Sammanfattning

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson's correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.

Publicerad i

Scientific Reports
2023, Volym: 13, nummer: 1, artikelnummer: 9947

SLU författare

• Ortiz Rios, Rodomiro Octavio

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

• Reslow, Fredrik

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

Globala målen

SDG2 Avskaffa hunger, uppnå tryggad livsmedelsförsörjning och förbättrad nutrition samt främja ett hållbart jordbruk


UKÄ forskningsämne

Genetik och förädling
Jordbruksvetenskap
Trädgårdsvetenskap/hortikultur


Publikationens identifierare

DOI: https://doi.org/10.1038/s41598-023-37169-y

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/122513