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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 Forecasting of yield of lemongrass (Cymbopogon flexuosus Nees ex. Steud Wats) based on weather parameters(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 lemongrass 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 (°C), minimum temperature (°C) 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 Probability models for rainfall in tropical monsoon climates(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2004) Swapna, K; KAU; Laly, John CThe influence of soil moisture regimes and stage of host introduction on seedling growth of sandal provenances was investigated in a pot culture experiment at the College of Forestry, Kerala Agricultural University, Vellanikkara. Two provenances in the South India, Shimoga (Karnataka) and Marayoor (Kerala) were selected for this study. The results showed that the seedlings of Marayoor provenance were taller and having a higher collar diameter as compared to seedlings of Shimoga provenances. The stage of introduction of host did not have any effect on the growth of sandal seedlings. The seedlings where the host was introduced at the time of planting sandal had comparatively higher total chlorophyll in both the provenances as compared to seedlings where the host was introduced three and six months after planting sandal. Highest Nitrogen and Calcium content was observed in Marayoor provenance when the host was introduced at the time of planting sandal, whereas the P content was higher in both the provenances where the host was introduced at the time of planting sandal. The parameters like seedling height, collar diameter, number of leaves, leaf area, dry matter and chlorophyll content decreased due to water stress. The haustorial connections were found only at 300 days after planting sandal. The seedlings of Marayoor provenance recorded lower pre-dawn water potential as compared to seedlings of Shimoga provenance. Introducing host at the time of planting sandal or three months after planting sandal, in Marayoor provenance resulted in higher plant water potential. The leaf diffusive resistance was relatively high in Marayoor provenance when the host was introduced at the time of planting sandal. The leaf diffusive resistance was high in water stressed plants. As the haustorial connections were found only at 300 days after planting sandal, it can be concluded that the host need to be planted only six to nine months after planting sandal. This will avoid the early competition between sandal and host. Fast growing pot host during the early phase of its growth may suppress sandal by competition.ThesisItem Open Access Statistical models for the assessment of yield loss due to weeds(Department of Agricultural Statistics, College of Horticulture, Velllanikkara, 2002) Priyalakshmi, M; KAU; Prabhakaran, P VA study was undertaken to identify suitable functional models for assessing the effect of weeds on the yields of three major crops of Kerala Viz. Rice, Tapioca and Sesame and to estimate the loss in yield in these crops caused -by the major weeds. The data required for the study were gathered from the available records of A.I.C.R.P on weed control . Multivariate techniques such as multiple linear regression analysis, step wise regression analysis and principal component analysis were used along with univariate techniques for the prediction of yield and yield loss. The study undoubtedly revealed the importance of weed in suppressing the potential yield of plants. The effect of weeds on crops depended on the type of management , crop and season. Crop loss estimates showed wide variation between seasons and locations. The estimate of loss ranged from 5.3% to 68.4% in rice, 3l.4% to 46.3% in sesame and '12.8% to 40.6% in tapioca. The percentage of avoidable loss' in different crops varied from 5.3% to 93.4%. Weed dry matter (W.D.M.) was found to be the most important weed character in , ' predicting crop yield and yield loss. Echinocloa was found to be one of the major weeds causing considerable havoc to rice crop. In general non linear mod~ls were more efficient than linear model in predicting crop yield. The cauchy function, reciprocal hyperbola, second order hyperbola and reciprocal straight line were adjudged to be the most prormsmg univariate functional models in des£ribing the yield-weed relation ship. Multivariate regression models were found to be more powerful in predicting crop yield than univariate models. In most of the cases the fitted statistical models described the proposed relation ship with satisfactorily high degree of precision.
