Comparison of models used for national agricultural ammonia emission inventories in Europe: Litter-based manure systems

Atmospheric Environment 43 (2009) 1632–1640

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Comparison of models used for national agricultural ammonia emission inventories in Europe: Litter-based manure systems B. Reidy a, J. Webb b, *, T.H. Misselbrook c, H. Menzi a, H.H. Luesink d, N.J. Hutchings e, B. Eurich-Menden f, H. Do¨hler f, U. Da¨mmgen g a

Swiss College of Agriculture, Laenggasse 85, CH-3052 Zollikofen, Switzerland AEA, Gemini Building, Harwell Business Centre, Didcot, Oxfordshire OX11 0QR, United Kingdom North Wyke Research, Okehampton, Devon EX20 2SB, United Kingdom d LEI, P.O. Box 29703, 2502 LS The Hague, The Netherlands e Dept. of Agroecology, University of Aarhus, Research Centre Foulum, 8830 Tjele, Denmark f Association for Technology and Structures in Agriculture (KTBL), Bartningstrasse 49, 64289 Darmstadt, Germany g Johann Heinrich von Thunen-Institute, Federal Research Institute for Rural Areas, Forestry and Fisheries, Institute of Agricultural Climate Research, Bundesallee 50, 38116 Braunschweig, Germany b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 April 2008 Received in revised form 27 November 2008 Accepted 1 December 2008

Six N-flow models, used to calculate national ammonia (NH3) emissions from agriculture in different European countries, were compared using standard data sets. Scenarios for litter-based systems were run separately for beef cattle and for broilers, with three different levels of model standardisation: (a) standardized inputs to all models (FF scenario); (b) standard N excretion, but national values for emission factors (EFs) (FN scenario); (c) national values for N excretion and EFs (NN scenario). Results of the FF scenario for beef cattle produced very similar estimates of total losses of total ammoniacal-N (TAN) (6% of the mean total), but large differences in NH3 emissions (24% of the mean). These differences arose from the different approaches to TAN immobilization in litter, other N losses and mineralization in the models. As a result of those differences estimates of TAN available at spreading differed by a factor of almost 3. Results of the FF scenario for broilers produced a range of estimates of total changes in TAN (9% of the mean total), and larger differences in the estimate of NH3 emissions (17% of the mean). The different approaches among the models to TAN immobilization, other N losses and mineralization, produced estimates of TAN available at spreading which differed by a factor of almost 1.7. The differences in estimates of NH3 emissions decreased as estimates of immobilization and other N losses increased. Since immobilization and denitrification depend also on the C:N ratio in manure, there would be advantages to include C flows in mass-flow models. This would also provide an integrated model for the estimation of emissions of methane, non-methane VOCs and carbon dioxide. Estimation of these would also enable an estimate of mass loss, calculation of the N and TAN concentrations in litter-based manures and further validation of model outputs. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Inventory Ammonia Emission Emission factor N-flow model Total ammoniacal-N (TAN) Solid manure Agriculture

1. Introduction The Gothenburg Protocol of the UN Convention on Long-range Transboundary Air Pollution (UNECE, 1999) and the EU National Emission Ceiling Directive (EC, 2001) require the reporting of national annual emissions of ammonia (NH3). Accurate inventories of agricultural NH3 emissions are required since they commonly account for more than 80% of the total (EMEP, 2005), and are needed to identify the major sources and to develop effective abatement strategies.

* Corresponding author. Tel.: þ44 870 190 6159; fax: þ44 870 190 6318. E-mail address: [email protected] (J. Webb). 1352-2310/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.12.015

The inventory approach should give a true picture of emissions, reliably and reproducibly show relatively small changes over time and take into account all relevant and measurable variables that influence emissions. Furthermore, to allow a co-ordinated implementation of the Protocol, different national inventories should be comparable. Emissions calculated by these models are indirectly used in the international negotiations to reduce atmospheric emissions through the RAINS (Amann et al., 2008) model, the output of which should be consistent with that produced by these models. The first NH3 emission inventories from livestock production were calculated by multiplying livestock numbers by emission factors (EFs) per animal (e.g. Buijsman et al., 1987). This approach did not allow for significant differences in NH3 emissions due to

