Author Information

Aziz Bouzaher
Alicia L. Carriquiry

Is the first presenter a student?

No

Type of Submission

Paper/Presentation

Abstract

The yield of products in the dry milling industry is largely determined by the physical properties of the corn kernel. The main objective of this paper is to investigate several statistical models of dry milling yield prediction based on physical characteristics of corn. Data consisting of one hundred corn samples representing a range of genetic traits and quality differences are used. For each corn sample, sixteen physical and chemical properties together with six dry milling product yields were measured, in a controlled laboratory environment .

For each corn sample, we consider a vector of dry milling product yields, and a vector of physical corn characteristics. Several single product models are investigated, two of which implicitly take into account the simplex sample space of product yields. A multivariate model is considered which consists of mapping the sample space from a simplex to unrestricted Euclidean space. Comparisons are performed using a jack-knife like approach .

Keywords

Dry milling, Quality characteristics, Yield prediction, Production function, Linear models, Compositional data, Cobb-Douglas, Translog, Continuation ratios, Jack-knife, Multivariate analysis.

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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Apr 28th, 1:00 PM

MULTI-PRODUCT DRY MILLING YIELDS PREDICTION WHEN PRODUCTS ARE NOT INDEPENDENT

The yield of products in the dry milling industry is largely determined by the physical properties of the corn kernel. The main objective of this paper is to investigate several statistical models of dry milling yield prediction based on physical characteristics of corn. Data consisting of one hundred corn samples representing a range of genetic traits and quality differences are used. For each corn sample, sixteen physical and chemical properties together with six dry milling product yields were measured, in a controlled laboratory environment .

For each corn sample, we consider a vector of dry milling product yields, and a vector of physical corn characteristics. Several single product models are investigated, two of which implicitly take into account the simplex sample space of product yields. A multivariate model is considered which consists of mapping the sample space from a simplex to unrestricted Euclidean space. Comparisons are performed using a jack-knife like approach .