NGAUGE: A decision support system to optimise N fertilisation of British grassland for economic and environmental goals

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Abstract

The poor efficiency with which nitrogen (N) is often used on grassland farms is well documented, as are the potential consequences of undesirable emissions of nitrogen. As fertiliser represents a major input of nitrogen to such systems, its improved management has good potential for increasing the efficiency of nitrogen use and enhancing environmental and economic performance. This paper describes the development, structure and potential application of a new decision support system for fertiliser management for British grassland. The underlying empirically-based model simulates monthly nitrogen flows within and between the main components of the livestock production system according to user inputs describing site conditions and farm management characteristics. The user-friendly decision support system (‘NGAUGE’) has a user interface that was produced in collaboration with livestock farmers to ensure availability of all required inputs. NGAUGE is an improvement on existing nitrogen fertiliser recommendation systems in that it relates production to environmental impact and is therefore potentially valuable to policy makers and researchers for identifying pollution mitigation strategies and blueprints for novel, more sustainable systems of livestock production. One possible application is the simulation of the phenomenon of pollution swapping, whereby, for example, the adoption of strategies for the reduction of nitrate leaching may exacerbate emissions of ammonia and nitrous oxide. Outputs of the decision support system include a field- and target-specific N fertiliser recommendation together with farm- and field-based N budgets, comprising amounts of N in both production and loss components of the system. Recommendations may be updated on a monthly basis to take account of deviations of weather conditions from the 30-year mean. The optimisation procedure within NGAUGE enables user-specified targets of herbage production, N loss or fertiliser use to be achieved while maximising efficiency of N use. Examples of model output for a typical grassland management scenario demonstrate the effect on model predictions of site and management properties such as soil texture, weather zone, grazing and manure applications. Depending on existing management and site characteristics, simulations with NGAUGE suggest that it is possible to reduce nitrate leaching by up to 46% (compared with a fertiliser distribution from existing fertiliser recommendations), and fertiliser by 33%, without sacrificing herbage yield. The greatest improvements in efficiency are possible on sandy-textured soils, with moderate N inputs.

Introduction

Experimental evidence, collected over the last three decades, of nitrogen (N) emissions from grassland (Ryden, 1981, Ryden, 1984, Scholefield et al., 1993) has demonstrated the inefficiency with which N is frequently used. The loss of N has both economic and environmental consequences. The N loss pathways of primary concern to society are nitrate leaching and emission of the gases nitrous oxide (N2O) and ammonia (NH3). The increase in nitrate concentration in water bodies in recent decades has been a cause of concern because of the perceived potential threat to human health and because of the ecological and aesthetic consequences of eutrophication. In the UK, agriculture is the main source of nitrate in most UK rivers and groundwaters (Powlson, 2000) and is estimated to account for 69% of the emission of N2O (Salway et al., 2001), which contributes both to global warming and to the depletion of the stratospheric ozone layer. Ammonia emission and subsequent deposition may contribute to water and soil acidification (Van Breemen et al., 1982) and is one of the main sources of the increased N supply to natural areas that may cause eutrophication of terrestrial and aquatic ecosystems (Isermann, 1990).

It has been shown (Scholefield et al., 1991) that there is a strong linear relationship between total annual inorganic N input to a grassland system and percentage recovery of that N by plants, such that in systems of low N flux, a larger proportion of the total N is recovered by the plant than in systems of higher N flux. Agricultural systems can be manipulated to changed efficiency simply by increasing or decreasing N input. Additionally, efficiency of plant uptake of N changes seasonally with weather and soil conditions and with physiological traits of the plant. Nitrogen fertilisers are the major N input to a typical dairy farm in the UK, comprising as much as 74% of the total N input (Jarvis, 1993), and are the input to the grassland N cycle that is most easily managed. It appears that there is much potential, therefore, to manipulate the efficiency of the system by appropriate management of fertilisers. However, simply reducing the fertiliser N input moves the system along the established efficiency relationship, and although losses can be reduced, production is also compromised. The challenge lies in the development and implementation of a system which lies above this line, i.e. is genuinely of greater N efficiency for the same total flux of N. This will involve both temporal and quantitative adjustment to fertiliser patterns.

