As expected, significant relationships of various strength were found between plant growth, soil texture or FWC and Δ¹³C. The relationships with aerial data are being analyzed; preliminary results show good relations to plant growth and soil properties.

<figure><img src="image_38.png" /><figcaption>Figure 1. Relationship between wheat and pea seeds ¹³C discrimination, biomass and subsoil water capacity.</figcaption></figure>

## CONCLUSION

The impact of water stress on plants reliably indicated spatially variable soil conditions related to soil vulnerability to water shortage and nitrate leaching.

**Acknowledgements:** Supported by Ministry of Agriculture of the Czech Republic CRI RO0418, Technology Agency of the Czech Republic TH02030133 and by Ministry of Industry and Trade TRIO FV10213.

## REFERENCES

Bruthans J., Kadleova R., Rybarova M., Hronec L., Kurkova I., Buzek F., 2015. High nitrate content in river bank filtration water supply in alluvial aquifer: Integrated approach, in 42nd IAH Congress - AQUA 2015, Rome, Italy (Abstract ID: 759).

Cameira, M.R., Mota, M., 2017. Nitrogen Related Diffuse Pollution from Horticulture Production—Mitigation Practices and Assessment Strategies. Horticulturae 3, 25.

Duffková D., Brom J., Žížala D., Zemek F., Procházka J., Nováková E., Zajíček A., Kvítek T., 2012. The determination of infiltration regions using water stress indices of vegetation from remote sensing and field measurement. Certified Methods, VÚMOP, Praha 2012. 64 pp. (In Czech). ISBN: 978-80-87361-15-3.

Klíř J., Šimon T., Svoboda P., Kurešová G., Haberle J. 2017. The distribution of mineral nitrogen in soil in relation to risk of nitrogen leaching in farms with irrigated vegetables, in: Dalgaard T., Olesen J.E., et al. (Eds), Innovative solutions for sustainable management of Nitrogen, Copenhagen, Aarhus University, June 2017, p. 79.

Raimanová I., Svoboda P., Kurešová G., Haberle J., 2016. The effect of different post-anthesis water supply on the carbon isotope discrimination of winter wheat grain. Plant, Soil & Environment, 62 (7), 329-334.

Váša J., 1960. Soil moisture characteristics (Půdní vláhové charakteristiky). Vodohospodárský časopis SAV, 8: 235-243 (In Slovak).
ASSESSMENT OF ARTIFICIAL WETLAND FOR NITRATE REMOVAL FROM SUBSURFACE DRAINED WATERSHED

TOURNEBIZE J.<fnref n="1" />, CHAUMONT C.<fnref n="1" />, BLANDIN M.<fnref n="1" />, SOOSAAR K.², HANSEN R.², MUHEL M.², TEEMUSK A.², PÄRN J.², MANDER U.²

INTRODUCTION

To reduce agricultural pollutants in drained watersheds, artificial wetlands showed a real potential as a mitigation strategy. Several publications have previously carried out experiments at plot scale to evaluate their efficacy, but few are dealing with the watershed scale. It is also the case that the long-term efficiency and greenhouse gas emission potential of these systems are poorly known. This study fills some of these gaps in our knowledge in this field.

MATERIAL AND METHODS

An off-stream artificial wetland (5270 m², 0.1 to 1 m deep), designed initially to abate pesticide pollution, intercepts drainage water from a 355 ha watershed in Rampillon, France (03°03′37.3″ E, 48°32′16.7″ N). A sluice gate at the inlet controls the rate of flow during peak events specially during winter season The flow entering the wetland fluctuates from 0 to 120 L s⁻<fnref n="1" />. The wetland is partially colonised by sedges, reed, cattails and free floating phytoplankton. Since 2012, an automatic water quality monitoring system has measured water discharge, temperature, dissolved O₂, conductivity pH, and NO₃⁻ and DOC concentrations in both the inlet and outlet artificial wetland. High frequency monitoring (discharge with an waterflow probe based on Doppler principle and Nitrate concentration with a spectro UV device based on light adsorption at 253nm by nitrate ion, at an hourly time step), supported with weekly flow weighted samples to assess Nitrate flux removal between the INLET and OUTLET of the artificial wetland. In May and November 2014 and March, November 2015 one-week high frequency measurement campaigns were conducted to study N₂O fluxes using 6 manually operated opaque floating static chambers and 12 floating automatic dynamic chambers (ADC). The latter were operated via multiplexer and had an incubation time of 5 minutes, whereas the gas flow was continuously measured using the Aerodyne TILDAS quantum cascade laser system. During the campaign, changes in the NO₃⁻ concentration was measured in nine reactor pipes. Also, water samples were collected for N₂O and N₂ isotope analysis, and sediments were collected for potential N₂ emission measurements using He/O₂ method.

RESULTS AND DISCUSSION

Nitrate removal in drained water

The 5 years monitoring data for the INLET/OUTLET sampling points (table 1) showed contrasting hydrological season with cumulative drain flow ranging between 89 up to 269 mm/years. The INLET average concentrations were above 54 mg_NO₃·L⁻<fnref n="1" />, whereas OUTLET concentrations were 19% lower in average for the 5 year's period. The percentage of exceeding threshold 50mg/L time is reduced from 45% to 17% due to artificial wetland mitigation. Mitigation efficiency due to denitrification processes accounting for the removal of about 24% of Nitrate fluxes from INLET to OUTLET of the experimental artificial wetland. Looking in detail the time series' removal efficiency is strongly dependent to hydraulic residential time and water temperature. However, nitrate removal was evidence in the datasets over all flow periods.

<footnote n="1">¹Irstea, UR HYCAR, 1 Rue Pierre-Gilles de Gennes, CS 10030, F-92761 Antony Cedex, France, ²University of Tartu, Institute of Ecology and Earth Sciences, 46 Vanemuise St, 51014 Tartu, Estonia</footnote>
<table><caption>Table 1. Summary of monitored data and calculated fluxes for the period 2012 to 2017</caption><thead><tr><th>Hydrological Year<br>(from N/10/01 to N+1/09/30)</th><th>2012-13</th><th>2013-14</th><th>2014-15</th><th>2015-16</th><th>2016-17</th></tr></thead><tbody><tr><td>Rainfall (mm)</td><td>790</td><td>921</td><td>652</td><td>869</td><td>692</td></tr><tr><td>Reference Evapotranspiration (mm)</td><td>595</td><td>621</td><td>734</td><td>638</td><td>730</td></tr><tr><td>Average Temperature during drained flow period (°C)</td><td>12.6</td><td>15.3</td><td>10.8</td><td>10.6</td><td>10.7</td></tr><tr><td>Drained Flow (mm)</td><td>269</td><td>225</td><td>192</td><td>199</td><td>89</td></tr><tr><td>Intercepted Volume by Artificial Wetland (mm)</td><td>25</td><td>19</td><td>120</td><td>94</td><td>29</td></tr><tr><td>Intercepted Rate of total drained flow (%)</td><td>9%</td><td>8%</td><td>63%</td><td>47%</td><td>33%</td></tr><tr><td>Flow weight INLET Nitrate concentration (mgNO<sub>3</sub>.L<sup>-1</sup>)</td><td>60.6</td><td>54.5</td><td>57.6</td><td>65.7</td><td>70.7</td></tr><tr><td>Flow weight OUTLET Nitrate concentration (mgNO<sub>3</sub>.L<sup>-1</sup>)</td><td>46.1</td><td>39.4</td><td>52.3</td><td>57.2</td><td>55.9</td></tr><tr><td>% Nitrate removal (%)</td><td>31.4</td><td>38.5</td><td>10.2</td><td>14.9</td><td>26.4</td></tr><tr><td>Over 50mg/L Nitrate concentration frequency INLET (%)</td><td>55%</td><td>NC</td><td>55%</td><td>32%</td><td>37%</td></tr><tr><td>Over 50mg/L Nitrate concentration frequency OUTLET (%)</td><td>15%</td><td>8%</td><td>27%</td><td>10%</td><td>27%</td></tr><tr><td>Yearly average Nitrate removal rate (mgN-NO<sub>3</sub>.day<sup>-1</sup>m<sup>-2</sup>)</td><td>269</td><td>207</td><td>441</td><td>254</td><td>270</td></tr></tbody></table>

## N₂O emission from artificial wetland

Measured N₂O emissions were low with an average of 1.1 µg N₂O-N.m⁻².h⁻¹, ranging from -25 to 62.8 µg N₂O-N.m⁻².h⁻¹. Extrapolated fluxes of N₂O were not significant at artificial wetland with less than 0.3 kg N-N₂O emitted over 312kgN-NO₃ removed (<0.1%).

<figure><img src="image_39.png" /><figcaption>Figure 1. N₂ and N₂O emissions from sediment incubation.</figcaption></figure>

## CONCLUSION

The Nitrate removal potential of artificial wetlands is clear, including high efficiency variability during the season due to hydraulic residential time and temperature. Denitrification is the main process involved, without significant N₂O emissions (less than 0.1% of removed Nitrate). A modelling approach based on the Tank In Series concept allows extrapolation of Rampillon's results and supports a design tool recommending a ratio between restored artificial wetland over connected drained area of 1% to remove 40% of annual Nitrate coming drained agricultural watershed.
EVALUATING SCENARIOS OF LAND MANAGEMENT PRACTICES IN CONTRASTED LANDSCAPES USING
A NITROGEN LANDSCAPE MODEL

CASAL L. ¹; DURAND P. ¹*; AKKAL-CORFINI N. ¹; BENHAMOU C. ²; SALMON-MONVIOLA J. ¹; FERRANT S. ³; PROBST A. ⁵; PROBST J.L. ⁴; SAUVAGE S. ⁴; VERTES F. ¹

¹UMR SAS, AGROCAMPUS OUEST, INRA 35000 Rennes, France; ²UMR ECOSYS, AgroParisTech, INRA 78850 Thiverval-Grignon, France; ³Centre National d'Etudes Spatiales (CNES), CESBIO, F-31000 Toulouse, France; ⁴ECOLAB, Université de Toulouse, CNRS, INPT, UPS, France

INTRODUCTION

Designing mitigation strategies at the landscape scale to improve overall N efficiency and reduce undesirable emissions is a major challenge (Cellier et al., 2011). Most often, mitigation measures are conceived at field or farm scale to reduce one main type of N emission. As part of the ESCAPADE project (ANR-12-AGRO-0003), agri-environmental scenarios of N management were built in contrasted rural landscapes to better understand the way reactive nitrogen transforms and transfers into, out of and within the agro-ecosystem (Galloway et al., 2003) depending on farm management and landscaping. The main objectives were (1) to simulate spatio-temporal scenarios under different environmental and agricultural conditions using the TNT2 model and (2) to evaluate the relative efficiency and possible complementarity of field-oriented and landscape-oriented measures to reduce N emissions in contrasted landscapes.

MATERIAL AND METHODS

The two study sites are headwater catchments located in Kervidy-Naizin (Western France) and in Auradé (South-West of France). They are contrasted in terms of agriculture type (mix farming with high livestock density and cereal cropping, respectively), soil, and landscape structures. Surveys were held to describe agricultural practices exhaustively (crop rotations and crop management practices over 15 years). The scenarios were built to investigate the different ways of mitigating the nitrogen cascade 1) optimization of agricultural practices (BMP scenarios), 2) management of landscape structure according to two strategies: (i) riparian buffer installation (ie distributing ecological structures such as vegetated/forested buffer strips around major sources or in riparian position in the landscape RI_scenarios), and (ii) set-aside patches (implanting few patches of natural vegetation HD_scenarios). Control scenario O_N aimed at estimating the N legacy of the catchment and the time lag necessary to go back to nearly pristine conditions. The scenarios are simulated using TNT2, a spatially distributed agro-hydrological model focusing on the spatial interactions within the landscape (Beaujouan et al., 2002; Ferrant et al., 2013; Oehler et al., 2009).

RESULTS AND DISCUSSION

<figure><img src="image_40.png" /><figcaption>Figure 1. Nitrogen mass balance at the catchment scale for the last three hydrologic years of simulation from 2012 to 2015.</figcaption></figure>
The figure 1 highlights the differences of the nitrogen mass balance between these contrasted sites. Auradé has a N surplus half of that of Kervidy-Naizin (40 and 83 kgN/ha/year, respectively). The diverse N inputs (organic, mineral inputs and grazing) in Naizin site generates a lossy nitrogen management enhanced by the high density of livestock and the wetter climate (runoff of 340 mm vs 100 mm in Auradé). The inputs on Auradé site are mineral fertilizers only. As a result the losses in the stream are only 13 in Auradé vs 64 kgN.ha⁻¹. year⁻¹ in Kervidy-Naizin.

