{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97265"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97265","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"US yield forecasting using crop condition ratings","abstract":"This thesis studied the relationship between US average yield for the five main crops grown in the United States, corn, soybeans, upland cotton, winter wheat, and spring wheat, and crop condition ratings from 1986 to 2015. Three statistical models were tested, the first one including all categories of condition ratings, a trend, and no intercept. The second model addresses the issue of abandoned acres following the ideas of Fackler & Norwood and included all categories less the Very Poor category, a trend, and no intercept. The last model investigated is the sum of Good and Excellent categories, a trend, and intercept. The models were used to forecast US yield out-of-sample as an alternative to the benchmark forecast WASDE. Major findings include: 1) crop condition ratings will not be a good predictor of US average yield if there is an increase in the WASDE yield forecasting and the conditions are not aligned accordingly, 2) when weather becomes an issue these models perform very well compared to WASDE as it was the case in 2012, 3) the models are a good forecast beating the USDA WASDE in almost every instance for the trend-yield months, 4) almost in every instance the best performing model did not use USDA’s original ratings, which implies that the corrections made to the data are rational, 5) except for soybeans and spring wheat, the WASDE survey-yield months are a better forecast than the statistical models developed in this thesis given the nature of the commodities and USDA’s superior method, 6) weighting by production instead of planted acres becomes particularly important when there is a wide range of yield variability between states like it is the case for corn and winter wheat, 7) correction for bias becomes particularly important the more the crop is sensitive to adverse events that affect yield during its reproduction stage.","abstract_html":"This thesis studied the relationship between US average yield for the five main crops grown in the United States, corn, soybeans, upland cotton, winter wheat, and spring wheat, and crop condition ratings from 1986 to 2015. Three statistical models were tested, the first one including all categories of condition ratings, a trend, and no intercept. The second model addresses the issue of abandoned acres following the ideas of Fackler & Norwood and included all categories less the Very Poor category, a trend, and no intercept. The last model investigated is the sum of Good and Excellent categories, a trend, and intercept. The models were used to forecast US yield out-of-sample as an alternative to the benchmark forecast WASDE. Major findings include: 1) crop condition ratings will not be a good predictor of US average yield if there is an increase in the WASDE yield forecasting and the conditions are not aligned accordingly, 2) when weather becomes an issue these models perform very well compared to WASDE as it was the case in 2012, 3) the models are a good forecast beating the USDA WASDE in almost every instance for the trend-yield months, 4) almost in every instance the best performing model did not use USDA’s original ratings, which implies that the corrections made to the data are rational, 5) except for soybeans and spring wheat, the WASDE survey-yield months are a better forecast than the statistical models developed in this thesis given the nature of the commodities and USDA’s superior method, 6) weighting by production instead of planted acres becomes particularly important when there is a wide range of yield variability between states like it is the case for corn and winter wheat, 7) correction for bias becomes particularly important the more the crop is sensitive to adverse events that affect yield during its reproduction stage.","abstract_has_math":false,"creators":["de Barros Dias, Fernanda"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Applied Economics","degree_department":null,"school":null,"contributors":["Irwin, Scott H.","Good, Darrel","Schnitkey, Gary"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:14:30Z","date_published":"2017-08-10T19:14:30Z","updated_at":"2026-07-22T22:24:32Z","subjects":["US yield","Forecasting","Crop conditions","World Agricultural Supply and Demand Estimates (WASDE)"],"languages":["en"],"rights":["Copyright 2017 Fernanda de Barros Dias"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97265","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Irwin, Scott H.","Good, Darrel","Schnitkey, Gary"]},{"key":"dc:creator","label":"Author","values":["de Barros Dias, Fernanda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:14:30Z","2017-03-08","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Applied Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["US yield","Forecasting","Crop conditions","World Agricultural Supply and Demand Estimates (WASDE)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Fernanda de Barros Dias"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97265"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis studied the relationship between US average yield for the five main crops grown in the United States, corn, soybeans, upland cotton, winter wheat, and spring wheat, and crop condition ratings from 1986 to 2015. Three statistical models were tested, the first one including