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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 Statistical assessment of banana ripening using smartphone - based images(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2022) Haritha, R Nair; KAU; Pratheesh, P GopinathThe research work entitled “Statistical assessment of banana ripening using smartphone-based images” was carried out at College of Agriculture, Vellayani during the period 2019 to 2021. The objectives were the development of suitable model to establish the relationship between Total Soluble Solids (TSS) and L*(lightness), a*(green-red ratios), b*(blue-yellow ratios) values and for prediction of TSS values using L*, a*, b* values. Development of a protocol for accurate data collection to assess TSS content in Banana using smart-phone-based images. Good quality Nendran variety with only minor shape and peel colour flaws were obtained from a nearest field randomly chosen for the study. Each time 3 hands at the ripening stage 1 (green) with 10 fingers by hand were collected. The fruits were stored in a normal day/ night cycle. Bananas were taken randomly from each hand and their color changes and development of brown spots were measured daily during 10-12 days. Banana samples were placed on the table covered with a non-reflecting white paper as a background of the image. For white light illumination, two of 36 W fluorescent lamps were fixed at ceiling above the experiment setup. Three smartphones were used for image acquisition. Smart phones were placed at a distance of 20 cm above the banana. Samples of banana were blended using a fruit juicer. The TSS were determined using a digital refractometer. For the images obtained, RGB and L*a*b* were extracted using ImageJ software. The observations on TSS, R, G, B, L*, a*, b* were used for fitting regression models after splitting the data into train (80%) and test (20%) sets. When linear model was fitted between TSS and R, G, B values for all the three devices, each of the independent variables were found to be significant. Adjusted Rsquared values obtained were 0.80, 0.80, and 0.84 for the three devices. It means about 80% of the variation in the TSS was explained by R, G, B values. For the predicted values of TSS R-squared values were 0.84, 0.90, and 0.95. Hence linear model was found to be better fit for predicting TSS. Since RGB color model is device dependent model, it may not always represent the same colour on different devices. But in case of CIE L*a*b*, it is device independent and shadows and areas of glossiness on the object surface had less impact. Therefore, linear model was fitted between TSS and L*, a*, b* values. Adjusted R-squared values obtained were 0.78, 0.81, and 0.85 for the three 126 devices. For the predicted TSS values R-squared values were 0.84, 0.76, and 0.95. Therefore, linear model between TSS and RGB model found to predict TSS much accurately than L*a*b* color space when prediction accuracy was compared. On visualization of data, TSS and L*a*b* found to have non-linear relationship for all the devices. When spline regression was fitted between TSS and L*, a*, b* values R-Squared obtained were 0.91, 0.90, and 0.89, which was higher compared to Rsquared values for linear model. Also, deviance explained by the models were 92%, 92.3%, and 90.7% for corresponding device 1,2 and 3. Therefore, spline regression found to be better model for TSS and L*, a*, b* data and for prediction of TSS values. Protocol for accurate data collection was developed with modification in the procedure performed. Possibility of Deep learning was explored in the study using CNN. Convolutional neural network (CNN) was developed using 3 categories Raw (TSS 4-10), Medium (TSS 11-17) and Ripe (TSS 18-32) with 30 samples each. 25 images from each category were taken as training set and 5 were taken as test set. 100 epochs were performed to mitigate overfitting and to increase the generalization capacity of the neural network. Model evaluation of training set gave an accuracy of 84% with loss value 0.45. For the training set, all 25 from ripe category were able to identify into that particular category. In case of raw 24 were identified as raw with 1 identified as medium. For medium 14 were identified as medium,3 identified as ripe and 8 identified as raw. Model evaluation of test set provided 73% accuracy with 0.81 loss. The model successfully classified 5 ripe bananas, 4 raw bananas (1 classified as medium) and 2 medium bananas (3 classified as raw). The results of the research work to identify the best fitting model concluded that RGB model found to predict TSS much accurately than L*a*b* color space when linear regression model was fitted and spline regression model was found to be the best fit for L*, a*, b* and TSS values, R-squared values were much higher with a good percentage of variation explained. The CNN developed classified images into raw, medium, and ripe with approximate accuracy of 74%. Therefore, CNN can be used to predict range of TSS in no time, if a large number of images are uploaded into this model. The CNN can be optimized further with higher number (atleast 10,000 samples) of samples to improve the efficiency of classification.ThesisItem Open Access Geostatistical analysis of groundwater level in Thiruvananthapuram District(Department of Agricultural