The 2012 ASHS Annual Conference

Despite its singular importance in sweet cherry (Prunus avium L.) production budgets, harvest efficiency is not well studied. Intuitively, there are biological, technological, and sociological factors that will affect harvest efficiency, yet there are limited empirical reports on these for tree fruit. Herein we report on the use of a novel real-time labor monitoring system (LMS) to investigate the role of training system on harvest efficiency in sweet cherry. The LMS consists of: a) a digital scale; b) a RFID reader/writer; c) a computational unit (CU) and d) wearable dataloggers. In 2011 the LMS was used to study picker efficiency in 11 commercial sweet cherry orchards throughout the Pacific Northwest. Four different architectures were evaluated: 1) Upright Fruiting Offshoots (UFO), a planar architecture comprised of unbranched vertical wood; 2) Y-trellised, an angled planar architecture; 3) Kym Green Bush (KGB), a multi-leader bush system; and 4) traditional open center (TOC), large trees comprised of 3–5 main leaders. A consistent picking crew was utilized to facilitate comparisons among systems. In each orchard the LMS calculated the harvest rate, weight of harvested fruit, number of harvested buckets, range in fruit weight per bucket, and mean fruit weight per bucket, for each individual picker. Our testing revealed a dramatic effect of canopy architecture on labor efficiency with a ca. 2-fold range among orchards from 0.46 kg/min to 0.93 kg/min. The highest mean harvest rates (0.93 ± 0.19 kg/min and 0.77 ± 0.12 kg/min) were in UFO orchards of ‘Cowiche’/‘Gisela®5’ and ‘Tieton’/‘Gisela®5’, respectively. High harvest efficiency in these two orchards was due to most fruit being readily accessible from the ground and the simplified architecture. The third highest efficiency was recorded for the KGB system with mean harvest rate of 0.71 kg/min in a fully pedestrian orchard.  Interstingly, inexperienced pickers improved to a greater extent (+130%) than experienced pickers (+80%) when comparing efficiency in TOC with pedestrian and planar systems (e.g., UFO and KGB). Furthermore, picking rate for individual pickers varied within a day by more than 100%. This range is likely due to variability in fruit density within trees, tree size, as well as fruit accessibility. The LMS also recorded significant variability in final bin weight of more than 35 kg, across all orchards, and a range in bucket weight between 7.00–13.00 kg was documented. These results suggest that current systems of picker reimbursement, by bucket or bin, are beset with inaccuracy.