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2020 - 20236
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Subject: Agricultural Statistics and Informatics × End date: 2023 × Start date: 2020 ×
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Now showing 1 - 6 of 6
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    ThesisItemOpen Access
    PRODUCTION OF MAJOR FRUIT CROPS IN ASSAM A TIME SERIES ANALYSIS
    (2023) Bhattacharjya, Dipankar; Phukon, Manashi Hazarika
    Pomology, one of the key subfields of horticulture, is the study of fruit cultivation. It has been practiced in India for many centuries. Nutritional importance of fruits for human being is well known. Man cannot survive just on cereal. Fruits and vegetables must be consumed for a balanced diet and optimum health. Nearly 90% of the nation's overall horticultural production is made up of fruits and vegetables. India is presently the world's second-largest producer of fruits and vegetables and the world's top producer of various kinds of horticulture crops, including okra, cashew nuts, bananas, papayas, and mangoes. Horticulture crops cover approximately 15% of the total cultivable area of Assam. Assam’s favorable agro-climatic conditions foster the growth of a wide range of fruits and vegetables in the state.For conducting this research secondary data was collected for the period from 1991 to 2020 from the Directorate of Economics and Statistics, Government of Assam. The objectives of my research topic were-  To study the growth trends of area, production and productivity of Major fruits grown in Assam.  To study the instability in area, production and productivity of Major fruits grown in Assam.  To develop a suitable forecast model for describing the production of Major fruits grown in Assam. From CAGR analysis, it was observed that the area, production and productivity of banana, pineapple, orangeshowed positive growth rate. On the other hand, the area and production of coconut also showed positive growth rate. For papaya also production and productivity showed positive growth rate. From CDVI analysis, it was observed that the area, production and productivity of banana, pineapple, papaya and coconut showed low instability while in case of orange the area and productionshowed medium instability but the productivityshowed low instability. For banana production, ARIMA (3,3,1), for pineapple production, ARIMA (6,3,1) and for orange production,ARIMA (2,3,1) were found to be suitable models for forecasting. Similarly, in case of papaya and coconut, ARIMA (2,3,3) and ARIMA (5,3,1) were found to be suitable models for forecasting respectively.
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    ThesisItemOpen Access
    INTERVAL ESTIMATION FOR DECISION MAKING IN CROP YIELD OF ASSAM
    (2023) GAYAN, ANKUR JYOTI; Saikia, Hemanta
    Crop yield is an important factor in agricultural sector because it is directlyrelated to food security, essential for economic development, important for employmentgeneration and has significant impact on export revenue. So estimation of crop yield isequally important because it helps in better planning for farmers, optimizing resourceallocation and forecasting market supply and demand. In this study crop yield of the stateof Assam was taken into consideration. Under this study, all the necessary data has beencollected from secondary sources, which include district level data from ICRISAT website,Statistical handbook of Assam and Economic survey of Assam. The objectives of the study are: 1. Estimation of parameters and their applications with inequalities for the normalpopulation 2. Application of the bootstrap method for the non-normal population to test thevariances. Simple linear regression was used to estimate prediction interval and it wasfound that for 23 districts of Assam yield of rice, pigeon pea, sesamum, rapeseed &mustard the prediction interval is true for 85.21%, 83.48%, 86.95% and 85.21%respectively for the period of 2018-2022. P-P plot was used to distinguish betweennormally distributed data and non-normally distributed data where it was found that foryield of rice, pigeon pea, sesamum, rapeseed & mustard there were 8, 5, 10 and 13 districtsof Assam respectively which were normally distributed. For normally distributed data theaverage rice yield, pigeon pea yield, sesamum yield, rapeseed & mustard yield ofHailakandi, Dibrugarh, Karbi Anglong and Jorhat respectively were the highest. Also,Chebyshev’s inequality was used to estimate confidence interval for normally distributeddata. At last bootstrapping method was used to test the equality of variances for non-normal data.
