# Impact of Combine Harvester Technological Operations on Global Warming Potential
## 3 Results and Discussion  
### Results of the Telematics Data Analysis  
Table 4 provides the summarized results of the telematics data analysis. The study revealed the following key results:  
* On average, each CH spent 68.2% \(\pm\) 1.1% of its time in direct harvesting work between 2016 and 2020. Accordingly, in this mode, a GWP of 87.2% \(\pm\) 0.7% was released into the environment via the exhaust gases. In the other modes (idle/transport), the emission of air pollutants occurred while the CH was engaged in indirect work. A similar value was calculated in previous studies and for other CH models [42]. * The maximum recorded engine operation time of a single CH per year (in all modes) was 1036.9 h year\({}^{-1}\) with a minimum of 18.1 h year\({}^{-1}\). * The highest total annual GWP (in all modes) of a single CH was 94,262.7 kg year\({}^{-1}\), while the lowest was 1974.7 kg year\({}^{-1}\). * Despite significant differences in absolute values between individual machines, the overall proportions of time spent engaged in direct work and in idle/transport modes were deemed comparable. The calculated correlation coefficient between the work (_TW_) and the idle (_TI_) modes was \(R^{2}\) = 0.88 (_TW_ = 2.03_TI_ + 72.87), whereas the coefficient between the work and the transport (_TT_) modes was \(R^{2}\) = 0.75 (_TW_ = 4.73_TT_ + 18.88). The calculated correlation coefficient between the GWP emitted in the work (_FW_) \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**1 Machine**} & \multirow{2}{*}{**1 GWP, kg h\({}^{-1}\)**} & \multirow{2}{*}{**1 GWP, kg Year\({}^{-1}\)**} \\  & & & \\  & **Utilization, h Year\({}^{-1}\)** & & & \\ \hline \multicolumn{4}{c}{Idle mode} \\ \hline  
\({}^{2}\) 2016–2020 & 282.2 \(\pm\) 32.5 & 99.7 \(\pm\) 2.0 & 27,417.2 \(\pm\) 2879.2 \\ \hline \end{tabular} \({}^{1}\) The arithmetical means of working time and global warming potential were obtained from the telematics database containing data from 67 CHs. \({}^{2}\) 2016–2020: averages of parameters over 5 years.  
\end{table} 
Table 4: Combine harvester operating time structure and global warming potential (GWP) in various engine modes. and idle (_Fl_) modes was \(R^{2}\) = 0.81 (\(FW\) = 14.72\(FI\) + 6761.8), while the coefficient between the GWP emitted in the work and transport (\(FT\)) modes was \(R^{2}\) = 0.75 (\(FW\) = 8.61\(FT\) + 3961.1). These strong correlations indicate a stable relationship between the different modes (idle, transport, and work); however, they can also suggest that implementing changes to reduce both indirect working time and GWP emissions can be problematic. The data analysis revealed the potential to reduce both the duration of indirect work and the associated negative environmental impacts. However, previous research has identified the influence of poorly organized work, inappropriate operator practices, and lack of knowledge and skills in the inefficient use of agricultural machinery [43; 44]. Influence of Technological Operations on the Global Warming Potential in Idle, Transport, and Harvesting Modes  
It can be concluded from the telematics data that during idling, the length of time spent in this mode (20.3%) and the GWP released (4.3%) account for fairly significant shares of the totals. By comparison, the idle time of other non-road machines can be between 20% and 70% [45; 46; 47]. The 2019 field tests explored the impact of fuel consumption on the GWP and the use of various technological operations at the idle speed of the CH engine. Figure 1 presents the obtained results. These results confirm that engine speed had the most significant impact on the hourly GWP in idle mode. An increase in engine speed from 1200 to 2200 min\({}^{-1}\) represented a very significant 2.7-fold increase in GWP emissions.