Modeling Grazing Systems' Nutrient Dynamics

To mimic grazing systems, daily grass intake and excretion of sheep and cattle in the field were quantified based on literature and experimental results from our site. Detailed information that is used to estimate inputs to various N pools in the model is given in Tables 2, 3, 4. As the model does not simulate volatilization, N input from excretion was reduced by a proportion equivalent to 0.6 of the total N in the farm‐yard manure (FYM) without considering the effect of temperature on the volatilization process. It is difficult to model individual animals in the field so we assumed a live weight of 600 kg per animal for beef cattle, 75 kg per animal for ewes and, for fields that were grazed, a spatially uniform distribution of grass intake over the grazing period.
Soil physical and chemical properties of the selected fields were based on baseline field surveys conducted in 2012. Agronomic management quantified for the simulation was interpreted from the farm records for the NWFP. The concentrations of nutrients in applied farmyard manure were estimated based on the DEFRA fertilizer manual (Department for Environment, Food and Rural Affairs, 2010). We assumed that the reported available N content of FYM in the manual is incorporated fully into the soil without further loss.
The SPACSYS model has been parameterized previously for the processes of soil water, soil heat transformation, and C and N cycling (Wu & Shepherd, 2011). Parameters related to grass species were adopted from a previous study. Those parameters were used directly in the simulations.
The data extracted from the UK Climate Projection 2009 (UKCP09) for future climate projections were applied to this study. The UKCP09 weather generator provides probabilistic projections of climate change (Jones et al., 2009). Medium (SRES A1B) and large (SRES A1F1) emission scenarios based on future projections of greenhouse gas and aerosol levels according to the IPCC (IPCC, 2007) were used to generate future climate conditions. The scenarios at the time slices of the 2020s, 2050s and 2080s were considered. One hundred files of 30‐year daily weather variables for each time‐slice under each emission scenario and a baseline representing the 1961–1990 period were generated for the site. To avoid the need for hundreds of simulations with SPACSYS, the mean daily value across the hundred files of each weather element (except precipitation) for each day of the 30 years of data was calculated. Because of its skewed distribution, daily means of precipitation across the files cannot be taken. Therefore, the monthly mean precipitation and the number of rain days per month were calculated for each file, and then both of these elements were averaged across the 100 files. The daily precipitation for a given month was then distributed randomly across the month. As wind speed is not included in UKCP09, it was obtained from the 11‐member Regional Climate Model dataset (Met Office, 2003). Six UKCP09 projections were produced, three time‐slices for medium (represented as 2020med, 2050med and 2080med, respectively) and three time‐slices for large emissions (represented 2020lar, 2050lar and 2080lar, respectively), plus historic climate data over the period 1961–1990 (symbolized as baseline) for the site. Annual mean climate characteristics for the time‐slices under the emission scenarios are given in Table 5.
To avoid further complexity in future scenarios, the current atmospheric CO₂ concentration (695 mg CO₂ m⁻³) was applied to all the simulations. Meanwhile, the current farm management practices for the individual fields (e.g. timing and amount of fertilizer or slurry application, grass‐cutting dates, start and end dates of grazing and number of animals) were kept the same for all simulations in the field. Therefore, any change in the fluxes of water, N and C as a result of the treatments would be the consequence of climate change scenarios.