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    ThesisItemOpen Access
    Impact of Weather Parameters on Winter Rice Productivity in North Bank Plains Zone of Assam
    (AAU, Jorhat, 2021) BURAGOHAIN, RABIJITA; Saikia, Hemanta
    Weather parameters play a very significant role in the diversity of agriculture from region to region. The impact of weather parameters on crop health is mostly influenced by the variabilities in local or regional climate rather than the global climate patterns. Assam is well-recognized for its rich genetic diversity of rice. The climatic condition and geographical location cause greater production of rice over the past in Assam. Winter rice is the leading rice crop, accounting for a major portion of the total rice production in Assam as well as in India. In this study, an attempt has been made to find a better implementable plan for the farmers and policy makers to increase the winter rice productivity in North Bank Plains Zone (NBPZ) of Assam by analyzing over thirty years of data. NBPZ is one of the main regions, where most of its livelihood depends on agriculture. The purpose of the study was to analyze the trend of area, production and productivity of winter rice and to estimate the impact of weather parameters in winter rice productivity in NBPZ of Assam. Moreover, the analysis also revealed inter-district and intra-district variation of weather impacts on winter rice productivity. Overall thirty years of secondary data were evaluated for the study. To make a more clear vision, again data were sub-divided into three decadal periods viz. period I (1988-89 to 1997-98), period II (1998-99 to 2007-08) and period III (2008-09 to 2017-18) respectively. Different statistical tools viz. homogeneity test and change point detection of the data series over the decadal and overall thirty years were evaluated. Pettitt‟s test and Buishand‟s test were used to confirm the change point of the period. The robust non-parametric Mann-Kendall test confirmed the trend pattern of winter rice productivity in NBPZ along with Sen‟s slope estimate of the rate of change per year of area, production and productivity. Although, stepwise multiple linear regression was performed to estimate the effects of climate change on winter rice productivity. The four weather parameters viz. maximum temperature, minimum temperature, rainfall and rainy days were considered for weather impact assessment. These parameters possess the most significant fluctuations in the NBPZ of Assam. Analyzing over thirty years of the dataset for the aggregate of NBPZ also resulted that the productivity of winter rice being highly influenced by maximum temperature and minimum temperature. Although, Period III of Sonitpur and Lakhimpur district, the number of rainy days were found significant for winter rice productivity. The analysis over the past decades of NBPZ was to provide essential information to the agricultural planner and policymakers responsible for designing efficient agricultural policies and for making significant decisions concerning resources allocation for the development of the agricultural sector in NBPZ as well as for Assam. The study revealed that the NBPZ is not limited to studying climate-resilient productivity of winter rice only, there is a great need for implementation of effective measures as crop production and productivity has a greater influence on the socio-economic needs of the people. Keywords: Winter rice, NBPZ, weather parameters, MK-test, Stepwise multiple linear regression. .
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    ThesisItemOpen Access
    A STATISTICAL STUDY ON THE GROWTH OF AGRICULTURAL SECTOR ON INDIAN ECONOMY WITH SPECIAL REFERENCE TO ASSAM
    (AAU, Jorhat, 2021) Saikia, Dikhita; Mahanta, Supahi
    Agriculture contributes significantly to India's productivity and employment as well as that of Assam, which is predominantly agricultural and overpopulated. Agriculture production in the state is below the national average. Knowledge of productivity trends of major crops is critical in various decision-making plans for the benefit of farmers. Several methods are used to calculate income of a state, the most important of which is Gross State Domestic Product (GSDP). Assam's GSDP growth rate is critical for assessing the state of the economy. As a major component of a state's GDP, the agricultural growth rate should be prioritized in order to increase farmer income and the per capita income of Assam's rural community. Keeping the aforementioned facts in mind, the present study was planned with the objectives: To study the trends and prospects of the productivity of the major crops and its contribution to GSDP of Assam; To study the growth of agricultural GDP versus total GDP growth rate in the state; Modeling of the economic growth rate of the agricultural sector in the state; Forecasting of GSDP with time series models. The data pertaining to the study were based on secondary data for the period of 31 years (1990 to 2020). From the results it was observed that rice, jute, rapeseed and mustard had a significant increasing trend in productivity. About 95.14 per cent of the variation in the GSDP of Assam was explained by the production of the six major crops viz. rice, jute, wheat, potato, sugarcane, rapeseed and mustard. Agricultural GSDP contributed a large percentage of total GSDP during the nineteenth century. It was found that total GSDP had a higher growth rate than agricultural GSDP growth rate but Agricultural GSDP had the highest standard deviation, indicating greatest variability in growth rate. By comparing the results of the Solow Growth model to actual data, a very close relationship was discovered between the actual (11.28 per cent) and calculated (12.13 per cent) average growth rates from 1990 to 2020. The four stages of the Box-Jenkins approach were used to create an appropriate ARIMA model for Assam's GSDP, which later used to forecast Assam's GSDP for the next ten years (from 2020 to 2030). Based on the forecasted values from our model, we expect Assam's GSDP continue to rise.
