Simulation of Long-Term Carbon and Nitrogen Dynamics in Grassland-Based Dairy Farming Systems to Evaluate Mitigation Strategies for Nutrient Losses

Abstract

Many measures have been proposed to mitigate gaseous emissions and other nutrient losses from agroecosystems, which can have large detrimental effects for the quality of soils, water and air, and contribute to eutrophication and global warming. Due to complexities in farm management, biological interactions and emission measurements, most experiments focus on analysis of short-term effects of isolated mitigation practices. Here we present a model that allows simulating long-term effects at the whole-farm level of combined measures related to grassland management, animal housing and manure handling after excretion, during storage and after field application. The model describes the dynamics of pools of organic carbon and nitrogen (N), and of inorganic N, as affected by farm management in grassland-based dairy systems. We assessed the long-term effects of delayed grass mowing, housing type (cubicle and sloping floor barns, resulting in production of slurry and solid cattle manure, respectively), manure additives, contrasting manure storage methods and irrigation after application of covered manure. Simulations demonstrated that individually applied practices often result in compensatory loss pathways. For instance, methods to reduce ammonia emissions during storage like roofing or covering of manure led to larger losses through ammonia volatilization, nitrate leaching or denitrification after application, unless extra measures like irrigation were used. A strategy of combined management practices of delayed mowing and fertilization with solid cattle manure that is treated with zeolite, stored under an impermeable sheet and irrigated after application was effective to increase soil carbon stocks, increase feed self-sufficiency and reduce losses by ammonia volatilization and soil N losses. Although long-term datasets (>25 years) of farm nutrient dynamics and loss flows are not available to validate the model, the model is firmly based on knowledge of processes and measured effects of individual practices, and allows the integrated exploration of effective emission mitigation strategies. © 2013 Shah et al.