The Prognostic Breeding Application JMP Add-In Program

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.

[1]  Michele Morgante,et al.  Evolution of DNA Sequence Nonhomologies among Maize Inbredsw⃞ , 2005, The Plant Cell Online.

[2]  V. A. Fasoula,et al.  Honeycomb breeding: principles and applications. , 2010 .

[3]  P. A. Peterson The plant genetics discovery of the century: transposable elements in maize. Early beginnings to 1990 [Zea mays L.] , 2005 .

[4]  Christopher A. Cullis,et al.  DNA Rearrangements in Response To Environmental Stress , 1990 .

[5]  V. A. Fasoula,et al.  Competitive Ability and Plant Breeding , 2010 .

[6]  Donald N. Duvick,et al.  Long‐Term Selection in a Commercial Hybrid Maize Breeding Program , 2010 .

[7]  T. Richmond,et al.  The Composition and Origins of Genomic Variation among Individuals of the Soybean Reference Cultivar Williams 821[W][OA] , 2010, Plant Physiology.

[8]  V. A. Fasoula,et al.  Application of prognostic breeding in maize , 2016, Crop and Pasture Science.

[9]  J. Dudley,et al.  100 Generations of Selection for Oil and Protein in Corn , 2010 .

[10]  R. Phillips,et al.  Plant Breeding Progress and Genetic Diversity from De Novo Variation and Elevated Epistasis , 1997 .

[11]  V. A. Fasoula,et al.  Improving the phenotypic expression of rice genotypes: Rethinking “intensification” for production systems and selection practices for rice breeding , 2015 .

[12]  V. A. Fasoula,et al.  Gene Action and Plant Breeding , 2010 .

[13]  V. A. Fasoula,et al.  Two novel whole-plant field phenotyping equations maximize selection efficiency. , 2008 .

[14]  T. Rocheford,et al.  Maize selection passes the century mark: a unique resource for 21st century genomics. , 2004, Trends in plant science.

[15]  V. A. Fasoula,et al.  The impact of plant population density on crop yield and response to selection in maize , 2005 .

[16]  V. A. Fasoula,et al.  Prognostic Breeding: A New Paradigm for Crop Improvement , 2013 .

[17]  V. A. Fasoula,et al.  Honeycomb Selection Designs , 2010 .

[18]  V. A. Fasoula,et al.  Principles underlying genetic improvement for high and stable crop yield potential , 2002 .