Elsevier

Geoderma

Volume 151, Issues 3–4, 15 July 2009, Pages 327-337
Geoderma

Application of the DNDC model to predict emissions of N2O from Irish agriculture

https://doi.org/10.1016/j.geoderma.2009.04.021Get rights and content

Abstract

Models are increasingly used to examine the potential impacts of management and climate change in agriculture. Our aim in this paper was to assess the applicability of the field-DeNitrification DeComposition (DNDC) model in Irish agriculture. This study provides the results of that evaluation, which is a prerequisite for using the model for assessing management impacts in the future. The DNDC model was tested against seasonal and annual data sets of nitrous oxide flux from a spring barley field and a cut and grazed pasture at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. In the case of the arable field, predicted fluxes of N2O agreed well with measured fluxes for medium to high fertilizer input (70–160 kg N ha 1) but poorly described those fluxes from zero fertilizer treatments. In terms of cumulative flux values, the relative deviation of the predicted fluxes from the measured values was a maximum of 6% for the highest N fertilizer inputs but increased to 30% for the medium N and more than 100% for the zero N fertilizer treatments. There is a linear correlation of predicted against measured flux values for all fertilizer treatments (r2 = 0.85) but the model underestimated the seasonal flux by 24%. Incorporation of literature values from a range of different studies on arable and pasture land did not significantly improve the regression. The description by DNDC for measured fluxes of N2O from reduced tillage plots was poor with underestimation of up to 55%.

For the cut and grazed pasture the relative deviations of predicted to measured fluxes were 150 and 360% for fertilized and unfertilized plots. A sensitivity analysis suggests that the poor model fit is due to DNDC overestimating WFPS and the effect of initial soil organic carbon (SOC) on N2O flux. As the arable and grassland soils differed only in SOC content, reducing SOC of the grassland field to that of the arable field value significantly improved the fit of the model to measured data such that the relative deviations decreased to 9 and 5% respectively. Sensitivity analysis highlighted air temperature as the main determinant of N2O flux, an increase in mean daily air temperature of 1.5 °C resulting in almost a 65% increase in the annual cumulative flux. This is interesting as with future global warming, N2O flux from the soil will have a strong positive feedback. It can be concluded that DNDC is unsuitable for predicting N2O from Irish grassland due to its overestimation of WFPS and effect of SOC on the flux.

Introduction

Nitrous oxide contributes to climate change by virtue of having a global warming potential (GWP) 298 times greater than that of carbon dioxide (IPCC, 2007). The atmospheric concentration of this greenhouse gas has increased from approximately 275 ppb in pre-industrial times to a present day concentration of 314 ppb (Harrison and Webb, 2001, IPCC, 2007). Agricultural land is the most important source of N2O emissions, contributing approximately 46–52% of the global anthropogenic N2O flux (Mosier et al., 1998, Olivier et al., 1998, Kroeze et al., 1999). Primary reasons for enhanced N2O emissions from cultivated soils are increased N inputs by mineral fertilizers, animal wastes and biological N fixation (IPCC, 1996, 2007). Other factors which affect N2O emissions are temperature, moisture, crop type, fertilizer type, soil organic carbon content, soil pH, tillage and soil texture (Dobbie et al., 1999, Stehfest and Bouwman, 2006, IPCC, 2007, Metay et al., 2007).

Management can influence soil fertility directly through fertilizer inputs (Bouwman, 1996, Makarov et al., 2003) and indirectly via management-induced changes in plant composition (Collins et al., 1998, Patra et al., 2006) and consequently increase N2O flux from soils. For example mowing and grazing accelerate the N cycle (Bardgett et al., 1998, Güsewell et al., 2005) and encourage increased above- and below-ground plant growth (Leriche et al., 2001) and root exudation (Lipson and Schmidt, 2004). Plants in mown grasslands must complete their life cycle relatively early in the season, thus the recycling of roots from early-season species in mown fields boosts soil nutrient contents sooner than in un-mown fields (Bardgett et al., 1998). Mowing enables short-lived herbs, that exploit early-season ecological niches (Louault et al., 2005), to flourish, and grazing reduces the dominance of grasses in favor of short-lived rosette species (Bullock et al., 2001).

National inventories of N2O fluxes from agricultural soils as required by signatory countries to the United Nations Framework Convention of Climate Change (UNFCC), are mainly derived from the use of the default IPCC Tier 1 method, where 0.9–1.25% of applied inorganic nitrogen to agricultural soils is assumed to be released to the atmosphere as nitrous oxide-N (Bouwman, 1996, IPCC, 1997, IPCC, 2000, IPCC, 2007). This standard reporting procedure has advantages in collating annual inventories but may mask significant variations in emission factors (EFs) on a regional scale (Schmid et al., 2001, Laegreid and Aastveit, 2002, Flynn et al., 2005). For instance in Ireland, published EFs derived from field measurements of N2O using either eddy covariance or static chamber methods vary depending on soil type, land management, climate and year and range from 3.4% for a grassland in Cork to 0.7 to 4.9% for Carlow and Wexford grasslands (Hsieh et al., 2005, Hyde et al., 2006, Fl et al., 2007).

