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Final Report - AC0116:

Improvements to the national agricultural inventory - Nitrous oxide

The InveN2Ory Project (2010-2016)

Principal Authors:

Dave Chadwick (Bangor University) [Project leader, WP1, WP2]

Bob Rees (SRUC) [WP2 lead]

Madeleine Bell (SRUC) [WP2]

Laura Cardenas (Rothamsted Research North Wyke) [WP2]

John Williams (ADAS Boxworth) [WP3 lead, WP2]

Fiona Nicholson (ADAS Gleadthorpe) [WP2]

Catherine Watson (AFBI Hillsborough) [WP2]

Karen McGeough (AFBI Hillsborough) [WP2]

Kevin Hiscock (University of East Anglia) [WP2]

Pete Smith (University of Aberdeen) [WP4 lead]

Kairsty Top (SRUC) [WP4]

Nuala Fitton (University of Aberdeen) [WP4]

Ute Skiba (CEH Bush) [WP5 lead]

Nick Cowan (CEH Bush) [WP5]

Alistair Manning (MetOffice) [WP5]

Tom Misselbrook (Rothamsted Research North Wyke) [WP6 lead]

Citation: Dave Chadwick, Bob Rees, Madeleine Bell, Laura Cardenas, John Williams, Fiona Nicholson,

Catherine Watson, Karen McGeough, Kevin Hiscock, Pete Smith, Kairsty Top, Nuala Fitton, Ute Skiba,

Nick Cowan, Alistair Manning and Tom Misselbrook (2016). Final Report - AC0116: Improvements to

the national agricultural inventory - Nitrous oxide. The InveN2Ory Project (2010-2016). Report to

Defra, DARDNI, Scottish Government and Welsh Government. pp77.

Funded by: Defra, DARDNI, Scottish Government and Welsh Government

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EXECUTIVE SUMMARY

This aim of this multidisciplinary, multi-organisation consortium project was to generate data by

both modelling and measurements from which country specific nitrous oxide (N2O) emission factors

could be derived to contribute to the UK agriculture greenhouse gas inventory improvement

programme. Modelling and measurements were designed to provide the evidence base to develop a

reporting tool for agriculture that better reflected the effects of UK soils, climate and N management

on N2O emissions. This project, the InveN2Ory project (AC0116), was one of three projects funded by

Defra and Devolved Administrations as part of the UK Greenhouse Gas Platform

(http://www.ghgplatform.org.uk/). The scope of the project required input from nitrous oxide (N2O)

experts from seven UK research organisations; viz ADAS, AFBI, Bangor University, CEH Edinburgh,

Rothamsted Research, Scotlands Rural University College, University of East Anglia, and an

independent consultant, Prof Keith Smith. The project comprised six work packages, which are

described below, and delivered data to the Data Synthesis Project (AC0114).

WP1: Prioritisation

There were two key objectives of the prioritisation phase of the InveN2Ory project: i) to develop

standard experimental protocols and identify initial proxies appropriate to the plot- and field-scale,

and ii) to confirm the key UK soil / climate zones and sources of N.

Since different research organisations were to provide N2O emission measurements that would be

used to generate IPCC compliant N2O EFs, it was essential that they all followed the same protocols

for experimentation and data handling. The measurement team in WP2 developed Joint

Experimental Protocols (JEPs) for the fertiliser N, livestock manures, and dung & urine experiments,

providing standardised approaches to experimental design; amendment applications; gas, soil and

vegetation sampling; data processing; and weather measurements. The team published their

chamber deployment protocol (Chadwick et al., 2014), and contributed to the GRA Guidelines for

using chambers to quantify N2O fluxes from agricultural soils (de Klein and Harvey, 2012).

Existing N2O measurement datasets were identified and the N source, application method and

timing, soil type, cropping system and measurement duration were recorded. In addition, we

preformed broadscale modelling runs (DNDC) to assess the relative effects of rainfall and soil clay

content on emissions. Using this information we identified the gaps in the N source, soil type,

cropping scenarios that needed filling via new plot-scale field experiments. The prioritisation phase

resulted in the identification of 9 experimental sites, 5 grassland and 4 arable, and the 37 plot-scale

field experiments required to help generate new country specific N2O EFs. The industry stakeholder

group was used to confirm the sites and experimental treatments.

WP2: Measurements of direct and indirect N2O emissions

Plot-scale experiments were conducted over 12-month durations to generate IPCC compliant N2O

EFs using static chambers. The fertiliser experiments addressed the effect of application rate and

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fertiliser type on N2O emissions. Increased numbers of split doses and use of a nitrification inhibitor,

dicyandiamide (DCD) were included as specific mitigation treatments. The experiments to derive

manure N2O EFs addressed the effect of manure type, method of slurry application and timing of

applications (spring or summer), as well as the effect of DCD on N2O emissions. Urine and dung were

applied separately at early-, mid- and late-grazing times to determine if different N2O EFs would be

needed depending on the time of deposition to the soil. Again, DCD was included to determine its

potential to mitigate N2O emissions. The results of these experiments were that the average N2O EF

for ammonium nitrate applied to arable land was 0.52%, whilst that for grassland was 1.12%. These

lower EFs associated with arable crops are consistent with a larger set of data collected in the DEFRA

MinNO project which reported average EFs for arable crops of <0.5% (Thorman et al,. 2013). The

average urea N2O EF was much lower, between 0.34 (arable) and 52% (grassland). The efficacy of

DCD on grassland was inconsistent after fertiliser addition, but reduced emissions by an average of

30% on arable land. The average N2O EF for livestock slurry was 0.57%, and 0.43% for farmyard

manure, and 0.88% for poultry manure. Whilst the average cattle urine N2O EF, 0.69%, was

significantly greater than that for cattle dung (0.17%), with the combined excretal N N2O EF being

between 0.5 and 0.6% (depending on the chosen urine N:dung N). DCD reduced the urine N2O EF by

an average of 36%. Hence in all cases except the fertiliser applications to grassland, the N2O EFs

were lower than the IPCC (2006) default values of 1% for fertiliser N and manures, and 2% for cattle

excreta deposited during grazing.

A laboratory experiment using the 9 soils from the experimental sites was conducted to determine

the effect of soil and climate factors on the efficacy of DCD as a nitrification inhibitor. The results

showed that the efficacy of DCD to reduce nitrification was affected by both temperature and by soil

type. Grassland soils resulted in a reduced half-life of DCD and reduced efficacy compared to arable

soils, and may be in part due to increased microbial breakdown of DCD in soils with greater organic

matter content.

A mixed sampling strategy was adopted to determine the nitrate concentrations and dissolved N2O

concentrations in the three Defra Demonstration Test Catchments (DTCs), the Wensum (an arable

farming dominated area with heavy clay soils, The Eden and the Hampshire Avon. The sampling

frequency allowed the estimation of the indirect N2O loss to groundwater (EF5g) from the three

catchments to compare with the IPCC (2006 Guidelines) default value of 0.0025%. Results showed

the importance of the predominant land use, the physical characteristics controlling runoff, and the

seasonality and timing of rainfall periods on the resulting EF (EF5g). The mean EF5g values obtained

using the datasets were typically lower by a factor of 0.5 or by an order of magnitude, in comparison

with the IPCC (2006) default value of 0.0025.

WP3: Proxies of N2O emissions

The overall objective of this work package was to identify proxy measurements which can be useful

(to both policy makers and modellers) to assess the impact of changes in agricultural practices and

soil and climatic conditions on N2O emissions at a national, regional, farm and field level.

Manufactured fertiliser nitrogen use and livestock numbers are useful primary proxies for indicating

changes in both direct and indirect N2O emissions at the national, regional and farm scale and are

currently the only proxies that will be reflected in the UK agricultural GHG inventory. Proxies that

describe and encourage management strategies for improving nitrogen use efficiency are important

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to inform progress with GHG reductions. The use of nitrification inhibitors is also a valuable proxy to

demonstrate mitigation of direct N2O emissions. Soil nitrate, soil moisture and clay content, as well

as annual rainfall are potential proxies for direct N2O emission at the field scale. A simple, widely-

usable model to predict direct N2O emissions following the application of manufactured nitrogen

fertiliser and based on proxy measurements of soil water filled pore space and soil nitrate was

developed for use at the field scale.

WP4: Modelling N2O emissions

The initial aim of WP4 was to provide a site level analysis of both the DDC and DNDC models against

nine experimental sites identified within the prioritisation phase. DNDC was substituted by L-DNDC

after an evaluation of its performance. Once completed, both a sensitivity and uncertainty analysis

were carried out for both models separately. Modelled outputs of annual N2O emissions were

compared to the AC0116 experimental sites. Finally, to provide an assessment of temporal and

spatial emissions across the UK, simulations of annual N2O emissions for the period 2001 to 2010

were run for three land use types: Cropland, Grasslands and Semi-natural land.

Both L-DNDC and DDC modelled estimates tended to follow the same pattern of emissions, and in

terms of annual estimates, were within the measured range. However, limitations in both model

calibration and input information led to uncertainty in modelled estimates.

Sensitivity analysis of the L-DNDC model revealed that the most important parameters involved in

N2O emission simulations are related to the simulation of microbial growth and maintenance, and

the efficiency involved in NO2 and N2O production. Whilst for DDC, the effect of changes in site level

inputs varied between the sites, which is not entirely unexpected as there was a range of

management histories, average climate data and a range of fertiliser types used across all

experimental plots. The underlying causes for some differences in the sensitivity of DDC to changes

in the inputs were also reflected across the sites. However, the primary causes of change in N2O

fluxes and yield estimates are: sensitivity of DDC to changes in SOM pools and total system C;

interaction of changes to total C and incorrectly simulated snow fall, and sensitivity of DDC to

interactions between changing inputs and fertiliser type.

Finally, the spatial framework that has been developed for DDC can provide a basis for Tier 3 EF

derivation, but is currently limited by lack of reliable spatial land management activity data such as;

a) crop rotation, and b) different grazing/ silage grassland systems both spatially and temporally.

Thus, modelled estimates can only currently provide a guide to emissions at a national scale. To take

advantage of this modelling system, data collection of spatially disaggregated activity / management

data to drive the models should be a priority. The combination of these new data with the spatial

modelling system described here, would deliver a powerful Tier 3 system for simulating and

reporting N2O emissions from UK agriculture.

WP5: Verification of N2O emissions (addressing spatial and temporal uncertainty)

This WP addressed spatial and temporal uncertainties of N2O fluxes, and used novel measurement

approaches, modern statistical analyses and modelling to explore the precision and scaling of N2O

fluxes from plot to field to regional emissions. Specifically, research determined i) the temporal

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uncertainty of chamber derived N2O fluxes by comparing N2O flux measurements from static

chambers and autochambers at the experimental sites in WP2, ii) spatial variability of emissions at

the plot and field scale using a newly developed ‘roving’ dynamic chamber, iii) the scaling of fluxes

from plot (chambers) to field (eddy covariance), and iv) the inverse modelling of atmospheric N2O

concentrations from tall towers around the UK resulted in a better fit to the newly derived EFs (from

WP2) compared with the EFs used in the official UK inventory reporting approach used in 2014.

Temporal uncertainty: The comparison of N2O emissions obtained with autochamber systems and

manual chambers at the WP2 plot experiments sites revealed that whilst some peaks and troughs of

N2O fluxes were not accounted for by the lower frequency of static chamber sampling, the dynamics

of N2O emission are generally similar between the two methodologies. In general, there was no

significant difference in the cumulative fluxes between static and autochamber measurements at

the same site, although the autochamber flux data were generally lower than the static chamber.

The autosampler data also confirmed that sampling of chambers between 10am and 2pm resulted in

fluxes that were typical of the daily average.

Measurement precision: A high precision closed loop dynamic chamber system was developed to

assess the precision of the WP2 static chambers. The comparisons of methods suggest that the static

chamber method may underestimate N2O flux measurements by as much as 20% as it assumes

linear change in gas concentrations during enclosure periods which may not always be the case.

However, this was acknowledged before the project started, as the project team believed it more

important to account for spatial variability of fluxes within the WP2 experimental plots. Furthermore

the more precise dynamic chamber revealed that negative fluxes of N2O are rare.

Spatial uncertainty: The ‘roving’ dynamic chamber was used to identify the spatial variability across a

grazed grassland field and across a whole livestock farm. The typical log-normal distribution of N2O

fluxes required development of Bayesian approaches to provide a robust and transparent method

for translating small-scale observations to larger scales, with appropriate propagation of uncertainty.

This new data analysis approach showed that both at the field and farm scale (i) N2O fluxes

correlated most strongly with soil NO3 concentrations, and (ii) that features which only occupy small

areas, such as animal feeding areas, manure heaps, animal barns can contribute to a large

percentage of the total estimated daily N2O flux, and therefore should not be excluded from farm

scale budgets.

Upscaling from plot to field scale: We used eddy covariance to integrate N2O fluxes at the field scale

and compare with chamber-derived N2O fluxes at six agricultural fields (4 grassland and 2 arable)

from five locations across the UK. The two measurement methods resulted in a similar range and

magnitude of N2O fluxes; however there was a tendency for chamber methods to report higher

fluxes than eddy covariance. Direct differences in reported fluxes between the methods are likely

due to the inability to accurately interpolate spatial data from the static chamber measurements.

Verifying GB N2O emission maps: To verify the newly derived N2O EFs (from WP2), a new inventory

dataset was constructed. In addition to the normal spatial aggregation (5 km2) we developed a

temporal disaggregation (monthly) in order to provide a better comparison with the continuous

atmospheric N2O concentration measurements available from 3 towers at Mace Head, Tacolneston

and Ridge Hill. Using the Met Office atmospheric dispersion model (NAME) maps derived from the

official N2O EFs (default IPCC 2006 GL guidelines) and the newly derived N2O EFs (AC0116 & AC0114)

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were compared with the atmospheric N2O concentration measurements for the year 2013. In

general the temporal pattern of the emissions corresponds with observed concentrations measured

at Ridge Hill and Tacolneston. Emissions associated with mineral N fertiliser application are

dominant at all sites. The performance of the disaggregated maps based on default or newly derived

EF’s is very similar. This comparison clearly shows the strength of comparing of bottom-up

inventories with top down inversion modelling to constraining annual UK emission inventories, and

should be done routinely annually.

WP6: Ammonia emissions and nitrogen excretion from grazing cattle

Ammonia (NH3) emissions by grazing livestock represent a source of indirect N2O emissions,

associated with re-deposited nitrogen and nitrate leaching. Because of the difficulties in measuring

NH3 emissions and nitrogen loading in urine and dung from grazing livestock there is a lack of

information from which to generate NH3 EFs. The aim of this work package was to conduct a series

of experiments where beef cattle were fitted with urine sensors, to quantify urination events

(volume and N content), and new laser technology was used to measure the atmospheric NH3

concentration up- and down-wind of grazing cattle. Whilst the urine sensors provided some useful

data on numbers of urination events per day, and individual urination volume and total N content, it

is unfortunate that the NH3 concentration analysers were not sensitive or stable enough to quantify

emissions.

The urine sensors provided an automated means of monitoring urination events from grazing cattle,

enabling the collection of a large amount of data. However, improvements to sensor attachment and

location monitoring are needed. There was clear diurnal variation in the N loading per urination

event, which may have implications for potential N losses depending on livestock management and

locations at different times of day. More sensitive measurement techniques than those used in this

study are required for the estimation of NH3 emissions from grazing cattle at the stocking rates used

here. Based on the outcomes of this study, no changes to the current NH3 emission factor for grazing

cattle are recommended. However, subject to improved NH3 emission measurement techniques,

further combined measurements of urine N excretion and NH3 emissions from grazing cattle are

recommended for a range of grazing management systems and environmental conditions.

Outputs

To date (November 2016), 27 papers have been published in a range of refereed journals, and many

others are in advanced preparation. This growing peer reviewed evidence base represents an

important resource to support revisions of the inventory country specific EFs. In addition, the field

plot-scale N2O flux data, EFs and associated metadata are being archived via the project: Ensuring

Public Access to Historic Defra Sponsored GHG Measurement Data.

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WP1. Prioritisation phase

Introduction

The aim of project AC0116 (and AC0114) was to develop a reporting tool for agriculture that better reflected the effects of UK soils, climate and N management on N2O emissions through an increased understanding of the processes and factors controlling emissions. The project was also designed to quantify the uncertainty of the total N2O emissions, and importantly to determine where relative uncertainties lie within the increased complexity of an improved inventory. There were two key objectives of the prioritisation phase of the InveN2Ory project:

1. Develop standard protocols and identify initial proxies appropriate to the plot and field scale 2. Confirm the key UK soil / climate zones and sources of N

In this Chapter we summarise progress made against these two objectives; detailed explanation can be found in Appendix 1.1. 1. Develop standard protocols and identify initial proxies appropriate to plot-field scale Since different research organisations were to provide N2O emission measurements that would be used to generate IPCC compliant N2O EFs, it was essential that they all followed the same protocols for experimentation and data handling. The measurement team in WP2 developed Joint Experimental Protocols (JEPs) for the fertiliser N, livestock manures, and dung & urine experiments, providing common approaches to experimental design, amendment applications, gas and soil sampling, and weather measurements (see Appendix 1.2). The JEPs also considered aspects of Health and Safety, although individual organisations have their own Risk Assessment procedures in place. In addition, a common data handling excel spreadsheet was produced to ensure a standardised method is used to calculate N2O fluxes, and ensure that N2O flux data as well as soil and climate data were presented in a uniform format for statistical analyses. Since the InveN2Ory project commenced, the UK has participated in a Global Research Alliance (GRA) sponsored workshop in Lincoln (New Zealand) to develop standardised methods for using chambers to quantify N2O fluxes from agricultural soils (de Klein and Harvey, 2012), and the UK chamber deployment methodology was published in 2014 (Chadwick et al., 2014). Field-scale proxies were identified during consultation between the WP2 measurement team and the project modellers, to ensure that the appropriate model starting conditions (e.g. bulk density, total soil C, total soil N, permanent wilting point, field capacity) were determined, and that edaphic and climatic variables such as soil ammonium and nitrate, water filled pore space, soil temperature and rainfall measurements were measured at an appropriate frequency to aid the optimisation of the DNDC and DAYCENT models. These proxy measurements have been built into the Joint Experimental Protocols. In addition, part of the prioritisation phase was to recruit and train researchers to be competent in the range of field and laboratory methodologies and to order and commission appropriate equipment. This was all completed on time. 2. Confirm the key soil / climate zones and sources of N The project team made an initial ‘gap analysis’ prior to the proposal submission, of what additional N2O EFs would be required to complement the number of existing and already planned experiments under other government funded projects that will deliver IPCC compliant N2O EFs under UK conditions. In this prioritisation phase, a more complete evaluation was made. The details of this process can be found in Appendix 1.1, but here we briefly summarise the rationale and approach for

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the selection of experimental platforms which we ultimately recommended (to the Project Management Group and presented to the Stakeholder Group) to generate new spatially and temporally explicit N2O EFs to compliment AC0116 modelling and the database of existing and planned EF data from current projects in order to improve the N2O inventory from Tier 1 to Tier 2. The approach taken is shown schematically in Figure 1.1. A geographical assessment was made of the land area (ha) under a range of soil type-rainfall zone combinations for grassland and arable land (1). The sensitivity of the N2O EF to these combinations of soil type and rainfall was assessed following typical N management on arable and grassland soils using the DNDC94 model (2), and scaled indicative N2O EFs generated for these soil-rainfall-N management combinations (3). This generated information to establish the relative importance of the individual soil type-rainfall zones to the total UK indicative N2O emission. Additional information used in this assessment was provided by a collation of UK N2O EF from existing and planned experiments in current projects (4). Not all of these EF measurements could be used, as some were not IPCC compliant, i.e. they were not of 12-months duration and/or did not include a non-amended control (5). These were our primary filters for removing experimental measurements and deriving a list of IPCC compliant EFs for the key N sources applied to agricultural soils (for grass and arable land) (see Table 1.). These current and planned N2O EFs were then ‘mapped’ onto the spatially explicit scaled indicative N2O emissions to generate an index of the number of EF measurements per unit of emission (6). The UK IPCC N2O inventory was used to prioritise the N sources to be included (7), and it was these final steps that allowed us to assess if the proposed AC0116 experiments were planned in the most relevant soil-climate zones (8).

Figure 1.1. Schematic process of assessing the proposed AC0116 experimental sites and treatments.