B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

differences in performance, diet and hence nitrogen (N) excretion, or differences in livestock and manure management practices among countries and regions. More recent inventories have replaced EFs per animal with partial EFs for grazing, livestock housing, manure storage and manure spreading. However, increasing the number of EFs to discriminate among emissions at each stage of manure management and among systems is insufficient, since it cannot account for interactions among the stages of manure management that occur when abatement measures are applied. In particular, such an approach may fail to recognise that introducing abatement at an early stage of manure management (e.g. housing) will, by conserving ammonium (NHþ 4 )-N, increase the potential for NH3 emissions later (e.g. during storage or after spreading). Thus a mass-flow approach is needed, in which the fate of N is followed throughout the manure management system. This is particularly important when attempting to rank the costs of introducing measures to reduce NH3 emissions or the impacts on other gaseous N species: nitrous oxide (N2O); nitric oxide (NO); dinitrogen (N2). Such a mass-flow approach was used by Van der Hoek (1994) in the MESTAMM model, Menzi and Katz (1997) and in the MARACCAS model (Cowell and ApSimon, 1998). Such models tend to be based on the flow of TAN rather than of N. There are four reasons for this. First, TAN, rather than total N, is the direct source of NH3-N emissions to which NH3-N emissions may be correlated (e.g. James et al., 1999; Ku¨lling et al., 2001). Second, it makes it possible to estimate the effects of changing livestock diets, e.g. when essential amino acids are added to the feed, there is a disproportionately greater reduction in the TAN content of excreta, since reduction of N in animal feed mainly reduces urine-N. This can be taken into account by reducing the proportion of N excreted as TAN as well as reducing total-N excretion. Third, this approach can automatically take into account the effect of emissions that occur in an ‘upstream’ part of the manure management system (e.g. livestock housing) on emissions in the subsequent ‘downstream’ parts (e.g. storage). This enables the impact of abatement measures to be assessed at the system scale. Finally, by deducting NH3-N emissions from both the masses of N and TAN, and by estimating transformations of N and TAN during manure management (e.g. immobilization of TAN in straw and mineralization of N during manure storage) it is possible to check the ratios of TAN to N at stages of manure management when measurements are available for comparison, e.g. before and after manure storage, and hence to check the reliability of model output. This approach was used to validate the output of the NARSES model (Webb and Misselbrook, 2004). In addition, models do not have the single purpose of producing estimates of emissions etc. They are also a useful tool to summarize our knowledge of systems into a dynamic simulation of that system and to identify where our understanding needs improving. Such models have been developed to estimate emissions and abatement potential in a number of European countries: Switzerland (‘DYNAMO’, Menzi et al., 2003; Reidy et al., 2007a); UK (‘NARSES’, Webb and Misselbrook, 2004); Germany (‘GAS-EM’, Da¨mmgen et al., 2003); Netherlands (‘MAM’, Groenwold et al., 2002; Luesink and Kruseman, 2007; ‘FarmMin’, Van Evert et al., 2003); Denmark (‘DAN-AM’, Hutchings et al., 2001). Coordination of model development pools knowledge, creates synergies and improves congruency among emission models. To enable such coordination a core group of emission inventory experts, which included authors of the models mentioned above, was formed in 2003 to develop a network and joint programme. The aim was to achieve a detailed overview of the currently best available inventory techniques, compile and harmonize the available knowledge on EFs for mass-flow emission calculation models and initiate a new generation of emission inventories.

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The first step was to determine how far the results obtained with different models used to calculate agricultural NH3 emissions agree for defined livestock scenarios. A detailed comparison of the models and the underlying EFs permitted common calculation principles to be described and the most important reasons for disagreements to be identified. The first comparison was restricted to slurry-based manure management systems and the results reported by Reidy et al. (2007a). In order to fully account for changes in total ammoniacal nitrogen (TAN; for broilers TAN includes uric acid N) during manure management, estimates need to be made of other gaseous N emissions and, for litter-based manures, immobilization. These transformations have a much greater impact on TAN flow during management of litter-based manures than during management of slurry (e.g. Webb and Misselbrook, 2004). Hence, we considered that a special comparison should be made of emission estimates from litter-based manures, which account for almost half of emissions from European agriculture as estimated by the RAINS model (Amann et al., 2008). 2. Material and methods 2.1. Livestock and manure management systems examined The models simulated emissions from two livestock categories: fattening beef cattle and broilers. Livestock numbers and manure management practices were chosen to reflect typical conditions in the participating countries (Table 1) but without grazing or access to outside areas, since a comparison of emissions during grazing by cattle was part of the previous study (Reidy et al., 2007a). Both livestock categories were treated as being housed on litter: straw for beef cattle and wood shavings for broilers. Calculations were made on an annual basis. 2.2. General model description The models compared in this paper have been used to produce nation-specific NH3 emission inventories, and all use the mass-flow approach, starting with a specific amount of N excreted by a defined livestock category (Fig. 1). In these models, a livestock category describes a group of animals of the same species that are managed with the same production objective, and the numbers of which are recorded in national statistics (e.g. fattening beef cattle). If census data permit a description of homogeneous animal populations that differ in age, sex, feeding or manure management, a livestock category can be further disaggregated into subcategories. The N excreted is dependent on the livestock category and is influenced by the amount and composition of the feed and performance of the animal. The models simulate separately the TAN and organic N flows over the different stages of emission (grazing, housing, manure storage and application). Emissions are calculated with EFs that are a proportion of the annual flow of TAN through the source that is emitting. Emissions from excreta deposited in animal housing are calculated from the annual amount of TAN (uric acid is included in TAN for poultry) deposited in the house and an EF depending on the housing type. The remaining TAN and the entire organic N deposited then pass to manure storage, with the addition of any N added in litter, minus TAN lost either through immobilization in straw (Kirchmann and Witter, 1989) or nitrification/denitrification (Chadwick et al., 1999). Some models take into account mineralization of organic N to TAN or losses of TAN via nitrification and denitrification that occur during manure storage. For the manure stored outside the house emissions are calculated as the product

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B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

Table 1 Activity data used for the solid manure scenarios. Beef

Broilers

Animal numbers Fattening period

100

100 000 places

150–500 kg liveweight

Average daily gain [g day1] Feed ration Housing system

1200 g

Approximately 40 days; end weight approximately 0.0–2.0 kg liveweight 42

Maize/grass silage, concentrates Naturally ventilated deep litter system, 4 kg1 straw day1 animal1 Deep litter

Concentrates Mechanically ventilated loose housing with litter bed. Water supplied via nipple drinkers Broiler litter

50% uncovered outdoor storagea Broadcasting on uncovered soil, no subsequent incorporation

100% in the house during production, uncovered outdoor storage Broadcasting on uncovered soil, no subsequent incorporation

25/75

25/75

Arable land

Arable land

Type of collected manure Manure storage Manure application Application season Summer/ spring or autumn [%] Application crop a

50% is directly spread after removal from the house.

