Precision Crop Load Management

My research team and I are focusing our efforts on resolving the critical challenge of excessive flowering and fruiting in apple and peach trees. The consequences of this overproduction are smaller fruit sizes and reduced bloom in subsequent years. To enhance crop quality, value, and promote sustainable annual bearing, our research aims to optimize crop load. We leverage predictive models and chemical interventions to achieve this balance, recognizing the substantial financial implications of crop load management.

A primary objective of our research is to evaluate current and potential chemical fruit thinners. The goal is to optimize fruit thinning potential while preserving high fruit yield and quality. We utilize the MaluSim model, a predictive tool developed by Cornell University, to determine the optimal timing for these thinning applications. We have conducted numerous field trials on major apple cultivars, including Fuji, Gala, Honeycrisp, and Red Delicious, to assess various materials for fruit thinning, such as 6-BA, NAA, ethephon, and ACC. We also explore blossom thinning materials like liquid lime sulfur and ammonium thiosulfate, and substances like ethephon and NAA for return bloom.

Additionally, we investigate chemicals and models for blossom thinning. We have expanded upon the predictive "Pollen Tube Growth Model" (PTGM) initially developed by researchers at Virginia Tech. This model provides precise timing for bloom thinning sprays. We have developed new models for two apple cultivars and are part of a multi-state research project to create a "universal" PTGM model for commercial apple cultivars across the USA. Our research, therefore, seeks to develop efficient and effective strategies for managing crop load, ultimately improving the sustainability and profitability of apple and peach industries.