R-based workflow to estimate chilling requirements in multiple fruit tree genotypes using Partial Least Squares regression: Prunus armeniaca L. case.

Abstract

Accurate estimation of chilling requirements (CR) is essential for breeding and selecting temperate fruit trees adapted to specific agroclimatic conditions, particularly under global warming scenarios. Among the available methodologies to determine CR, the Partial Least Squares (PLS) regression procedure, based on long-term phenological and temperature records, offers a suitable approach to delineate the effective chill accumulation period. In this study, we present an R-based workflow developed using the agroclimatic functions integrated into the chillR package for R to determine the genotype-specific CR of 282 apricot ( Prunus armeniaca L.) seedlings from two progenies grown in southwestern Spain. The pipeline generates standardized CR datasets suitable for downstream applications, including QTL mapping and the selection of promising genotypes for breeding purposes. This tool streamlines the estimation process, reducing the technical expertise and time required for CR estimation, thereby supporting efficient phenotypic selection and accelerating genetic research in temperate fruit trees. The complete code and associated datasets are freely available in a public repository (https://github.com/CEBASFruitBreed/R-workflow-ChillPLS), promoting the use across a range of temperate fruit species.•Uses long-term flowering observations and temperature records to determine genotype-specific chilling requirements.•Integrates PLS regression procedure within an R-based workflow to estimate chilling requirements from datasets comprising multiple genotypes.•Generates standardized outputs suitable to support genetic analysis and informed breeding decisions.

Plant phenotyping relevance

複数のアンズ遺伝子型の低温要求量という植物生理形質を、PLS回帰とRワークフローで推定する方法自体が中心であり、再利用可能なコードとデータも提供している。

Code and data availability

The authors explicitly state that the complete R code (workflow for PLS-based chilling requirement estimation) and the associated datasets (flowering records and temperature data) are freely available in a public GitHub repository.