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As a result of this process, i.e. having taken account of a) the land area under different soil type – rainfall zones, b) the sensitivity of N2O EFs to soil type and rainfall (via the DNDC modelling), and c) an improved stock-take of existing and planned experiments which will deliver IPCC compliant EFs for different N sources, we re-assessed the initial geographical location of the proposed sites (included in the original proposal). The complete set of field experiments (showing sites and N source combinations) are shown below in Table 1. Fertiliser applications to arable: there were potentially 45 EFs for use in the ‘filtered’ dataset, the majority in the medium and low rainfall areas, typical of this land use. Almost half of these would be generated by the MIN-NO project (LK09128). Only 6 of these were for urea fertiliser, the vast majority being for (C)AN. Given the large number of potential N2O EFs from N fertilisers we recommended retaining 3 of the initially planned experiments, but to replace one of the planned fertiliser experiments with a manure to tillage experiment – see below. Fertiliser to grass: there were potentially 24 EFs for use in the ‘filtered’ dataset, and all but one were within the high and moderate rainfall areas, which would be typical of grazed grass. All of these EFs were for clay and medium soils and most were for (C)AN (8 were EFs for urea). Provisionally, AC0116 included five fertiliser to grass experiments, two of which started in spring 2011; Crichton (High rainfall - Medium soil) and Hillsborough (Medium rainfall – Medium soil). Given the large proportion of the total grassland area on the medium soil texture in all three rainfall zones, and the small number of EFs from fertiliser applied to clay soils in high rainfall zones, we recommended retaining the original experiments for fertiliser applied to grass. Manure applications to arable: there were 7 EFs for use in the ‘filtered’ dataset. All were for cattle slurry, and they were all in high and low rainfall areas. There were no solid manure or pig slurry EFs in this dataset. However, there were two proposed AC0116 experiments planned; one in the Wensum (Low rainfall – light sand soil) where treatments would include pig slurry, pig FYM, poultry manure and poultry litter applied in autumn and spring; and one at Rosemaund (low rainfall – medium soil) where cattle slurry, cattle FYM, poultry manure and poultry litter would be applied in autumn and spring. Given the proportion of the arable land area on the medium soil texture, we recommended that these experiments be retained. But we also recommended the replacement of the fertiliser to tillage experiment at Gilchriston with a manures to tillage experiment in the Edinburgh area to provide additional EFs from a medium soil in a high rainfall zone. Manure applications to grass: there were 14 IPCC compliant EFs for livestock manures applied to grass. These were nearly all for cattle slurry, with the majority in a high rainfall area, which would be typical for grass. There was one EF for pig slurry on a light sand in a low rainfall area. However, ca. 45% of pig slurry is applied to grass. There were no solid manure IPCC compliant N2O EFs. Provisionally, there were four planned AC0116 experiments applying livestock manure to grass, comparing cattle slurry and cattle FYM timings, with and without a nitrification inhibitor. These AC0116 experiments were planned for Rowden (High rainfall – clay soil), Pwllpeiran (High rainfall – medium soil), Hillsborough (Moderate rainfall – medium soil) and Drayton (Low rainfall – clay soil). Given the large proportion of the grassland area in the medium soil category, we recommend retaining the Pwllpeiran and Hillsborough experiments. The Rowden and Drayton experiments provide contrasting rainfall zones for clay soils, and hence offered an important opportunity to fully explore the range of potential EFs from the manure type/soil/rainfall combinations. We also recommended replacing cattle slurry and cattle FYM with pig slurry and pig FYM at the Drayton experimental site.

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Urine/dung applications to grass: there were only 4 urine EFs and no dung EFs available in the ‘filtered’ dataset, in contrasting rainfall zones. In AC0116, we planned five urine/dung experiments; Drayton (Low rainfall – clay soil), Crichton (High rainfall – medium soil), Hillsborough (Moderate rainfall – medium soil), Rowden (High rainfall – clay soil) and Pwllpeiran (High rainfall –medium soil). A similar rationale was used to justify retaining these planned experiments to the manure to grass experiments. Given the large proportion of the grassland area in the medium soil category, the Pwllpeiran and Hillsborough experiments are essential. The Rowden and Drayton experiments provided contrasting rainfall zones for clay soils, and hence offered an important opportunity to fully explore the range of potential EFs from the urine&dung/soil/rainfall combinations. We recognise that there is a lack of N2O EFs for dung & urine from organic soils on semi improved and rough pasture. But there are a limited number of dung & urine EFs per se across all soil types, and these 5 sites would improve upon our current knowledge on clay and medium soils.

Table 1. Combinations of site / N source field experiments.

Land use / N source Detail Treatments

Arable

Fertiliser Gilchristan Spring crop Control, 5 rates of ammonium nitrate, 1 rate of

urea, 1 rate of AN+DCD, 1 rate of urea+DCD, 1 rate of AN with more frequent doses

Rosemaund Spring crop

Woburn Spring crop

Manure

Boghall Autumn application Control, cattle FYM, poultry litter, layer manure, cattle slurry (SBC), cattle slurry (TH)

Boghall Spring application Control, poultry litter, layer manure, cattle slurry (SBC), cattle slurry (TH)

Rosemaund Autumn application Control, cattle FYM, poultry litter, layer manure, cattle slurry (SBC), cattle slurry (TH)

Rosemaund Spring application Control, poultry litter, layer manure, cattle slurry (SBC), cattle slurry (TH)

Wensum Autumn application Control, pig FYM, poultry litter, layer manure, pig slurry (SBS), cattle slurry (TH)

Wensum Spring application Control, poultry litter, layer manure, pig slurry (SBC), pig slurry (TH)

Grassland

Fertiliser

Crichton In 3 splits Control, 5 rates of ammonium nitrate, 1 rate of urea, 1 rate of AN+DCD, 1 rate of urea+DCD, 1 rate of AN with more frequent doses

Drayton In 3 splits

Hillsborough In 3 splits

Pwllpeiran In 3 splits

Rowden In 3 splits

Manure

Drayton Autumn application Control, cattle slurry (TS), cattle slurry (SBC), cattle slurry+DCD (TS), cattle slurry+DCD (SBC), cattle FYM

Drayton Spring application

Pwllpeiran# Autumn application

Pwllpeiran# Spring application

Rowden Autumn application

Rowden Spring application

Hillsborough Autumn application Control, cattle slurry (TS), cattle slurry (SBC), cattle slurry+DCD (TS), cattle slurry+DCD (SBC) Hillsborough Spring application

Urine & Dung

Crichton Early grazing Control, urine, urine+DCD, artificial urine, dung

Crichton Mid grazing

Crichton Late grazing

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Drayton Early grazing

Drayton Mid grazing

Drayton Late grazing

Hillsborough Early grazing

Hillsborough Mid grazing

Hillsborough Late grazing

Pwllpeiran Early grazing

Pwllpeiran Mid grazing

Pwllpeiran Late grazing

Rowden Early grazing

Rowden Mid grazing

Rowden Late grazing

TH=trailing hose, TS=trailing shoe, SBC=surface broadcast.#Note: experiments were curtailed before

they started due to Defra cuts.

Note: the AC0116 experimental sites did not include shallow soils, peat soils or organic soils, although the Hillsborough site has a high OM content in the top soil. These soil types represent relatively small land areas under improved grassland and arable land in the UK, although some of the DAs may have greater proportions of these soils in upland areas of unimproved grassland. For example, in the UK, shallow soils, peat soils and organic soils represent ca. 6%, ca. 1% and ca. 3% of the arable land, and ca. 4%, ca. 2% and 8% of the improved grassland areas that would receive the majority of N inputs as fertiliser and managed manures. We understood that Defra had awarded a new project on GHG emissions and C balances on lowland peat as we started the AC0116 project, which should be able to contribute N2O EFs to the wider AC0116 database. The selection of the nine experimental platforms above was primarily based on the relative proportion of land area under each combination of soil type and annual rainfall, the modelled sensitivity of N2O EFs to soil type and rainfall, and the number and geographical spread of IPCC compliant EFs from the N2O UK emission factor database. The choice of N source (urine, dung, livestock manure and fertiliser) applied at these nine experimental sites reflects the major sources of N2O identified by the current UK N2O inventory. But the detail and rationale for the actual treatments to be included at each site requires brief explanation, as the aims of AC0116 were to;

generate new (gap filling) N2O EFs for the typical range of N sources (fertiliser N type, manure type, urine and dung)

provide an understanding of the relationship between N application rates and the N2O EFs

determine the effect of N application timings on the N2O EF

explore mitigation methods for N2O emissions which could be included in the new inventory structure (e.g. split doses of mineral N fertiliser and use of nitrification inhibitors)

generate EFs that future-proof the improved inventory for potential ammonia emission mitigation, e.g. use of low trajectory slurry application techniques.

Each of these aims is discussed briefly below. Forms of Nitrogen Both nitrification and denitrification require a suitable form of N as a substrate for N2O production; nitrification requires a source of ammonium-N, whilst denitrification requires a source of nitrate-N. Hence the form of N applied/deposited to soil is important in controlling the timing and magnitude of an N2O flux. A number of studies comparing N2O EFs following the application of different N fertiliser types to soil have concluded that ammonium nitrate fertiliser resulted in greater emissions than urea fertiliser. The application of manures to soil also supplies carbon and water, as well as N,

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so manure applications can be significant sources of N2O. The type of manure may influence the N2O flux. Therefore, experimental treatments in AC0116 included a comparison of N2O emissions from ammonium nitrate and urea fertiliser applications, and following the applications of a number of key contrasting manure types including slurry and solid manure from cattle and pigs, as well as poultry manure. Cattle manures were applied to grassland plots, whilst pig and poultry manures were applied to plots on arable land, as this would be common commercial practice. Nitrogen Rates There is a growing body of evidence showing that the relationship between N2O emission and N application rate is not necessarily linear (e.g. Cardenas et al., 2010). It is important to determine this relationship within the experimental treatments for fertiliser N on both grassland and arable sites in project AC0116. Application Timings The timing of the N application/deposition to the soil will also affect both direct and indirect N2O emissions since this affects the soil conditions at the time of application. It was therefore important to assess the effect of typical agronomic timing of applications/deposition of the different N sources on N2O EFs in the experimental treatments. Hence, fertilisers were applied in split doses (according to recommendations), manure were applied in both spring and autumn, whilst urine and dung were ‘deposited’ in the spring (early grazing), summer and autumn (later grazing). Mitigation - Split Doses of Fertiliser Nitrogen Fertiliser N is normally applied in split doses (typically 2-4 depending on the weather conditions and the type of crop being grown) throughout the growing season to ensure that the crop makes efficient use of the N. By increasing the number of smaller applications there is a reduced risk of excess N remaining in the soil at risk of loss to water or air (e.g. as N2O). Hence, one treatment on both grassland and arable sites was to increase the number of doses of fertiliser N (maintaining the same total N application in the season) from e.g. 3 to 5. Of course, this increases the risk of a rainfall event coinciding with an application of an N source, but this treatment would allow us to test the potential for managing fertiliser N in smaller more frequent doses as a means of trying to reduce overall N2O emissions from the soil, and improve nitrogen use efficiency. Mitigation - Nitrification Inhibitors Nitrification inhibitors are chemical compounds that can be applied to the soil to slow down the metabolism of the nitrifiers in the soil. In doing so, they reduce the rate of conversion of ammonium to nitrate and therefore reduce the potential for N2O emissions from a) nitrification and b) by reducing the pool of nitrate at risk of denitrification. Nitrification inhibitors have been shown to be successful in reducing N2O emissions after fertiliser N applications (Misselbrook et al., 2014), slurry applications (Minet et al., 2016) and deposition of urine (Di and Cameron, 2016). Defra project AC0213 assessed the efficacy of nitrification inhibitors to reduce N2O emissions from a number of N sources at a limited range of grassland and arable sites throughout the UK. These data have now been made available to AC0114 and AC0116 for inclusion in the N2O EF database and in the assessment of mitigation practices. AC0116 provided the opportunity to extend our knowledge of the efficacy of nitrification inhibitors from a wider range of N sources and on a greater number of soil/climate combinations. Hence, we included treatments with the inhibitor DCD on the fertiliser, slurry and urine experiments.

13

Future proofing the inventory - Slurry Application Methods It is important that the improved GHG inventory is future proofed for a range of potential changes in management practices. For example, although currently only a small percentage of all livestock slurry in the UK is applied to land by band-spreading / shallow injection (compared with surface broadcasting), increases in spring/summer application timings (as a result of the Nitrate Vulnerable Zone Action programme) along with the need to maximise slurry N use efficiencies and reduce ammonia loss will lead to a greater proportion (perhaps a significant proportion) of slurry being applied using these techniques. Band-spreading / shallow injection slurry application methods should retain more slurry N in the soil so there is the potential for higher N2O emissions. Whilst this has been shown to occur in some studies, it is not always the case (Chadwick et al., 2011). Few studies have compared N2O emissions from contrasting slurry application techniques. It is important that we generate country specific N2O emission factors for slurry applied by low trajectory spreading methods. Hence, slurry treatments under AC0116 included a comparison of N2O EFs from surface broadcast slurry and slurry applied by trailing hose or trailing shoe.

14

WP2. Field experiments for determine emission factors and effects of mitigation strategies, and a laboratory experiment to better understand the factors controlling efficacy of the nitrification inhibitor, DCD.

Bob Rees*, Laura Cardenas, Catherine Watson, John Williams, Fiona Nicholson, Dave Chadwick.

*WP leader

Summary Thirty-five separate experiments were conducted at the large plot-scale, to generate an evidence base from which country specific N2O emission factors could be calculated. Common experimental protocols were adopted to quantify N2O fluxes from multiple static chambers per plot in randomised block designed experiments following the application of different fertiliser N types, different manure types, and urine and dung. Experiments also explored the importance of fertiliser application rate, season of manure timing and timing of urine/dung deposition on N2O fluxes. Two specific mitigation practices were explored; use of a nitrification inhibitor, and increased numbers of smaller doses of fertiliser N. In brief, N2O EFs from the fertiliser N applied to arable crops, were <1%, the IPCC default, with an average of 0.52% from ammonium nitrate and 0.34% from urea. In contrast, the average N2O EF from ammonium nitrate applied to grassland was similar to the IPCC default, at 1.12%. The application of manure to arable crops resulted in a wide range of EFs (0.15-2.73), which was a consequence of high level of variability associated with the data. However, higher emission factors were generally associated with autumn manure applications. The average N2O EFs from the 15 dung and urine experiments were 0.17 and 0.69, respectively. A weighted new combined excretal value of 0.5-0.6% (depending on the proportion of excreted N apportioned to dung or urine) is also much lower than the IPCC 2% value for cattle (and 1% for sheep). Aims A large programme of experimental work was one of the key deliverables of the ACO116 (InveN2Ory) project. The prioritisation phase of the project was used to identify gaps in our knowledge of soil/ climate and emission source interactions that were underrepresented in measurement data (see WP1 and Appendix 1.1). Using this knowledge a comprehensive programme of field and laboratory research was implemented. A network of experimental sites was established with the following aims:

1. To make direct nitrous oxide (N2O) measurements at a range of soil-climatic zones to generate data to support the derivation of new country specific emission factors from key sources (nitrogen fertilisers, manure applications and urine and dung)

2. To undertake indirect emission measurements associated with N deposition (ammonia emissions) and nitrate leaching (at the plot and field scale), and assess mitigation methods e.g. the use of a nitrification inhibitor, building on Defra project AC0213, and N application timings

3. To assess mitigation strategies for N2O emissions, e.g. more split doses of fertiliser

applications, and application of a nitrification inhibitor (DCD).

4. To understand the role of DCD as an inhibitor of the nitrification process and consequent N2O emissions in a controlled environment study.

Methodology

15

Direct nitrous oxide emissions Sites for N2O EF measurement were selected following a geographical assessment of UK arable/grassland area under a range of soil/climatic zones, and a ‘gap analysis’ to identify zones lacking in current/planned experimental data. Figure 2.1 shows the location of the 4 arable and 5 grassland experimental locations across, England, Wales, Northern Ireland and Scotland. Table 2.1 summarises some of the key characteristics of the experimental locations. Note: different experimental sites (fields) were used for different N source experiments within a location.

Figure 2.1. Map showing the 9 experimental locations used in WP2 of the InveN2Ory project in relation to the major geoclimatic zones in the UK. Red circles represent grassland sites; yellow circles represent arable sites.

Table 2.1. Summary characteristics of the 10 experimental sites used in WP2 of the InveN2Ory

project. Locations/ N sources

Grid Ref.

Land use

Soil type

Altitude (m asl)

Clay content (%)

Bulk density (g cm3)

pH Annual total rainfall (mm)*

Annual Av Air temp. (oC)*

Site management

Crichton Fert, U/D

9867 7302

Grass SL 50 15 1.07 5.6 1140 9.1 SRUC

Drayton# Fert, U/D

160 554

Grass C 47 59 0.85 7.6 628 10.3 ADAS

Bush Man

248 653

Arable SL 190 25 1.1 6.0 1669 7.18 SRUC

Gilchriston Fert

479 658

Arable SL 165 13 1.20 6.3 676 9.0 SRUC

Hillsborough Fert, U/D, Man

3231 3569

Grass SCL 128 22 0.90 6.0 908 9.0 AFBI

Rowden Fert, U/D, Man

643 994

Grass CL 185 37 0.62 5.7 1037 10.1 Rothamsted

Pwllpeiran Fert, U/D, Man

542 614

Grass CL 213 29 0.91 5.5 1570 10.0 ADAS

Rosemaund Fert, Man

5364 4648

Arable CL 80 21 1.24 6.5 591 10.0 ADAS

Wensum Man

891 254

Arable SL 60 11 1.44 6.7 724 10.0 ADAS

Woburn Fert

948 353

Arable LS 96 11 1.5 7.0 525 10.9 Rothamsted

Where; C=clay, CL=clay loam, LS=loamy sand, SL=sandy loam, SCL=sandy clay loam. *30 year average. #Manure experiments were planned for Drayton (including pig slurry), but were cut by the funders. Fert=N fertiliser forms, U/D=urine and dung, Man=manure.

16

Treatment rates and applications Common experimental protocols (developed in WP1) were used for each N source (fertiliser N, livestock manures and urine/dung deposition – see Appendix 1.2) and implemented across all sites. In all experiments the treatments and a control were replicated in a randomised block design, with three blocks, and five static chambers per individual plot, giving a total of 15 chambers for each treatment. Generally, plots all had a similar layout, with dedicated areas for chamber measurements, soil sampling and harvest areas. Figure 2.2 shows a schematic of a typical plot layout, which ensured that chamber measurements were not unduly influenced by regular soil sampling nearby, or that harvest areas were not compromised by frequent trampling.

Figure 2.2. A typical plot layout of experiments in WP2 of the InveN2Ory project. Synthetic fertiliser application to arable and grassland Fertiliser application rates were based on guidance given in Defra’s Fertiliser Manual (RB209; Defra, 2010) and for arable experiments varied according to crop, soil type and soil N status. In experiments investigating the effect of ammonium nitrate (AN) rates, the standard recommended rates were adjusted accordingly at each site to produce treatments both above and below the recommended rate. A urea treatment was included, to compare with AN, and was applied at the same rate as the current recommended AN rate. Fertilisers were applied evenly across the plot by hand to simulate agronomic practice, with timings based on commercial practice. DCD was applied at a rate equivalent to 10 kg DCD ha-1. The DCD was applied as a 2% solution one hour after fertiliser, using a knap-sack sprayer. As DCD contains 65% N the amount of AN or urea applied to these plots was reduced to match the target total rate of N application. An additional mitigation treatment was the application of the recommended rate of AN in more, but smaller, split doses. This meant generally applying the recommended rate of AN fertiliser in 5 doses, rather than 3. Fertiliser treatments in Arable experiments (Gilchristan, Rosemaund, Woburn) 1. Untreated control (no fertiliser N) 2. Ammonium nitrate at rate 1 and RB209 timings 3. Ammonium nitrate at rate 2 and RB209 timings 4. Ammonium nitrate at rate 3 and RB209 timings

17

5. Ammonium nitrate at Rate 4 (RB209 recommended rate) and RB209 timings 6. Ammonium nitrate rate 5 (Excess of RB209 recommended rate) and RB209 timings 7. Ammonium nitrate fertiliser N at rate 4 and RB209 timings + NI (DCD*) 8. Urea fertiliser N (at rate (4) and timings) 9. Urea fertiliser N at rate 4 + NI (DCD*) 10. Ammonium nitrate (at rate 4) – but more splits (e.g. 5 instead of 3) to reduce excess mineral N in the soil at any point in time. * DCD treatments were included to match the total N applied at rate 4. Fertiliser treatments in grassland experiments (Crichton, Drayton, Hillsborough, North Wyke, Pwllpeiran) 1. Untreated control (no fertiliser N) 2. Ammonium nitrate at rate 70 kg/ha N and RB209 timings 3. Ammonium nitrate at rate 140 kg/ha N and RB209 timings 4. Ammonium nitrate at rate 210 kg/ha N and RB209 timings 5. Ammonium nitrate at rate 280 kg/ha N and RB209 timings 6. Ammonium nitrate at 350 kg/ha N and RB209 timings 7. Ammonium nitrate fertiliser N at 210 kg/ha N and RB209 timings + NI (DCD*) 8. Urea fertiliser N (210 kg/ha N and timings) 9. Urea fertiliser N at rate 210 kg/ha N+ NI (DCD*) 10. Ammonium nitrate (at 280 kg/ha N) – but more splits (e.g. 3 for 1st cut, 2 for 2nd cut and 2

for 3rd cut to reduce excess mineral N in the soil at any point in time. * DCD treatments were included to match the total N application of 210 kg/ha N. Manure application to arable and grassland Livestock manures were source locally from commercial farms. The same manures were used in experiments in both the autumn and spring, so manures were stored (covered) over-winter on site. The target application rate for all treatments was approx. 180 kg N ha-1 (not exceeding 250 kg N ha-

1), but depended on locality and land-use, with actual rates varying due to N concentrations after storage, and application practicalities. Slurries were analysed for nutrient content, mixed before application and applied using buckets to simulate surface broadcast and watering cans to simulate trailing hose (30 cm spacing on arable land and 20 cm on grassland). Solid manures were applied evenly across the plots by hand. All manures were analysed to determine exact N application rates. Manure treatments in arable experiments (Bush, Rosemaund, Wensum) 1. Untreated control (no livestock manure) 2. FYM– autumn; Pig FYM in Wensum, Cattle FYM at Rosemaund

(incorporated within 24 hours) 3. Poultry litter - autumn (incorporated within 24 hours) 4. Poultry litter – spring topdressed 5. Layer manure – autumn (incorporated within 24 hours) 6. Layer manure – spring topdressed 7. Slurry surface broadcast – autumn; pig slurry in Wensum, cattle slurry at Rosemaund

(incorporated within 24 hours) 8. Slurry trailing hose – autumn; pig slurry in Wensum, cattle slurry at Rosemaund 9. Slurry surface broadcast – spring; pig slurry in Wensum, cattle slurry at Rosemaund 10. Slurry trailing hose – spring; pig slurry in Wensum, cattle slurry at Rosemaund Manure treatments in grassland experiments (Hillsborough, North Wyke, Pwllpeiran) 1. Untreated control 2. Cattle slurry surface broadcast– autumn

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3. Cattle slurry trailing shoe – autumn 4. Cattle slurry surface broadcast autumn + DCD 5. Cattle slurry trailing shoe autumn + DCD 6. Cattle slurry surface broadcast – spring 7. Cattle slurry trailing shoe – spring 8. Cattle slurry surface broadcast spring + DCD 9. Cattle slurry trailing shoe spring +DCD 10. Cattle FYM– autumn# 11. Cattle FYM – spring# # cattle FYM treatments were not possible at Hillsborough due to restrictions on importing manure onto the farm.

