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Rootstock selection and crop load adjustment are key practices in apple orchard management; nevertheless, the effects of rootstocks and crop load levels on important physiological processes of the scions, such as photosynthetic performance and carbohydrate accumulation, are still unclear. To investigate the impact of different rootstocks and crop load levels on scion photosynthesis and carbohydrate buildup, in 2020, 'Honeycrisp' trees grafted on rootstocks 'G.41', 'G.935', and 'M.9-T337' were thinned to low and high crop load levels, and photosynthetic performance and carbohydrate accumulation in leaves and fruit were evaluated. Leaves from 'G.935' showed the highest net photosynthesis and electron use efficiency of photosynthesis and the lowest activity for non-net carboxylative processes, all together indicative of enhanced photosynthetic performance. High crop load determined an increase in gas exchange, suggesting a positive feedback of high fruit competition on carbon assimilation. While rootstock 'M.9-T337' showed a higher accumulation of starch in leaves, no pattern regarding the composition of leaf-soluble sugars among rootstocks could be identified. Conversely, by the end of the harvest season, leaves from low-cropping trees had higher fructose, glucose, and sorbitol than those from high-cropping trees, but differences in starch content were not significant. Fructose and sorbitol concentrations were affected by rootstock and crop load, respectively. Overall, this study showed that high cropping enhanced photosynthesis in 'Honeycrisp' apple and determined lower accumulation of some soluble carbohydrates (fructose, glucose, sorbitol) in leaves. This study also provided insights into how rootstocks affect photosynthetic performance of 'Honeycrisp', highlighting 'G.935' as the rootstock conferring the highest photosynthetic capacity under the present experimental conditions.

Flower receptivity is a limiting factor for the fertilization of several tree fruit. The effective pollination period (EPP) can be used to determine flower longevity and identify limiting factors by assessing stigmatic receptivity, pollen tube growth rate, and ovule longevity. EPPs were determined for three apple cultivars under natural field conditions in Washington State in 2019 and 2020. In addition, a greenhouse study, performed under semi-controlled conditions, evaluated the influence of six maternal parents on the pollen tube growth performance of six pollen sources. The duration of the stigmatic receptivity ranged from 6.3 to 8.1 days, depending on the cultivar and year-pollen tubes required between 5.5 and 7.0 days from the stigma to reach the ovules. Ovule longevity of non-pollinated flowers varied between 8.2 and 11.3 days. Combinations of these factors resulted in EPPs ranging from 3.0 days for 'Rubinstar' to 5.6 days for 'Olsentwo Gala' in the present experimental conditions. The greenhouse study revealed that parentage affected pollen tube growth performance. Importantly, a significant interaction between maternal and paternal factors indicated that the performance of different pollen sources depended on the maternal parent and that general recommendations on pollination need to account for the maternal parent.

In vitro germination assays are frequently used in screening trials to evaluate the pollen viability of pollinizers. To be effective, screening trials must have defined threshold criteria, from which individuals can then be assessed. However, despite decades of research on pollen viability, no established threshold is available to categorize apple cultivars based on their in vitro pollen tube lengths. This study aimed to identify and characterize the subgroups of cultivars based on their pollen tube growth performance. In vitro pollen tube lengths of 41 individuals were determined by incubating samples on artificial germination media at 15 and 25 °C. A six-number summary statistic was calculated, and hierarchical clustering on principal component (HCPC) analysis was used to determine and characterize subgroups. Furthermore, a decision tree model was used to predict class membership for future datasets. HCPC analysis partitioned the 41 individuals into three subgroups with different performances. The decision tree quickly predicted the cluster membership based on the second quartile at 15 °C and the third quartile at 25 °C. The thresholds from the decision tree can be used to characterize new observations. The use of the methods will be demonstrated using a case study with 29 apple accessions.