Statistics, College of Agiculture, Vellayani, 2021) Harinath, A; KAU; Pratheesh P, GopinathThe research work entitled “Geostatistical analysis of groundwater level in Thiruvananthapuram district” was carried out at the College of Agriculture, Vellayani during 2019-2021. The objective of the study was to analyze the spatiotemporal variations in the groundwater level, identify the relationship between groundwater and climatic factors (i.e., rainfall and temperature), and to prepare the thematic map for the location. To characterize the spatiotemporal fluctuations in groundwater level within the research region, various geostatistical approaches were used. The WRIS [Water Resource Information System] website was used to collect groundwater level data for 29 different locations within the study area for 10 years, from 2008 to 2017. The selection of data points was based on the even spatial distribution such that all the locations in the district are entirely covered. The NASA satellite website data was used to collect the rainfall and temperature data for the 29 distinct sites throughout a ten-year period. The semivariogram models were fitted to assess the spatial continuity of groundwater level. The nugget to sill ratio is also identified for detecting the spatial dependency. In the research region, the kriging interpolation approach was used to assess the spatiotemporal fluctuations in groundwater levels. If the data sets are normally distributed, the kriging interpolation technique will be more successful. Thus, the data points were subjected to exploratory data analysis to test the normality of the data set. The normality of the data sets is found out by Shapiro-Wilk’s normality test. The results showed that the years 2010 and 2017 are not normally distributed as the null hypothesis of the test is rejected. And also, in the case of temperature and rainfall, all the data points were not normally distributed. Thus, for the proper analysis, the log transformation was performed to the data sets which are not normally distributed and proceeded to further steps. The relationship of groundwater and climatic factors were accounted with the correlation analysis. The results showed that the temperature is having more dependency with the groundwater level fluctuation than the rainfall. 88 The semivariogram fitting were done to the groundwater level drop for each location, groundwater level over the years, and for the average groundwater level to identify the spatial and temporal variations in the study area. The drop was found out for each location by taking the difference between the groundwater levels of the years 2008 and 2017. The positive drop refers the depletion in the groundwater level and the negative drop refers the increment in the groundwater level. The nugget to sill ratio explains that the groundwater level drop is having a relatively strong spatial dependence. The three models, Spherical, Exponential and Gaussian models were fitted to the groundwater level for each year. The best fit model was selected by accounting the Adjusted R2 value. The spatiotemporal variation was studied by kriging interpolation method. The thematic maps were created to analyze the groundwater level variations. The maps were created in the ArcGIS 10.4 software. By investigating the maps prepared, the groundwater level depletion is observed severely in the Varkala region, and the Parassala region. The groundwater level at the high ranges like Ponmudi, Bonacaud, and Neyyar regions are maintaining a decent amount of groundwater level. From the PCA biplots prepared, the study concluded that there is a gradual groundwater depletion happening from 2008 to 2017. And from the biplot of years, the temperature is relatively high in 2016, 2017 where the groundwater level is also high. And the temperature is relatively low in 2008, 2009 where the groundwater level is also low. Thus, it can be concluded that the groundwater is having some dependency with the temperature variations which have been detected in the correlation analysis. From the biplot of different locations, it can be analyzed that the Varkala, Sreekariyam, Pothencode, Chengal, Neyyattinkara regions are having high groundwater depth. And Kattakkada, Kallar, Palode, Ariyanadu, Maruthamoola, Peringamala regions are having low groundwater depth. From the research performed, it can be concluded that, most of the locations are having a positive drop in the groundwater, which represents that the groundwater depletion is happening in temporal structure in the study area. The highest depletion in the 89 groundwater is seen in Pothencode, Chengal, Varkala, Neyyattinkara regions. The rate of groundwater level drop is 1.49 meters, which is positive, and can be inferred that there is depletion in the groundwater level. The nugget to sill ratio of the groundwater level drop in the study area is 0.367, which refers that the depletion is moderately spatially dependent. From the correlation analysis, it can be concluded that the temperature is a major factor influencing the groundwater depletion than the rainfall, because there is a positive significant correlation between groundwater and temperature. The groundwater depth of Varkala, Pothencode, Sreekariyam, Neyyattinkara, Chenkal, Kulathoor is high, and at Kattakkada, Palode, Kallar, Ariyanadu have low groundwater depth which