US yield forecasting using crop condition ratings

de Barros Dias, Fernanda

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https://hdl.handle.net/2142/97265 Copy

Description

Title

US yield forecasting using crop condition ratings

Author(s)

de Barros Dias, Fernanda

Issue Date

2017-03-08

Director of Research (if dissertation) or Advisor (if thesis)

Irwin, Scott H.

Committee Member(s)

• Good, Darrel
• Schnitkey, Gary

Department of Study

Agr & Consumer Economics

Discipline

Agricultural & Applied Economics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• US yield
• Forecasting
• Crop conditions
• World Agricultural Supply and Demand Estimates (WASDE)

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.

Graduation Semester

2017-05

Type of Resource

text

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http://hdl.handle.net/2142/97265

Copyright and License Information

Copyright 2017 Fernanda de Barros Dias

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