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Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
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Executive Summary

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.

Virtually all statistical agencies are seeing declines in survey response rates, and NASS is no exception (see National Research Council, 2013). As a result, the number of counties for which NASS has been able to publish estimates for crop acreage, yield, and cash rents has been declining over time. This has caused challenges for key data users that must develop alternative estimates for those counties without official NASS estimates. Those alternative estimates are neither as justifiable nor as transparent as the estimates from sound probability samples or consistent, high-quality, model-based approaches.

PURPOSE OF THIS STUDY

In September 2014, NASS entered into a cooperative agreement with the Committee on National Statistics of the National Research Council to

Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×

assess county-level crop and cash rents estimates and to offer 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. Multiple sources of data that could be used for this purpose are potentially available, including NASS surveys, data from other agencies, and automated field-level data collected by farm equipment. The panel was asked to consider technical issues involved in using these 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.

The current NASS approach to integrating multiple data sources is through its Agricultural Statistics Board (ASB). While the current process follows specific steps and guidelines, it is inherently subjective and neither transparent nor reproducible. This could be improved if the ASB were provided with high-quality, model-based estimates that synthesize multiple data sources.

NASS itself has observed that if it could develop and adopt model-based approaches to integration of multiple data sources, it could bring the agency into conformance with the statistical standards promulgated by the U.S. Office of Management and Budget (OMB) (2006).1 These standards lay out the steps to be taken to ensure that published statistical information is transparent, reproducible, and reliable.

The panel’s recommendations are based largely on presentations and deliberations at its four open meetings, during which it heard from many NASS staff about the agency’s surveys, frames, remote sensing indications, auxiliary data sources, the process used to prepare estimates, and work on developing and implementing model-based estimates; staff of other USDA agencies about administrative data, use of NASS estimates, and the potential for use of remote sensing information; representatives of other statistical agencies that have developed and implemented model-based approaches to small-area estimation; and academics with expertise in model development.

KEY RECOMMENDATIONS

The panel has chosen to present its major recommendations in terms of a vision for NASS in 2025. This vision has three components.

First, in the future NASS prepares for the ASB county-level estimates based on models that incorporate multiple data sources, as well as uncertainty measures for the estimates.

___________________

1 See especially Standard 4.1.

Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×

RECOMMENDATION 2-1: The National Agricultural Statistics Service should evolve the Agricultural Statistics Board role from one of integrating multiple data sources to one of reviewing model-based predictions; macro-editing; and ensuring that models are continually reviewed, assessed, and validated.

RECOMMENDATION 2-2: The National Agricultural Statistics Service should achieve transparency and reproducibility by developing, evaluating, validating, documenting, and using model-based estimates that combine survey data with complementary data in accordance with Office of Management and Budget standards.

RECOMMENDATION 2-3: The National Agricultural Statistics Service (NASS) should adopt and use the following publication standard:

  • County-level estimates may be withheld to protect confidentiality.
  • County-level estimates may be withheld because NASS deems them unreliable for any use, based on its measure of uncertainty.
  • All other county-level estimates will be published, along with their measures of uncertainty.

RECOMMENDATION 2-4: The National Agricultural Statistics Service should develop and publish uncertainty measures for county-level estimates.

Second, the NASS list frame is a georeferenced farm-level database, serving as a sampling frame for surveys and facilitating the use of farm data in statistical analysis.

RECOMMENDATION 2-8: The National Agricultural Statistics Service should adopt the Farm Services Agency’s Common Land Unit as its basic spatial unit.

RECOMMENDATION 2-9: The National Agricultural Statistics Service should be prepared to maintain alternative geospatial field-level boundaries (e.g., resource land units and precision agriculture measurements) in its databases to facilitate completing the geospatially referenced farm-level database.

RECOMMENDATION 2-10: The National Agricultural Statistics Service (NASS) should update its list frame to make use of Common Land Unit information as geospatial building blocks by developing linkages

Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×

between the NASS list frame and the Farm Services Agency and the Risk Management Agency administrative data.

Third, NASS acquires all georeferenced administrative and remotely sensed and ground-gathered data relevant to developing estimates and uses these data to complement its survey data.

RECOMMENDATION 3-1: The National Agricultural Statistics Service (NASS) should collaborate with the Risk Management Agency to obtain relevant individually identifiable acreage and production data and to conduct comparisons with NASS data for the same entity.

RECOMMENDATION 3-3: The National Agricultural Statistics Service should collaborate with farmer cooperatives to ensure that it is one of the government agencies with which farmers can choose to share their relevant precision agriculture data.

RECOMMENDATION 3-5: The National Agricultural Statistics Service should develop a precision agriculture reporting option for the County Agricultural Production Survey/Acreage, Production, and Stocks survey system. Farmers who reported relevant precision agriculture data would either not receive an additional survey form or receive one that was simplified and easy to use.

RECOMMENDATION 3-8: The National Agricultural Statistics Service should explore collaboration with other U.S. Department of Agriculture agencies that are actively involved in remote sensing applications to obtain access to data with finer spatial resolution and possibly also to share in the costs of processing those data.

RECOMMENDATIION 3-9: The National Agricultural Statistics Service (NASS) should keep abreast of emerging data sources; how they are used; and how they might be used to improve county estimates, especially of yield. Based on a careful evaluation, NASS might consider purchasing data.

Finally,

RECOMMENDATION 5-1: The National Agricultural Statistics Service should undertake a staged, systematic effort to implement the vision presented in Chapter 2 of this report.

Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
Page 1
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
Page 2
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
Page 3
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
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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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