can be concluded from PCA biplot of different locationsThesisItem Open Access Structural equation modelling in paddy(Department of Agricultural Statistics, College of Agriculture, Vellanikkara, 2021) Pooja, B N; KAU; Ajitha, T KAgriculture is the largest sector of economic activity in Kerala and has a crucial role to play in economic development by providing food and raw materials, employment to a very large proportion of the population, capital for its own development and surpluses for economic development. In this context a study on the analysis of the trends regarding the data for a period from 1960-‘61 to 2019-‘20 on area under cultivation, production and productivity of paddy in Kerala has great importance. An empirical study was also attempted to identify the vital factors leading to the enhancement of net income of paddy farmers using Structural equation modelling on the primary data collected from 150 registered paddy farmers of Ollukkara block of Thrissur district. The trend analysis of area, production and productivity of paddy in Kerala for the period from 1960-‘61 to 2019-‘20 pertaining to autumn, winter and summer seasons revealed that area under paddy and production had a declining trend in autumn and winter seasons whereas an increasing trend in the case of summer paddy. Paddy productivity has an increasing trend in all the seasons. Employing Bai and Perron (1998) methodology, breaks in the time series of area, production and productivity of paddy in Kerala for different seasons were identified and were used to explore volatilities of paddy production in different phases. Compound Annual Growth Rate (CAGR) and instability indices were computed with respect to each break points of the trend and used to explain the growth pattern of the variables over the study period owing to the fact that paddy is one of the most essential food crops in Kerala. In the recent past, area under cultivation of paddy had been declining due to several factors including the adoption of non-agricultural food crops like rubber and coconut which would provide better returns to farmers. CAGR on area, production and productivity showed a declining trend upto 2008-‘09. Area under autumn paddy was the most affected variable resulted in negative growth rates in all phases. In contrast, the growth rate was positive for productivity in almost all phases of different seasons. However, in the subsequent year to the enactment of the Kerala paddy conservation and wetland act, the area and production of paddy for all the three seasons gradually started increasing depicting a positive impact of the act on paddy cultivation in the state. The growth instability was maximum for summer paddy production. Time series modelling and forecasting analysis identified Browns’ exponential smoothing model as the best with significantly high value R2 = 0.99 for area under paddy in autumn, ARIMA (0,1,0) with R2 = 0.98 in winter and Simple exponential smoothing model resulted in an R 2 = 0.93 in summer. Coming to paddy production in autumn, Browns’ exponential smoothing model resulted in an R 2 = 0.95 and Simple exponential smoothing model seemed to be the best for winter and summer season with R 2 = 0.87 and R 2 = 0.60 respectively. Holts’ exponential smoothing model was found as the best with R 2 = 0.87 to predict paddy productivity in autumn and summer and Browns’ exponential smoothing model with R 2 = 0.87 for winter paddy productivity. The secondary data collected from the year 1996-‘97 to 2018-‘19 on area, production, productivity and price of paddy from the official website (DES), Kerala were made use of to forecast the yearly change in paddy production by fitting a regression of change in production on yearly change in cultivated area, yearly change in productivity, yearly change in price, and the interaction of yearly change in price and area. The regression equation resulted in an adjusted R 2 of 0.73 and yearly change in area and productivity were the significant regressors. An empirical analysis was conducted using a sample of 150 registered paddy farmers from Ollukkara block of Thrissur district to determine the factors considered by farmers to influence their paddy production and, ultimately leading to their net income. Several linear regression equations could be constructed simultaneously from a path analysis using structural equation modelling, leading to prediction equations for paddy production and net income. The final model iterated, resulted in goodness of fit measures viz; comparative fit index = 0.90 and Tucker Lewis index = 0.90 and RMSEA = 0.08 emphasising the potential of SEM in plant science studies as powerful as in social science. Finally the constraints faced by the farmers in paddy farming were ranked according to their severity and coefficient of concordance was computed as w= 0.423 which was significant at 1 per cent level showing strong agreement among the farmers to rank the constraints as “financial, labour management, pest, disease and animal attack, marketing and lack of knowledge in paddy farming”. However in spite of all these constraints farmers are now attracted towards paddy farming because of the enriched net returns from it.ThesisItem Open Access Cointegrated movement of food grains production and agricultural inputs: a time series assessment(Department of Agricultural Statistics, College of Agriculture, Vellanikkara, 2021) Sisira, P; KAU; Ajitha, T KIntroduction of the green revolution, modernization of agriculture, encouragement to research and extension in agriculture are some of the factors that contributed to the growth in agriculture. Increasing crop production and productivity are not just about the new technologies or crop management. Environmental sustainability is also of vital importance. The complexity of these issues now faced make improving crop production and productivity a more challenging task. Water, fertilisers, crop protection-inputs and professional advice all need to be managed in the most efficient manner. Fertiliser use has seen a tremendous increase in India and in other parts of the world with the spread of green revolution. Fertiliser was identified as one of the three most important factors, along with seed and irrigation for raising agricultural production and sustaining food self-sufficiency in India. In Kerala, farmers mostly depend on agriculture as a means to earn more money and concentrate more on cash crops other than crops those belong to staple food grains category which is one of the most important factors for human existence. The study intends to scrutinize the movement of food grains production and agricultural inputs through a time series assessment in India and three selected states viz., Kerala, Andhra Pradesh and Tamil Nadu using secondary information collected from various official sources. To identify the trend in production of food grains and agricultural inputs in India for the period 1950-2020 and the states (1980-2020), the linear, quadratic and cubic functional forms were selected with high values of adjusted R 2 . Trend analysis for India depicted an overall growth in an upward direction for the variables under study realizing almost linear trend. Whereas the trend analysis for Kerala, AP and TN with respect to total cropped area, fertilizer consumption and pesticide consumption showed a declining trend. In the case of food grain production, a slow increase was noted in very recent years for all the three states. CAGR was computed to observe the growth rate of the variables and for India, overall growth rate in the variables under study was positive. For total cropped area it was +0.006, +0.089 for fertiliser consumption and +0.048 for pesticide consumption and +0.026 for food grains production. However, in Kerala, the total cropped area (+0.001) and fertiliser consumption (+0.01) showed positive CAGR whereas negative growth rate for pesticide consumption (-0.01) and for food grains production (-0.002). In Andhra Pradesh, CAGR was -0.02 showing a negative growth rate in the case of total cropped area and 0.03 for fertiliser consumption, -0.03 for pesticide consumption and 0.02 for food grain production. In the case of Tamil Nadu, for total cropped area and fertiliser consumption CAGR was 0.004 and 0.02 respectively. Whereas for pesticide consumption it was -0.002 and for food grain production it was 0.02. Overall pesticide use had a negative CAGR in the states of Kerala, AP and TN. Also, the negative growth rate of food grain production in Kerala needs serious attention and it is also worth to identify the factors which discriminates Kerala from AP and TN. Time series model building was used to determine the best fit model and forecast future values of the variables under consideration. In India, Holts’ model was identified as the best to forecast total cropped area, fertiliser consumption and food grains production with adjusted R2 values as 0.96, 0.99 and 0.98 respectively. Regarding pesticide consumption Simple exponential smoothing model was the best with adjusted R 2 = 0.95. For Kerala, Simple exponential smoothing model, ARIMA (1,0,0) and Holts’ model were obtained for total cropped area (adj. R2=0.76), fertiliser consumption (adj. R2=0.66) and food grains production (adj. R2=0.85) respectively. For Andhra Pradesh, ARIMA (0,1,0) model was identified for total cropped area with adj. R2= 0.80, Simple exponential smoothing model for fertiliser consumption with adj. R2=0.93, for pesticide consumption with adj. R2=0.82 and for food grains production with adj. R2=0.82. When coming to Tamil Nadu, ARIMA (0,1,0) was the best for modeling total cropped area with adj. R2=0.76, ARIMA (0,1,6) for fertiliser consumption with adj. R2=0.74, Simple exponential smoothing model for pesticide consumption with adj R2= 0.84 as well as for food grains production with adj. R2=0.43. It is well known that Kerala imports food grains mainly cereals and vegetables from Andhra Pradesh and Tamil Nadu. To examine the pattern and dispersion of variables viz; total cropped area, fertiliser consumption, pesticide consumption and food grains production in Kerala, AP and TN, Box plot analysis was done and found that AP had highest dispersion and Kerala showed lowest dispersion with respect to variables under study. Since variability was found among the states, Mahalanobis D2 was used to estimate the pairwise distance between the states with respect to variables under study. The distance between Kerala - TN (1.94) was more when compared with Kerala - AP (1.93) and the distance between AP - TN (1.74) was the lowest. Discriminant analysis paves a way to pinpoint the casual factors which contribute to the discrepancy between the states and it identifies the