of the annual TAN flow through storage and an EF. The loss of NH3 following field application of manure is calculated as the product of the annual amount of TAN applied and an EF for application. The models only consider the NH3 emission from the field application of the manure and not any subsequent emission of N2 or N2O. All the field-applied manure-N that is not emitted as NH3 enters the soil. Each model checks to ensure that mass conservation is obeyed i.e. the total N excreted equals the total emission of gaseous N via

NH3 volatilization and denitrification plus the TAN and organic N entering the soil. Despite the common underlying approach, the models used in the study differ with respect to livestock categories, management systems and the degree to which additional processes are taken into account. 2.2.1. DYNAMO The Swiss DYNamic Ammonia MOdel (DYNAMO) Menzi et al. (2003) is used for the calculation of the Swiss NH3 emission inventory (Reidy et al., 2007b) and the abatement potential (Reidy and Menzi, 2007). DYNAMO differs from the generalized model in that housing emissions are calculated in percent of total N and for all sources in poultry production. For beef all other emissions are expressed as a percentage of TAN. 2.2.2. DAN-AM The Danish NH3 emission inventory system follows the generalized model (Hutchings et al., 2001). The calculation of emissions from field-applied manure uses a statistical model based on the Michaelis–Menten equation, similar to the ALFAM model of NH3 emissions from slurry (Sogaard et al., 2002). A single parameterization is used for all types of farmyard manure (FYM) based on data for typical TAN and dry matter contents of Danish cattle and pig FYM. Seasonal EFs were developed for applications made in spring, summer, autumn and winter, using average Danish meteorological conditions, to account for the effect of weather. Annual EFs were calculated as weighted averages of these values, based on the seasonal distribution of FYM applications. Finally, the annual EFs are adjusted to account for the distribution of manure among application techniques (e.g. surface-applied, rapidly incorporated by plough, disc or tine). 2.2.3. GAS-EM GAS-EM (GASeous EMissions) has been used to calculate the German agricultural emission inventory (Da¨mmgen et al., 2003)

Fig. 1. Schematic N flow used in mammalian livestock in the different models used in the scenario calculations (TAN ¼ total ammoniacal-N) (Da¨mmgen and Hutchings, 2008). mexcreted ¼ mass of N excreted; mbedding ¼ mass of N in bedding; myard, TAN ¼ mass of TAN deposited on yards (example); myard, org ¼ mass of organic N deposited on yards (example); Eyard ¼ emissions from yard.

B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

and as a policy advice tool. In contrast to the generalized model, it calculates not only emissions for NH3 but also of greenhouse gases (e.g. CH4, N2O), particulate matter (PM10 and PM2.5), NO and N2. Immobilization and mineralization are considered in storage. N inputs with bedding material are taken into account. For the latest version see Da¨mmgen and Hutchings (2008). 2.2.4. NARSES The NARSES model (Webb and Misselbrook, 2004) is a nationalscale model to estimate the magnitude, spatial and temporal distribution of agricultural NH3 emissions and the potential applicability of abatement measures with associated costs in order to produce cost curves. NARSES differs from the generalized model in three respects: (1) partitioning of N excretion by cattle between housing and grazing is not linearly related to the length of time but reflects the greater N concentration of grazed herbage. (2) A proportion of manure is spread directly from buildings and therefore not subject to storage losses. (3) Prior to storage or spreading to land a proportion of TAN, related to the amount of straw per animal typically used for bedding, is immobilized. 2.2.5. MAM The Manure and Ammonia Model (MAM) (Groenwold et al., 2002; Luesink and Kruseman, 2007) is primarily a tool for Dutch manure policy analysis (Luesink et al., 2004; Luesink et al., 2008), to calculate farm level manure balances and to optimize the distribution of manure at a national scale. MAM differs from the generalized model as follows: NH3 emissions are calculated by EFs expressed in percent of total N except for field application (percent of TAN). For ruminants the EFs for housing are seasonally differentiated and portioning of N excretion by cattle between housing and grazing is not linearly related to the length of time but to excretion activity at the milking period and the rest of the day. 2.3. Scenarios Three model comparisons were made (Table 2). Calculating emissions with standardized values allows differences among models in the calculation of the N flow to be detected. For this purpose the national specific N excretion rates and initial TAN contents at excretion, as well as the EFs, were replaced in each model by a set of standardized, fixed (F) values (scenario FF; Tables 3 and 4). The TAN contents at subsequent stages of manure management were calculated by the models. At a second level of comparison, only N excretion and initial TAN contents were standardized (scenario FN; Tables 3 and 4), whereas national EFs were used for the calculations (scenario FN; Tables 3 and 4). Thus, differences among the calculated emissions were related to different EFs which reflect differences in manure management systems and climate, and differences in the scientific basis for the EFs. Finally, emissions were calculated using the national N excretion rates, TAN contents and EFs (scenario NN, Tables 3 and 4). As for the FN scenario, differences were expected to be primarily the result of differences among country-specific livestock and manure management systems. However, because N excretion rates and EFs

Table 2 Description of the different scenarios. Scenario

Nitrogen excretion [kg a1 N]

Emission factors

FF FN NN

Fixeda Fixeda Nationalb

Fixeda Nationalb Nationalb

a b

Same value used in all models. Model-specific values used.

