Precision crop load management is important for increasing yield, fruit size, and quality of apple production, particularly for the ‘Honeycrisp’ variety, which is highly susceptible to biennial bearing and difficult to thin. The MaluSim model, developed at Cornell University, can be used to guide crop load management by modeling carbohydrate balance to optimize fruitlet thinning. However, this model was developed using tree performance in New York and may not account for higher temperatures in Washington State, which may affect canopy growth and development. The objective of this study was to assess early season canopy growth and evaluate carbon partitioning of Honeycrisp apples grown in Washington state climatic conditions. The first experiment compared the canopy growth of Honeycrisp topworked in 2016 onto a ‘Granny Smith’ planting with M.9T337 as a rootstock. The topworked trees were trained to single, double, or triple-axis trees. The second experiment assessed carbon partitioning of Honeycrisp trees conducted under single, double, and triple leader(s) training systems. Training systems significantly impacted shoot length and the number of shoots. Single-axis trees had significantly longer shoot lengths and higher shoot numbers than the double and triple-axis training systems. Canopy imaging was also used to assess canopy infill and light interception. The second experiment weighed the biomass partitioning of the various tree parts (spurs, 1-year growth, leaves, branches, roots, rootstock, inter-stem, and fruits). Below-ground portions of all training systems accounted for more biomass than above-ground portions. There was smaller wood and a greater proportion of spur buds in the double and triple-axis trees compared to single-axis trees. The differences in carbon partitioning and canopy development among different training systems compared to the types of trees used when developing the MaluSim model may affect how the MaluSim model performs.
