Agricultural Crop Forecasting for Large Geographical Areas

Literature Cited

1. Adrian D. 2012. A model-based approach to forecasting corn and soybean yields. Proceedings of the Fourth International Conference on Establishment Surveys, 2012 Alexandria VA: Am. Stat. Assoc http://www.amstat.org/meetings/ices/2012/papers/302190.pdf
   [Google Scholar]
2. Allen R. 2007. Safeguarding America's Agricultural Statistics: A Century of Successful and Secure Procedures, 1905–2005 Washington, DC: USDA NASS https://www.nass.usda.gov/About_NASS/pdf/asb_historical.pdf
3. Baruth B, Royer A, Klisch A, Genovese G 2008. The use of remote sensing within the MARS crop yield monitoring system of the European Commission. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing 2008, Commission VIII935–940 Hannover, Ger: ISPRS http://www.isprs.org/proceedings/XXXVII/congress/8_pdf/10_WG-VIII-10/02.pdf
   [Google Scholar]
4. Basso B, Cammarano D, Carfagna E 2013. Review of crop yield forecasting methods and early warning systems Rep., FAO Rome: http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/GS_SAC_2013/Improving_methods_for_crops_estimates/Crop_Yield_Forecasting_Methods_and_Early_Warning_Systems_Lit_review.pdf
5. Battese GE, Fuller WA 1981. Prediction of county crop areas using survey and satellite data. Proceedings of the Section on Survey Research Methods, American Statistical Association500–505 Alexandria VA: Am. Stat. Assoc
   [Google Scholar]
6. Battese GE, Harter RM, Fuller WA 1988. An error-components model for prediction of county crop areas using survey and satellite data. J. Am. Stat. Assoc. 83:40128–36
   [Google Scholar]
7. Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C et al. 2010. Monitoring global croplands with coarse resolution Earth observations: the Global Agriculture Monitoring (GLAM) project. Remote Sens 2:61589–609
   [Google Scholar]
8. Bédard F, Reichert G 2013. Integrated crop yield and production forecasting using remote sensing and agri-climatic data Final Rep., Anal. Proj. Initiat., Remote Sens. Geospat Anal., Agric. Div Ottawa, Can.:
9. Benedetti R, Rossini P 1993. On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ. 45:311–26
   [Google Scholar]
10. Berliner LM. 1996. Hierarchical Bayesian time-series models. Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics KM Hanson, RN Silver 15–22 Dordrecht, Neth: Springer
    [Google Scholar]
11. Boogaard HL, De Wit AJW, te Roller JA, van Diepen CA 2014. WOFOST Control Centre 2.1 and WOFOST 7.1.7: User's Guide for the WOFOST Control Centre 2.1 and WOFOST 7.1.7 Crop Growth Simulation Model Wageningen: Alterra, Wageningen Univ. Res. Cent https://www.wur.nl/en/Research-Results/Research-Institutes/Environmental-Research/Facilities-Products/Software-and-models/WOFOST/Documentation-WOFOST.htm
12. Boryan C, Yang Z, Mueller R, Craig M 2011. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto Int 26:5341–58
    [Google Scholar]
13. Brick JM, Williams D 2013. Explaining rising nonresponse rates in cross-sectional surveys. Ann. Am. Acad. Political Soc. Sci. 645:36–59
    [Google Scholar]
14. Brisbane J, Mohl C 2014. The potential use of remote sensing to produce field crop statistics at Statistics Canada. Proceedings of the Statistics Canada Symposium 2014 Ottawa, Can: Stat. Can https://www.statcan.gc.ca/eng/conferences/symposium2014/program/14259-eng.pdf
    [Google Scholar]
15. Carfagna E, Gallego FJ 2005. Using remote sensing for agricultural statistics. Int. Stat. Rev. 73:3389–404
    [Google Scholar]
16. Carletto G, Beegle K, Himelein K, Kilic T, Murray S et al. 2010. Improving the availability, quality and policy-relevance of agricultural data: the Living Standards Measurement Study—Integrated Surveys on Agriculture Work. Pap., LSMS-ISA World Bank Washington, DC: http://www.fao.org/fileadmin/templates/ess/pages/rural/wye_city_group/2010/May/WYE_2010.2.1_Carletto.pdf
17. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM 2006. Measurement Error in Nonlinear Models: A Modern Perspective Boca Raton, FL: Chapman & Hall/CRC, 2nd ed..
