Improving Crop Estimates by Integrating Multiple Data Sources

Improving Crop Estimates by Integrating Multiple Data Sources
Author :
Publisher : National Academies Press
Total Pages : 149
Release :
ISBN-10 : 9780309465298
ISBN-13 : 030946529X
Rating : 4/5 (29X Downloads)

Book Synopsis Improving Crop Estimates by Integrating Multiple Data Sources by : National Academies of Sciences, Engineering, and Medicine

Download or read book Improving Crop Estimates by Integrating Multiple Data Sources written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-01-26 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: The National Agricultural Statistics Service (NASS) is the primary statistical data collection agency within the U.S. Department of Agriculture (USDA). NASS conducts hundreds of surveys each year and prepares reports covering virtually every aspect of U.S. agriculture. Among the small-area estimates produced by NASS are county-level estimates for crops (planted acres, harvested acres, production, and yield by commodity) and for cash rental rates for irrigated cropland, nonirrigated cropland, and permanent pastureland. Key users of these county-level estimates include USDA's Farm Services Agency (FSA) and Risk Management Agency (RMA), which use the estimates as part of their processes for distributing farm subsidies and providing farm insurance, respectively. Improving Crop Estimates by Integrating Multiple Data Sources assesses county-level crop and cash rents estimates, and offers recommendations on methods for integrating data sources to provide more precise county-level estimates of acreage and yield for major crops and of cash rents by land use. This report considers technical issues involved in using the available data sources, such as methods for integrating the data, the assumptions underpinning the use of each source, the robustness of the resulting estimates, and the properties of desirable estimates of uncertainty.


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