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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.
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ThesisItem Open Access Pre-harvest forecasting models and trends in production of banana (Musa spp.) in Kerala(Department of Agricultural Statistics, College of Agriculture, vellayani, 2016) Sharath Kumar, M P; KAU; Vijayaraghava KumarThe study entitled “Pre-harvest forecasting models and trends in production of banana (Musa spp.) in Kerala” was conducted at Instructional farm, College of Agriculture, Vellayani. The objectives of the study were to develop models for early forecasting of yield in four major banana cultivars grown in Kerala viz., Nendran, Robusta, Redbanana and Njalipoovan and also to carry out the time series analysis of the trend in area and production of banana in Kerala. The study was based on both primary and secondary data. Initial and monthly observations on growth habits and yield of commonly grown banana cultivars were used for forecasting. Secondary data on area, production and productivity over a period of twenty five years (1991-2015) were collected from published sources of Directorate of Economics and Statistics, Govt. of Kerala and State Department of Agriculture. Additional information on price change and climatic factors were also incorporated in state level time series analysis. . Pre-harvest forecasting models developed for the first three months, using sucker characters and numbers of leaves were not found to be sufficient in forecasting yield and best models were identified from the fourth month onwards in all cultivars. Correlation analysis of yield (bunch weight) with biometrical characters in all four cultivars showed that correlation is positive and significant in 4th, 5th and 6th months of growing. Among biometrical characters, plant height and plant girth showed significant relationship with yield in all cultivars. In Njalipoovan, in addition to plant height and plant girth, number of leaves and leaf area (D-leaf) had some positive relationship with the ultimate yield. Meanwhile fruit characters like number of fruits, weight of second hand, fruit weight had significant correlations with yield in all cultivars. Stepwise multiple linear regressions were attempted to primary data at every month. The statistically most suited forecasting models were selected on the basis of coefficient of determination (R2), adjusted R2 and mallow‟s Cp criteria. It resulted that, in nendran variety, plant height and plant girth were contributing to yield with highest R2 of 0.80 in the 5th month (model Y= -1.37+0.025 H4+0.10 G5). Fruit characters were statistically significant to making of a 55 per cent of variation in total yield. In Njalipoovan, models from 4th month onwards were found good for early forecasting of yield. Number of leaves, plant height, and leaf area and plant girth could predict yield with R2 of 81.7%, while fruit characters, viz., number of fruits, fruit length, fruit girth and fruit weight could predict yield with an R2 of 71.88 %. In Red banana, it was found that plant height and plant girth at fourth gave suitable prediction with an R2 of 0.762, meanwhile fruit characters could predicted yield with an R2 of 71 .28%. In Robusta variety, prediction can be made from 4th month onwards as best predictor as plant height and girth (with an R2 of 75.24 %). At harvesting stage, fruit characters could predict the maximum yield up to 96.76 %. Principal component analysis resulted that first three principal components are sufficient for getting maximum information from explanatory variables in all four cultivars with 75 % explained variation. Linear and nonlinear growth models were developed for the purpose of estimating the growth rate and fitting the best model. The use of R2, criteria of randomness and normality of time series data were used as a measure of goodness of fit. Cubic model was found as best fit for estimated trends in area, productivity, whole sale price and cost of cultivation under banana production. Quadratic function was selected as best suited for production trend. However, rainfall and rainy days were found to have less effect on changing in area, production and productivity of banana. Area, production, wholesale price and cost of cultivation showed a positive trend during past twenty- five years. Hence, reliable estimate of a crop yield, well before harvest can be made of from 4th month onwards in all cultivars studied. Policy decisions regarding planning of crop procurement, storage, distribution, price fixation, movement of agricultural processing commodity, import-export plans, marketing can be formulated based on these forecasts.ThesisItem Open Access Multivariate analysis for the classification of locations using soil parameters in central districts of Kerala(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2018) Muhsina, A; Brigit JosephThe research entitled ‘Multivariate analysis for the classification of locations using soil parameters in central districts of Kerala’ was conducted with the objectives to find out appropriate multivariate method to classify the locations based on soil fertility parameters and also to find out the interrelations among the soil parameters. Data on thirteen soil parameters of 17 panchayats (678 samples) in Ernakulam (EKM) and 28 panchayats (789 samples) in Kottayam (KTM) as per “Revolving Fund Mode Project- Soil Testing Lab” of Department of Soil Science and Agricultural Chemistry, College of Agriculture, Vellayani were subjected to various multivariate techniques. Initial data analysis using box plot technique was carried out to remove the