Discipline: environment; Keywords: Greenhouse gas, system dynamic model, methane, FPCM, feed sources, cow’s physiological processes.

A web-based carbon footprint assessment tool was developed by the researchers cited below that will assist dairy farmers in understanding their GHG emissions and developing effective mitigation strategies and sustaining farmlands. The tool also provide a means with which to communicate and report back to stakeholders and key audiences, such as consumers, about the real impact of dairy farmers in South Africa on the environment. It is fundamental to identify and understand the complexity of the dairy production system and to account for all the different variables that contribute to such an integrated system. The key GHGs with important heat trapping traits concerning dairy production included in the model includes methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2).

Through implementing and using this tool, data from various farms can be collected and analysed to create a scientifically sound and defensible baseline for GHG emissions from milk produced on farms. To allow for a point of reference between farms, a unit for energy-corrected milk (FPCM) produced will be used to report on emissions. The model will assist dairy farmers in calculating and monitoring the impact of key environmental indicators such as energy and N-use efficiency, key emission sources and the economic outcome of their operations or any changes thereof.

The system dynamics model behind the tool is divided into five different sub-models which can be analysed to identify key drivers and their impact on a farm. Based on the management practices implemented on a farm, the different GHG emissions can be expected as a result from biological processes that occur because of these practices. The initial assessment will be reported as the baseline footprint of a specific farm due to the current situation, which will be followed up annually and track changes over time. Through the identification of key environmental indicators, emission reduction opportunities can be identified. Furthermore, the economic sub-model included in the model provides additional insight into potential benefits or impacts any changes may have. To enable this advantage, the model allows for simulation of possible management changes to anticipate the effects it might have on the emissions as well as the economic impact of such changes. FPCM

The herd management sub-model covers the general herd structure and size and the management thereof. The flow of animals, herd growth and productive stock on the farm is included in this section. Following is the herd energy sub-model which requires information related to the dairy herds’ biological processes and its energy requirements, such as maintenance, growth, milk production and pregnancy, energy and protein intake as well as all procured feed included in the diet. The feed management sub-model includes aspects related to land use and pasture or crop management. Factors such as pasture utilisation, type of forage planted, the nutritional value in terms of energy and protein produced, fertiliser type and quantity applied, yields and feed allocation are included.

The emissions sub-model is mostly model derived from inputs obtained in the first three sub-models. This section only requires information related to manure management systems and factors contributing to direct carbon emissions such as fuel and electricity. From the emissions sub-model, reports can be generated to reflect the emissions and identify those emissions with the highest impact. CH4 emissions are generated from enteric fermentation processes and manure management; N2O emissions from soil and manure management and CO2 emissions from direct sources.

The economic sub-model does not represent a detailed economic or financial report but only account for high-level entities. However, it will provide additional insight into potential benefits or impacts when management changes are considered. Simulation of potential changes will allow a farmer to compare the possible gains and trade-offs for different strategies and implement those most suited to their farm situation and targets.

After the development phase, the model was applied to several farms to calibrate the model and evaluate its feasibility. An article published through Dairy New Zealand in February 2021, entitled “Mapping the carbon footprint of milk for dairy cows”, stated that New Zealand dairy is the world’s most emission efficient with the lowest emissions per kg FPCM at 0.74 kg CO2-eq and from the 86 countries studied, while the highest was from Peru at 3.29 kg CO2-eq per kg FPCM. From the farms evaluated in this study, the emissions ranged from 0.89 to 1.13 kg CO2-eq per kg FPCM.

Interestingly, the farm that showed lowest emissions per kg FPCM also had the highest profit per kg FPCM for the year. In contrast to this, the farm where the highest energy intake was observed also had highest milk production and biggest cows; however, it was the smallest herd and had the lowest profit compared to the other farms.

From the initial results it seems like the important and practical opportunities to reduce emissions can also drive improvement to farms, such as enhanced feed efficiencies, improved herd management strategies and increased productivity, which all have a positive effect on the economic stability of the farm. The model calculates the protein intake from both procured feed and farm-produced feed. Depending on the forage type, the protein content will be calculated in the model. The evaluated farms indicated that farms with the highest fertiliser N-application rates also had a higher protein intake from cows and subsequently had an increased N-excretion rates, which in return increase emissions and have a negative economic impact.

Dairy sustainability and profitability are not conflicting objectives and those farms that are environmentally sustainable tends to be more profitable. The developed model can calculate the emissions from a dairy farm, identify the critical environmental indicators and simulate scenarios to determine the best and most profitable mitigation strategies, as well as track progress over time which can be used to report on actual data. Since dairy producers have already adapted to more sustainable practices over many years and have introduced more efficient use of natural resources, further addition towards emission reductions to evidently increase profitability and simultaneously help promoting the sector as more environmentally friendly to the consumer, should not be too far from reality. Furthermore, many opportunities to improve on the environmental impact from dairy production and greater emission reduction strategies still exist. The model will be under continuous development based on feedback from users. Also, when improved input options from updated science and data become available, it will be made freely available and can be accessed on the website: https://assetresearch.org.za/environmental-indicators-dairy-production-systems/.


Blignaut, J.N1,3., Reinecke, R2. & Swanepoel, P.A3., 2022. A system dynamics approach to incorporate environmental indicators into economic outcomes of dairy production systems in South Africa. Asset Research1; FarmVision2; University of Stellenbosch3. Project funded by Milk SA.