Spatial hierarchical Bayes Small Area Model for disaggregated level crop acreage estimation

Abstract

Crop area statistics in most of the states in India are provided based on complete enumeration or census approach. But, shortage of man power, failure of the primary and revenue staffs to devote adequate time and attention in collection and compilation of data has deteriorated the quality of area statistics as well as increased the time lag in availability of data in hand. In the view of above problem, a well-designed sample survey has the ability to cater the need of accurate and timely crop area information with utilization of limited resources. A pilot study conducted by ICAR- Indian Agricultural Statistics Research Institute attempts to estimate disaggregated level crop yield based on reduced number of Crop Cutting Experiments (CCEs) while crop acreage estimation has been done through sample survey approach. But, traditional sampling theory has also some limitations in providing reliable and valid estimates particularly for districts/areas with few or negligible sample sizes. To tackle this need Small Area Estimation (SAE) approach has been considered in this paper. In particular, using Hierarchical Bayes spatial small area model disaggregated level crop area has been estimated for two major crops, rice and wheat respectively in the state of Uttar Pradesh for Agriculture year 2015-16. Estimates produced using SAE technique has acceptable precision level.