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are to some extent interdependent variables, differences of the observed emissions in the second scenario may be partly compensated (e.g. larger EFs may compensate for reduced N excretion). The calculated emissions can thus be assumed to truly represent the specific NH3 emissions of a given livestock category influenced by the specific livestock and manure management systems of the respective country. The purpose of the paper is not to compare inventories. This could be achieved by listing nation-specific EFs and accounting for the differences. The object was to compare the model structures, in particular, we wished to assess the impact of steps introduced to account for other losses of TAN and their impact on the estimate of NH3 emission. For example, two models could have the same EF for emissions from stored FYM, e.g. 20% of TAN. However, if one model makes an estimate of TAN immobilization in straw of 40% of that remaining after NH3 emission from the building, whereas another model makes no estimate of NH3 immobilization, then the estimate of NH3 emission during storage made using the second model will be 67% greater than the estimate made by the first model, despite their using the same EF for storage emissions. 3. Results and discussion The results presented in this section, in kg N, were estimated from 100 beef cattle or 100 000 broiler places. 3.1. Beef 3.1.1. Total ammonia emissions All models gave similar FF estimates of total losses (i.e. from housing through to spreading) of TAN for beef cattle, ranging from 1822 to 2041 kg N (Fig. 2). However, total emissions of NH3-N differed considerably, from 933 (NARSES) to 1511 kg (DAN-AM). Scenario FN gave greater differences in total TAN losses ranging from 1645 kg for DAN-AM to 2100 kg for MAM. Estimates of NH3-N emissions also differed, from 877 kg (NARSES) to 1396 kg (MAM) (Fig. 3). Under NN the greater N excretion estimated for DAN-AM and MAM (Table 3), compared with the other three models, led to those two models calculating the greatest NH3 emissions. Differences in estimates of N excretion for a livestock class arise because each country uses its own excretion model and defines cattle groups in different ways and with different degrees of detail (in breed, feeding regimes etc.). 3.1.2. Housing emissions As expected from the FF scenario, all models estimated the same NH3-N housing emission. MAM is the only model that considers other gaseous N losses (NO, N2O, N2) during housing, 14.1% of total N (Ntot) during housing and 14.1% of Ntot during storage (Oenema et al., 2000). The ratios NO, N2O, N2 are c. 1:1:7 (i.e. assuming the same emission ratios as in soil emissions). In FN, housing EFs ranged from 12% of TAN (DAN-AM) to 37% (DYNAMO) (Table 3). The DAN-AM EF was derived from measurements made in a number of countries (Sommer et al., 2006). Although no DK studies of litter-based housing emissions have been made, in the deep litter housing common in DK, the suggestion is that the FYM accumulates on the floor and is compacted by the feet of the cattle. The lack of porosity and air access reduces the transport of NH3 to the manure surface. The housing EF used in NARSES was derived from data collected in the UK (Misselbrook et al., 2006) which are reported as being c. 25% less than from slurry-based systems. However, a recent review of UK data on NH3 emissions, concluded those from buildings housing livestock on straw, were particularly uncertain (Webb et al., 2005). The GAS-EM, NARSES and MAM EFs were similar at c. 20% of TAN. Both GAS-EM

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B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

Table 3 Nitrogen excretion and TAN content of excreta (fresh manure). Emission factors used for the different beef scenarios by the different models. FF, fixed N excretion values and emission factors; FN, fixed N excretion values and national emission factors; NN, national N excretion values and emission factors. Scenario

FF

FN and NN DYNAMO

DAN-AM

GAS-EM

NARSES

MAM

37 60 20 60 90

38 60 23 35 81

45 NA 17c 3 100

NH3-N emitted as % of TAN entering the stage N excretion (kg place1 a1 N) TAN content (% of N excretion) Housing Storage Application a b c

35a 60a 30 28 80

38 60 37b 30 60

45 66 12b 9 64

FF and FN. Transformed from total N to TAN. Average value, weighted with respect to manure production, using 6.6% for November to April and 16.9% from May to October from total N and transformed to TAN.

Table 4 Nitrogen excretion and TAN content of broiler excreta and emission factors used for the different broiler scenarios by the different models. FF, fixed N excretion values and emission factors; FN, fixed N excretion values and national emission factors; NN, national N excretion values and emission factors. Scenario

FF

FN and NN DYNAMO

DAN-AM

GAS-EM

NARSES

MAM

0.41 70 14.0b 4.0 90.0

0.56 70 9.0 10.0 63.0

0.53 NA 14.0b 3.0b 100.0

NH3-N emitted as % of TAN entering the stage N excretion (kg place1 a1 N) TAN content (% of N excretion) Housing Storage Application

0.40 NA 40.0b 10.0b 15.0b

0.52 60 42.0 32.0 64.0

FF and FN. EF as percent of total N.

and MAM assume the same EF for straw-based as for slurry-based housing systems. The greater EF used in DYNAMO is partly attributed to the EF being based on the assumption that emissions from deep litter housing systems are greater than those from slurry housing systems by half of the emissions that would occur from outdoor storage of solid manure. In the NN scenario it is likely that the greater N excretion in DK and NL is due to greater fertilizer-N application (in the NL in recent years c. 150 kg N ha1 to grassland) compared with other countries (Fig. 4). 3.1.3. Storage emissions From FF large differences were calculated for NH3-N emission during storage, from 69 (NARSES) to 206 kg (DAN-AM and MAM). The greater losses during storage estimated by DAN-AM and MAM were due to no estimate being made of TAN immobilization during housing and hence there being more TAN available for NH3 emission during storage. The NARSES estimate of immobilization is related to the amount of straw used with 0.0068 kg TAN immobilized per kg straw added (Kirchmann and Witter, 1989), which for this scenario equated to 47% of the TAN remaining after NH3-N emission in the building. GAS-EM assumes that 40% of the TAN is immobilized. This is also derived from the data of Kirchmann and Witter (1989) but applied to typical straw use in Germany due to concerns that calculating immobilization proportional to straw use might lead to unrealistically small concentrations of TAN in manures at storage and spreading. DYNAMO uses expert judgement that one third of the TAN excreted is immobilized by the straw. Within heaps of stored solid manure, conditions may arise favourable to both nitrification and denitrification leading to emissions of N2O, NO and N2. N balance studies have reported

losses of between 13 and 33% N by denitrification (including N2O) (Petersen et al., 1998). In contrast Sommer (2001) estimated only 1% of total N was lost as N2 (with N2O-N emissions of c. 0.1–0.3% N). The difference in results between these two studies may have been due to the greater C:N ratio (20:1) in the manure used by Sommer (2001) compared with that used by Petersen et al. (1998) of 8:1– 10:1, with wider C:N ratios favouring immobilization of TAN. All

