James R. Schupp

Prediction of Bitter Pit in ‘Honeycrisp’ Apples and Best Management Implications

During a 3-year study of bitter pit in commercial ‘Honeycrisp’ apple (Malus 3domestica) orchards, incidence was associated with low calcium (Ca) levels in fruit peel; high ratios of nitrogen (N), potassium (K), and/or magnesium (Mg) to Ca in fruit peel; excessive terminal shoot length; and low crop load. Peel N and Mg concentrations were negatively correlated and peel Ca concentration positively correlated with crop density (CD). Shoot length (SL) was not consistently correlated with peel N, Mg, or phosphorus (P) and was negatively correlated with only Ca. A two-variable model that included SL and the ratio of N to Ca explained more than 65% of bitter pit incidence. The model has implications for best management of the cultivar in the field and during storage. The high susceptibility of ‘Honeycrisp’ to bitter pit is not well understood. Crassweller and Smith (2016) found levels of Ca in foliar tissue were lower in ‘Honeycrisp’ than in ‘Cameo’. Cheng (2016) reported lower fruit levels of Ca in ‘Honeycrisp’ compared with ‘Gala’. Fruit levels of K, Mg, and P were similar in the two cultivars, and he proposed the resulting nutrient imbalance predisposed ‘Honeycrisp’ to a deficiency of Ca and Carelated disorders. Research in New Zealand on mineral movement in bitter pit–prone cultivars indicated rapid early season uptake of Ca and poor to no late season uptake, whereas K andMg continued to increase over the course of the season (Ferguson, 2001; Ferguson and Watkins, 1989). Studies conducted on bitter pit development at the cellular level have improved the understanding of Ca localization in cells of pitted fruit. De Freitas et al. (2010) reported evidence of a connection between bitter pit and Ca binding to cell walls as well as Ca accumulating in storage organelles. Additional cytochemical research (De Freitas et al., 2015) demonstrated an association between higher levels of water-insoluble pectin Ca and bitter pit. Hocklin et al. (2016) proposed a possible role of apoplasmic calcium-pectin crosslinking. Bitter pit management in the orchard is central to disorder prevention but is not always effective, and the reasons are often unclear. Research conducted by Rosenberger et al. (2004) demonstrated that season-long Ca treatments were required for reducing bitter pit incidence in ‘Honeycrisp’ grown in New York. Bitter pit control was not enhanced by supplementing Ca sprays with trifloxystrobin fungicide, boron, or harpin protein treatments. Trials by Biggs and Peck (2015) showed that rates ranging as high as 26.3 kg·ha per season of elemental Ca were needed to significantly reduce bitter pit incidence in ‘Honeycrisp’ apples grown in Virginia and West Virginia orchards. Foliar Ca products were evaluated in both studies, and none were better than calcium chloride (CaCl2). Telias et al. (2006) reported that crop load had a more significant effect on bitter pit than Ca sprays, with bitter pit incidence being positively correlated to low yield and large fruit. Mitcham (2008) and Silveira et al. (2012) demonstrated that shoot growth suppression reduced bitter pit incidence. Research results reported by other investigators on the effects of Ca, crop load (CD), and shoot growth have at times been contradictory, and predictive tools are needed to assist producers in developing site-specific best management programs for managing bitter pit. Fruit mineral analysis has the potential to assist producers in managing nutrient imbalances in the orchard while also providing a possible predictive tool. In research by Ferguson et al. (1979), low Ca in ‘Cox’s Orange Pippin’ fruit sampled 3 weeks before harvest was associated with an increased risk of bitter pit development. Amarante et al. (2013), De Freitas et al. (2015), Dris et al. (1998), Ferguson and Watkins (1989), and Lanauskas and Kvikliene (2006) suggested high N, K, and/or Mg to Ca ratios in fruit of bitter pit–prone cultivars could improve the prediction of susceptibility to the disorder. Al Shoffe et al. (2014) reported significant correlations between bitter pit and levels of N, P, K, N/Ca, Mg, and (Mg + N)/Ca ratio in ‘Honeycrisp’ fruit. The fruit tissue sampling procedure affects the reliability of bitter pit prediction from mineral analysis, and Amarante et al. (2013) demonstrated tissue should be sampled from the calyx end of the fruit. The best tissue to sample from ‘Fuji’ was the peel, whereas the flesh was a better predictor for ‘Caterina’. Before the research reported in this article, the authors compared peel and flesh nutrient measurements for ‘Honeycrisp’ and found improved correlations to bitter pit with nutrients measured in peel tissues (Baugher et al., 2014). We also found peel tissues could be prepared by air-drying rather than freeze-drying, which made the technique more practical for commercial growers (unpublished data). The objectives of a 3-year study of ‘Honeycrisp’ grown at three crop densities in six commercial orchards were to 1. improve guidelines for balancing CD, terminal SL, and fruit nutrient levels to reduce bitter pit incidence in ‘Honeycrisp’ orchards and Received for publication 10 July 2017. Accepted for publication 21 Aug. 2017. This research was supported by the State Horticultural Association of Pennsylvania, the Pennsylvania Apple Program, and the Pennsylvania Department