REPRESENTATIONS OF MULTI-ATTRIBUTE GRAIN QUALITY

Grain quality is typically measured via several attributes. As these attributes vary across shipments and time, grain quality can be described using multivariate probability or frequency distributions. These distributions are important in modeling blending opportunities inherent in various grain shipments. For computational reasons, it is usually necessary to represent these distributions with a small set of discrete points and probabilities. In this analysis, we suggest a representation method based on Gaussian quadrature. This approach maintains the blending opportunities available by preserving moments of the distribution. The Gaussian quadrature method is compared to a more commonly used representation in a barley blending model.


Issue Date:
2001-07
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/31149
Published in:
Journal of Agricultural and Resource Economics, Volume 26, Number 1
Page range:
275-290
Total Pages:
16




 Record created 2017-04-01, last modified 2017-08-24

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