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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.ThesisItem Open Access Multiphase analysis of cocoa production in Kerala(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2020) Shivakumar, M; KAU; Ajitha, T KCocoa (Theobroma cacao L.) is a very important crop as it provides food, income, employment and resources for poverty reduction. It ensures livelihood for millions of small holder farmers and offers raw material for the multibillion global chocolate industries. Despite the fact that Kerala has enormous potential in terms of suitable agricultural land, cocoa has failed to become a significant crop. As its domestic production is not sufficient to meet the increased demand, the industry has to resort to substantial imports. So, a comprehensive study titled “Multiphase analysis of cocoa production in Kerala” has been made on different aspects of cocoa cultivation, management practices, production and the constraints faced by actual growers. The trend analysis and forecasting of yearly area, production and productivity of cocoa in Kerala using advanced time series models employed on the data for the period from 1980-2017 revealed a distinct quadratic trend for the area under cocoa, having an increasing trend now and more or less linear stochastic trends for production and productivity. The Holt’s exponential smoothing model was identified as the best to predict yearly area under cocoa with an adjusted R2 equal to 0.94. The yearly production of cocoa could be well modelled by ARIMA (0,1,1) with an adjusted R2 = 0.72. By incorporating area under cocoa as an independent variable, ARIMAX (0,1,0) model could improve the R2 to 0.84 to predict the yearly production of cocoa. The productivity of cocoa seemed to be constant for several years (0.45tonnes/ha) which was well predicted through the simple exponential smoothing model with an adjusted R2 = 0.84. Evaluation of the performance of 100 selected cocoa hybrids in the Cocoa Research Centre, College of Horticulture, KAU, Vellanikkara showed that the peak average monthly yield was in the month of November (18.14pods) followed by the yield in October (18.04) and December (14.56). A pattern of biennial tendency persisted for the yearly yields of the hybrids. The results of General linear model repeated measures ANOVA highlighted the existence of a significant Time x Factor interaction with a partial eta squared equal to 0.14 where factor denotes different subgroups of cocoa hybrids with homogeneous yield. After the first harvest, the peak average yield was noticed during the fifth year irrespective of different low and high yielding groups. The income from cocoa farming depends on healthy pods harvested. So, an attempt was also made to account for the frequency of number of infected pods from each tree and it could be well demonstrated by geometric distribution which is a special case of Negative binomial distribution. Owing to the fact that the infected pods might be the outcome of external factors like weather variables, the influence of those factors with cocoa yield was also investigated. A stepwise regression of yield on previous five month’s accumulated weather variables resulted in a parsimonious prediction equation with total number of rainy days as the single regressor which could explain 66% of the variation in yield. The adjusted R2 could be enhanced to 69% by incorporating maximum temperature as the second most important regressor. The vide variation realised in the average monthly yield of cocoa hybrids could be well captured through SARIMA (1,0,0) (1,1,0)12 model with an adjusted R2 = 0.92. An empirical analysis to identify the factors perceived by farmers to influence their cocoa production and ultimately their income was performed taking a total sample of 100 farmers from Veliyamattom Panchayat of Idukky district and Iritty Panchayat of Kannur district. From a path analysis through structural equation modelling several linear regression equations could be generated simultaneously leading to prediction equations for cocoa yield and income. The final model iterated resulted in goodness of fit measures viz; comparative fit index = 0.96 and Tucker Lewis index = 0.94. Price of cocoa turned out to be the most prominent factor which contributed to the income of a cocoa farmer highlighting the importance of fixing the marketing price of cocoa. Second factor was yield per tree which was the outcome of good quality seedlings, efficient cultivation practices, plant protection and disease management measures, protection from rodent attacks etc. Importance of access to credit which would help to overcome the problems of lack of capital was emphasised. Financial problems such as inability to get assistance from financial institutions, lack of proper marketing facilities including drying and fermentation facilities of cocoa beans also were noticed. Probit analysis identified the factors viz; age