18. Chen F, Crow WT, Colliander A, Cosh MH, Jackson TJ et al. 2017. Application of triple collocation in grand-based validation of Soil Moisture Active/Passive (SMAP) level 2 data products. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 10:2489–502
    [Google Scholar]
19. Cochran WG. 1938. Discussion: crop estimation and its relation to agricultural meteorology. Suppl. J. R. Stat. Soc. 5:112–20
    [Google Scholar]
20. Cohen J. 1960. A coefficient of agreement for nominal scales. Educ. Psych. Meas. 20:37–46
    [Google Scholar]
21. Congalton RG, Green K 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices Boca Raton, FL: CRC
22. Cressie N, Kornak J 2003. Spatial statistics in the presence of location error with an application to remote sensing of the environment. Stat. Sci. 18:4436–56
    [Google Scholar]
23. Cressie N, Wikle CK 2011. Statistics for Spatio-Temporal Data Hoboken, NJ: Wiley
24. Cruze NB. 2015. Integrating survey data with auxiliary sources of information to estimate crop yields. Proceedings of the Survey Research Methods Section565–578 Alexandria, VA: Am. Stat. Assoc
    [Google Scholar]
25. Cruze NB. 2016. A Bayesian hierarchical model for combining several crop yield indications. Proceedings of the Survey Research Methods Section2045–53 Alexandria, VA: Am. Stat. Assoc
    [Google Scholar]
26. Cruze NB, Benecha HK 2017. A model-based approach to crop yield forecasting. Proceedings of the Survey Research Methods Section2724–33 Alexandria, VA: Am. Stat. Assoc
    [Google Scholar]
27. Czajka JL, Beyler A 2016. Declining response rates in federal surveys: trends and implications Rep., US Dep. Health Hum. Serv Washington, DC: https://aspe.hhs.gov/system/files/pdf/255531/Decliningresponserates.pdf
28. Czaplewski RL, Catts GP 1992. Calibration of remotely sensed proportion or area estimates for misclassification error. Remote Sens. Environ 39:29–43
    [Google Scholar]
29. Di Paola A, Valentini R, Santini M 2016. An overview of available crop growth and yield models for studies and assessments in agriculture. J. Sci. Food Agric. 96:709–714
    [Google Scholar]
30. Doraiswamy PC, Cook PW 1995. Spring wheat yield assessment using NOAA AVHRR data. Can. J. Remote Sens. 21:143–51
    [Google Scholar]
31. Eur. Comm. Sci. Hub 2016. Crop yield forecasting Rep., JRC Sci. Hub, Eur. Comm., Brussels. https://ec.europa.eu/jrc/en/research-topic/crop-yield-forecasting
32. FAO (UN Food Agric. Organ.) 2015. Report of in-depth country assessment: Georgia Rep., FAO, Rome Italy: http://www.fao.org/fileadmin/templates/rap/files/Project/Global_Strategy_Country_Pages/Georgia/IdCA_Report__Georgia.pdf
33. FAO (UN Food Agric. Organ.) 2016a. Crop Yield Forecasting: Methodological and Institutional Aspects Rome: FAO http://gsars.org/wp-content/uploads/2016/03/AMIS_CYF-Methodological-and-Institutional-Aspects_0303-web.pdf
34. FAO (UN Food Agric. Organ.) 2016b. The Agricultural Integrated Survey (AGRIS): Producing Cost-Efficient Data on Farms for Policymaking Broch., FAO, Rome Italy: http://gsars.org/en/the-agricultural-integrated-survey-agris-poster/
35. FAO (UN Food Agric. Organ.) 2017. Recent Practices and Advances for AMIS Crop Yield Forecasting at Farm and Parcel Level: A Review Rome: FAO http://www.fao.org/3/a-i7339e.pdf
36. Fieuzal R, Baup F 2017. Forecast of wheat yield throughout the agricultural season using optical and radar satellite images. Int. J. Appl. Earth Obs. Geoinform. 59:147–56
    [Google Scholar]
37. FSA (USDA Farm Serv. Agency) 2004. FSA handbook: common land unit USDA Farm Serv. Agency Washington, DC: https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-products/common-land-unit-clu/index
38. FSA (USDA Farm Service Agency) 2017. Farm programs fact sheet Pamphlet, USDA Farm Serv. Agency Washington, DC:
39. Fuller WA. 2006. Measurement Error Models New York: Wiley Intersci
40. Gallego J, Carfagna E, Baruth B 2010. Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. Agricultural Survey Methods R Benedetti, M Bee, G Espa, F Piersimoni 193–211 Chichester, UK: Wiley