<table><caption>Table 1: (a) Main features of the set of scenarios with N_sc for Kervidy-Naizin and Au_sc for Auradé site (the units are specified in brackets) and (b) corresponding results. The concentration is in mgN-NO₃·L⁻¹, N input is in kgN.ha⁻¹. year⁻¹ and N excess is a dimensionless ratio.</caption><thead><tr><th></th><th>N_BAU</th><th>N_BMP</th><th>N_HD14</th><th>N_RI14</th><th>N_ON</th><th>Au_BAU</th><th>Au_BMP</th><th>Au_HD18</th><th>Au_RI18</th><th>Au_ON</th></tr></thead><tbody><tr><td><strong>a. Scenarios</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fertiliser reduction (%)</td><td>0</td><td>9</td><td>23</td><td>19</td><td>100</td><td>0</td><td>6</td><td>28</td><td>13</td><td>100</td></tr><tr><td>Uncut grassland (%)</td><td>5</td><td>5</td><td>16</td><td>15</td><td>100</td><td>4</td><td>4</td><td>18</td><td>18</td><td>100</td></tr><tr><td><strong>b. Results - Fluxes</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(average of 3 last years)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NO₃-N concentration</td><td>15.2</td><td>14.1</td><td>13.6</td><td>11.7</td><td>9</td><td>8.8</td><td>8.6</td><td>7.6</td><td>8.1</td><td>4.4</td></tr><tr><td>N input by agriculture</td><td>207</td><td>188</td><td>160</td><td>168</td><td>0</td><td>107</td><td>100</td><td>79</td><td>93</td><td>0</td></tr><tr><td>N excess</td><td>83</td><td>68</td><td>45</td><td>46</td><td>-94</td><td>40</td><td>38</td><td>24</td><td>33</td><td>-24</td></tr></tbody></table>

The results show that on both sites, scenarios rank in the same way BAU > BMP > HD > RI > ON. However, the scenarios have different efficiencies according to the sites. The control scenario ON (zero input and 100% grassland) represents a decrease in stream concentration of 50 % in Auradé vs. 41 % in Kervidy-Naizin compared to Business as usual scenario (BAU). The scenarios of landscape management are more efficient in Kervidy-Naizin with a decrease of 11 % and 23% ((BAU-sc)/ BAU) for HD and RI scenarios, respectively, while in Auradé the decrease is only 8 % and 14 %, respectively, although the surface converted in environmental zone is larger in Auradé (18%) than in Naizin (16 %).

## CONCLUSION

This study shows the interest of distributed modelling to compare contrasted sites. Assessing the effect on the gaseous emissions and deposition of ammonia and the indirect N₂O emissions should be the next step for better assessment of these scenarios. More generally, these types of landuse management are also potentially beneficial for the mitigation of other pollutant and for preservation of biodiversity. This suggests that such policy should be submitted to a multicriteria evaluation, in terms of ecosystem services and economic impacts taking into account the agricultural and pedoclimatic context of the catchments.

**Acknowledgements:** This work was funded by the French National Research Agency (ESCAPADE project ANR-12-AGRO-0003). For Naizin-Kervidy, the farm surveys were performed within the MOSAIC project ANR-12-AGRO-0005). The grant of the 1st author was co-funded by Arvalis. The two experimental catchments, Kervidy-Naizin and Auradé, belong to the French Research Infrastructure OZCAR (Observatory of the Critical Zone: http://www.ozcar-ri.org/).

## REFERENCES

* Beaujouan V, Durand P, Ruiz L, Aurousseau P, Cotteret G. A hydrological model dedicated to topography-based simulation of nitrogen transfer and transformation: rationale and application to the geomorphology-denitrification relationship. Hydrological Processes 2002; 16: 493-507.

* Cellier P, Durand P, Hutchings N, Dragosits U, Theobald M, Drouet JL, et al. Nitrogen flows and fate in rural landscapes. In: Sutton, M. A., Howard, C. M., Erisman, J. W., Billen, G., Bleeker, A., Grennfelt, P., van Grinsven, H., Grizzetti, B., (eds.) The European Nitrogen Assessment Sources, Effects and Policy Perspectives. Cambridge University Press 2011: 229-248.

* Ferrant S, Durand P, Justes E, Probst J-L, Sanchez-Perez J-M. Simulating the long term impact of nitrate mitigation scenarios in a pilot study basin. Agricultural Water Management 2013; 124: 85-
96.

Galloway JN, Aber JD, Erisman JW, Seitzinger SP, Howarth RW, Cowling EB, et al. The nitrogen cascade. BioScience 2003; 53: 341–356.

Oehler F, Durand P, Bordenave P, Saadi Z, Salmon-Monviola J. Modelling denitrification at the catchment scale. Science of the Total Environment 2009; 407: 1726-1737.
N₂O EMISSIONS IN RESPONSE TO THE ADDITION OF NITRIFICATION INHIBITORS TO DIGESTATE: A
CASE STUDY IN THE PO VALLEY (NORTHER ITALY)

CHIODINI, M.E.<fnref n="1" />, PEREGO, A.<fnref n="1" />, SANZ-GOMEZ, J.², TARLAZZI, S.³ ACUTIS, M.<fnref n="1" />,

INTRODUCTION

Agricultural soils are the main anthropogenic source of nitrous oxide (N₂O) and its emission is related to fertiliser-N, particularly in over-fertilized systems (Shcherbak et al., 2014). Addition of nitrification inhibitors, such as 3,4-dimethylpyrazolphosphate (DMPP), to ammonium-based fertilizers was shown to decrease N₂O emissions (Severin et al., 2016). The aim of this study was to estimate the effect of a DMPP-base product (BASF commercial name: Vizura®) added to digestate in decreasing the N₂O emission in a field site in the Po valley (Northern Italy). This area is characterized by high temperature in summer and in turn high rate of nitrification, along with large fertilization rate and irrigation water availability (Perego et al., 2016).

MATERIAL AND METHODS

The N₂O emissions were observed in two monitoring campaigns (each 5 weeks long, sampling every 2
days) in a field located in Valera Fratta (45°15′N 9°20′E, Soil organic carbon=1.3%, June-August mean
air temperature=25°C) after top-dressing fertilization in maize in 2016 and prior to maize sowing in
2017. Compared treatments were: non-fertilized plot (NO), D: digestate - Vizura® injection, V: digestate
+ Vizura® injection. Digestate derived from silage maize + dairy slurry anaerobic digestion. The
fertilization rate in 2016 was 61 kg N ha⁻<fnref n="1" />; it was 283 kg N ha⁻<fnref n="1" /> in 2017. In V, Vizura® (2 l ha⁻<fnref n="1" />) was
injected in the topsoil (0-0.20 m).

For each treatment and each sampling day, three non-static chambers (NSCs, made of polypropylene, thermally insulated, 0.3 m long X 0.3 m wide X 0.3 m height) were used in order to collect replicates. NSCs were built according to Chadwick et al. (2014). The samples were taken by a syringe through a tube and valve having a Luer Lock system for the connection. For each sampling date and NSC, samples were taken at 0, 20 and 40 minutes after the installation of the NSC on the framework placed in the topsoil. Before taking the sample, the fan system was run for 10 seconds in order to accurately mixing the air into the NSC. Then 30 ml of inner air was collected by 60 ml syringe and then put into 12 ml under vacuum LabcoExetainer® glass vials.

As for the flux determination, we used the linear regression method wherein N₂O fluxes for each NSC was calculated as follows: F = H*dC/dt, where F is flux (µg m⁻²s⁻<fnref n="1" />); H is the ratio of the internal NSC volume (m³) over the surface area (m²) in contact with the soil; C is the N₂O concentration in the NSC (µg m⁻³); t is time (seconds); dC/dt is used to represent the time rate of change of C (µg m⁻³ s⁻<fnref n="1" />). Cumulated fluxes were calculated by the estimation of missing data through linear interpolation between the mean fluxes obtained by samples on successive sampling date. In order to define the standard deviation of cumulated fluxes (dotted lines in Fig. 1) a bootstrap based method using IBM SPSS 24 statistical software was applied.

**RESULTS AND** discussion

Both in 2016 and 2017, results demonstrate that the addition of Vizura® to the digestate significantly
reduce the cumulative N₂O emissions. As shown in Figure 1, similar emission trends were observed
over the experiments. In 2016, when an irrigation event occurred (about 30 mm; 20 days after
injection), V did not result in an increase in emissions unlike it was found in D. Similarly, in 2017 the
irrigation water, which was applied the day after the injection, caused a rapidly increase in N₂O
emission in D unlike in V. Moreover, the inhibition effect was still present at the end of the experiment.
The N₂O emissions were 50% and 81% lower in V than in D in 2016 and 2017, respectively. The emission

<footnote n="1">¹Department of Agricultural and Environmental Sciences, University of Milano, via Celoria, Italy, ² BASF SE, ³BASF Italy Crop Protection Division</footnote>
factor estimated for V was comparable to the IPCC value (1%; Tab.1). Our results of reduction are in agreement with Severin et al. (2016).

<figure><img src="image_41.png" /><figcaption>Table 1. Cumulative N₂O emission and EF factor (in % of N applied, subtracted by emissions from N0) of digestate - Vizura® (D) and digestate + Vizura® (V) treatments in 2016 and 2017.</figcaption></figure>

<table><thead><tr><th>Year</th><th>Treatment</th><th>Application method</th><th>Total N supplied (kg N ha<sup>-1</sup>)</th><th>TAN supplied (kg NH<sub>4</sub>-N ha<sup>-1</sup>)</th><th>N emitted (N<sub>2</sub>O-N kg ha<sup>-1</sup>)</th><th>EF (%)</th></tr></thead><tbody><tr><td rowspan="2">2016</td><td rowspan="2">D V</td><td rowspan="2">Inter-row injection</td><td rowspan="2">61</td><td rowspan="2">27.8</td><td>1.10</td><td>1.8%</td></tr><tr><td>0.52</td><td>0.9%</td></tr><tr><td rowspan="2">2017</td><td rowspan="2">D V</td><td rowspan="2">Tines harrow injection</td><td rowspan="2">238</td><td rowspan="2">167.5</td><td>9.22</td><td>3.9%</td></tr><tr><td>1.73</td><td>0.7%</td></tr></tbody></table>

## Conclusion

The addition of the DMPP inhibitors to digestate significantly reduced cumulative N₂O emissions from maize under the climatic conditions of the Po valley.

**Acknowledgements:** We acknowledge BASF Italy Crop Protection Division for generous provision of Vizura.

## REFERENCES

* Chadwick D.R., Cardenas L., Misselbrook T.H., Smith K.A., Rees R.M., Watson C.J., Dhanoa M.S., 2014. Optimizing chamber methods for measuring nitrous oxide emissions from plot-based agricultural experiments. Eur. J. Soil Sci., 65-2, 295-307.

* Perego A., Wu L., Finco A., Gerosa G., Chiazzese M., Amaducci S., 2016. Field evaluation combined with modelling analysis to study fertilizer and tillage as factors affecting N₂O emissions: A case study in the Po valley (Northern Italy). Agric. Ecosys. Environ., 225, 72-85.

* Shcherbak, I., Millar, N., & Robertson, G.P., 2014. Global metaanalysis of the nonlinear response of soil nitrous oxide (N₂O) emissions to fertilizer nitrogen. PNAS, 111-25, 9199-9204.

* Severin M., Fuß R., Well R., Hähndel R., Van den Weghe H., 2016. Greenhouse gas emissions after application of digestate: short-term effects of nitrification inhibitor and application technique effects. Arch. Agron. Soil Sci., 62-7, 1007-1020.
# CHANGES IN LAND USE AND IMPACT IN WATER QUALITY FOR WATER SUPPLY. EXAMPLE OF "CASTELO DE BODE" IN PORTUGAL

CORDOVIL, C.M.d.s.¹, REIS, R.², MIRANDA, R. ¹; VALE, M.²

¹ Universidade de Lisboa, Instituto Superior de Agronomia LEAF, Portugal; ² Direcção Geral do Território, Portugal

## INTRODUCTION

Territory management is closely linked to water management. Portugal has recently made a legislative revision of the land planning and territorial management policy. In this work we review the conceptual strategies defined for the efficient management of the territory and water resources, based on the work experience of two decades acquired on the sub-basin of "Castelo do Bode", to make an analysis of the transformations of land use, integrating the changes induced by the recent fires in the year 2017 which had a substantial risk impact on the decline of the raw water quality. A retrospective analysis of the evolution of the territorial occupation of the drainage basin is made for the official water supply facility (EPAL) in "Castelo de Bode". At the same time, the evolution of water quality parameters, in particular those associated with quality monitoring, is evaluated, with emphasis on the behavior of nitrogen and phosphorus. At the same time, and because the Portuguese mainland suffered in 2017 the most serious fires with more than 400 thousand hectares of burned area, we try to integrate in the analysis the phenomenon of fires and their impact on water quality. This risk of deterioration of the quality of the supercritical water is associated with the increase in the surface runoff, which carries with it the burning materials containing nutrients such as nitrogen and phosphorus and other elements whose excessive presence in the water is undesirable. The basin under study has undergone changes of use over the years with the consequent impacts on soil in terms of organic matter and its physical characteristics, namely drainage, which directly affects the quality of surface and groundwater. In this process of deterioration of quality, nitrogen (N) and phosphorus (P) are particularly important, two essential nutrients for agricultural production, but with special attention to their impact on water quality.

