25107 Evaluation of Field Book: An Open-source Android App for Collecting Phenotypic Data in a Peach Breeding Program

Wednesday, August 10, 2016: 2:00 PM
Capitol Center Room (Sheraton Hotel Atlanta)
Ksenija Gasic , Clemson University, Clemson, SC
Trevor Rife , Kansas State University, Manhattan, KS
Jesse A. Poland , Kansas State University, Manhattan, KS
Sook Jung, Assistant Research Professor , Washington State University, Pullman, WA
Kate M. Evans , Washington State University, TFREC, Wenatchee, WA
Nnadozie Oraguzie , Washington State University, Pullman, WA, United States
Lisa Wasko DeVetter , Washington State University, Mount Vernon, WA
Cameron Paul Peace , Washington State University, Pullman, WA
Mercy A. Olmstead , University of Florida, Gainesville, FL
Dorrie Main, Professor of Bioinformatics , Washington State University, Pullman, WA
Concomitant advances in high-throughput sequencing, phenotyping, and computational technologies, are generating petabytes of genomic, genetic, and breeding data for Rosaceae crops. Efficient utilization of this data by the research community requires analysis, integration, and visualization. The Genome Database for Rosaceae (GDR) is working towards enabling high-resolution dissection of traits and relating molecular diversity to functional variation for efficient development of new cultivars through marker-assisted breeding. Plant breeding programs generate and search through thousands of plants to find the best plant types which will become new improved cultivars. A typical breeding program produces hundreds of thousands of phenotypic data points in a given year. Inefficient and poor handling of this data decreases the genetic gain of the breeding program, consequently reducing its efficiency. While data handling has been a limiting area for breeding programs in the past, advances in computing have provided simple solutions that can address the needs of plant breeding programs. Using open-source software and relatively inexpensive phone and tablet hardware, GDR is supporting development of a platform that will allow researchers to replace hard-copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. The peach breeding program at Clemson University is testing the Field Book app for collecting data on field research plots. Field Book data input is customized for a large number of data types including numeric, percentage, categorical, multicategorical, date, Boolean, text, counter, rust score, photo, and audio. These data types allow the user to create custom traits to suit their own personal needs. We report our experience of Field Book app in a peach breeding program and further development plans.
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