    [Google Scholar]
41. Gehlke CE, Biehl K 1934. Certain effects of grouping upon the size of the correlation coefficient in census tract material. J. Am. Stat. Assoc. 29:169–70
    [Google Scholar]
42. Genovese G, Bettio M, Orlandi S, Boogaard HL, Petrakos M et al. 2004. Methodology of the MARS Crop Yield Forecasting System 4: Statistical Data Collection, Processing and Analysis Luxembourg: Eur. Comm
43. Goedhart PW, Hoek SB, Boogaard HL 2017. The CGMS Statistical Tool: User Manual v 3.0. Ispra, Italy: Eur. Comm http://spirits.jrc.ec.europa.eu/files/CGMS-Statistical-Tool-v300.pdf
44. Good DL, Irwin SH 2005. Understanding USDA corn and soybean production forecasts: methods, performance and market impact over 1970–2004 Rep. 2005–03, AgMAS Proj., Dep. Agric. Consum. Econ., Univ. Ill., Urbana-Champaign
45. Gotway CA, Young LJ 2002. Combining incompatible spatial data. J. Am. Stat. Assoc. 97:458632–48
    [Google Scholar]
46. Groten SME. 1993. NDVI-crop monitoring and early yield assessment of Burkina Faso. Int. J. Remote Sens. 14:1495–515
    [Google Scholar]
47. Groves RM, Lyberg L 2010. Total survey error: past, present, and future. Public Opin. Q. 74:5849–79
    [Google Scholar]
48. Han W, Yang Z, Di L, Mueller R 2012. CropScape: a web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Comput. Electron. Agric. 84:111–123
    [Google Scholar]
49. Hansen JW, Jones JW 2000. Scaling-up crop models for climate variability applications. Agric. Syst. 65:43–72
    [Google Scholar]
50. Hayes MJ, Decker WL 1996. Using NOAA AVHRR data to estimate maize production in the United States corn belt. Int. J. Remote Sens. 17:163189–200
    [Google Scholar]
51. Heald J. 2002. USDA establishes a common land unit. ESRI ArcUser Online Apr.–June. http://www.esri.com/news/arcuser/0402/usda.html
    [Google Scholar]
52. Hoek S, Goedhart PW, Akkermans W 2009. The CGMS statistical tool for yield forecasting. Agro Inform 22:219–20
    [Google Scholar]
53. Homer CG, Dewitz JA, Yang L, Jin S, Danielson P et al. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81:5345–54
    [Google Scholar]
54. Hoogenboom GJ, White JW, Messina CD 2004. From genome to crop: integration through simulation modelling. Field Crop Res 90:1145–63
    [Google Scholar]
55. Hunt LA, Boote KJ 1998. Data for model operation, calibration, and evaluation. Understanding Options for Agricultural Production GY Tsuji, G Hoogenboom, PK Thornton 9–39 Dordrecht, Neth: Springer
    [Google Scholar]
56. Irwin JO. 1938. Crop estimation and its relation to agricultural meteorology. Suppl. J. R. Stat. Soc. 5:11–45
    [Google Scholar]
57. Irwin S, Good D 2016. Opening up the black box: more on the USDA corn yield forecasting methodology. farmdoc dly 6:162
    [Google Scholar]
58. Johnson DM. 2014. An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 99:341–56
    [Google Scholar]
59. Johnson DM. 2016. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. Int. J. Appl. Earth Obs. Geoinform. 52:65–81
    [Google Scholar]
60. Jones JW, Antle JM, Basso B, Boote KJ, Conant RT et al. 2017. Brief history of agricultural systems modeling. Agric. Syst. 155:240–254
    [Google Scholar]
61. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD et al. 2003. The DSSAT cropping system model. Eur. J. Agron. 18:235–65
    [Google Scholar]
62. Karlsson AM, Grönvall A 2016. Using administrative registers for making a sample frame for agricultural statistics—methodologies, techniques and experiences. Proceedings of the Seventh International Conference on Agricultural Statistics (ICAS-VII) Rome: FAO https://www.istat.it/storage/icas2016/f36-karlsson.pdf
    [Google Scholar]
63. Kennedy MC, O'Hagan A 2001. Bayesian calibration of computer models. J. R. Stat. Soc. B 63:425–64
    [Google Scholar]
64. Kouadio L, Newlands NK, Davidson A, Zhang Y, Chipanshi A 2014. Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sens 6:10193–214
    [Google Scholar]
