## 3 Results and Discussion  
Each milker scanned and read 48 files during milking operations to select animals with high levels of milk yield. Grouping ewes are generally performed several times per year during the milking routine and the time to complete this task depends on the number of operators involved. Conventionally, two operators--as well as the milkers--are needed, one to read the animal ID and another to read the related information. The smartglasses for AR enabled to conduct animal selection and grouping, during milking procedures, involving only one working unit instead of three, obtaining a work completion time per side (24 milking stalls) averaged 11.1 min (Table 1). When the milking routine is modified by the addition of a new task, such as pressing a button and scanning a code, the milking operating time invariably increases. In fact, the milking sessions with a conventional routine, where only milking procedures are carried out, take about 3 minutes to attach 24 clusters [46]. Anyway, the use of SG to read the information related to each animal is strongly recommended when a specific activity is required for effective management of the flock. We also measured text comprehension based on the number of reading errors by asking milkers to identify the ewes that had a \"yearly milk yield higher than 250 kg\". In every case, the errors during the trials were caused by a failure to recognize the information requested. The frequency of failures per side of milking parlor was approximately 3.3%, which did not seem to affect the quality of work. Specifically, the total number of errors was 16 and 11 for graphic and text composition, respectively, which corresponded to 16.6% and 11.5% of all milk yields signed per each of them. The 43.8% of milkers correctly read all the information reported in the QR codes scanned, allowing the recognition of the animals that need to be separated. This is an encouraging result since the operators performed an agricultural task using for the first time a modern device with which they did not have a high level of confidence. Four workers out of nine made errors reading both graphic and text composition files; furthermore, these were responsible for the highest number of errors. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Work Completion Time (min)** & **Number of Errors** \\ \hline Total (N = 32) \({}^{\ddagger}\) & 11.1 \(\pm\) 3.70 & 0.8 \(\pm\) 1.44 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) sixteen milkers per two milking parlor sides.  
\end{table} 
Table 1: Means and standard deviations of milkers’ operating time and number of errors per side (24 stalls) of the milking parlor for animal selection and grouping. Table 2 summarizes the results for NASA-TLX scores. The statistical analysis has no underlined significant differences on the overall scores or for each categories of the NASA-TLX but we can observe that the perceived workload for the TXT composition type were basically higher (overall workload 40.21 vs. 34.90), as obtained also by [35] in a study where they compared two different user interface designs in simulated warehouse order picking. In particular, the graphic-based (vs. text-based) information led to decreases of 21.1% for mental demand, 19.7% for temporal demand and 6.4% for physical demand. As a consequence, the graphic-based information type was more usable by the farmers. These results are also in accordance with the visual dominance theory where visual inputs tend to dominate other modalities in perceptual and memorial reports and speeded responses [47]. Nonetheless, these low difference percentages may be explained by the human propensity for memorizing the position of the information in the text-based type requested during the experiment. Observing the perceived overall workload for each milker, the text-based information was higher in 75% of milkers than was the graphic-based information (Figure 4). These results confirmed that the arrangement of information influences its receptivity. Table 3 summarizes the score of the IBM CSUQ.