Statistical Science

Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges

Nathan B. Cruze, Andreea L. Erciulescu, Balgobin Nandram, Wendy J. Barboza, and Linda J. Young

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

In the United States, county-level estimates of crop yield, production, and acreage published by the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS) play an important role in determining the value of payments allotted to farmers and ranchers enrolled in several federal programs. Given the importance of these official county-level crop estimates, NASS continually strives to improve its crops county estimates program in terms of accuracy, reliability and coverage. In 2015, NASS engaged a panel of experts convened under the auspices of the National Academies of Sciences, Engineering, and Medicine Committee on National Statistics (CNSTAT) for guidance on implementing models that may synthesize multiple sources of information into a single estimate, provide defensible measures of uncertainty, and potentially increase the number of publishable county estimates. The final report titled Improving Crop Estimates by Integrating Multiple Data Sources was released in 2017. This paper discusses several needs and requirements for NASS county-level crop estimates that were illuminated during the activities of the CNSTAT panel. A motivating example of planted acreage estimation in Illinois illustrates several challenges that NASS faces as it considers adopting any explicit model for official crops county estimates.

Article information

Source
Statist. Sci., Volume 34, Number 2 (2019), 301-316.

Dates
First available in Project Euclid: 19 July 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1563501643

Digital Object Identifier
doi:10.1214/18-STS687

Mathematical Reviews number (MathSciNet)
MR3983330

Keywords
Agricultural surveys auxiliary data benchmarking official statistics small area estimation

Citation

Cruze, Nathan B.; Erciulescu, Andreea L.; Nandram, Balgobin; Barboza, Wendy J.; Young, Linda J. Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges. Statist. Sci. 34 (2019), no. 2, 301--316. doi:10.1214/18-STS687. https://projecteuclid.org/euclid.ss/1563501643


