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In this paper, we introduce a unique new plant breeding decision support software tool DeltaGen, implemented in r and its package Shiny. DeltaGen provides plant breeders with a single integrated solution for experimental design generation, data quality control, statistical and quantitative genetic analyses, breeding strategy evaluation, simulation, and cost analysis, pattern analysis, index selection, and underlying basic theory on quantitative genetics. Key analysis procedures in DeltaGen were demonstrated using three datasets generated from forage breeding trials in Australia, New Zealand, and the United States. Analyses of the perennial ryegrass seasonal growth data in Case Study 1 was based on residual maximum likelihood analysis and pattern analysis. A graphical summary of the performance of entries across locations was generated, and entries with specific and broad adaptation were identified. The quantitative genetic analysis and breeding method simulation procedures applied to the perennial ryegrass half-sib (HS) family data in Case Study 2 enabled estimation of quantitative genetic parameters, prediction of genetic gain, and calculation of costs per selection cycle. These results enabled comparison of three breeding methods, which also included genomic selection, and their simulation. Data from Case Study 3 were analyzed to investigate a multivariate approach to identify HS families of switchgrass with breeding values that would enable an increase in biomass dry matter yield (DMY) and cell wall ethanol (CWE) and a decrease in Klason lignin (KL). The Smith– Hazel index developed enabled identification of HS families with genetic worth for increasing DMY and CWE and reducing KL, in contrast with individual trait selection. Analysis of the datasets in all three case studies provides a snapshot of the key analyses available within DeltaGen. This software tool could also be used as a teaching resource in plant breeding courses. DeltaGen is available as freeware at http://agrubuntu.cloudapp.net/plantBreedingTool/ M.Z.Z. Jahufer and D. Luo, AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, New Zealand. Received 28 July 2017. Accepted 26 Jan. 2018. *Corresponding author (zulfi.jahufer@ agresearch.co.nz). Assigned to Associate Editor Jeffrey Endelman. Abbreviations: ApWFgsy–HS, among and within half-sib family; BLUP, best linear unbiased predictor; CWE, cell wall ethanol; DMY, dry matter yield; FS, full-sib; DG, genetic gain; GEBV, genomic estimated breeding value; GS, genomic selection; HS, half-sib; HSPT, half-sib family with progeny testing; KL, Klason lignin; MANOVA, multitrait analysis of variance; REML, residual maximum likelihood; SH, Smith– Hazel; TAFE, Technical and Further Education. Published in Crop Sci. 58:1118–1131 (2018). doi: 10.2135/cropsci2017.07.0456 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). Published March 15, 2018