outliers present in soil fertility parameters of both districts. Descriptive statistics of each soil parameters in the different panchayats in both districts were worked out and it was found that pH ranged from 4.2-5.8 in EKM with a coefficient of variation (CV) of 10.6 per cent, whereas that of KTM ranged from 4.61-5.95 with a CV of 11.28 percent. Oxidisable organic carbon (OC) (range: 1.23-2.47 %), phosphorus (range: 16.51-113.59 kg ha-1) and potassium (range: 45.86-570.21 kg ha-1) content had a CV of 46.84, 78.80 and 86.54 percent respectively in EKM while KTM showed a variation of 56.92, 79.24 and 60.21 percent respectively for OC (range: 0.98-3.39 %), P (range: 16.89-104 kg ha-1) and K (range: 179.14-580.64 kg ha-1). Multivariate analysis of variance (MANOVA) was done in order to test whether the panchayat means were significantly different with respect to 13 soil parameters. Results of MANOVA revealed that there was significant difference between the mean vector of 17 panchayats in EKM and 28 panchayats in KTM. Principal Component Analysis (PCA) generated five PCs which together accounted for 80 per cent variation in data of EKM and in KTM. Sampling adequacy for PCA and factor analysis was tested by Kaiser-Meyer-Olkin measure and it was found that sample sizes were adequate to conduct PCA in EKM (0.571) and KTM (0.464). Correlation among the variables was tested using Bartlett’s chi square test of sphericity and it was found that variables were correlated to each other. Variables were plotted using factor loadings and it was observed that EC, S and B had high positive loadings on factor 1 and 2 in EKM while none of the variables had positive loadings on F1 and F2 in KTM. Score plot obtained from PCA in EKM showed that Chengamanadu, Manjapra and Thirumarady panchayats had high content of available S and B. In Kottayam, Score plot drawn showed that Cu and Zn were predominant in Akalakkunnam, Kadaplamattom, Meenachil, Melukavu, Poonjar and Ramapuram panchayats. Hierarchical clustering (HC) and K –means clustering were performed to group the panchayats in both districts based on soil fertility status and thereafter comparison of various clustering procedures was done using Davies – Bouldin (DB) index. Different dissimilarity measures- Euclidean, squared Euclidean, Chebychev distance and Mahalanobis D2 were determined and single linkage, complete linkage and average linkage methods were adopted under these measures. The results in EKM showed that Mahalanobis D2 was the better clustering procedure with seven clusters (DB index: 0.120) followed by average linkage method under Euclidean distance (DB index: 0.306) with seven clusters. Manjapra (C6) and Keerampara (C7) remained as individual clusters. Keerampara had strongly acidic soils (pH -5.17) with high available Mg (158 mg kg-1) while Manjapra soils had low Mg availability (19 mg kg-1) and high S content (57 mg kg-1). Kakkad, Kalady and Vengoor came under C1 which possessed approximately same EC (0.15-0.19 dS m-1), OC (2-2.4%) and Mg (71-73 mg kg-1) content. Chengamanadu and Vengola came under C3 while Ayyampuzha and Mudakkuzha came under C4. Clustering of panchayats in KTM using Mahalanobis D2 resulted in better clusters as DB index was 0.120. Twenty eight panchayats in KTM were classified into 8 clusters. Only Akalakkunnam panchayat remained as a separate cluster with pH less than 4.8 and EC 0.08 dS m-1. Aymanam and Elikulam came under a cluster while Pallikkathode and Puthuppally came under another cluster. Bharananganam, Chempu and Melukavu came under a cluster as they had similarity in pH (5.35 -5.48), Mg (46-49 mg kg-1), S (23-27 mg kg-1) and Fe (23-29 mg kg-1). Inter relations among the variables were determined by using Pearson’s correlation and it was found that Ca, Fe and P was positively and significantly correlated with EC in KTM whereas S and P were positively related with EC in EKM. Mg and B were negatively related (-0.495). Similarly Fe and Mn had a negative correlation with each other (-0.467) in KTM district. Ca and B was negatively correlated in EKM (-0.525) which indicated the antagonistic effect. The results of the study indicated that Mahalanobis D2 gave the optimum clusters in both EKM and KTM followed by Euclidean distance using average linkage.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 Trends in production and bienniality of coconut (cocos nucifera L.) var.wct.(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2018) Fallulla, V K; Vijayaraghava KumarThe study entitled "Trends in production and bienniality of coconut (Cocos nucifera L.) var.WCT" was carried out based on data on the number of nuts harvested from 525 WCT palms planted in 1966 at Coconut Research Station, Balaramapuram with its five to six harvests per year, for a period of 25 years viz. 1993 to 2017. The objectives of the study are to identify the extent of bienniality, variations in repeatability and type of yield fluctuations over years and over different harvests. The effect of meteorological factors like rainfall, number of rainy days, maximum and minimum temperature and wind velocity of the above period also formed part of this study. Initial data analysis using box plot technique was carried out to remove the outliers present in the data. The number of nuts produced by a palm in an year was found to be 68.5 with an overall standard deviation of 37.83 nuts. A plot of the average number of nuts produced in an year against the growing periods showed a steady decrease in yield from 2012 onwards (i.e. after 50 years of planting). Preliminary statistical analysis by applying Analysis of variance (ANOVA) for the number of nuts produced by individual