Because of the importance of nitrous oxide emission for global warming, regional or even global emission estimates are needed for policy and decision makers. Given the considerable expense of establishing and maintaining relevant flux measurement sites, the use of simulation models to estimate N2O fluxes from agricultural soils, using soil and climate data, has obvious benefits. Modelling also allows the complex links between soil physical, chemical and microbial processes that underpin nitrification, denitrification and decomposition to be examined. Models can simulate the processes responsible for production, consumption and transport of N2O in both the long and short term, and also allow spatial simulation (Willams et al., 1992).

Simulation models range from simple empirical relationships based on statistical analyses to complex mechanistic models that consider all factors affecting N2O production in the soil (Li et al., 1992, Frolking et al., 1998, Stenger et al., 1999, Freibauer and Kaltschmitt, 2003, Roelandt et al., 2005, Jinguo et al., 2006). These factors include soil moisture, soil temperature, carbon and nitrogen substrate for microbial nitrification and denitrification which are critical to the determination of N2O emissions (Cho et al., 1979, Batlach and Tiedje, 1981, Frissel and Van Veen, 1981, Tanji, 1982, Leffelaar and Wessel, 1988). One widely used mechanistic model is DeNitrification DeComposition (DNDC) developed to assess N2O, NO, N2 and CO2 emissions from agricultural soils (Li et al., 1992, Li et al., 1994, Li, 2000). The rainfall driven process-based model DNDC (Li et al., 1992) was originally developed for USA conditions. It has been used for simulation at a regional scale for the United States (Li et al., 1996) and China (Li et al., 2001). Advantages of DNDC are that it has been extensively tested and has shown reasonable agreement between measured and modelled results for many different ecosystems such as grassland (Brown et al., 2001, Houghton et al., 1996, Saggar et al., 2007), cropland (Li, 2003, Cai et al., 2003, Yeluripati et al., 2006, Pathak et al., 2006, Tang et al., 2006) and forest (Li, 2000, Stange et al., 2000, Kesik et al., 2006). The model has reasonable data requirement and is suitable for simulation at appropriate temporal and spatial scales.

The Field-DNDC model contains four main sub-models (Li et al., 1992, Li, 2000); the soil climate sub-model calculates hourly and daily soil temperature and moisture fluxes in one dimension, the crop growth sub-model simulates crop biomass accumulation and partitioning, the decomposition sub-model calculates decomposition, nitrification, NH3 volatilization and CO2 production, whilst the denitrification sub-model tracks the sequential biochemical reduction from nitrate (NO3) to NO2, NO, N2O and N2 based on soil redox potential and dissolved organic carbon.

This paper presents a field evaluation of DNDC for an Irish sandy loam soil under both arable and grassland crops with different fertilizer and tillage regimes. Results are discussed in terms of the suitability of this model for estimating annual and seasonal fluxes of N2O from Irish agriculture.

Section snippets

Experiments

Measurements of N2O flux were carried out for a spring barley field from April to August for two consecutive seasons (2004/05), and for a cut and grazed pasture from October 2003 to November 2004. Both fields were located at the Oak Park Research Centre, Carlow, Ireland (52°86' N, 6°54' W). The arable field was seeded with spring barley (cv. Tavern) at a density of 140 kg ha 1 and managed under two different tillage regimes; conventional tillage where inversion ploughing to a depth of 22 cm was

Results and discussion

Climate and soil input variables for DNDC are listed in Table 1. Field data measurements were used for all of the variables listed except for atmospheric CO2, rainfall N, clay fraction and depth of the soil water retention layer. Here default values were used. Collectively, DNDC was better at predicting N2O fluxes for high inputs of N fertilizer (> 140 kg N ha 1) than for zero or low N input treatments (0–70 kg N ha 1). In addition the model appeared to be unduly sensitive to the influence of

Conclusions

In its present form DNDC is suitable for simulation of C and N dynamics in medium to high N input systems, but less suitable for low input systems, with the accuracy of the prediction being highly dependent on the level of fertilizer application. High fertilizer inputs produce low relative deviations between modelled and measured fluxes (∼ 1–6%) for the arable field under conventional tillage. Prediction of N2O fluxes from reduced tillage plots however, was poor, with DNDC consistently

Acknowledgement

This work was funded by the EU sixth framework program (contract EVK2-CT2001-00105, Greengrass Project Europe) and Irish EPA project no: 2001-CD-C1M1. PS is a Royal Society-Wolfson Research Merit Award holder.

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