Export citation

References

  • Adrian, D. W. (2012). A model-based approach to forecasting corn and soybean yields. In Proceedings of the Fourth International Conference on Establishment Surveys Amer. Statist. Assoc., Montreal, QC. https://ww2.amstat.org/meetings/ices/2012/papers/302190.pdf [Accessed: 2019-01-31].
  • Bailey, J. T. and Kott, P. S. (1997). An application of multiple list frame sampling for multi-purpose surveys. In JSM Proceedings, Survey Research Methods Section 496–500. Amer. Statist. Assoc., Alexandria, VA.
  • Battese, G. E., Harter, R. M. and Fuller, W. A. (1988). An error-components model for prediction of county crop areas using survey and satellite data. J. Amer. Statist. Assoc. 83 28–36.
  • Bell, J. and Barboza, W. (2012). Evaluation of using CVs as a publication standard. In Proceedings of the Fourth International Conference on Establishment Surveys Amer. Statist. Assoc., Montreal, QC. https://ww2.amstat.org/meetings/ices/2012/papers/302002.pdf [Accessed: 2019-01-31].
  • Bell, W., Basel, W. W. and Maples, J. J. (2016). An overview of the U.S. census bureau’s small area income and poverty estimates program. In Analysis of Poverty Data by Small Area Estimation (M. Pratesi, ed.) 349–378 19. Wiley, Hoboken, NJ.
  • Bellow, M. E. and Lahiri, P. (2010). Empirical Bayes methodology for the NASS county estimation program. In JSM Proceedings, Survey Research Methods Section 343–355. Amer. Statist. Assoc., Alexandria, VA.
  • Bellow, M. E. and Lahiri, P. (2011). An empirical best linear unbiased prediction approach to small area estimation of crop parameters. In JSM Proceedings, Survey Research Methods Section 3976–3986. Amer. Statist. Assoc., Alexandria, VA.
  • Bellow, M. E. and Lahiri, P. (2012). Evaluation of methods for county level estimation of crop harvested area that employ mixed models. In Proceedings of the DC-AAPOR/WSS Summer Conference, Bethesda, MD Amer. Statist. Assoc., Alexandria, VA.
  • Boryan, C. (2010). The USDA NASS Cropland Data Layer Program: Transition from Research to Operations (2006–2009. USDA NASS Education and Outreach: Research Reports https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/Boryan_CDL_Program_Research_to_Operations_2006-2009_final.pdf [Accessed: 2019-01-31].
  • Boryan, C., Yang, Z., Mueller, R. and Craig, M. (2011). Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, cropland data layer program. Geocarto Int. 26 341–358.
  • Cruze, N. B. (2015). Integrating survey data with auxiliary sources of information to estimate crop yields. In JSM Proceedings, Survey Research Methods Section 565–578. Amer. Statist. Assoc., Alexandria, VA.
  • Cruze, N. B. (2016). A Bayesian hierarchical model for combining several crop yield indications. In JSM Proceedings, Survey Research Methods Section 2045–2053. Amer. Statist. Assoc., Alexandria, VA.
  • Cruze, N. B. and Benecha, H. K. (2017). A model-based approach to crop yield forecasting. In JSM Proceedings, Survey Research Methods Section 2724–2733. Amer. Statist. Assoc., Alexandria, VA.
  • Cruze, N. B., Erciulescu, A. L., Nandram, B., Barboza, W. J. and Young, L. J. (2019). Supplement to “Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges.” DOI:10.1214/18-STS687SUPP.
  • Czaplewski, R. L. (1992). Misclassification bias in areal estimates. Photogramm. Eng. Remote Sens. 58 189–192.
  • Erciulescu, A. L., Cruze, N. B. and Nandram, B. (2016). Small area estimates incorporating auxiliary soucres of information. In JSM Proceedings, Survey Research Methods Section 3591–3605. Amer. Statist. Assoc., Alexandria, VA.
  • Erciulescu, A. L., Cruze, N. B. and Nandram, B. (2017). Small area estimates of end-of-season agricultural quantities. In JSM Proceedings, Survey Research Methods Section 541–560. Amer. Statist. Assoc., Alexandria, VA.
  • Erciulescu, A. L., Cruze, N. B. and Nandram, B. (2018). Benchmarking a triplet of official estimates. Environ. Ecol. Stat. 25 523–547.
  • Erciulescu, A. L., Cruze, N. B. and Nandram, B. (2019). Model-based county level crop estimates incorporating auxiliary sources of information. J. Roy. Statist. Soc. Ser. A 182 283–303.
  • Fay, R. E. III and Herriot, R. A. (1979). Estimates of income for small places: An application of James-Stein procedures to census data. J. Amer. Statist. Assoc. 74 269–277.
  • Fuller, W. A. and Battese, G. E. (1973). Transformations for estimation of linear models with nested-error structure. J. Amer. Statist. Assoc. 68 626–632.
  • Gallego, F. J. (2004). Remote sensing and land cover area estimation. Int. J. Remote Sens. 25 3019–3047.
  • Iwig, W. (1996). The national agricultural statistics service county estimates program. In Indirect Esitmators in U.S. Federal Programs (W. Schaible, ed.) 129–144 7. Springer, New York.
  • Johnson, D. M. (2014). An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141 116–128.
