Predictive characterisation identifies global sources of acyanogenic germplasm of a key forage species
Rosa María García Sánchez
A B F , Mauricio Parra-Quijano C , Stephanie Greene D and José María Iriondo E
+ Author Affiliations
- Author Affiliations
A Escuela Internacional de Doctorado, Universidad Rey Juan Carlos, Calle Tulipán s/n, 28933 Móstoles, Madrid, Spain.
B Centro Nacional de Recursos Fitogenéticos, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Autovía A-2 Km 36, 28800 Alcalá de Henares, Madrid, Spain.
C Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, Ciudad Universitaria, A.A. 14490, Bogotá D.C., Colombia.
D National Laboratory for Genetic Resources Preservation, USDA ARS, Fort Collins, CO, USA.
E Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Calle Tulipán s/n, 28933 Móstoles, Madrid, Spain.
F Corresponding author. Email: rosamaria.garcia@inia.es
Crop and Pasture Science 70(6) 546-554 https://doi.org/10.1071/CP18346
Submitted: 19 July 2018 Accepted: 23 April 2019 Published: 19 June 2019
Journal Compilation © CSIRO 2019 Open Access CC BY-NC-ND
Abstract
Forage breeding is essential for animal production, and its effectiveness depends on available genetic diversity. However, breeding is challenged when there is limited evaluation of genebank accessions. Predictive characterisation based on ecogeographic information is a promising approach to address the urgent need to expedite evaluation of target traits in existing collections of forage genetic resources. Using white clover (Trifolium repens L.) as an example, we applied predictive characterisation to model the expression of cyanogenesis, an important process related to the generation of anti-quality compounds. Data on genebank accessions and other population occurrences were divided into two subsets, one including accessions that had been evaluated for this trait, and the other with those that had not. The occurrence sites of the records with the best geo-referencing quality were characterised ecogeographically. The cyanogenesis trait was predicted using the calibration method, in which some selected ecogeographic variables were used as independent variables. Thus, we identified 470 populations with high probability of being acyanogenic. A small sample of populations (18 accessions) was evaluated to ratify the usefulness of this approach. Seventeen of the evaluated accessions showed a complete acyanogenic response and one showed 95% acyanogenic plants. Our study also expanded the areas previously rated as highly acyanogenic. In conclusion, our results contribute in a predictive way and with minimum cost to increase the knowledge of wild populations and genebank accessions in relation to a target trait. This facilitation in the generation of evaluation data may encourage greater investment in forage plant breeding and boost germplasm utilisation.
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