Development of Livelihood Index for Different Agro-Climatic Zones of India

A livelihood index has been developed for different agro-climatic zones of India, based on the secondary data for TE 2003. Six different sub-indices obtained are indicators of Infrastructure Status, Agricultural Status, Nutritional Status, Economic Status, Health and Sanitation Status and Food Availability Status in respective zones. A total of 57 variables have been considered for this study. Finally, a composite integrated livelihood index has been developed which indicates the livelihood status of different agro-climatic zones in the country. Also, 103 districts of low agricultural productivity have been identified within low livelihood regions. The results of this study have been compared with those of backward districts identified under Wage Employment Program by the Task Force of Planning Commission of India. It is found that about 60 per cent districts identified in this study are the same as identified by the Task Force. Further, the spatial distributions of the identified districts under the study have been mapped using GIS maps and it has been observed that almost same region of the country has been found to be most backward in both the studies. The study has revealed regional disparity in the development process and has suggested to formulate appropriate policies to bridge this disparity gap.Productivity Analysis, Resource /Energy Economics and Policy,

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Not AvailableSample survey is a cost effective mean to collect reliable information about a finite population. There are various sampling methodologies, among them two-phase sampling is generally used for estimating population mean or total under the two different situations. First, when the information of the auxiliary variable is not readily available and the other condition is when it is vey expensive to gather information on characteristic under study y, but it is comparatively cheaper to gather information on the variables which are highly correlated with the characteristic under study. In large scale surveys, two-phase sampling approach is proposed in order to reduce the number of sampled units which require the more expensive objective methods. Prediction approach is applied to predict the non-sampled units in surveys. In the large preliminary sample (first phase sample) of two-phase sampling, there are total n'- n non-sampled units having auxiliary information, so there is a need to develop an estimator based on prediction approach under finite population. In the present study, we have proposed a new estimator of finite population total based on prediction approach in the context of two-phase sampling.Not Availabl

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Not AvailableIn India, Food and Agriculture Organization of the United Nations (FAO) was implementing a project “Strengthening Agriculture Market Information System (AMIS) in India using Innovative Methods and Digital Technology” and supporting the efforts of the Ministry of Agriculture and Farmers Welfare, Govt. of India. This project identified the potential of improving the data coverage on ‘on-farm’ post-harvest management of food grains through Input Survey carried out in Agriculture Census. Therefore, a pilot study on private food grains stock estimation at farm level aligned with Input Survey of Agriculture Census in India funded by FAO-India was conducted by ICAR-Indian Agricultural Statistics Research Institute (ICAR‑IASRI). Under this study, a suitable sampling methodology aligned with existing Input Survey for estimation of private food grain stock at farm level has been developed. A suitable questionnaire aligned with existing Input Survey of Agriculture Census has been developed covering different food grains stock at farm level. Under this study, a pilot survey was conducted in two states namely Haryana and Madhya Pradesh. The four crops under AMIS study i.e. wheat, paddy, maize and soybean along with pulses were covered under this pilot survey. The data was collected for all the three seasons. The estimates of food grains stock, pre-harvest opening stock, production obtained, quantity sold, quantity stored, quantity disposed and percentage stock at farm level were obtained along with its percentage Coefficient of Variation (% CV) and were found to be reasonably good for overall size classes. Therefore, it is expected that for overall holding size classes, the proposed methodology will provide farm level reliable estimates of food grains stock at district level. The study has established the feasibility of inclusion of developed questionnaire in the future Input Survey of Agriculture Census in India in order to estimate the food grains stock at farm level which will bridge the gap on private food grains stock in on-farm and off-farm domains of the supply chain.Not Availabl

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Not AvailableAgroforestry is an integrated self-sustainable land use management system that is not only capable of producing food from marginal agricultural land but also capable of maintaining and improving the quality of environment. It plays a vital role in achieving integrated rural and urban development. But reliable data on area under agroforestry is not available as methodology for estimation of area under agroforestry is not yet standardized. In this paper, we propose to use remote sensing techniques for estimation of area under agroforestry using high resolution satellite data. Area under agroforestry in Ludhiana district of Punjab State, India has been estimated using the proposed methodology. Land use/land cover map of the district has also been generated using ERDAS IMAGINE software. It has been observed from the accuracy assessment that the estimate of area under agroforestry obtained using the proposed methodology under this study is reliable. Further, area under agroforestry has been classified into detailed classes within agroforestry.National Research Centre for Agroforestry (NRCAF), Jhansi - Indian Council of Agricultural Research (ICAR) Plan Schem

