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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 Nonlinear models for major crops of Kerala(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2007) Joshy, C G; KAU; Krishnan, SNonlinear modelling techniques are the most suited tools for describing any time series phenomenon. Among the various nonlinear models in vogue monomolecular, logistic, gompertz and mixed-influence models find a prominent place. With this idea the agricultural scenario of Kerala was measured through the three important descriptors namely area, production and productivity of the major crops viz; coconut, rubber, paddy, pepper, tapioca, cashew and banana for all the districts and the state as such. Monomolecular model was the most apt model in most of the cases. The data sets were further explored based on the carrying capacity achieved by 2002-03 coupled with intrinsic growth rate. When none of the nonlinear models were found satisfactory either simple linear regression model or quadratic model was tried to explore the nature of trend. Coconut production was found to have reached its near maximum in all the districts where it was a major crop but the productivity figures gave a warning note for increasing the productivity. Rubber was found to be one of the most gifted crops, which was not devoid of proper attention. Even with this stature, production of rubber can be improved through uniform management practices. Usually nonlinear and quadratic models aptly describe a time series data on crop production. It is astonishing that simple linear regression model aptly described the paddy production in the state. The regressive value of the regression coefficients indicated that paddy production in the state is facing extinction.Paddy production in the state has at least to be protected. The lack of fit of most of the nonlinear models and even quadratic models to the data of pepper production indicate the various devastating hazards that the crop faced with. These contrasting features bring out the fact that pepper cultivation be not allowed to be toyed with. The area specific crops like cashew, cardamom, coffee and banana be made nonspecific through innovative technologies. A concerted effort with valid stresses specific to each crop will make the agricultural scenario bright.ThesisItem Open Access Interaction effect under ammi model(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2006) Eldho, Varghese; KAU; Krishnan, SThe study of interaction is one of the major objectives of most of agricultural experiments. Conceptually this is done based on regression technique. Among the interactions studied, two factor interaction derives its importance as it is the simplest of the interactions. The joint regression technique is employed to study the G x E interaction. The regression techniques are having the assumption of additivity of effects. When there is departure from these assumption the joint regression technique fails. Additive Main effects and Multiplicative Interaction studies have helped a lot at this juncture. Raju (2002) derived a more comprehensive measure of interaction based on AMMI model. This was achieved using the spectral decomposition of the relevant interaction matrix which enabled the study of interaction with the same precision as that of studying the main effects. Biplots formulations of interaction effects based on the PCA vector scores are the most simplest and explicit representation of interaction. The study of interaction based on spectral decomposition has been illustrated using the secondary data on the biometric, chemical and qualitative characters from the projects “Development of a bimodal phasic management system to improve both quantity and quality in Kacholam (Kaempferia galanga)” and “Development of a bimodal phasic management system to improve both quantity and quality in Njavara (Oriza Sativa)”. The DMRT tests for each level of the factors viz., calcium and source were carried out for the parameters viz., percentage content of phosphorus in rhizome, percentage content of potassium in rhizome and North – South foliage spread. In all these characters no specific interaction effect could be sorted out. These interactions when studied based on the factor analytical technique revealed that source II and second level of calcium had the highest positive interaction as regards the percentage content of phosphorus; source III and third level of calcium for percentage content of potassium and source II and third level of calcium for North – South foliage spread. When the order of the interaction matrix was high as in the case of the second experiment, DMRT tests failed to highlight the appropriate interactive effect in the characters viz., grain yield, percentage content of nitrogen in grain, percentage content of phosphorus in grain, percentage content of phosphorus in straw and percentage content of potassium in straw. The study based on the factor analytical technique revealed that the treatments T15, T8, T3, T1 and T4 respectively had the highest interactive effect with Payyanur for the above said characters where as for Badagara they were T3, T14, T4, T5 and T8 .ThesisItem Open Access Spatial and temporal variations in the development of agriculture in Kerala(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2002) Allahad, Mishra; KAU; Ajitha, T KAgricultural scenario of Kerala is unique as compared to other states of India. The present study entitled "Spatial and temporal variations in the development of agriculture in Kerala" was undertaken mainly with an objective of constructing composite indices to quantify the development of agriculture based on suitable indicator variables for each district or region of Kerala. The significance