## DRIVERS AND IMPACTS

The adverse effects of forest fires have a variable impact on the various environmental compartments: soil, water, air and biota. Fire intensity is directly related to the impacts caused on the environment. A low-intensity fire can raise the surface temperature of the soil up to 100 °C and a temperature of 50 °C can be recorded at 5 cm depth. If the fire is of high intensity, then the temperature of the soil surface can reach 700 °C, and a temperature of 250 °C can be found at 10 cm depth (Neary et al., 1999). One of the consequences of forest fires may be the loss of soil organic matter (SOM). In fact, the volatilization of organic compounds starts at 100 to 180 °C and at 200 °C the decomposition of resistant compounds such as hemicellulose and lignin occurs. Forest fires can also result in structural changes in SOM, with their consequences in the lower retention of elements by soil colloids. These elements include both nutrients such as nitrogen and phosphorus, such as micronutrients and metals which, downstream can reach surface water by runoff or groundwater by leaching. However, fires also contribute to the entry of organic material from partially burnt materials which in the long term may even result in increased accumulation of OM in soils due to mineralization deficiency. Irrespective of fire intensity, there is a significant increase of N-NH4, with consequent loss of N by volatilization and, if the conditions favor nitrification, loss of nitrates by leaching. This increase in N-NH4 is mainly due to the increase number of dead plants and microbial biomass. In fact, another consequence of forest fires is the impact of temperature on soil microorganisms, which can be killed between 50 and 120 °C (Silva et al., 2016). Microbial biomass can even be completely destroyed in the surface layer of the soil and take many years to recover pre-fire levels. In this context, where both SOM and microbial biomass have been totally or partially destroyed, soil disruption necessarily leads to a reduction in its capacity for adsorption and water infiltration. After a fire occurs, a hydrophobic layer is formed in the soil and changes the porosity, the infiltration capacity and the water storage, as a consequence of the structural
changes occurred. In fact, the absence of vegetation, and the newly acquired depletion of the soil structure, greatly promotes erosion which, following the natural drainage trend of the basins, leads to varying amounts of nutrients in the surface water bodies (eg N and P) and organic compounds from the ashes formed by the vegetal material consumed by the fire. Not only is SOM and its mineralized elements lost, but also the organic compounds resulting from the burning of woody species will, in time, supply more mineral elements that can potentially reach the water bodies, thus increasing their pollution. Of particular note are nitrogen, phosphorus, micronutrients and metals, as well as organic compounds, for example polycyclic aromatic compounds (Silva et al., 2016). The most notable effect of the fires is on the level of the flora, by the partial or complete alteration or elimination of the vegetation (dead blanket, arboreal and shrub vegetation). As a result of the loss of vegetation, the soil is more exposed and with a greater vulnerability to the erosive processes, namely water erosion type in the first rains after the fire. The hydrological regime is also affected indirectly by changes in vegetation and soil level, with lower evaporation and higher volumes of surface runoff in burned areas compared to non-burned areas (Silva et al., 2016). Once again, this impact will determine and favor the nutrient runoff among which N and P stand out. In fact, forest fires should be seen as a source of diffuse pollution for bodies of water downstream of. In the present work, it is possible to determine the presence of the compounds of the present invention. These substances, from certain concentrations, present high toxicity to living beings, and have a potential for bioaccumulation and high environmental persistence (Silva et al., 2016). All pyrolytic substances, which also include N and P nutrients, can reach aquatic systems downstream from the burned areas, through surface run-offs (other means of transport include leaching and atmospheric deposition), compromising water quality and affecting the aquatic biota (Silva et al., 2016). For example, particular groups such as phytoplankton, amphibians and fish. However, re-vegetation of burned areas occurs spontaneously, but steps should be taken to promote plant growth to protect soils from the increased risk of erosion following a fire, with the negative consequences on water quality noted above. Promoting the recovery of the vegetation cover, which may include shrubby and woody herbaceous plants, necessarily requires restructuring of the soil, which necessarily implies the restoration of organic matter levels in the soil through conservation practices. A recommended practice for a shorter-term recovery may be the use of organic waste, which does not represent an increased risk of contamination of soil and water, to promote the increase of soil organic matter and provide nutrients to growing species (Cordovil et al., 2011).

## CONCLUSION

Taking into account future climate change scenarios, which provide for a longer dry season and an increase in extreme summer temperatures, forest fires are expected to increase, with obvious damage and levels. Thus, it is imperative to correctly evaluate the effects of forest fires on aquatic systems as they may be responsible for drastic and immediate changes in the structure and functioning of ecosystems.

**Acknowledgements:** Acknowledgements, if any, may be placed here.

## REFERENCES

Cordovil CMdS, A. de Varennes, R. Pinto, RC Fernandes 2011. Changes in mineral nitrogen, soil organic matter fractions and microbial community level physiological profiles after application of digested pig slurry and compost from municipal organic wastes to burned soils.

DOI: 10.1016/j.soilbio.2010.12.023

Neary, D.G., Klopatek, C.C., DeBano, L.F., Folliot, P.F., 1999. Fire effects on belowground sustainability: a review and synthesis. Forest Ecol Manag. 122, 51-71.

Silva V, Pereira JL, Gonçalves F, Keizer IJ, Abrantes N 2016. Efeitos dos fogos florestais nos sistemas aquáticos. Captar 6(2), 68-77.
# EUTROPHICATION: CAUSES, MECHANISMS, CONSEQUENCES AND PREDICTABILITY

PINAY, G.<fnref n="1" />, GASCUEL-ODOUX, C.², MENESGUEN, A.³, SOUCHON, Y.⁴, LE MOAL, M. ⁵, DURAND, P.², LEVAIN, A.⁶, PANNARD, A.<fnref n="1" />, SOUCHU, P.³, ETRILLARD, C.⁷, MOATAR, F.⁸.

## INTRODUCTION

Eutrophication is one of the most common alterations of inland and marine waters. Its best-known manifestations are toxic cyanobacteria blooms in lakes and waterways and proliferations of green macroalgae in coastal areas. These phenomena are generating major disruptions to aquatic ecosystems and have impacts on related goods and services, on human health and on the economic activities of the territories where they occur. In some areas, these environmental crises have become an urgent societal issue, involving a wide variety of stakeholders with contrasting values and interests. The term eutrophication is used by both the scientific community and public policy-makers, and therefore has multiple definitions. The introduction by the public authorities of regulations to limit eutrophication is a source of tension and debate on the activities identified as contributing or having contributed decisively to these phenomena.

Debates on the identification of the factors and risk levels of eutrophication, seeking to guide public policies, have led the ministries in charge of the environment and agriculture to ask for a joint scientific appraisal (Expertise Scientifique Collective, or ESCo) to be conducted on the subject. French research institute (CNRS, Ifremer, INRA and Irstea) were therefore mandated to produce a critical situational analysis on the latest knowledge of the causes, mechanisms, consequences and predictability of eutrophication phenomena.

## ORGANIZATION AND PRINCIPLES OF THE JOINT SCIENTIFIC APPRAISAL

40 French and foreign experts were mobilized for the joint scientific appraisal on eutrophication, with skills in the following disciplines: ecology, hydrology, biogeochemistry, biotechnical sciences, social sciences, law, economics, and covering the various types of aquatic ecosystems: lakes, streams, estuaries, marine coastal and offshore environment, as well as the concept of continuum between these systems. The experts' work drew on a bibliographic corpus of around 4,000 references, composed essentially of scientific articles validated by peers, and supplemented, for a number of topics, by technical or scientific reports and legal texts. This exercise culminated with the production of a report compiling the experts' contributions, a synthesis, as well as a symposium, on September 19th, 2017.

## CONTENTS OF THE REPORT

1. What is eutrophication? Why and how does it occur?

1.1. Definition of eutrophication

1.2. What are the factors responsible for eutrophication?

1.3. What are the mechanisms of eutrophication?

1.4. What are the manifestations of eutrophication?

1.5. What are the environmental, economic and social impacts inventoried?

2. What criteria can one use to characterize the eutrophication of environments?

Indicators of eutrophication are generally classified into indicators of pressure, chemical status and impact. Pressure and status indicators relate respectively to the identification and quantification of pollutant sources and their concentrations, whereas the impact indicators use the biological responses of the living communities specific to each type of environment.

<footnote n="1">¹ CNRS, Univ. Rennes 1, UMR Ecobio, Rennes, ² Inra, UMR SAS, Rennes, ³ Ifremer, Brest, ⁴ Irstea UR Riverly Lyon, ⁵ CNRS, Rennes, ⁶ Inra, UMR Lisis, Marne la Vallée, ⁷ Inra, UMR SMART, Rennes, ⁸ Univ. Tours. France.</footnote>
4. Can the risk of eutrophication be characterized and predicted? If so, how?

4.1. Transfers, retention and transformation of nitrogen and phosphorus along the land-sea continuum

4.2. Taking account of climate change is essential

4.3. The vulnerability of ecosystems to eutrophication

4.4. Modelling: a tool for understanding ecosystems

5. What are the strategies and frameworks to combat eutrophication?

5.1. Engineering in aquatic ecosystems: an ad hoc solution

5.2. Managing phosphorus and nitrogen inputs in aquatic environments is essential

5.3. Are regulatory monitoring frameworks well adapted to monitor eutrophication?

5.4. Socio-economic support for remediation

6. Future areas of investigation

6.1. Developing a methodology for analyzing the eutrophication risk

6.2. Moving towards systemic research approaches

<figure><img src="image_42.png" /><figcaption>Figure 1. Conceptual diagram of the transfer, retention, and purification zones of nitrogen (N) and phosphorus (P) along the land-sea continuum. Source: Joint scientific appraisal on eutrophication.</figcaption></figure>

## CONCLUSION

the synthesis, report, and presentation of the results symposium are available here: www.cnrs.fr/inee

## REFERENCE

Gilles Pinay, Chantal Gascuel, Alain Ménesguen, Yves Souchon, Morgane Le Moal, Alix Levain, Alexandrine Pannard, Philippe Souchu, Claire Etrillard, Florentina Moatar. L'eutrophisation : manifestations, causes, conséquences et prédicibilité. Expertise scientifique collective, rapport, CNRS- Ifremer-INRA-Irstea (France), 148 pages (French and English versions).
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes – France

**Session II: Regional studies – Oral presentations**
KEYNOTE PRESENTATION : NITROGEN DYNAMICS IN AGRICULTURAL SYSTEMS UNDER MEDITERRANEAN CLIMATE

LUIS LASSALETTA

CEIGRAM, Universidad Politécnica de Madrid, Spain; IMBE-CNRS, Aix en Provence, France

luis.lassaletta@upm.es

The Mediterranean climate is characterized by having dry and warm summers and mild winters. It is found in some areas between 30º and 40º both in the North and South hemisphere. About 50% of the area is found in the countries of the Mediterranean basin, the rest is distributed in California (USA), Chile, South Africa and Australia. The study of agronomic and environmental sustainability of Mediterranean agricultural systems is of particular interest because: 1) it is present in highly populated areas; 2) it is a biodiversity hotspot; 3) the agriculture is unique in terms of species and varieties; 4) population increases and dietary changes are increasing the food demand; 5) the scarce water is a key production factor for agriculture but also a first necessity for the people; 6) climate change is exacerbating the most extreme Mediterranean traits; 7) some temperate areas in Europe and South America are experiencing transitions towards the Mediterranean climate. The role of nitrogen is central because it as a key production factor highly linked with water management and it is also a source of water pollution. Herein a summary is presented on the particular characteristics of nitrogen dynamics in agricultural systems that make the Mediterranean region different to other more studied areas such as the temperate one (e.g. North Europe). The agronomic and environmental implications of these particularities together with their connection with the configuration of the agro-food systems are discussed.

The temporal gap between the period of maximum water availability and of maximum irradiance has a deep impact on the configuration of the Mediterranean agricultural systems such as the selection of species, varieties, rotations and management practices (Aguilera et al. 2013). The response of crops to irrigation is disproportionally higher than in the countries with temperate climate (e.g. net irrigation requirements range 1000-2500 mm/y in Southern Europe, while in Denmark they are only 50 mm/y, Wriedt et al. 2009). As a consequence, the agricultural surface under irrigation has significantly grown in the Southern European countries during the last 30 years. The differences between these two worlds, namely rainfed and irrigated, are enormous and they have to be considered separately. Since the potential yields are much higher in irrigated crops, their fertilization is generally higher. Anyhow, the N input in irrigated systems is in many cases lower than in Northern Europe countries where the environmental conditions permit higher yields. The Yield vs. N Input response curves are very different when comparing rainfed and irrigated systems. Anyhow, there is also a diversity of Mediterranean climatic conditions from arid to humid and the maximum potential yield of rainfed systems is much closer to irrigated systems in the humid Mediterranean areas. The important climatic interannual variability clearly modulates the optimal N rate in rainfed systems. Thus, pollution risk and N management require taking into account the important effect of: 1) the spatial variability; 2) the interannual variability; 3) the significant water-nitrogen interactions (Quemada and Gabriel 2016).

