## 3 Evaluation Results and Discussion
### Information Flow and Product Traceability Performance of the Smart System  
The information gathered using the smart system was rich enough to construct an effective traceability map of each registered product along the pruning supply chain [11]. Detailed pruning quality parameters and the product traceability system are provided in [12; 13]. The SLS was integrated with the biomass traceability system, with pruning quality and related traceability parameters. The usability testing indicated that all of the traceability-related parameters were implemented on the information platform of the SLS (see Figure 3). The identification of producers (provider ID), transporters (transporter ID), and consumers (consumer ID), and the specific name (title) and production lot number assigned to each registered product are mandatory parameters to trace product movement along the pruning supply chain (see Table 4). Table 7 indicates that except for smart box number (Cargoog serial no. ), delivery code, pickup date, and delivery date, all important parameters are recorded correctly. The errors in records of Cargoog serial no., pickup date, and delivery date could be mainly due to barriers in data transfer from the smart box to the central database. In the case of Spain, the Cargoog serial no., pickup date, and delivery date were not recorded at all in the central database, indicating that the information was not properly transferred from the smart box tool to the central database. For illustration purposes, a product from the test in Germany is considered here. For a product registered with the title \"Field7\" and lot number 151008B05R1, the producer, transporter, and consumer are identified as FDEU020, DDEU012, and CDEU013, respectively (see Appendix A). The corresponding complete product identification code can be constructed as FDEU020-DDEU012-CDEU013. Whenever a product traceability issue is initiated, further detailed traceability-related information can be retrieved from gathered data for each stage of the logistics chain. This can be done by an experienced administrator of the smart system who can interpret all recorded values in each of the 41 columns of the spreadsheet-based central database. The data barriers in delivery date (column 38) and Cargoog serial number (column 7) may hinder the system administrator from confirming the final delivery of the product to the intended consumer. In such cases, the administrator should find additional information by telephone or internet conversation, for instance, by introducing an option (on the platform) for the end consumer to confirm the product delivery. This enables the system administrator to easily construct the product delivery identification code and strengthen the traceability and management of the entire pruning supply [10]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Product** \\ **(Lot Number)** \\ \end{tabular} } & \multicolumn{3}{c}{**Recorded Moisture Content (\%)**} & \multirow{2}{*}{**Remark**} \\ \cline{2-2} \cline{4-5}  & **Storage** & **Storage** & **Relative Humidity (RH)** & \\  & **(start)** & **(end)** & **Measured by Smart Tool** & \\ \hline Field1 & \multirow{2}{*}{31.68} & \multirow{2}{*}{31.68} & \multirow{2}{*}{NA} & \multirow{2}{*}{Data were not transferred properly} \\ (151007B01R1) & & & & \\ \hline Field3 & \multirow{2}{*}{31.68} & \multirow{2}{*}{13.36} & \multirow{2}{*}{78.5–88.9} & \multirow{2}{*}{RH measured during transport} \\ (151008B03R1) & & & & \\ \hline Field7 & \multirow{2}{*}{31.68} & \multirow{2}{*}{13.36} & \multirow{2}{*}{91.8–93.2} & \multirow{2}{*}{RH measured during transport} \\ (151008B05R1) & & & & \\ \hline Field8 & \multirow{2}{*}{31.68} & \multirow{2}{*}{31.68} & \multirow{2}{*}{NA} & \multirow{2}{*}{Route planned, but pruning} \\ (151008B04R1) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Example of moisture content data compiled from test in Germany.