of the districtwise and temporal disparities in agricultural development have been studied. The agricultural growth with respect to acreage and gross production of major crops • is also estimated using different growth curves. The time series data from 1970-71 to 1997-98 collected from State Planning Board and Directorate of Economics and Statistics, Government of Kerala, Trivandrum were used for the study. As all the districts were not present before 1985-86 state was divided into several regions. Districts wise analysis was carried out from 1985-86 to 1997-98, whereas region wise analysis was carried out from 1970-71 to 1997-98. For measuring the diversification level of districts or regions five indices viz., Herfindahl Index, Entropy Index, Modified Entropy Index, Composite Entropy Index and Ogive Index were computed. All the quantitative indices were constructed by using the total cropped area of seven major crops of Kerala. It was found that in most of the periods the diversification in cropping pattern was mainly towards plantation crops. The most diversified district was Kollam, where the cropping pattern had equal importance to all the major crops. Based on the real situation, out of the five measures of diversification Composite Entropy Index was found to be better suited. It was also noticed that as time progressed the diversification level among the districts or regions decreased. The Compound growth rates of both production and acreage were computed and it was found that rubber recorded the highest C.G.R. The food crops viz., rice and tapioca showed negative C.G.R whereas cash crops viz., coconut and pepper showed positive C.G.R for both production and acreage. Productivity index were constructed for each district taking into consideration the variety of crops and their relative importance in a particular district. The results revealed that different districts behaved differently with respect to the rate of growth of productivity. Development is a multidimensional process, so instead of analysing a single variable, composite index or development index for different districts or regions were computed by using several indicators, which contributed to the development of agriculture. In the present study three methods were used to compute the development index based on seven indicators. In the first approach i.e. Taxonomic approach during 1985-86, 1990-91 and 1995-96 Emakulam occupied the first place in agriculture development. However, Wayanad and Kasargode were the two least agriculturally developed districts during the above said periods. It was also observed that there was hardly any change in the level of development of agriculture over different periods of study. In Taxonomic approach each variable was considered to have equal contribution towards the development of agriculture. However, it is unlikely to happen so. With this fact, the Taxonomic approach was modified in Modified Taxonomic approach by giving separate weightage to the indicators based on the score given by experts. In the present study separate weightage did not have any significant impact on the classification of districts or regions on their agricultural development status. Obviously the selected variables might be highly correlated. Characteristics in biological experiment are highly correlated. In the present study Principal Component analysis was used to overcome this problem. The first component of both district wise and region wise analysis contributed around 99.5 per cent of total variation. Therefore, without loosing any information supplied by the seven variables, the first component score was taken as the composite index of development. Hence in the present context Principal Component analysis could be considered as the best method, as no approximation is involved. It could be considered as a more comprehensive method. The Potential targets for the under developed districts or regions are also estimated to assess the position of those districts or regions compared to the model • districts or regions. Accordingly suitable development programmes can be launched or special care can be taken to allocate resources optimally on per capita basis to reduce spatial disparities in development.ThesisItem Open Access Forcasting of lemongrass (Cymbopogon flexuosus Nees ex.Steud Wats) yield based on weather(Department of Agricultural Statistics, College of Horticulture,Vellanikkara, 2001) Sajitha Vijayan, M; KAU; Soudamini, PThe grass and oil yield obtained from comparative yield trials conducted at Aromatic and Medicinal Plants Research Station from 1965-1989 and the weather observations corresponding to the same period have been analysed in order to evaluate the effect of different climatic factors on lomongrass yield and to develop suitable prediction models for the pre-harvest forecasting of grass yield with sufficient degree of precision. The variety viz., OD-19 (Sugandhi) was considered and the crop was raised as rainfed for the entire period of investigation. The meteorological variables included in the study were number of rainy days, total rainfall (mm), maximum temperature (0C), minimum temperature (0C) and relative humidity (%). Coefficients of correlation of weather variables and their logarithms with grass and oil yield for the growing period of the crop (six weeks or three fortnights) were worked out. Two stage regression models for each week of the growing period were developed to predict grass and oil yield using observations on weather variables up to the week of forecast as the explanatory variables. Predictability of model obtained for earlier week of crop growth were over 70% for first, second, fourth and fifth harvests. Fortnightly prediction models were also developed making use of weather variables and their logarithms. In addition to these, logarithms of weather variables were also used as explanatory variables to predict logarithm of grass and oil yields. In the case of fortnightly weather variables composite regression model proposed by Agrawal et al.