Abstracts

Please follow the instructions below for the preparation of your abstract.

Please note the abstract example provided as sample to assist you to prepare your abstract in a consistent style and format.

Many thanks in advance for your abstract submission in strict adherence to the instructions below.

        1. Please use 15px. bold Arial font for the Title of the abstract.

        2. Please use 15px. Arial font for the Author name as: “Surname, N.” Please use number in             

            parenthesis as:  “(1)” for first affiliation, “(2)” for second affiliation, etc. with no space after name 

            initial.

        3. If there is a coauthor separate with “,” with no space before and one space after. 

        4. Please use 13px. Arial font for affiliations, using superscript numerals in separate paragraphs to

            provide authors’ details.

        5. Please write text of the abstract as a single paragraph, with no indentation, with a maximum   

            count of 350 words, using 15px. Arial font.

        6. Please complete the following additional information using fonts and formats as per Example                  below.

Keywords: (maximum 5 keywords, use 15px. Arial italics)

Funding: (use 13px. Arial)

Contact Information: (provide full contact details i.e. full name, full postal address, contact phone number, contact emails; use 15px. Arial italics)

Poster or oral presentation (underline your preferred choice of presentation; underline both options in case the program is unable to accommodate the abstract for oral presentation)

Session(s): (indicate title of sessions that you wish your abstract be considered for inclusion)

Example:

Modelling genotype by environment interaction improved the prediction accuracy of alfalfa growth traits

Jighly, A.(1,2), Joukhadar, R.(1,2), Spangenberg, G.(1,3)

¹ Qingdao Agricultural University, Qingdao, Shandong Province, P.R. China

² AgriSapiens, Melbourne, Victoria 3081, Australia

³ La Trobe University, AgriBio Centre for AgriBioscience, Victoria 3086, Australia

Alfalfa (Medicago sativa) plays an important role as animal feed due to its high nutritional value being rich in protein. Its multiple cuttings per year make it a sustainable and economical feed option. Moreover, its deep root system allows it to thrive in various environmental conditions. Advancements in predicting and enhancing dry matter yield and other growth characteristics are main objectives in predictive breeding of alfalfa. This study investigated the growth characteristics of alfalfa by incorporating genotype by environment (GxE) interactions into the predictive model through the reproducing kernel Hilbert space machine learning algorithm. We used publicly available data of 400 global alfalfa accessions genotyped with 122,763 genotyping-by-sequencing single nucleotides polymorphisms, and phenotyped in four and five field trials (two locations) for dry matter yield and plant height; respectively. Our results showed that the GxE model demonstrated a significant improvement in prediction accuracy for dry matter yield compared to the standard model that does not consider GxE. The average prediction accuracy increased from 0.50 to 0.56 by modelling GxE, and this improvement was consistent across different field trials ranging from 0.03 to 0.10 per trial. On the other hand, the GxE model had slight improvement in prediction accuracy for plant height increasing to 0.57 compared to 0.55 for the standard model. However, this improvement was not statistically significant, as expected given that plant height is usually controlled by a small number of genes that have low interaction with the environment. These results highlight the potential of GxE modelling in enhancing our understanding of alfalfa growth dynamics and improving biomass yield in alfalfa breeding programs. Further research is required to validate and extend these findings across different traits, germplasm sources, and environmental conditions.

Keywords: predictive breeding, alfalfa, growth and biomass yield traits, genomic selection, GxE modelling

Funding: Qingdao Agricultural University, China and AgriSapiens, Australia

Contact Information: Dr. German Spangenberg FTSE PSM, Qingdao Agricultural University, College of Grassland Science, 700 Changcheng Road, Chengyang District, 266109 Qingdao, Shandong Province, P. R. China, Phone: +86 150 5325 3830, Emails: germancspangenberg@gmail.com; germancspangenberg@qau.edu.cn; g.spangenberg@latrobe.edu.au

Poster or oral presentation

Session(s): Genetic and Genomic Tools; Forage Quality