Concepts in modelling N2O emissions from land use

Abstract

Modelling nitrous oxide (N 2 O) emissions from soil is challenging because multiple biological processes are involved that each respond differently to various environmental and soil factors. Soil water content, organic carbon, temperature and pH are often used in models that predict N 2 O emissions, yet for each of these factors there are concepts that are not fully understood. Though a ubiquitous measure of soil water for models, the application of functions based on water filled pore space across soils that vary in bulk density is not ideal. Diffusion of gases and solutes in soil are controlled by the volume fractions of air and water present. Across soils with different bulk densities, both of these terms vary at constant water filled pore space. Soil organic carbon influences N 2 O emissions in two ways: as a source of energy for denitrifiers and also by driving biological oxygen demand and the creation of anaerobic zones in the soil. Soil temperature influences N 2 O emissions through its effect on the activity of microorganisms and enzymes. A variety of temperature response functions have been proposed. The preferred response function should contain a temperature optimum that can be varied in response to climatic conditions to account for microbial adaptation. Soil pH can have direct and indirect influences on rates and product ratios of nitrification and denitrification. The concepts of pH optima and microbial adaptation need to be considered in modelling. Methodological issues such as microsite versus bulk soil measurements and apportioning N 2 O fluxes to the various N transformation processes remain an impediment to characterising the influence of pH and other factors on N 2 O emissions. Quantifying the response of N 2 O emissions to individual factors using regression analysis requires all other factors to be controlled experimentally. Boundary line analysis provides a way of defining the response to a single input variable where other influencing variables are not controlled. Such analyses can aid in the definition of the shape and magnitude of response functions to be incorporated into process simulation models. Process/mechanistic simulation models offer a greater transferability than empirical models but careful consideration of temporal and spatial scale and the availability of data to run these models is critical in developing model structure.

Nitrification rates and associated nitrous oxide emissions from agricultural soils – a synopsis

“…Indeed, comparing the response of processes to water content across different soils is difficult because different metrics can be used, and water content is usually a proxy for several factors that influence process rates. With respect to N 2 O emissions and soil water metrics, some progress has been made since Farquharson and Baldock (2008) highlighted this issue, with metrics such as volumetric water content and relative diffusivity providing improvements in explaining N 2 O emissions (Castellano et al 2010;van der Weerden et al 2012;Balaine et al 2013;Klefoth et al 2015;Jamali et al 2016). Ultimately, the modelling of oxygen demand and transport (Cook et al 2013) is likely to provide the best prospects for improving simulations of N 2 O dynamics.…”

Benchmarking nitrous oxide emissions in deciduous tree cropping systems

“…Furthermore, volumes of air and water in soil at given values of WFP will be influenced by the bulk density of the soil (Castellano et al 2010). As such, Farquharson and Baldock (2008) proposed that VWC is more appropriate for estimating the spatial and temporal variability of N 2 O emissions for different soils, and further supported by van der Weerden et al (2012). In our study, relationships between VWC and WFP and N 2 O emissions were consistently weak but almost identical for each site, sampling location and season with the exception of winter, when VWC was more strongly correlated.…”

Biological Feedstocks for Biofuels

“…Therefore, when we change crops and associated N fertilizer application, it will lead to different NO x emissions. Qin et al [28] developed an agroecosystem model (AgTEM) that was incorporated with biogeochemical and ecophysiological processes. They found that N 2 O emitted from croplands with high N application rates is mostly larger than those with lower N input levels.…”