(1980) was also developed.ThesisItem Open Access Changing scenario of Kerala agriculture- an overview(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2009) Unnikrishnan, T; KAU; Ajitha T KThe present investigations on “Changing scenario of Kerala agriculture – an overview” was carried out in the Department of Agricultural Statistics, College of Horticulture, Vellanikkara during 2006 – ’09. The secondary data on area, production, productivity and price of major crops of Kerala viz; coconut, rubber, paddy(season wise), pepper, cashew, arecanut, coffee, tapioca and banana collected from the Directorate of Economics and Statistics for the period from 1952-53 to 2006-07 were used for the analysis. The main objectives of the study included assessment of trend and growth rates of area, production, productivity and price, testing of the cointegrated movement of price and respective area of each crop, identification of the best ARIMA(Auto Regressive Integrated Moving Average) model for prediction of area, production, productivity and price and comparison of predictability of forecasting models developed by different techniques. Modified P-Gan’s method helped to understand whether the growth rate in crop production was mainly due to area or productivity. The series of prices and areas of respective crops could be co-integrated and the regression models evolved through this technique resulted in moderately high values of predictability. ARIMA models were superior to other models developed achieving a maximum value of R2 = 99.8% for the prediction of area of rubber with a very low value of MAFPE = 1.23%. Excellent parsimonious forecasting equations could be generated using the ARIMA technique for all the crops studied. The general findings of the study showed that there was a shift in area from food crops to non-food crops. The production of major food crops, rice and tapioca reached at negative growth rates due to the declining trend of their areas. But production rate of banana has increased due to increase in both area and yield. Among cash crops, both area and productivity growths influenced the production rates. The major cash crops coconut, arecanut and pepper showed positive growth rates. Compared to food crops, cash crops in general showed better growth trends in production. Negative growth rate in the production of cashewnut was due to the decline in area. Among plantation crops, rubber and coffee attained a high production growth rate due to the combined growth of area and productivity. The highest production growth rate and area growth rate were recorded by rubber among all the crops studied.ThesisItem Open Access Relationship between weed density and yield loss in semi- dry rice(Department of Agricultural Statistics, College of Horticulture,Vellanikkara, 2001) Shiji, C P; KAU; Krishnan, SSacciolepis interrupta and Isachne miliacea are two major problem weeds of rice in Kerala. An investigation on the quantum of crop loss incurred due to different densities of these weeds was undertaken to study the extent of damage inflicted on the crop which would necessitate early control of these weeds. The observations recorded on the various crop and weed characteristics were analysed as a 52 factorial experiment. It was found that crop characteristics like total bio- mass of paddy at harvest, number of tillers of paddy at harvest, number of productive tillers at harvest, grain yield and strain yield. And weed characteristics like number of tillers of S. interrupta at 60 DAS, height of S. interrupta at 60 DAS, number of tillers of S. interrupta at harvest of rice, dry matter production of S. interrupta and drymatter production of 1. miliacea were found to be affected by the weeds. The intra and interspecific competition was also brought to light based on the analysis. Single weed species models like that of Cousens (1985), Hakansson (1983), the first model of Watkinson (1981), Marra and Carlson (1983), Wilson and Cussans (1983), Wilcockson (1977) and Carlson et al. (1981) fitted well to the yield loss - S. interrupta/ 1. miliacea density relationship whereas those models proposed by Ngouajio et al. (1999), Kropff and Spitters (1991), Dew (1972), Zakharenko (1968) and Chisaka (1977) fitted well only to the yield loss- S. interrupta density relationship. The extended version of the Cousens (1985) model by Swinton et al . . (1994a) to a multi-species model was also fitted to the data and the same explained the yield loss - S. interrupta + 1. miliacea densities relationship to a considerable extent. The reduced form of the multispecies model to an equivalent single species model as worked out by Swinton et al. (1994b) also had a good fit. The numerical assessment of yield loss _. S. interrupta + 1. miliacea density relationship as illustrated by Berti and Zanin (1994) revealed the extent of damage on the crop by the weeds. The new curvilinear models tried also explained the yield loss - weed density relationship with the exception that the role of 1. miliacea deterring the yield of crop could not be highlighted due to its peculiar way of growth. The threshold weed densities worked out on a economic loss basis revealed that even the presence of two S. interrupta plants in a square meter area was hazardous for the crop whereas even the presence of 321. miliacea plants in the same stipulated area was not as detrimental as S. interrupta.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 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.
