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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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2001 - 200992010 - 201916
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Subject: Agricultural Statistics and Informatics × End date: 2019 × Start date: 2000 ×
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Now showing 1 - 9 of 25
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    ThesisItemOpen Access
    Nonlinear models for major crops of Kerala
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2007) Joshy, C G; KAU; Krishnan, S
    Nonlinear 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.
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    ThesisItemOpen Access
    Interaction effect under ammi model
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2006) Eldho, Varghese; KAU; Krishnan, S
    The 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 .
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    ThesisItemOpen 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 K
    Agricultural 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.
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    ThesisItemOpen 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, P
    The 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.
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    ThesisItemOpen Access
    Changing scenario of Kerala agriculture- an overview
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2009) Unnikrishnan, T; KAU; Ajitha T K
    The 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.
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    ThesisItemOpen Access
    Comparison of methods for optimum plot size and shape for field experiments on paddy (Oryza sativa)
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2019) Athulya, C K; KAU; Brigit Joseph
    The research work entitled “Comparison of methods for optimum plot size and shape for field experiments on paddy (Oryza sativa)” was conducted with the objective of estimation and comparison of methods for optimum plot size and shape for field experiments on high yielding variety of paddy. The study was based on primary data collected from a uniformity trial conducted in an area of 800m2 with Uma variety of paddy in virippu season 2018 at Integrated Farming System Research Station (IFSRS), Karamana. The crop was transplanted at a spacing of 20 cm × 15 cm. The field was divided in to 1.2 m × 1.2 m (1.44 m2) plots, after leaving a border of one meter from all the sides of the plot to eliminate the border effects, thus give rise to 400 basic units. Observations on plant height and number of tillers were recorded separately from each basic unit at monthly intervals and number of productive tillers, thousand grain weight, grain yield and straw yield were recorded separately from each basic unit at the time of harvest. The average height of the plant increased from 40.55 cm at one month after planting (MAP) to 121.37 cm at four MAP. The number of tillers per plant varied from 4 at two MAP to 14 at four MAP. The grain yield per basic unit varied from a minimum of 200 g to a maximum of 650 g with an average yield of 391.13 g per plot. The average straw yield was 0.501 kg. The first quartile (Q1) was observed at 0.410 kg and third quartile (Q3) was at 0.572 kg. The estimated average harvest index was 0.438 with a coefficient of variation (CV) of 20.78 per cent. The mean productive tillers estimated was 9 per plant. The correlation between productive tillers and grain yield was significant (0.128). Harvest index showed a very high significant correlation of 0.744 with grain yield. Soil fertility contour map was constructed based on yield data of all original basic units and by taking 3 × 3 and 5 × 5 moving average and the results of the analysis have shown that 3 × 3 moving average provided a more prominent picture of fertility status of the field and thus concluded that fertility gradient was more in horizontal direction. Serial correlation of horizontal and vertical strip and mean squares between vertical and horizontal strips also revealed that fertility gradient was more pronounced in horizontal direction. The optimum plot size estimated by combining the basic units of 1.44 m2 into plots of different sizes along with CV for each plot size. The different methods used for the estimation of optimum plot size are maximum curvature method, Fairfield Smith’s variance law method, modified maximum curvature method, comparable variance method, cost ratio method, covariate method, based on shape of the plot method and Hatheway’s method. Generally these methods need not provide a unique estimate. The optimum plot size estimated under maximum curvature method and comparable variance method was 34.56 m2 (24 basic units) with rectangular shape and it was same for both methods. The optimum plot size estimated under covariate method by taking harvest index as covariate was also 34.56 m2. The optimum plot size estimated by considering length (X1) and breadth (X2) also provided same plot size (34.56 m2) with X1 =3 units and X2 =8. Optimum plot size under Hatheway’s method was estimated by choosing varying number of replications and difference between treatment means. A plot size of 37.44 m2 (26 basic units) for four replications and 10 per cent difference between the treatment means was found to be optimum under this method. The optimum plot size estimated under Fairfield Smith’s variance law method and modified maximum curvature method was 8.64 m2 and it was not considered as optimum because it was smaller in size. Optimum plot size under cost ratio method was obtained by considering different cost ratios of fixed cost K1 and variable cost K2. The estimated plot size under cost ratio method was 5.95 units with K1 = 10 and K2 = 1. The comparison of methods for optimum plot size was done based on CV. The maximum percentage reduction in CV was found to be with a plot size of 24 basic units and percentage reduction was very low thereafter. Hence maximum curvature method, comparable variance method, covariate method and shape of the plot methods can be recommended for estimating optimum plot size for Uma variety of paddy for field experiments and the estimated optimum plot size was 34.56 m2 and the recommended shape was rectangular
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    ThesisItemOpen 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, S
    Sacciolepis 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.
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    ThesisItemOpen 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 Kumar
    The 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.
