|GARCIA, ROSA - National Institute For Agricultural Research (INIAP)|
|PARRA-QUIJANO, MAURICIO - National University Of Colombia|
|IRIONDO, JOSE - Juan Carlos University|
Submitted to: Crop and Pasture Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/21/2018
Publication Date: 6/19/2019
Citation: Garcia Sánchez, R.M., Parra-Quijano, M., Greene, S.L., Iriondo, J.M. 2019. Predictive characterisation identifies global sources of acyanogenic germplasm of a key forage species. Crop and Pasture Science. 70(6):546-554. https://doi.org/10.1071/CP18346.
Interpretive Summary: Forage breeding is essential to maximize the performance of animals and its effectiveness depends on available genetic diversity, which is often not known. Predictive characterization based on ecogeographic information is a promising approach to address the urgent need to evaluate large gene bank collections. Using white clover as an example, we applied this approach to predict which accessions in a genebank collection may likely be acyanogenic and where in the world we could collect white clover with this desirable trait. Occurrence data on genebank accessions and wild populations was divided into two subsets based on if they had been evaluated and not for cyanogenesis. The occurrence locations were characterized for their habitat parameters. Multivariate analysis was employed to examine the association between cyanogenesis and environmental variables, and eight different modelling approaches were evaluated. We identified 470 populations with a high probability of being acyanogenic and the procedure was validated by morphologically evaluating a subset; all were acyanogenic. Our study also expanded the geographic areas previously thought to support acyanogenic white clover. In conclusion, our work demonstrated the value of predictive characterization in identifying useful accessions in a germplasm collection, as well as the geographic regions where desirable traits can be found. This procedure can be added to the plant breeder's toolbox, making it easier and more cost effective to identify breeding material with valuable traits.
Technical Abstract: Forage breeding is essential to maximize the performance of animals consuming the forage and its effectiveness depends on available genetic diversity. But breeding is challenged when there is limited evaluation of genebank accessions. Predictive characterization based on ecogeographic information is a promising approach to the urgent need to expedite the evaluation of target traits in existing collections of forage genetic resources. Using Trifolium repens as an example, we applied the calibration predictive characterization method to model the expression of cyanogenesis in T. repens and predicted which accessions and wild populations would have desired levels of this trait. Data on genebank accessions and other population occurrences was divided into two subsets, considering the availability of evaluation data for cyanogenesis: evaluated and non-evaluated sets. The occurrence sites of the records with the best geo-referencing quality were characterized ecogeographically. Multivariate analysis was employed for studying the association between cyanogenesis and these ecogeographic variables, and eight alternative modelling approaches were applied on the evaluated set. We identified 470 populations with a high probability of being acyanogenic. The procedure was validated by evaluating a sample of the top-rank predicted acyanogenic accessions with a 100% success. Our study also expanded the areas previously rated as highly acyanogenic. In conclusion, our results contribute to increase, in a predictive way and with a minimum cost, the knowledge on natural 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 utilization.