ProbApple – A probabilistic model to forecast apple yield and quality

CONTEXT

Fruit yield and quality are critical determinants of the economic performance of apple orchards. However, these economic metrics are highly uncertain due to various quality-reducing factors during the growing season, and fruit growers would greatly benefit from reliable predictions.

OBJECTIVE

In this study, we aim at developing a new tool to support fruit growers in anticipating yield and potential quality losses under the specific conditions of their orchards. The tool should allow application at four key time points during the growing season (at full bloom, before fruit thinning, after June drop, and four weeks before harvest) and capture uncertainty in the quality-reducing factors and the resulting yield parameters.

METHODS

Using expert knowledge, we designed and parameterized the probabilistic ‘ProbApple’ model and conducted Monte Carlo simulations to project probability distributions for total and high-quality apple yield for a ‘Gala’ orchard in the German Rhineland. We compared scenarios with and without anti-hail netting to demonstrate the use of the model for predicting apple yield.

RESULTS AND CONCLUSIONS

Applying the model four weeks before harvest, the median forecasted apple yield was 50.4 t/ha (25 %-quantile: 44.0; 75 %-quantile: 57.8 t/ha) with anti-hail netting and 49.3 t/ha (25 %-quantile: 42.7; 75 %-quantile: 56.5 t/ha) without. The forecasted high-quality yield was 34.9 t/ha (25 %-quantile: 27.5; 75 %-quantile: 41.6 t/ha) with the protection measure and 30.0 t/ha (25 %-quantile: 15.8; 75 %-quantile: 38.7 t/ha) without. These results are in line with commonly achieved ‘Gala’ apple yields in the Rhineland region.

SIGNIFICANCE

We show that ProbApple is a customizable tool for forecasting apple yield and quality, offering producers valuable insights for operational planning and informed management decisions.