of the farmers, land holding size, experience in cocoa cultivation, membership in organisations like Krishibhavan, farmer’s club, Cooperative society, Banks, SHGs etc. and frequency of contact with extension personnel to be significant for decision making to implement plant protection measures which were inevitable for successful crop management and ultimately leading to the net income of farmers. The yield gap analysis revealed that as against the potential yield (dry bean weight) of 4kg/tree/year, the national average yield from cocoa farmers was only 2.5 kg/tree/year resulting in a yield gap of 37.5% which need adequate attention.ThesisItem Open Access Inter-regional disparity in soil fertility status of southern Kerala-a statistical analysis(Department of Agricultural Statistics, College of Agriculture, Vellayani, 2020) Nayana, Narayanan; KAU; Brigit, JosephThe research work entitled ‘Inter-regional disparity in soil fertility status of southern Kerala – a statistical analysis’ was carried out at College of Agriculture Vellayani during 2018-2020. The objective was to develop soil fertility status index to assess regional disparity among the panchayats of Thiruvananthapuram, Kollam and Pathanamthitta districts of southern Kerala and to classify the panchayats based on soil fertility index. Secondary data on 12 soil fertility parameters, collected as a part of Kerala State Planning Board Project conducted in 2013 were used for the analysis. Detection and elimination of outliers using box plot was the first step in the analysis followed by estimation of mean vector of 12 soil fertility parameters from 43 panchayats of Kollam, 50 panchayats of Pathanamthitta and 44 panchayats of Thiruvananthapuram. The descriptive statistics used include range, mean and coefficient of variation (CV) of all the parameters. The entire analysis was done with the help of SPSS software. Among the 12 soil fertility parameters the range of Cu (0.34 - 49.89 mg kg-1), S (0.19 - 91.78 mg kg-1) Fe (7.07 - 391.84 mg kg-1) and K (17.49 - 851.85 kg ha-1) was observed to be very high with CV 249.62, 188.98, 154.22 and 92.32 per cent respectively in Kollam district. This is an indication of deficiency to above adequate availability of these nutrients in the panchayats. Among all the parameters highest consistency was observed in pH with mean value of 6.01 and the values varied between 4.66 and 8.13. EC values varied between 0.03 and 0.73dS m-1 showing a mean valueof 0.17 dS m-1 with a CV of 72.03 per cent. Almost a similar CV was observed for both OC (40.45 %) and P (44.94 %) with their values in the range 0.46 to 2.06 per cent and 9.67 to 111.49 kg ha-1 respectively. There observed a wide spread deficiency of Ca and Mg in almost all the panchayats. Among the micronutrients considered highest consistency was shown by B with a CV of 39.78 per cent and the lowest observed value of B was 0.13 mg kg-1 and the highest was 1.17 mg kg-1. Mean value of Mn was 17.47 mg kg-1 with a CV of 63.59 per cent. Available content of Mn varied between 2.33 and 46.32mg kg-1. PCA (Principal Component Analysis) performed on the mean vector of 12 soil fertility parameters of 43 panchayats in Kollam, extracted four PCs which accounted for 72.73 per cent variation of the data and based on the extracted PCs weighted aggregate index known as Soil Fertility Index (SFI) was developed to quantify the soil fertility status. The SFI thus estimated varied from 16.34 to 435.12 in Kollam with a mean of 111.31 and CV of 82.45 per cent. Further, factor analysis was performed to reduce the dimension and the soil nutrients P, Ca, K, OC, Fe, S and Cu had loading above 0.5 with a communality of more than 50 percent were retained and PCA was repeated and SFI was re-estimated. The estimated SFI was normalized using min-max normalization and based on this, the panchayats were grouped into four categories as low (SFI from 0-25%), medium (25-50%), high (50-75%) and very high (75-100%). In Kollam about 70 per cent of the panchayats were included in low fertility class with respect to initial SFI and 79 per cent with respect to SFI- FA. The soils in the panchayats listed in the low fertile category reported to have deficiency in Ca, Mg, S and Cu. While considering Pathanamthitta, EC has shown highest CV with its values range from 0.06 to 2.75 dS m-1. All the soil nutrients except B and Mg were adequate in the soils of most of the Panchayats in Pathanamthitta. pH was said to have high consistency with a CV of 8.92 per cent with its values in the range 4.20 to 6.56. CV of 27.70 and 28.80 per cent were observed for OC and K respectively with their values in the range of 0.64 to 3.26 per cent and 148.32 to 475.67 kg ha-1. CV of P was 61.85 per cent with mean value 66.62 kg ha-1. Lowest observed value of P was 4.63kg ha-1 and highest was 195.87 kg ha-1. Ca content had shown variation from 82.10 to 1000.00 mg kg-1. Mean value of Ca was 634.92 mg kg-1 with a CV of 48.60 per