Dung and urine application to grassland Application rates were based upon typical N loadings (Yamulki et al. 1998). Five individual urine patches (60cm x 60cm) were located randomly across the plot, applied at a rate equivalent to 5l/m2 using a watering can fitted with a rose attachment with an average urine-N deposition rate equivalent to 455 kg N ha-1. Prior to application a wooden frame was placed around the patches to avoid runoff, and removed once the urine had soaked into the soil. Dung was applied evenly within each chamber at a rate equivalent to 20 kg m2, with average deposition rate equivalent to 855 kg N ha-1. Artificial urine was made in the laboratory following the method outlined in Kool et al. (2006a) for recipe 2 (R2). Dung and real urine was collected from cattle < 7 days prior to the experiment start date and stored in a refrigerator at < 4 °C. The nitrification inhibitor, dicyandiamide (DCD), was applied at a rate equivalent to 10 kg DCD ha-1 to maintain consistency with other published research, and to match recommended commercial guidelines. In the urine experiments the DCD was applied as a 1% solution and mixed with the urine to ensure an even distribution over the patch. Urine and dung treatments (Crichton, Drayton, Hillsborough, North Wyke, Pwllpeiran) 1. Untreated control 2. Urine – autumn 3. Urine - spring 4. Urine – summer 5. Urine – autumn +DCD 6. Urine – spring + DCD 7. Urine – summer +DCD 8. Dung – spring 9. Dung - summer 10. Dung – autumn N2O emission measurements In compliance with IPCC guidelines, measurements were made for 12 months to determine annual EFs. Daily gas samples were taken on ten occasions over the first two weeks after N application. Sampling frequency was reduced to two days/week for the following three weeks. A fortnightly sampling strategy was then implemented for the next five months (or until the next fertiliser application when applied in split doses), and reduced to monthly sampling for the remaining six months. This sampling strategy was followed after each split fertiliser application, reverting to the start of the strategy after each application. In addition, one set of background N2O measurements was taken in the week prior to application. A closed static chamber technique was used at all sites according to the protocol published by Chadwick et al. (2014), where five chambers were inserted

19

5cm into the soil on each plot). Chambers were either circular opaque polypropylene (400 mm diameter, 300 mm height, soil surface area coverage approx. 0.126 m²), or square opaque polypropylene (400 mm x 400 mm x 400 mm, soil surface area coverage of 0.16 m2). Before and after chamber sampling, ten ambient air samples were collected, representative of N2O concentration at time zero. Lids were placed onto chambers, sealed, and left in place for 40 minutes. At the end of the 40 minute closure period a 50 ml sample of gas was extracted from each chamber using a syringe, through a valve with a three-way tap. Each gas sample was transferred to a pre-evacuated 20 ml glass vial. The order in which the blocks were sampled was randomised each day. Gas samples were analysed for N2O concentration in the ‘local’ laboratory using Gas Chromatographs (GC) fitted with an electron capture detector (ECD)*. GC response was calibrated using certified standard N2O gas mixtures. N2O flux from each chamber was calculated by measuring the increase in chamber headspace concentration, above that of the average concentration in ambient air samples at the end of the 40 minute closure period. The assumption of linear gas accumulation in the chamber is based on evidence provided in (Chadwick et al. 2014). The detection limit of the GC measurement systems used in WP2 of the project ranged from 1.4 – 2.1 g N2O-N ha-1 d-1. *all GCs were tested with ‘blind’ standard samples containing different mixed concentrations of N2O and CO2, as high CO2 concentrations have been shown to affect ECD sensitivity. All GC’s performed well, providing confidence that they measured the same N2O concentration (see Appendix 2.1). . N2O flux and EF calculation N2O flux was calculated using N2O concentration, chamber height, the ideal gas law, air temperature and chamber closure time. The mean flux for the five chambers for each plot was calculated and used to derive the mean flux and standard error (SE) for each treatment on any sampling occasion. Cumulative fluxes were calculated by interpolating the area under the curve between sampling points, and a mean cumulative flux and SE was calculated for each treatment using plot means. Emission Factors were calculated by subtracting the cumulative emission from the control treatment in each block from the cumulative emission from individual treatments in the same block, as in the IPCC methodology, displayed in Equation 1. A common data spreadsheet was used by each research group to automatically calculate the N2O fluxes in this was.

𝐸𝐹 = (𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑁2𝑂 𝑓𝑙𝑢𝑥 (𝑘𝑔 𝑁2𝑂-𝑁) − 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑁2𝑂 𝑓𝑙𝑢𝑥 𝑓𝑟𝑜𝑚 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 (𝑘𝑔 𝑁2𝑂-𝑁)

𝑁 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 (𝑘𝑔𝑁))

× 100 Equation 1.

Measurements of Soil moisture, mineral N and climate Soil moisture, soil mineral N and climate were measured throughout the experiments to assess their role in N2O emission generation. Plot sizes were large enough to allow dedicated areas for N2O chamber placement, soil sampling and yield measurements. Soils for mineral N analysis were collected weekly in the month following N applications, with frequency then reduced to one sample every four to seven weeks for the remaining period. The measurements were made on one representative bulked sample from each plot, made up of five random samples from the 0-10 cm soil layer. These samples were analysed for NH4

+-N and NO3--N by colorimetric analysis (Singh et al.

2011), using a Skalar SAN++ segmented flow analyser, after 2M KCl extraction of a sieved (<4 mm) sample, with a soil: extractant ratio of 1:2. Soil samples for gravimetric soil moisture measurement were collected on every N2O emission sampling day and consisted of five randomly located 0-10 cm samples from each block, which were then bulked to provide three soil moisture samples per day.

20

Soil bulk density was also measured to convert gravimetric to volumetric moisture content and % water filled pore space (%WFPS). Further soil samples were taken at the beginning of the experiment to measure field capacity and permanent wilting point. Meteorological stations recorded daily air temperature and precipitation for the duration of the experiment. Crop/grass yield measurements Crop and grass yields were measured from each treatment to assess the impact of N2O mitigation measures on crop/grassland production. A small-plot harvester or hand harvest was used to harvest an area of approx.15-20 m2 at the sites, and grass was cut from the yield measurement areas when it had reached the height of the chambers. The fresh yield for each patch was measured, and a representative sample of the grass was dried and weighed to allow conversion of fresh weight to dry matter yields. A subsample from each plot was milled and analysed for C/N to determine N uptake. Other measurements Nitrate leaching Potential nitrate-N and ammonium-N losses to water were measured using Prenart SuperQuartz soil water samplers from the autumn livestock manure application timings at the Wensum site only. This was the only site where porous cups would be effective and potential leaching could occur, i.e. an autumn application to a sandy soil. Five samplers were installed on each plot, and samples taken every 2 weeks or after 25mm of rainfall, whichever was the sooner. Samples of leachate were analysed for nitrate-N and ammonium-N. Effective drainage was calculated between each sampling using the Irriguide model, and multiplied by the NO3-N or NH4-N concentration to estimate total N leached. Ammonia emissions

Ammonia losses were measured from the livestock manure treated plots using small-scale windtunnels (Lockyer, 1984). Ammonia emissions were measured for up to 7 days from the slurry and FYM treatments and up to 21 days from the poultry manure treatments. Bubblers (containing 0.02M orthophosphoric acid) samples were changed at the following times after livestock manure application: 1 hour, 3 hours, 6 hours, 24 hours and then daily until the end of the measurement period, and analysed for NH4-N.

Results Synthetic fertiliser application to arable and grassland: When AN and urea applied at current recommended fertiliser application rates were compared, the highest emissions were measured from AN application at the Higher Wheaty grassland site (10.20 kg N2O-N ha-1). Lowest emissions were from urea application at the Rosemaund arable site (0.98 kg N2O-N ha-1). At Crichton (Bell et al. 2015a) and Rowden there was an increase in EFs in response to increasing fertiliser N applications. Two of the five grassland sites had much higher emissions than the three arable sites (Figure 2.3). Emissions from AN were generally greater than from urea, although this was not always statistically significant. Emission Factors measured from arable sites were generally below the 1% default value (Bell et al., 2015b), with an average N2O EF for the AN treatments of 0.52 ± 0.10. The average urea EF was 0.34 ± 0.16 (Table 2.2). DCD reduced the N2O EF from AN applied to arable land by an average of 30%. However, at the grassland sites EFs of up to 2.86 were reported at Rowden (Table 2.3). The average N2O EF for AN treatments on grassland was 1.12±0.13, whilst the average for urea was 0.52±0.12. DCD had an inconsistent effect on N2O emissions from AN applied to grassland.

21

Figure 2.3. Cumulative nitrous oxide emissions from arable and grassland sites receiving synthetic fertilisers (Arable - recommended RB209 rate, R4; Grassland - 210 kg N ha-1). (N=3). AN=ammonium nitrate. Table 2.2. Nitrous oxide Emission Factors from arable sites receiving synthetic fertilisers. (N=3). Arable sites N2O EF (%)

Gilchriston N rate (kg/ha)

EF (%)

Rosemaund N rate (kg/ha)

EF (%)

Woburn N rate (kg/ha)

EF (%)

AN1 40 0.84 60 0.41 60 0.78

AN2 80 0.28 120 0.29 120 0.29

AN3 120 1.42 180 0.2 180 0.43

AN4 160 0.98 240 0.18 240 0.22

AN5 200 1.06 300 0.13 300 0.35

Urea 120 0.65 240 0.13 180 0.24

AN3/AN4 + DCD 120 0.41 240 0.05 180 0.15

Urea + DCD 120 -0.28 240 0.11 180 0.15

AN Split 120 1.11 240 0.1 180 0.32

AN=ammonium nitrate.

Table 2.3. Nitrous oxide Emission Factors from grassland sites receiving synthetic fertilisers. (N=3). Grassland sites N2O EF (%)

N rate (kg/ha)

Crichton

Hillsborough

Drayton

Rowden

Pwllpeiran

AN1 80 1.06 0.65 1.18 2.04 0.70

AN2 160 1.14 0.59 1.03 1.29 0.61

AN3 240 1.23 0.64 0.74 1.36 0.57

AN4 320 1.34 0.44 0.87 2.86 0.77

AN5 400 1.74 0.65 0.86 2.86 0.69

Urea 320 0.91 0.31 0.24 0.62 0.52

AN3 + DCD 320 1.00 0.53 0.86 1.85 0.37

Urea + DCD 320 0.60 0.03 0.08 0.29 0.03

AN Split 320 1.60 0.44 0.71 2.47 0.84

AN=ammonium nitrate.

Manure application to arable and grassland: Highest annual N2O emissions following manure application to arable and grassland were measured from autumn application of broiler litter at the Wensum arable site (8 kg N2O-N ha-1). Lowest emissions were measured from spring applications of cattle slurry at the Boghall arable site (Bell et al. 2016). Emissions from arable and grassland sites were greater (or not significantly different) for all types of manure following autumn rather than spring application (Figure 2.4). Arable sites generally had higher annual emissions than grassland sites, partly due to the application of the highest emitting manure types of BL and LM to arable land only (Figure 2.4). Mean EFs associated with manure application to grassland and arable soils were

02468

101214

AN Urea AN Urea AN Urea AN Urea AN Urea AN Urea AN Urea AN Urea

Rosemaund Woburn Gilchriston Drayton Pwllperian Hillsborough HigherWheaty

Crichton

An

nu

al N

2O e

mis

sio

ns

(kg

N2O

-N h

a-1)

Mineral fertiliser to grassMineral fertiliser to arable

22

amongst the most variable for any N source (Table 2.4 and Table 2.5), ranging from -0.83 (Boghall spring) to 2.59% (Boghall autumn). The negative emission factors calculated in some of these experiments reflect the variability in the cumulative N2O emissions from the different treatments, including the control plots. In any derivation of the revised overall EF for manures, there will need to be consideration about whether these values are included within the statistical analysis.

Figure 2.4. Nitrous oxide emissions from arable and grassland sites receiving manures and slurries. Where: SSB=slurry surface broadcast, STH=slurry trailing hose, CFYM=cattle farmyard manure, PFYM=pig farmyard manure, BL=broiler litter, LM=layer manure. (N=3). Table 2.4. Nitrous oxide Emission Factors from grassland sites receiving manures and slurries. (N=3). N2O EF (%) Pwllpeiran

autumn Pwllpeiran Spring

Hillsborough autumn

Hillsborough spring

North Wyke - autumn

North Wyke – spring

Cattle slurry broadcast

1.06 0.35 1.31 0.19 0.39 0.25

Cattle slurry trailing shoe

0.91 0.95 0.74 0.42 -0.20 0.27

Cattle slurry broadcast + DCD

1.19 0.31 0.55 -0.01 -0.52 -0.03

Cattle slurry trailing shoe + DCD

1.44 0.37 0.35 0.20 -0.02 0.52

Cattle FYM 0.39 0.58 - - 0.61 0.13

Table 2.5. Nitrous oxide Emission Factors from arable sites receiving manures and slurries. (N=3). N2O EF (%) Wensum

autumn Wensum spring

Boghall autumn

Boghall spring

Rosemaund autumn

Rosemaund spring

Pig slurry broadcast 0.41 0.42 - - - -

Pig slurry bandspread 0.20 0.76 - - - -

Pig FYM 0.25 0.15 - - - -

Broiler litter 2.30 0.56 1.08 0.36 1.39 0.51

Layer manure 1.31 0.73 0.77 0.21 1.03 0.27

Cattle slurry broadcast - - 2.59 -0.79 -0.05 0.33

Cattle slurry bandspread

- - 2.53 -0.83 1.19 0.43

Cattle FYM - - 0.34 0.46

Urine and dung application: There were large variations in N2O emissions between sites, emission sources, and season of application (Bell et al. 2015c). Highest emissions were measured from spring applied urine at Beacon Field (13.3 kg N2O-N ha-1) and lowest emissions from autumn applied urine

-2

0

2

4

6

8

10

SSB

STH

CFY

M BL

LM SSB

STH

PFY

M BL

LM SSB

STH

CFY

M BL

LM SSB

STH

CFY

M

SSB

STH

SSB

STH

CFY

M

Rosemaund Wensum Boghall Pwllperian Hills Higher Lea

An

nu

al N

2O

em

issi

on

s(k

g N

2O

-N h

a-1)

autumn spring

Manure to grassManure to arable

23

at Hillsborough (0.31 kg N2O-N ha-1). There was no consistent trend between season of application and emissions between sites (Fig.2.5). Emissions from a typical urine N application were generally higher than from a typical dung N application, but again variation between sites and seasons was observed (Table 2.6). The average calculated EFs for dung and urine were 0.17 and 0.69, respectively. A weighted new combined excretal value of 0.5-0.6% (depending on the proportion of excreted N apportioned to dung or urine) is also much lower than the IPCC 2% value for cattle (and 1% for sheep). The average values for the urine N2O EF is similar to that report by Kelliher et al. (2014) for New Zealand for dairy cattle (in hill country, low slope) of 0.84%. The same paper reports much lower N2O EFs for dung (0-06 – 0.32%) for cattle. Recent email discussions with Richard Eckhart have also revealed similar EFs to those measured by Australian researchers.

Figure 2.5. Nitrous oxide emissions from grassland sites receiving dung and urine.

Table 2.6. Nitrous oxide Emission Factors from grassland sites receiving dung and urine N2O EF (%)

Crich

ton

sprin

g

Crich

ton

sum

mer

Crich

ton

autu

mn

Hillsb

oro

ug

h sp

ring

Hillsb

oro

ug

sum

mer

Hillsb

oro

ug

autu

mn

Ro

wd

en

sprin

g

Ro

wd

en

sum

mer

Ro

wd

en

autu

mn

Drayto

n

sprin

g

Drayto

n

sum

mer

Drayto

n

autu

mn

Pw

llpeiran

sprin

g

Pw

llpeiran

sum

mer

Urine 0.20 1.09 0.33 1.02 0.29 0.05 2.96 0.56 0.11 0.34 0.18 1.64 0.52 0.3

Artificial urine

-0.02 1.1 0.16 2.06 0.34 0.1 2.23 0.7 0.07 0.34 0.16 1.31 0.54 0.28

Urine+ DCD

0.06 1.06 0.23 0.25 0.38 0.02 1.09 0.49 0.12 0.21 0.15 1.00 0.19 0.09

Dung 0.12 0.2 0.11 0.17 0.15 0.04 0.14 0.39 0.10 0.08 0.12 0.32 0.22 0.21

Temporal trend in emissions: Emissions varied throughout the experimental periods, with the highest emissions most often observed in the first few weeks following fertiliser/manure application. The time between nitrogen application and peak N2O emissions varied between sites, with emission peaks often observed when large rainfall and an increase in soil water filled pore space followed N application (e.g. Figure2.6, fertiliser application to Crichton grassland).

02468

10121416

Urine Dung Urine Dung Urine Dung Urine Dung Urine Dung

Drayton Pwllperian Hillsborough Beacon Field Crichton

An

nu

al N

2O e

mis

sio

ns

(kg

N2O

-N h

a-1)

SprngSummerAutumn

Urine and dung application to grass

24

Figure 2.6. Temporal changes in N2O emissions, rainfall and soil wetness at Crichton following fertiliser N treatments. Use of AC0116 treatment N2O emissions and emission factors Whilst the synthesis of the AC0116 experiments by N source and land use is a useful exercise (and has generated a number of papers to date), and provides a sense of direction for the new N2O EFs that will be used in the revised inventory structure, these data have been collated with other experimental datasets that have used the same/similar methods as part of a wider synthesis by the AC0114 project team. The AC0114 team have used statistical modelling approaches to derive country specific N2O EFs from the AC0116 N2O emissions data as well as other appropriate datasets. This is especially so for the urine and dung data reported above. AC0116 experimental data availability The N2O flux data from each treatment replicate in each WP2 field experiment are being archived in the FBA’s Agricultural and Environmental Data Archive (AEDA, www.environmentdata.org), via the Defra project SCF0114, Compilation of Defra’s Nitrous Oxide Emission Data Archive. Additional metadata required to interpret the N2O emissions data and to aid with the development of any future modelling work is also being included in the archive. The metadata measurements necessary for developing models were discussed with the modelling group and were included in the joint experimental protocols. Where possible, nitrous oxide data and associated metadata from additional research projects where N2O flux measurements have been made, and that are likely to be included in statistical analyses to derive new EFs, are also being archived.

25

WP2. Calculation of emission factor EF5g for indirect emissions of nitrous

oxide in drainage waters of the Demonstration Test Catchments Kevin Hiscock

Summary A mixed sampling strategy was adopted to determine the nitrate concentrations and dissolved N2O concentrations in the three Defra Demonstration Test Catchments (DTCs), the Wensum (an arable farming dominated area with heavy clay soils, The Eden and the Hampshire Avon. The sampling frequency allowed the estimation of the indirect N2O loss to groundwater (EF5g) from the three catchments to compare with the IPCC (2006 Guidelines) default value of 0.0025%, whilst the more frequent sampling of the river Wensum and drainage water allowed us to explore the factors controlling the seasonal fluctuations in observed nitrate and dissolved N2O concentrations. In the Eden, the nitrate concentrations ranged between 2.93 – 14.26 mg N l-1, and the mean N2O concentrations were <0.001 mg N l-1, and the calculated mean emission factor (EF5g) for the Eden was 0.000202. For the Hamsphire-Avon with a geology of clays and greensand over Chalk, the dissolved N2O concentrations ranged from 0.005 to 0.013 mg N l-1, and the nitrate concentrations ranged from 1.27 – 8.00 mg l-1, with a resulting mean emission factor (EF5g) for the Hampshire Avon samples of 0.00270 (similar to the IPCC default). For the Wensum, the range of the calculated EF5g was 0.0001 – 0.0004. Seasonal variations in N2O concentrations were evident in all three DTC catchments and further detailed sampling of field drains in the Blackwater sub-catchment of the Wensum showed how N2O concentrations measured at 12 drain locations experienced a gradual decrease from 0.008 mg N l-1 in Spring, to less than 0.001 mg N-1 in the late summer. Introduction Of the emissions of N2O from agricultural land, about 80% is direct emissions arising from fertiliser and manure applications of nitrogen, with the remainder occurring as indirect emissions from agricultural drainage waters. However, this 20% of indirect N2O emissions accounts for two-thirds of the uncertainty in current estimates of agricultural emissions. To improve estimates of indirect emissions, this research used the opportunity in the Defra Demonstration Test Catchments (DTCs) Project in the Eden, Hampshire Avon and Wensum to collect samples of stream and field drainage waters for the analysis of dissolved N2O and nitrate concentrations. To calculate emission factors for N2O from agriculture, ideally the input of N into the system (FNin) should be known, as well as the flux of N2O out of the system (FN2Oout), so that an emission factor (EF) can be calculated according to: EFA = FN2Oout/FNin. EFA is likely to yield more realistic estimates, but is more data intensive to compute. Hence, in common with the IPCC (2006), the catchment data collected in this study do not include a value for FNin and so the following equation is used instead: EFB = cN2Oaq/cNO3aq, where cN2Oaq and cNO3aq are the dissolved concentrations of N2O and nitrate, respectively. In the IPCC (2006) methodology, the indirect emission factor, EF5, was revised from 0.025 to 0.0075 kg N2O-N/kg N leached in runoff and incorporates the following components: EF5g (groundwater, equal to 0.0025); EF5r (surface drainage and rivers, equal to 0.0025); and EF5e (estuaries, equal to 0.0025). Hence, the aim of this study was to compare indirect emission factors calculated from measured N2O and nitrate concentrations with the IPCC (2006) value for possible application to drainage waters in England. Methodology In the Wensum catchment, the sampling programme was based on 26 sampling sites visited monthly for the period February 2011 to June 2013. Samples were collected from surface waters using glass syringes and returned to the laboratory for analysis by gas chromatography. Sampling effort in the Eden and Hampshire Avon DTCs was less intensive and was carried out quarterly during the hydrological year 2012-13, at 21 and 12 sampling locations, respectively.

