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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 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 this paper Geographic Information System (GIS) based spatial sampling procedures have been proposed for environmental studies in agriculture. In the proposed sampling procedures, an attempt is made to suggest a stratified sampling design in which not only the spatial nature of the environmental variables has been taken care by incorporating spatial correlation based on auxiliary character in selecting the sample but also the effect of clustering properties of environmentally polluted neighboring areal units is considered. It is proposed to stratify the study region based on neighborhood properties of the sampling units.Not Availabl

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Not AvailableIn this paper, an attempt has been made to obtain the reliable estimates of crop yield at block level by adopting double sampling approach. For estimating crop yield at block level, alternative estimators have been tried by adopting double sampling ratio, double sampling regression, multivariate ratio and regression methods of estimation in case of double sampling using farmer's eye estimate and area of the fields as auxiliary variables. The efficiencies of these estimators have been compared among each other. It has been observed that among all the estimators, multivariate regression estimator using double sampling is the most efficient estimatorNot Availabl

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Not AvailableGeneral Crop Estimation Surveys (GCES) Scheme is adopted in all the states of country to estimate crop yield at higher level (state, district). With progress in planning in agriculture, especially in case of the cotton crop, we need estimate of cotton crop yield at tehsil/block level with the desired degree of precision. Application of GCES as such, as tehsil/block level with the same number of crop cutting experiments (CCEs) may yield estimates with less degree of precision. If the simple crop-cutting approach is to be adopted directly for this purpose, the present number of crop cutting experiments will have to be increased significantly. In such case, use of information getting from auxiliary variable correlated with variable under study may increase the degree of precision of estimates at tehsil level. In this study, double sampling regression approach under stratified two stage sampling design framework has been proposed for estimation of the average yield of cotton at the tehsil level using the picking having highest correlation with total yield as auxiliary variable.Not Availabl

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Not AvailableRanked Set Sampling (RSS) is preferred to Simple Random Sampling (SRS) when measuring an observation is expensive or time-consuming, while ranking small subset of observations is relatively easy. Estimating the variance of RSS estimator has been found cumbersome under finite population. In this study, we propose two rescaling bootstrap variance estimation techniques in RSS under finite population framework viz. Strata Based Rescaling Bootstrap (SBRB) and Cluster Based Rescaling Bootstrap (CBRB) methods. Simulation as well as real data application results suggest that SBRB method performs better than CBRB method for different combination of set size (m) and number of cycles (r).Not Availabl

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Not AvailableWhen measuring an observation is expensive, but ranking a small subset of observations is relatively easy, ranked set sampling (RSS) can be used to increase the precision of the estimators. Estimating the variance in case of RSS has been found to be cumbersome in the context of finite population. Therefore, in this paper, we propose two different variance estimation procedures using Jackknife method in RSS under finite population framework. We compare the efficiency of these proposed variance estimation procedures with each other through a simulation study.Not Availabl

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Not AvailableBootstrap technique is used in the estimation of variance of non-linear statistics in case of complex surveys. This technique is gaining popularity for survey data with missing observations. In this paper, bootstrap techniques with missing observations have been compared through a simulation study under different imputation techniques. The technique namely "Proportional Bootstrap Without Replacement (PBWO)" for missing observations has also been compared with the Rescaling Bootstrap Without Replacement (RSBWO) method for complete data set. Further, the efficiency of Proportional Bootstrap With Replacement (PBWR) technique for missing observations has been compared with the standard bootstrap technique for complete data set. An optimum number of bootstrap samples required for the reliable estimation of variance in the case of missing observations has also been obtained.Not Availabl

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Not AvailableIn this study, an attempt was made to develop bootstrap variance estimation procedure for Spatial Estimator (SE) of finite population mean in presence of missing observations under Simple Random Sampling Without Replacement. The Proportional Spatial Bootstrap (PSB) method has been proposed considering spatial relationship between sampling units. Under this technique, different spatial imputation techniques based on the spatial dependency of data were used to impute missing observations in the observed sample. The statistical properties of the proposed PSB techniques were studied empirically through a simulation study. The simulation results reveal that using appropriate spatial data-dependent imputation techniques, the proposed PSB technique performed better than its existing techniques available in the literature.Not Availabl

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Not AvailableWhen measuring an observation is expensive, but ranking a small subset of observations is relatively easy, ranked set sampling (RSS) can be used to increase the precision of the estimators. Estimating the variance in case of RSS has been found to be cumbersome in the context of finite population. Therefore, in this paper, we propose two different variance estimation procedures using Jackknife method in RSS under finite population framework. We compare the efficiency of these proposed variance estimation procedures with each other through a simulation study.Not Availabl