all categories of condition ratings, a trend, and no intercept. The second model addresses the issue of abandoned acres following the ideas of Fackler & Norwood and included all categories less the Very Poor category, a trend, and no intercept. The last model investigated is the sum of Good and Excellent categories, a trend, and intercept. The models were used to forecast US yield out-of-sample as an alternative to the benchmark forecast WASDE. Major findings include: 1) crop condition ratings will not be a good predictor of US average yield if there is an increase in the WASDE yield forecasting and the conditions are not aligned accordingly, 2) when weather becomes an issue these models perform very well compared to WASDE as it was the case in 2012, 3) the models are a good forecast beating the USDA WASDE in almost every instance for the trend-yield months, 4) almost in every instance the best performing model did not use USDA’s original ratings, which implies that the corrections made to the data are rational, 5) except for soybeans and spring wheat, the WASDE survey-yield months are a better forecast than the statistical models developed in this thesis given the nature of the commodities and USDA’s superior method, 6) weighting by production instead of planted acres becomes particularly important when there is a wide range of yield variability between states like it is the case for corn and winter wheat, 7) correction for bias becomes particularly important the more the crop is sensitive to adverse events that affect yield during its reproduction stage.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Fernanda de Barros Dias, accepted the attached license on 2017-03-06 at 16:19.","The student, Fernanda de Barros Dias, submitted this Thesis for approval on 2017-03-06 at 16:31.","This Thesis was approved for publication on 2017-03-08 at 09:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10577 on 2017-08-10 at 13:37:53","Made available in DSpace on 2017-08-10T19:14:30Z (GMT). No. of bitstreams: 2 DEBARROSDIAS-THESIS-2017.pdf: 3018528 bytes, checksum: 9206d83a6ff910156b9562af446e741e (MD5) LICENSE.txt: 4220 bytes, checksum: 40a32ef81402ccf84d8418e10d09fec4 (MD5) Previous issue date: 2017-03-08"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["US yield forecasting using crop condition ratings"]}]}],"canonical_facts":{"dc:contributor":["Irwin, Scott H.","Good, Darrel","Schnitkey, Gary"],"dc:creator":["de Barros Dias, Fernanda"],"dc:date":["2017-08-10T19:14:30Z","2017-03-08","2017-05"],"dc:description":["This thesis studied the relationship between US average yield for the five main crops grown in the United States, corn, soybeans, upland cotton, winter wheat, and spring wheat, and crop condition ratings from 1986 to 2015. Three statistical models were tested, the first one including all categories of condition ratings, a trend, and no intercept. The second model addresses the issue of abandoned acres following the ideas of Fackler & Norwood and included all categories less the Very Poor category, a trend, and no intercept. The last model investigated is the sum of Good and Excellent categories, a trend, and intercept. The models were used to forecast US yield out-of-sample as an alternative to the benchmark forecast WASDE. Major findings include: 1) crop condition ratings will not be a good predictor of US average yield if there is an increase in the WASDE yield forecasting and the conditions are not aligned accordingly, 2) when weather becomes an issue these models perform very well compared to WASDE as it was the case in 2012, 3) the models are a good forecast beating the USDA WASDE in almost every instance for the trend-yield months, 4) almost in every instance the best performing model did not use USDA’s original ratings, which implies that the corrections made to the data are rational, 5) except for soybeans and spring wheat, the WASDE survey-yield months are a better forecast than the statistical models developed in this thesis given the nature of the commodities and USDA’s superior method, 6) weighting by production instead of planted acres becomes particularly important when there is a wide range of yield variability between states like it is the case for corn and winter wheat, 7) correction for bias becomes particularly important the more the crop is sensitive to adverse events that affect yield during its reproduction stage.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Fernanda de Barros Dias, accepted the attached license on 2017-03-06 at 16:19.","The student, Fernanda de Barros Dias, submitted this Thesis for approval on 2017-03-06 at 16:31.","This Thesis was approved for publication on 2017-03-08 at 09:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10577 on 2017-08-10 at 13:37:53","Made available in DSpace on 2017-08-10T19:14:30Z (GMT). No. of bitstreams: 2 DEBARROSDIAS-THESIS-2017.pdf: 3018528 bytes, checksum: 9206d83a6ff910156b9562af446e741e (MD5) LICENSE.txt: 4220 bytes, checksum: 40a32ef81402ccf84d8418e10d09fec4 (MD5) Previous issue date: 2017-03-08"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/97265"],"dc:language":["en"],"dc:rights":["Copyright 2017 Fernanda de Barros Dias"],"dc:subject":["US yield","Forecasting","Crop conditions","World Agricultural Supply and Demand Estimates (WASDE)"],"dc:title":["US yield forecasting using crop condition ratings"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural & Applied Economics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:32Z"}