The objective was to develop a plant-process corn yield forecasting model and examine how effective these forecasts might be in improving corn yield forecasts made at the state and crop reporting district level in Iowa. This study was conducted in light of recent budget cuts by the National Agricultural Statistics Service (NASS) and elimination of the reporting of district corn yield forecasts in Iowa;Results indicated that district and state plant-process model (PPM) corn yield forecasts perform well compared to NASS forecasts in August and September but not very well in October and November. Also, the PPM forecasts did not perform well in the southern districts of Iowa. The PPM does not appear to be a comparable substitute for past NASS district corn yield forecasts;Combining PPM and NASS forecasts gives composite yield forecasts that are superior to both PPM and NASS forecasts, especially for the August forecast. At this stage in development, therefore, the usefulness of PPM forecasts would come from supplementing NASS sample yield information.
","abstract_html":"<p>The objective was to develop a plant-process corn yield forecasting model and examine how effective these forecasts might be in improving corn yield forecasts made at the state and crop reporting district level in Iowa. This study was conducted in light of recent budget cuts by the National Agricultural Statistics Service (NASS) and elimination of the reporting of district corn yield forecasts in Iowa;Results indicated that district and state plant-process model (PPM) corn yield forecasts perform well compared to NASS forecasts in August and September but not very well in October and November. Also, the PPM forecasts did not perform well in the southern districts of Iowa. The PPM does not appear to be a comparable substitute for past NASS district corn yield forecasts;Combining PPM and NASS forecasts gives composite yield forecasts that are superior to both PPM and NASS forecasts, especially for the August forecast. At this stage in development, therefore, the usefulness of PPM forecasts would come from supplementing NASS sample yield information.</p>","abstract_has_math":false,"creators":["Krog, David"],"institution":null,"degree_name":"Doctor of Philosophy","degree_level":"dissertation","degree_discipline":null,"degree_department":"Department of Economics (LAS)","school":null,"contributors":[],"advisors":["Stanley R. Johnson"],"committee_chairs":[],"committee_members":[],"year":1988,"date_issued":"1988","date_published":"1988","updated_at":"2026-07-24T02:37:49Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.31274/rtd-180813-9307"],"render_values":[{"text":"https://doi.org/10.31274/rtd-180813-9307","href":"https://doi.org/10.31274/rtd-180813-9307","code":true}]},{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/rtd/9687/"],"render_values":[{"text":"archive/lib.dr.iastate.edu/rtd/9687/","href":null,"code":true}]}]},"links":{"outbound_url":"https://dr.lib.iastate.edu/handle/20.500.12876/82812","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Stanley R. Johnson"]},{"key":"dc:contributor.department","label":"Department","values":["Department of Economics (LAS)"]},{"key":"dc:creator","label":"Author","values":["Krog, David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-08-17T01:14:13.000"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-07-02T06:15:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-07-02T06:15:02Z"]},{"key":"dc:date.issued","label":"Date","values":["1988"]},{"key":"dc:type","label":"Dc Type","values":["dissertation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/rtd/9687/"]},{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.31274/rtd-180813-9307"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://dr.lib.iastate.edu/handle/20.500.12876/82812"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The objective was to develop a plant-process corn yield forecasting model and examine how effective these forecasts might be in improving corn yield forecasts made at the state and crop reporting district level in Iowa. This study was conducted in light of recent budget cuts by the National Agricultural Statistics Service (NASS) and elimination of the reporting of district corn yield forecasts in Iowa;Results indicated that district and state plant-process model (PPM) corn yield forecasts perform well compared to NASS forecasts in August and September but not very well in October and November. Also, the PPM forecasts did not perform well in the southern districts of Iowa. The PPM does not appear to be a comparable substitute for past NASS district corn yield forecasts;Combining PPM and NASS forecasts gives composite yield forecasts that are superior to both PPM and NASS forecasts, especially for the August forecast. At this stage in development, therefore, the usefulness of PPM forecasts would come from supplementing NASS sample yield information.
"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Plant-process model corn yield forecasts for Iowa"]}]}],"canonical_facts":{"dc:contributor.advisor":["Stanley R. Johnson"],"dc:contributor.department":["Department of Economics (LAS)"],"dc:creator":["Krog, David"],"dc:date":["2018-08-17T01:14:13.000"],"dc:date.accessioned":["2020-07-02T06:15:02Z"],"dc:date.available":["2020-07-02T06:15:02Z"],"dc:date.issued":["1988"],"dc:description.abstract":["The objective was to develop a plant-process corn yield forecasting model and examine how effective these forecasts might be in improving corn yield forecasts made at the state and crop reporting district level in Iowa. This study was conducted in light of recent budget cuts by the National Agricultural Statistics Service (NASS) and elimination of the reporting of district corn yield forecasts in Iowa;Results indicated that district and state plant-process model (PPM) corn yield forecasts perform well compared to NASS forecasts in August and September but not very well in October and November. Also, the PPM forecasts did not perform well in the southern districts of Iowa. The PPM does not appear to be a comparable substitute for past NASS district corn yield forecasts;Combining PPM and NASS forecasts gives composite yield forecasts that are superior to both PPM and NASS forecasts, especially for the August forecast. At this stage in development, therefore, the usefulness of PPM forecasts would come from supplementing NASS sample yield information.
"],"dc:format.mimetype":["application/pdf"],"dc:identifier":["archive/lib.dr.iastate.edu/rtd/9687/"],"dc:identifier.doi":["https://doi.org/10.31274/rtd-180813-9307"],"dc:identifier.uri":["https://dr.lib.iastate.edu/handle/20.500.12876/82812"],"dc:language.iso":["en"],"dc:title":["Plant-process model corn yield forecasts for Iowa"],"dc:type":["dissertation"],"thesis:degree_level":["dissertation"],"thesis:degree_name":["Doctor of Philosophy"]},"updated_at":"2026-07-24T02:37:49Z"}