cent. Values of Mg were in the range of 26.62 to 461.50 mg kg-1showing 94.30 per cent CV. CV of S was 36.89 per cent with mean value 20.60 mg kg-1. A CV of 102.39 per cent was observed for B with values in the range 0.05 to 3.06 mg kg-1. CV of Cu and Fe was 35.86 per cent (for values between 0.87 to 5.51 mg kg-1) and 41.56 per cent (values in the range 20.27 to 101.62mg kg-1) respectively with their mean values as 2.73 mg kg-1 and 35.20 mg kg-1. Values of available Mn varied between 3.73 to 69.88 mg kg-1 with a CV of 63.17 per cent. PCA extracted four PCs which accounted for 68.70 per cent variation in the data. The mean and CV of the estimated SFI of Panchayats were respectively 131.75 and 33.49 based on initial PCA. The parameters OC, Cu, K, B and Mn were exempted based on factor analysis and the mean and CV of SFI- FA was respectively 185.90 and 38.30 per cent. Classification based on SFI- FA has shown an improvement in fertility status and the results of classification revealed that about 56 percent Panchayats were in high to very high soil fertility category in Pathanamthitta.This was because the relatively good availability of most of the soil parameters like OC, P, K, S, Cu, Fe and Mn in the panchayats of Pathanamthitta. In Thiruvananthapuram also more consistency was shown by pH with a CV of 3.67 per cent with values in the range 5.01 to 6.32. Most of the soil fertility parameters except EC (152.15%), Cu (130.98%), K (80.66%), B (54.24%) and Fe (59.15%) were found to have CV below 50 percent indicating less variability among the Panchayats in Thiruvananthapuram. The range of values of OC and P were respectively 0.48 to 1.70 percent, 13.11 to 38.94 kg ha-1. CV of Ca was 34.94 per cent with majority of the panchayats having available Ca above 300 mg kg-1. For Ca, lowest value observed was 170.80 mg kg-1 and highest value was 882.50 mg kg-1. Mg availability varied between 29.60 to 200.79 mg kg-1 and that of S was 12.00 to 61.38 mg kg-1. There noticed an adequacy in Fe and Mn availability but B was deficient. PCA extracted five PCs which accounted for 75.06 per cent variation in Thiruvananthapuram. SFI constructed had a mean of 95.93 with a CV of 27.68 per cent. The result of factor analysis concluded that all the parameters were found to be relevant in explaining the variation in soil fertility. Based on SFI, it was observed that 51.35 percent of panchayats in Thiruvananthapuram were in the medium fertility status category with almost all the parameters were found to be sufficiently available in most of the panchayats except B and Mg in Thiruvananthapuram. The results of the study based on the developed SFI confirmed inter-regional disparity between panchayats within in each district as well as between districts. The soil fertility status of panchayats in Kollam was poor as compared to Panchayats in Pathanamthitta and Thiruvananthapuram, but soil fertility status of Panchayats in Thiruvananthapuram was low as compared to Pathanamthitta and this conclusion was statistically supported by Kruskal-Wallis test.ThesisItem Open Access Development of a suitable model for ascertaining the growth and egg production in quails(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1991) John Thomas, M; KAU; George, K CAn investigation was carried out into the growth and egg production aspect of Japanese quails at the Kerala Agricultural University Poultry Farm, Mannuthy on 1st February, 1989 with the following objectives. 1. to find a suitable relationship between age and body weight. 2. to investigate the" trend of egg production in quails through suitable mathematical models. ,3. to study the impact of climate parameters (temperature, ; , humidity) on egg production in quails. The birds were reared under uniform feed formula and ^identical management practices (recommended by Kerala Agricul tural University Package of Practices). The investigation mainly depended on' data consisting of weekly body weights of -ii^-dividual birds, daily egg production of birds (beginning from age at sexual maturity) and daily climatological para meters (temperature and humidity) from beginning till the end of experiment of 30th September, 1989. Mathematical models such as linear, quadratic, exponential, .Von-Bertalanffy, modified exponential, logistic and Gompertz were fitted for the purpose using body weights of ) individual birds as well as average body weights over twelve weeks and the fitted models were compared using coefficient of 2 determination (r ) and standard error of estimate(s). Mathematical models such as linear, exponentialf parabolic exponential, inverse polynomial. Gamma function. Gamma-type functic^n, quadratic function, quadratic function in logari'thmic scale, quadratic-cum-log, emperical and linear hyperbolic functions were fitted for the development of suitable models for ascertaining egg production using total weekly, fortnightly egg production, hen housed and hen day egg production and fitted models