Fertiliser recommendations for N have been produced in a similar format for England and Wales since 1973. With the exception of the most recent edition, recommendations have given little or no consideration to the potential environmental impacts of N application and have been rather generalised in relation to site variables. In the current version (RB209, MAFF, 2000), there is more site-specificity, in terms of soil types (three classes), rainfall (three classes) and previous management and N use. Although the publication points out the importance of achieving the right balance between profitable agricultural production and environmental protection, it also states that ‘the primary aim of the recommendations is to maximise the economic return from the use of fertilisers’. Improvement of the current UK recommendation system to effect improvements in efficiency would necessitate a change in emphasis from production/economic targets to a system driven, to a greater degree, by limitation of the undesirable exports: nitrate lost to surface water and N2O and NH3 emitted to the atmosphere. The application of such an approach would be especially beneficial in areas of particular sensitivity such as Nitrate Vulnerable Zones (NVZs, implemented under the Nitrates Directive, 91/676/EC), where the nitrate concentration of water draining from farmland is a fundamental consideration in the selection of agricultural management. The improved recommendation would seek to strike a compromise between production and environmental impact since the farmer still needs to achieve an acceptable level of income.

The objective of the research presented in this paper was to produce a decision support system (DSS) which would enable the efficiency of N use in grassland fields to be improved, by calculating the optimal temporal distribution of N fertiliser for a given field. In order to achieve this aim, the NGAUGE DSS was developed, to provide field-specific monthly N fertiliser recommendations, which improve the efficiency with which N is used, for user-specified targets. This necessitated simulating flows of N on a site-specific basis, with sensitivity to climate, soil properties, sward management and on-going weather, and the development of the means of determining the best distribution of fertiliser N through the year to improve the efficiency of N use.

Section snippets

Model development

An existing empirically-based model of N cycling in grassland soils, NCYCLE (Scholefield et al., 1991), was taken as the basis for the new model and DSS. NCYCLE is an annual, empirical model, based on published multi-site grassland data sets and has, since its creation, been validated for many of its key components (Rodda et al., 1995). NCYCLE simulates N flows through the major processes of N transformation in the soil and therefore links the input, production and loss components of the

Model validation

The performance of NGAUGE was evaluated in two ways: (a) assessment of the closeness of predictions and observations of N loss and transformation; and (b) investigation of the effect of NGAUGE fertiliser recommendations on N losses on paddocks of commercial dairy farms. Results of the latter will be presented in another paper. Data from a purpose-built cut-plot experiment in mid Devon, UK were used to evaluate the predictions of NGAUGE against field measurements. The site was on an old sward

User interface description

NGAUGE was programmed in Borland Delphi 5. This is an object-oriented language, which associates portions of code with ‘events’ that happen to objects (e.g. a click on the ‘run’ button). It was written in a modular structure, using procedures and functions that can be called from any part of the program.

The user interface was designed and constructed in consultation with farmers, advisors, computer programmers and others with experience in DSS software development. User preferences suggested

Use of NGAUGE for prediction of existing and optimised N flows on livestock farms

The degree to which optimisation is able to improve upon the predicted performance of a conventional system is dependent on the characteristics of the system (weather, soil type, fertiliser use, etc.) and the optimisation performed. Some examples of NGAUGE runs are given below to exemplify its capability. For each scenario, the results from an optimised and non-optimised run are given. The conventional or non-optimised fertiliser distribution is based on MAFF (2000), for a fertiliser input of

Discussion

The simulation of existing fertiliser, manure and grazing practices in the non-optimised mode of NGAUGE enables the user to investigate the likely effects of changed management in any of these areas on both production and losses of N through the main processes of volatilisation, denitrification and leaching. The simulation of all of these processes also allows the potential effects of ‘pollution swapping’ to be monitored, as strategies for the abatement of individual loss processes are

