A decision-support model for optimizing pruning efficiency in juvenile Gmelina arborea plantations

Pruning is essential for clearwood production in commercial Gmelina arborea plantations, yet tool selection is often based on experience rather than evidence, despite its effects on operational efficiency and pruning quality. This study developed a rule-based decision-support model integrating operational performance and biological responses to optimize pruning in juvenile clonal plantations. Four treatments were evaluated at two sites: handheld pruning saw, handheld pruning saw with sealant, hacksaw, and pruning shears. At each site, treatments were assigned in a completely randomized design with 30 trees per treatment (60 trees per treatment across sites). The first pruning lift removed lower branches to 2.5 m above ground. Pruning time, cut quality, and operator workload, assessed using heart-rate metrics for three operators, were recorded. Wound closure was quantified by image analysis two months after pruning on the two largest pruned branches of a subsample. Baseline tree size and branching attributes were comparable among treatments. Pruning time increased significantly with branch diameter for all tools (p < 0.05), but the increase was smallest for pruning shears and greatest for the handheld pruning saw. Final operator heart rate ranged from 98.1 to 113.3 bpm among treatments. Wound closure was highest for pruning-saw treatments (93.6%), intermediate for the hacksaw (84.5%), and lowest for pruning shears (78.1%). These results provide an evidence-based framework for selecting pruning tools by balancing operational efficiency, operator workload, cut quality, and early wound recovery.