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    ThesisItemOpen Access
    STRUCTURAL BREAK ANALYSIS OF RAPESEED AND MUSTARD PRODUCTION IN JORHAT DISTRICT OF ASSAM
    (AAU, Jorhat, 2020-10) BARUAH, SUJATA; Paswan, R. P.
    Oilseed crops play a vital role in the Indian agricultural economy and so does in any parts of Assam in terms of area and production. Rapeseed and mustard production in Jorhat have increased from 7817 tonnes in 2012-13 to 9118 tonnes in 2013-14 and then decreased to 8129 tonnes in 2014-15. This reflects the structural change in the economy of the district. The present study is performed to determine the exact time of the structural break in the rapeseed and mustard production, followed by the identification of the factors affecting the crop’s production and finally by examining the presence of cointegration between the crop productivity and the various variables under investigation. The data collected for the study pertained to the annual time series of area, production, productivity, maximum temperature, minimum temperature, total rainfall, bright sunshine hours, and wind speed for the periods 1988-89 to 2014-2015. The results of the structural break analysis reveal that the variables for the crop are non-stationary at levels, indicating the existence of structural breaks. The production of rapeseed and mustard is found to have breaks in the years 1995-96 and 1996-97. Amongst all the factors under investigation, the area is found to have a significant effect on the production of the crop in Jorhat district of Assam. This implies that increasing the land area in the study location may increase the production of the crop in the same place. Johansen’s cointegration test was used to check for the presence of the cointegration between the variables under the crop. It is concluded that the variables in the model are cointegrated. This is followed by the employment of the Vector Equilibrium Correction Model, finally proving the presence of a long-run relationship between the variables. It is found that minimum temperature has a negative relationship with the productivity of the crop whereas area and total rainfall have positive and significant short-run effects on the productivity of rapeseed and mustard crop in the study location.
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    ThesisItemOpen Access
    CLASSIFYING THE STATES OF INDIA THROUGH RICE, WHEAT, AND GROUNDNUT USING STATISTICAL GRAPHICS
    (AAU, Jorhat, 2020-07) TSIGBEY, STANLEY TORNAM; Saikia, Hemanta
    India’s economy is mainly contingent on agriculture which accounts for 17-18 percent of India's gross domestic product (GDP) and provides sufficient employment to 50-60% of the total population. The India position in terms of rice, wheat, and groundnut production all over the world call for vital information on area, production, and productivity as well as agricultural states of India. In this study an effort has been made to classify the agriculture states of India using statistical graphics i.e. regression analysis and tri linear plot of the three selected major crops; rice, wheat, and groundnut. And in order to do that the 50 years data (1966-2016) trend of area, production, and productivity of the various crops are converted into index number. Thereafter a scatter plot is depicted considering ‘area index’ as an independent variable and ‘productivity index’ as a dependent variable and then a linear regression line is being fitted along with confidence band for classification of the states. Tri-linear plot was considered as one of the graphics in classifying the states where the indices values of the three variables (area, production, productivity) were first brought within the range 0 to 1. Afterwards percentage contributions of each of the three variables are taken where the total of the three variables sum to unity (100%) and are represented as one point on a triangular diagram. The study reveals that some states recorded less productivity despite of being adequate increasing area trend and some states also shown productivity increased with decreased area. The classification of Indian states is to provide essential information to the planners and policymakers responsible for designing efficient agricultural policies, and for making significant decisions concerning resources allocation for the development of agricultural sector in the various states.