The purpose of this research is to compare and analyze several different yield forecasting methods. The study analyzes corn yields in Ohio and South Dakota for the years 1986 through 2012. A base model, with a trend and state dummy variable is developed. Two competing models, one with objective variables and one with subjective variables, are then developed as additions to the base model. The competing objective model is developed by adding accumulated growing degree days (GDD) and accumulated rainfall variables. The competing subjective model is developed by adding a USDA crop conditions index (CCI) variable. The models are estimated weekly between weeks 24 and 36 of the calendar year. The three models are compared using several different criteria. Examinations of adjusted R2 values, F-test values, and root Mean Squared Error (MSE) values are conducted, as well as statistical tests of the competing model forecast errors. The results show that the competing subjective (CCI) model performs the best at forecasting corn yield during the growing season. It outperforms the base and objective models for the entire study period. With a minimum MSE of 8 bushels per acre, it is over 7 bushels per acre more accurate at forecasting yield than its competitors.
","abstract_html":"<p>The purpose of this research is to compare and analyze several different yield forecasting methods. The study analyzes corn yields in Ohio and South Dakota for the years 1986 through 2012. A base model, with a trend and state dummy variable is developed. Two competing models, one with objective variables and one with subjective variables, are then developed as additions to the base model. The competing objective model is developed by adding accumulated growing degree days (GDD) and accumulated rainfall variables. The competing subjective model is developed by adding a USDA crop conditions index (CCI) variable. The models are estimated weekly between weeks 24 and 36 of the calendar year. The three models are compared using several different criteria. Examinations of adjusted R2 values, F-test values, and root Mean Squared Error (MSE) values are conducted, as well as statistical tests of the competing model forecast errors. The results show that the competing subjective (CCI) model performs the best at forecasting corn yield during the growing season. It outperforms the base and objective models for the entire study period. With a minimum MSE of 8 bushels per acre, it is over 7 bushels per acre more accurate at forecasting yield than its competitors.</p>","abstract_has_math":false,"creators":["Jorgensen, Nicholas"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - Open Access","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Matthew Diersen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T04:27:51Z","subjects":["corn yields","yield forecasting models","crop conditions","CCI models","Agricultural Economics"],"languages":["en"],"rights":["Copyright © 2014 Nicholas Jorgensen. All rights reserved."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/339","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Matthew Diersen"]},{"key":"dc:creator","label":"Author","values":["Jorgensen, Nicholas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-25T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["corn yields","yield forecasting models","crop conditions","CCI models","Agricultural Economics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © 2014 Nicholas Jorgensen. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/339"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The purpose of this research is to compare and analyze several different yield forecasting methods. The study analyzes corn yields in Ohio and South Dakota for the years 1986 through 2012. A base model, with a trend and state dummy variable is developed. Two competing models, one with objective variables and one with subjective variables, are then developed as additions to the base model. The competing objective model is developed by adding accumulated growing degree days (GDD) and accumulated rainfall variables. The competing subjective model is developed by adding a USDA crop conditions index (CCI) variable. The models are estimated weekly between weeks 24 and 36 of the calendar year. The three models are compared using several different criteria. Examinations of adjusted R2 values, F-test values, and root Mean Squared Error (MSE) values are conducted, as well as statistical tests of the competing model forecast errors. The results show that the competing subjective (CCI) model performs the best at forecasting corn yield during the growing season. It outperforms the base and objective models for the entire study period. With a minimum MSE of 8 bushels per acre, it is over 7 bushels per acre more accurate at forecasting yield than its competitors.
"]},{"key":"dc:title","label":"Title","values":["A Comparison of Corn Yield Forecasting Models"]}]}],"canonical_facts":{"dc:contributor":["Matthew Diersen"],"dc:creator":["Jorgensen, Nicholas"],"dc:date.available":["2016-02-25T08:00:00Z"],"dc:description.abstract":["The purpose of this research is to compare and analyze several different yield forecasting methods. The study analyzes corn yields in Ohio and South Dakota for the years 1986 through 2012. A base model, with a trend and state dummy variable is developed. Two competing models, one with objective variables and one with subjective variables, are then developed as additions to the base model. The competing objective model is developed by adding accumulated growing degree days (GDD) and accumulated rainfall variables. The competing subjective model is developed by adding a USDA crop conditions index (CCI) variable. The models are estimated weekly between weeks 24 and 36 of the calendar year. The three models are compared using several different criteria. Examinations of adjusted R2 values, F-test values, and root Mean Squared Error (MSE) values are conducted, as well as statistical tests of the competing model forecast errors. The results show that the competing subjective (CCI) model performs the best at forecasting corn yield during the growing season. It outperforms the base and objective models for the entire study period. With a minimum MSE of 8 bushels per acre, it is over 7 bushels per acre more accurate at forecasting yield than its competitors.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/339"],"dc:language":["en"],"dc:rights":["Copyright © 2014 Nicholas Jorgensen. All rights reserved."],"dc:subject":["corn yields","yield forecasting models","crop conditions","CCI models","Agricultural Economics"],"dc:title":["A Comparison of Corn Yield Forecasting Models"],"thesis:degree_discipline":["Economics"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T04:27:51Z"}