When timber is harvested, trees are felled and cut (“bucked”) into pieces (so called “assortments”) of different length. Pricing of the assortments depends on their length, diameter class and quality and is given in € per m³, which are then multiplied with the respective volume. In motor-manual felling and bucking operations, the chainsaw operator has to determine which assortments to cut from a given tree. By separating the tree into the most suitable combination of assortments, the highest value can be gained from a given tree. In contrast to fully mechanized harvesting operations, chainsaw operators are at the moment not aided by computers when taking this value-defining decision. Further, they are lacking a proper performance documentation in the form of an assortment list and aggregated volumes per assortment, species, quality, length or diameter class.
The Bucking App is an application for Android OS mobile devices, which aims to close this gap. With the Bucking App, a value optimized bucking scheme can be determined for a given tree from tree and contractual parameters entered by the user, thereby assisting the chainsaw operator in this critical decision. Further, the Bucking App provides after-operation statistics, which can be utilized for documentation and planning purposes.
However, the Bucking App does not aim to replace, but to assist the human brain when taking the bucking decision. While on the one hand enabling fully computer-aided operation, the user is on the other hand able to select a customized bucking scheme, which can be compared to the value-optimized scheme. This functionality aims at providing the user with a training option, which can be employed for both personal and institutionalized education and training purposes. This functionality is of further use when defects that go unnoticed during the visual pre-bucking inspection require to alter the original bucking scheme. In these cases, it can easily be done.
This App is part of Project that has received funding from the Bio Based Industries Joint Undertaking under the European
Union’s Horizon 2020 research and innovation programme under grant agreement No 20757.