Human activity has greatly perturbed the nitrogen cycle through increased fixation by legumes, by energy and fertilizer production, and by the mobilization of N from long-term storage pools. This extra reactive N is readily transported through the environment, and there is increasing evidence that it is changing ecosystems through eutrophication and acidification. Rothamsted Experimental Station, UK has been involved in research on N cycling in ecosystems since its inception in 1843. Measurements of precipitation composition at Rothamsted, made since 1853, show an increase of nitrate and ammonium N in precipitation from 1 and 3 kg N ha −" yr −" , respectively, in 1855 to a maximum of 8 and 10 kg N ha −" yr −" in 1980, decreasing to 4 and 5 kg N ha −" yr −" today. Nitrogen inputs via dry deposition do, however, remain high. Recent measurements with diffusion tubes and filter packs show large concentrations of nitrogen dioxide of c. 20 µg m −$ in winter and c. 10 µg m −$ in summer ; the difference is linked to the use of central heating, and with variations in wind direction and pollutant source. Concentrations of nitric acid and particulate N exhibit maxima of 1n5 and 2 µg m −$ in summer and winter, respectively. Concentrations of ammonia are small, barely rising above 1 µg m −$ .Taking deposition velocities from the literature gives a total deposition of all measured N species to winter cereals of 43n3 kg N ha −" yr −" , 84 % as oxidized species, 79 % dry deposited. The fate of this N deposited to the very long-term Broadbalk Continuous Wheat Experiment at Rothamsted has been simulated using the SUNDIAL N-cycling model : at equilibrium, after 154 yr of the experiment and with N deposition increasing from c. 10 kg ha −" yr −" in 1843 to 45 kg ha −" yr −" today, c. 5 % is leached, 12 % is denitrified, 30 % immobilized in the soil organic matter and 53 % taken off in the crop. The ' efficiency of use ' of the deposited N decreases, and losses and immobilization increase as the amount of fertilizer N increases. The deposited N itself, and the acidification that is associated with it (from the nitric acid, ammonia and ammonium), has reduced the number of plant species on the 140-yr-old Park Grass hay meadow. It has also reduced methane oxidation rates in soil by c. 15 % under arable land and 30 % under woodland, and has caused N saturation of local woodland ecosystems : nitrous oxide emission rates of up to 1n4 kg ha −" yr −" are equivalent to those from arable land receiving 200 kg N ha −" yr −" , and in proportion to the excess N deposited ; measurements of N cycling processes and pools using "&N pool dilution techniques show a large nitrate pool and enhanced rates of nitrification relative to immobilization. Ratios of gross nitrification : gross immobilization might prove to be good indices of N saturation.

SUMMARYA computer model is presented that describes the flow of nitrogen between crop and soil on the field scale. The model has a compartmental structure and runs on a weekly time-step. Nitrogen enters via atmospheric deposition and by application of fertilizer or organic manures, and is lost through denitrification, leaching, volatilization and removal in the crop at harvest. Organic nitrogen is contained within three of the model compartments – crop residues (including plant material dying off through the growing season), soil microbial biomass and humus. Inorganic nitrogen is held in two pools as NH4+ or NO3-. Nitrogen flows in and out of these inorganic pools as a result of mineralization, immobilization, nitrification, leaching, denitrification and plant uptake. The model requires a description of the soil and the meteorological records for the site – mean weekly air temperature, weekly rainfall and weekly evapotranspiration. The model is designed to be used in a ‘carry forward’ mode – one year's run providing the input for the next, and so on. The model also allows the addition of 15N as labelled fertilizer, and follows its progress through crop and soil. Data from a Rothamsted field experiment in which the fate of a single pulse of labelled N was followed over several years were used to set the model parameters. The model, thus tuned, was then tested against other data from this and two contrasting sites in south-east England. Over a period of 4 years, the root mean square (R.M.S.) difference between modelled and measured quantities of labelled N remaining in the soil of all three sites was c. 7·5 kg N/ha, on average. The root mean square error in the measurements was c. 2·5 kg/ha. Similarly, the R.M.S. difference between modelled and measured recovery of labelled N by the crop was 0·6, compared with 0·3 kg/ha in the measurements themselves.