65. Liu F, Bayarri MJ, Berger JO 2009. Modularization in Bayesian analysis, with emphasis on analysis of computer models. Bayesian Anal 4:1119–50
    [Google Scholar]
66. Lopresti MF, Di Bella CM, Degioanni AJ 2015. Relationship between MODIS-NDVI data and wheat yield: a case study in northern Buenos Aires province, Argentina. Inf. Process. Agric. 2:273–84
    [Google Scholar]
67. Martin J, Matheson J 1999. Responses to declining response rates on government surveys. Surv. Methodol. Bull. 45:33–37
    [Google Scholar]
68. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ et al. 2004. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–72
    [Google Scholar]
69. Myneni RB, Williams DL 1994. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 49:3200–11
    [Google Scholar]
70. Nandram B, Berg E, Barboza W 2014. A hierarchical Bayesian model for forecasting state-level corn yield. Environ. Ecol. Stat. 21:3507–30
    [Google Scholar]
71. Nandram B, Sayit H 2011. A Bayesian analysis of small area probabilities under a constraint. Surv. Methodol. 37:137–52
    [Google Scholar]
72. NASS (USDA Natl. Agric. Stat. Serv.). 2012. The Yield Forecasting Program of NASS SMB Staff Rep. SMB12–01, USDA NASS Washington, DC: https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/Yield_Forecasting_Program.pdf
73. NASS (USDA Natl. Agric. Stat. Serv.) 2018. Crop Production: 2017 Summary Washington, DC: USDA NASS http://usda.mannlib.cornell.edu/usda/current/CropProdSu/CropProdSu-01-12-2018.pdf
74. Natl. Res. Counc 2013. Nonresponse in Social Science Surveys: A Research Agenda Washington, DC: Natl. Acad. Press
75. Natl. Acad. Sci. Eng. Med 2017a. Federal Statistics, Multiple Data Sources, and Privacy Protection: Next Steps Washington, DC: Natl. Acad. Press
76. Natl. Acad. Sci. Eng. Med 2017b. Improving Crop Estimates by Integrating Multiple Data Sources Washington, DC: Natl. Acad. Press
77. Natl. Acad. Sci. Eng. Med 2017c. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy Washington, DC: Natl. Acad. Press
78. Openshaw S, Taylor P 1979. A million or so correlation coefficients. Statistical Methods in the Spatial Sciences N Wrigley 127–44 London: Pion
    [Google Scholar]
79. Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Morjondo M et al. 2011. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agron. 35:103–14
    [Google Scholar]
80. Pan Y, Wang M, Wei G, Wei F, Shi K et al. 2010. Application of area-frame sampling for agricultural statistics in China. Proceedings of the Fifth International Conference on Agricultural Statistics (ICAS-V) Rome: FAO http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/ICAS5/PDF/ICASV_5.3_034_Paper_Wang.pdf
    [Google Scholar]
81. Patel NK, Ravi N, Navalgund RR, Dash RN, Das KC, Patnaik S 1991. Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa. Int. J. Remote Sens. 12:2259–66
    [Google Scholar]
82. Pettorelli N, Laurance WF, O'Brien TG, Wegmann M, Nagendra H et al. 2014. Satellite remote sensing for applied ecologists: opportunities and challenges. J. Appl. Ecol. 51:839–48
    [Google Scholar]
83. Presser S, McCulloch S 2011. The growth of survey research in the United States: government-sponsored surveys, 1984–2004. Soc. Sci. Res. 40:41019–24
    [Google Scholar]
84. Quinlan JR. 1993. C4.5: Programs for Machine Learning San Francisco: Morgan Kaufmann
85. Quinlan JR. 2003. C5.0 Online Tutorial Empire Bay, Aust: RuleQuest Res https://www.rulequest.com/see5-unix.html
86. Rasmussen MS. 1992. Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. Int. J. Remote Sens. 13:3431–42
    [Google Scholar]
87. Reichert G, Bédard F, Mohl C, Benjamin W, Jiongo VD et al. 2016. Canada—crop yield modelling using remote sensing, agroclimatic data, and statistical survey data. Proceedings of the Seventh International Conference on Agricultural Statistics (ICAS-VII) Rome: FAO https://www.istat.it/storage/icas2016/g43-reichert.pdf
    [Google Scholar]