26

Results The results from the Wensum are presented in Figure 2.7 and show two clusters. Sites in the upper catchment where chalk outcrop dominates have higher N2O concentrations of less than 0.005 mg N l-1 in contrast to sites in the middle and lower catchment where runoff from glacial deposits predominates and N2O concentrations are of the order of 0.001 mg N l-1. The leaching of nitrate to groundwater in the Chalk aquifer, with concentrations of dissolved gas maintained by higher water pressures below the water table, is in contrast to more rapid runoff of drainage from the lower permeability glacial deposits. These glacial deposits comprise silts and clays and subsurface flow is relatively shallow, with drainage often controlled by land drains. The land drains allow rapid runoff of recent rainfall to stream courses where N2O can be lost to the atmosphere. As shown in Figure 2.8, these two contrasting runoff mechanisms result in emission factors (EF5g) that range from 0.00013 at site W01 (Wendling Beck, a clay sub-catchment west of Dereham) to 0.00037 at site W20 (South Raynham on the upper Wensum, a Chalk sub-catchment).

Figure 2.7. Regression of N2O against nitrate for all sites in the Wensum catchment for the period February 2011 to June 2013.

Figure 2.8. Example emission factors (EF5g) for the Wensum catchment for the hydrological year October 2011 to September 2012. Sites W01, W15 and W12 are sites on glacial deposits and sites W05 and W20 are sites on Chalk.

27

Sampling effort in the Eden and Hampshire Avon DTCs was less intensive and was carried out quarterly during the hydrological year 2012-13, at 21 and 12 sampling locations, respectively. The Eden catchment is dominated by livestock and dairy farming and the underlying geology at the sampling sites includes glacial till over Carboniferous limestone, Borrowdale Volcanics and various sandstones, siltstones and mudstones. As shown in Table 2.7, this combination of organic nitrogen inputs from manure spreading and the relatively rapid runoff of rainfall in low permeability catchments results in mean N2O concentrations of less than 0.001 mg N l-1, comparable to the concentrations recorded in runoff from clay-dominated sub-catchments in the Wensum. Given the associated range in nitrate concentrations of 2.93 – 14.26 mg N l-1, the calculated mean emission factor (EF5g) for the Eden samples is 0.000202. In contrast to the Eden, the Hampshire Avon is dominated by mixed livestock farming and has an underlying geology of clays and greensand over Chalk. The importance of Chalk groundwater in this catchment is noticeable in terms of the measured mean N2O concentrations, which range from 0.005 to 0.013 mg N l-1 (Table 2.7). The measured nitrate concentrations range from 1.27 – 8.00 mg l-1 and the resulting mean emission factor (EF5g) for the Hampshire Avon samples is 0.00270. Table 2.7. Summary of nitrate, dissolved N2O and calculated emission factors (EF5g) for the Eden and Hampshire Avon catchments. Catchment Sampling date Nitrate

(mg N ml-1) Mean N2O (mg N l-1)

EF5g mean EF5g min EF5g max

Eden Mar 2012 0.00055

June 2012 0.00099

Oct 2012 0.00063

Mar 2013 0.00075

2.93−14.26

0.000202 0.000057 0.000496

Hampshire Avon Feb 2012 0.00506

June 2012 0.01143

Oct 2012 0.01313

Mar 2013 0.01150

1.27−8.00

0.00270 0.00011 0.01369

Seasonal variations in N2O concentrations are evident in all three DTC catchments and this was further investigated by a field-scale investigation in the Blackwater sub-catchment of the Wensum in which weekly field drain sampling was undertaken. As shown in Figure 2.9, N2O concentrations measured at 12 drain locations experienced a gradual decrease from 0.008 mg N l-1 in March 2013 to less than 0.001 mg N l-1 in the late summer 2013. In the spring and early summer, when field conditions were still wet and the field drains were flowing freely, the effect of periods of rainfall is to flush N2O from the soil profile leading to peaks in concentration. These peaks are noticeable on a cross-plot of N2O versus nitrate (Figure 2.10) that otherwise shows a positive trend of N2O increasing with nitrate concentrations. Based on the dataset of 98 samples collected from mid-March to the end of August 2013, the calculated emission factor (EF5g) for the field drains varied between 0.00016 and 0.006, with a mean value of 0.0012.

28

Figure 2.9. Temporal variation of main rainfall events (upper diagram) and dissolved N2O concentrations in water from land drain outfalls (lower diagram) in the Blackwater sub-catchment of the Wensum. Notice the peaks in N2O concentration associated with the three rain events 1 to 3.

Figure 2.10. Relationship between dissolved N2O and nitrate in land drain outfalls collected from mid-March to end of August 2013 (n = 98) in the Blackwater sub-catchment of the Wensum. In summary, the three catchments surveyed as part of this study demonstrated a range of measured

N2O and nitrate concentrations that reflected three factors: the predominant land use; the physical

characteristics controlling runoff; and the seasonality and timing of rainfall periods. Table 2.8

summarises the mean emission factor values obtained using the datasets and, in comparison with

the IPCC (2006) default value of 0.0025, the calculated EF5g values are typically lower by a factor of

0

5

10

15

20

25

0 2 4 6 8 10 12 14

Nit

rou

s o

xid

e (µ

g N

L-1

)

Nitrate (mg N L-1)

0

10

20

30

40

50

60

Wee

kly

rain

fall

(mm

)

2

3

4

0

1

2

3

4

5

6

7

8

9

Nit

rou

s o

xid

e (µ

g N

L-1

)

1

29

0.5 or by an order of magnitude. Only the Hampshire Avon with an emission factor of 0.0027 is

equivalent to the IPCC value. The observed wide variability in EF5g values between sites is likely

driven by site-specific geological conditions such that emission factors should be calculated

separately, highlighting the need to distinguish between different hydrological environments. The

results, however, do indicate that the IPCC (2006) default value for EF5g may still overestimate

indirect N2O emissions globally.

Table 2.8. Summary of mean indirect N2O emission factors (EF5g) determined for the Eden, Hampshire Avon and Wensum catchments.

Catchment Emission factor, EF5g

Eden 0.0002

Hampshire Avon 0.0027

Wensum 0.0001 – 0.0004

Blackwater (Wensum) 0.0025

30

WP2. Effects of soil properties and temperature on the efficacy of

dicyandiamide. Catherine Watson, Karen McGeough, Ronnie Laughlin

Methodology A laboratory incubation study was conducted using soil from the nine UK Platform sites (five grassland soils and four arable soils) to determine the effect of soil physical and chemical properties and temperature on the efficacy of DCD as a nitrification inhibitor. An N rate of 100 µg N g-1 oven-dry soil was applied as ammonium chloride with and without DCD and the soils were incubated at 60% water-filled-pore-space and at three temperatures (5, 15, 25˚C). The NH4

+ pool was enriched with 15N to 60 atom %. DCD was applied at a rate equivalent to 10% of the NH4-N concentration. Soil was extracted with 2M KCl on 8 occasions over a period of 60 days post application, where the concentration and enrichment of the NH4

+ and NO3- pools was determined. Gross soil N

transformations were quantified with a 15N tracing model. Nitrous oxide measurements were made on the jars incubated at 15˚C at 0, 6, 12, 24, 36, 48 hours and subsequently at days 5, 10, 15, 20, 30, 40 and 60.

Results A full account of the experiment can be found in McGeough et al. (2016). Here we provide a summary of the results. The persistence of DCD was strongly related (P<0.001) to temperature with the measured half-life across all soils decreasing with increasing temperature: 89.1, 37.3 and 18.4 days at 5, 15, and 25˚C respectively, demonstrating a 79.3% reduction in the longevity of DCD between 5 and 25˚C. There was wide variation in the half-life of DCD between soils: at 5˚C the half-life ranged from 33.6 to 254 days, at 15˚C from 13.8 to 119 days, and at 25˚C from 6.8 to 63.1 days.

Oxidation of the soil ammonium pool was the dominant gross N transformation process in all soils. DCD significantly decreased the rate of ammonium oxidation (ONH4) (P < 0.001) by an average of 57.1%. At each temperature and for each soil the application of DCD significantly decreased the rate of ONH4, with an average % inhibition of 67.6, 69.6 and 47.5% at 5, 10, and 25˚C respectively. The % inhibition was significantly greater (P<0.001) at both 5ºC and 15ºC compared to 25ºC. Similarly, DCD was highly effective in lowering net nitrate production on all soils at all temperatures. It was established that at 5ºC sufficient DCD was present over the duration of the study to inhibit nitrification in all soils, but the range in % inhibition of ONH4 was still large (35.1 to 85.1 %), indicating that soil properties were affecting the efficacy of DCD as a nitrification inhibitor.

The DCD first-order rate constant was not correlated with the % inhibition in net nitrate production confirming that the difference in the efficacy between soils was not related to the amount of DCD present (at 5 and 15ºC). Furthermore there was no correlation between the rate of net nitrate production and the % inhibition of net nitrate production by DCD, indicating that it was an effective nitrification inhibitor over a range of nitrification rates. The efficacy of DCD in inhibiting net nitrate production was most highly correlated (P<0.001) with soil Cu (r=-0.82) and % clay (r=-0.71). However, there were also significant (P<0.01) negative correlations with total N (r=-0.66) and loss on ignition (LOI) (r=-0.61). Many of the soil properties were highly correlated with each other. For example, Cu was correlated with % clay content (r=0.75, P<0.05) and total N was strongly correlated (P<0.001) with LOI (r=0.98), total C (r=0.96), total organic C (r=0.95) and cation exchange capacity (CEC) (r= 0.92), which are indicators of soil organic matter content. The grassland soils were characterised as having significantly higher (P<0.01) total N concentrations than the arable soils (0.49 vs. 0.13 %). Stepwise multiple regression showed that three soil properties (Cu, oxalate extractable Fe and oxalate extractable Al) contributed significantly to explaining 85.0% of the variation in % inhibition of net nitrate production by DCD. The % inhibition in cumulative N2O

31

emissions was significantly (P<0.001) negatively correlated with CEC (r= -0.72), LOI (r= -0.69), total N (r= -0.68), total C (r= -0.63) and % clay (r= -0.60). CEC alone explained 49.8% of the variation (P<0.001) and adding other soil properties in stepwise multiple regression analysis did not significantly improve the % variation.

The current study showed that DCD was an effective inhibitor of ammonium oxidation, net nitrate production and N2O emissions. However, its efficacy was adversely affected by high temperatures and soils with high clay and high organic matter content. Hence the % inhibition in net nitrate production by DCD was significantly (P<0.001) lower in grassland soils than in arable soils and resulted in an overall % inhibition in N2O emissions of 57.6% and 81.2%, respectively (Figure 2.11).

Figure 2.11. % Inhibition in rate of net nitrate production by DCD averaged across all temperatures

(error bars are 1 standard error) (A = arable, G = grassland)

Conclusions from WP2 The project has resulted in a step change in our understanding of the interactions between management and climate on nitrous oxide emissions from UK agricultural soils. The full synthesis of emission factor data from the AC0116 project and other related project was undertaken by ACO114, however this section of work has been pivotal in contributing to this wider synthesis and some clear conclusions can be drawn. The biggest difference between observations and current emission factors used in the UK’s inventory related to grazing returns from livestock. Results from the AC0116 experiment have shown that the average emission factor associated with urine deposition by cattle was 0.69%. For dung the value was 0.15%. A combined excretal N2O EF would be ca. 0.55% (depending on the relative proportion of total N excreted that is attributed to the urine and dung), which represents a significantly lower value than the default inventory emission factor of 2% for excretal N deposited during grazing (i.e. pasture, range and paddock). The application of synthetic fertiliser-N to grasslands was associated with a wide range of EFs (0.44-2.86%), a variability related to site and climatic factors. However, the average EF for across different

0

20

40

60

80

100

% In

hib

itio

n in

rat

e o

fn

et

NO

3p

rod

uct

ion

32

sites in different years was 1.12% (± 0.13%) for AN, and 0.52% (±0.12%) for urea. There were lower EFs reported where DCD was added to fertilisers, which was particularly significant for urea with DCD where EFs as low as 0.03% were reported. The observed EFs associated with synthetic fertilisers used on arable crops (average of 0.52%) were significantly lower EFs observed than the Tier 1 default value of 1% used in the UK Inventory (Hinton et al. 2015) . These lower EFs associated with arable crops are consistent with a larger set of data collected in the DEFRA MinNO project which reported average EFs for arable crops of <0.5% (Thorman et al,. 2013). The application of manure to arable crops resulted in a wide range of EFs (0.15-2.73), which was a consequence of high level of variability associated with the data. However, higher emission factors were generally associated with autumn manure applications. The results of the experimental work programme provide strong evidence for the effectiveness of different mitigation options. DCD proved to be a powerful inhibitor of N2O emissions. When applied with fertilisers used on grassland sites the average reduction in the N2O EF was 27%. On arable sites the average reduction in the EF was even higher at 58%. This pattern of greater emissions reduction in the arable soils was also demonstrated in the laboratory incubation. The application of DCD with urine also resulted in a large 48% reduction in the N2O EF, but application with slurries did not result in such clear overall effects. In most cases N2O emissions from urea were lower than those from AN, although not all experiments included measurements on NH3 volatilisation, which is associated with indirect N2O emissions. The splitting of fertiliser applications into a greater number of smaller applications did not result in any consistent mitigation.

33

WP3. Proxies John Williams*, Andy Whitmore, Gordon Dailey, et al.

*WP leader

Summary Proxy measurements are useful to assess impacts of changes in agricultural practices and soil and climatic conditions on both direct and indirect nitrous oxide emissions. This workpackage identified Proxies at different scales. Field-scale: soil nitrate, soil moisture, clay content and annual rainfall are potential proxies for direct N2O emission. A simple, widely useable model to predict direct N2O emissions following manufactured nitrogen fertiliser was developed using proxy measurements of soil water filled pore space and soil nitrate. Farm-scale: Proxies that describe and encourage management strategies methods for improving nitrogen use efficiency (e.g. fertiliser recommendation systems, nutrient management planning, investment in farm infrastructure to improve manure N use efficiency) are important to inform progress with GHG reductions. Regional-scale: manufactured fertiliser nitrogen use and livestock numbers are useful primary proxies for indicating changes in both direct and indirect N2O emissions at the national and regional (and farm scale). Introduction The overall objective of this work package was to identify proxy measurements which can be useful (to both policy makers and modellers) to assess the impact of changes in agricultural practices and soil and climatic conditions on nitrous oxide (N2O) emissions at a national, regional, farm and field level. Proxies are parameters that help explain and predict nitrous oxide emissions. They can be useful to assess the impact of changes in agricultural practices, as well as soil and climatic conditions on direct and indirect N2O emissions. Direct N2O emissions from soils are mainly produced from the microbially mediated processes of nitrification (Bremner and Blackmer, 1978) and denitrification (Firestone and Davidson, 1989). Indirect N2O emissions also occur following deposition of emitted ammonia (e.g. following applications of livestock manures or urea based fertilisers) or following the denitrification of leached nitrate-N. Factors that influence the rate of nitrification and denitrification are likely to be good proxies for understanding the effect that changes in farm practice are likely to have on nitrous oxide emissions from agricultural systems. Important factors controlling the production of nitrous oxide from soils; the magnitude of nitrogen inputs (principally as manufactured fertiliser N, livestock manure additions and urine deposition), soil moisture/soil aeration status and soil temperature. Indirect nitrous emissions will be influenced by factors controlling ammonia volatilisation (e.g. the numbers of animals in grazed and fully housed management systems, delay between application and soil incorporation for organic material applications, application method for livestock slurries quantity of urea fertiliser used) and nitrate leaching losses (e.g. N inputs in fertilisers and organic manures, fertiliser/manure application timings, fate of crop residues etc.) Methodology Identification of appropriate proxies for predicting nitrous oxide emissions A meeting was held at ADAS Drayton in August 2012 with representatives from AC0114 and AC0116 project teams to identify the most appropriate proxies that could explain and describe changes in the amounts and patterns of N2O emissions at a range of scales, viz. field, farm and national/regional scales. The following proxies were identified as having most potential.

34

1. National regional and farm scale:

Manufactured fertiliser N use, and form of N fertiliser.

Livestock numbers, including N feed inputs and associated manure N production.

Timing of manufactured fertiliser N applications.

Timing of manure N applications.

N use efficiency per unit of output e.g. N per litre milk, tonne of grain, kg of meat etc.

Manure type - high or low readily available N content.

Climate – temperature (grass growing days) and rainfall amount / timing.

Farm-gate balances using PLANET methodology; www.planet4farmers.co.uk .

N use efficiency per unit of output e.g. N per litre/milk, tonne of grain, kg meat etc.

Manure type - high or low readily available N content.

Climate – temperature and rainfall.

Uptake of low ammonia emission application technologies for slurries, and associated N use efficiency.

Uptake of nitrification inhibitor use.

Soil physical factors influencing wetness.

2. Field scale:

Soil wetness (e.g. water filled pore space), related to soil type - pore size distribution infiltration rate, hydrological classification etc.

Soil mineral N content.

Crop type, crop yields and crop N offtake.

Soil structure and soil bulk density.

Soil management i.e. cultivation strategies / compaction by machinery and animals. It was suggested that ‘good’ proxies were:

Cheap/easy to measure

Universally available

Sector specific

Had defined boundaries and not a replacement for life cycle analysis

Based on a strong scientific evidence base

Reflected changes in direct and indirect emissions

Able to describe for production intensities Overall, Proxies were considered to be most useful as indicators of change, rather than for quantitative assessment of effects. Proxies at national, regional and farm scale It was identified that proxies largely fell into two categories: primary and secondary: Primary proxies Primary proxies are quantitative and can be used as a measure of production efficiency at farm, regional and national scales. The most robust primary proxies clearly describe the amount of nitrogen entering and leaving the agricultural system and include the amount of manufactured fertiliser N use, livestock numbers, the amount and type of animal feed use and crop/meat/milk yields. At the national and regional scale data on manufactured fertiliser N use are readily available via estimates of crop areas from the Devolved Administrations of the UK (i.e. England, Wales, Scotland

35

and Northern Ireland), and N application rates from the British Survey of Fertiliser Practice (plus country-specific data for Northern Ireland provided by DARDNI - Department of Agriculture and Rural Development, Northern Ireland). Livestock population data are reported annually as statistical outputs of the four Devolved Administrations and are based on the annual June Agricultural Survey for each country. An assessment of the impact of reductions in nitrogen fertiliser use and livestock numbers on national nitrous oxide emissions was carried out based on the 2012 UK agricultural GHG Inventory are shown in Figure 3.1. A 10% reduction in manufactured fertiliser N use was predicted to reduce direct nitrous oxide emissions by 4.5% compared with the 2012 baseline. A 10% reduction in livestock numbers was predicted to reduce nitrous oxide emissions by 6.5%. Reductions in livestock numbers and in the fertiliser N application use would also be reflected in reduced NH3 volatilisation losses and nitrate leaching losses which would lead to reductions in indirect nitrous oxide emissions.

Figure 3.1. The effect of a theoretical 10% reduction in animal numbers and in the manufactured nitrogen fertiliser application rate in the UK agricultural greenhouse gas inventory. Recent evidence from the field experiments carried out in in Defra project AC0213 and in work package 2 of project AC0116, suggests that nitrification inhibitors can be effective at reducing nitrous oxide emissions from fertiliser, manure applications and excretal returns. Knowledge of the quantity and areas of NI use would be a useful Proxie to estimate reductions in N2O emissions. Secondary proxies Secondary proxies are qualitative and reflect changes in practice that are likely to increase nitrogen use efficiency by minimising excessive manufactured fertiliser and manure applications and minimising nitrogen losses via ammonia volatilisation to air and nitrate leaching losses to the environment. It is important to demonstrate that secondary proxies reflect changes in practice that lead to reductions in nitrogen inputs without reducing product yields. Examples of secondary proxies include: (i) The use of fertiliser recommendation systems to match fertiliser/manure N use to crop demand. e.g. Defra’s Fertiliser Manual (RB209) and PLANET nutrient management programme AHDB Topic sheets and SRUC Technical notes. The MANNER-NPK decision support software (Nicholson et al., 2013) provides information on organic manure nutrient supply based on manure type, application timing, weather conditions and for slurries application method.

