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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Bouzaher, Aziz and Carriquiry, Alicia L.
(1991).
"MULTI-PRODUCT DRY MILLING YIELDS PREDICTION WHEN PRODUCTS ARE NOT INDEPENDENT,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1421
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 .
