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The livestock sector is one of the fastest growing subsectors of the agricultural economy and, while it makes a major contribution to global food supply and economic development, it also consumes significant amounts of natural resources and alters the environment. In order to improve our understanding of the global environmental impact of livestock supply chains, the Food and Agriculture Organization of the United Nations has developed the Global Livestock Environmental Assessment Model (GLEAM). The purpose of this paper is to provide a review of GLEAM. Specifically, it explains the model architecture, methods and functionality, that is the types of analysis that the model can perform. The model focuses primarily on the quantification of greenhouse gases emissions arising from the production of the 11 main livestock commodities. The model inputs and outputs are managed and produced as raster data sets, with spatial resolution of 0.05 decimal degrees. The Global Livestock Environmental Assessment Model v1.0 consists of five distinct modules: (a) the Herd Module; (b) the Manure Module; (c) the Feed Module; (d) the System Module; (e) the Allocation Module. In terms of the modelling approach, GLEAM has several advantages. For example spatial information on livestock distributions and crops yields enables rations to be derived that reflect the local availability of feed resources in developing countries. The Global Livestock Environmental Assessment Model also contains a herd model that enables livestock statistics to be disaggregated and variation in livestock performance and management to be captured. Priorities for future development of GLEAM include: improving data quality and the methods used to perform emissions calculations; extending the scope of the model to include selected additional environmental impacts and to enable predictive modelling; and improving the utility of GLEAM output.

In the face of global climate threats, farm and land-management decisions must balance climate concerns with profitability, animal welfare, and ecosystem health. However, few comprehensive studies have quantified the relationship between animal welfare and greenhouse gas (GHG) emissions, and no study focuses specifically on sheep farms. The present study aims to quantify the effects of impaired welfare on GHG emissions for common welfare challenges faced in UK lowland (L) and hill (H) sheep farming systems. Two case study research farms in Scotland, representative of high welfare conditions, were used as baselines for semi-intensive L and extensive H systems. In this study, "high welfare conditions" are defined as situations where animals have access to adequate feeding, suitable housing, good health, and opportunities to express natural behaviours. From each high-welfare baseline, scenarios representing common levels of impaired welfare conditions were modelled, using parameters retrieved from the published literature. The selected poor-welfare scenarios included lameness, gastrointestinal nematodes, blowfly strike, liver fluke, inadequate shelter provision, inadequate feeding during lamb growth and late gestation, and high lamb mortality rate. GHG emissions were estimated "from-cradle-to-farm-gate" using Agrecalc ©, a Life Cycle Assessment tool for the agricultural sector. Total GHG emissions and emission intensities (EI) in kg of CO2 equivalent per kg live weight were compared across the baseline and the scenarios. Gross farm emissions and product-level EIs demonstrated divergent patterns in response to impaired welfare. Most impaired welfare scenarios led to a slight decrease in total farm emissions (0.03-3%), with a few exceptions. On the other hand, EI increased across all impaired welfare scenarios relative to the baseline, because meat production decreased by 1.3-16.6% across all impaired welfare scenarios, reducing resource use efficiency. Lameness was identified as particularly impactful, resulting in 18 and 10% increases in EI on H and L farms, respectively. This was primarily due to the high lamb mortality associated with lameness in published studies. Inadequate shelter provision was associated with an 8-15% increase in EI. Scenarios related to ineffective parasite control contributed to an EI increase ranging from 1 to 13%, while inadequate feeding management caused a 3-4% increase in EI. This study highlights the potential for reducing emission intensity through system-specific interventions, emphasising the importance of integrating animal welfare into GHG mitigation strategies.

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding.