NUTGRANJA 2.0

Farm nutrient management has been identified as one of the most important factors determining the economic and environmental performance of dairy cattle (Bos taurus) farming systems. Given the environmental problems associated with dairy farms, such as emissions of greenhouse gases (GHG), and the complex interaction between farm management, environment and genetics, there is a need to develop robust tools which enable scientists and policy makers to study all these interactions. This paper describes the development of a simple model called NUTGRANJA 2.0 to evaluate GHG emissions and nitrogen (N) and phosphorus (P) losses from dairy farms. NUTGRANJA 2.0 is an empirical mass-balance model developed in order to simulate the main transfers and flows of N and P through the different stages of the dairy farm management. A model sensitivity test was carried out to explore some of the sensitivities of the model in relation to the simulation of GHG and N emissions. This test indicated that both management (e.g. milk yield per cow, annual fertiliser N rate) and site-specific factors (e.g. % clover (Trifolium) in the sward, soil type, and % land slope) had a large effect on most of the model state variables studied (e.g. GHG and N losses).<p>Farm nutrient management has been identified as one of the most important factors determining the economic and environmental performance of dairy cattle (Bos taurus) farming systems. Given the environmental problems associated with dairy farms, such as emissions of greenhouse gases (GHG), and the complex interaction between farm management, environment and genetics, there is a need to develop robust tools which enable scientists and policy makers to study all these interactions. This paper describes the development of a simple model called NUTGRANJA 2.0 to evaluate GHG emissions and nitrogen (N) and phosphorus (P) losses from dairy farms. NUTGRANJA 2.0 is an empirical mass-balance model developed in order to simulate the main transfers and flows of N and P through the different stages of the dairy farm management. A model sensitivity test was carried out to explore some of the sensitivities of the model in relation to the simulation of GHG and N emissions. This test indicated that both management (e.g. milk yield per cow, annual fertiliser N rate) and site-specific factors (e.g. % clover (Trifolium) in the sward, soil type, and % land slope) had a large effect on most of the model state variables studied (e.g. GHG and N losses).