References
Adrian, D.W. (2012). A Model-Based Approach to Forecasting Corn and Soybean Yields. Paper presented at the Fourth International Conference on Establishment Surveys, Montréal, Québec, Canada, June 11–14. Available: http://www.amstat.org/meetings/ices/2012/papers/302190.pdf [September 2017].
Atwood, J.A., Shaik, S., and Watts, M.J. (2002). Can normality of yields be assumed for crop insurance? Canadian Journal of Agricultural Economics, 50(2), 177–184.
Atwood, J.A., Shaik, S., and Watts, M.J. (2003). Are crop yields normally distributed? A reexamination. American Journal of Agricultural Economics, 85(4), 888–901.
Babcock, B.A. (2008). Corn belt contributions to the crop insurance industry. Iowa Ag Review, 14(2), 1–3.
Bailey, J.T., and Kott, P.S. (1997). An application of multiple list frame sampling for multipurpose surveys. In Proceedings of the Section on Survey Research Methods. Washington, DC: American Statistical Association. http://ww2.amstat.org/sections/srms/Proceedings/papers/1997_084.pdf [September 2017].
Baker, R., Brick, M., Bates, N., Battaglia, M., Couper, M., Dever, J., Gile, K., and Tourangeau, R. (2017). Report of the AAPOR Task Force on Non-Probability Sampling. Available: https://www.aapor.org/AAPOR_Main/media/MainSiteFiles/NPS_TF_Report_Final_7_revised_FNL_6_22_13.pdf [August 2017].
Banerjee, S., Gelfand, A., Finley, A.O., and Sang, H. (2008). Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(4), 825–848.
Barboza, W.J., and Iwig, B. (2010). Assessing an Administrative Data Source as a Sampling Frame. Available: http://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2010/jsm2010-paper-Barboza-Iwig.pdf [September 2017].
Battese, G.E., and Fuller, W.A. (1981). Prediction of county crop areas using survey and satellite data. In Proceedings of the Section on Survey Research Methods. Washington, DC: American Statistical Association.
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. Journal of the American Statistical Association, 83(401), 28–26.
Bédard, F., and Reichert, G. (2013). Integrated Crop Yield and Production Forecasting Using Remote Sensing and Agri-Climatic Data. Analytical Projects Initiatives Final Report. Ottawa, Ontario: Statistics Canada.
Bell, J., and Barboza, W. (2012). Evaluation of Using CVs as a Publication Standard. Paper presented at the Fourth International Conference on Establishment Surveys, Montréal, Québec, Canada, June 11–14.
Bellow, M.E. (2007). Comparison of Methods for Estimating Crop Yield at the County Level. RDD Research Report RDD-07-05. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service. Available: http://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/Crop_Yield_Research_Report_MEB.pdf [September 2017].
Bellow, M.E., and Lahiri, P. (2012). Evaluation of Methods for County Level Estimation of Crop Harvested Area That Employ Mixed Models. Paper presented at the Fourth International Conference on Establishment Surveys, Montréal, Québec, Canada, June 11–14.
Bellow, M.E., Cruze, N., and Erciulescu, A. (2017). Developments in model-based county-level estimation of agricultural cash rental rates. Proceedings of the Section on Survey Research Methods Section of the Joint Statistical Meetings. Alexandria, VA: American Statistical Association.
Berg, E., Cecere, W., and Ghosh, M. (2014). Small area estimation for county-level farmland cash rental rates. Journal of Survey Statistics and Methodology, 2(1), 1–37.
Berrocal, V.J., Gelfand, A.E., and Holland, D.M. (2010). A spatio-temporal downscaler for output from numerical models. Journal of Agricultural, Biological, and Environmental Statistics, 15(2), 176–197.
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B, 36(2), 192–236.
Besag, J., York, J., and Mollié, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistics and Mathematics, 43(1), 1–59.
Bradley, J.R., Holan, S.H., and Wikle, C.K. (2015a). Multivariate spatio-temporal models for high-dimensional areal data with application to longitudinal employer–household dynamics. Annals of Applied Statistics, 9(4), 1761–1791.
Bradley, J.R., Wikle, C.K., and Holan, S.H. (2015b). Spatio-temporal change of support with application to American Community Survey multi-year period estimates. Stat, 4(1), 255–270.