2500

Application

Immobilization

Denitrification

Housing

Storage

2000

234 530

1500

675 0 108

kg

a b

0.60a 70a 8.5 10.0 63.0

101

1000

700

379

130 69

125 100 978

206

448

704

588 331

206 0

500 630

630

630

630

630

DYNAMO

DanAm

GAS-EM

NARSES

MAM

0

Model Fig. 2. Fate of TAN for the beef FF scenario (fixed N excretion values and emission factors, 100 heads) as calculated by the different models. The stacked columns represent amount of N emitted or immobilized in the respective part of the manure handling chain.

B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

2500

Application

Immobilization

Denitrification

Housing

Storage

2000 282

1500

364

455

921

112 133

0 134

kg

169

809

239

1000

978 700

101 101

500

704

675

38 0

425 507

413

483

438

GAS-EM

NARSES

MAM

210

0 DYNAMO

DanAm

Model Fig. 3. Fate of TAN for the beef FN scenario (fixed N excretion values and national emission factors, 100 heads) as calculated by the different models. The stacked columns represent amount of N emitted or immobilized in the respective part of the manure handling chain.

models except DYNAMO estimate losses of NO, N2O, N2 during storage. In NARSES and GAS-EM emissions are calculated with NO, N2O, N2 EF of 0.1:1:3% N based on the EMEP/CORINAIR Guidebook. The NARSES model includes a 12% loss of TAN as effluent draining from manure heaps. This was the mean loss measured in four studies cited in Webb and Misselbrook (2004). In GAS-EM, a ratio of 10% of organic N was assumed by German experts to be mineralized during manure storage. Thus for NARSES and GAS-EM total losses attributed to nitrification and denitrification will total 4.1% of TAN entering the manure store. In contrast the total estimate in MAM, from both housing and storage, will be c. 34% of TAN excreted. This large loss explains the very small storage NH3-N EF. In the FN scenario, calculated NH3 emissions from storage were extremely variable, the EF ranging from 3% (MAM) to 60% (GAS-EM) of TAN. The large range of EFs for storage emissions is likely to be

3500 3000

Application

Immobilization

Denitrification

Housing

Storage

730

2500 426

kg

2000 1500 1000

282 455

1172

249

1078

507

0 DYNAMO

3.1.4. Spreading emissions In consequence of the differences in estimates of immobilization, other N losses and mineralization, FF estimates of TAN available at spreading differed by a factor of almost 3, from 293 (NARSES) to 844 kg (DAN-AM). NARSES estimates the greatest losses of TAN via immobilization and other N losses, while DAN-AM estimates the smallest losses of TAN by those pathways. NARSES calculates that cattle FYM, after storage, will only contain c. 6% of the N in that manure as TAN. Recent UK analyses of the N and TAN content of cattle FYM indicate that the average TAN content at spreading is only c. 7% (Anon., 2003). The FN EFs for NH3 emissions following FYM application were all large, ranging from 60% (DYNAMO) to 100% (MAM). The greater EF used in the Dutch model was based on the results of Huijsmans et al. (2001). The EF used in GAS-EM was derived from a study reported by Do¨hler et al. (2002) using fresh deep litter manure. The NARSES EF was derived from 41 UK data sets (Misselbrook et al., 2006). The EF in DAN-AM has been derived from the statistical model ALFAM (Sogaard et al., 2002) using data for a typical TAN and dry matter content of Danish FYM and average Danish meteorological conditions. The smallest EF, used in DYNAMO, was based on field measurements lasting for only 2–4 days (Menzi et al., 1997) and hence may have underestimated emissions. However, the considerable differences in emissions at spreading between the models were not primarily due to differences in the EFs, but rather a consequence of the different treatment of immobilization and mineralization. The greater the immobilization assumed the less were emissions at spreading. The greatest emissions were estimated by MAM which assumed net mineralization during storage and does not take into account immobilization. 3.2. Broilers

905

124 124 713

48 0

604

500

in part a consequence of different storage conditions. The EF of 3% used in MAM was reported by Van der Hoek (1994). The EFs used in DAN-AM were those reported by Poulsen et al. (2001). The NARSES EF was derived from the weighted mean of three UK studies (Misselbrook et al., 2006), albeit there was substantial variation in the emissions measured in those studies. The Swiss EF was derived from Swiss data which found a wide range of total-N losses with an average of about 9%. Based on a TAN proportion of 30% at the start of storage an EF of 30% of TAN was derived (Menzi et al., 1997). The EF for GAS-EM was derived by an expert group considering a (very) limited database including mass balance calculations and may therefore include other N losses, for details see Do¨hler et al. (2002). In addition NH3 emissions from storage will vary, in particular in response to the degree to which composting occurs. However, models of the type evaluated here have the object of estimating national emissions and if the constituent EFs are averages of studies which reported substantial variation, this may reflect the variability of emissions within the country for which the national total is being calculated.