palms revealed high significant difference between different palms with respect to each harvest and also with respect to each year. Pearson’s correlation coefficient between yield data of different harvests in an year as well as previous years were estimated and a significant correlation were observed for the previous harvest and rest of the coefficient were non significant. Statistical tools in respect of graphical, parametric and non parametric approaches were tried as an attempt to detect and quantify the biennial bearing tendency. Graphical approach confirmed biennial bearing tendency among different years as well as among different harvests. The parametric study was carried out using orthogonal contrasts developed by Saraswathi (1983). This method used four F ratios F1, F2, F3 and F4 , the significance of which provide biennial tendency and time-trend each for four year periods. F1 ratio is used to test the biennial tendency under the assumption of absence of time trend and then confirmed by F2 ratio. F3 is used to test time trend effect under the assumption of absence of biennial effect. This assumption confirmed by F4 ratio. For the period 1997-2000, F1 was found to be significant at 5 per cent level indicating biennial tendency for this period in the absence of time trend, which was then confirmed using F2 criterion. But this method didn’t confirm bienniality for other periods The non parametric approach using biennial bearing index ‘B’ (Hoblyn et al., 1936) was made for the period of 1993-2017. The ‘B’ factor was based on 23 pairs of successive signs positive or negative indicating fall or rise in yield over continuous years for each of the palms. A test of significance of bienniality was obtained by calculating the binomial probabilities. Number of successive change in signs of 16 or above for this period indicate significant departure from the equiprobable hypothesis. Therefore a palm showing a B factor equal to or higher than 16/23 can consider as significantly biennial in bearing; and on this basis 41.1 per cent of the palms were found to be biennial in bearing. Intensity or degree of crop fluctuations was measured by the ‘I’ factor (Hoblyn et al., 1936). All palms showed an intensity of crop fluctuations less than 50 per cent; of which in 81.8 per cent, the intensity ranged from 20 to 30 per cent. A zero percent ‘I’ indicates regular bearing or no alternate bearing behavior. Regular bearing was not observed for any of the palms. 100 per cent I indicates strict alternate bearing behavior. No palms were found to be strict in alternate bearing also. Maximum number of palms were found to exhibit the biennial bearing pattern but are not strict ( 100 per cent) in bienniality. Spearman’s rank correlation coefficients were calculated for all 23 pair of alternate years and all 24 pair of adjacent years. For palms possessing biennial tendency the coefficients for alternate years should be higher than that of adjacent years and this is tested by rank sum test (Z). The Z value was found to be non significant for the period 1993-2017 indicating no strict alternate bearing behavior in the selected palms. As production is found to be in a a steady decrease from 2002 onwards ‘Z’ is separately estimated for the period 1993-2001, and found to be significant for this period indicating alternate bearing behavior for this period. Repeatability was estimated for number of nuts per tree using ANOVA estimator for different periods. While considering the whole period 1993-2017 and 2013-2016 repeatability coefficient was very low 0.13 and 0.06 respectively with variances 0.00015 and 0.00052 respectively. High estimate of repeatability, 0.397, 0.355 respectively were observed for the period 1993-1996 and1997-2000. Correlation between climatic factors in the current year, previous year, two years before and three years before with the production of nuts for the current year were estimated and were not significant except for the minimum temperature of the current year. It indicated that the parameters of annual climatic factors were not adequate to explain the temporal variation in yield. However Correlation between number of days without rain in summer (dry spell ) and yield in succeeding season of next year was found to be -0.43 which is negatively significant, showing this factor will inversely affect the yield of the next harvest. Bienniality also found to be not directly influenced by the climatic factors. A linear regression model with high R2 value, 0.98 were fitted with current year yield as dependent variables and previous year yield, Number of trees in the ‘on’ phase, Rainy days and Wind velocity as independent variables.ThesisItem Open Access Multivariate clustering techniques- a comparison based on rose (rosa spp.)(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2018) Arya, V Chandran; KAU; Vijayaraghava KumarThe study entitled “Multivariate clustering techniques – a comparison based on rose (Rosa spp.)” was undertaken to compare different clustering techniques, to identify the suitable technique for different types of qualitative and quantitative data and to illustrate the procedures using data based on a field experiment on rose (Rosa spp.). Data on quantitative and qualitative traits collected from a field experiment on “Characterization and genetic improvement in Rose (Rosa spp.) through mutagenesis” done during 2014-2017 at College of Agriculture, Vellayani and Regional Agriculture Research Station (RARS), Ambalavayal, Wayanad was used for the study. Twenty five cultivars each coming under the Hybrid Tea and Floribunda groups of rose were evaluated for the study. There were nine quantitative