Bradley, J.R., Cressie, N., and Shi, T. (2016a). A comparison of spatial predictors when datasets could be very large. Statistics Surveys, 10, 100–131. doi: 10.1214/16-SS115.
Bradley, J.R., Wikle, C.K., and Holan, S.H. (2016b). Bayesian spatial change of support for count-valued survey data with application to the American Community Survey. Journal of the American Statistical Association, 111(514), 472–487.
Bradley, J.R., Wikle, C.K., and Holan, S.H. (2017). Regionalization of multiscale spatial processes using a criterion for spatial aggregation error. Journal of the Royal Statistical Society, Series B, 79(3), 815–832.
Bunn, D., and Wright, G. (1991). Interaction of judgmental and statistical forecasting methods: Issues and analysis. Management Science, 37(5), 501–518.
Busselberg, S. (2011). Bridging Livestock Survey Results to Published Estimates through State–Space Models: A Time Series Approach. Available: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2011/JSM-2011-Busselberg.pdf [September 2017].
Cecere, W., Berg, E., and Ghosh, M. (2012). A Problem in Small Area Estimation: Cash Rental Rates. Paper presented at the Fourth International Conference on Establishment Surveys, Montréal, Québec, Canada, June 11–14. Available: www.amstat.org/meetings/ices/2012/papers/301998.pdf [September 2017].
Chipanshi, A., Zhang, Y., Kouadio, L., Newlands, N., Davidson, A., Hill, H., Warren, R., Qian, B., Daneshfar, B., Bedard, F., and Reichert, G. (2015). Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agricultural and Forest Meteorology, 206, 137–150. doi: 10.1016/j.agrformet.2015.03.007.
Coble, K.H., Knight, T.O., Goodwin, B.K., Miller, M.F., and Rejesus, R.M. (2010). A Comprehensive Review of the RMA APH and COMBO Rating Methodology. Available: http://www.rma.usda.gov/pubs/2009/comprehensivereview.pdf [December 2017].
Congressional Research Service. (2017). NASS and U.S. Crop Production Forecasts: Methods and Issues. Report 7-5700. Available: https://fas.org/sgp/crs/misc/R44814.pdf [July 2017].
Cressie, N.A.C. (1993). Statistics for Spatial Data. New York: Wiley.
Cressie, N.A.C., and Johannesson, G. (2008). Fixed rank kriging for very large spatial data sets. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 70(1), 209–226. Available: http://www.jstor.org/stable/20203819 [September 2017].
Cressie, N.A.C., and Wikle, C.K. (2011). Statistics for Spatio-Temporal Data. Hoboken, NJ: John Wiley & Sons.
Crouse, C. (2000). Evaluation of the Use of Spatial Modeling to Improve County Yield Estimates. Research Report RD-00-05. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service.
Cruze, N.B. (2015a). A Bayesian Hierarchical Model for Combining Several Crop Yield Indications. Briefing presented at Federal Committee on Statistical Methodology Research Conference, Washington, DC, December 1. Available: http://fcsm.sites.usa.gov/files/2016/03/C3_Cruze_2015FCSM.pdf [September 2017].
Cruze, N.B. (2015b). Integrating Survey Data with Auxiliary Sources of Information to Estimate Crop Yields. Proceedings of the Survey Research Methods Section of the Joint Statistical Meetings, Seattle, WA, August 13. Available: https://ww2.amstat.org/meetings/jsm/2015/onlineprogram/AbstractDetails.cfm?abstractid=314604 [September 2017].
Cruze, N.B., Erciulesscu, A.L., Nandram, B., Barboza, W.J., and Young, L.J. (2016). Developments in Model-Based Estimation of County-Level Agricultural Estimates. Paper presented at the Fifth International Conference on Establishment Surveys, Geneva, Switzerland, June 20–23. Available: http://ww2.amstat.org/meetings/ices/2016/proceedings/131_ices15Final00229.pdf [September 2017].
Diggle, P.J., Moraga, P., Rowlingson, B., and Taylor, B.M. (2013). Spatial and spatio-temporal log-Gaussian Cox processes: Extending the geostatistical paradigm. Statistical Science, 28(4), 542–563.
Dorfman, A. (2018). Towards a Routine External Evaluation Protocol for Small Area Estimation. To appear in International Statistical Review.