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    ThesisItemOpen Access
    Time series modelling for comparitive performance and influencing factors of production on paddy and coconut in South India
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2019) Suresh, A; KAU; Brigit Joseph
    The research entitled “Time Series modelling for comparative performance and influencing factors of production on paddy and coconut in South India” was conducted with the objective of developing statistical models on trend in area, production and productivity of paddy and coconut across Kerala, Karnataka and Tamil Nadu and to develop different statistical models for analysing the price movement of these crops across the states overtime and to develop models for analysing the influencing factors of production. Secondary data regarding area, production, productivity and rainfall were collected for a period of past 25 years from Directorate of Economics and Statistics (Govt. of Karnataka), Department of Economics and Statistics (Govt. of Kerala and Tamil Nadu) and Coconut Development Board. Secondary data on price was collected for major markets of paddy (Thanjavur and Raichur) and copra (Kochi, Kangayam and Tumkur) from indiastat and Agmarknet. Trend analysis was used to understand the trends in area, production and productivity using different linear and nonlinear growth models. Compound Annual Growth Rate (CAGR) was estimated using exponential model to compare the performance in area, production and productivity of paddy and coconut in South India. Johansen’s co-integration technique was used to understand the price movement in the markets across the states for price of paddy and copra. Panel data regression analysis was done to identify the climatic variables that influence the production of paddy and coconut. From trend analysis, the best model was selected based on adj. R2, criteria of randomness, normality and Root Mean Square Error (RMSE). In paddy, quadratic model was found to be the best fitted model for area and production in Karnataka, production and productivity in Kerala and area in Tamil Nadu. Cubic model was found to be the best model for area in Kerala, productivity in Tamil Nadu and power model for productivity in Karnataka and compound model for production in Tamil Nadu. In case of coconut, quadratic model was found to be the best fitted model for area, production and productivity in Karnataka and area and productivity in Tamil Nadu. Cubic model was found to be the best model for area, production and productivity in Kerala and production in Tamil Nadu. Comparative performance of paddy and coconut in Southern states was compared based on CAGR for a period from 1987-2017. CAGR revealed that production (1.1%) and productivity (1.0%) of paddy in Karnataka and productivity (1.5%) in Kerala was found to be positive and significant. Area (-4.5%) and production (-3.0%) of paddy in Kerala and area (-0.7%) in Tamil Nadu was found to be negative and significant. In case of coconut, positive and significant CAGR was noticed for area, production and productivity in Karnataka and Tamil Nadu and production (1.4%) and productivity (2.0%) in Kerala where as a declining trend in area (-0.6%) was noticed in Kerala. Stationarity is the prime requirement for co-integration analysis of price of paddy and coconut in various markets and it was tested using Augmented Dickey Fuller test (ADF). The results of ADF test indicated that price of paddy in Thanjavur (TN) and Raichur (Karnataka) markets and price of copra in Kochi (Kerala), Kangayam (TN) and Tumkur (Karnataka) markets were stationary after taking the first difference which suggested that all the price series were integrated of order one I(1). The result of Johansen’s co-integration test revealed that monthly wholesale price of paddy in Thanjavur and Raichur markets were co-integrated. Similarly price of copra in Kochi (Kerala), Kangayam (TN) and Tumkur (Karnataka) markets was also co-integrated which means that price in different markets are moving together. Granger Causality test was applied to find the direction of causality from one market to another and it revealed that there was a bidirectional influence in Thanjavur and Raichur market price of paddy. In case of copra, there was a bidirectional influence between Kochi and Kangayam market price and unidirectional influence on prices of Kochi and Tumkur. The effect of climatic factors on production was analysed using panel data regression with fixed effect model suggests that average rainfall during Q3 (July - September) and Q4 (October - December) had a positive and significant effect on production of paddy. In case of coconut, previous year average rainfall during Q1t-1 (January - March) and Q4t-1 (October - December) had a positive and significant influence on production of coconut. Trend in area, production and productivity was well explained by cubic and quadratic model for paddy and coconut with high adj R2 and least RMSE. CAGR of productivity of paddy in three South Indian states has shown a positive trend but there was a declining trend in area under paddy in Kerala and Tamil Nadu. There was a significant positive growth rate in area, production and productivity of coconut in Karnataka and Tamil Nadu and production and productivity in Kerala. However, the productivity in Tamil Nadu (14251 nuts ha-1) and Karnataka (13181 nuts ha-1) was far ahead as compared to that of Kerala (9664 nuts ha-1). The monthly wholesale price of paddy in Thanjavur and Raichur markets and price of copra in Kochi, Kangayam and Tumkur markets were co-integrated which indicates that any price change in one market influence the price in other markets. Production of paddy was influenced by Q3 (July - September) and Q4 (October - December) rainfall, in case of coconut, production was influenced by previous year average rainfall during Q1t-1 (January - March) and Q4t-1 (October - December).