Improving Crop Estimates by Integrating Multiple Data Sources (2017) / Chapter Skim
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5 Implementing the Vision and Beyond
5 Implementing the Vision and Beyond
Pages 89-102
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From page 89... ...
from integrating multiple data sources through a process that appears subjective to one of evaluating estimates prepared through a statistical model-based integration of these alternative information sources (Recommendation 2-1) , preparing its county estimates using a transparent and well-documented process (Recommendation 2-2)
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From page 90... ...
; developing useful small-area models for acreage and yield; planning for the inclusion of CLUs and equivalent geospatial information (such as RLUs and boundary information from precision agriculture) in the Enhanced List Management Operations (ELMO)
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From page 91... ...
-- NASS leadership could encourage ERS to extend its analysis of farm values (Nickerson et al., 2012) to include similar research on the impact of parcel-specific variables on cash rents so as to identify variables that might improve models (Recommenda tion 4-1)
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From page 92... ...
accumulation, growing season precipitation accumulation, water stress indices, and other variables scientifically recognized as being potentially useful for explaining crop growth through the growing season. A model such as the VSMB could prove very helpful to NASS in identifying and preparing relevant independent variables that could be used to improve both remote
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From page 93... ...
The cash rents model developed by Berg and colleagues (2014) integrates survey data from 2 years and was originally developed for use when the Cash Rents Survey was conducted annually.
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From page 94... ...
However, this type of model considers direct survey estimates to be primary and does not account for the error structure of other potential input variables. Use of a more complete measurement error structure might evolve the current approach into one that is capable of integrating multiple data sources.
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From page 95... ...
NASS needs to publish county-level data, and there are legitimate reasons for the aggregation of county estimates within a state to add to previously published state estimates. If alternative grouping of counties leads to measurably more accurate results relative to ASDs and fewer outliers, NASS may choose to benchmark county estimates directly to state totals, computing ASD totals as the sum of benchmarked county estimates.
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From page 96... ...
to the NASS list frame is a high-priority, first-stage project. Achieving complete linkage with these administrative sources will be time consuming, so NASS's work on this effort needs to begin right away, starting with the development of an approach that will build to success.
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From page 97... ...
NASS could develop revised survey forms for matched farms that take advantage of the administrative data to simplify reporting and reduce respondent burden (Recommendation 2-11)
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From page 98... ...
SECOND-STAGE PROJECTS: PROJECTS OCCURRING ONCE FSA, RMA, AND NASS FARMS HAVE BEEN LINKED AND INCLUDED IN THE LIST FRAME OR OTHER ACCESSIBLE DATABASE Projects in the second stage of implementation of the panel's vision will depend on progress on first-stage projects described above. Collaborative efforts under first-stage Project 1 will lay the foundation by obtaining access to administrative and auxiliary data and facilitating the development and use of new data sources.
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From page 99... ...
By the end of the first-stage projects, the cash rents model will have been refined and built into a new and more transparent ASB process, and models that successfully integrate multiple data sources for crop estimates will have been developed and adopted. Second-stage projects include improving surveys forms, improving survey imputation and estimation, developing unit-level models, and potentially redesigning NASS surveys to take advantage of newly available information.
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From page 100... ...
These auxiliary data for these well-defined subpopulations could be used to provide more efficient estimators relative to the current NASS sampling and estimation methods. Data from precision agriculture databases, FSA program participants, and RMA purchasers of insurance clearly are not probability samples and are not likely to be representative of the population.
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From page 101... ...
In this situation, NASS could consider revamping its survey program to take advantage of available administrative data and models and to refocus survey efforts on areas or commodities not well covered by alternative sources. CONCLUSION The panel's vision for the future of NASS is for 2025.
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