Dow AgroSciences. (2016). Dow AgroSciences puts growers at the center of a new precision agronomy program. Dow AgroSciences Newsroom, March 3. Available: http://www.dowagro.com/en-US/usag/News%20and%20Resources/NewsRoom/2016/March/03/Dow%20AgroSciences%20Puts%20Growers%20at%20the%20Center%20of%20a%20New%20Precision%20Agronomy%20Program [July 2017].
Erciulescu, A.L., Cruze, N.B., and Nandram, B. (2016). Model-based county-level crop estimates incorporating auxiliary sources of information. In Proceedings of the Survey Research Methods Section of the Joint Statistical Meetings. Alexandria, VA: American Statistical Association.
Fay, R.E., and Herriot, R.A. (1979). An application of James–Stein procedures to census data. Journal of the American Statistical Association, 74(366), 269–277.
Franco, C., and Bell, W.R. (2016). Borrowing information over time in binomial/logit normal models for small area estimation. In M. Hidiroglou and W. Okrasa (Eds.), Joint Issue of Statistics in Transition New Series and Survey Methodology Small Area Estimation 2014, 16(4), 563–584. Available: http://stat.gov.pl/en/sit-en/joint-issue-part-i-sae-poznan-2014 [September 2017].
Fuentes, M., and Raftery, A.E. (2005). Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models. Biometrics, 61(1), 36-45.
Fuller, W.A., and Goyeneche, J.J. (1998, June). Estimation of the State Variance Component. Working Paper. Ames, IA: Iowa State University, Statistical Laboratory.
Gallagher, P. (1987). U.S. soybean yields: Estimation and forecasting with nonsymmetric disturbances. American Journal of Agricultural Economics, 69(4), 796–803.
Gelfand, A.E. (2010). Misaligned spatial data. In A.E. Gelfand, P.J. Diggle, M. Fuentes, and P. Guttorp (Eds.), Handbook of Spatial Statistics (pp. 517–539). Boca Raton, FL: CRC Press.
Gershunskaya, J. (2012). Estimation for detailed publication levels in the current employment statistics survey. In Proceedings of the American Statistical Association, Survey Research Methods Section. Alexandria, VA: American Statistical Association.
Gershunskaya, J., and Lahiri, P. (2010). Robust small area estimation using a mixture model. In Proceedings of the American Statistical Association, Survey Research Methods Section. Alexandria, VA: American Statistical Association. Available: http://www.amstat.org/sections/srms/proceedings/y2010/files/307425_58536.pdf [September 2017].
Glauber, J.W. (2004). Crop insurance reconsidered. American Journal of Agricultural Economics, 86(5), 1179–1195.
Good, D.L., and Irwin, S.H. (2016). Using FSA acreage data to project NASS January planted acreage estimates for corn and soybeans. Farmdoc Daily, 6, 17. Available: http://farmdocdaily.illinois.edu/2016/01/using-fsa-acreage-data-project-nass-january-planted.html [July 2017].
Goodwin, B.K., and Ker, A.P. (1998). Nonparametric estimation of crop yield distributions: Implications for rating group-risk crop insurance contracts. American Journal of Agricultural Economics, 80(1), 139–153.
Gotway, C., and Young, L. (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97(458), 632–648.
Graham, M., and Iwig, W. (1996). County estimation of crop acreage using satellite data. In W. Schaible (Ed.), Indirect Estimators in U.S. Federal Programs (Ch. 6). New York: Springer. Available: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/GIS_Reports/The%20National%20Agricultural%20Statistics%20Service%20County%20Estimates%20Program.pdf [July 2017].
Haining, R.P. (2003). Spatial Data Analysis: Theory and Practice. New York: Cambridge University Press.
Harri, A., Erdem, C., Coble, K.H., and Knight, T.O. (2009). Crop yield distributions: A reconciliation of previous research and statistical tests for normality. Review of Agricultural Economics, 31(1), 163–182.
Hatfield, J. (1983). Remote sensing estimators of potential and actual crop yield. Remote Sensing of Environment, 13(4), 301–311.
Hazell, P.B.R. (1984). Sources of increased instability in Indian and U.S. cereal production. American Journal of Agricultural Economics, 66(3), 302–311. doi: 10.2307/1240797.
Hodges, J.S., and Reich, B.J. (2010). Adding spatially-correlated errors can mess up the fixed effect you love. The American Statistician, 64(4), 325–334.
