Predicting apple yield and quality remains a challenge for growers worldwide. Numerous factors, such as weather conditions, pests and diseases, contribute to uncertainty in production. Accurate forecasts can help fruit growers make informed decisions and optimize orchard management, storage and sales.
We developed the ProbApple tool, which utilizes a probabilistic modeling approach based on decision analysis techniques to forecast both total yield and quality of apples at four critical stages of the production cycle: (i) full bloom, (ii) pre-thinning, (iii) post-June drop, and (iv) four weeks before harvest. While natural variability and uncertainty in input parameters lead to broad outcome ranges, these distributions provide a realistic representation of potential harvest results. Such predictions help fruit growers in both short-term operational decisions throughout the growing season and long-term strategic planning.
In our publication, we present a case study on ‘Gala’ apples in the German Rhineland, comparing yields in orchards with and without anti-hail netting. The model can easily be applied to similar orchards, requiring only a few orchard-specific estimates and minimal measurement efforts.
Find the full publication here:
Schmitz, C., Zimmermann, L., Schiffers, K., Balmer, M., Luedeling, E., 2025. ProbApple – A probabilistic model to forecast apple yield and quality. Agricultural Systems 226, 104298. https://doi.org/10.1016/j.agsy.2025.104298
Interested to play around with a slightly simplified version of the model? Visit our shiny App!
