Browsing by Author "Ajitha, T K"

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Changing scenario of Kerala agriculture- an overview
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2009) Unnikrishnan, T; 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.
Cointegrated movement of food grains production and agricultural inputs: a time series assessment
(Department of Agricultural Statistics, College of Agriculture, Vellanikkara, 2021) Sisira, P; Ajitha, T K
Introduction 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 study
Forecasting of rice yield using climatological variables
(Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1986) Ajitha, T K; Prabhakaran, P V
Multiphase analysis of cocoa production in Kerala
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2020) Shivakumar, M; Ajitha, T K
Cocoa (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.
Optimisation techniques in long term fertilizer trials: rice-rice system
(Department of Agricultural Statistics , College of Horticulture, Vellanikkara, 2019) Jesma, V A; Ajitha, T K
Optimization through response surface methodology
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2019) Shivakumar, M; Ajitha, T K
Spatial and temporal variations in the development of agriculture in Kerala
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2002) Allahad Mishra; 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.