To avoid the emissions of reactive nitrogen compounds such as ammonia, nitrous oxide and nitrate the recipes coming from studies performed in the temperate areas are not valid. Ammonia emissions associate with urea applications are in many cases lower due to higher atmospheric stability during the dry summers. The emission of ammonia is however becoming a first order problem in some Mediterranean countries due to structural changes occurred during the last decades in the agro-food system configuration, as the net import of feed is reaching similar levels to domestic crop production (Lassaletta et al. 2014). As a result, the amount of manure to be managed is increasing every year. The organic carbon content of Mediterranean soils is significantly low. This amount of manure could represent an opportunity for improving soil quality and for accomplishing the objectives of the '4 per 1000' initiative. The nitrous oxide emission factors (EF) are in general much lower than in temperate
systems. In a recent meta-analysis, Cayuela et al. (2017) have shown how the semiarid conditions, together with lower water input and moderate application rates clearly reduce emissions factors. For example, the weighted averaged nitrous oxide EF for rainfed systems is 0.27 %, a value that contrasts with 1% considered in the IPCC Tier 1 methodology for National Emission Inventories. Only sprinkler irrigation showed an EF not statistically different from 1%, while furrow and drip systems averaged 0.5%. The transition to Tier 2 or 3 is therefore important for Mediterranean countries (Sanz-Cobeña et al. 2017). Concerning nitrate leaching in the Mediterranean region, the nitrification process is faster, but nitrate leaching from rainfed systems is highly variable depending on the climatic conditions, with huge interannual variations. In irrigated systems, particularly those heavy fertilized with manures in areas of animal concentration, the leaching can be very high after the irrigation events. A good water management could reduce 58% of the nitrate leaching. During the last decades, the pollution of freshwater has increased in several Mediterranean basins such as the Ebro (Lassaletta et al. 2009). Once the nitrogen has been exported from the cropping systems to the freshwaters, the N that is retained in the basin without being exported to the coastal zone is very high: 95% of the N that is applied to the crops is retained while in temperate systems this proportion is much lower (75%). This is a consequence of the high-water regulation (irrigation channels and dams) associated with the water scarcity of these areas (Lassaletta et al. 2012; Romero et al. 2016). As a result, despite the risk of coastal eutrophication is lower, the threat for terrestrial pollution rises. In conclusion, nitrogen dynamics deeply depend on the water input and management and on the climatic variability. Rainfed and irrigated systems represent two contrasted worlds that do not match with the knowledge gained in the temperate systems. Mitigation, adaptation and water quality strategies have to be constructed in coordination and tailored at the regional scale.

**Acknowledgements:** L. Lassaletta was supported by the FP7 project LUC4C and by a 'Ramon y Cajal' research contract from the Spanish Ministry of Economy and Competitiveness (RYC-2016-20269).

## REFERENCES

Aguilera, E., Lassaletta, L., Sanz-Cobena, A., Garnier, J., Vallejo, A., 2013. The potential of organic fertilizers and water management to reduce N2O emissions in Mediterranean climate cropping systems. A review. Agriculture, Ecosystems & Environment 164, 32-52.

Cayuela, M.L., Aguilera, E., Sanz-Cobena, A., Adams, D.C., Abalos, D., Barton, L., Ryals, R., Silver, W.L., Alfaro, M.A., Pappa, V.A., Smith, P., Garnier, J., Billen, G., Bouwman, L., Bondeau, A., Lassaletta, L., 2017. Direct nitrous oxide emissions in Mediterranean climate cropping systems: Emission factors based on a meta-analysis of available measurement data. Agriculture, Ecosystems & Environment 238, 25-35.

Lassaletta, L., García-Gómez, H., Gimeno, B.S., Rovira, J.V., 2009. Agriculture-induced increase in nitrate concentrations in stream waters of a large Mediterranean catchment over 25 years (1981-2005). S 407, 6034-6043.

Lassaletta, L., Romero, E., Billen, G., Garnier, J., García-Gómez, H., Rovira, J.V., 2012. Spatialized N budgets in a large agricultural Mediterranean watershed: high loading and low transfer. Biogeosciences 9, 57-70.

Lassaletta, L., Billen, G., Romero, E., Garnier, J., Aguilera, E., 2014. How changes in diet and trade patterns have shaped the N cycle at the national scale: Spain (1961-2009). Reg. Environ. Change 14, 785-797.

Quemada, M., Gabriel, J.L., 2016. Approaches for increasing nitrogen and water use efficiency simultaneously. Global Food Security 9, 29-35.

Romero, E., Garnier, J., Billen, G., Peters, F., Lassaletta, L., 2016. Water management practices exacerbate nitrogen retention in Mediterranean catchments. Science of the Total Environment 573, 420-432.

Sanz-Cobena, A., Lassaletta, L., Garnier, J., Smith, P., 2017. Mitigation and quantification of greenhouse gas emissions in Mediterranean cropping systems. Agriculture, Ecosystems & Environment 238, 1-4.

Wriedt, G., Van der Velde, M., Aloe, A., Bouraoui, F., 2009. Estimating irrigation water requirements in Europe. Journal of Hydrology 373, 527-544.
# POTENTIALS FOR SIMULTANEOUS IMPROVEMENT OF PHOSPHORUS AND NITROGEN MANAGEMENT IN AUSTRIA

TANZER, J.<fnref n="1" />, RECHBERGER, H.², ZESSNER, M.², ZOBOLI, O.²

## INTRODUCTION

Both phosphorus (P) and nitrogen (N) are essential plant nutrients and, as fertilizers, play an essential role in ensuring global food security. However, they are currently mainly used in a linear way, with high imports or production of mineral fertilizer and high losses to water, air and landfills. This creates various problems: P is a non-renewable resource and reserves are concentrated in few and geopolitically unstable regions, giving rise to high price fluctuations. Moreover, P emissions to water bodies are a main cause for eutrophication. Contrary to phosphorus, nitrate fertilizer can be industrially produced in the Haber-Bosch-Process; however, this requires high energy input. Reactive N released into the environment contributes to problems like air and ground water pollution, acidification, eutrophication and climate change.

Recently, efforts to make management of P and N more efficient and sustainable have grown; however, most studies to date are restricted to one substance or a single sector or process. Given the close connection of their cycles, interactions between measures targeting P and N management are to be expected though. This study simultaneously looks at P and N management and options for improvement in the national context of Austria, in order to identify co-benefits and negative side-effects between the two substances.

## MATERIAL AND METHODS

The analysis is based on a coupled material flow analysis (MFA) for P and N, following the methodology described by Brunner and Rechberger (2017) and building on the Austrian MFA for P (Zoboli et al., 2015). The freeware STAN (Cencic and Rechberger, 2012) was used to calculate mass balances and to perform error propagation and data reconciliation. Uncertainty of model input data was characterized according to a method developed by Laner et al. (2014), in which qualitative data classification and exponential-type uncertainty characterization functions are combined.

The coupling of the P and N balance was achieved by introducing a goods-layer into the system, which represents the total mass of a stock or flow. Coupling takes place during data reconciliation, when masses and concentrations of flows are adjusted so that the mass balance for all processes is kept on both the P and the N layer.

To evaluate the effects of different management options, scenarios looking at measures to reduce P and/or N-demand, increase recycling or reduce their losses to air or water were compared to a reference state, representing the actual situation in 2015. Indicators used for assessment include the demand of mineral P- and N-fertilizer, emissions to air (N only) and water bodies, P accumulation in agricultural soil as well as losses in the waste sector.

## RESULTS AND DISCUSSION

The analysis confirmed the close connection between the P- and N-system. Overall measures to improve P-management have positive effects on the N-system and vice versa, which is why highest efficiency gains can be achieved by a combination of all the 16 measures studied (see Table 1). Large potentials lie in the reduction of mineral fertilizer demand, whereas emissions to water bodies can only be reduced to a lesser extent. This is partly an effect of successful emission reduction measures

<footnote n="1">¹ Center for Water Resource Systems, TU Wien, Karlsplatz 13/222, A-1040, Vienna, Austria; ² Institute for Water Quality and Resource Management, TU Wien, Karlsplatz 13/226, A-1040 Vienna, Austria</footnote>
in the past and shows that society to date has made more progress in environmental- than in resource protection.

Few measures directly target the reduction of N-emissions to the atmosphere. Efforts are being made to convert reactive nitrogen into N₂ prior to emission; however, distinction of different N species was out of the scope of this study. N₂ can be regarded as unproblematic in terms of air pollution and climate change. Nevertheless, measures to keep N within the system similar to the ones currently under development for P could be beneficial in terms of reducing the energy demand for N-fertilizer production.

<table><caption>Table 1. Improvement potential compared to the status quo. An improvement potential of 1 refers to a state, where mineral fertilizer is completely replaced by recycled products and no losses to the waste sector, soil, water and atmosphere occur. Numbers in brackets indicate data uncertainty. Only measures with a significant total improvement potential are shown.</caption><thead><tr><th>Improvement potential</th><th>Min. P-fertilizer demand</th><th>P-losses in waste sector</th><th>P water emissions</th><th>P soil accumulation</th><th>Min. N-fertilizer demand</th><th>N-losses in waste sector</th><th>N water emissions</th><th>N air emissions</th><th>Total</th></tr></thead><tbody><tr><td>Combined measures</td><td>0.121 (0.008)</td><td>0.095 (0.007)</td><td>0.062 (0.010)</td><td>0.090 (0.015)</td><td>0.10 (0.03)</td><td>0.079 (0.018)</td><td>0.08 (0.04)</td><td>0.043 (0.010)</td><td>0.67 (0.05)</td></tr><tr><td>Improved fertilizer application</td><td>0.086 (0.015)</td><td>0.002 (0.014)</td><td>0.031 (0.014)</td><td>0.079 (0.013)</td><td>0.07 (0.04)</td><td>-0.000005 (0.036318)</td><td>0.06 (0.05)</td><td>0.018 (0.012)</td><td>0.34 (0.07)</td></tr><tr><td>Improved meat and bone meal recycling</td><td>0.021 (0.021)</td><td>0.028 (0.011)</td><td>0.00028 (0.02149)</td><td>-0.000005 (0.025431)</td><td>0.01 (0.04)</td><td>0.060 (0.018)</td><td>0.01 (0.07)</td><td>0.002 (0.015)</td><td>0.13 (0.10)</td></tr><tr><td>Improved sewage sludge recycling</td><td>0.043 (0.020)</td><td>0.054 (0.009)</td><td>0.001 (0.021)</td><td>0.003 (0.025)</td><td>-0.0001 (0.0433)</td><td>0.01 (0.04)</td><td>-0.001 (0.071)</td><td>-0.0001 (0.0139)</td><td>0.11 (0.10)</td></tr></tbody></table>

## CONCLUSION

By coupling flows of P and N in a combined MFA, the interrelations in management of these two substances could be revealed and co-benefits of measures identified that might have been overlooked in a single substance analysis. This opens the door to including other substances that might be affected by P and N management into the system and to developing frameworks for the analysis of coupled resource systems in general.

**Acknowledgements:** This research was funded by the Austrian Science Fund (FWF) as part of the Vienna Doctoral Programme on Water Resource Systems (DK-plus W1219-N22).

## REFERENCES

* Brunner P., Rechberger H., 2017. Handbook of material flow analysis: for environmental, resource and waste engineers. Second edition. Taylor & Francis, CRC Press, Boca Raton, London.

* Cencic O., Rechberger H., 2012. STAN 2.5.1302. Institute for Water Quality, Resources and Waste Management, TU Wien.

* Laner D., Feketitsch J., Rechberger H., Fellner J., 2015. A novel approach to characterize data uncertainty in MFA and its application to plastic flows in Austria. Journal of industrial ecology, 20 (5), 1050-1063.

* Zoboli O., Laner D., Zessner M., Rechberger H., 2015. Added Values of Time Series in Material Flow Analysis – The Austrian Phosphorus Budget from 1990 to 2011. Journal of industrial ecology, 20 (6), 1334-1348.
# GREENHOUSE GASES EMISSIONS FROM AGRICULTURE IN THE NORTH OF FRANCE (1852-2014): CONSEQUENCE OF SPECIALISATION AND INTENSIFICATION

GARNIER J.<fnref n="1" />, LE NOË J<fnref n="1" />, MARESCAUX A.<fnref n="1" />, SANZ-COBENA A.², THIEU V.<fnref n="1" />, BILLEN G.<fnref n="1" />

## INTRODUCTION

The Seine basin is a region particularly dedicated to intensive cereal farming, while livestock is now only present in its periphery. This specialization of the Seine basin in cereals increased in the second half of the 20th century, along with an extreme specialization of Brittany into intensive livestock farming. Before that period, livestock kept a central place in cropping systems and local recycling of nutrients limited the need for exogenous fertilizers. We aimed at analysing the spatial-temporal trajectory of greenhouse gas emission from agriculture to disentangle the respective role of specialization and intensification in greenhouse gas emissions.

