A MODEL TO CALCULATE GHG EMISSIONS ON SA DAIRY FARMS.

Studies estimating the contribution of milk production to global greenhouse gas (GHG) emissions do not really reflect the diversity of dairy farm systems. Such systems include complex and integrated biological components, such as type or cow breed, the physiological status of the herd, type and composition of the feed, various energy sources, the heterogeneity of the systems and the climatic region. As a result, it is almost impossible to develop generic methodologies that would simulate all parameters equally and therefore a comprehensive modelling approach is required. Such a model should be scientifically founded and based on a whole farm model (WFM) approach, using a combination of existing sub-models and various underlying simulation methodologies to calculate GHG emissions. Furthermore, the model should accommodate a large amount of data from which to extract information for logical interpretation and should include two widely used principles: lifecycle assessment (LCA); and the guideline methodology of the IPCC (Intergovernmental Panel on Climate Change). The model should assist farmers to better understand the carbon footprint and the environmental impact of dairy farms with the possibility of identifying mitigation strategies. In addition, it should provide the ability to benchmark results with national and global GHG emissions among dairy production systems. The aim of the authors cited below was therefore to draw production and input data from commercial dairy farms and to devise a descriptive model for GHG emissions that could be applied as a predictive model in various dairy farming scenarios. 

Their paper presents a model to quantify total GHG emissions from dairy farms. The model, which is based on the discussion above accounts for the variability that occurs in GHG emissions among farm production and management practices. The variation is accommodated in six dairy farm management systems (FMS), which broadly include typical dairy production systems in South Africa. These are pasture-based with high or low stocking rates, total mixed ration with high or low stocking rates, and partial mixed ration with high or low stocking rates. Three variations of functional units that were used to evaluate the environmental impacts of various FMS are defined as per animal unit = kg CO₂-equivalent (eq) per head per year; per unit of farm area = kg CO₂-eq per ha per year, and per unit of product = kg CO₂-eq per kg FPCM, where FPCM is fat and protein corrected milk. 

The results show a range of GHG emissions in CO₂-eq among the FMS with various equation approaches because of the large impact from different emission factors, which vary between accounting methods. The more detailed equations were recommended to effectively improve environmental impacts. They are mostly non-linear equations which predict more biologically realistic emissions when compared with the linear equations in which over or under-predictions of GHG were observed. The prominent drivers for GHG emissions across all FMS were from enteric methane (CH₄) and nitrous oxide (N₂O) from soil management. Rankings among FMS varied according to output methodology and functional units. GHG emissions expressed per animal or per unit area differ greatly from those expressed from a given level of product. 

In conclusion, the accounting methodologies that are described in the paper to predict GHG emissions of animal-related origin performed satisfactory across all FMS, and could be applied to quantify the carbon footprint of dairy production systems in South Africa. 

Reference: 

R. Reinecke & N. H. Casey., 2017. A whole farm model for quantifying total greenhouse gas emissions on South African dairy farms. S. Afr. J. Anim. Sci. 47, 883-894.