A hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources.

Abstract

A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variability.

Plant phenotyping relevance

リンゴ器官の成長形質を階層的・反復的に収録した再利用可能なデータセットであり、成長率やアロメトリーの推定・モデル較正に用いるデータ資源として方法論的価値がある。

Code and data availability

The paper is a Data in Brief article describing its own apple tree growth phenotype dataset (shoot, fruit, trunk measurements) deposited publicly on Mendeley Data with DOI 10.17632/852r5dnzd5.1 and a direct URL. This is a paper-specific, public, actionable phenotype dataset.