36

(ii) Soil management plans. Good soil management is essential to ensure good soil structure that allows roots to take up water and applied nutrients. Compacted soils restrict root growth, limit water and nutrient uptake, which reduce yield potentials and nitrogen use efficiency which will increase the risks of both direct and indirect nitrous oxide emissions. (iii) Nutrient management plans Nutrient management planning which includes routine soil analysis and to ensure adequate soil pH and P, K, Mg and S supply are vital to for optimum crop yields that maximise nitrogen use efficiency . For example, Defra’s Fertiliser Manual suggests target Olsen extractable soil P levels of between 16-25 mg l-1 P for arable, forage crops and grassland and 26-45 mg l-1 P for vegetables. Similarly, target ammonium nitrate extractable K levels for arable forage crops and grassland are 121-180 mg l-1 K and 181-240 mg l-1 K for vegetables. Historically, deposition of sulphur dioxide emitted from power stations and industry have supplied sufficient sulphur for optimal crop growth. A recent Defra funded review (Webb, 2012) reported that sulphur deposition had reduced by 94% between 1970 and 2010 and currently most areas in the UK receive less than 15 kg ha-1 SO3 from the atmosphere. Recent AHDB, Industry and water industry funded research has highlighted the need for sulphur fertiliser applications to ensure optimum yields. Experiments carried out on oilseed rape crops on light sandy soils in Suffolk and Hertfordshire showed yield responses of c. 4 t ha-1 where sulphur fertiliser was applied to crops receiving 190 kg ha-1 fertiliser N. Such results clearly demonstrate the need to ensure that all plant nutrients are adequately supplied to maximise N use efficiency and minimise the risks of losses to the environment. Improved slurry spreading timings and technologies. Improving N use efficiency from organic materials will reduce the need for manufactured fertiliser N applications to meet crop demand. Improvements in farm infrastructure are usually required to increase N use efficiency and indicators such as the volume of slurry storage capacity on-farm (to ensure that slurries are applied at times when nutrients are most likely to be taken up by crops) and the quantity of slurry spread using precision application technologies are useful proxies for identifying improvements in manure N use efficiency. A large body of evidence which demonstrates the effect of slurry application timing on nitrate leaching losses (Beckwith et al., 1998, Chambers et al., 2000). On arable soils, nitrate leaching losses following September, October and November applications are typically in the range 10-20% of total N applied, whilst N losses following applications in December or January are typically not significantly elevated above background levels. Data from the WRAP/Defra/Wrap Cymru/Zero Waste Scotland funded DC-Agri (Nicholson et al. 2016) showed that precision techniques were effective at reducing ammonia emissions from slurry and digestate applications compared with surface broadcasting. Precision application techniques also have the advantage of spreading slurry evenly across known bout widths and reduce crop contamination compared with surface broadcast applications. Proxies at the field scale Identifying proxies for nitrous oxide emissions at the field scale is complicated by the complex interactions between factors that control nitrification and denitrification. These factors include soil mineral nitrogen content, soil moisture status, soil texture, temperature, climate conditions before and following application of the nitrogen source, soil organic matter content, previous crop residues etc.

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Work carried out as part of the Link funded MIN-NO project (Sylvester-Bradley et al., 2015) used data from 24 experiments on arable land growing either feed or bread making wheat varieties, spring and winter barley, winter oilseed rape, or sugar beet to produce a statistical model to predict direct N2O emissions. ‘The MIN-NO model’ related the natural logarithm of observed total annual direct soil N2O emissions to the manufactured fertiliser N applied, annual rainfall and soil clay content. Figure 3.2 shows the relationship between measured and predicted annual emissions of direct N2O and the model which provided the best relationship was: ln(𝑁2𝑂+710)=5.567(±0.3490)+0.002402(±0.0001062)∗𝑁+0.002349(±0.0005046)∗𝑅𝑎𝑖𝑛+ 0.02344(±0.009793 )∗𝐶𝑙𝑎𝑦% −0.003096 (±0.0014161 )∗𝑅𝑎𝑖𝑛∗𝐶𝑙𝑎𝑦%/100 [Equation 2] where,

N2O is the cumulative annual N2O-N emission (g ha-1)

710 is added to avoid any negative values, N is the rate (kg ha-1) at which fertiliser N is applied

Rain is the annual rainfall (mm, January to December)

Clay% is the percentage of clay in the soil. It was found that soil organic carbon and pH did not add to model performance, so these were excluded.

Figure 3.2. Comparison of measured N2O emissions (g N2O-N ha-1 year-1) with emissions predicted by ‘the MIN-NO model’ using N application rate, annual rainfall and soil clay content. Further work carried out as part of this work package investigated a simple model of N2O emissions based on measurements carried out in the work package 2 experiments. The model, which was applied to the manufactured N fertiliser experiments, functions with measurements or estimates of WFPS (Vw) and soil nitrate (NO3) only, both of which are known to be controlling variables of direct N2O emission ( Firestone et al., 1980; Dobbie and Smith, 2003). The simple function that we used to estimate N2O emissions is as follows: f = Vw -Wc if Vw -Wc >0; 0 otherwise [Equation 2] N2O = NO3 * f * K Where,

Vw: volumetric water content divided by the total pore space

6

7

8

9

10

6 7 8 9 10

Mea

sure

d E

mis

sion

s ln

(N2O

+710)

g/h

a

Predicted Emissions ln(N2O+710 g/ha)

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NO3: soil nitrate N in the top 10 cm of the soil; kg N ha-1

N2O: nitrous oxide N emission; g N ha-1

Wc: fitted site-dependent threshold value of the soil water content. When wetter than Wc, denitrification can occur, and increases linearly with increased water as determined by Equation 2

K: fitted rate parameter common to all sites The model was run for the eight experimental sites that received manufactured fertiliser N. this required some additional parameters to be measured, namely the soil water content at field capacity and permanent silting point. These data are provided in Table 3.1, alongside other soil physical characteristics of the sites. Table 3.1. The sites used with the model, and the measured soil properties.

Site number

Site

Cropping

BD (g cm3)

Clay (%)

Silt (%)

Sand (%)

FC

WP

1 Woburn Arable 1.60 11 23 66 27.9 9.2

2 Rosemaund Arable 1.35 21 52 27 33 15.0

3 Gilchriston Arable 1.51 12.7 21.4 65.9 31.2 8.0

4 Crichton Grass 1.12 15 30 56 50.1 12.1

5 Hillsborough Grass 0.93 25.7 30.3 44 61.8 21.5

6 Drayton Grass 0.86 56.5 21.5 22 61.2 29.7

7 Pwllpeiran Grass 0.95 31 40.3 28.7 55.5 31.1

8 Higher Wheaty Grass 0.72 41 46 13 55 20.0

BD: soil dry bulk density; Clay, Silt, Sand: percent by weight in the mineral fraction of sieved soil, of clay, silt and sand respectively; FC, WP: field capacity and wilting point; the % volumetric water content at 0.05 and 15 bars tension, respectively.

The simulation using only days on which N2O was measured gave the better fit of model to the measurements (Figure 3.3), but the simulation of all days provided an estimate of the total N loss during the period of the experiment. Conclusions

Manufactured fertiliser nitrogen use and livestock numbers are useful primary proxies for indicating changes in both direct and indirect N2O emissions at the national, regional and farm scale and are currently the only proxies that will be reflected in the UK agricultural GHG inventory.

Proxies that describe and encourage management strategies methods for improving nitrogen use efficiency (e.g. fertiliser recommendation systems, nutrient management planning, investment in farm infrastructure to improve manure N use efficiency) are important to inform progress with GHG reductions. The use of nitrification inhibitors is a valuable proxy to demonstrate mitigation of direct N2O emissions.

Soil nitrate, soil moisture and clay content, as well as annual rainfall are potential proxies for direct N2O emission at the field scale.

A simple, widely-usable model to predict direct N2O emissions following the application of manufactured nitrogen fertiliser and based on proxy measurements of soil water filled pore space and soil nitrate was developed for use at the field scale.

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Figure 3.3. Simulated versus observed N2O emission totals, g ha-1. For 8 sites. Blue: control (no fertiliser N). Red: fertiliser N treatments. Dotted line: perfect fit. Solid line and regression stats: trend on all treatments. a) N2O summed for days of N2O measurement only. b) N2O integrated over whole period of measurement.

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WP4. Modelling

Nuala Fitton, Arindam Datta, Matthias Kuhnert, Vasilis Myrgiotis, Kairsty Topp and Pete Smith*

*WP leader

Summary

The work undertaken in WP4 has shown that biogeochemical models such as DailyDayCent (DDC) and Landscape DNDC (L-DNDC) models can provide a reasonable estimation of daily and annual N2O emissions. This however varies across the different cropland and grassland experimental sites. While common causes of uncertainty in our estimates were a) data limitation and b) model processes/uncertainty, overall, our results indicate that the models are particularly effective at replicating the spatial variation in N2O fluxes in response to changes in soil inputs and changes in emissions due the application of different N sources and at different points during the year. Models are less effective at capturing the pattern of daily emissions, particularly from grasslands, and when the chemical composition of N sources is unknown. This is confounded by the remaining uncertainty over the uptake of N from the atmosphere. Future needs include a continuation of the site level comparisons, especially as models are updated and improved. In addition, the spatial framework that has been developed for DDC can provide a basis for Tier 3 EF derivation, but is currently limited by lack of reliable spatial land management activity data.

Introduction and Aims

The magnitude of N2O emissions is dependent on a number of factors including the application of N, soil texture, available mineral N, temperature and precipitation, and land use type. Due to the temporal and spatial variation in N2O emissions, process based models such as DailyDayCent (DDC) and Landscape DNDC (L-DNDC), have been developed to help improve our understanding of the underlying soil and crop processes that drive emissions. In addition they can help quantify GHG emissions from different ecosystems and climates. Prior to use in this manner, an assessment of their ability to accurately predict emissions must be undertaken. This must then be combined with an assessment of how site inputs influence processes within the biogeochemical models, and hence how they alter modelled projections of annual emissions or crop yields. Therefore the initial aim of WP4 was to provide a site level analysis of both the DDC and L – DNDC models against nine experimental sites identified within the prioritisation phase. Once completed, both a sensitivity and uncertainty analysis were carried out for both models separately. This enables important gaps to be identified in the input information used to drive model simulations, and importantly also allows limitations of the processes within each model to be identified. From this, modelled outputs of annual N2O emissions were compared to the AC0116 experimental sites funded directly under the InveN2Ory project. These sites provide us with a more robust estimate of the accuracy of models, as measured N2O emissions provided are (a) from a range of treatments that test both the timing of N application but also different N sources, (b) the same management protocol was followed in each site and (c) each site was located in a different climatic zone. Therefore by comparing modelled estimates with measured values an assessment of how DDC interprets annual emissions temporal, spatial and from different N sources can be made. Finally, to provide an assessment of temporal and spatial emissions across the United Kingdom (UK), simulations of annual N2O emissions for the period 2001 to 2010 were run for three land use types: Cropland, Grasslands and Semi-natural land.

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Modelling of historical and AC0116 sites

Methods LDNDC and DDC were used to simulate N2O emissions at a range of arable and three grassland sites to which synthetic and organic fertilisers were applied. Both models were calibrated independently and according to best practice for each model framework, and daily fluxes were then plotted against the simulations at each site to test model performance for both DDC and L-DNDC.

The prediction of N2O fluxes by LDNDC at the 7 experimental sites (i.e. 4 arable and 3 grassland) was found to be particularly sensitive to 8 biochemical parameters. This was identified through the performance of sensitivity analysis (using outputs for a subset of these sites) and information found in the relevant literature. These 8 parameters were calibrated using the measured N2O data from the 7 experimental sites. The calibration process resulted at a parameter vector was able to reduce the RMSE between measured and simulated data points across all 7 sites. The 8 parameters that were calibrated can be split into 2 groups: (1) 5 parameters affecting the efficiency of microbial activities that are directly responsible for the production N2O and NO2 through nitrification and denitrification; and (2) 3 parameters related to the decomposition of the microbial C pool of the simulated soil (i.e. the dynamics of the microbial population).

Crop growth-related parameters were calibrated using a subset of a dataset of yield measurements from 22 arable sites (including the 4 experimental sites considered in this report). The model’s crop yield prediction, when using the calibrated parameters, was evaluated against a different/independent subset of the aforementioned 22 arable sites. Grass growth-related parameters were altered so that simulated grass yields match those measured at the 4 grassland sites examined. In other words, the calibration of crop growth parameters was global while that of grass growth parameters was local (specific to the examined grassland sites).

Details on the calibration methodology adopted for the DailyDayCent model are detailed in Appendix 4.1 Section 2.1, however in general we adopted a generic approach to model calibration was used for both the historical and AC0116 sites. Across all sites and their experimental plots, the same soil carbon (C) calibration, equilibrium establishment, crop growth and model process were applied. In addition only site level inputs parameters i.e. soil, climate and land management were to be changed between the sites and for experimental sites testing the implications of N timings only management schedules were altered. A site level sensitivity analysis for the historical sites was also undertaken using the DDC model. This was adopted to test the influence that the model inputs, and their uncertainty at a site level, available at all of the historical sites have on the nitrous oxide emissions.

Key Results Historical sites: L-DNDC and DailyDayCent A comparison between measured and simulated daily N2O emissions is presented in Table 4.1. A detailed comparison of both daily and annual estimates between DDC-simulated, and measured outputs are detailed in Appendix 4.1. The low R2 across all sites using LDNDC shows that the trends in the measured data were simulated poorly, and that DDC performed better in this respect. The average R2 values for both models across all sites were below 0.8.

The RMSE values (g N/ha) represent total error, the total difference in the simulated and measured daily N2O values, and using both models are similar across all nine sites. Across all sites, the RMSE and E (show bias; under- or over-prediction) are well below the corresponding values at the 95% confidence limits, showing that both total error and bias are not significant, and thus both models are performing within the accuracy of the measurement error. Soil moisture and soil temperature were simulated accurately at all sites using LDNDC (Figure 4.1). Annual emissions calculated by DDC

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tended to fall within the range of measured values. However there are a number of model and site input limitations that cause uncertainty in modelled outputs.

AC0116 sites: DailyDayCent Detailed daily and annual comparisons between modelled and measured values are detailed in Appendix 4.3. Modelled and measured annual emissions for the grassland experimental sites with a) Dung and Urine, b) Fertiliser and c) Manure are detailed in Appendix 4.3 (Tables 5.1, 5.2, 5.3 respectively). For grassland sites, where dung and urine was applied, the accuracy of DDC to predict daily fluxes was mixed, both within sites and between sites. When annual emissions were compared, modelled outputs were of the same magnitude as the corresponding measured values. When patterns of emissions were compared i.e. across the seasons and between experimental sites, changes in DDC in relation to both was statistically significant (Appendix 4.3, Figure 5.2).

Table 4.1. Statistical comparison of measured and simulated data in nine experimental sites using DDC and LDNDC models.

Figure 4.1. Measured and simulated (LDNDC) soil moisture content at Crichton (left) and Gleadthorpe Lamb Field (right).

As a consequence, when emission factors were calculated (Appendix 4.3 Table 5.5) measured values showed good agreement between estimates. Where AN or CAN fertiliser was applied to grasslands, the pattern in annual emissions at each site exhibited the expected response i.e. for every increase in the rate of N application, there was a corresponding increase in N emissions (Appendix 4.3 Table

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5.1, Figure 5.1). This was also replicated by DDC, however for some sites DDC had difficulty in replicating the pattern in daily emissions. For example at the Drayton site DDC tended to overestimate site WFPS and other soil parameters such as soil N. This highlights one of the disadvantages of using a single generic spin up across all sites as part of model application, as here it maybe that more site calibration at the Drayton site maybe required as these processes tended to be better represented in the other experimental sites. However, as DDC in spatial mode is run in a similar manner, if site level DDC is over calibrated to site level inputs, the pattern of emissions may improve. However, the spatial runs would become defunct as the level of detail required to specifically calibrate each soil/climate/land use combination in each 1km grid to the same specification does not exist. Table 5.4 (Appendix 4.3) outlines the modelled and measured emission factors calculated for each experimental site. While for some of the sites values were comparable, at the Higher Wheaty site, the modelled EFs for all treatments were lower than those derived from measured values. This was because as the N application rate increased from 240 to 320 kg N annual, emissions increased from 4 to 10 kg N2O – N ha-1 yr-1. DDC also simulated an increase in emissions, though the response was an order of magnitude lower, and it is unclear from the input data what could have driven the different amplitude of response. When emissions were compared across all the sites, the R2 is lower than where dung and Urine are applied (R2= 0.49). Both the annual emissions and EFs for sites with slurry (broadcast) and FYM applied are outlined in Appendix 4.3 (Tables 5.3 and 5.6). Modelled daily, and in some cases annual, emissions varied significantly between the sites where manure and slurry was applied. Differences between modelled and measured estimates are driven by uncertainties in the input information provided and a lack of flexibility within the model. In DDC, Manure, Slurry and Dung are applied as organic matter characterised by a C/N ratio which subsequently is used to estimate the N application rate. This information, however, was not consistently provided for each site and thus estimated from the RPBS fertiliser handbook. An additional uncertainty is that DDC does not discriminate between different types of organic fertilisers. Manure, Slurry and Dung are fundamentally different in how they interact with soil processes and in the manner in which they are applied. This was also true for the cropland experimental sites where organic material was applied. As a consequence, both daily and annual emissions showed better agreement where AN was applied compared to regions where manure was applied (Appendix 4.3 Table 5.7 and 5.8, Figure 5.7 and 5.8). Tables 5.9 and 5.10 (Appendix 4.3) outline the calculated EFs. While DDC tended to overestimate the rate of annual emissions in terms of spatial variation in EFs, DDC provides a good estimate of the potential emissions.

Recommendations and conclusions Both L-DNDC and DDC can provide acceptable estimates of the rate of N2O emissions from crop and grassland ecosystems. Differences in the pattern of daily emissions are detailed in Appendices 4.1, 4.2 and 4.3. Of particular interest was (a) the ability of the models to provide acceptable estimates of annual emissions (b) that biogeochemical models replicate the spatial pattern in fluxes due to site (soil type and climate) differences and (c) that modelled estimates change in relation to experiment type. Modelled estimates tended to follow the same pattern of emissions, and in terms of annual estimates, were within the measured range. However, limitations in both model calibration and input information led to uncertainty in modelled estimates These include:

1) Short term or climate data used to drive site specific emissions will have a direct effect on N2O fluxes, as they will alter crop production rates, the soil C spin up and the soil moisture content of the soil. It is, therefore, essential that this type of input data is supplied for each of the experimental sites.

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2) A continuation of experimental work described in this report would be beneficial, but it should be expanded to continuous measurements, and comparisons with model outputs will help to improve our understanding of underlying processes. This is because N2O measurements are non-continuous and include large uncertainties, the statistical methods most commonly used to evaluate model performance (e.g. RMSE, r2, E etc.) can be limited. As part of a research paper (in preparation), the limitations of these statistics for model evaluation have been reviewed by SRUC, as have the role of time lags between measured and simulated soil N2O.

3) Uptake of N2O was measured across all of the experimental sites and DDC and L-DNDC assume there is a net emission from the soil. As a consequence, both the annual emissions and correlations between daily fluxes will always incorporate some bias. In circumstances where there are large gaps in measurements (up to and including 1 month), if an uptake of N2O is used for calculating emissions in this interval, then modelled values will continually overestimate the cumulative flux.

4) The chemical composition of AN fertilisers and carbon content of organic manures are essential inputs for the models as both will alter how emissions propagate through the model.

Sensitivity Analyses

L- DNDC: Methodology

Considerable effort has been made to understand the internal structure and mechanisms of L-DNDC, and to assess the sensitivity of simulated N2O to the model’s parameters. This sensitivity analysis is related to the on-going model parameter calibration, which aims to achieve overall realistic predictions of soil water, soil N and C dynamics, crop yields and N fluxes. An analysis of correlation between various model outputs was also undertaken to assess the relationship between model drivers and fluxes.

