**1. Introduction**

The growing global population leads to an increasing need for resources—food, energy, and materials. This expanding demand forces the shift from a fossil-based linear economy to a sustainable biobased economy. Bioeconomy demands biological feedstock that has the potential to generate a spectrum of bio-based products by involving multidisciplinary areas of science, management, and engineering [1]. Agriculture is the primary supply of nutrition and bioenergy and a substantial contributor to the bioeconomy. Yet, agriculture is also linked with environmental, economic, and social aspects of climate change. For example, climate change affects the productivity of the agriculture sector, and thus change in the agricultural practices feedback to the greenhouse gas (GHG) balance. Therefore, climate change and agriculture are linked by complex relations, which can be difficult to define or measure [2].

The vast majority of the studies that are looking into the quantification of the climate impacts use the Global Warming Potential (GWP) for a 100-year time horizon as the default metrics. Since the development of GWP metric in the early nineties, there have been updates only on the numerical value of this metric, rather than the development of the assessment methodology itself [3].

The use of GWP is an accepted measure within the Kyoto Protocol to the United Nations Framework Convention on Climate Change as a measure to weigh the impact of climate due to the emissions of GHGs. Although the use of GWP has received various criticism due to underlying assumptions, it became widely accepted measure because of transparency and ease of use [4]. There were numerous alternative methods developed to substitute use of GWP, such as Global Temperature Change Potential (GTP) by Shine et al. [4], Global Warming Potential using cumulative CO2 forcing-equivalent (GWP\*) by Allen et al. [5] and other normalized point and integration metrics (see the review by Levasseur et al. [6]). Current studies on climate science show that processes occurring in the natural environment sometimes cannot be reasonably well quantified using a single value for measuring the impact created in the 100-year perspective. This quantification using a single value cannot be done due to the non-linear nature of the emission dissipation in various environments that leads to spatial and temporal heterogeneities. Misinterpretation of these e ffects can lead to policy decisions that underestimate the impacts of the emissions with a short lifetime and with a dominating local pollution e ffect. An example of this phenomenon is given in the thesis work by Shimako [7] and published in the paper by Shimako et al. [8]. This work shows that the same amount of emissions might have di fferent influence if the timing of emissions is considered. Another limitation of GWP is that it estimates the forcing of the climate but does not characterize the impact of climate dynamics. Although climate dynamics are included in the global temperature change potentials (GTPs), they are not intended to illustrate the influence of radiative forcing and enable a qualitative interpretation of causes [9].

The GWP, including the Bern Carbon Cycle Model (BCCM), was proposed as an alternative method to take into consideration both amount and time of emission, as well as the fraction of emissions remaining in the atmosphere from previous emission periods. Furthermore, BCCM considers the e ffect of GHG emissions estimated as a continuous pattern that handles removals (via sinks) and addition of new emissions to the "stock" of the atmosphere hence also considering the climate system response to emissions.

Thus, this study aims to compare two methods for GHG emission accounting from the agriculture sector. Firstly, the constant GWP values for a 100-year time horizon (GWP100) and, secondly, the time dynamic GWP values for a 100-year time horizon obtained by using the BCCM to find whether the obtained results will lead to similar or contradicting conclusions. Also, the e ffect of global temperature potential (GTP) of the studied system is summarized.

The agriculture sector is the world's leading source of non-CO2 GHG emissions and the secondlargest GHGs emission source overall. On the global scale, in 2010, the non-CO2 GHG emissions from agriculture accounted for 10–12% of the total annual anthropogenic emissions or 5.2–5.8 Gt CO2 eq. [10]. The same share of the GHGs emissions from agriculture is also evident in the European Union (EU), where 0.442 Gt CO2 eq. originated from agriculture that corresponds to around 10% of the total annual GHG emissions in the EU. Based on the EU strategy for a low-carbon economy by 2050 [11], non-CO2 GHG emissions or GHG emissions not covered by the EU Emissions Trading Scheme (non-ETS) should be cut down by 30% in the comparison to the emission in 2005 [12]. Thus, these emission reductions should also substantially relay on the emission cutbacks in the agriculture sector. Therefore, a lot of research is put into the evaluation of emission mitigation potential in the EU Member states, including a detailed analysis of the agriculture sector [13,14].
