The Sustainable and Integrated Management Systems for Dairy Production or SIMSDAIRY, is a whole farm system-based model that considers the interactions between climatic conditions, biophysical characteristics, and farm management practices on the carbon and nitrogen footprint of dairy production systems (DPS). In the context of the MilKey platform, SIMSDAIRY is used to examine several emission mitigation strategies (greenhouse gas and other nitrogen emissions) for a dairy cattle farm and the size of this mitigation effect for this farming system.

In the context of the MilKey platform, SIMSDAIRY is used to examine several emission mitigation strategies (greenhouse gas and other nitrogen emissions) for a dairy cattle farm and the size of this mitigation effect for this farming system.

 

 

The Sustainable and Integrated Management Systems for Dairy Production or SIMSDAIRY, is whole farm system-based model that considers the interactions between climatic conditions, biophysical characteristics, and farm management practices on the carbon and nitrogen footprint of dairy production systems (DPS). These interactions are simulated on a monthly basis within a year. The semi-mechanistic methodology followed by the model applies empirical and dynamic approaches to simulate different environmental aspects of sustainability making it sensitive to different climatic and biophysical conditions. SIMSDAIRY estimates the methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O), ammonia (NH3), nitrogen oxide (NOX) and nitrate (NO3) emissions from animals, manure management, fertilizer application, feed/fertilizer purchase and energy use of conventional and organic DPS. The different greenhouse gas (GHG) and air pollutant emissions are aggregated to determinate carbon/nitrogen footprints and individual emission intensities (per liter of milk and per hectare). In addition, the model allows for the identification of hotspots for nitrogen emissions. Some farm management decisions could be adjusted, allowing for the assessment of their effect on a wide range of dairy farming scenarios.

As for CH4, the model considers both the enteric and manure emissions. Regarding CH4 from enteric fermentation, SIMSDAIRY analyses the relationship between dry matter intake (DMI) and the degree of unsaturated fatty acids in the diet as a proxy for estimating these emissions. For CH4 emissions from manure management, these are calculated using specific emission factors (EF) for the manure excreta and application and IPCC methodology for the manure storage stage. In addition, CO2 emissions from energy use as well as concentrate and mineral fertilizers purchases are estimated. Specific EF for electricity and fuel use are applied for the estimation of the emissions derived from energy use. As for manure N losses and flows, these are simulated following the principles of a mass-balance approach. Relevant emissions for the nitrogen footprint such as NO2 together with NH3, and NOx, are simulated from the pool of total ammonium nitrogen (TAN) in manure nitrogen according to different EF for different management stages. Furthermore, NO3 emission are estimated taking into account the biological processes of plant uptake, denitrification, nitrification and mineralization as well as the weather conditions, soil texture, fertilization and grazing management .

Case studies from MilKey have been used to compare organic and conventional DPS in different key regions for milk production across Europe. Selected mitigation options aimed at reducing GHG and NH3 emissions have been implemented in the model. The results obtained from using SIMSDAIRY show how different structural and management characteristics largely condition DPS emissions. Far from being a problem, analyzing this variability facilitates applying mitigation measures adapted to each production context allowing knowledge-based decisions.

Previous works have fully described and discussed the limitations of the model.

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Chadwick DR, Pain BF (1997) Methane fluxes following slurry applications to grassland soils: Laboratory experiments. Agric Ecosyst Environ 63:51–60. https://doi.org/10.1016/S0167-8809(96)01119-X

DEFRA (2010) Guidelines to Defra/DECC’s greenhouse gas conversion factors for company reporting

Del Prado A, Chadwick D, Cardenas L, et al (2010) Exploring systems responses to mitigation of GHG in UK dairy farms. Agric Ecosyst Environ 136:318–332. https://doi.org/10.1016/j.agee.2009.09.015

Del Prado A, Chadwick D, Scholefield D (2009) New integrated dairy production systems: Specification, practical feasibility and ways of implementation

Del Prado A, Misselbrook T, Chadwick D, et al (2011) SIMSDAIRY: A modelling framework to identify sustainable dairy farms in the UK. Framework description and test for organic systems and N fertiliser optimisation. Sci Total Environ 409:3993–4009. https://doi.org/10.1016/j.scitotenv.2011.05.050

Del Prado A, Scholefield D (2008) Use of SIMSDAIRY modelling framework system to compare the scope on the sustainability of a dairy farm of animal and plant genetic-based improvements with management-based changes. J Agric Sci 146:195–211. https://doi.org/10.1017/S0021859608007727

Giger-Reverdin S, Morand-Fehr P, Tran G (2003) Literature survey of the influence of dietary fat composition on methane production in dairy cattle. Livest Prod Sci 82:73–79. https://doi.org/10.1016/S0301-6226(03)00002-2

IPCC (1997) IPCC Guidelines for National Greenhouse Gas Inventories. Paris

Webb J, Misselbrook TH (2004) A mass-flow model of ammonia emissions from UK livestock production. Atmos Environ 38:2163–2176. https://doi.org/10.1016/j.atmosenv.2004.01.023

Yamulki S, Jarvis SC, Owen P (1999) Methane Emission and Uptake from Soils as Influenced by Excreta Deposition from Grazing Animals. J Environ Qual 28:676–682. https://doi.org/10.2134/jeq1999.00472425002800020036x