Key Results:

Sensitivity analysis revealed that the most important parameters involved in N2O emission simulations are related to the simulation of microbial growth and maintenance, and the efficiency involved in NO2 and N2O production (Appendix 4.2 Figure 5). This information is also useful in reducing the number of model parameters that should be calibrated (~12). Initial results of the calibration of L-DNDC’s crop-related parameters are presented in Figures 3 and 4 (Appendix 4.2). Results from the analysis of correlation are presented in Figure 6 (Appendix 4.2). Some of these outputs are drivers of N2O fluxes and other N flows (e.g. soil moisture and T), and others are related to the conversion pathways through which N is released from the soil as N2O (e.g. heterotrophic and autotrophic CO2). A weak relationship between soil moisture and N2O and NO emission is revealed, suggesting that either the production of N2O under anaerobic conditions is not as strong as it should be, or that N2O produced in the soil is not diffusing effectively. Another interesting point is that denitrification by autotrophic nitrifiers (i.e. nitrifier denitrification) has a stronger relationship with NO than N2O flux (i.e. a weak autotrophic CO2 relation to N2O in comparison to NO). This suggests that the model’s parameters prioritise NO fluxes over N2O fluxes during nitrifier denitrification. DailyDayCent: Methodology Each of the key soil and climatic inputs used to drive the DDC model has an associated uncertainty in the measured site value, which can in turn introduce a degree of uncertainty in the baseline N2O emissions. Detailed methodology and results are outlined in Appendix 4.3 and Fitton et al., 2014 (a and b). In general, to define model behaviour to input parameters and uncertainty in their values; a) it was assumed there is a normal distribution in the uncertainty, and the uncertainty in each input was the same for each experimental plot, b) used the site value as the median point, and standard deviation of the different input parameters to define the uncertainty range, and c) simulated a 10

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step-wise change in the key site parameters, where the increase or decrease in the range of values simulated was constrained by the maximum and minimum values. In addition, for each simulation, only a single soil input was varied and all other soil input and climate values remained the same as measured. For the climate inputs, DDC uses daily climate information; therefore, the uncertainty values of 1oC or 1mm refers to uncertainty in daily values. Consequently, daily values in temperature or precipitation were adjusted to reflect the uncertainty interval. To test the potential interactions between the soil and climate input uncertainties on modelled N2O emissions, an uncertainty analysis using Monte Carlo simulations was performed. This analysis was only carried out on the historical sites. Published scientific papers as a result of this work are in Appendix 4.1, 4.5 and 4.6

Key Results:

The effect of changes in site level inputs varied between the sites, which is not entirely unexpected as there was a range of management histories, average climate data and a range of fertiliser types used across all experimental plots. The underlying causes for some differences in the sensitivity of DDC to changes in the inputs were also reflected across the sites. However, the primary causes of change in N2O fluxes and yield estimates are:

(i) Sensitivity of DDC to changes in SOM pools and total system C: changing soil inputs, even within a narrow range, can lead to the model assuming that there is a change in soil texture.

(ii) Interaction of changes to total C and incorrectly simulated snow fall: Within the DDC model, sequential sub-zero temperatures combined with precipitation, is assumed to lead to snowfall. Once temperatures increase above zero degrees, it is assumed that snow melt occurs. This leads to a large spike in emissions

(i) Sensitivity of DDC to interactions between changing inputs and fertiliser type: The sensitivity of emissions to changes in inputs is also dependent on the fertiliser type. This is primarily because AN and Urea are simulated in the model based on their N contents, whereas organic amendments are added to the soil C pool. In addition, the type of input also drives differences in the response, where any change in soil pH may increase the magnitude, but not alter the pattern, of emissions. Changing precipitation can lead to saturation, or extend already dry periods, therefore altering the pattern of emissions.

Mitigation modelling

The methodology, results and conclusions of the mitigation study will be outlined in detail in the AC0114 report and are not, therefore, included here.

Spatial modelling

To provide and assessment of spatial emissions, spatial data on soil, climate, land use and land management was collated for the UK. Soil input information was obtained from the Harmonised Soil World Database (HWSD) which contains information on soil pH, bulk density (BD), and sand, silt and clay content for the 10 dominant soil series in each 1 km grid square. This database also defines the land use within each grid square and contains separate soil information for grass and crop land uses. This database was then combined with the MORECS gridded dataset, which provides the source of climate information for each of the 1km grid squares. In addition, the MORECS grid data also provides harvest and planting dates for a range of different crop types including barley, winter wheat and winter barley.

In terms of land management, there is no spatially disaggregated data set for the UK, so for all grassland points across the UK for the period 2001 to 2010, the grasslands were assumed not to be grazed, with grass sward instead harvested in the summer (June/July) and before the start of winter

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(October). The date of fertilizer application was delayed by 10 days for MORECS squares with winter average temperature less than 5oC and brought forward by 10 days for MORECS squares with winter average temperature higher than 6 oC. For the cropland area, crop rotations for each region were set following Holman et al. (2005). Planting and harvest dates of major crops were obtained from the MORECS grid and fertiliser application rates were set according to crop type. As mentioned previously, DDC requires a spin up phase to generate a steady turnover in the soil C pools, therefore it was initially assumed that for both crop and grassland grids, land was assumed to be under native vegetation. This was then replaced by a grazed grassland system and then by a conversion to the LU or land management similar to the period of interest.

As DDC is a stand-alone model, outputs are on a per-hectare basis, so to provide spatial simulations an automated framework was adopted which converted the soil, climate and land management information into a form that can be used by DDC.

Results

Figures 3 and 4 (Appendix 4.4) represent the rate of annual N2O emissions for the grassland and semi-natural land across the UK for the years 2001 to 2010. The range of values simulated was large, ranging from a minimum of 0.5 kg N2O – N ha-1 yr-1 to a maximum of 7 kg N2O – N ha-1 yr-1 over the ten year period. To test for relationships between annual emissions, and some of the key soil and climate inputs, a Pearson correlation analysis was used (Table 1 Appendix 4.4), and this was coupled with a single regression analysis to create a simple explanatory model to account for the variation of annual emissions. This analysis was performed on the 10 year average annual emissions. Across the UK, emissions from grasslands were closely correlated with soil texture i.e. clay content and bulk density, with temperature and precipitation having little impact on national emissions. However, when taken on a regional level, the response of emissions can differ. For example, in Scotland, long term annual emissions are higher in general than other regions of the UK, but rates are a function of soil texture, and increasingly by soil pH and precipitation. This is not unexpected as Scottish soils tend to have high carbon and slightly acidic soils; this, coupled with higher precipitation (provided it does not become saturating), can lead to a higher rate of N2O from soils. In terms of croplands (Appendix 4.4 Figure 5 and Table 2), annual emissions are primarily a function of soil texture and crop yield, and this is replicated across each region.

Conclusions and further work

The extent of the experimental platform has allowed for a robust test on the effectiveness of the different models and modelling approaches in replicating nitrous oxide emissions. The ability of models to accurately replicate annual emissions means that for policy makers tools such as L-DNDC and DailyDayCent can be used in Tier 3 inventory reporting and also allow policy makers to test and examine the impact of mitigation strategies on national emissions.

Despite this there are a number of both model and data limitations that could be addressed to improve model performance which will in recue uncertainties in estimates. L-DNDC’s main limitation is the requirement from the user to provide measured input data corresponding to discrete soils layers. This requirement can, under certain conditions, affect the simulated N2O fluxes as a result of its impact on vertical nutrient flows in the soil. Also, the model appears to be sensitive to user-defined hydrological characteristics of the soil; mainly the depth/dynamics of the water table. Overall, the performance of L-DNDC is characterised by a –generally-- successful prediction of N2O fluxes during the high-peak periods (i.e. first to last fertiliser application) and an underestimation of background N2O fluxes (i.e. period before first and after last application). It can be argued that the

47

model performs better at grassland rather than at arable sites even though this conclusion is based exclusively on the results for the sites examined in this study.

For DailyDayCent there is currently no limited data version, that exist for other models, or simplified structure that allows for spatial simulations to be easily made. As a consequence while on a site level a wide range of fluxes are easily replicated spatially an automated framework must be constructed. In addition there is a lack of elasticity in how the model replicates different N sources such as manure or dung, as the model does not take into account the texture or for the case of dung application consistency.

While the HWSD soils database and MORECS gridded data provide an excellent foundation for simulating spatial emissions, the lack of activity data on management information such as; a) crop rotation, and b) different grazing/ silage grassland systems both spatially and temporally, mean that modelled estimates can only currently provide a guide to emissions at a national scale. To take advantage of this modelling system, data collection of spatially disaggregated activity / management data to drive the models should be a priority. The combination of this new data with the spatial modelling system described here, would deliver a powerful Tier 3 system for simulating and reporting N2O emissions from UK agriculture.

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WP5 Verification Ute Skiba*, Nick Cowan, Peter Levy, Dave Reay, Daniela Famulari, Margaret Andersen, Julia Drewer,

Laura Cardenas, Neil Donovan, Ed Carnell, Ulrike Dragosits, Nuala Fitton, Tim Arnold, Meneguz,

Elena, Alistair Manning

*WP leader

Summary This WP addressed spatial and temporal uncertainties of N2O fluxes, and used novel measurement approaches, modern statistical analyses and modelling to explore the precision and scaling of N2O fluxes from plot to field to regional emissions. Specifically, research determined i) the temporal uncertainty of chamber derived N2O fluxes by comparing N2O flux measurements from static chambers and autochambers at the experimental sites in WP2, ii) spatial variability of emissions at the plot and field scale using a newly developed ‘roving’ dynamic chamber, iii) the scaling of fluxes from plot (chambers) to field (eddy covariance), and iv) the inverse modelling of atmospheric N2O concentrations from tall towers around the UK resulted in a better fit to the newly derived EFs (from WP2) compared with the EFs used in the official UK inventory reporting approach used in 2014. Temporal uncertainty: The comparison of N2O emissions obtained with autochamber systems and manual chambers at the WP2 plot experiments sites revealed that whilst some peaks and troughs of N2O fluxes were not accounted for by the lower frequency of static chamber sampling, the dynamics of N2O emission are generally similar between the two methodologies. In general, there was no significant difference in the cumulative fluxes between static and autochamber measurements at the same site, although the autochamber flux data were generally lower than the static chamber. The autosampler data also confirmed that sampling of chambers between 10am and 2pm resulted in fluxes that were typical of the daily average. Measurement precision: A high precision closed loop dynamic chamber system was developed to assess the precision of the WP2 static chambers. The comparisons of methods suggest that the static chamber method may underestimate N2O flux measurements by as much as 20% as it assumes linear change in gas concentrations during enclosure periods which may not always be the case. However, this was acknowledged before the project started, as the project team believed it more important to account for spatial variability of fluxes within the WP2 experimental plots. Furthermore the more precise dynamic chamber revealed that negative fluxes of N2O are rare. Spatial uncertainty: The ‘roving’ dynamic chamber was used to identify the spatial variability across a grazed grassland field and across a whole livestock farm. The typical log-normal distribution of N2O fluxes required development of Bayesian approaches to provide a robust and transparent method for translating small-scale observations to larger scales, with appropriate propagation of uncertainty. This new data analysis approach showed that both at the field and farm scale (i) N2O fluxes correlated most strongly with soil NO3 concentrations, and (ii) that features which only occupy small areas, such as animal feeding areas, manure heaps, animal barns can contribute to a large percentage of the total estimated daily N2O flux, and therefore should not be excluded from farm scale budgets. Upscaling from plot to field scale: We used eddy covariance to integrate N2O fluxes at the field scale and compare with chamber-derived N2O fluxes at six agricultural fields (4 grassland and 2 arable) from five locations across the UK. The two measurement methods resulted in a similar range and magnitude of N2O fluxes; however there was a tendency for chamber methods to report higher

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fluxes than eddy covariance. Direct differences in reported fluxes between the methods are likely due to the inability to accurately interpolate spatial data from the static chamber measurements. Verifying GB N2O emission maps: To verify the newly derived N2O EFs (from WP2), a new inventory dataset was constructed. In addition to the normal spatial aggregation (5 km2) we developed a temporal disaggregation (monthly) in order to provide a better comparison with the continuous atmospheric N2O concentration measurements available from 3 towers at Mace Head, Tacolneston and Ridge Hill. Using the Met Office atmospheric dispersion model (NAME) maps derived from the official N2O EFs (default IPCC 2006 GL guidelines) and the newly derived N2O EFs (AC0116 & AC0114) were compared with the atmospheric N2O concentration measurements for the year 2013. In general the temporal pattern of the emissions corresponds with observed concentrations measured at Ridge Hill and Tacolneston. Emissions associated with mineral N fertiliser application are dominant at all sites. The performance of the disaggregated maps based on default or newly derived EF’s is very similar. This comparison clearly shows the strength of comparing of bottom-up inventories with top down inversion modelling to constraining annual UK emission inventories, and should be done routinely annually. Introduction Knowledge of rates and processes responsible for soil N2O fluxes is mostly based on measurements from small static chambers (< 1m2) at rather coarse time intervals of daily to monthly. Relatively recent developments of reasonably user friendly high frequency high resolution quantum cascade lasers have made it much easier to measure N2O fluxes at the field scale and a temporal resolution of >10 Hz. The purchase of an Aerodyne quantum cascade laser fitted with a N2O/CO2 diode through the InveN2Ory project has made it possible for CEH to take advantage of this new development and deliver on our objectives:

Evaluate the spatial and temporal uncertainty of static chamber derived N2O flux measurements made in WP2.

Evaluate the uncertainty associated with upscaling from chamber to the field scale.

Quantify N2O emissions and verify models of N2O emissions at the farm-scale.

Verify the process based models developed in WP4 at the field scale.

Verify N2O emissions at the country scale.

1) Temporal uncertainty Background The great majority of measurements of N2O emissions from agricultural and other soils have been undertaken with manually operated closed static chambers (Rochette and Ericksen-Hamel, 2008). Typically, a small number (e.g. 4-8) of replicate chambers are dispersed across an experimental plot. Chamber closure and gas sampling is carried out either at regular intervals or according to an event-determined schedule – for example daily sampling for a few days following N fertiliser application, then at widening intervals as an initial flush of N2O emission subsides (e.g. Smith et al., 2012). Because of concerns that significant short-term variations in N2O (and other trace gas) emissions might be missed by such procedures, a limited number of studies have been carried out entirely with automated chamber/sampler systems. However, resource limitations have required the continued widespread use of simple manual chambers, so efforts have been made to evaluate the effect of sampling frequency on the results obtained with them, by comparisons with the results obtained at higher frequencies with automated systems. These comparisons have employed both laboratory-constructed and industrially manufactured auto-systems. Some investigations have also included the effects of spatial variability on the manual chamber data.

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Studies using laboratory-built auto-systems include several N2O emission studies in Scotland: on agricultural grassland receiving mineral N fertiliser (Smith & Dobbie, 2001) and sewage sludge (Scott et al., 1999), and arable land receiving N-rich crop residues and paper mill waste (Baggs et al, 2002). Smith and Dobbie (2001) found that estimates of cumulative N2O emission based on samples taken at 3-7 day intervals deviated by no more than 14%, on average, from those based on samples taken at 8-hr intervals, and that when estimates were based on daily sampling intervals immediately after N fertilisation, the agreement was closer still. Parkin (2008) obtained a very similar outcome, in work in a maize field in Iowa, USA. The most detailed work so far appears to be that of Morris et al. (2013) and van Zwieten et al. (2013), who compared N2O emissions from a sub-tropical Australian soil sown to maize and fertilised with poultry litter or urea, over 57 days. Each of 12 plots contained an autochamber to which a mobile gas chromatography system could be connected, with measurements taken every three hours, and seven manual chambers measured every three days. The authors reported that, apart from a large and unexplained initial deviation between auto and manual chambers, there was acceptable agreement in the cumulative emissions determined, and that manual chamber numbers could have been reduced by a third without increasing errors. In the work reported here, we compare the N2O emissions obtained with AGPS autochamber systems (UIT, Dresden, Germany) and with manual chambers, in a number of the plot-scale experiments described in WP2. Methods Measurements were carried out at four of the UK sites used in WP2. Six experiments were located on three different fields at N Wyke (SW England), Woburn (SE England), Boghall (Scotland) and Hillsborough (Northern Ireland). There was a range of fertiliser and manure types and application rates as described in Table 5.1. Table 5.1. Site and treatment details of the sites used for eddy covariance and static chamber comparison. SITE Year Season

application Measurement Period

Crop Treatments N application (kg ha-1)

Woburn, Beds. 2011 Spring 05/03/11-08/03/12

Winter Wheat

Ammonium nitrate

180

N Wyke, Higher Lea

2011 Autumn 28/09/11-11/04/12

Grass Broadcast slurry

60

N Wyke, Higher Lea

2012 Spring 17/04/12-21/06/12

Grass Broadcast slurry

77

N Wyke, Beacon Field

2012 Spring 15/05/12-05/09/12

Grass Artificial urine

405

N Wyke, Beacon Field

2012 Summer 03/07/12-18/09/12

Grass Artificial urine

429

N Wyke, Beacon Field

2012 Autumn 26/09/12-22/01/13

Grass Artificial urine

435

N Wyke, Higher Wheaty

2013 Spring 27/02/13-20/11/13

Grass Ammonium nitrate

320

Boghall, Midlothian

2013 Spring 16/04/13-26/06/13

Winter Wheat

Poultry Litter

121

Hillsborough, Co Down

2013 Spring 04/04/13-14/09/14

Grass Broadcast cattle slurry

77

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The daily flux from the static chambers were calculated from the accumulation of the gas in the headspace after 40 minutes taken from one sampling time in the day (between 10:00 and 14:00 hours), with replicate ambient samples serving as the t=0 reference. In contrast the AGPS sampled four times in every 24h period (between the hours of 4 & 5 o’clock and 10 & 11 o’clock both in the am and pm). Each sampling time furnished three headspace samples, T0, T20 and T40 (0, 20 and 40 minutes after chamber closure). An hourly flux was then estimated from the rate of increase in headspace N2O concentration. To calculate the daily mean from the 4 measurement periods from the AGPS autochamber, an average of these hourly flux values was taken and multiplied by 24 to provide an estimate of the daily flux, which could be compared to those obtained from the mean of the 5 static chambers on the same plot. The cumulative flux from each static chamber were obtained by calculating the areas under the curve (trapezoidal method) using the daily fluxes, and averaging the five totals for the static chambers on the same plot as the AGPS autochamber. The cumulative flux from the AGPS was similarly estimated by calculating the area under the curve (trapezoidal method) from the daily flux estimate. Comparisons of the cumulative emissions between the two methods were based on values obtained across the same calendar period. Results A comparison of the calculated daily N2O fluxes from the static (mean of 5) and automated chambers can be seen in Figure 5.1, following the application of urine to a clay soil at the North Wyke WP2 experimental site in Spring (early grazing period). The greater frequency of sampling by the autochamber shows peaks and troughs in N2O fluxes that are not measured by the lower frequency of static chamber sampling. However, the dynamics of N2O emission are generally similar between the two methodologies.

Figure 5.1. N2O emissions from static and automated chambers at Beacon field, North Wyke, from a urine treatment in one of the WP2 plot-scale experiments. The daily flux was estimated from the separate hourly flux sampled by the AGPS chamber at the four occasions during the day, .i.e. at 5am, 10am, 5pm and 10pm. These values were compared with the daily flux from the 5 static chambers on the same plot. An example of the comparison of these daily fluxes is shown in Figure 5.2, for a sampling date from a late grazing urine application experiment at the same site as shown in Figure 5.1.

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Figure 5.2. Comparison of daily N2O fluxes calculated using the hourly flux measured at four times during the day by the autochamber (AC – solid black line) and the one measurement time (SC – black square). The average daily N2O flux using the four sampling times by the autochamber is shown as the open circle. In this example (Fig. 5.2), the daily flux measured by the static chamber method is not significantly different to the average flux calculated from the mean of the four measurement periods by the autochamber, providing confidence that the sampling time between 10am-2pm was representative of the daily flux. This was not always the case, with the static chambers sometimes resulting in a greater daily flux. But we must take into account that we only had one autochamber in each site, and 5 static chambers. So, spatial variability of N2O production and emission will also complicate interpretation of the temporal comparisons we have made. Figure 5.3 summarises the comparative cumulative emissions from the static and autochambers for all 6 experiments. The cumulative N2O emissions from the control plots (static chambers) in each experiment are included in this figure for reference. The data show that for all sites except for Beacon field spring and Higher Wheaty, there was no nitrogen treatment effect (i.e. the difference between SC control and SC treatment was not significant). Fluxes were generally low (except for Beacon Field spring and Higher Wheaty). The autochamber flux data were generally lower than the static chamber, but only significantly for Woburn and Higher Wheaty.

Figure 5.3. A comparison of cumulative N2O fluxes from different experimental sites using static chambers and an automatic chamber.

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2) Measurement precision

In order to investigate spatial uncertainty we developed a high precision closed loop dynamic chamber system, compared WP2 static chamber with dynamic chamber measurements and used this new method to measure fluxes to assess field scale variability and farm scale emissions. Developing a dynamic chamber for high precision N2O flux measurements A non-steady-state flow-through (or closed loop dynamic) chamber system was designed in which a closed volume of air was circulated between a flux chamber and a quantum cascade laser gas analyser via a pump (Cowan et al. 2014a, Figure 5.4). Two 30 m lengths tubing were attached to both the inlet of the QCL and the outlet of the pump which provided a 30 m radius from the QCL in which the chamber could be placed. This method could be operated from a stationary location or from a vehicle with a mobile generator power supply.

Figure 5.4. The dynamic chamber is placed on pre-inserted rings for 3 – 5 min with direct analysis by the quantum cascade laser situated in a mobile vehicle or field cabin.

