Toward a 21st Century National Data Infrastructure: Enhancing Survey Programs by Using Multiple Data Sources (2023) / Chapter Skim
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8 Using Multiple Data Sources for County-Level Crop Estimates
8 Using Multiple Data Sources for County-Level Crop Estimates
Pages 167-186
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From page 167... ...
reviewed procedures then used by the USDA National Agricultural Statistics Service (NASS) to produce 1 For the history of agricultural statistics in the United States, see U.S.
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Section 8.1 briefly reviews data sources that might be used for producing crop estimates. Sections 8.2 and 8.3 discuss statistical modeling
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Section 8.4 explores opportunities for continued improvement of agricultural statistics. 8.1 DATA SOURCES FOR CROP ESTIMATES This section summarizes the main data sources that NASS has used to make county-level crop estimates in the United States, as well as other data sources with potential to improve model-based estimates: private-sector data and data obtained from social media, webscraping, and crowdsourcing.2 2 See also Stubbs (2016, p.
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5) noted that NASS uses these surveys to publish "about 400 national agricultural statistical reports and thousands of additional state agricultural statistical reports covering more than 120 crops and 45 livestock items." 4 See https://www.nass.usda.gov/AgCensus/ for a description of the Census of Agriculture and https://www.census.gov/history/www/programs/agriculture/census_of_agriculture.html for its history.
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For example, NASS estimates remain most accurate at the national level, but low response rates become increasingly important for estimates at the state and especially county levels." Increasing nonresponse to agricultural surveys suggests that assessment of alternate data sources is an appropriate next step, as recommended by the National Academies' report on Improving Crop Estimates by Integrating Multiple Data Sources (NASEM, 2017b; see Box 8-1)
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, which collects individual farm yield and loss information to administer the Federal Crop Insurance program.8 Farmers who elect to participate in FSA programs provide the agency with planted acreages and crop types. Because participation in FSA programs is voluntary, estimates of planted acreage from FSA data alone will usually underestimate the total amount of planted acreage for a crop (which will also include acreage from farmers who do not participate in FSA programs)
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Important survey-related uses of remotely sensed data include improving coverage of list frames and improving the efficiency of sampling designs; many agricultural surveys use information on land cover to stratify the sampling design (Carfagna & Carfagna, 2015) .13 Various remote sensing sources could be used as inputs to crop m odels: "An increasing number of satellites, aircraft, drones, flux towers, and weather stations collect geospatially referenced data that may be useful for monitoring crop-growing conditions.
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Satellite imagery is a valuable resource for producing crop estimates, but comes with challenges described by workshop participants.15 NkwimiTchahou et al.
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described uses of linked geospatial and 16 With the advent of precision agriculture (see below) , some farmers may use tractor based data to report the actual acreage planted, which may differ from acreage that would be reported using Common Land Units.
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The National Academies' report on Improving Crop Estimates by Integrating Multiple Data Sources (NASEM, 2017b, p.
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Data from Social Media, Webscraping, and Crowdsourcing The unknown coverage of social media, webscraping, and crowd sourcing data makes it difficult to use these data as a single source to produce statistics, but they can provide valuable information when verified and combined with other sources. In agricultural statistics, webscraped and crowdsourced data have been used for expanding sampling frames and providing "ground truth" to verify data obtained from other sources such as satellite images.
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The National Academies' report Improving Crop Estimates by Integrating Multiple Sources recommended that NASS revise the county-level
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Successive stages in the model-development process to include non-survey sources of data have been documented in a series of journal articles and conference presenta tions.18 The current panel anticipates that NASS will issue an official meth odology report that consolidates the information in the research reports and describes the current production models, as that will provide important documentation for data users. The models that have been developed can be viewed as extensions of those used for the Small Area Income and Poverty Estimates program, with additional features to meet the special challenges of producing crops county estimates, such as the additional information from FSA and RMA that can be used to set a lower bound on planted acreage in each county.
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Totals from the administrative records could, however, be viewed as "informative lower bounds" for the planted acreage in each county, and Chen, Nandram, and Cruze (2022) incorporated constraints into the planted acreage model by requiring the county-level estimate of planted acreage to be at least as large as the maximum acres planted to the crop, as determined from FSA and RMA values.
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. CONCLUSION 8-1: The National Agricultural Statistics Service has made substantial progress in the difficult process of developing models to produce crop estimates at different levels of geography.
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investigated models that could be used to produce mid-season estimates of crop yield and production that could, potentially, replace estimates from the September Farm Survey. Predictor variables included Normalized Difference Vegetation Index and agroclimatic data available in August, as well as information from the July Farm Survey; these models did not include crop insurance data.
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The modeling efforts of Statistics Canada demonstrate the promise of using satellite imagery along with administrative records data for producing crop estimates that could replace estimates from surveys. As a result, Statistics Canada was able to reduce the number of Field Crop Surveys from six to four (March, June, November, and December)
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4) argued that it is important for population groups to have more than mere representation in the data and "that there is a compelling need to improve the participation of women, people living with disabilities, and other marginalized groups in all aspects of open data for agriculture and nutrition." For county-level crop estimates, equity aspects could be explored by comparing measures of uncertainty about model inputs and outputs with county-level statistics about poverty, race, ethnicity, and other characteristics calculated from the decennial census or American Community Survey.
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. CONCLUSION 8-2: Remotely sensed data have great potential for improving agricultural production models.
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