Bitter Pit: Can We See It Coming?

There are a few options available for growers to get a better idea of bitter pit pressure within their blocks before storage of apples occurs. There are 3 main methods being used:

• Passive Prediction Model
• Fruit Peel Sap Analysis
• Shoot Length

These methodologies are not precise (~65% accuracy), have shown to underestimate bitter pit across multiple regions and cultivars and have not been thoroughly researched in Ontario.

Recent research indicates that using multiple prediction methods together may improve the accuracy of bitter pit predictions. Growers should weigh the costs, time requirements, and management benefits of each method to determine the most practical approach for their orchard.

Passive Bitter Pit Prediction Model

The ‘Passive Method’ of bitter pit incidence was developed by Dr. Chris Watkin’s out of the Cornell University research lab. This method, as described below, looks at a large sample of apples prior to harvest in hopes of estimating the total bitter pit occurrence in storage.

Passive Bitter Pit Prediction Methodology

1. Collect 100 fruit from representative trees in one block three weeks before harvest
   1. Harvest fruit from as many trees as possible, with a minimum of 10 trees per block.
   2. Blocks that have a variation in crop load, only pick fruit from representative trees AND/OR take separate samples from lower cropping trees.
2. Store fruit at room temperature (20°C) for three weeks
3. Assess bitter pit (internal and external) of each sample
4. Express data as a percent for bitter pit incidence (shown in highlighted text box).

% of predicted bitter pit = (amount of fruit with bitter pit ÷ total number of fruit) × 100

Passive Prediction Model

Fruit Peel Sap Analysis

The Peel Sap Analysis is a newer test looking at the differences and ratios of calcium (Ca), magnesium (Mg), potassium (K), and nitrogen (N). This method was developed by Cornell University as well, but by Dr. Lailiang Cheng.  

Fruit Peel Sap Analysis Methodology

1. Collect fruit samples (minimum 20, ideally 100) from representative trees in one block when the fruit is roughly 55 grams in size (golf ball size)
   1. Harvest fruit from as many trees as possible, with a minimum of 10 trees per block, and no more than 2–3 apples per tree.
2. Send samples in labelled bags for lab, with the following information:
   1. Name, Farm, Cultivar, Block, Date
   2. If submitted multiple samples from the same block distinguish between them with identifier (i.e. low crop load, more vigour, rootstock).
3. Place samples in a cooler for transport. If needed, place bags in freezer for future transport to the lab.
4. After analysis occurs, look at K/Ca and N/Ca ratios and compare bitter pit risk as outlined in Table 1.

Table 1. Fruit peel sap analysis nutrient ratios and their risk indicators for bitter pit predictions (Cheng, 2025)

Fruit Peel Sap Analysis

Average Shoot Length

There is a high correlation of bitter pit incidence with excessive vegetative growth, this is due to competition between leaves and fruit for calcium. As a result, shoot length can serve as a useful indicator of bitter pit risk. Researchers at Penn State University developed a prediction model for Honeycrisp that incorporates both the fruit peel N/Ca ratio and average terminal shoot length, demonstrating that combining measures of tree vigour and fruit nutrient status can improve bitter pit prediction

Average Shoot Length Methodology

1. Three weeks before harvest select 20 representative trees per block
2. Measure the length (inches) of 5 current season terminal shoots from each tree
3. Determine the average length of shoot growth (shown in highlighted box)
4. Collect fruit samples for Fruit Peel Sap Analysis
5. Compare N/Ca ratio and shoot length to Table 2.

Average terminal shoot length = total lengths (inches) ÷ total number of measurements taken

Table 2. Estimated percent of apples on Honeycrisp trees developing bitter pit after cold storage as affected by shoot length and N/Ca. (Marini, 2023)

Average Shoot Length & N/Ca Ratio

Bitter Pit Correlations

Across all three approaches, the strongest and most consistent bitter pit associations are: low crop loads, excessive vegetative growth, large fruit size, and calcium imbalance within the fruit. Rootstocks are a contributing factor and can either enhance or suppress bitter pit onset.

For additional information on bitter pit understanding and mitigation please refer to the following articles:

• A Pitfall in the Pipeline – Part I: Calcium’s One-Way Ticket
• A Pitfall in the Pipeline – Part 2: Challenging the Pit Problem

Resources & References

Al Shoffe, Y., & Watkins C. Prediction of Bitter Pit In  ‘Honeycrisp’ Using the Passive Method. Cornell University.

Basedow, M., Al Shoffe, Y., & Galimberti, A. 2019. Predication of Bitter Pit in ‘Honeycrisp’ Apples Before Storage. Northern New York Agricultural Development Program 2019 Project Report.

Baugher, T.A., Marini, R., Schupp, J.R., & Watkins, C.B. 20157. Prediction of Bitter Pit in ‘Honeycrisp’ Apples and Best Management Implications. HortScience, 52:10. 1368–1374.

Cheng, L. 2025. Diagnosis of Tree & Fruit Nutrient Status via Sap Analysis. Cornell University. PowerPoint.

Marini, R. P., Baugher, T. A., Muehlbauer, M., Sherif, S., Crassweller, R., & Schupp, J. R. 2020. Verification and Modification of a Model to Predict Bitter Pit for ‘Honeycrisp’ Apples, HortScience, 55:12. 1882–1887.

Robinson, T., & Cheng, L. n.d. Managing Pruning and Fertilization of Honeycrisp to Balance Growth, Cropping and Fruit Quality. Cornell Fruit Resources: Tree Fruit.

Tracton, T., & Farcuh, M. 2024. Controlling Bitter Pit in Apples: Best Practices for Growers. University of Maryland Extension. FS-2023-0701.

Wallis, A., Miranda Sazo, M., Sallato, B. 2021. Takeaways From the 2021 Honeycrisp Virtual Meetups. Michigan State University.

Watkins, C. Passive Prediction Model for Determining Storage Potential of Honeycrisp. Cornell University.

Erika DeBrouwer

OMAFA Tree Fruit Specialist

erika.debrouwer@ontario.ca