Simulation of nitrous oxide emissions at field scale using the SPACSYS model.

Nitrous oxide emitted to the atmosphere via the soil processes of nitrification and denitrification plays an important role in the greenhouse gas balance of the atmosphere and is involved in the destruction of stratospheric ozone. These processes are controlled by biological, physical and chemical factors such as growth and activity of microbes, nitrogen availability, soil temperature and water availability. A comprehensive understanding of these processes embodied in an appropriate model can help develop agricultural mitigation strategies to reduce greenhouse gas emissions, and help with estimating emissions at landscape and regional scales. A detailed module to describe the denitrification and nitrification processes and nitrogenous gas emissions was incorporated into the SPACSYS model to replace an earlier module that used a simplified first-order equation to estimate denitrification and was unable to distinguish the emissions of individual nitrogenous gases. A dataset derived from a Scottish grassland experiment in silage production was used to validate soil moisture in the top 10 cm soil, cut biomass, nitrogen offtake and N2O emissions. The comparison between the simulated and observed data suggested that the new module can provide a good representation of these processes and improve prediction of N2O emissions. The model provides an opportunity to estimate gaseous N emissions under a wide range of management scenarios in agriculture, and synthesises our understanding of the interaction and regulation of the processes.

An objective approach to model reduction: Application to the Sirius wheat model.

An existing simulation model of wheat growth and development, Sirius, was evaluated through a systematic model reduction procedure. The model was automatically manipulated under software control to replace variables within the model structure with constants, individually and in combination. Predictions of the resultant models were compared to growth analysis observations of total biomass, grain yield, and canopy leaf area derived from 9 trials conducted in the UK and New Zealand under optimal, nitrogen limiting and drought conditions. Model performance in predicting these observations was compared in order to evaluate whether individual model variables contributed positively to the overall prediction. Of the 1 1 1 model variables considered 16 were identified as potentially redundant. Areas of the model where there was evidence of redundancy were: (a) translocation of biomass carbon to grain; (b) nitrogen physiology; (c) adjustment of air temperature for various modelled processes; (d) allowance for diurnal variation in temperature; (e) vernalisation (f) soil nitrogen mineralisation (g) soil surface evaporation. It is not suggested that these are not important processes in real crops, rather, that their representation in the model cannot be justified in the context of the analysis. The approach described is analogous to a detailed model inter-comparison although it would be better described as a model intra-comparison as it is based on the comparison of many simplified forms of the same model. The approach provides automation to increase the efficiency of the evaluation and a systematic means of increasing the rigour of the evaluation.

Evaluating and reducing a model of radiocaesium soil-plant uptake.

An existing model of radiocaesium transfer to grasses was extended to include wheat and barley and parameterised using data from a wide range of soils and contact times. The model structure was revised and evaluated using a subset of the available data which was not used for model parameterisation. The resulting model was then used as a basis for systematic model reduction to test the utility of the model components. This analysis suggested that the use of 4 model variables (relating to radiocaesium adsorption on organic matter and the pH sensitivity of soil solution potassium concentration) and 1 model input (pH) are not required. The results of this analysis were used to develop a reduced model which was further evaluated in terms of comparisons to observations. The reduced model had an improved empirical performance and fewer adjustable parameters and soil characteristic inputs.