## MATERIAL AND METHODS

$N_2O$ and $CH_4$ budgets have been established in the Seine basin for the current period with emission coefficients determined from field (Garnier et al., 2009, 2013; Benoit et al. 2015; unpublished) and literature data. $N_2O$ emissions from cropland were linked to mineral fertilizer use and rainfall (Figure 1). $CH_4$ emissions were calculated according to the composition of livestock and its excretion (Garnier et al., 2013), taking also into account for the long term, the evolution of excretion factors for each class of animal (Le Noë et al., submitted and ref. therein). Historical trends in agricultural greenhouse gas emissions ($CH_4$ and $N_2O$) were then reconstructed from the late 19th century to the present day based on the GRAFS analysis (Generalized Representation of Agro-Food System, after Billen et al., 2013).

<figure><img src="image_43.png" /><figcaption>Figure 1. a. Relationship between $N_2O$ emissions and cropland fertilisation for e.g. two classes of rainfall</figcaption></figure>

## RESULTS AND DISCUSSION

The trajectories of $N_2O$ emissions were parallel all over the studied period, 30-40% higher for the Great West compared to the Seine Basin. Rather stable from the end of the 19th century to the middle of the 20th century, $N_2O$ emission doubled in the 1980s, and decreased from the 1980's, when fertilisation was decreased too. $CH_4$ emissions were rather stable in the Seine basin although the livestock was concentrated at the periphery of the Basin to allow cropping intensification in fertile loam soils in the center of the basin. In the Great West, $CH_4$ was multiplied by about four during the same time with intensified livestock breeding (Figure 2). Manure management which represented half of the enteric fermentation until the 1950s increased to 75% in the recent year reflecting a change in grazing practices.

<footnote n="1">¹ Sorbonne Université, UPMC, CNRS, EPHE, UMR 7619 METIS, 4 place Jussieu, Paris, France; ² UPM, Universidad Politécnica de Madrid, ETSI Agrónomos, Av. Complutense s/n, 28040, Madrid, Spain</footnote>
<figure><img src="image_44.png" /><figcaption>Figure 2. Long term trajectories of a. N₂O emissions and b. CH₄ emissions for the two contrasted regions, Seine Basin and Great West</figcaption></figure>

The GRAFS approach also allowed running prospective scenarios showing that the pursuit of specialization would level off N₂O emissions in both regions, but would increase CH₄ in the Great West and decreased it in the Seine Basin. On the contrary, in a more virtuous scenario with a reconnection of crop and livestock farming and general transition to organic farming would increase CH₄ emissions in the Seine Basin by a factor of two, while reducing four time those in the Great West.

## CONCLUSION

The GRAFS analysis of historical farming systems makes it possible, in addition to exploring agricultural performance and leakage to hydrosystems, to estimate emissions to the atmosphere.

The transition to industrial agriculture and its territorial specialization were accompanied by a significant increase in GHG emissions, that deep changes in agriculture could reverse.

## REFERENCES

* Benoit M., Garnier J., Billen G., Tournebize J., Gréhan E., Mary B. (2015). Nitrous oxide emissions and nitrate leaching in an organic and a conventional cropping system (Seine basin, France). *Agric. Ecosyst. Environ.* 213: 131–141. doi:10.1016/j.agee.2015.07.030

* Billen G., Garnier, J., Lassaletta L. (2013). Modelling the nitrogen cascade from watershed soils to the sea: from regional to global scales. *Phil. Trans. R. Soc. B* 2013 **368**, 20130123. DOI: 10.1098/rstb.2013.0123

* Garnier J., Billen G., Vilain G., Martinez A., Mounier E., Silvestre M., Toche F. (2009). Nitrous oxide (N2O) in the Seine river and basin: observations and budgets. *Agric. Ecosyst. Environ.* 133: 223–233. doi:10.1016/j.agee.2009.04.024.

* Garnier J., Vilain G., Jehanno S., Silvestre M., Billen G., Poirier D., Martinez A., Decuq C., Cellier P., Abril G. (2013). Methane emissions from land use, livestock farming, and the river network of the Seine basin (France), *Biogeochemistry*, 116:199–214. DOI 10.1007/s10533-013-9845-1

* Le Noë J., Billen G., Esculier F., Garnier J. (submitted). Long term socio-ecological trajectories of agro-food systems revealed by N and P flows: the case of French regions from 1852 to 2014. *Agric. Ecosyst Environ.*
# DRIVING FORCES OF FOOD SYSTEM NITROGEN FLOWS IN CHINA, 1990 TO 2012

GAO BING<fnref n="1" />,², HUANG, WEI<fnref n="1" />,², CUI SHENGHUI<fnref n="1" />,²*

## INTRODUCTION

As the largest Nr producer and consumer, the largest population in the world and undergoing the largest urbanization in the world in the last few decades (Bai et al., 2014), China is an interesting case that demonstrates how population growth, dietary changes, people's movements in urbanization and N management practices can affect long-term trends in N use and loss from food production and consumption system by an emerging economy. This study aimed (i) to analyze the historical trends in per capita food N consumption and the differences in per capita food N consumption between urban and rural areas from 1990 to 2012; (ii) to estimate the changes in animal-food N (AN) and plant-food N (PN) consumption, driven by population growth, dietary changes associated with income growth and rural-urban migration; (iii) to quantify the contributions of the above key drivers to inputs of N through the food system.

## MATERIAL AND METHODS

The material flow analysis approach was adapted for quantifying the flows of N in the Chinese food system, which was divided into five categories: crop production, animal-food production, food processing, household consumption (including both urban and rural households), and waste disposal. Mass balance calculations as a basic principle (inputs = outputs + accumulations) were adopted for the calculations of N input, output and accumulations in different sectors of the entire food system, if no data were available for calculating them directly (Ma et al., 2012; Cui et al., 2016). The impacts of population growth, dietary changes associated with income growth, and rural-urban migration on the national AN and PN consumption was quantified by setting up different scenarios (Table 1). The impacts of these factors on N input to the Chinese food system was calculated by the variation of AN and PN, driven by the three factors multiplied by the N costs of AN and PN consumption.

<table><caption>Table 1 Description of the scenarios and the calculations of driving force effects</caption><thead><tr><th>Code</th><th>Scenario description</th><th colspan="2">Driving force effect calculation</th></tr></thead><tbody><tr><td>S1</td><td>Urban and rural populations maintained at level of 1990, dietary unchanged as 1990 and no people moved to cities</td><td>S1<sub>2012</sub>-S1<sub>1990</sub></td><td>N management practices effect (this effect only for N input)</td></tr><tr><td>S2</td><td>Urban and rural dietary changes based on S1</td><td>S2-S1</td><td>Dietary changes effect</td></tr><tr><td>S3</td><td>Population growth based on S2</td><td>S3-S2</td><td>Population growth effect</td></tr><tr><td>RS</td><td>Real situation, population growth, dietary changes and people moved to cities</td><td>RS-S3</td><td>People's movements in urbanization effect</td></tr></tbody></table>

## RESULTS AND DISCUSSION

New N input to Chinese food system increased by 16.7 Tg from 1990 to 2012. China's population growth, dietary changes and rural-urban migration created a need for 7.6 Tg increased N to support these changes over the past 20 years. The remaining 54%, increase in N input was mainly due to the decrease in the NUE of the Chinese food system, from 16% to approximately 9%, between 1980 and 2009 (Ma et al., 2012). These results indicate that the increased N input to the Chinese food system is not merely due to over-fertilization by Chinese farmers (Chen et al., 2014), but has been driven by population growth, dietary changes and rural-urban migration.

<footnote n="1">¹ Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, PR China; ² Xiamen Key Lab of Urban Metabolism, Xiamen 361021, PR China</footnote>
<figure><img src="image_45.png" /><figcaption>Figure 1 The changes in N input to Chinese food system, driven by population growth, dietary changes and rural-urban migration relative to 1990, during 1990–2012</figcaption></figure>

CONCLUSION

Our analysis indicates that China is facing higher risks of environmental N pollution with urbanization, because of the higher N input to the food system caused by the additional demand for AN precipitated by population growth and rural-urban migration (and this trend will continue, and even increase, under China's urbanization). The strategies for reducing the risks of N losses to the environment involve guiding the urban and rural dietary changes to the recommended energy standards, and to mitigate the increase in demand for AN, etc.

**Acknowledgements:** This manuscript is sponsored by the National Basic Research Program of China (2014CB953801), the Young Talents Projects of the Institute of Urban Environment, the Chinese Academy of Sciences (IUEMS201402).

REFERENCES

Bai, X.M., Shi, P.J., Liu, Y.S., 2014. Society: realizing China's urban dream. Nature 509 (7499), 158–160.

Cui, S.H., Shi, Y.L., Malik, A., Lenzen, M., Gao, B., Huang, W., 2016. A hybrid method for quantifying China's nitrogen footprint during urbanization from 1990 to 2009. Environ. Int. 97, 137–145.

Ma, L., Velthof, G.L., Qin, W., Zhang, W.F., Liu, Z., Zhang, Y., Wei, J., Lesschen, J.P., Ma, W.Q., Oenema, O., Zhang, F.S., 2012. Nitrogen and phosphorus use efficiencies and losses in the food chain in China at regional scales in 1980 and 2005. Sci. Total Environ. 434, 51–61.
# ASSESSMENT OF SPATIALLY EXPLICIT NEEDED INCREASE IN NITROGEN USE EFFICIENCY IN EUROPEAN AGRICULTURAL SOILS IN VIEW OF AIR AND WATER QUALITY

DE VRIES, W.<fnref n="1" />,², KROS, J.², VOOGD, J.C.², LOUWAGIE, G³

## INTRODUCTION

The intensification of European agriculture, including large inputs of nitrogen (N) to soil by fertilizers and manure, has led to an increase in crop growth but also to adverse effects on the environment (Sutton et al., 2011). In several regions in Europe, high N inputs have led to: (i) eutrophication of surface waters due to increased N runoff, (ii) increased nitrate (NO₃) levels in drinking water reservoirs due to elevated NO₃ leaching and (iii) loss of terrestrial biodiversity due to increased emission and deposition of ammonia (NH₃). Inversely, in other regions there is still room for increasing N inputs without exceeding critical thresholds for N losses. When current N inputs exceed critical N inputs, environmental objectives can only be reached by lowering N input, which likely would cause a loss in crop production, unless the nitrogen use efficiency (NUE) is increased. When the NUE is increased, the same crop yield can be reached with less N fertilizer, due to an enhanced N uptake fraction, while the critical N input increases since a lower fraction of N is lost to the environment. In this study we calculated critical N inputs and their exceedances (current N inputs minus critical N inputs) for agricultural soils in the EU-27 in view of effects on terrestrial and aquatic ecosystems due to NH₃ emissions to air and N runoff to surface water. The increase in NUE that is required to attain current crop yields while reducing NH₃-N emissions to air and N runoff to surface water to acceptable environmental levels, was also calculated.

## MATERIAL AND METHODS

The derivation of critical N inputs in view of adverse environmental impacts consisted of three consecutive steps, i.e. (i) identification of critical values for defined N indicators, (ii) back-calculation of critical N losses to surface water or air that correspond to these critical values and (iii) back-calculation of critical N inputs from critical N losses. Calculations were carried out for approximately 40.000 unique combinations of soil type, slope class and altitude class, within a given socio-economic NUTS3 region. The results thus obtained were aggregated at NUTS3 level, country level and EU-27 level. The acceptable NH₃-N emissions were set equal to mean critical N load in view of terrestrial ecosystem functioning. The acceptable N runoff to surface water was based on a critical concentration of dissolved total N in surface water of 2.5 mg N l⁻<fnref n="1" />, in view of eutrophication of aquatic ecosystems (Liu et al., 2011). The INTEGRATOR model was used to calculate current and critical N inputs at EU-27 level (De Vries et al., 2011) using each criterion separately and the minimum of both criteria. The calculated, spatially-explicit critical N inputs in view of losses to air and water were compared with current N inputs (reference year 2010). For areas where current N inputs exceeded critical N inputs, we calculated the needed increase in NUE, defined as crop N removal divided by the total N input by fertilizer, manure, deposition and fixation, to attain environmental objectives at current crop yields.

## RESULTS AND DISCUSSION

The calculations at EU-27 level showed that current (2010) N inputs exceeded critical N inputs by approximately 20%, using either critical N concentrations in surface water or critical NH₃-N emission-deposition rates as criterion (Table 1). Relatively high exceedances were found in regions with high total N inputs, such as Ireland, the Netherlands, Belgium and Luxembourg, Bretagne in France and the Po valley in Italy. In several regions, the current N input was, however, lower than the critical N input, e.g. due to high precipitation allowing high N runoff rates or to low N inputs.

