Title

Session 3 - Precision Agriculture: Imputing Data without Replication

Presenter Information/ Coauthors Information

Jixiang Wu, South Dakota State UniversityFollow

Presentation Type

Oral

Student

No

Track

Methodology

Abstract

It is sometimes important to revisit the historical crop field trial data. However, many historical data are available in a format of entry means under different environments rather than repeated field plot data. In this presentation, I will present a recently proposed methodology, which can be used to impute replicated trial data sets to reveal the original information harbored in the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data.

Start Date

2-11-2020 9:30 AM

End Date

2-11-2020 10:25 AM

This document is currently not available here.

Share

COinS
 
Feb 11th, 9:30 AM Feb 11th, 10:25 AM

Session 3 - Precision Agriculture: Imputing Data without Replication

Dakota Room 250 A/C

It is sometimes important to revisit the historical crop field trial data. However, many historical data are available in a format of entry means under different environments rather than repeated field plot data. In this presentation, I will present a recently proposed methodology, which can be used to impute replicated trial data sets to reveal the original information harbored in the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data.