were compared using Furnival index, r^ and s. Multiple linear regression equation was fitted using average weekly egg production per bird as dependent variable and weekly temperature and humidity as explanatory variable to study the impact of climatological parameters on egg production in quails. The investigation has the following, salient features. (i) The hatching weight of Japanese quails were 7.1369 g. (ii) The females weighed more than the males during the entire period of experiment and the body weights have shown an increasing trend. At the end of 12th week the average body weights of males and females were 157.6552 g and 179.2500 g respectively. (iii) Rao's method justified that initial body weights • had no significant effect on growth rate. • (iv) Gompertz curve = a exp [-b exp(-kt)'] was most , suitable for , ascertaining growth in quails on individual basis as well as on the basis of • average body weights over twelve weeks. (v) Average age at sexual maturity (females) was found to be approximately 10 weeks and on an average the eggs weighed 12.20 g. (vi) Quadratic function in logarithmic scale ; = a f b(logJ^) + c(log^)^ was most suitable , for ascertaining egg production in quails (weekly, , fortnightly, hen housed and hen day production • basis). (vii) Climatic parameters had significant impact on egg production in quails.ThesisItem Open Access Study of genetic correlations under full -SIB mating system (Two loci case)(Department of Statistics, College of veterinary and animal sciences Mannuthy, Thrissur, 1985) Khin Moe Moe; KAU; George, K CA purely theoretical investigation entitled ,JA Study of Genetic Correlations under Fu ll-s ib Mating System (two lo c i case)*1 was carried out with the following objectives, i ) to derive the joint distribution (correlation table) and to find the correlation between fu ll -s ib pairs under fu l l -s ib mating system in the case of two lo c i when there i s no linkage as well as when there i s complete linkage. l i ) to derive the joint distribution (correlation table) and to find the correlation between parent-offspring pair© under fu l l -s ib mating system in the case of two loci when there is no linkage as well as when there is complete linkage, i i i ) to derive the joint distribution (correlation table) and to find the correlation between fu l l -s ib pairs under paront-offspring mating system in the case of two lo c i when there is no linkage as well as when there is complete linkage, iv) to derive the joint distribution (correlation table) and to find the correlation between parent-offspring pairs under parent-offspring mating system in the case of two lo c i when there is no linkage as well as when there is complete linkage. 2 Th© joint distributions of fu ll -s ib pairs and parent- ©Ffspring pairs undor fu ll-s ib gating system wore derived with the help of generation matrix technique and th© correlations wore worked out therefrom, assuming additive genie e ffec ts and using the product-momeni correlation coefficient formula. The correlations were worked out for tho f i r s t ten generations of fu ll -s ib mating in both cases of no linkage and complete linkage, & comparative study of fu ll -s ib correlations and parent-offspring correlationsf conducted both numerically and graphically, revealed that £i) evonthough fu ll -s ib correlation was greater than parent-offspring correlation in in i t ia l generation (random mating) when there was complete linkage, the la tte r increased more rapidly than the former from in it ia l generation to f ir s t generation and ( i i ) from the second generation onwards, the rate of increase in both o f correlations were nearly the same upto tenth generation. I t was interesting to note that the parent-offspring correlations wore of comparatively higher order than th© fu ll-s ib correlations in both cases of complete linkage and no linkage. Similarly, th© joint distributions (correlation tables) for fu ll-s ib pairs and parent-offspring pairs under parentoffspring mating system were derived employing generation matrix approach and the correlations for the f i r s t ten 3 generations of parent—offspring mating in both cases of no linkage and complete linkage were worked out therefrom. A comparative study of those correlations was carried out both numerically and graphically. It was found that the trend in both correlation curves remain the same, but the value of parent-offspring correlation was always greater than that of full-sib correlation in case of no linkage as well as in caso of complete linkage. In comparison of all these correlations, it was found that the correlations increased as the number of generation increased and ultimately reached the limit unity when the number of generations increased indefinitely large. It was also observed that the magnitude of correlation in case of complete linkage was more than that of correlation In case of no linkage even under the same system of mating*
