Greenhouse gas emissions from contrasting beef production systems
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Date
30/06/2014Author
Ricci, Patricia
Metadata
Abstract
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.