provides better yield prediction model of cassava as compared to linear prediction model on the basis of R2 and Mallow’s Cp. Moreover, the biometric characters such as number of functional leaves and inter nodal length were the most significant predictor variables in all short duration varieties included in this study.