Provincial cattle carbon emissions from enteric fermentation and manure management in South Africa

Future prediction of biogas potential and CH4 emission with boosting algorithms: the case of cattle, small ruminant, and poultry manure from Turkey

Animal waste can be converted into a renewable energy source using biogas technology. This process has an impact on greenhouse gas emissions and is a sustainable source of energy for countries. It can reduce the effects of climate change and protect the planet for future generations. Tier1 and tier2 approaches are commonly used in the literature to calculate emissions factors. With boosting algorithms, this study estimated each animal category's biogas potential and CH4 emissions (tier1 and tier2 approach) for 2004-2021 in all of Turkey's provinces. Two different scenarios were created in the study. For scenario-1, the years 2020-2021 were predicted using data from 2004 to 2019, while for scenario-2, the years 2022-2024 were predicted using data from 2004 to 2021. According to the scenario-1 analysis, the eXtreme Gradient Boosting Regressor (XGBR) algorithm was the most successful algorithm with an R2 of 0.9883 for animal-based biogas prediction and 0.9835 and 0.9773 for animal-based CH4 emission predictions (tier1 and tier2 approaches) for the years 2020-2021. When the mean absolute percentage error was evaluated, it was found to be relatively low at 0.46%, 1.07%, and 2.78%, respectively. According to the scenario-2 analysis, the XGBR algorithm predicted the log10 values of the animal-based biogas potential of five major cities in Turkey for the year 2024, with 11.279 for Istanbul, 12.055 for Ankara, 12.309 for Izmir, 11.869 for Bursa, and 12.866 for Antalya. In the estimation of log10 values of CH4 emission, the tier1 approach yielded estimates of 3.080, 3.652, 3.929, 3.411, and 3.321, respectively, while the tier2 approach yielded estimates of 1.810, 2.806, 2.757, 2.552 and 2.122, respectively.

Long-term relationships of beef and dairy cattle and greenhouse gas emissions: Application of co-integrated panel models for Latin America

The cattle sector plays a pivotal role in the economies of numerous Latin American and Caribbean countries. However, it also exerts a significant impact on environmental degradation, including substantial contributions to greenhouse gas emissions (accounting for 23.5 % of global livestock emissions) and deforestation (70 % attributed to livestock in South America). This article aims to investigate the complex, long-term, and short-term relationships between population growth, pastureland expansion, deforestation, and the cattle sector in 15 countries across the region, focusing on their effects on greenhouse gas emissions as well as beef and dairy production. Utilizing data from FAOSTAT spanning the period from 1990 to 2019, a cointegrated panel model was developed using the Pooled Mean Group technique, resulting in the estimation of six models. The aggregate-level results for the region reveal the presence of relatively stable long-term relationships. This implies that over time, the influence of population growth, pastureland expansion, and deforestation on greenhouse gas emissions from cattle production tends to diminish in significance. This long-term behavior may be particularly pronounced in countries with more developed cattle sectors, where efforts to mitigate the environmental impacts of cattle production, such as promoting improved forage technologies, silvo-pastoral systems, grazing management practices, and the implementation of policies, regulatory frameworks, and incentives, have gained traction. These progressive countries can serve as regional benchmarks, and the lessons they have learned hold valuable insights for the sustainable intensification of cattle production in countries with less-developed cattle sectors.