<footnote n="1">¹ Environmental Systems Analysis Group, Wageningen University and Research, Wageningen, Netherlands; ² Wageningen Environmental Research, Wageningen, Netherlands; ³ European Environment Agency, Copenhagen, Denmark</footnote>
<table><caption>Table 1. Average current (2010) and critical annual N budgets of agricultural land in EU-27 calculated by INTEGRATOR based on a critical N concentration in surface water of 2.5 mg N l⁻¹, a critical NH₃-N emission and the minimum of both criteria.</caption><thead><tr><th rowspan="2">Source</th><th colspan="4">N budget EU-27 (kg N ha<sup>-1</sup> yr<sup>-1</sup>)</th></tr><tr><th>Current</th><th>Critical N runoff 2.5 mg N l<sup>-1</sup></th><th>Critical NH<sub>3</sub>-N emission</th><th>Minimum critical runoff and emission</th></tr></thead><tbody><tr><td colspan="5"><em>Input to land</em></td></tr><tr><td>Fertilizer</td><td>79</td><td>73</td><td>67</td><td>62</td></tr><tr><td>+fixation</td><td></td><td></td><td></td><td></td></tr><tr><td>Excretion+</td><td>59</td><td>45</td><td>35</td><td>33</td></tr><tr><td>biosolids</td><td></td><td></td><td></td><td></td></tr><tr><td>Total input</td><td>138</td><td>118</td><td>102</td><td>95</td></tr><tr><td colspan="5"><em>Output from land</em></td></tr><tr><td>Crop uptake</td><td>101</td><td>93</td><td>80</td><td>75</td></tr><tr><td>Total surplus</td><td>37</td><td>25</td><td>22</td><td>20</td></tr><tr><td>Denitrification</td><td>28</td><td>18</td><td>15</td><td>14</td></tr><tr><td>Leaching + runoff</td><td>18</td><td>7</td><td>7</td><td>66</td></tr><tr><td>Soil</td><td></td><td></td><td></td><td></td></tr><tr><td>accumulation</td><td>-9</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Total output</td><td>138</td><td>118</td><td>102</td><td>95</td></tr></tbody></table>

The calculated NUE values that are needed to attain the current crop yield while not exceeding critical environmental thresholds nearly always ranged between 50 and 90% (Figure 1). There is thus a clear need for a spatial reallocation of the N inputs to N-deficient regions and an increase in NUE in highly productive regions to avoid environmental impacts while maintaining crop production.

<figure><img src="image_46.png" /><figcaption>Figure 1. The ratio between N output and input (NUE) when using the current input and output data (left) and NUE increase required to attain the current crop yield while NH3 losses to air (middle) or N losses to surface water (right) are acceptable.</figcaption></figure>

## CONCLUSION

Current (2010) N inputs were approximately 20% higher than critical N inputs related to critical N concentrations in surface water or critical NH₃-N emission rates. The NUE generally needs to increase
to 70-90% to attain current crop yields at acceptable NH₃-N emissions to air and acceptable N runoff rates to surface water.

REFERENCES

De Vries W., Leip A., Reinds G.J., Kros J., Lesschen J.P., Bouwman A.F., 2011. Comparison of land nitrogen budgets for European agriculture by various modelling approaches. Environ. Pollution, 159-11, 3254-3268.

Liu C., Kroeze C., Hoekstra A.Y., Gerbens-Leenes W., 2011. Past and future trends in grey water footprints of anthropogenic nitrogen and phosphorus inputs to major world rivers. Ecol. Indic., 18, 42-49.

Sutton M.A., Howard C.M., Erisman J.W., Bealey W.J., Billen G., Bleeker A., Bouwman A.F., Grennfelt P., van Grinsven H., Grizzetti B., 2011. The challenge to integrate nitrogen science and policies: the European Nitrogen Assessment approach. In: Sutton M.A., Howard C.M., Erisman J.W., Billen G., Bleeker A., Grennfelt P., van Grinsven H., Grizzetti B. (Eds.), The European Nitrogen Assessment. Cambridge University Press, Cambridge, pp. 82-96.
# HISTORIC TRENDS IN N AND S DEPOSITION IN THE UK: 1800 TO PRESENT

DRAGOSITS U.<fnref n="1" />, TOMLINSON S.J.<fnref n="1" />, CARNELL E.J.<fnref n="1" />, DORE A.J.<fnref n="1" />, MISSELBROOK T.H.², SIMPSON D.³,⁴,
LANGFORD B.<fnref n="1" />, MULLINGER N.<fnref n="1" />, NEMITZ E.G.<fnref n="1" />, SUTTON M.A.<fnref n="1" />

## INTRODUCTION

The legacy of nitrogen (N) and sulfur (S) accumulation and its spatial distribution is an important part of assessing current threats to ecosystems and the ecological response to accumulated N-pools over time. High atmospheric pollutant concentrations also significantly affect human health. As part of a UK project (NERC Macronutrients Long-Term Large-Scale (LTLS); e.g. Tipping et al., 2017), historic N and S deposition and its spatial distribution in the UK from 1800 to present was estimated. The main sources of N and S deposition are NOₓ and SO₂ emissions from combustion (industry, transport etc.), and ammonia (NH₃, largely from agriculture). The magnitude of atmospheric SO₂ concentrations affects the chemical transformations and therefore the lifetime and transport distance of N-containing gases and particles before deposition, especially for NH₃ which is highly reactive. Given the importance of N input to terrestrial and freshwater systems regarding macronutrient pools and fluxes, quantifying these is a key input to wider historic modelling of macronutrients, to answer fundamental questions that can be simply summarised as ‘how did we get to where we are today?’ and consider the implications on future trends and mitigation strategies.

## MATERIAL AND METHODS

This study reconstructed atmospheric emission, concentration deposition timelines back to 1800 over six time slices (2010, 1990, 1970, 1950, 1900 and 1800). Key tasks involved research into historical trends of emissions of nitrogen and sulfur, their sources, emission source strengths and respective spatial distributions, and utilised auxiliary data including historic population, agricultural statistics and practice as well as land cover data. A key consideration was to develop a consistent multi-year spatial dataset that can be analysed, interpreted and compared across the 200 year time period. The Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model (e.g. Matejko et al., 2009) was used to calculate UK concentration and deposition estimates of N and S compounds at a 5km grid resolution.

## RESULTS AND DISCUSSION

Results show that spatial patterns of N and S deposition have changed considerably over the 200+ year period, and how the local/regional deposition history (as well as the current and predicted future patterns of atmospheric deposition) vary widely across the UK (Figure 1). Spatial patterns also vary due to the non-linearity of changes in atmospheric chemistry, with decreasing presence of S compounds in the atmosphere prolonging the atmospheric lifetime of NH₃. There is also considerable spatial and temporal variability in the distribution of wet and dry components of deposition, which in turn are dependent on precipitation, wind and orography.

Historically, both SO₂ and NOₓ emissions and deposition increased rapidly from the Industrial Revolution (~1760–1830) until mitigation measures were implemented through effective legislation from the 1980s, both at the international level (Sulfur and NOₓ Protocols of the UN ECE) and through national efforts regarding combustion and transport sources. By contrast, NH₃ emissions have only decreased very slowly over the last two decades, with only a small proportion of agricultural emissions regulated under the Industrial Emissions Directive (large pig and poultry farms). These data enable a wealth of more detailed assessments of cumulative atmospheric N and S inputs (through deposition as well as atmospheric pollutant concentrations) and their impacts, on habitats and species as well as on human health.

<footnote n="1">¹ NERC Centre for Ecology & Hydrology (CEH) Edinburgh, UK; ² Rothamsted Research North Wyke, UK; ³ EMEP/MSC-W Norwegian Meteorological Institute, Norway, ⁴ Chalmers University of Technology Gothenburg, Sweden</footnote>
<figure><img src="image_47.png" /></figure>

Figure 1. Total N deposition estimated for the UK for example time slices (1800, 1900, 1950, 1970, 1990, 2010) and predicted deposition for 2030 (considering policies currently in place, i.e. business-as-usual). Values represent grid average estimates rather than habitat specific estimates, taking account of land cover present in each grid square.

## CONCLUSION

Historic spatial trends of N and S deposition were shown to have changed substantially across the UK over the last 200+ years, both in terms of magnitude and spatial patterns. Considerable reductions in S emissions and deposition in particular, but also for NOx can be clearly attributed to international and national legislation. Reduced N deposition (from NH₃ emissions) is now the largest source of N deposition in the UK, with only relatively small decreases in the recent past. Historical time series datasets such as the study presented here may allow further investigation of legacy effects as well as the interpretation of current distributions of habitats and species sensitive to N inputs and acidification, and population-level human mortality rates or shortened life expectancy.

**Acknowledgements:** This research was funded by the UK Natural Environment Research Council Macronutrient Cycles Programme (LTLS project, grants NE/J011533/1 and NE/J011703/1) and by the Rural & Environment Science & Analytical Services Division of the Scottish Government. Online datasets used include Vision of Britain (http://www.visionofbritain.org.uk/) and Edina AgCensus data (http://edina.ac.uk/agcensus/) at Edinburgh University Data Library, which are gratefully acknowledged.

## REFERENCES

Matejko M., Dore A.J., Hall J., Dore C.J., Blas M., Kryza M., Smith R.I. and Fowler D., 2009. The influence of long term trends in pollutant emissions on deposition of sulphur and nitrogen and exceedance of critical loads in the United Kingdom. Environmental Science & Policy 12, 882-896.
https://doi.org/10.1016/j.envsci.2009.08.005

Tipping E., Davies J., Henrys P., Kirk G., Lilly A., Dragosits U., Carnell E., Dore A., Sutton M. and Tomlinson S., 2017. Increases in soil carbon due to ecosystem fertilization by atmospheric nitrogen deposition. Nature Scientific Reports 7:1890. https://doi.org/10.1038/s41598-017-02002-w
# THE NITROGEN OPERATING SPACE OF AGRICULTURE: CONNECTING NITROGEN SELF-SUFFICIENCY TO MAXIMUM NET PRODUCTION

HARCHAOUI, S.<fnref n="1" />, CHATZIMPIROS, P.<fnref n="1" />

## INTRODUCTION

Industrial nitrogen (N) fertilizers ($N_{ind}$) have played a key role in global agricultural productivity and are estimated to have underpinned a net doubling in global population over the 20th century (Smil, 2002; Erisman et al. 2008). N inputs to agriculture rely on two additional natural mechanisms, namely atmospheric N deposition ($N_{atm}$) and biological nitrogen fixation (BNF), but unlike $N_{ind}$, the supply capacity of those mechanisms is restricted by land availability. BNF is not performed by cash crop, but only by leguminous crops, mostly fodder crops, which compete for land with cash crops. In preindustrial systems, the necessity to set aside land for BNF was a major limiting factor of total production. The use of $N_{ind}$ lifts this constraint, but increases the dependency of food production on fossil fuels and induces emission of greenhouse gases. In addition, the resulting abundance of $N_{ind}$ may trigger poor N management practices and high N losses with severe consequences for ecosystems and human health (Foley et al., 2011). The abundance of $N_{ind}$ also drives the decoupling between vegetal and animal farming systems, which hampers the recovery of manure N into crop production. The use of $N_{ind}$ has deeply transformed the N operating space of agriculture by disconnecting total production from N self-sufficiency. The implications of a sharp reduction of $N_{ind}$ (either due to climate policy, fossil fuel use restrictions, societal demand for organic agriculture) on the feeding capacity of world agriculture have received little scrutiny (Erisman et al. 2008). This paper provides a modeling approach of the nitrogen operating space of agriculture and assesses scenarios of agricultural productivity and N self-sufficiency as a function of key structural agricultural variables.

## MATERIAL AND METHODS

The study system is the utilized agricultural area considering N fixing land (sum of all N fixing grasslands, fodder and food crops) and non-fixing land (major arable crops). Total N input ($N_{tot}$) to the system is the sum of BNF, $N_{ind}$ and $N_{atm}$, which is set constant to 5 kgN ha⁻<fnref n="1" /> yr⁻<fnref n="1" />. The N self-sufficiency is defined as the ratio of BNF and $N_{atm}$ to $N_{tot}$. BNF is transferred from fixing to non-fixing land through manure N recovery and cropland rotations. Manure N is calculated as a function of the nitrogen conversion efficiency (NCE) of feed to food by livestock. For ruminants, feed is provided by BNF land and equals the above-ground N yield (BNF<sub>yield</sub>). Total BNF rate ($r_{BNF}$) is the sum of BNF<sub>yield</sub> plus the non-extracted N calculated from the N harvest index of fixing crops and assumed to be transferred to non-fixing land through crop rotations. The N net production is defined as the sum of the vegetal and livestock production of agriculture, minus the feed calculated from livestock NCE. The share of grain used as feed (α) is a key parameter. Table 1 summarizes the structural variables of the model and the values used in the simulations inspired by results from historical analysis for France (Harchaoui and Chatzimpiros, accepted) and by current world estimates. The simulations consider constant fertilization rate on arable land for a crop yield of 110 kgN ha⁻<fnref n="1" /> obtained with nitrogen use efficiency (NUE) of 70 % which can both be considered as optimal in current agriculture.