Conclusions

NGAUGE provides a basis for improved decision-making about fertiliser management on grassland farms. It is a tool which enables users to be more aware of the magnitude of N losses and provides a means of improving the efficiency with which N used on grassland fields. The potential for improvement in efficiency was found to be dependent on site characteristics and existing management, with the greatest improvement possible on sandy-textured soils with moderate N inputs. It was possible to reduce

Acknowledgements

The development of NGAUGE was funded by DEFRA, London (NT1601, NT1603). We thank Eunice Lord (ADAS) for analysis of the long-term weather data and helpful comments during the development of NGAUGE, and Colin Brown (NSRI) for the soil moisture algorithms. The input of farmers to the user interface evaluation is gratefully acknowledged. IGER is sponsored by the Biotechnology and Biological Sciences Research Council.

References (58)

  • M.S. Aulakh et al.

    Soil denitrification—significance, measurement, and effects of management

    Adv. Soil Sci.

    (1992)
  • L.D. Bailey et al.

    Effects of moisture, added NO3 and macerated roots on NO3 transformation and redox potential in surface and subsurface soils

    Can. J. Soil Sci.

    (1973)
  • Blantern, P., 1991. Factors affecting nitrogen transformations in grazed grassland soils with specific reference to the...
  • N.C. Brady

    The Nature and Properties of Soils

    (1984)
  • D.R. Chadwick et al.

    Plant uptake of nitrogen from the organic nitrogen fraction of animal manures: a laboratory experiment

    J. Agric. Sci. Camb.

    (2000)
  • C.M. Cho et al.

    Denitrification intensity and capacity of three irrigated Alberta soils

    Soil Sci. Soc. Am. J.

    (1979)
  • E.A. Davidson et al.

    Processes regulating soil emissions of NO and N2O in a seasonally dry tropical forest

    Ecology

    (1993)
  • M.L. Decau et al.

    AzoPât: une description quantifiée des flux annuels d’azote en prairie pâturée par les vaches laitières2. Les flux du système sol-plante

    Fourrages

    (1997)
  • L. Delaby et al.

    AzoPât: une description quantifiée des flux annuels d’azote en prairie pâturée par les vaches laitières1. Les flux asocies à l’animal

    Fourrages

    (1997)
  • H.J. Di et al.

    Calculating N leaching losses and critical nitrogen application rates in dairy pasture systems using a semi-empirical model

    J. Agric. Res.

    (2000)
  • P. Dosch et al.

    Reducing N losses (NH3, N2O, N2) and immobilization from slurry through optimized application techniques

    Fertil. Res.

    (1996)
  • M.K. Firestone et al.

    Microbial basis of NO and N2O production and consumption in the soil

  • J. Frame et al.

    Extending the grazing season

    Forage Matters

    (2001)
  • J.T. Gilmour

    The effects of soil properties on nitrification and nitrification inhibition

    Soil Sci. Soc. Am. J.

    (1984)
  • Hall, D.G.M., Reeve, M.J., Thomasson, A.J., Wright, V.F., 1977. Water retention, porosity and density of field soils....
  • A.C. Hansson et al.

    Uptake and above- and below-ground allocation of soil mineral-N and fertilizer-15N in a perennial ryegrass ley (Festuca pratense)

    J. Appl. Ecol.

    (1989)
  • D.J. Hatch et al.

    Field measurement of nitrogen mineralisation using soil core incubation and acetylene inhibition of nitrification

    Plant Soil

    (1990)
  • A. Hopkins et al.

    Response of permanent and reseeded grassland to fertilizer nitrogen1. Herbage production and herbage quality

    Grass Forage Sci.

    (1990)
  • K. Isermann

    Share of agriculture in nitrogen and phosphorus emissions into the surface waters of Western Europe against the background of their eutrophication

    Fertil. Res.

    (1990)
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