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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.

Greenhouse gas (GHG) emissions from livestock ruminants, particularly methane (CH4), nitrous oxide, and indirectly ammonia (NH3) significantly contribute to climate change and global warming. Conventional monoculture swards for cattle feeding, such as perennial ryegrass or Italian ryegrass, usually require substantial fertiliser inputs. Such management elevates soil mineral nitrogen levels, resulting in GHG emissions and potential water contamination. Mitigating the environmental footprint of these farming practices requires sustainable alternative feeding strategies for cattle production. Multispecies grassland swards (grass + legumes or legumes + herbs) represent a promising alternative to monoculture grassland swards for cattle nutrition due to their reduced nitrogen requirements, excellent herbage yield, and polyphenolic compounds (tannins, formononetin, luteolin, quercetin, and acteoside) which may have positive effects on animals. This study investigated the effects of selected multispecies grassland swards (plant blends) on in vitro ruminal fermentation and DM digestibility. Three experimental blends of plants cultivated without fertilisers were utilised: (1) perennial ryegrass (PRG) + red clover (RC), (2) chicory (C) + red clover (RC), and (3) Tonic plantain (PLA) + red clover (RC). The control blend included perennial ryegrass (PRG), and red clover (RC) cultivated with fertiliser. The in vitro trial showed a reduction in CH4 production and ruminal NH3 concentration (by 14.7 and 28.8%, respectively; P < 0.01) in the PLA+RC blend compared to the control. This plant blend also increased propionate concentration (P < 0.05) and reduced acetate and butyrate concentrations and the acetate-propionate ratio (P < 0.01). Additionally, the total protozoal and methanogen counts were mostly reduced by the PLA+RC blend (P < 0.01) among all blends investigated. In conclusion, the Tonic plantain and red clover blend (PLA+RC) cultivated without fertilisers have the potential to be utilised as a sustainable alternative feed source for climate-friendly cattle production, aligning with the aims of the European Climate Care Cattle Farming project.

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.

Animal nitrogen (N) partitioning is a key parameter for profitability and sustainability of ruminant production systems, which may be predicted from N isotopic discrimination or fractionation (Δ15N). Both animal genetics and feeding level may interact and impact on N partitioning. Therefore, this study aimed to assess the interactive effects of genetic merit (G) and feed allowance (F) on N partitioning and Δ15N in sheep. The sheep were drawn from two levels of G (high G vs. low G; based on New Zealand Sheep Improvement Limited (http://www.sil.co.nz/) dual (wool and meat) growth index) and allocated to two levels of F (1.7 (high F) vs. 1.1 (low F) times Metabolisable Energy requirement for maintenance) treatments. Twenty-four Coopworth rams were divided into four equal groups for a N balance study: high G × high F, high G × low F, low G × high F, and low G × low F. The main factors (G and F) and the interaction term were used for 2-way ANOVA and regression analysis. Higher F led to higher N excretions (urinary N (UN); faecal N (FN); manure N), retained N, N use efficiency (NUE), and urinary purine derivatives excretion (P < 0.05). On the other hand, higher UN/N intake, and plasma Δ15N were observed with the lower F (P < 0.05). Higher G led to increased UN, FN, manure N, apparent N digestibility, and urinary purine derivatives excretion (P < 0.05). Higher F only increased UN in high G sheep, with no effect on low G sheep (P < 0.05). Regression analysis results demonstrated potential to use plasma Δ15N to reflect the effects of G and F on NUE and UN/N intake. Further research is urged to study interactive effects of genetic and feeding level on sheep N partitioning.

AbstractLarge efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with anin silicomodel for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). DMI and IT were recorded with load cells. A total of 37 individual dynamic patterns of methane production were analysed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. Since IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates ourin silicomodel with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.ImplicationsReducing methane emissions from ruminants is a major target for sustainable and efficient livestock farming. For the animal, methane production represents a loss of feed energy. For the environment, methane exerts a potent greenhouse effect. Methane mitigation strategies require accurate, non-invasive and inexpensive techniques for estimating individual methane emissions on farm. In this study, we integrate measurements of feeding behaviour in cattle and a mathematical model to estimate individual methane production. Together, model and measurements form a software sensor that efficiently predicts methane output. Our software sensor is a promising approach for estimating methane emissions at large scale.