In the past decades, a considerable body of literature has emerged on timber tracking and tracing in the forest and timber industry supply chain. Therefore, a systematic literature review was conducted using an established method (»PRISMA«). To help define the relevance of timber tracking and tracing, this review addresses the subject areas, objectives and characteristics from scientific studies conducted over the past 25 years. In total 213 papers were included in the qualitative synthesis of the subject areas and objectives, with a 160 of those included in the characteristics synthesis to analyse comparable publication contents.This study demonstrates the rationale behind the research efforts in the field of timber tracking and tracing. The results showed that the main key objectives were to combat illegal logging and trade, provide sustainable forest management, enable tracking and tracing, enhance efficiency, ensure legal compliance, determine the origin of timber and to identify species. The characteristics of the analysis methods used showed that genetic methods, physical chemistry methods, image methods, geomatics, certification, Radio-Frequency Identification (RFID) and smart technologies and software applications were most common. Most research activities were conducted in Asia and Europe. The majority of tracking and tracing methods were found to be highly practical. The application along supply chain dominated because of the high number of publications in genetic methods where a comprehensive application is possible. Furthermore, the forest, harvesting, and manufacturing were identified as core application areas. Most studies lacked an economic evaluation of the developed solutions, which is a crucial aspect to consider for future successful implementation. The number of tree/wood species involved was notably extensive with a considerable diversity observed across continents. It will be essential that future research incorporates new technologies such as artificial intelligence (AI) that is currently emerging in the field of timber traceability. This can help achieve the identified objectives and address existing and future challenges through the self-learning property of AI.
Gerhard et al. (Fri,) studied this question.