88. Rembold F, Atzberger C, Savin I, Rojas O 2013. Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens. 5:1704–33
    [Google Scholar]
89. Rice K, Boone EL 2016. Bayesian measures of goodness of fit. Wiley StatsRef: Statistics Reference Online N Balakrishnan, T Colton, B Everitt, W Piegorsch, F Ruggeri, JL Teugels New York: Wiley https://doi.org/10.1002/9781118445112.stat05739.pub2
    [Crossref] [Google Scholar]
90. Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C et al. 2014. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. PNAS 111:93268–73
    [Google Scholar]
91. Schnepf R. 2017. NASS and U.S. Crop Production Forecasts: Methods and Issues Rep. 7-5700, Congr. Res. Serv Washington, DC: https://fas.org/sgp/crs/misc/R44814.pdf
92. Smith TW. 1995. Trends in non-response rates. Int. J. Public Opin. Res. 7:2157–71
    [Google Scholar]
93. Son NT, Chen CF, Chen CR, Minh VQ, Trung NH 2014. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. Forest Meteorol. 197:53–64
    [Google Scholar]
94. Stat. Can 2018a. Business register (BR) Stat. Can., Ottawa, Can., updated Feb. 7. http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=469734
95. Stat. Can 2018b. Field crop reporting series Stat. Can., Ottawa, Can., updated Feb. 14. https://www.statcan.gc.ca/eng/survey/agriculture/3401
96. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:1267–88
    [Google Scholar]
97. UNECE (UN Econ. Comm. Eur.) 2007. Register-Based Statistics in the Nordic Countries: Review of Best Practices with Focus on Population and Social Statistics Geneva: UN
98. Van der Goot E, Supit I, Boogaard HL, van Diepen K, Micale F et al. 2004. Methodology of the MARS Crop Yield Forecasting System Vol. 1. Meteorological Data Collection, Processing and Analysis Luxembourg: Eur. Comm
99. van Diepen CA, Wolf J, van Keulen H, Rappoldt C 1989. WOFOST: a simulation model of crop production. Soil Use Manag 5:116–24
    [Google Scholar]
100. van Diepen K, Boogaard HL, Supit I, Lazar C, Orlandi S et al. 2004. Methodology of the MARS Crop Yield Forecasting System 2 Agrometeorological Data Collection, Processing and Analysis Luxembourg: Eur. Comm
101. Walker G, Sigman R 1982. The use of LANDSAT for county estimates of crop areas: evaluation of the Huddleston–Ray and Battese–Fuller estimators Rep., USDA NASS Washington, DC: 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
102. Wallgren A, Wallgren B 2016. Administrative data and agricultural statistics—what strategy and methods should we adopt?. Proceedings of the Seventh International Conference on Agricultural Statistics (ICAS-VII) Rome: FAO https://www.istat.it/storage/icas2016/f33-wallgren.pdf
     [Google Scholar]
103. Wang JC, Holan SH, Nandram B, Barboza W, Toto C, Anderson E 2012. A Bayesian approach to estimating agricultural yield based on multiple repeated surveys. J. Agric. Biol. Environ. Stat. 17:184–106
     [Google Scholar]
104. Wikle CK. 2003. Hierarchical models in environmental science. Int. Stat. Rev. 71:2181–99
     [Google Scholar]
105. Wu F, Meng J, Li Q, Yan N, Du X, Zhang M 2014. Remote sensing-based global crop monitoring: experiences with China's CropWatch system. Int. J. Digital Earth 7:2113–37
     [Google Scholar]
106. Yohai VJ. 1987. High breakdown-point and high efficiency robust estimates for regression. Ann. Stat. 15:2642–56
     [Google Scholar]
107. Young LJ, Hyman M, Rater RR 2018. Exploring a big data approach to building a list frame for urban agriculture: a pilot study in the city of Baltimore. J. Off. Stat 34:2323–40
     [Google Scholar]
108. Yuan Y, Johnson VE 2012. Goodness-of-fit diagnostics for Bayesian hierarchical models. Biometrics 68:1156–64
     [Google Scholar]
109. Zhao J, Zhou W 2010. The integrated survey framework in the redesign of sample surveys in China agricultural and rural statistics. Proceedings of the Fifth International Conference on Agricultural Statistics (ICAS-V) Rome: FAO http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/ICAS5/PDF/ICASV_5.2_065_Paper_Zhao.pdf
     [Google Scholar]