Hurst, B. (2015). Big Data and Agriculture: Innovation and Implications. Statement of the American Farm Bureau Federation to the House Committee on Agriculture. Available: https://agriculture.house.gov/uploadedfiles/10.28.15_hurst_testimony.pdf [July 2017].
Iwig, W. (1996). The National Agricultural Statistics Service County Estimates Program. In W. Schaible (Ed.), Indirect Estimators in U.S. Federal Programs (Ch. 7, pp. 129–144). New York: Springer. Available: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/GIS_Reports/The%20National%20Agricultural%20Statistics%20Service%20County%20Estimates%20Program.pdf [July 2017].
JASON Deliberative Study. (2016). New Techniques for Evaluation of Crop Production. JST-16-Task-006. McLean, VA: The MITRE Corporation.
Johansson, R., Effland, A., and Coble, K. (2017). Falling response rates to USDA crop surveys: Why it matters. Farmdoc Daily, January 19. Available: http://farmdocdaily.illinois.edu/2017/01/falling-response-rates-to-usda-crop-surveys.html [July 2017].
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 Sensing of Environment 141, 116–128. doi: 10.1016/j.rse.2013.10.027.
Johnson, D.M. (2016). A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geoinformation, 52, 65–81. doi: 10.1016/j.jag.2016.05.010.
Kalton, G. (2002). Models in the practice of survey sampling (revisited.). Journal of Official Statistics, 18(2), 129–154.
Kott, P.S. (1989). Assessing linearization variance estimators. In Proceedings of the American Statistical Association, Survey Research Methods Section. Alexandria, VA: American Statistical Association. Available: http://ww2.amstat.org/sections/SRMS/Proceedings/papers/1989_030.pdf [September 2017].
Krause, P., Boyle, D.P., and Base, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5, 89–97.
Leroux, B.G., Lei, X., and Breslow, N. (1999). Estimation of disease rates in small areas: A new mixed model for spatial dependence. In M.E. Halloran and D.A. Berry (Eds.), Statistical Models in Epidemiology, the Environment and Clinical Trials (pp. 179–192). New York: Springer.
Li, Y., Brown, P., Gesink, D.C., and Rue, H. (2012). Log Gaussian Cox processes and spatially aggregated disease incidence data. Statistical Methods in Medical Research, 21(5), 479–507.
Lindgren, F., Rue, H., and Linstrom, J. (2011). An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic differential equation approach (with discussion). Journal of the Royal Statistical Society, Series B, 73(4), 423–498.
Liu, B., Stokes, S.L., Topping, T., and Stunz, G. (2017). Estimation of total from a population of unknown size and application to estimating recreational red snapper catch in Texas. Journal of Survey Statistics and Methodology, 5(3), 350–371. doi: 10.1093/jssam/smx006.
Lokupitiya, E., Breidt, F.J., Williams, S., and Keith, P. (2007). Deriving comprehensive county-level crop yield and area data for U.S. cropland. Agronomy Journal, 99, 673–681. doi: 10.2134/agronj2006.0143.
McCarl, B.A., Villavicencio, X., and Wu, X. (2008). Climate change and future analysis: Is stationarity dying? American Journal of Agricultural Economics, 90(5), 1241–1247.
Mercer, L., Wakefield, J., Pantazis, A., Lutambi, A., Masanja, H., and Clark, S. (2015). Small area estimation of child mortality in the absence of vital registration. The Annals of Applied Statistics, 9(4), 1889–1905.
Nandram, B., Berg, E., and Barboza, W. (2014). A hierarchical Bayesian model for forecasting state-level yield. Environmental and Ecological Statistics, 21(3), 507–530.
National Academies of Sciences, Engineering, and Medicine. (2017). Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press.
National Research Council. (1980). Estimating Population and Income of Small Areas. Panel on Small-Area Estimates of Population and Income. Committee on National Statistics, Assembly of Behavioral and Social Sciences. Washington, DC: National Academy Press.