139

0 134

760

597

380

1637

773 271

DanAm

437

GAS-EM

NARSES

561

MAM

Model Fig. 4. Fate of TAN for the beef NN scenario (national N excretion values and emission factors, 100 heads) as calculated by the different models. The stacked columns represent amount of N emitted or immobilized in the respective part of the manure handling chain.

3.2.1. Total ammonia emissions Estimates of total TAN losses from the FF scenario for broilers ranged from 28 755 (GAS-EM) to 34 292 kg N (DAN-AM) (Fig. 5). Total TAN losses were greatest from DAN-AM as that is the only model which assumes immobilization of TAN in poultry litter during storage at 21% of TAN ex-housing. This contrasts with the treatment of TAN for livestock housed on straw where no immobilization is regarded as taking place. None of the models which take account of TAN immobilization in straw in the beef scenarios do so for broiler litter as the high lignin content of wood shavings used as litter is considered to preclude immobilization (Kirchmann

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B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

40000 35000

Application

Immobilization

Denitrification

Housing

Storage

30000 13125

13998

kg

25000 20000

21722

15000

20883

20039

5684 3843

12830

10000 5000

0 3950 0 3570

DYNAMO

1448

1440 3382

3570

3698 0 3570

3570

3843 0 3570

DanAm

GAS-EM

NARSES

MAM

8070

0

Model Fig. 5. Fate of uric acid nitrogen (UAN) for the broiler FF scenario (fixed N excretion values and emission factors, 100 000 places) as calculated by the different models. The stacked columns represent amount of ammonia emitted in the respective part of the manure handling chain.

and Witter, 1989). Other gaseous N losses ranged from c. 1440 (GASEM and MAM) to c. 12 830 kg N (NARSES). Dynamo takes no account of denitrification losses and hence produced the largest estimate of NH3-N emissions at 29 242 kg, while DAN-AM, which estimated the greatest decrease in TAN via other pathways produced the smallest estimate of NH3-N emissions (20 538 kg) (Fig. 5). As for beef cattle, substantial differences of total emissions were observed between the broiler FF and FN scenarios (Fig. 5 and 6). The greatest losses of TAN were estimated by DAN-AM and GAS-EM (38 408 and 39 060 kg respectively, Fig. 6). These greater losses were due to DAN-AM being the only model which assumes immobilization of TAN in broiler litter and the large EF used for spreading emissions in GAS-EM. In consequence GAS-EM estimated the greatest NH3 emissions of (36 340 kg), while NARSES estimated the smallest total NH3 loss (20 694 kg). Total NH3 emissions

40000

Immobilization

Denitrification

Housing

3.2.3. Storage emissions DAN-AM estimates other N losses as 10% of total N during storage. Whereas the FF housing emissions were identical for all models, smaller emissions during storage were obtained with NARSES because of the much greater estimate of other N losses from buildings compared with the other models. Storage losses

35000

Storage

35000

6386

30000

4541

25000

kg

Application

3.2.2. Housing emissions NARSES and MAM were the only models to assume losses of N2O/N2 from buildings. The estimate of 12% TAN used in NARSES was based on data reported in the review of Chadwick et al. (1999), while the EF for emissions of N2O during storage was based on the default EF provided by IPCC (1997). As with beef FYM the ratio of N2:N2O was taken as 3:1. In MAM 1.2% of total N is estimated to be lost as N2O/N2 in both the buildings and during storage (Oenema et al., 2000). There were some large differences in national (FN) NH3-N EF for buildings, ranging from 8% of TAN for NARSES to 57% of TAN for DYNAMO (Table 4). The NARSES EF was derived as the weighted mean of 6 UK studies (Misselbrook et al., 2006). Recently the results of one study, which reported much larger emissions, were deleted from the constituent database. This was because the work was carried out in buildings in which the use of water by the broilers was uncontrolled leading to wet manure and hence increased emissions. The large Swiss EF was derived from balance measurements which obviously underestimated other N losses (Menzi and Katz, 1997).

3240 0 3600 0

6811

26800

14337

20000 5670

Immobilization Housing

Storage 5508

25000

3928

20000 16888

Application Denitrification

30000

kg

45000

estimated by the other three models were similar and intermediate between those two estimates. The NN estimates of N excretion used in DYNAMO, GAS-EM and NARSES were similar at c. 400 g place1 a1, while those used in DAN-AM and MAM were c. 500 g place1 a1 (Table 4). As at least the excretion values from Switzerland, Netherlands and Denmark are taken from regularly updated national values derived from standard production data, it can be assumed that these differences reflect the differing feeding regimes and management conditions in the respective countries. Estimates of annual N excretion by all livestock classes in the UK have been recently reviewed and updated (Fig. 7).

15000

2160 0 2400 0

5892 18300

10275

15855

4898

15000 10000

24000 15000

5000

2720 1260 0

DanAm

1440 1392 0

8280

2955 0 3402

8460

GAS-EM

NARSES

MAM

0 DYNAMO

10000

12886

16000 12958

5000

9234

1272 1229 0

5660

2118 0 2438

7473

GAS-EM

NARSES

MAM

1860 860 0

0

Model Fig. 6. Fate of uric acid nitrogen UAN for the broiler FN scenario (fixed N excretion values and national emission factors, 100 000 places) as calculated by the different models. The stacked columns represent amount of ammonia emitted in the respective part of the manure handling chain.

DYNAMO

DanAm

Model Fig. 7. Fate of uric acid nitrogen UAN for the broiler NN scenario (national excretion values and emission factors, 100 000 places) as calculated by the different models. The stacked columns represent amount of ammonia emitted in the respective part of the manure handling chain.