characters and three qualitative characters. Statistical studies were carried out with the help of statistical packages SPSS, STATA, SAS, R and NTSYS. Preliminary statistical analysis by applying Analysis of variance (ANOVA) for all quantitative characters under study revealed significant difference among different genotypes with respect to each character. Multivariate analysis of variance (MANOVA) was carried out to test the significance of varietal means for each group. The results indicated difference among the cultivar means for both groups with respect to all quantitative characters. Linear discriminant function developed using nine quantitative characters for each of the groups were used to elucidate the differences between them. The average score obtained was 11.01 for the Hybrid Tea type and – 2.34 for Floribunda type with an overall average of 4.38. Discriminant function analysis reassured the difference between the two groups under study. Cluster analysis on Hybrid Tea type and Floribunda type were performed for quantitative, qualitative and mixed data. Association measures used were Euclidean distance, Squared Euclidean, Chebychev distance, City Block distance and Mahalanobis D2 for quantitative data, Jaccard, Dice, Simple matching and Hamann’s coefficient for qualitative data and Gower’s measure for mixed data. Different methods such as single linkage, complete linkage, Unweighted Pair Group Average Method (UPGMA), Weighted Pair Group Average Method (WPGMA), Unweighted Pair Group Centroid Method (UPGMC), Ward’s method, modified Tocher method, k means clustering and Principal Component Analysis (PCA) were adopted for the clustering of cultivars. Optimum numbers of clusters were determined by Pseudo t2 statistics for hierarchical clustering and by Pesudo F statistics for k means clustering. SD ( Scatterness- Distance) index was used to test validity of clustering based on quantitative data. Clustering based on qualitative data was carried out using seven characters, three of which are qualitative traits and all others are quantitative characters converted to qualitative traits. Jaccard and Dice coefficient were used for binary data while Simple matching and Hamann’s were used for multi-state data. The result of different clustering techniques based on Squared Euclidean distance gave approximately the same result as that of Euclidean distance. The Jaccard and Dice coefficients were found to be very similar, so that there was no difference in topology of dendrogram but only in branch length. Clustering pattern under Simple matching and Hamann’s coefficient provided were of similar type. For both groups among all the clustering methods, single linkage clustering under different distance measures tends to create a set of one or two clusters including majority of the genotypes and the remaining genotypes are single or two member clusters. Single linkage clustering tends to produce long chain types clusters as opposed to bunched clusters. On the other hand, the single linkage algorithm suffers chaining effect. Among other clustering algorithms, complete linkage method and Ward’s clustering method showed similar results under Squared Euclidean distance. UPGMA, WPGMA and UPGMC methods under Squared Euclidean method gave comparable results. Clustering using UPGMA and WPGMA method gives almost same clustering pattern under different distance measures for qualitative and quantitative data. Results obtained from k means clustering are comparable with results obtained from hierarchical clustering except for single linkage clustering. A certain degree of similarity was observed between k means and D2 analysis but not to up that between other clustering methods. Under Hybrid Tea genotypes, H16 (Mary Jean) formed a single cluster under single linkage method using different distance measures for quantitative, qualitative and mixed data analysis. Under complete linkage method H7 (Alaine Souchen) and H25 (Josepha) came under same cluster, in clustering based on quantitative and qualitative characters. H22 (Mom’s Rose) and H23 (Lois Wilson) came under same cluster in clustering based on complete linkage, UPGMA and WPGMA except under Hamann’s coefficient. These came under the same cluster under D2 analysis also. Among Floribunda genotypes F2 (Tickled Pink) and F5 (Princess de Monaco) were included in the same cluster under UPGMA method for both quantitative and qualitative data. F1 (Versailles) and F24 (Golden Fairy) also came under the same cluster except for multistage distances under UPGMA. Clustering based on mixed data gave approximately the same results as that of quantitative data under different clustering algorithms except for single linkage clustering. Comparison using SD index indicated high index value for clustering based on Gower’s measure. Comparison among single linkage, complete linkage and Average linkage under different association measures using SD index were carried out. Average linkage method under Squared Euclidean was found to be the best for both type with SD index 0.651 for Hybrid Tea and 0.659 for Floribunda type. Clustering pattern observed from score plot of PCA is comparable with the pattern obtained from quantitative data especially with D2 analysis. Contribution of characters towards variance obtained D2 analysis and PCA showed similar results. From the study it is possible to compare different methods and exclude inappropriate methods. Groups formed from modified Tocher method and PCA are different from other methods. SD index indicated that UPGMA under Squared Euclidean distance is the best for quantitative data.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 security