Finlay–Wilkinson random regression for yield and yield stability prediction in cereals
Pablo Sandro, Justin Blancon, Jeffrey Neyhart, Julie Dawson, Lucia Gutiérrez · 2026
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Description
Pablo Sandro and four co-authors test a random regression method for predicting crop yield stability in this 2026 paper. Year-to-year climate swings make yields jumpy, so breeders want lines that hold steady. The Finlay-Wilkinson approach models each line's response to environment quality, and the random version can borrow strength from relatives' DNA.
They fit both versions on oat, barley, and wheat datasets with differing balance. On balanced data the two gave similar stability estimates. On sparse oat data, the random version still predicted stability moderately well for unseen lines. Choosing the right test environments mattered more than adding more of them. The authors suggest the method can flag stable lines earlier and design leaner testing schemes.
Catalog record
| CREATOR | Pablo Sandro, Justin Blancon, Jeffrey Neyhart, Julie Dawson, Lucia Gutiérrez |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |