Greenhouse gas emissions from contrasting beef production systems

Agriculture has been reported to contribute a significant amount of greenhouse gases to the atmosphere among other anthropogenic activities. With still more than 870 million people in the world suffering from under-nutrition and a growing global food demand, it is relevant to study ways for mitigating the environmental impact of food production. The objective of this work was to identify gaps in the knowledge regarding the main factors affecting greenhouse gas (GHG) emissions from beef farming systems, to reduce the uncertainty on carbon footprint predictions, and to study the relative importance of mitigation options at the system level. A lack of information in the literature was identified regarding the quantification of the relevant animal characteristics of extensive beef systems that can impact on methane (CH4) outputs. In a meta-analysis study, it was observed that the combination of physiological stage and type of diet improved the accuracy of CH4 emission rate predictions. Furthermore, when applied to a system analysis, improved equations to predict CH4 from ruminants under different physiological stages and diet types reduced the uncertainty of whole-farm enteric CH4 predictions by up to 7% over a year. In a modelling study, it was demonstrated that variations in grazing behaviour and grazing choice have a potentially large impact upon CH4 emissions, which are not normally mentioned within carbon budget calculations at either local or national scale. Methane estimations were highly sensitive to changes in quality of the diet, highlighting the importance of considering animal selectivity on carbon budgets of heterogeneous grasslands. Part of the difficulties on collecting reliable information from grazing cattle is due to some limitations of available techniques to perform CH4 emission measurements. Thus, the potential use of a Laser Methane Detector (LMD) for remote sensing of CH4 emissions from ruminants was evaluated. A data analysis method was developed for the LMD outputs. The use of a novel technique to assess CH4 production from ruminants showed very good correlations with independent measurements in respiration chambers. Moreover, the use of this highly sensitive technique demonstrates that there is more variability associated with the pattern of CH4 emissions which cannot be explained by the feed nutritional value. Lastly, previous findings were included in a deterministic model to simulate alternative management options applied to upland beef farming systems. The success of the suggested management technologies to mitigate GHG emissions depends on the characteristics of the farms and management previously adopted. Systems with high proportion of their land unsuitable for cropping but with an efficient use of land had low and more certain GHG emissions, high human-edible returns, and small opportunities to further reduce their carbon footprint per unit of product without affecting food production, potential biodiversity conservation and the livelihood of the region. Altogether, this work helps to reduce the uncertainty of GHG predictions from beef farming systems and highlights the essential role of studies with a holistic approach to issues related to climate change that encompass the analysis of a large range of situations and management alternatives