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Agricultural input and output prices have become extremely volatile in recent years and the global meat industry faces sustainability challenges related to climate change, resource competition, environmental regulations, animal welfare concerns, consumer preferences and industry policies. Additionally, the economic situation of cattle fattening farms has been significantly impacted by two major shocks: the COVID-19 pandemic and the onset of the war in Ukraine. This has led to a growing demand for microsimulation tools that can analyse how these conditions affect the operations of agricultural farms and address various technological challenges at both the farm and sector levels. In this paper, we present a farm model to analyse the cattle farming sector for the pre-Alpine region, using Slovenia, a typical example of this region, as a case study. These farms are particularly important from both social and environmental sustainability perspectives, and it is crucial that economic sustainability follows suit. The results of the SiTFarm model show that, on average, farms in the cattle farming sector achieved modest results between 2018 and 2022, with an average gross margin of 9.57 €/h. However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. At the sector level, the gross margin decreased by 12% in 2020 but increased by 99% in 2022 compared to the reference year 2018, highlighting the incredible price volatility over a short period. The model results also indicate greenhouse gas emissions ranging from 5.01 to 7.77 kg CO2 eq. per kg of daily body weight gain on the analysed farms. Nearly half of the farms have GHG emissions for cattle fattening exceeding 6.1 kg CO2 eq. per kg daily body weight gain, while about 10% of farms achieve a sustainability target of approximately 5 kg CO2 eq. per kg of daily body weight gain., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Brečko et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The COVID-19 epidemic has shown that efficient prediction models are required, and the well-known SI, SIR, and SEIR models are not always capable of capturing the real dynamics. Modified models with novel structures could help identify unknown mechanisms of COVID-19 spread., Objective: Our objective is to provide additional insights into the COVID-19 spread mechanisms based on different models' parameterization which was performed using evolutionary algorithms and the first-wave data., Methods: Data from the Our World in Data COVID-19 database was analysed, and several models-SI, SIR, SEIR, SEIUR, and Bass diffusion-and their variations were considered for the first wave of the COVID-19 pandemic. The models' parameters were tuned with differential evolution optimization method L-SHADE to find the best fit. The algorithm for the automatic identification of the first wave was developed, and the differential evolution was applied to model parameterization. The reproduction rates (R0) for the first wave were calculated for 61 countries based on the best fits., Results: The performed experiments showed that the Bass diffusion model-based modification could be superior compared to SI, SIR, SEIR and SEIUR due to the component responsible for spread from an external factor, which is not directly dependent on contact with infected individuals. The developed modified models containing this component were shown to perform better when fitting to the first-wave cumulative infections curve. In particular, the modified SEIR model was better fitted to the real-world data than the classical SEIR in 43 cases out of 61, based on Mann-Whitney U tests; the Bass diffusion model was better than SI for 57 countries. This showed the limitation of the classical models and indicated ways to improve them., Conclusions: By using the modified models, the mechanism of infection spread, which is not directly dependent on contacts, was identified, which significantly influences the dynamics of the spread of COVID-19., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Stanovov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)