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SUNDIAL (SimUlation of Nitrogen Dynamics In Arable Land) is a user‐friendly, PC‐based version of the Rothamsted Nitrogen Turnover Model. It comprises a menu‐driven system that allows agricultural advisers, even with minimal computing experience, to enter details of a particular field or farm and simulate N turnover. The processes involved are described by a set of parameterized zero and first‐order equations. The addition of a novel facility for calculating crop parameters enables instantaneous estimation of parameters for rough but immediate simulations of a new crop, graphical visualization of the expressions used in SUNDIAL, comparison of the parameterized expressions with experimental measurements, and further refinement of parameters by fitting individual expressions to measured data or by iteratively adjusting parameters to improve the fit of simulated results to soil and crop N and 15N measurements. The facilities in SUNDIAL for displaying the various outputs in graphical form make it particularly useful for examining the impact of different management strategies on the N cycle in arable agriculture. Graphical facilities include balance sheets, graphical plots, pie charts, and flow diagrams.

Some form of critical evaluatory procedure for models is needed to maintain the integrity of modeling and to ensure that the increasingly widespread use of models does not result in the propagation of misleading information. The term validation must be used with the clear understanding that no model can be validated in the sense that it has been unequivocally justified. All that can be achieved is to show how small the probability is that the model has been refuted. Whether this probability is acceptable is a subjective decision. The type of statistical test that is appropriate depends on the quality of the data against which the model is tested. Using a procedure that compares the sums of squares that result from the model not fitting the data with the sums of squares due to error in the data, gives a stringent test, but it requires the replication of measurements. Rigor is as important in evaluating parameters as it is in testing models. Direct measurement is the best option, but where a parameter has to be obtained by fitting, the statistical procedures used for validation are appropriate. In general, the further the data used for parameterization are removed from the data to be simulated the better. Problems can arise in both parameterization and validation if the model is nonlinear regarding its parameters, and the latter have appreciable variances. Parameterization and validation become more difficult as the complexity of the model or the scale at which it is used increase.

An effective fertilizer recommendation system requires information on seasonal, soil-related and cultural variations in soil mineral nitrogen (N) and nutrient requirements of the crop. This can be provided by dynamic N turnover models, such as listed by Plentinger 8~ Penning De Vries (1996). In this paper, we describe a survey of farmer opinion designed to ascertain what farmers want from such a decision support system. Over 100 farmers were surveyed. Surveyed farmers requested that default values be available for all model inputs. Inputs should be entered both by windows-based menu (for clarity) and tabular format (for speed), have userselected units, and be fully supported by context-sensitive help. The system should have a hierarchical structure allowing access to fixed parameters, and be compatible with commonly used farm recording packages. Recommendations should be provided both for the field (single and optional application rates), and in tabular format across the whole farm. Simulations should be easily rerun using more recent crop and weather data. Turnover processes underlying recommendations should be illustrated by flow diagrams of flux between pools, pie charts of fertilizer fate, bar charts of movement down the soil profile and graphical plots of changes in N status against time.

Labelling using the stable 15 isotope of nitrogen allows a close monitoring of the fate of the fertilizer applied to arable crops. Because 15N data give direct estimates of nitrogen transformation rates, they also provide more stringent tests for N models than those based on bulk inorganic N dynamics. They may therefore point at flaws in models that had previously gone unnoticed, especially if 15N was monitored on short time steps which capture even rapid processes like nitrification.Here we tested the simple, process-based model SUNDIAL on two such data sets obtained in Northern France under winter wheat and winter rape crops receiving various doses and forms of fertilizer N. In both experiments, microplots (≈ 1 m2 in size) within larger blocks were dressed with 2·0 atom-% enriched labelled 15N, as urea, or ammonium-nitrate as NH+4–15NO3 or 15NH+4–H–NO3. Replicate micro-plots were subsequently sampled on four occasions after fertilizer application, and 15N enrichment was monitored in plant roots and tops, and at several depths in the soil in inorganic and organic forms.Comparison between observed and simulated data showed that, shortly after application, SUNDIAL either underestimated (rapeseed) or overestimated (wheat) the rates of crop uptake. Also, the gradual incorporation of 15N into soil organic matter was too quick in autumn and too slow in spring under the rapeseed crop. The simulation of the rapid depletion of the labelled soil inorganic N pool was correct under wheat, whereas under rape, SUNDIAL predicted an accumulation of nitrate which was not observed.After a longer time interval (1–2 months), the simulated and observed amounts of fertilizer-derived N in the crop and in the soil became more comparable. However, SUNDIAL only accounted for part of the unrecovered labelled N. Additional measurements indicated that denitrification and ammonia volatilization were responsible for most of the losses, with discrepancies occurring because SUNDIAL failed to volatilize ammonia after fertilizer spreading. The other major source of error lay in the simulation of crop demand for nitrogen, which may be improved based on sounder eco-physiological concepts, such as that of a regulation of plant uptake by shoot biomass.

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