The first study introduces a fruit set prediction model based on distributions of fruitlet mass, offering a reliable method to estimate fruit drop, or abscission, during the growing season. Tested across four distinct U.S. regions, the model demonstrated consistent accuracy, giving growers a valuable tool to make timely thinning and management decisions that directly impact yield and fruit size.
The second study examines how crop load affects key fruit quality attributes, including size and color, as well as return bloom in 'Honeycrisp' apple trees. Conducted across multiple U.S. locations, the research highlights the balance required between maximizing current-season yield and ensuring strong flowering and productivity in the following year. Findings confirm that excessive crop load can reduce fruit quality and negatively impact return bloom, while optimized crop levels support more consistent annual production.
Together, these studies underscore the importance of precise crop load management in modern apple production. By combining predictive tools with a deeper understanding of crop load effects, growers can make more informed decisions that improve fruit quality, stabilize yields, and enhance long-term orchard performance.
This work contributes to ongoing efforts to refine orchard management practices through data-driven approaches, helping apple producers optimize productivity, fruit quality, and sustainability in an increasingly complex growing environment.
The collaborations for both publications emerged from a NIFA-USDA-SCRI project entitled Precision Apple Crop Load Management led by Dr. Terence Robinson of Cornell University. The subject of our prediction model publication was the basis of Dr. Laura Hillmann's PhD research led by our group here at Michigan State University. This research was prompted by an elegant fruit set prediction model previously developed by Dr. Duane Greene (UMass) and colleagues at Cornell. Despite the model's accuracy, grower adoption was limited by the relatively large time investment to implement the model. Our work was an attempt to redesign the model using a different approach without compromising precision.
Todd Einhorn is the Martin & Judith Bukovac Endowed Associate Professor of Tree Fruit Physiology at Michigan State University. Co-author Luis Gonzalez Nieto is a professor in the College of Agriculture and Natural Resources, Michigan State University
The full articles can be read on the ASHS HortScience electronic Journal website at:
https://doi.org/10.21273/HORTSCI18854-25 and https://doi.org/10.21273/HORTSCI19058-25
Established in 1903, the American Society for Horticultural Science is recognized around the world as one of the most respected and influential professional societies for horticultural scientists. ASHS is committed to promoting and encouraging national and international interest in scientific research and education in all branches of horticulture.
Comprised of thousands of members worldwide, ASHS represents a broad cross-section of the horticultural community - scientists, educators, students, landscape and turf managers, government, extension agents and industry professionals. ASHS members focus on practices and problems in horticulture: breeding, propagation, production and management, harvesting, handling and storage, processing, marketing and use of horticultural plants and products. To learn more, visit ashs.org.
