NitroScape: a model to integrate nitrogen transfers and transformations in rural landscapes.

Modelling nitrogen transfer and transformation at the landscape scale is relevant to estimate the mobility of the reactive forms of nitrogen (N(r)) and the associated threats to the environment. Here we describe the development of a spatially and temporally explicit model to integrate N(r) transfer and transformation at the landscape scale. The model couples four existing models, to simulate atmospheric, farm, agro-ecosystem and hydrological N(r) fluxes and transformations within a landscape. Simulations were carried out on a theoretical landscape consisting of pig-crop farms interspersed with unmanaged ecosystems. Simulation results illustrated the effect of spatial interactions between landscape elements on N(r) fluxes and losses to the environment. More than 10% of the total N(2)O emissions were due to indirect emissions. The nitrogen budgets and transformations of the unmanaged ecosystems varied considerably, depending on their location within the landscape. The model represents a new tool for assessing the effect of changes in landscape structure on N(r) fluxes.

Effects of farm heterogeneity and methods for upscaling on modelled nitrogen losses in agricultural landscapes.

The aim of this study is to illustrate the importance of farm scale heterogeneity on nitrogen (N) losses in agricultural landscapes. Results are exemplified with a chain of N models calculating farm-N balances and distributing the N-surplus to N-losses (volatilisation, denitrification, leaching) and soil-N accumulation/release in a Danish landscape. Possible non-linearities in upscaling are assessed by comparing average model results based on (i) individual farm level calculations and (ii) averaged inputs at landscape level. Effects of the non-linearities that appear when scaling up from farm to landscape are demonstrated. Especially in relation to ammonia losses the non-linearity between livestock density and N-loss is significant (p > 0.999), with around 20-30% difference compared to a scaling procedure not taking this non-linearity into account. A significant effect of farm type on soil N accumulation (p > 0.95) was also identified and needs to be included when modelling landscape level N-fluxes and greenhouse gas emissions.

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N(2)O emissions simulated by the CERES-EGC model.

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N(2)O emissions were mainly sensitive to the fraction of N(2)O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.

Effect of 3D nitrogen, dry mass per area and local irradiance on canopy photosynthesis within leaves of contrasted heterogeneous maize crops.

BACKGROUND AND AIMS: Nitrogen partitioning within stands has been described fairly comprehensively, especially for C(3) plants in dense stands where the horizontal heterogeneity of foliage distribution is relatively small. Nitrogen has been shown to be distributed vertically and in parallel to light, maximizing carbon assimilation and stand productivity. Conversely, row crops such as maize (C(4) plants) are characterized by strong horizontal heterogeneity of foliage distribution, and a three-dimensional (3D) approach is required to investigate the combined effect of spatial distribution of nitrogen and light on canopy photosynthesis. MODEL: The 3D geometry of maize canopies was modelled with varying densities and at different developmental stages using plant digitizing under field conditions. For lamina parts, photosynthesis was measured and nitrogen content per unit area (N(a)) was described from analysis of nitrogen content per unit mass (N(m)) and dry mass per unit area (M(a)). Hyperbolic relationships between photosynthesis at irradiance saturation (P(max)) and N(a) were established as well as a linear relationship between dark respiration (R(d)) and N(a), whereas quantum efficiency (alpha) was found to be independent of N(a). KEY RESULTS AND CONCLUSIONS: N(m), M(a) and N(a) were shown to change over time vertically (i.e. between laminae), which has been largely reported previously, and horizontally (i.e. within laminae), which has scarcely been described previously. Even if M(a) played a major role in N(a), a strong relationship between N(a) and M(a) could not be demonstrated, whereas several previous studies have found that N(a) was essentially related to M(a) rather than N(m). From simulations of radiative exchange using a 3D volume-based approach and lamina photosynthesis using a hyperbola, it was shown that real patterns of N(a) partitioning could increase daily crop photosynthesis by up to 8 % compared with uniform patterns of N(a), especially for the earliest stages of stand development.