of Agriculture Research Program. We acknowledge the valuable contributions of Tom Jarvinen, Michael Basedow, Erin Dugan, Kristi Kraft, Danielle Ryan, Montserrat Fonseca Estrada, Alana Anderson, Ryan Hilton, Sladjana Prozo, and Gustavo Salazar (Penn State Extension); Tom Kon, Edwin Winzeler, and Melanie Schupp (Penn State Fruit Research and Extension Center); Dave and Jim Benner, Clint and Bill Lory, Ben and Joe Lerew, Chris Baugher, and Dave and John Wenk (grower cooperators); Lee Showalter, Leighton Rice, David Rice, Ben Rice (Rice Fruit Company); Ryan Hess (Hess Brothers Fruit Company); John Spargo, and Denyce R. Matlin (Penn State Agricultural Analytical Services Laboratory); and Jacqueline F. Nock and Yosef Al Shoffe (Cornell Apple Postharvest Physiology Laboratory). The mention of a trademark, proprietary product, or vendor does not constitute a guarantee or warranty of the product and does not imply its approval to the exclusion of other products or vendors that also may be suitable. Corresponding author. E-mail: tab36@psu.edu. 1368 HORTSCIENCE VOL. 52(10) OCTOBER 2017 2. develop predictive models for determining how to improve management and postharvest handling of ‘Honeycrisp’ apples. Packinghouses in major fruit growing regions use various fruit nutrient models to predict the potential for bitter pit in storage (Ferguson, 2001; Hanson, 2012). This investigation was designed to assess both field measurements and fruit nutrient measurements with the objective of developing a model that would guide both fruit producers and fruit packers. Materials and Methods Field trial design. During 2014 to 2016, uniform field trials were established in six high-density ‘Honeycrisp’ blocks in orchards with varying histories of bitter pit incidence. The studies included two orchard blocks each with histories of high, moderate, and low levels of bitter pit. The six blocks were the same each year with the exception of a change in 2016 because of two blocks (history of low bitter pit) receiving hail damage. At the start of the project, the trees ranged in age from 5to 8-year old, and no prohexadione-calcium was applied to suppress shoot growth in any of the blocks or years. Within each block, three trees each were selected with high, medium, and low crop loads. Individual trees were handled as replications, and the following data were collected: 1. Trunk diameter at a 20-cm height and the number of fruit at harvest for determinations of CD (fruit/cm trunk cross-sectional area); 2. Average SL calculated from 10 representative terminal shoots per tree after terminal bud set; 3. Fruit peel nutrient levels at 3 weeks before harvest (composite sample from 15 randomly selected fruit of similar size per tree); 4. Average fruit weight, soluble solids concentration, and flesh firmness at harvest (five representative fruit per tree); and 5. Bitter pit incidence at harvest and again following long-term storage (the percentage of 20 randomly selected fruit of similar size per tree with bitter pit symptoms). Fruit tissue preparation and analysis. Selection of the fruit tissue to sample was based on a 2012 preliminary study in which bitter pit incidence was more closely correlated to minerals in fruit peel than in fruit tissue (Baugher et al., 2014). Based on research by Amarante et al. (2013), 1-cm wide peel samples were taken from around the circumference at the calyx end of the fruit, using a potato peeler and exercising care to prevent removal of fruit flesh. Fruit peel samples were air-dried and then ground into a fine powder. Analyses for N, P, K, Ca, Mg, Mn, Fe, Cu, B, and Zn were conducted by the Penn State Agricultural Analytical Services Laboratory (procedures described at http:// agsci.psu.edu/aasl/plant-analysis/plant-methods; Penn State College of Agricultural Sciences, 2017). Bitter pit assessment. Fruit samples were collected at optimum maturity for long-term storage based on ground color and starch measurements (Blanpied and Silsby, 1992; Greene et al., 2015). The samples were stored at the Penn State Fruit Research and Extension Center, Biglerville, PA, in air storage maintained at 3.3 C. Fruit were assessed for the incidence of bitter pit after 4 months in storage plus 7 d at 20 C. Statistical analysis. Relationships between variables of the study were first evaluated as scatter plots with SAS’s PROC GPLOT and G3D before using PROC CORR (Freund and Littell, 2000) to verify the linear relationships between the response variable (percentage of fruit with bitter pit symptoms after storage) and 21 potential regressor variables [average SL (cm), CD (fruit/cm trunk cross-sectional

Autonomous Mechanical Thinning Using Scanning LIDAR

Hand thinning is a labor-intensive and expensive peach production practice. Mechanical thinning has been shown to be an economical method of reducing thinning cost. However, current mechanical thinning systems applied to perpendicular V systems require the operator to constantly steer the tractor to maintain engagement. This paper presents a system using a LIDAR to sense the canopy and automatically control the position of a modified Darwin string thinner position to maintain engagement. We demonstrate that the automated system is approximately as good as a human at maintaining canopy engagement by presenting blossom removal counts, and suggest that this may be an economically viable method of augmenting mechanical thinning.