National Research Council. (1991a). Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume I, Review and Recommendations. Panel to Evaluate Microsimulation Models for Social Welfare Programs. C.F. Citro and E.A. Hanuschek (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council. (1991b). Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers. Panel to Evaluate Microsimulation Models for Social Welfare Programs. C.F. Citro and E.A. Hanuschek (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council. (1997). Assessing Policies for Retirement Income: Needs for Data, Research and Models. Panel on Retirement Income Modeling. C.F. Citro and E.A. Hanuschek (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council. (2000a). Small-Area Estimates of School-Age Children in Poverty: Evaluation of the Current Methodology. Panel on Estimates of Poverty for Small Geographic Areas. C.F. Citro and G. Kalton (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council. (2000b). Small-Area Estimates of School-Age Children in Poverty: Priorities for 2000 and Beyond. Panel on Estimates of Poverty for Small Geographic Areas. C.F. Citro and G. Kalton (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council. (2008). Understanding American Agriculture: Challenges for the Agricultural Resource Management Survey. Panel to Review USDA’s Agricultural Resource Management Survey, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
National Research Council. (2013). Nonresponse in Social Science Surveys: A Research Agenda. Panel on a Research Agenda for the Future of Social Science Data Collection. R. Tourangeau and T.J. Plewes (Eds.). Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
Neill, J. (2011). FSA programs explained: Reconstitution. Tri-State Livestock News, June 3. Available: http://www.tsln.com/news/fsa-programs-explained-reconstitution [September 2017].
Newlands, N., Zamar, D., Kouadio, L., Zhang, Y., Chipanshi, A., Potgeiter, A., Toure, S., and Hill, H. (2014). An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty. Frontiers in Environmental Science, 2(17). doi: 10.3389/fenvs.2014.00017.
Nickerson, C., Morehart, M., Kuethe, T., Beckman, J., Ifft, J., and Williams, R. (2012). Trends in U.S. Farmland Values and Ownership. Economic Information Bulletin 92. Washington, DC: U.S. Department of Agriculture, Economic Research Service. Available: https://www.ers.usda.gov/webdocs/publications/44656/16748_eib92_2_.pdf?v=41055 [July 2017].
Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F., and Sain, S. (2015). A multiresolution Gaussian process model for the analysis of large spatial datasets. Journal of Computational and Graphical Statistics, 24(2), 579–599.
Pfeffermann, D. (2013). New important developments in small area estimation. Statistical Science, 28(1), 40–68.
Porter, A.T., Wikle, C.K., and Holan, S.H. (2015). Small area estimation via multivariate Fay-Herriot models with latent spatial dependence. Australian & New Zealand Journal of Statistics, 57, 15–29.
Reichart, G., Bédard, F., Mohl, C., Benjamin, W., Dongmo Jiongo, V., Chipanshi, A., and Zhang, Y. (2016). Crop Yield Modelling Using Remote Sensing, Agroclimatic Data and Statistical Survey Data. Briefing presented at Seventh International Conference on Agricultural Statistics, Rome, Italy, October 26–28. Slides Available: https://www.dropbox.com/s/v5j50nqenuikciy/CROP_YIELD_MODELLING_USING_REMOTE_SENSING_AGROCLIMATIC_DATA_AND_STATISTICAL_SURVEY_DATA_Reichert_Statistics_Canada_ICAS_VII.pptx?dl=0 [September 2017}.
Riebler, A., Sorbye, S., Simpson, D., and Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4), 1145–1165.
Rue, H., and Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. Boca Raton, FL: Chapman and Hall/CRC Press.
Rue, H., Martino, S., and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series B, 71(2), 319–392.
Schaible, W.L. (Ed.). (1996). Indirect Estimators in U.S. Federal Programs. New York: Springer.
Schimmelpfennig, D. (2016). Precision Agriculture Technologies and Factors Affecting Their Adoption. Washington, DC: U.S. Department of Agriculture, Economic Research Service. Available: https://www.ers.usda.gov/amber-waves/2016/december/precision-agriculture-technologies-and-factors-affecting-their-adoption [June 2016].
Schleusener, M. (2016). NASS surveys have direct impact on critical farm programs. Farmdoc Daily, 6, 222. Available: http://farmdocdaily.illinois.edu/2016/11/nass-surveys-impact-critical-farm-programs.html [July 2017].
Scott, A., and Smith, T.M. (1969). Estimation in multi-stage surveys. Journal of the American Statistical Association, 64(327), 830–840.
Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory for Kriging, New York: Springer.
Szulczewski, W., Zyromski, A., Biniak-Pierog, M. (2012). New approach in modeling spring wheat yielding based on dry periods. Agricultural Water Management, 103, 105–113.
