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Agronomy 2019, 9(1), 25; https://doi.org/10.3390/agronomy9010025

The Prognostic Breeding Application JMP Add-In Program

1
Institute of Plant Breeding, Genetics & Genomics and Dept. of Crop and Soil Sciences, University of Georgia, 111 Riverbend Road, Athens, GA 30602, USA
2
Agricultural Statistics Laboratory, University of Arkansas, Fayetteville, AR 72701, USA
*
Author to whom correspondence should be addressed.
Received: 4 December 2018 / Revised: 3 January 2019 / Accepted: 7 January 2019 / Published: 9 January 2019
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Abstract

Prognostic breeding is a crop improvement methodology that utilizes prognostic equations to enable concurrent selection for plant yield potential and stability of performance. There is a necessity for plant breeders to accurately phenotype plants in the field and select effectively for high and stable crop yield in the absence of the confounding effects of competition. Prognostic breeding accomplishes this goal by evaluating plants for (i) plant yield potential and (ii) plant stability, in the same generation. The plant yield index, stability index and the plant prognostic equation are the main criteria used for the selection of the best plants and the best entries grown in honeycomb designs. The construction of honeycomb designs and analysis of experimental data in prognostic breeding necessitate the development of a computer program to ensure accurate measurement of the prognostic equations. The objective of this paper is to introduce the Prognostic Breeding Application JMP Add-In, a program for constructing honeycomb designs and analyzing data for the efficient selection of superior plants and lines. The program displays powerful controls, allowing the user to create maps of any honeycomb design and visualize the selected plants in the field. Multi-year soybean data are used to demonstrate key features and graphic views of the most important steps. View Full-Text
Keywords: crop improvement; genetic gain; plant yield index; stability index; crop yield potential; honeycomb selection designs; selection efficiency; prognostic equations; moving replicates; moving grids crop improvement; genetic gain; plant yield index; stability index; crop yield potential; honeycomb selection designs; selection efficiency; prognostic equations; moving replicates; moving grids
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Fasoula, V.A.; Thompson, K.C.; Mauromoustakos, A. The Prognostic Breeding Application JMP Add-In Program. Agronomy 2019, 9, 25.

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