Influence of Microbial Community on Nitrogen Loss through Denitrification in Agriculture

Global food demand is expected to rise by up to 62% by 2050, with an increasing population and finite land available for food production. Denitrification causes loss of fixed nitrogen from soil systems, thereby reducing crop productivity. Current nitrogen cycling models cannot fully explain the variation in nitrogen use efficiency at the field scale; this variation is thought to be associated with microbial community dynamics. The dynamics of denitrification and denitrifying communities are affected by the physicochemical properties of the soil, and therefore the land management practices which alter them. In this thesis, we look at the effects of land management history and innate environmental variation using a long-term factorial field trial, a working commercial farm, and laboratory-based experiments. By studying the denitrifier community composition, abundance and diversity in the context of environmental data, we were able to address the question of how specific land management practices and innate spatiotemporal variation affects the distribution of denitrifiers within arable soils.

It was found that bacterial and denitrifier communities are largely unaffected by land management history, and cluster predominantly along environmental gradients. Additionally, there was a high degree of spatiotemporal variation in soil denitrifying communities, with implications for future sampling strategies as well as computer modelling. Finally, it was established that isolation of bacteria from the environment is a viable method for culturing non-model organisms that can be characterized and used to inform nitrogen-cycling models. In this combination of well-established agricultural field trial and working commercial farm, the major identifiable determinant of bacterial community dissimilarity was a blend of physicochemical factors. The importance of selection on bacterial communities, relative to ecological drift, may have consequences for attempts to understand and model nutrient availabilities in arable farming systems, and improve agricultural productivity to feed the growing global population.