Fruit quality of apple is heavily influenced by crop load management practices. During the developmental window between bloom and 15mm fruitlet diameter, the general commercial practice is to apply chemical thinners to reduce the initial crop load based on the cultivar and market. However, thinning success varies from year to year, depending on bloom density, thinner selection and dose, climatic factors, tree health and cultivar. Precision crop load models such as the pollen tube growth model, the MaluSim model, and the fruit growth rate model have improved thinning success; however, labor-intensive measures has hindered broad adoption of the latter model which effectively predicts fruit set following thinner application. To improve grower adoption, we developed an alternative, time-efficient fruit set prediction model. The model has been tested on multiple cultivars over a three-year period at four distinct apple production regions throughout the United States. Abscission probabilities and fruit set predictions are based on the distribution of fruitlet mass and confirmed by actual measures of fruit set. The model starts at 6 mm fruitlet diameter and accounts for all thinners up to this time. A prediction is returned in ~8 days to inform repeat thinner applications while fruitlets are still highly sensitive to thinners. A user-guide, digital balance that automatically exports fruit mass to a laptop/tablet, and a macro-enabled Excel spreadsheet file have been developed and are available to growers to increase the precision around crop load management.