root cause for the distance obtained by Mahalanobis D2 among states. Food grain production followed by fertiliser consumption was found to be the discriminating factors in Kerala - AP analysis. The distinguishing factors in Kerala - TN analysis was fertiliser consumption followed by total cropped area. Consumption pattern of fertiliser nutrients such as N, P and K in Kerala was entirely different from the recommended dose. On all Kerala basis, the average use of N, P and K were significantly lower than that of the recommended quantity depicting imbalanced use of fertilisers during the period 1995 - 2020 and for the period 1993 - 2009 for all districts in Kerala. Kerala showed highest imbalance index of 0.24 during the study period. None of the years showed perfect balance or extreme imbalance in Kerala. For district wise study it could be observed that the district Wayanad was having the highest imbalance index (0.212) followed by Kozhikode (0.205) and Idukki (0.202). The Palakkad district was having the least value of imbalance index which was equal to 0.099. To assess the co integrated movement of food grains production and agricultural inputs in India and the states under study, Vector Auto Regression was used by modeling each variable as a linear combination of past values of itself and past values of other variables in the system. The VAR models resulted in an adjusted R2 ranging from 0.95 - 0.99 for India with respect to different variables and for all the states also with significantly high values of adjusted R 2 showing the potential of the VAR approach to quantify the co integrated movement of the variables under studyThesisItem Open Access Time series modeling and forecasting of tea prices in India(Department of Agricultural Statistics, College of Agriculture , Vellanikkara, 2021) Deenamol Joy; KAU; Laly John, CThe study entitled “Time series modeling and forecasting of tea price in India” was conducted to study the components of time series data on prices of tea in India, to develop time series forecast models for the prices, to develop statistical models for price volatility and to study the integration between international and Indian tea prices. Monthly auction prices of tea for North India, South India and All India for the period from January 1980 to December 2020 collected from the Tea Board formed the main database for the present study. International price of tea for Colombo (Sri Lanka) and Mombasa (Kenya) for the period from January 1980 to December 2020 were collected. To have an idea about the trend in A- Pr- Pd of tea in India, annual data on A- Pr- Pd of tea from 1970 to 2019 in North India, South India and All India were also collected. To have a general idea about trend in A- Pr- Pd of tea in North India, South India and All India, models like exponential, quadratic, cubic etc were fitted. From among several models tried, quadratic model was found to be the best fit for area under tea in North India, while, cubic model was found to be the appropriate fit for production and productivity of tea in North India and, A- Pr- Pd of tea in South India as well as All India. North India and South India tea price data was decomposed to time series components like trend, seasonal variation, cyclic variation and irregular variation. North India and South India showed an overall increasing trend and a prominent seasonal variation. Cyclic variations showed that South India exhibited more cycle of price volatility compared to North India. All India tea price was found to be the simple average of North India and South India tea prices. Compound Annual Growth Rate (CAGR) was estimated for A- Pr- Pd of tea in North India and South India for the period from 1970 to 2019. For North India, growth rate in production was more during 1996-2019 compared to period 1970-1995. For South India, a decline in production was observed during 1970 to 1995. Price forecast models like exponential Smoothing models and ARIMA models were fitted to forecast the tea prices in North India and South India from January 2021 to April 2021. For North India tea price, SARIMA (0,1,3)(0,1,1)12 was identified as the best forecast model whereas for tea price of South India SARIMA (0,1,1)(1,0,1)12 was selected to forecast tea prices. For tea prices in North India and South India, volatility in prices were estimated using intra and inter annual volatility and its significance was tested by fitting suitable ARCH model. Intra annual volatility indices of monthly tea prices in both regions were varying irregularly. In most of the years, North India showed large variation in tea price compared to South India. ARCH (1) model was fitted to check the significance of tea prices and the estimate of ARCH parameter showed high volatility for tea prices for North India and South India. Cointegration analysis was carried out for tea prices to study the integration between international and domestic Indian tea markets. One cointegrating relationship exists between the market pairs, North India - South India, North India – Mombasa and South India – Colombo. No cointegration exist between the market pairs, All India -Mombasa and All India - Colombo. Unidirectional causality was observed between South India and Colombo whereas, bidirectional causality was observed between market pairs, North India - Mombasa and North India - South India.