B. Reidy et al. / Atmospheric Environment 43 (2009) 1632–1640

calculated by GAS-EM and DAN-AM were smaller than those of DYNAMO, at 3698, 3843 and 3950 kg, respectively. GAS-EM takes into account that broiler manure is typically left in the house during the production cycle. Thus the house is also, in part, the storage system. In consequence the GAS-EM calculation routines assign the N2O, NO and N2 emissions to the house. The FN EF for storage emissions ranged from 4% of TAN (GASEM) to 32% of N (DAN-AM). However, apart from the NARSES EF, which was derived from 5 studies (Misselbrook et al., 2006) the EFs were generally based on scant data. The EF used for GAS-EM, 13.8% of total N, was derived by expert judgement from a limited experimental background (Do¨hler et al., 2002). That used in MAM, 3.0% of total N, is from Van der Hoek (1994). Due to the limited importance of poultry production in DK, compared with that of cattle and pigs, little work has been carried out to determine EFs for poultry and there have been no updates in nearly 10 years. The DAN-AM EF, 25% of Ntot, was based on Groot Koerkamp (1994) and Elwinger and Svensson (1996). However, that study may have been carried out under conditions of uncontrolled access to drinking water that are no longer commonplace. A further explanation of the deviation in EFs is perhaps the dry matter content of the manure. 3.2.4. Spreading emissions As a result of the estimated losses and immobilization of TAN, the TAN pool decreases and hence reduces emissions following application. Hence much smaller FF emissions following application were obtained with NARSES (13 998 kg) and DAN-AM (13 125 kg) because those models allow for denitrification losses during housing and storage and, in the case of DAN-AM, immobilization as well. The greatest emissions of NH3 following spreading were estimated by DYNAMO (21722 kg) as the TAN pool was not diminished by other losses. Estimates of FN NH3 emissions following application to land were critically dependent on estimates not only of NH3 emissions during housing and storage but also estimates of other N losses as recounted in Section 2.3 above. There were also large differences in the national EFs ranging from 10% of total N in DYNAMO to 100% of TAN in MAM. Possible causes for differences in EFs were postulated as: differences in the interpretation of the relatively limited and variable data; differences how ‘‘up to date’’ EFs for ‘‘insignificant’’ sources are; real differences in climate, management, production, etc. The EF used in NARSES (63% of TAN) was based on 30 data sets (Misselbrook et al., 2006), while that used in DAN-AM (64% TAN) was derived from the ALFAM database (Sogaard et al., 2002) which included the UK measurements. The EF used in GAS-EM, 90% TAN, is the same as that for FYM without incorporation. The EF used in DYNAMO, 10% of Ntot, was based on Rodhe and Karlsson (2002) and a small number of Swiss measurements over only 3–4 days for field measurements and 4–6 days for wind tunnel measurements (Menzi et al., 1997). The large data set available from the UK reflects the greater contribution of the poultry sector to UK NH3 emissions, currently accounting for 13% of NH3 emissions from agriculture, compared with, for example, 9.2% for Germany in 2006 (Da¨mmgen et al., 2007) and 4% in Switzerland (Reidy et al., 2007b). 3.3. Possible reasons for differing results Comparing emission inventories on the basis of EFs and N excretion rates can identify differences among models but not in all cases the respective reasons. These can be divided into four main types: (1) errors i.e. a limited database from which EFs were derived, e.g. EFs for buildings housing poultry and poultry manure storage in GAS-EM; (2) differences in agricultural practice. Such differences will be reflected in different model structures rather than in different EFs; (3) differences in the model structure; (4) differences

1639

in model parameterization. The differences in agricultural practice may include different excretion rates resulting from different feeding practice and production intensities (e.g. the protein concentration in the diet, growth rate per animal and slaughter weight), variations in the types of livestock housing, storage and application technology and from variations in climate. These factors are fully valid and explain why it is necessary to construct emission inventories at a national or even local scale. Differences in the model structure may be related to the inclusion of additional sources (e.g. hard standings) or processes (e.g. immobilization, denitrification, particularly in NARSES and GAS-EM) not included in others. Differences in parameterization of EF for what are essentially similar husbandry systems arise due to the access to different sources of information, different interpretations of the same information or different assumptions for special situations (e.g. emissions in buildings and manure storage when cattle are mainly outside during the grazing season). Such differences are inevitable; given the varied backgrounds of the scientists involved and the ample variation in the experimental data. Nevertheless, differences reported here for beef cattle reflect some profound differences in the concepts underlying some of the models. For example, while DAN-AM, GASEM and NARSES all estimate losses of other N gases and immobilization of TAN, the size of the losses estimated varies by a factor of c. 3 for immobilization and c. 7 for other gaseous N losses. With respect to other gaseous losses few studies have been carried out to quantify these, and fewer still have attempted to measure emissions of N2. In consequence estimates for these processes have been based on interpretation at least as much as on published EFs. 4. Conclusions Each country uses its own excretion model and defines cattle groups in different ways and with different degrees of detail (in breed, feeding regimes). In this paper we have restricted ourselves to the manure management and not considered differences in excretion. To examine excretion models, their parameterization and the inputs assumed would require a separate paper and one of our conclusions is that there needs to be a Europe-wide comparison of excretion models. Output of the models tested proved to be much more variable for solid manure than for slurry. There are fewer published results of NH3 emissions from solid manure than for slurry and the introduction of litter leads to more complex interactions (e.g. immobilization and mineralization) and greater, and highly variable, emissions of other N gases. In addition, the state of development of the models with respect to immobilization and denitrification is quite different. We conclude that it is important to get a better overview of the existing knowledge, an identification of research gaps and a thorough re-editing of some of the models (especially DYNAMO and MAM) for processes other than NH3 emission. Due to the introduction of varying amounts of litter, and to the variability of the composition of that litter, the variability of emissions found in practice is likely to be much greater for straw-based systems than for slurry systems. A recent review of UK NH3 research (Webb et al., 2005) found that among the larger sources of NH3 emissions, those from buildings housing cattle and pigs on straw were the most uncertain. The mass-flow approach to estimating NH3 emissions has evolved from initially estimating only NH3 emissions (Cowell and ApSimon, 1998), to including processes such as immobilization, mineralization, nitrification and denitrification in order to properly quantify the TAN flow (Webb and Misselbrook, 2004) and to improve the accuracy of the NH3 emission calculations. Given that immobilization and denitrification depend also on the C:N ratio in manure there have also been discussions of how to include C flows