Tang, M., Slud, E., and Pfeiffer, R. (2014). Goodness of fit tests for linear mixed models. Journal of Multivariate Analysis, 130, 176–193. doi: 10.1016/j.jmva.2014.03.012.
Taylor, B.M., Davies, T.M., Rowlingson, B., and Diggle, P.J. (2015). An R package for inference with spatial and spatio-temporal log-Gaussian Cox processes. Journal of Statistical Software, 63, 1–29.
Torabi, M., and Rao, J.N.K. (2014). On small area estimation under a sub-area model. Journal of Multivariate Analysis, 127, 36–55
Traxler, G., Falck-Zepeda, J., Ortiz-Monasterio, J.I., and Sayre, K. (1995). Production risk and the evolution of varietal technology. American Journal of Agricultural Economics, 77(1), 1–7.
U.S. Department of Agriculture. (2016). Requirement to Report Field Location on the Acreage Report. Bulletin No. MGR-16-005. Available: https://www.rma.usda.gov/bulletins/managers/2016/mgr-16-005.pdf [July 2017].
U.S. Department of Agriculture-National Agricultural Statistics Service. (2007). Safeguarding America’s Agricultural Statistics: A Century of Successful and Secure Procedures, 1905–2005. Available: https://www.nass.usda.gov/About_NASS/pdf/asb_historical.pdf [July 2017].
U.S. Department of Agriculture-National Agricultural Statistics Service. (2014). Cash Rents Methodology and Quality Measures. Available: https://www.nass.usda.gov/Publications/Methodology_and_Data_Quality/Cash_Rents/08_2014/rentqm14.pdf [August 2017].
U.S. Department of Agriculture-National Agricultural Statistics Service. (2016). Cash Rents Methodology and Quality Measures. Available: https://www.nass.usda.gov/Publications/Methodology_and_Data_Quality/Cash_Rents/08_2016/rentqm16.pdf [August 2017].
U.S. Department of Agriculture-Natural Resources Conservation Service. (2012). User Guide for the National Commodity Crop Productivity Index (NCCPI). Version 2.0. Washington, DC: Author.
U.S. Office of Management and Budget. (2006). Standards and Guidelines for Statistical Surveys. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/inforeg/statpolicy/standards_stat_surveys.pdf [July 2017].
Wakefield, J. (2008). Ecologic studies revisited. Annual Review of Public Health, 29, 75–90.
Walker, G., and Sigman, R. (1982). The Use of LANDSAT for County Estimates of Crop Area: Evaluation of the Huddleston–Ray and Battese–Fuller Estimators. Available: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/GIS_Reports/The%20Use%20of%20LANDSAT%20for%20County%20Estimates%20of%20Crop%20Areas%20Evaluation.pdf [September 2017].
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. Journal of Agricultural, Biological, and Environmental Statistics, 17(1), 84–106.
Williams, M. (2013). Small Area Modeling of County Estimates for Corn and Soybean Yields in the U.S. Paper presented at the Federal Committee on Statistical Methodology (FCSM) Research Conference, Washington, DC, November 4–6. Available: http://fcsm.sites.usa.gov/files/2014/05/C2_Williams_2013FCSM.pdf [September 2017].
Wood, J.D., Griffis, T.J., Baker, J.M., Frankenburg, C., Verma, M., and Yuen, K. (2017). Multiscale analyses of solar-induced florescence and gross primary production. Geophysical Research Letters, 44(1), 533–541. Available: https://www.ars.usda.gov/research/publications/publication/?seqNo115=331937 [July 2017].
Yang, S.-R., Koo, W.W., and Wilson, W.W. (1992). Heteroskedasticity in crop yield models. Journal of Agriculture and Resource Economics, 17(1), 103–109.
You, Y., and Zhou, Q.M. (2011). Hierarchical Bayes small area estimation under a spatial model with application to health survey data. Survey Methodology, 37, 25–37.
Zhu, Y., Goodwin, B.K., and Ghosh, S. (2008). Time-Varying Yield Distributions and the Implications for Crop Insurance Pricing. Paper presented at the NC State Agricultural Economics Workshop, Raleigh, NC, May 6. Available: http://ageconsearch.umn.edu/bitstream/104420/2/presentation_13783_zhu_2011.pdf [November 2017].