## RESULTS AND DISCUSSION

Figure 1 quantifies the net N production of agriculture as a function of the N self-sufficiency for the three scenarios of table 1. The share of grain to livestock is set at 35 % in all scenarios, which is the current value for both France and the world (Harchaoui and Chatzimpiros, accepted; Foley et al., 2011). In contrast, the production from ruminants increases with BNF through a structural correlation which is possibly counterintuitive. It highlights the pivotal role of ruminants in N self-sufficient systems (organic agriculture) and challenges the visions that combine agricultural N self-sufficiency and

<footnote n="1">¹ Laboratoire Interdisciplinaire des Energies de Demain (LIED), Université Paris Diderot, France.</footnote>
vegetarian diets. The results highlight the relevance of high manure recovery rates in particular in high N self-sufficiency regimes. Without N industrial fertilizers, the model predicts a drop by half of the global N net production which is in line with estimates from Smil (2002) updated by Erisman et al (2008). There is a variety of options to feed the 9.7 billion people projected by FAO in 2050. Under the S1 scenario, the N self-sufficiency would drop below 0.25. If the production was to be sustained solely by BNF, major changes including a tripling of rBNF (from 12 to 35 kgN ha⁻¹) and an increase of NUE to 70% would be necessary (S2 scenario). The very high scenario (S3) considers an increase by an order of magnitude of current world average rBNF and may only be conceivable for a handful of western countries. The trajectory of France in fig1 is driven by the concomitant increase of N yields and NUE.

<table><caption>Table1. Structural agricultural variables and values used in the simulations inspired by results from historical analysis for France (Harchaoui and Chatzimpiros, accepted) and from current world estimates. Sources: (1) Lassaletta et al. (2014) (2) Smil (2002), (3) FAOSTAT (2017), (4) Foley et al (2011), (5) Calculated.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">France</th><th>World</th><th colspan="3">Scenarios</th></tr><tr><th>1980-1991</th><th>1992-2002</th><th>2003-2013</th><th>2013</th><th>Current world rBNF (S1)</th><th>High rBNF (S2)</th><th>Very high rBNF (S3)</th></tr></thead><tbody><tr><td>rBNF fixing kgN ha<sup>-1</sup></td><td>35</td><td>38</td><td>35</td><td>12<sup>(2)</sup></td><td>12</td><td>35</td><td>35 -&gt; 100</td></tr><tr><td>Fixing to total area %</td><td>49</td><td>45</td><td>44</td><td>67<sup>(5)</sup></td><td>variable</td><td>variable</td><td>variable</td></tr><tr><td>N industrial kgN ha<sup>-1</sup></td><td>78</td><td>79</td><td>73</td><td>22<sup>(3)</sup></td><td>variable</td><td>variable</td><td>variable</td></tr><tr><td>Yield non-fixing kgN ha<sup>-1</sup></td><td>97</td><td>110</td><td>119</td><td>60<sup>(5)</sup></td><td>60</td><td>110</td><td>110</td></tr><tr><td>Grain to livestock α %</td><td>39</td><td>38</td><td>37</td><td>35<sup>(4)</sup></td><td>35</td><td>35</td><td>35</td></tr><tr><td>Livestock NCE %</td><td>21</td><td>21</td><td>20</td><td>-</td><td>22</td><td>22</td><td>22</td></tr><tr><td>NUE non-fixing <sup>(1)</sup> %</td><td>43</td><td>60</td><td>71</td><td>-</td><td>50<sup>(2)</sup></td><td>70</td><td>70</td></tr><tr><td>Net production kgN ha<sup>-1</sup></td><td>38</td><td>46</td><td>51</td><td>13<sup>(3)</sup></td><td></td><td></td><td></td></tr><tr><td>% Livestock in net N prod.</td><td>25</td><td>20</td><td>18</td><td>15</td><td></td><td></td><td></td></tr><tr><td>N self-sufficiency %</td><td>25</td><td>19</td><td>20</td><td>34<sup>(5)</sup></td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_48.png" /><figcaption>Figure 1. Relationship between maximum N net production per ha and N self-sufficiency for three scenarios. i) Current average world N fixing rate (rBNF = 12 kgN ha⁻¹) ii) High rBNF= 35 kg ha⁻¹ (western countries) iii) Increasing rBNF (from 35 to 100 kgN ha⁻¹) as a positive function of N self-sufficiency. In each scenario, the range of net N production is determined by the manure recovery rate β∈[0, 1]. The color code indicates the share of livestock in total net production. The trajectory for France (1980 to 2013) is indicated for comparison.</figcaption></figure>

## CONCLUSION

This paper models the N operating space of agriculture by connecting N self-sufficiency to net N production.

## REFERENCES

Erisman J. et al. 2008. How a century of ammonia synthesis changed the world. Nature Geoscience 1(10): 636 - 639.
Foley, J. et al. 2011. Solutions for a cultivated planet. Nature 478(7369): 337–342

Harchaoui S., Chatzimpiros P., accepted. Energy, nitrogen and farm surplus transitions in agriculture from historical data modeling. France, 1882-2013.

Lassaletta, L. et al. 2014. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environmental Research Letters 9(10): 105011.

Smil, V. 2002. Nitrogen and food production: proteins for human diets. *Ambio* 31(2): 126-131.
# IMPACT OF NITROGEN MANAGEMENT PLANS ON WINTER SOFT WHEAT YIELD AND NITROGEN BALANCE AT EUROPEAN SCALE, SIMULATED WITH THE STICS CROP MODEL

LECERF, R.¹, VAN DER VELDE, M.¹, DENTENER, F.¹

1 Food Security Unit - D5, JRC, European Commission, Italy

## INTRODUCTION

The nitrogen use efficiency of arable crops depends on climate and its variability, on soil types, the crops cultivated, and agro-management practices, a complex system that crop models can help to understand in order to improve current policies such as the EU's Nitrate directive (Asseng et al., 2001). Improving nitrogen use efficiency (NUE) to reduce nitrate leaching and greenhouse gas emissions related to arable crop cultivation, while maintaining an economically sustainable yield, is the overall target of precision farming by optimizing fertilization practices, quantities applied, and dates of application considering weather conditions and soils.

A European view would benefit from determining the best set of practices at regional scale and their sensitivity to climate. Therefore, the STICS crop model is being implemented at European scale to simulate the yields, nitrogen and carbon balances of the main arable crops. This activity started by simulating winter soft wheat, using past climate and fertilization rates in order to establish a baseline scenario and highlight the limits and uncertainties of the crop model. The goal is to determine regionally the optimal fertilisation practices that would allow farmers to get the best reward from their production while optimising nitrogen use and limiting the losses.

The yields, nitrogen and carbon balance of several fertilization scenarios were simulated by the STICS crop model (Brisson et al., 1998), using as input the nitrogen rates simulated by the CAPRI model (Henrichsmeyer et al., 1997), the soil and meteorological data from the Crop Growth Modeling System from 1995 to 2010 (Baruth et al. 2007). The main fertilization scenarios used are mimicking basic fertilization management practices and precision farming, ranging from one nitrogen application to split nitrogen applications, applied according to crop phenological stages and crop needs, remaining nitrogen residues in the soil, and meteorological conditions.

## MATERIAL AND METHODS

### Crop model inputs, simulations and aggregation

All input data required by the crop model are coming from the Food Security Unit of the Joint Research Center. The meteorological data and the soil hydraulic properties of the Crop Growth Modeling System (CGMS), available at a spatial resolution of 25km for the whole Europe are used. The sowing dates were also extracted from the CGMS database and a generic crop calibration was used, not accounting for the diversity of cultivars. The vernalisation requirements were adapted for each grid using a recent calibration produced for the WOFOST crop model simulations. Simulations, launched at a 25km resolution, are aggregated at NUTS-3 level using an arable land mask. From NUTS-3 level to NUTS-2 and higher hierarchical administrative levels, the cultivated area of winter soft wheat is used to weight the output results. The yields, mineral and organic nitrogen and carbon balances are thus available from the grid scale to the administrative scales relevant for policy making.

### Fertilization scenarios

Three fertilization scenarios are simulated for all grids identified as arable land in Europe. The first scenario (sc1) consists of applying the entire amount of nitrogen at the end of winter, toward the tillering stage. A second scenario (sc2), designed to mimic precision agriculture, starts by establishing a simple provisional balance considering the yield targeted by farmers equals the amount of nitrogen reported by CAPRI divided by the nitrogen
needed to produce 1 quintal of grain, assumed to be equal to 3. Nitrogen is then applied depending on the plant needs and the nitrogen available in the soil as simulated by the crop model, splitting the nitrogen application in a maximum of 5. The last scenario (sc3) is identical to the previous one but the provisional balance targets a yield of 9.5t/ha, thus increasing the amount of nitrogen applied in southern and eastern Europe.

## RESULTS AND DISCUSSION

The main preliminary results indicate that splitting fertilization does not necessarily improves the NUE of the crop cultivated, but this does tend to diminish nitrate leaching, and increase protein levels. To be efficient, enough rain should fall after a nitrogen application. For instance, a crop fertilized at the tillering stage can have a higher NUE than a crop receiving 3 applications but without sufficient amounts of precipitation following it.

Establishing a provisional nitrogen balance and applying N according to available soil N, can help to maintain yields close to their current level but decrease the nitrogen rates substantially. On the other hand, when increasing the amount of nitrogen compared to the current level in intensive systems, even when considering precision agriculture, sampling soil, and establishing a provisional N balance, would only slightly increase yields but increase the quantity of nitrogen used substantially.

<table><caption>Table 1: Simulated yield and amount of nitrogen applied for the scenarios simulated at European scale</caption><thead><tr><td>Scenario</td><td>Yields (t/ha)</td><td>N (kg/ha)</td></tr></thead><tbody><tr><td>One Application (sc1)</td><td>5.21</td><td>140</td></tr><tr><td>Precision agriculture, targeting the current yield (sc2)</td><td>4.88</td><td>111</td></tr><tr><td>Variation sc1/sc2 (%)</td><td>-6.33</td><td>-20.71</td></tr><tr><td>Precision agriculture, targeting a yield of 9.5t/ha (sc3)</td><td>5.65</td><td>189</td></tr><tr><td>Variation sc1/sc3 (%)</td><td>+8.45</td><td>+35.00</td></tr></tbody></table>

## CONCLUSION

Adapting nitrogen applications to meteorological conditions, plant needs, and establishing a proper nitrogen balance, notably by measuring the nitrogen remaining in the soil, leads to an improvement of the Nitrogen Use Efficiency. Increasing the quantity of nitrogen in already intensive systems will tend to have a limited impact on yields, while considerably increasing environmental impacts. In regions where water stress is the main limiting factor, increasing nitrogen rates would have a limited impact on yields as long as no additional irrigation water is supplied. However, in many such regions, for instance in the Mediterranean, this may not be a feasible option in given the already limited availability of water.

## REFERENCES

* Asseng, S., Turner, N.C., Keating, B.A., 2001. Analysis of water- and nitrogen-use efficiency of wheat in a Mediterranean climate. Plant and Soil 233, 127–143.

* Baruth, B., Genovese, G., Leo, O., 2007. CGMS version 9.2 User manual and technical documentation. JRC40605. EUR 22936.

* Brisson, N., Mary, B., Ripoche, D., et al., 1998. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18, 311–346.

* Henrichsmeyer, W., Britz, W., Heckelei, T., Meudt, M., Sander, R., 1997. Konzept eines regionalisierten, agrarsektoralen Politikinformationssystems für den Agarsektor der EU: das CAPRI-Modell. In: Agrarwirtschaft Jg. 46, Heft 8/9, S. 322 – 324.
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes – France

Session II: Regional studies - Highlighted posters
# MODELLING BIOMASS AND NUTRIENT FLOWS IN AGRO-FOOD SYSTEMS AT THE LOCAL SCALE. SCENARIO SIMULATION AND ASSESSMENT IN A FRENCH CASE-STUDY

FERNANDEZ-MENA H.<fnref n="1" />,²,³, NESME T.², MACDONALD GK⁴, PELLERIN S.<fnref n="1" />

## INTRODUCTION

Solutions for towards farming sustainability require to improve nutrient and biomass recycling in agro-food systems to move out from linear flows of fertilizers, crop products, feedstuff, by-products and organic wastes (Lassaletta et al., 2014). Innovative tools quantifying material exchanges between farms and their upstream and downstream partners in agro-food systems at the local scale may be useful to explore circular nutrient flows (Davis et al., 2016). Designing and assessing alternative scenarios of material flows within local agro-food systems can help decision-makers to identify the feasibility and pathways to move towards nutrient reuse and recycling in circular economy (Smith et al., 2016). However, assessment of alternative scenarios is challenged by the complex nature of agro-food networks, which involve flows and relationships among different economic agents in the food production sector (e.g., farms, fertilizer and feed suppliers, slaughterhouses, food processors, and waste managers). Agent-based modelling appears to be a particularly important tool that enables the simulation of complex material flows among a wide range of individual agents and it can also be useful to assess the emergent system outcomes of different scenarios of agent behaviors (Fernandez-Mena et al., 2016).

In this study, we aimed to simulate alternative scenarios of material flows in a specific farming region. To do so, we create the FAN ("Flows in Agricultural Networks") agent-based model and applied it to a case study, the Ribéracois, in Dordogne, France. The FAN model is a powerful tool to simulate different scenarios, to assess a range of environmental outputs, and to simultaneously simulate multiple bio-sourced and biomass materials as a network of local potential exchanges. Our scenarios, deal with good management practices, organic waste recycling, bioenergy production, crop-livestock symbiosis and chemical fertilizers removal. Thanks to FAN simulations, our scenarios performances are evaluated through various environmental indicators.