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Not AvailableIn this study, an attempt has been made to improve the sampling strategy incorporating spatial dependency at estimation stage considering usual aerial sampling scheme, such as simple random sampling, when the underlying population is finite and spatial in nature. Using the distances between spatial units, an improved method of estimation, viz. spatial estimation procedure, has been proposed for the estimation of finite population mean. Further, rescaled spatial bootstrap (RSB) methods have been proposed for approximately unbiased estimation of variance of the proposed spatial estimator (SE). The properties of the proposed SE and its corresponding RSB methods were studied empirically through simulation.Not Availabl

Development of Livelihood Index for Different Agro-Climatic Zones of India

A livelihood index has been developed for different agro-climatic zones of India, based on the secondary data for TE 2003. Six different sub-indices obtained are indicators of Infrastructure Status, Agricultural Status, Nutritional Status, Economic Status, Health and Sanitation Status and Food Availability Status in respective zones. A total of 57 variables have been considered for this study. Finally, a composite integrated livelihood index has been developed which indicates the livelihood status of different agro-climatic zones in the country. Also, 103 districts of low agricultural productivity have been identified within low livelihood regions. The results of this study have been compared with those of backward districts identified under Wage Employment Program by the Task Force of Planning Commission of India. It is found that about 60 per cent districts identified in this study are the same as identified by the Task Force. Further, the spatial distributions of the identified districts under the study have been mapped using GIS maps and it has been observed that almost same region of the country has been found to be most backward in both the studies. The study has revealed regional disparity in the development process and has suggested to formulate appropriate policies to bridge this disparity gap

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Not AvailableRanked Set Sampling (RSS) is preferred over Simple Random Sampling (SRS), when measuring an observation is expensive or time consuming, but can be easily ranked at a negligible cost. While working with spatial population, classical statistical methods fail to capture the dependency present in the underlying data. In this article, an attempt was made to develop efficient estimation procedure through RSS sampling design incorporating spatial dependency among sampling units of a spatial finite population. Distance between spatial units was taken as measure of spatial dependency. The properties of the proposed Spatial Estimator (SE) were further studied empirically through a simulation study. The proposed Spatial Estimator (SE) under RSS of population mean from spatial data was found to be better than usual RSS estimator.Not Availabl

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Not AvailableIn this study, an attempt has been made to improve the sampling strategy incorporating spatial dependency at estimation stage considering usual aerial sampling scheme, such as simple random sampling, when the underlying population is finite and spatial in nature. Using the distances between spatial units, an improved method of estimation, viz. spatial estimation procedure, has been proposed for the estimation of finite population mean. Further, rescaled spatial bootstrap (RSB) methods have been proposed for approximately unbiased estimation of variance of the proposed spatial estimator (SE). The properties of the proposed SE and its corresponding RSB methods were studied empirically through simulation.Not Availabl

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Not AvailableCrop Cutting Experiment (CCE) technique was developed in India as a method for estimating crop yield based on the sampling of small subplots within cultivated fields by pioneers in sampling and survey design especially Dr. P.V. Sukhatme of ICAR-Indian Agricultural Statistics Research Institute, New Delhi and Prof. P.C. Mahalanobis of Indian Statistical Institute (ISI), Kolkata independently. Within a decade, the crop-cut methods were quickly adopted as the standard method recommended by the Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, to estimate crop production. Since then, the crop cut method has been commonly regarded as a reliable and objective method for estimating crop yield. The crop-cut method is adopted by Directorate of Economics and Statistics (DES), Ministry of Agriculture and Farmers Welfare, Govt. of India for estimation of crop yield of major crops under General Crop Estimation Surveys (GCES) Scheme. This method is widely adopted by many African and Latin American countries also. Based on various independent pilot studies in different states of the country, different shapes and sizes of the crop-cut plots were recommended for different crops.Not Availabl

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Not AvailableRanked Set Sampling (RSS) is preferred over Simple Random Sampling (SRS) when measuring an observation is expensive or time consuming, but can be easily ranked at a negligible cost. Biswas et al. (2015) proposed a Spatial Estimator (SE) of population mean under RSS through prediction approach incorporating spatial dependency among sampling units of a spatial finite population. In this present article, an attempt has been made to propose bootstrap techniques viz. Rescaled Spatial Stratified Bootstrap (RSSB) and Rescaled Spatial Clustered Bootstrap (RSCB) methods for unbiased variance estimation of the SE under RSS from finite populations. Simulation study reveals that both the proposed methods give approximately unbiased estimation of variance of the SE under RSS for different combination of sample and bootstrap sample sizes, but while considering relative stability, RSSB method was found to be more stable.Not Availabl