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in mass-flow models as well. This would also have the advantage of providing an integrated model for the estimation of emissions of methane, non-methane VOCs and carbon dioxide. Estimation of the latter would also provide an estimate of mass loss which would enable calculation of the N and TAN concentrations in litter-based manures. This would enable further validation of model outputs. At present, outputs can be checked by means of the N:TAN ratio prior to storage and spreading (e.g. Webb and Misselbrook, 2004). However, the ability to check estimates of concentrations as well as ratios against measurements would enable more thorough output validation. References Amann, M., Bertok, I., Cofala, J., Heyes, C., Klimont, Z., Rafaj, P., Scho¨pp, W., Wagner, F., 2008. National Emission Ceilings for 2020 Based on the 2008 Climate & Energy Package. NEC Scenario Analysis Report Nr. 6. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, 72 pp. Anon., 2003. Final Report Project NT2006, Manure Analysis Database (MANDE). Defra, UK. Buijsman, E., Maas, J.F.M., Asman, W.A.H., 1987. Anthropogenic ammonia emissions in Europe. Atmospheric Environment 21, 1009–1022. Chadwick, D.R., Sneath, R.W., Phillips, V.R., Pain, B.F., 1999. A UK inventory of nitrous oxide emissions from farmed livestock. Atmospheric Environment 33, 3345–3354. Cowell, D.A., ApSimon, H.M., 1998. Cost-effective strategies for the abatement of ammonia emissions from European agriculture. Atmospheric Environment 32, 573–580. Da¨mmgen, U., Hutchings, N.J., 2008. Emissions of gaseous nitrogen species from manure management – a new approach. Environmental Pollution 154, 488–497. Da¨mmgen, U., Lu¨ttich, M., Haenel, H.-D., Do¨hler, H., Eurich-Menden, B., Osterburg, B., 2007. Calculations of Emissions from German Agriculture – National Emission Inventory Report (NIR) 2009 for 2007. Da¨mmgen, U., Lu¨ttich, M., Do¨hler, H., Eurich-Menden, B., Osterburg, B., 2003. GAS-EM – a procedure to calculate gaseous emissions from agriculture. Landbauforschung Vo¨lkenrode 52, 19–42. Do¨hler, H., Eurich-Menden, B., Da¨mmgen, U., Osterburg, B., Lu¨ttich, M., Bergschmidt, A., Berg, W., Brunsch R., 2002. BMVEL/UBA-Ammoniak-Emissionsinventar der deutschen Landwirtschaft und Minderungsszenarien bis zum Jahre 2010. UBA-Texte 05.02, Berlin (in German). EC (European Commission), 2001. Directive 2001/81/EC of the European Parliament and of the Council of 23 October 2001 on National Emission Ceilings for Certain Atmospheric Pollutants. Elwinger, K., Svensson, L., 1996. Effect of dietary protein content, litter and drinker type on ammonia emission from broiler houses. Journal of Agricultural Engineering Research 64, 197–208. EMEP, 2005. Database of the National Submissions to the UNECE LRTAP Convention Maintained at EMEP. Available from: http://webdab.emep.int/. Groenwold, J.G., Oudendag, D., Luesink, H.H., Cotteleer, G., Vrolijk, H., 2002. Het Mest- en Ammoniakmodel. LEI, Den Haag, Rapport 8.02.03 (in Dutch). Groot Koerkamp, P.W.G., 1994. Review on emissions of ammonia from housing systems for laying hens in relation to sources, processes, building design and manure handling. Journal of Agricultural Engineering Research 59, 73–87. Huijsmans, J.F.M., Hol, J.M.G., Hendriks, M.M.W.B., 2001. Effect of application technique, manure characteristics, weather and field conditions on ammonia volatilization from manure applied to grassland. Netherlands Journal of Agricultural Science 49, 323–342. Hutchings, N.J., Sommer, S.G., Andersen, J.M., Asman, W.A.H., 2001. A detailed ammonia emission inventory for Denmark. Atmospheric Environment 35, 1959–1968. IPCC/OECD, 1997. Revised 1997 IPCC Guidelines for National Greenhouse Gas Inventories. OECD, 2 rue Andre´ Pascal, Paris. James, T., Meyer, D., Esparza, E., Depeters, E.J., Perez-Monti, H., 1999. Effects of dietary nitrogen manipulation on ammonia volatilization from manure from Holstein heifers. Journal of Dairy Science 82, 2430–2439. Kirchmann, H., Witter, E., 1989. Ammonia volatilization during aerobic and anaerobic manure decomposition. Plant and Soil 115, 35–41. Ku¨lling, D.R., Menzi, H., Krober, T.F., Neftel, A., Sutter, F., Lischer, P., Kreuzer, M., 2001. Emissions of ammonia, nitrous oxide and methane from different types of dairy manure during storage as affected by dietary protein content. Journal of Agricultural Science 137, 235–250.

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