Comparison of dynamic and static chambers at WP2 manipulation sites The dynamic chamber method (Fig 5.4) was deployed at various InveN2Ory WP2 sites (North Wyke, Crichton, Rosemaund, Bush) and was compared with the traditional static chamber method, where discrete samples are analysed offline using gas chromatography. The detection limit for the dynamic chamber method was ± 0.04 nmol m-2 s-1 which was approximately 10 times lower than the more commonly used static chambers (± 0.39 nmol m-2 s-1). The comparisons of methods suggest that the static chamber method may underestimate N2O flux measurements by as much as 20% as it assumes linear change in gas concentrations during enclosure periods which may not always be the case (Cowan et al, 2014a). However, this was accepted, as the WP2 project team believed it more important to account for spatial variability of fluxes within the experimental plots (which can be orders of magnitude different from different chambers on the same plot), and use resources to make measurements from multiple chambers per plot, than to make more measurements of the chamber headspace to explore the potential non-linearity of the concentration accumulation from fewer chambers (Chadwick et al., 2014). Whatever method is used, it is important to know the limitations of the methodology employed to measure GHG fluxes, as demonstrated in the below example: The high precision flux measurements by the dynamic chamber connected to the QCL shed new light on apparent ‘negative’ fluxes. We analysed 1220 dynamic chamber N2O flux measurements from a variety of UK soils, mostly from WP2 locations. Of these 115 flux measurements indicated uptake by the soil. However, only four of these apparently negative fluxes were greater than the detection limit of the method which suggests that the vast majority of reported negative fluxes in the literature from such measurements are actually due to instrument noise (Cowan et al., 2014b). We therefore recommend that good scientific practice is followed at all times and that the detection limit and noise of the instruments used in greenhouse gas research are regularly assessed.

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N2O FluxLog scale

Shaded area Silage Remains Manure heap Manure Perimeter

3) Spatial uncertainty

Spatial variability at the field scale The spatial variability of N2O fluxes was assessed in detail using the dynamic chamber for one grazed grassland at Easter Bush (close to the Boghall WP2 field) featuring a dung heap, tree shelter belt, silage remains and manure heap (Cowan et al. 2015). Nitrous oxide fluxes ranged from 2 - 79000 μg N2O-N m-2 h-1 and correlated most strongly with soil NO3

- concentrations. Features within the field such as shaded areas and manure heaps contained significantly higher available nitrogen than the rest of the field (Figure 5.5.). Although these features only represented 1.1% of the area of the field, they contributed to over 55% of the total estimated daily N2O flux.

Figure 5.5. 100 N2O flux measurements on a sheep grazed grassland identified the large spatial variability and N2O hotspots.

The eddy covariance technique would be able to integrate most of these features, except the hotspots in the tree shelter belt, where the sheep congregated frequently. The shelter belt changes turbulence and thereby prevents eddy covariance analysis. We therefore recommend that eddy covariance measurements should be accompanied by occasional measurements from strategically placed chambers to a) include obvious hotspots and b) to understand the spatial heterogeneity of the field. Spatial variability at the farm scale The primary aim of the study was to identify and compare the most significant sources of N2O

emissions from the farm, with a focus on N2O emissions from sources which are not directly

associated with fertiliser application. The dynamic chamber method was used to measure N2O

fluxes, a total of 529 measurements over 1 year, from the diversity of terrestrial sources at Easter

Bush livestock farm in Central Scotland. Individual N2O flux measurements varied by four orders of

magnitude, with values ranging from -5.5 to 352,900 µg N2O-N m-2 h-1. The log-normal distribution of

the fluxes (Figure 5.6) required the use of more complex statistics to quantify uncertainty. We used a

Bayesian approach which provided a robust and transparent method for "upscaling" i.e. translating

small-scale observations to larger scales, with appropriate propagation of uncertainty.

N2O fluxes measured from intensively-used areas, such as animal feeding areas, manure heaps and

animal barns were typically one to four orders of magnitude higher than those measured on the

extensive arable and pasture fields. Soil mineral nitrogen (in the form of ammonium and nitrate) was

also measured from these areas and was found to correlate linearly on a log-scale with N2O flux.

Although fluxes measured from these sources were significantly larger than the majority field

coverage, these intensive areas were found to contribute less N2O at the farm scale (12 to 33 % of

the total) than the extensive fields, which dominated the area coverage (99.7 %). The contribution of

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these small scale features to total farm-scale N2O emissions are similar to those found by Matthews

et al (2010). The contribution from the small but intensively-used areas should still be considered at

the farm scale, given that at up to 33 % of the fluxes may come from only 0.3 % of the area under

certain conditions. This work is being prepared for publication as a manuscript titled, “Nitrous oxide

emissions from a Scottish dairy farm: influence of land management and soil chemistry”, to be

submitted by the end of 2016.

Figure 5.6. Frequency distribution of observed N2O fluxes at the farm scale, shown on a log transformed axis.

4) Upscaling from plot to field scale Measurements of soil N2O fluxes by eddy covariance has the advantage over chambers, that measurements are non-destructive and measure an integrated flux at the field to regional scale at a very high temporal resolution (>10 Hz), usually integrated over 30 min periods. Disadvantages are the need for a large uniform field and mains or generator power. The flux (FC) is calculated from the covariance between the vertical wind speed (w) and the gas concentration (C) measured at one

point as follows: 𝐹𝐶 = 𝑤′ ∙ 𝐶′̅̅ ̅̅ ̅̅ ̅̅ . Temporal data coverage is much better than for chambers, but due to changes in wind direction data coverage is never complete. For the InveN2Ory project we (1) compared eddy covariance and chamber methods on fields close to WP2 sites (North Wyke, Crichton, Rosemaund, Bush) and (2) used a combination of eddy covariance and chambers to assess the impact of ploughing on N2O and CO2 fluxes. Method In this project we compared these two approaches at six agricultural fields from five locations across the UK. These consisted of four intensively managed grassland fields (Crichton, North Wyke, Easter

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Bush-North and South fields (Figure 5.9), Easter Bush and two arable fields (Rosemaund and Lincolnshire), all very close to the WP2 manipulation sites. The measurements from the sites combined give a wide ranging set of conditions in which low background flux from agricultural soils and high fluxes from fertilisation events could be compared for both measurement techniques. Measurements from the intensively managed sheep grazed grassland ‘Easter Bush’ were made over a period of six months, while at the other sites measurements were short-term and lasted only several weeks. Key Results The log-normal distribution of data in the chamber measurements required the use of Bayesian statistics to estimate uncertainties when spatially interpolating data to the field scale for comparison with eddy covariance data. Comparisons of N2O fluxes measured using both techniques suggests that static chambers generally report higher fluxes than those measured using eddy covariance; however, the large range of uncertainty due to spatial interpolation of the chamber data overlaps well with fluxes measured by eddy covariance (Figure 5.7). Although differences in direct comparisons of the methods tended to be within an order of magnitude, cumulative flux estimates were not. Further work is required to compare these methods thoroughly which will then be reported in a manuscript titled “Comparisons of agricultural nitrous oxide fluxes measured using static chamber and quantum cascade laser-based eddy covariance techniques” to be published upon completion.

Figure 5.7 A direct comparison of fluxes measured using static chamber and eddy covariance techniques are made for all six field site locations. The 95 % confidence interval is included for both methods. The comparisons of eddy covariance and static chamber techniques show that the methods report a similar range and magnitude of N2O fluxes; however there is a tendency for chamber methods to report higher fluxes than eddy covariance. In order to compare chambers with eddy covariance methods some kind of spatial interpolation is required. We recommend that based on the log-normal distribution of flux data collected from chamber measurements that Bayesian statistics are applied to interpolate spatially and provide the relevant uncertainties rather than using the naïve/arithmetic mean.

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Example of using eddy covariance and chambers in tandem to assess the impact of ploughing on N2O eddy covariance fluxes Method N2O fluxes were measured using the eddy covariance method from an area of intensively managed, grazed grassland (Easter Bush, Scotland) before and after a tillage event on the 1st of May 2012, and were compared with fluxes measured from an adjacent grassland which remained un-tilled (Figure 5.6) (Cowan et al., 2016). Fertiliser (70 kg-N ha-1 ammonium nitrate) was applied to the un-tilled field on the 28th of May and to both fields on the 9th of August. N2O, H2O and CO2 mixing ratios were measured by a quantum cascade laser (QCL) rapid gas analyser. Measurements lasted from March to September 2012 during which the wind direction shifted between the fields (south-westerly and north-easterly). True temporal data coverage for the un-tilled and tilled fields were 34% and 32% respectively. In order to estimate cumulative fluxes from both fields, temporal interpolation of the missing data points was required. The most common approach (both for chamber and eddy covariance) is to linearly interpolate in time between flux measurements. This is a rather uncertain approach and we therefore used a general additive model (GAM), which accounted for temporal patterns at a range of timescales and nonlinear responses to environmental variables, implemented using the mgcv package in the R software. Key results Fluxes of N2O measured from the un-tilled field (Figure 5.8a) generally remained low until fertilisers were applied. Before the tillage event, N2O fluxes measured from the tilled field were similar in magnitude to the un-tilled field (~0.2 nmol m-2 s-1). During May and June, N2O fluxes increased considerably from the tilled field, similar in magnitude to a nitrogen fertilisation event; however, no additional nitrogen was applied in this period (Figure 5.8b).

Figure 5.8. Gap filled eddy covariance measurements for both the (a) un-tilled and (b) tilled fields at 30 min intervals. Fertiliser was applied on the 28th of May and 9th of August and tillage occurred on the 1st of May.

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An estimated total of 0.85 Kg N2O-N ha-1 additional N2O was released over a two month period after the tillage event than would be predicted had fluxes remained at background levels. Cumulative flux emissions associated with the fertilisation events on the un-tilled field were 0.55 and 0.76 Kg ha-1 on the 28th of May and 9th of August, respectively (Figure 5.9). Cumulative flux emissions associated with the fertilisation event on the tilled field was 0.77 Kg N2O-N ha-1. As each fertilisation event consisted of a 70 kg N ha-1 as ammonium nitrate, these cumulative fluxes accounted for 0.8%, 1.1% and 1.1% of the total applied nitrogen in each case.

Figure 5.9. Cumulative fluxes calculated for the tilled and un-tilled fields during the measurement period with the associated 95 % confidence intervals in uncertainty. In 2014 the North field was ploughed. The 2012 and 2014 ploughing events and effects on soil GHG fluxes (static chamber measurements) and net ecosystem exchange of CO2 (eddy covariance) were studied in details. The magnitude of the GHG fluxes was broadly similar after both ploughing events, but distribution of the fluxes was different due to different meteorological conditions. The increase in GHG fluxes was short term (< 60 days) and mostly affected N2O fluxes and to a much lesser extend CH4 and CO2 fluxes (Drewer et al., 2016). Recommendations/Conclusions Eddy covariance provides constant spatially integrated N2O fluxes which allows considerable improvements to be made when interpolating cumulative emissions at the field scale in comparison to chamber methods. Eddy covariance can measure flux events which last only a few hours and can monitor conditions in which fluxes vary erratically over short periods; however, the methodology is susceptible to changes in wind direction and this should be considered with importance in the setup of any eddy covariance experiment. Our results, both from the eddy covariance and chamber studies indicate that N2O fluxes associated with the grassland tillage event in the study were larger than those estimated from individual fertilisation events. It is unclear from the results if reduced fertiliser emissions are due to changes in soils caused by the tillage event. We would recommend further long term (several years) N2O flux monitoring field sites in which the effects of various agricultural management practices can be compared in terms of meteorological conditions and soil type in the future so that agricultural sources of N2O can be better characterized both spatially and temporally.

5) Regional verification of the improved N2O EF inventory approach Atmospheric Observations

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Measurements of N2O concentrations for 2013 were collected at three of the four GHG stations of the UK DECC (Deriving Emissions linked to Climate Change) network (http://www.metoffice.gov.uk/atmospheric-trends, note: N2O measurements are not available at the fourth station, Angus). The stations used in this study are: Mace Head (MH, 53.33N, 9.90W, 25 m a.s.l.) on the western coast of Ireland, telecommunication towers at Ridge Hill (RH, 52.00N, 2.54W, 204 m a.s.l.) in western England and Tacolneston (TN, 52.52N, 1.14E, 56 m a.s.l.) in eastern England. Measurements at MH are available every 40 minutes and at RH/TN approximately every 10 minutes. Figure 5.11 shows the location of the sites. It should be noted that no data are available at RH for January 2013, and for TN data are missing from 30th July at 09:00 to 6th August 2013 at 15:00 and from 2nd September at 13:00 to 12th September 2013 at 11:00.

Figure 5.11. Map of the three N2O monitoring sites chosen from the DECC GHG Measurements Network. MH: Mace Head, RH: Ridge Hill and TN: Tacolneston. Modelling technique The modelling framework used NAME (the Met Office’s atmospheric dispersion Lagrangian model, Jones et al. 2007) to model the dilution and dispersion of N2O emissions from across the UK and beyond for comparison at the observation stations for 2013, with a source-receptor methodology (e.g. Manning et al., 2011). This works by tracking modelled particles backwards from the observation points every two hours and recording the particle mass and the amount of time spent in a vertical layer close to the ground (more precisely, between 0 and 100 m a.g.l.). Background concentrations, which were used to create a baseline (i.e. the northern-hemispheric background level), were estimated using Mace Head observations for periods of clean air from the Atlantic under westerly conditions. NAME was driven by 3-D meteorology from the Met Office Unified Model at a global horizontal resolution of 25 km. Spatial and temporal disaggregation of emission data Emission estimates made using the IPCC guidelines from the 2013 annual agricultural N2O inventory (Cardenas et al., 2014, Carnell et al., 2015) were temporally disaggregated based on agricultural management (e.g. sowing dates) and environmental data (rainfall and soil texture from Harmonized World Soil Database, used for leaching and mineral fertiliser emissions). The temporal profile for emissions from the application of mineral fertilisers and livestock manures/slurries was derived

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using fertiliser guidelines (The Fertiliser Manual, RB209) and survey data of application timings (BSFP, 2014). The 2013 ammonia inventory (Misselbrook et al. 2014) was referred to for the temporal split of livestock emissions, as it contains detailed information on management practice (e.g. animal housing periods and manure management practice). Indirect soil emissions (N2O emissions following nitrate leaching and atmospheric N deposition from agricultural sources) were disaggregated using a number of proxies such as monthly rainfall (derived from MORECS rainfall data 2013 http://thredds.ceh.ac.uk/thredds/morecs.html) and crop/grassland distributions. The monthly estimates of N2O emissions were spatially distributed at a 5 km grid resolution (methods described in Carnell et al., 2015). In addition to the official best estimate of N2O emissions (as reported under the GHGI) for 2013, alternative emission estimates were created, which incorporate the latest results from recent and on-going research projects, the DEFRA funded projects MINNO and InveN2Ory (WP2). The main sources where changes were implemented in this modelling were the soil N2O emissions, whereas the livestock manure management source remained unchanged. Key changes (used in this modelling) compared with the official 2013 data are:

Direct emissions from fertiliser application to arable and grassland crops – variable emission rates have been introduced, which take into account the type of land the fertiliser is applied to (i.e. arable or grassland) and the type of fertiliser applied. These variable emission rates replace the default IPCC (2006 GL) constant emission rate of 1.25%.

Direct grazing emissions (including those from non-agricultural horses) - The default IPCC (2006 GL) emission rate has changed from 2 % to 0.44 %.

Direct emissions from manure application - The default IPCC (2006 GL) emission rate of 1.25% has been adjusted to 0.6% for livestock slurry and 0.36 % for farmyard manure and poultry litter applications.

Indirect emissions from mineral N fertiliser application – The default IPCC (2006 GL) volatilisation rate of N applied (to all land types) has been adjusted from 10 % to 2.2 % for applications to grassland and 3.5 % for arable land.

Indirect emissions from manure going to land – The default IPCC (2006 GL) volatilisation rate of 20 % has been replaced with rates specific to livestock and manure type (also dependant on manure application method/speed of incorporation). Rates for applied manures are specific for the devolved authorities of the UK. Rates for grazing returns are set at 3.6 % for cattle, sheep and horse manure, 24.5 % for poultry manure and 17.5 % for outdoor pigs.

Indirect emissions from manure management– The default IPCC (2006 GL) rate for volatilisation loss from manure management during housing and storage of 1 % have been replaced with rates that are specific to livestock type, manure management system and change for each of the devolved authorities of the UK

Indirect N2O emissions from leaching – The leaching rate of mineral N fertiliser application and grazing returns to grassland is now 10 %, replacing the default IPCC (2006 GL) rate of 30 %. The leaching rate of N applied to arable land remains at 30 % .

All other sources that contribute to soil emissions (biological fixation, emissions from histosols, re-emissions of N deposition and sewage) remain the same as the current 2013 GHG inventory. The spatial patterns of the annual average emissions of the two datasets were compared with the experimental dataset showing much lower agricultural emissions across the country. The temporal split shown in Figure 5.12 for both versions of the N2O inventory highlights the seasonal variability in emissions, due to agricultural management practices and environmental conditions. The graph also clearly highlights the reduction in total agricultural emissions in the experimental dataset (Figure 5.12b), when compared with the official estimates (Figure 5.12a).

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(a)

(b)

Figure 5.12. (a) Official 2013 N2O agricultural emission estimates, disaggregated by month and separated into dominant source sectors and (b) showing estimated emissions using the alternative emission factors derived from recent and on-going research. Non-UK European emissions from the EDGAR global inventory (EDGAR, JRC/PBL, 2008), scaled to individual country 2012 UNFCCC reported emissions, were obtained, with thanks, from Anita Ganesan, University of Bristol (Ganesan et al. 2015).

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Key Results Comparison between the official inventory and the experimental datasets We compared the 2013 ‘Official’ and ‘Alternative’ N2O inventories from agricultural sources with the concentration measurements for the same year at the three towers MH, TN, RH. At the monitoring stations, the modelled concentrations of N2O produced by the two agricultural emission estimates (default IPCC and alternative estimates) are compared to baseline concentrations (at MH) Figure 5.13. At Tacolneston the concentrations produced using the alternative emission factors are up to ~0.5 ppb lower than those obtained using the default IPCC methodology (2006 GL) and ~0.3 ppb at Ridge Hill (with the largest difference occurring in the spring/summer months). a)

b)

c)

Figure 5.13. Time series of N2O mole fractions (in ppb) showing the difference between modelling with the default IPCC (2006 GL) emission estimates (green), alternative emission estimates (red) and baseline emissions (black) at the three monitoring sites; a) MH, b) RH and c) TN.

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In order to assess whether the alternative emission estimates are more appropriate than those produced using the default IPCC methodology, we calculated the total modelled mole fractions of N2O (including influence from Europe and the non-agricultural sector) using the official inventory agricultural data disaggregated by month and we have compared them with observations available at the three monitoring sites. We then repeated the same calculation with the alternative emission estimates. Monthly averaged results are presented in Figure 5.14. a)

b)

Figure 5.14. Comparison between observations (black) and total modelled mole fractions of N2O at (a) RH and (b) TN, obtained using the official inventory agricultural data (green) and the new experimental dataset from ongoing research (red). The default IPCC (2006 GL) methodology appears to provide a better estimate of N2O concentration compared to estimates made using the alternative approach. In general, for both approaches, the temporal pattern of emissions corresponds with observed concentrations measured at RH and TN (and MH, not presented here). The correlation coefficients imply poor agreements between the observations and models for the months July (RH & TN) and August (MH). The reasons remain to be investigated.

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Emissions associated with mineral N fertiliser application are dominant at all sites, with peaks during months with high emissions, which can be associated with intensive application of nitrogen fertilisers and manures during the spring season. The largest concentrations were observed at Tacolneston, where the maximum pollution event is around 1 ppb higher than at Ridge Hill.

Uncertainties in model and observations Although it is clear that background mole fractions are dominant (e.g. ~326 ppb for MH in 2012), relative to the magnitude of the peaks (~2-3 ppb), there are uncertainties in both the modelled emissions and observations at the monitoring sites. Recommendations/Conclusions At all three sites (MH, TN, RH), the trends in the modelled data were found to fit with the observational data trends. In particular we can conclude that (1) The northern-hemispheric background trends, which reach a minimum in August, are due to atmospheric transport patterns rather than due to agricultural activities, (2) Emission and individual pollution event peaks tend to be lower in summer, primarily as a result of less fertiliser and manure application taking place during this period and reduced housing emissions from livestock, with animals primarily outdoors on pastures. In addition, soils are warmer and drier than during the rest of the year i.e. conditions less favourable for the emission of nitrous oxide, (3) As found in previous studies (e.g. Manning et al. 2010), the Irish site (Mace Head) shows significantly lower concentrations than the English sites, due to its different geographical location downwind of the Atlantic Ocean under prevailing winds. The suitability of alternative emission factors from ongoing work has also been investigated. Mole fractions derived from the alternative approach were estimated to be up to ~0.5 ppb lower than those made using the default IPCC (2006 GL) methodology. When compared to observations, monthly averages of N2O mole fractions estimated with the default IPCC methodology appear to correspond more closely to the measured N2O concentrations at Ridge Hill and Tacolneston, than for the alternative emission estimates. This comparison clearly shows the strength of comparing of bottom-up inventories with top down inversion modelling to constraining annual UK emission inventories, and should be done routinely annually.

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WP6. Ammonia Emissions from Grazing Livestock.