  • Johnson, D. M. (2016). A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. Int. J. Appl. Earth Obs. Geoinf. 52 65–81.
  • Kim, J. K., Wang, Z., Zhu, Z. and Cruze, N. B. (2018). Combining survey and non-survey data for improved sub-area prediction using a multi-level model. J. Agric. Biol. Environ. Stat. 23 175–189.
  • Kott, P. S. (1989). Robust small domain estimation using random effects modeling. Surv. Methodol. 15 3–12.
  • Nandram, B., Berg, E. and Barboza, W. (2014). A hierarchical Bayesian model for forecasting state-level corn yield. Environ. Ecol. Stat. 21 507–530.
  • National Academies of Sciences Engineering, and Medicine (2017). Improving Crop Estimates by Integrating Multiple Data Sources. The National Academies Press, Washington, DC.
  • National Research Council (1997). Small-Area Estimates of School-Age Children in Poverty: Interim Report 1, Evaluation of 1993 County Estimates for Title I Allocations. The National Academies Press, Washington, DC.
  • National Research Council (1998). Small-Area Estimates of School-Age Children in Poverty: Interim Report 2, Evaluation of Revised 1993 County Estimates for Title I Allocations. The National Academies Press, Washington, DC.
  • National Research Council (1999). Small-Area Estimates of School-Age Children in Poverty: Interim Report 3. The National Academies Press, Washington, DC.
  • National Research Council (2000a). Small-Area Estimates of School-Age Children in Poverty: Evaluation of Current Methodology. The National Academies Press, Washington, DC.
  • National Research Council (2000b). Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. The National Academies Press, Washington, DC.
  • National Research Council (2007). Using the American Community Survey: Benefits and Challenges. The National Academies Press, Washington, DC.
  • National Research Council (2008). Understanding American Agriculture: Challenges for the Agricultural Resource Management Survey. The National Academies Press, Washington, DC.
  • Rao, J. N. K. and Molina, I. (2015). Small Area Estimation, 2nd ed. Wiley, Hoboken, NJ.
  • Stasny, E. A., Goel, P. K. and Rumsey, D. J. (1991). County estimates of wheat production. Surv. Methodol. 17 211–225.
  • United States Census Bureau (2017). Small Area Income and Poverty Estimates (SAIPE) Program. https://www.census.gov/programs-surveys/saipe/about.html. [Accessed: 2019-01-31].
  • United States Government Publishing Office (2015). Big Data and Agriculture: Innovation and Implications–Hearing before the Committee on Agriculture, House of Representatives, 114th Congress. https://www.govinfo.gov/content/pkg/CHRG-114hhrg97412/pdf/CHRG-114hhrg97412.pdf. [Accessed: 2019-01-31].
  • United States Government Publishing Office (2016). Big Data and Agriculture: Innovation in the Air–Hearing before the Subcommittee on General Farm Commodities and Risk Management of the Committee on Agriculture, House of Representatives, 114th Congress. https://www.govinfo.gov/content/pkg/CHRG-114hhrg20574/pdf/CHRG-114hhrg20574.pdf. [Accessed: 2019-01-31].
  • USDA NASS (2010). Field Crops: Usual Planting and Harvesting Dates. Agricultural Handbook No. 628. https://downloads.usda.library.cornell.edu/usda-esmis/files/vm40xr56k/dv13zw65p/w9505297d/planting-10-29-2010.pdf [Accessed: 2019-01-31].
  • USDA NASS (2014). Small Grains 2014 Summary. https://downloads.usda.library.cornell.edu/usda-esmis/files/5t34sj573/2r36v149p/qj72p9909/SmalGraiSu-09-30-2014.pdf [Accessed: 2019-01-31].
  • USDA NASS (2015). Crop Production 2014 Summary. https://downloads.usda.library.cornell.edu/usda-esmis/files/k3569432s/5q47rr167/1n79h650b/CropProdSu-01-12-2015_revision.pdf [Accessed: 2019-01-31].
  • Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Fenimore, C., Gleason, K. and Arndt, D. (2014). Improved historical temperature and precipitation time series for U.S. climate divisions. J. Appl. Meteorol. Climatol. 53 1232–1251.
  • Walker, G. and Sigman, R. (1984). The use of LANDSAT for county estimates of crop areas: Evaluation of the huddleston-ray and the battese-fuller estimators for the case of stratified sampling. Commun. Statist. Theory Methods 13 2975–2996.
  • Wang, J. C., Holan, S. H., Nandram, B., Barboza, W., Toto, C. and Anderson, E. (2012). A Bayesian approach to estimating agricultural yield based on multiple repeated surveys. J. Agric. Biol. Environ. Stat. 17 84–106.
  • Williams, M. (2013). Small area modeling of county estimates for corn and soybean yields in the US. Presentation at the Federal Committee on Statistical Methodology Research Conference. http://www.copafs.org/UserFiles/file/fcsm/C2_Williams_2013FCSM.pdf [Accessed: 2019-01-31].
  • Young, L. J. (2019). Agricultural crop forecasting for large geographical areas. Ann. Rev. Stat. Appl. 6. 173–196.

Supplemental materials

  • Supplement to “Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges”. Supplementary information.