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2013 - 20162
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Subject: Agricultural Statistics and Informatics × Author: KAU × End date: 2016 × Start date: 2011 × Type: Thesis × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Pre-harvest forecasting models and trends in production of banana (Musa spp.) in Kerala
    (Department of Agricultural Statistics, College of Agriculture, vellayani, 2016) Sharath Kumar, M P; KAU; Vijayaraghava Kumar
    The study entitled “Pre-harvest forecasting models and trends in production of banana (Musa spp.) in Kerala” was conducted at Instructional farm, College of Agriculture, Vellayani. The objectives of the study were to develop models for early forecasting of yield in four major banana cultivars grown in Kerala viz., Nendran, Robusta, Redbanana and Njalipoovan and also to carry out the time series analysis of the trend in area and production of banana in Kerala. The study was based on both primary and secondary data. Initial and monthly observations on growth habits and yield of commonly grown banana cultivars were used for forecasting. Secondary data on area, production and productivity over a period of twenty five years (1991-2015) were collected from published sources of Directorate of Economics and Statistics, Govt. of Kerala and State Department of Agriculture. Additional information on price change and climatic factors were also incorporated in state level time series analysis. . Pre-harvest forecasting models developed for the first three months, using sucker characters and numbers of leaves were not found to be sufficient in forecasting yield and best models were identified from the fourth month onwards in all cultivars. Correlation analysis of yield (bunch weight) with biometrical characters in all four cultivars showed that correlation is positive and significant in 4th, 5th and 6th months of growing. Among biometrical characters, plant height and plant girth showed significant relationship with yield in all cultivars. In Njalipoovan, in addition to plant height and plant girth, number of leaves and leaf area (D-leaf) had some positive relationship with the ultimate yield. Meanwhile fruit characters like number of fruits, weight of second hand, fruit weight had significant correlations with yield in all cultivars. Stepwise multiple linear regressions were attempted to primary data at every month. The statistically most suited forecasting models were selected on the basis of coefficient of determination (R2), adjusted R2 and mallow‟s Cp criteria. It resulted that, in nendran variety, plant height and plant girth were contributing to yield with highest R2 of 0.80 in the 5th month (model Y= -1.37+0.025 H4+0.10 G5). Fruit characters were statistically significant to making of a 55 per cent of variation in total yield. In Njalipoovan, models from 4th month onwards were found good for early forecasting of yield. Number of leaves, plant height, and leaf area and plant girth could predict yield with R2 of 81.7%, while fruit characters, viz., number of fruits, fruit length, fruit girth and fruit weight could predict yield with an R2 of 71.88 %. In Red banana, it was found that plant height and plant girth at fourth gave suitable prediction with an R2 of 0.762, meanwhile fruit characters could predicted yield with an R2 of 71 .28%. In Robusta variety, prediction can be made from 4th month onwards as best predictor as plant height and girth (with an R2 of 75.24 %). At harvesting stage, fruit characters could predict the maximum yield up to 96.76 %. Principal component analysis resulted that first three principal components are sufficient for getting maximum information from explanatory variables in all four cultivars with 75 % explained variation. Linear and nonlinear growth models were developed for the purpose of estimating the growth rate and fitting the best model. The use of R2, criteria of randomness and normality of time series data were used as a measure of goodness of fit. Cubic model was found as best fit for estimated trends in area, productivity, whole sale price and cost of cultivation under banana production. Quadratic function was selected as best suited for production trend. However, rainfall and rainy days were found to have less effect on changing in area, production and productivity of banana. Area, production, wholesale price and cost of cultivation showed a positive trend during past twenty- five years. Hence, reliable estimate of a crop yield, well before harvest can be made of from 4th month onwards in all cultivars studied. Policy decisions regarding planning of crop procurement, storage, distribution, price fixation, movement of agricultural processing commodity, import-export plans, marketing can be formulated based on these forecasts.
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    ThesisItemOpen Access
    Formation and efficient estimation of stochastic frontier production functions
    (Department of Agricultural Statistics,College of Horticulture Vellanikkara, Thrissur-680 656, 2013) Dhanesh, N J; KAU; Krishnan, S
    Technological change and efficiency improvement are important sources of productivity growth in any economy. The concept of technical efficiency (TE) is based on input and output relationships. Technical inefficiency arises when actual or observed output from a given input mix is less than a possible mix. The analysis of technical efficiency involves the assessment of the degree to which the production technologies are utilized. The present investigation on “Formation and efficient estimation of stochastic frontier production functions” was carried out in the Department of Agricultural Statistics, College of Horticulture, Vellanikkara during 2010-13, to assess the present economics of pepper cultivation, to formulate new stochastic frontier production functions and to compare them. The secondary data collected from the Department of Plantation Crops and Spices, College of Horticulture, Vellanikkara on area of holdings, number of vines, yield, expenses for machinary, labour, manure, and other expenses for the cultivation of pepper in the three blocks viz; Mananthavady, Kalpetta and Bathery were used for the analysis. The summary statistics revealed that irrespective of the blocks, the expenditure on labour was the highest followed by expenditure on manure and it was increasing according to the increase in age of plants. For the stochastic frontier production model to be realistic, exact measurement of the cost of the inputs as also the realized output is extremely necessary. Very few farmers keep records of the expenditure incurred on the various inputs and very rarely the output realized. Vegetable crops have a short duration. So the farmer will be in a position to give realistic figures regarding the various inputs as also the outputs. As regards plantation crops, there will be a lag right from establishment of the crop to the steady bearing stage. Therefore, it will be very difficult to trace back the exact cost, as no records would be available about the costs incurred. A rapid estimation survey is the only feasibility where in simultaneous estimation of the costs involved at from the nursery through the various stages of growth can be observed. Since a farmer who is already having a steady bearing crop, would have incurred lesser costs through the previous stages of growth of the crop, it is most feasible to use the concept of present worth to arrive at exact costs of previous stages of the crop. The stochastic frontier analysis was done using the present value (PV) as also with the present cost. The stochastic frontier analysis (SFA) was done for all the three blocks compounding all the costs starting from the nursery stage (First age group) up to the steady bearing stage (Fourth age group) using PV. The mean technical efficiency was observed to be 0.93, 0.91 and 0.94 for Mananthavady, Kalpetta and Bathery Blocks respectively. The stochastic frontier approach for each age group by pooling over the blocks, were also worked out using PV and it revealed a mean technical efficiency of 0.95 and 0.92 for the plantations in the third and fourth age groups respectively. To assess the factors influencing technical efficiency, the regression of TE on the factors like area of holdings, number of vines, cost for implements and machinary, labour, manure and other expenses was fitted for each block. About 91 per cent of the variation in technical efficiency could be explained using these variables. When the area of holdings increased, the technical efficiency seemed to decrease. With proper labour management, the technical efficiency can be significantly improved.