Tom Misselbrook* H. Flemming, V. Camp, C. Umstatter, C.-A. Duthie, L. Nicoll, T. Waterhouse

Summary Ammonia (NH3) emissions by grazing livestock represent a source of indirect nitrous oxide emissions, associated with redeposited nitrogen and nitrate leaching. Because of the difficulties in measuring ammonia (NH3) emissions and nitrogen loading in urine and dung from grazing livestock there is a lack of information from which to generate NH3 EFs. The aim of this work package was to conduct a series of experiments where beef cattle were fitted with urine sensors, to quantify urination events (volume and N content), and new laser technology was used to measure the atmospheric NH3 concentration up- and down-wind of grazing cattle. Whilst the urine sensors provided some useful data on numbers of urination events per day, and individual urination volume and total N content, it is unfortunate that the NH3 concentration analysers were not sensitive or stable enough to quantify emissions. The urine sensors provided an automated means of monitoring urination events from grazing cattle, enabling the collection of a large amount of data. However, improvements to sensor attachment and location monitoring are needed. There was clear diurnal variation in the N loading per urination event, which may have implications for potential N losses depending on livestock management and locations at different times of day. More sensitive measurement techniques than those used in this study are required for the estimation of NH3 emissions from grazing cattle at the stocking rates used here. Based on the outcomes of this study, no changes to the current NH3 emission factor for grazing cattle are recommended. However, subject to improved NH3 emission measurement techniques, further combined measurements of urine N excretion and NH3 emissions from grazing cattle are recommended for a range of grazing management systems and environmental conditions. Introduction and Aims The UK inventory of ammonia emissions from agriculture is a nitrogen-flow, mass balance model, requiring emission factors to be expressed as a proportion of available nitrogen in the emitting pool. For emissions from grazing cattle, this can effectively be expressed as a proportion of the urine N deposited to the pasture. In previous studies of ammonia emissions from grazing cattle, urine N returns have been estimated based on literature values or animal chamber measurements. Direct measurements have also been made from urine applications to land, using a system of small wind tunnels (Lockyer and Whitehead, 1990), but the influence of the tunnel system can mean that measured emission can differ markedly from those under ambient conditions. There is a need, therefore, for further in-situ grazing emission measurements combined with measurements of urine N returns to pasture to provide a more robust emission factor for this source under a range of environmental and management conditions. The aims of this WP therefore were to conduct combined measurements of urine N deposition, using urine sensors specifically developed for this purpose by AgResearch (New Zealand), and ammonia (NH3) emissions using micrometeorological techniques from grazing cattle. A more complete report of this WP can be found in Appendix 6.1. Methods Six grazing trials were conducted across two sites: two trials at Easter Bush, Edinburgh, using 14 beef cattle (average live weight 630 kg) on a 1 ha permanent pasture and a further 4 trials at North Wyke, Devon, using up to 12 in-calf dairy heifers (average live weight 450 kg) on a 0.5 ha permanent pasture. The Easter Bush trials were conducted in September and October 2012 and the North Wyke trials between July and September 2013. Each trial lasted between 5 and 10 days.

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Urine deposition to the grazed pastures was estimated by fitting a proportion of the grazing cattle with purposely designed urine sensors (AgResearch, New Zealand). The urine sensors record the timing of each urination event, volume flow, N concentration and supposedly location (although this feature was subsequently found not to function correctly). At Easter Bush, cattle were also fitted with GPS collars. Sensors were calibrated using cattle urine of known volumes and N concentrations. Sensors remained on the cattle for up to four days. Ammonia emissions from the grazed plot were measured using an inverse dispersion modelling approach combining measurements of ammonia concentration at fixed points around the paddock (using either Los Gatos Economical Ammonia Analysers or ALPHA passive samplers) and wind speed and direction statistics. Results A total of 678 individual urination events were recorded, 119 from the beef cattle at Easter Bush and 559 from the dairy heifers at North Wyke. As the method of sensor attachment to the cow was improved throughout the trial, the number of sensors giving reliable data and the average duration of data recording also improved. Frequency of urination was greater for the dairy heifers than the beef cattle at 11.6 and 7.6 events per day, respectively. Mean values for urine volume, N concentration and N loading per urination event were similar between the cattle types (Table 6.1, Figure 6.1), but with the greater frequency of urination, total urine N excretion was greater for the dairy heifers than the beef cattle, with respective values of 255 and 194 g N per animal per day. Ammonia emissions from the grazed pastures were below the limit of detection for the measurement techniques used for all but two of the trials. Problems with sensitivity and drift meant that none of the Los Gatos instrument data were usable. For the July and August trials at North Wyke, ALPHA sampler data were used to estimate average fluxes of 0.11 and 0.57 kg ha-1 d-1 NH3-N for the July and August trials, respectively, which represented 8.6 and 33.5% of the estimated urine N deposition to the pastures during the grazing period. The current emission factor for grazing emissions used in the UK ammonia emission inventory is 6% of urine N deposition, with source data being in the range 0-10%. Table 6.1. Urine volume, N concentration and N loading statistics for the beef (119 observations) and dairy (559 observations) cattle urination events

Mean Median Range Lower quartile

Upper quartile

Standard deviation

Beef cattle:

Volume (L) 1.75 1.60 0.60-4.73 1.19 2.13 0.82

N concentration (g L-1) 14.4 15.1 2.2-20.6 13.3 16.8 3.6

N loading (g) 25.6 22.9 3.5-75.2 15.3 29.9 1.39

Dairy heifers:

Volume (L) 1.80 1.47 0.36-6.42 1.03 2.29 1.09

N concentration (g L-1) 13.1 13.9 0.6-34.4 8.6 16.8 6.2

N loading (g) 22.0 18.0 0.3-113.8 11.6 28.1 15.6

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Figure 6.1. Diurnal variation in urine N loading. Conclusions and recommendations

The urine sensors provided an automated means of monitoring urination events from grazing cattle, enabling the collection of a large amount of data. However, improvements to sensor attachment and location monitoring are needed.

There was clear diurnal variation in the N loading per urination event, which may have implications for potential N losses depending on livestock management and locations at different times of day.

More sensitive measurement techniques than those used in this study are required for the estimation of ammonia emissions from grazing cattle at the stocking rates used here.

Based on the outcomes of this study, no changes to the current ammonia emission factor for grazing cattle are recommended. However, subject to improved ammonia emission measurement techniques, further combined measurements of urine N excretion and ammonia emissions from grazing cattle are recommended for a range of grazing management systems and environmental conditions.

0

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InveN2Ory Published Outputs (as of 27th May 2016)

List of InveN2Ory project papers published, submitted, in preparation and planned

Published Smith, K.A., Dobbie, K.E., Thorman, R., Watson, C.J., Chadwick, D.R., Yamulki, S. & Ball, B.C. (2012). The effect of N fertilizer forms on nitrous oxide emissions from UK arable land and grassland. Nutrient Cycling in Agroecosystems 93, 127-149. Skiba, U., Jones, S.K., Dragosits, U., Drewer, J., Fowler, D., Rees, R.M., Pappa, V.A., Cardenas, L., Chadwick, D., Yamulki, S. & Manning, A.J. (2012). UK emissions of the greenhouse gas nitrous oxide. Philosophical Transactions of the Royal Society B, 367 no. 1593, 1175-1185. Cloy, J.M., Rees, R.M., Smith, K.A., Goulding, K., Smith, P., Waterhouse, A. & Chadwick, D. (2012). Impacts of Agriculture upon Greenhouse Gas Budgets. In Environmental Impacts of Modern Agriculture. Eds: R.E. Hester and R.M. Harrison), Issues in Environmental Science and Technology, Royal Society of Chemistry, Cambridge, Issue 34. Rees, R.M., Baddeley, J.A., Bhogal, A., Ball, B.C., Chadwick, D.R., MacLeod, M., Lilly, A., Pappa, V.A., Thorman, R.E., Watson, C.A. & Williams, J. (2013). Nitrous oxide mitigation in UK agriculture. Soil Science and Plant Nutrition 59, 3-15. Topp, C.F.E., Wang , W., Cloy , J.M., Rees R.M., & Hughes, G. (2013). Information Properties of Boundary Line Models for N2O Emissions from Agricultural Soils. Entropy 15, 972-987. Chadwick, D.R., Cardenas, L., Misselbrook, T.H., Smith, K.A., Rees, R.M., Watson, C.J., Mcgeough, K.L., Williams, J.R., Cloy, J.M., Thorman, R.E. & Dhanoa, M.S. (2014). Optimizing chamber methods for measuring nitrous oxide emissions from plot-based agricultural experiments European Journal of Soil Science, 65, 295–307. Fitton N., Datta A., Smith K., Williams J., Hastings A., Kunhert M., Topp K. & Smith P. (2014). Assessing uncertainties in modelled estimates of N2O emissions and yield at a UK cropland experimental site using the DailyDayCent model. Nutrient Cycling in Agroecosystems 99, 119-133. Gilhespy, S.L., Anthony, S., Cardenas, L., Chadwick, D., del Prado, A., Li, C., Misselbrook, T., Rees, R.M., Salas, W., Sanz-Coben, A., Smith, P., Tilston, E.L., Topp, C.F.E., Vetter, S., & Yeluripati, J.B. (2014). First 20 years of DNDC (DeNitrification DeComposition): Model Evolution. Ecological Modelling 292, 51–62. Misselbrook, T.H., Cardenas, L.M., Camp, V., Thorman, R.E., Williams, J.R., Rollett, A.J. & Chambers, B.J. (2014). An assessment of nitrification inhibitors to reduce nitrous oxide emissions from UK agriculture. Environmental Research Letters 9, doi:10.1088/1748-9326/9/11/115006. Cowan, N.J., Famulari, D., Levy, P.E., Anderson, M., Bell, M.J., Rees, R.M., Reay, D.S. & Skiba, U.M. (2014). An improved method for measuring soil N2O fluxes using a quantum cascade laser connected to a dynamic chamber. European Journal of Soil Science 65, 643-652 Fitton, N., Datta, A., Hastings, A., Kuhnert,M., Topp, K., Cloy, J., Rees, R.M., Cardenas, L., Williams, J., Smith, K., Chadwick, D. & Smith, P. (2014). The Challenge of Nitrogen Management at the Field

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Scale: Simulation, Sensitivity and Uncertainty Analysis of N2O Fluxes Across Nine Experimental Sites Using Dailydaycent. Special Issue of Environmental Research Letters on Focus on Nitrogen Management Challenges: From Global to Local Scales. Environmental Research Letters 9, 095003. Bell, M., Rees, R., Cloy, J., Topp, K., Bagnall, A. & Chadwick, D. (2015). Nitrous oxide emissions from cattle excreta applied to a Scottish grassland: effects of soil and climatic conditions and a nitrification inhibitor. Science of the Total Environment 508, 343-353. Cowan, N.J., Famulari, D., Levy, P.E., Anderson, M., Reay, D.S. & Skiba, U.M. (2014). Investigating negative fluxes of N2O in agricultural soils using a high precision dynamic chamber method. Atmospheric Measurement Techniques 7, 4455-4462. Cowan, N.J., Norman, P., Famulari, D., Levy, P.E., Reay, D.S. & Skiba, U.M. (2015). Spatial variability and hotspots of soil N2O fluxes from intensively grazed grassland. Biogeosciences 12, 1585 - 1596. Hinton, N.J., Cloy, J.M., Bell, M.J., Chadwick, D.R. Topp, C.F.E., & Rees, R.E. (2015). Managing fertiliser nitrogen to reduce nitrous oxide emissions and emission intensities from a cultivated Cambisol in Scotland. Geoderma Regional 4, 55-65. Yeluripati, J.B., del Prado, A., Sanz-Cobena,,A., Rees, R.M., Li, C., Chadwick, D., Tilston, E., Topp, C.F.E., Cardenas, L.M., Ingraham, P., Gilhespy, S., Anthony, S., Vetter, S.H., Misselbrook, T., Salas, W. & Smith, P (2015). Global Research Alliance Modelling Platform (GRAMP): An open web platform for modelling greenhouse gas emissions from agro-ecosystems. Computers and Electronics in Agriculture 111, 112-120. Bell, M.J., Cloy, J.M., Topp, K., Ball, B.C., Bagnall, A., Rees, R.M. & Chadwick, D. R. (2016). Quantifying N2O emissions from intensive grassland production: the role of synthetic fertiliser type, application rate, timing, and nitrification inhibitors. The Journal of Agricultural Science DOI: http://dx.doi.org/10.1017/S0021859615000945. Bell, M.J., Hinton, N., Rees, R.M., Cloy, J.M., Topp, K., Cardenas, L., Donovan, N., Scott, T., Webster, C., Whitmore, A., Williams, J., Balshaw, H., Paine, F., & Chadwick, D. (2015). Nitrous Oxide emissions from fertilised UK arable soils: Quantification and mitigation. Agriculture, Ecosystems and Environment, 212, 134-147. McGeough, K.L., Watson, C.J., Müller, C., Laughlin, R.J. & Chadwick, D.R. (2016). Soil properties play an important role in determining the efficacy of dicyandiamide (DCD) as a nitrification inhibitor in UK soils. Soil Biology and Biochemistry 94, 222-232. Bell,M.J., Hinton,N.J., Cloy,J.M., Topp,C.F.E., Rees,R.M., Williams,J.R., Misselbrook,T.H. & Chadwick,D.R. (2016). How do emission rates and emission factors for nitrous oxide and ammonia vary with manure type and time of application in a Scottish farmland? Geoderma, 264, 81-93. Cowan, N.J., Levy, P.E., Famulari, D., Anderson, M., Drewer, J., Reay, D.S., & Skiba, U.M. (2016) The influence of tillage on N2O fluxes from an intensively managed grazed grassland in Scotland. Biogeosciences 13, 4811 – 4821. Misselbrook, T., Flemming, H., Camp, V., Umstatter, C., Duthie, C.-A., Nicoll, L. & Waterhouse, T. (2016) Automated monitoring of urination events from grazing cattle. Agriculture, Ecosystems and Environment.

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Jones, S. K., Helfter, C., Anderson, M., Coyle, M., Campbell, C., Famulari, D., Di Marco, C., van Dijk, N., Topp, C. F. E., Kiese, R., Kindler, R., Siemens, J., Schrumpf, M., Kaiser, A., Nemitz, E., Rees, R. M., Sutton, M. A., and Skiba, U. M. (2016). The nitrogen, carbon and greenhouse gas budget of a grazed, cut and fertilised temperate grassland. Biogeosciences Discussion doi:10.5194/bg-2016-221. In press Drewer, J., Anderson, M., Scholtes, B., Helfter, C., Parker, J., Rees, R.M. & Skiba, U.M. The impact of

ploughing intensively managed temperate grasslands on N2O, CH4 and CO2 fluxes. Plant and Soil.

DOI 10.1007/s11104-016-3023-x

Cardenas, L.M.,Misselbrook, T. Hodgson, C., Donovana, N., Gilhespy, S, Smith, K., Dhanoa, M.S. & Chadwick, D. Effect of the application of cattle urine and dung on greenhouse gas emissions from a UK grassland soil. Agriculture, Ecosystems and Environment. Submitted Myrgiotis, M., Williams, M., Rees, R.M., Smith, K.E., Thorman, R.E., Sylvester-Bradley, R. & Topp, C.F.E. Model evaluation in relation to soil N2O emissions: An algorithmic method which accounts for variability in measurements and possible time lags. Environmental Modelling and Software. Levy, P; Cowan, N; van Oijen, M ; Famulari, D; Drewer, J; Skiba, U On the estimation of cumulative fluxes of N2O : uncertainty in temporal upscaling and emission factors. European Journal of Soil Science. In advanced preparation Carolan R., Donovan, N., Cardenas, L. et al. Autochamber vs static chamber comparison synthesis paper. For submission to Biogeosciences Cardenas, L. et al. A synthesis of N2O EFs from the InveN2Ory fertilised grassland experiments in the UK. Chadwick, D.R., Cardenas, L., Donovan, N, Misselbrook, T., Williams, J., Thorman, R, McGeough, K., Watson, C., Bell, B, Anthony, S., & Rees, B. Disaggregating grazing excretal N2O EFs to urine and dung: quantification of country specific emission factors and implications for national emissions. For submission to Journal of Environmental Quality. Fitton et al. Modelling spatial and inter-annual variations of nitrous oxide emissions from UK cropland and grasslands using DailyDaycent. For submission to Agriculture, Ecosystems and Environment. Hiscock et al. Factors influencing the variation of indirect N2O EFs in the Wensum catchment. Hiscock et al. Fluxes of indirect N2O emissions during a storm event in a mini-catchment in the Blackwater sub-catchment.

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Misselbrook et al. Summary of effects of nitrification inhibitors across multiple sites with different N sources. Misselbrook, T.H., Anthony, S.G., Chadwick, D.R.., Moorby, J.M., Jones, C.A., Spadavecchia, L. Improving the UK Agriculture Greenhouse Gas Emission Inventory. Nicholson, F. et al Evidence for different N2O EFs from different manure types applied to land. Topp et al. The impact of manure type and timing on N2O emissions and leaching: a modelling assessment. Cowan, N.J., Famulari, D., Levy, P.E., Anderson, M., Reay, D.S., Skiba, U.M. Nitrous oxide emissions

sources from a mixed livestock farm. For submission to Agricultural Ecosystems and Environment

Cowan, N.J., Famulari, D., Anderson, M., Levy, P.E., Reay, D.S., Skiba, U.M. Comparisons of agricultural nitrous oxide fluxes measured using static chamber and quantum cascade laser-based eddy covariance techniques. For submission to Biogeosciences Carnell, E., Meneguz, E., Arnold, T., Skiba, U., Misselbrook, T., Cardenas, L., Manning, A., Dragosits, U. Verifying the UK N2O emission inventory with tall tower measurements. For submission to Biogeosciences.

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Reference List Baggs, E., Rees, RM., Castle, K., Scott, A., Smith, KA. & Vinten, A.J.A. (2002). Nitrous oxide release from soils receiving crop residues and applications of paper waste. Agriculture Ecosystems and Environment 90, 109-123. Beckwith, C. P, Cooper, J., Smith, K. A. & Shepherd, M. A.(1998).. Nitrate leaching loss following application of organic manures to sandy soils in arable cropping. I. Effects of application time, manure type, overwinter crop cover and nitrification inhibition. Soil Use and Management 14, 123–130. Bell,M.J., Cloy,J.M., Topp,C.F.E., Ball,B.C., Bagnall,A., Rees,R.M. & Chadwick,D.R. (2015a). Quantifying N2O emissions from intensive grassland production: the role of synthetic fertiliser type, application rate, timing, and nitrification inhibitors. Journal of Agricultural Science. DOI: http://dx.doi.org/10.1017/S0021859615000945. Bell, M.J., Hinton, N., Rees, R.M., Cloy, J.M., Topp, K., Cardenas, L., Donovan, N., Scott, T., Webster, C., Whitmore, A., Williams, J., Balshaw, H., Paine, F., & Chadwick, D. (2015b). Nitrous Oxide emissions from fertilised UK arable soils: Quantification and mitigation. Agriculture Ecosystems and the Environment 212, 134-147. Bell,M., Rees,R.M., Cloy,J., Topp,K., Bagnell, A. & Chadwick,D. (2015c). Nitrous oxide emissions from cattle excreta applied to a Scottish grassland: effects of soil and climatic conditions and a nitrification inhibitor. Science of the Total Environment 508, 343-353. Bell,M.J., Hinton,N.J., Cloy,J.M., Topp,C.F.E., Rees,R.M., Williams,J.R., Misselbrook,T.H. & Chadwick,D.R. (2016). How do emission rates and emission factors for nitrous oxide and ammonia vary with manure type and time of application in a Scottish farmland? Geoderma 264, 81-93. Bremner, J.M., Blackmer, A.M. (1978). Nitrous oxide: Emissions from soils during nitrification of fertiliser nitrogen. Science (Washington, DC) 199, 295-296. BSFP (2013): The British Survey of Fertiliser Practice. Fertiliser use on farm crops for crop year 2012. Defra, London. 103pp. Cardenas, L.M., R.Thorman, R., Ashlee, N., Butler, M., Chadwick, D.R., Chambers, B., Cuttle, S., Donovan, N., Kingston, H., Lane, S. & Scholefield, D. (2010). Quantifying annual N2O emission fluxes from grazed grassland under a range of inorganic fertiliser nitrogen inputs. Agriculture, Ecosystems and Environment 136, 218-226. Cardenas L., Gilhespy S. & Misselbrook T. (2013) Inventory of UK emissions of methane and nitrous oxide from agricultural sources for the year 2012. MS EXCEL spreadsheet. Rothamsted Research, North Wyke, Devon. Carnell E.J., Tomlinson S.J. & Dragosits U. (2014) The spatial distribution of ammonia, methane and nitrous oxide emissions from agriculture in the UK 2012. CEH Report. Centre for Ecology & Hydrology, Edinburgh Research Station, Bush Estate, Penicuik. 11pp. Chadwick, D. R., Cardenas, L., Misselbrook, T. H., Smith, K. A., Rees, R. M., Watson, C. J., McGeough, K. L., Williams, J., Cloy, J., Thorman, R., & Dhanoa, M. S. (2014). A fit-for-purpose methodology for nitrous oxide measurements from plot-based agricultural experiments. European Journal of Soil Science 65[2], 295-307.

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List of Appendices Appendix 1.1. Prioritisation Report. Appendix 1.2. Joint Experimental Protocols for Fertiliser, Organic Manures, and Urine and Dung. Appendix 2.1. Results of the ‘blind’ ring-test of the different Gas Chromatographs used to measure N2O concentration. Appendix 4.1. Site simulation, sensitivity and uncertainty analysis of N2O fluxes across nine experimental sites using DailyDayCent. Appendix 4.2. Modelling work with L-DNDC. Appendix 4.3. Site simulation of N2O fluxes across the AC0116 experimental sites using DailyDayCent. Appendix 4.4. Spatial simulation of N2O emissions from UK cropland and grasslands using DailyDayCent. Appendix 6.1. Automated monitoring of urination events from grazing cattle.