## MATERIAL AND METHODS

FAN model represent the following steps during a simulation cycle (equivalent to a year):

(i) the production of fertilizing materials such as manure, digestates and sewage by farms, anaerobic digesters and wastewater treatment plants;

(ii) the organic or chemical fertilizers are then applied to agricultural soils according to each farm's nutrient demand; (iii) farm's crop production is calculated as a function of fertilizing material inputs to soils through a simple linear yield-response model;

(iv) animal feed requirements are estimated according to species-specific feed demand, and crop products are exchanged in order to meet these animal requirements. Once feed and forage requirements are satisfied, livestock production is calculated;

(v) fruits, vegetables, and animal products are exchanged with local food industries, where they are processed, generating processed food and food wastes;

(vi) anaerobic digesters inflow biomass wastes and perform the bioenergy production.

<footnote n="1">¹. INRA, UMR 1391 ISPA, F-33882 Villenave d'Ornon, France. ². Bordeaux Sciences Agro, Université de Bordeaux, UMR 1391 ISPA, F-33175 Gradignan, France. ³ Department of Agroecology and Environment, ISARA Lyon, U. Lyon, France. ⁴ Department of Geography, McGill University, Montreal, QC, H3A 2K6, Canada</footnote>
Note that global markets can create competition with local materials (e.g., imported chemical fertilizers can compete with local manures for fertilizing soils) or can compensate local production deficits (e.g., in feedstuff and forage to meet animal requirements).

The scenarios applied to FAN simulations for the riberacois are based on the efficiency substitution and redesign logic that can make each of them a progression on their environmental performance (Fig. 1).

<figure><img src="image_49.png" /><figcaption>Figure 1. Scenarios explored in the Ribéracois case-study using the FAN model.</figcaption></figure>

## RESULTS AND DISCUSSION

The production indicators calculated after FAN simulations include crop production, livestock production, and renewable energy production. In addition, the performance of the system is also measured through environmental indicators, including nutrient cycles and fertilization (Fig. 2), nutrient losses to water bodies, C sequestration and greenhouse gas emissions, and food and feed autonomy.

<figure><img src="image_50.png" /><figcaption>Figure 2. N flows from FAN scenarios output, in kg of N per year. N inputs to the system are on the left, and N outputs and losses on the right.</figcaption></figure>
## CONCLUSION

The environmental performance of the scenarios increased progressively through their application. However, several variables showed that this progression was not straightforward and sometimes food production could be drastically reduced. This information gave an insight on how different choices and development strategies can make agro-food systems change and adapt to local environmental issues and what are the drawbacks and the gains.

## REFERENCES

- Davis, S.C., Kauneckis, D., Kruse, N.A., Miller, K.E., Zimmer, M., and Dabelko, G.D. (2016). Closing the loop: integrative systems management of waste in food, energy, and water systems. J. Environ. Stud. Sci. 6, 11–24.

- Fernandez-Mena, H., Nesme, T., Pellerin, S., 2016. Towards an Agro-Industrial Ecology: A review of nutrient flow modelling and assessment tools in agro-food systems at the local scale. Sci. Total Environ. 543, 467–479.

- Lassaletta, L., Billen, G., Grizzetti, B., Garnier, J., Leach, A.M., and Galloway, J.N. (2014a). Food and feed trade as a driver in the global nitrogen cycle: 50-year trends. Biogeochemistry 118, 225–241.

- Smith, J., Lang, T., Vorley, B., Barling, D., 2016. Addressing policy challenges for more sustainable local–global food chains: Policy frameworks and possible food “futures.” Sustainability 8, 299.
# A COMPARISON OF DISAGGREGATED NITROGEN BUDGETS FOR DANISH AGRICULTURE USING EUROPE-WIDE AND NATIONAL APPROACHES

KROS, J.<fnref n="1" />, HUTCHINGS, N.J.², TOFT KRISTENSEN, I.², SILLEBAK KRISTENSEN, I.², DUUS BØRGESEN, C.², VOOGD, J.C.H.<fnref n="1" />, DALGAARD, T.², DE VRIES, W. <fnref n="1" />,³

## INTRODUCTION

Spatially detailed information on nitrogen (N) budgets is relevant to identify regions with a need to significantly reduce N pollution. Furthermore, disaggregation to smaller areas (landscapes and/or catchments) is relevant since implementation of the Water Framework Directive and to a lesser extent the Nitrates Directive will lead to significant changes in land use and land management at this scale. However, at the scale of the European Union, there is a lack of consistent, reliable, high spatial resolution data necessary for the calculation of regional N losses. One model that has been used at European scale to assess high spatial resolution N budgets is the model Integrator (De Vries et al., 2011; Kros et al., 2012). Despite using this detailed information, the model might still be quite inaccurate at the regional level within countries. To gain insight in the reduction in uncertainty that could be achieved by using higher resolution input data, spatially disaggregated agricultural N budgets for Denmark for the period 2000-2010 were generated by Integrator, using both high spatial resolution national data (Integrator-DK) and data available at the European scale (Integrator-EU). Here we report the approach and results of this study, focusing on the years 2000 and 2010, for which the quality of the regional Danish input data was considered best. The results provide insight in the quality of European-scale model results at regional scale (within country level). We also compared the results at national scale with those obtained using Danish national data and a Danish modelling approach.

## MATERIAL AND METHODS

The Integrator-EU model, developed to assess N-flows at European level in response to changes in climate, land use and land management, was used to assess N flows for Denmark. Integrator-EU comprises 27 Member States, including Denmark, that are subdivided into so-called NitroEurope Classification Units (NCUs). These NCUs, about 40,000, are composed of multipart polygons, each of the polygons being a cluster of 1 km × 1 km pixels. Integrator predicts the N (NH₃, NOₓ, N₂O and N₂) emissions and N leaching from housing and manure storage systems and agricultural soils. Integrator-EU calculates the total N manure production for each NCU using Eurostat data on animal numbers at the socio-economic NUTS3 level from Eurostat and excretion rates from CAPRI (Britz et al., 2014). Fertilizer N application is based on national fertilizer consumption rates for the year 2010. For each country, the mineral fertilizer is distributed over crops using weighing factors that are based on the N uptake of the crop. For the application of Integrator-EU with high spatial resolution national data for Denmark (DK) we developed Integrator-DK by (i) adaptation of the boundaries of the NCUs, (ii) adaptation of the manure distribution module and (iii) translation of the detailed Danish data, usually available at municipality level, to conform with the crop types and animal categories, used in Integrator.

## RESULTS AND DISCUSSION

Results showed that the national N fluxes in the N budgets calculated by the two versions of the model were within 1-5% for N inputs by fertilizer and manure excretion, but inputs by N fixation and N mineralisation differed by 50-100% and N uptake differed by ca 25%, causing a difference in N leaching and runoff by nearly 50%. Figure 1 presents a comparison of predicted N inputs and N losses by Integrator-EU and Integrator-DK for the year 2010

<footnote n="1">¹ Wageningen Environmental Research, Wageningen, Netherlands; ² Department of Agroecology, Aarhus University, Tjele, Denmark; ³ Environmental Systems Analysis Group, Wageningen University, Wageningen, Netherlands</footnote>
at 113 plots, where the Integrator-EU results for the 113 plots were compared with the mean of the corresponding Integrator-DK plots with underlying data of 610 plots.

<figure><img src="image_51.png" /><figcaption>Figure 1 XY-plots for N input by animal manure, NH₃-N emission and N leaching (in kg N ha⁻¹ yr⁻¹) as calculated by Integrator-EU and Integrator-DK for the year 2010 together with the 1:1 line and the R² of linear regression between the Integrator-EU and Integrator-DK results</figcaption></figure>

Results show a relatively large scatter around the 1:1 line, and a rather poor correspondence for manure application (R²=0.21) and NH₃ emission (R²=0.18) and reasonable correspondence for N leaching (R²=0.48). This means that the spatial distributions of Integrator-DK clearly differ from those of Integrator-EU. The spatial distribution of manure distribution and N losses from Integrator-DK were closer to observed distributions than those from Integrator-EU.

## CONCLUSION

Differences in spatial distribution between Integrator-EU and Danish results are mainly due to differences in the spatial distribution of livestock and the resulting manure distribution. This causes large differences between EU and DK versions in the spatial distribution of NH₃ emission and N leaching. Furthermore, differences in crop N offtake and leaching fractions used in Integrator causes large differences in N leaching and runoff. The incorporation of detailed national data rather than less detailed European data does, however, not necessarily result in more reliable national results. This comparative study between spatially detailed data based and generic national data based N budgets clearly illustrates the importance of good spatial resolution in input data. There is clearly a need for collection and submission of high resolution data from all Member States to EUROSTAT.

**Acknowledgement:** This project has been supported by the Strategic Research Alliance DNMARK (www.dnmark.org) funded by The Danish Council for Strategic Research (reference 12-132421).

## REFERENCES

* Britz, W., Witzke, P., 2012. CAPRI model documentation, URL: http://www.capri-model.org/docs/capri_documentation.pdf. University of Bonn.

* De Vries W., Leip A., Reinds G.J., Kros J., Lesschen J.P., Bouwman A.F., Grizzetti B., Bouraoui F., Butterbach Bahl K., Bergamaschi P., Winiwarter W., 2011. Geographic variation in terrestrial nitrogen budgets across Europe. In: Sutton, M.A., Howard C.M., Erisman J.W., Billen G., Bleeker A., Grennfelt P., Van Grinsven H., Grizzetti B. (Eds). The European Nitrogen Assessment. Cambridge, Cambridge University Press, Cambridge, pp. 317-344.

* Kros J., Heuvelink G.B.M., Reinds G.J., Lesschen J.P., Ioannidi, V., De Vries W., 2012. Uncertainties in model predictions of nitrogen fluxes from agro-ecosystems in Europe. Biogeosciences 9-11, 4573-4588.
# NITROGEN MANAGEMENT IN FRENCH DAIRY SYSTEMS: EVALUATION AND ENHANCEMENT OF NITROGEN EFFICIENCY AND ECONOMIC PERFORMANCE

FORAY, S.<fnref n="1" />, VERTES, F.², GODINOT, O.²

## INTRODUCTION

While nitrogen (N) management has improved significantly on livestock farms, with positive consequences for nitrate concentrations in surface waters (Manneville et al., 2017), the French dairy farming sector must continue its efforts. At the same time, dairy farmers have to cope with market developments (increased milk demand), price volatility and changing agricultural policies. Therefore, reconciling respect for the environment and economic competitiveness has become one of their main objectives. To support them, the "Inosys-Réseau d'Elevage" farms network associates 2,000 breeders and 240 engineers to analyze the management of the farms and disseminate the knowledge and tools needed to improve the systems. The analysis of these farms' data aims to identify sustainable production systems and key actions that can be implemented by farmers to improve nitrogen (and phosphorus) management, related to economic performances (full results in Foray et al., 2017).

## MATERIAL AND METHODS

Data used focus on the specialized dairy farms of the "Inosys" network for the years 2000-2004 and 2009-2013 and include a descriptive statistical analysis of several indicators of practices, emissions, and impacts related to N management. Five classes of dairy systems were described according to their production area (lowland vs mountain and piedmont) and their feeding systems: proportion of maize in the forage area <10%, 10 to 30%, >30%. A comparison between the most and least efficient systems regarding the nitrogen balance (Simon et al., 2000) is conducted. N surplus are defined as nitrogen inputs including (except in table 1) symbiotic fixation and atmospheric deposition minus nitrogen outputs.

## RESULTS AND DISCUSSION

### Intensification of dairy systems and environmental response

Despite the decrease in the number of dairy farm (-34% in 2015 compared to 2005), the national milk production has increased by around 9% between 2004/2005 and 2014/2015, reflecting both the evolution of the size of the dairy farms (France Agrimer, AGRESTE) and the intensification of dairy farms: more milk per cows (+ 595 l per cow per year) and more milk per ha of forage area (+ 434 l per ha of forage area). This intensification was achieved by an increase in feed concentrates.

However, the expansion and the intensification of the dairy farms have not led to a degradation of the nitrogen farm gate balance.

<table><caption>Table 1. Evolution of the milk production, feed concentrates intakes and N surplus between 2000-2004 and 2009-2013 – in the specialized dairy farms of Inosys Network data</caption><thead><tr><th></th><th>Average 2000-2004</th><th>Average 2009-2013</th><th>P value</th></tr></thead><tbody><tr><td>Feed concentrates - kg per cow per year (g per l milk)</td><td>1 388 (211)</td><td>1 590 (220)</td><td>&lt; 0.01 (&lt;0.03)</td></tr><tr><td>Milk production - l/cow/year ; l/ha forage area /year)</td><td>6 601 (6070)</td><td>7 196 (6 504)</td><td>&lt; 0.05 (&lt;0.05)</td></tr><tr><td>Livestock Unit - LSU/ha forage area</td><td>1.42</td><td>1.37</td><td>NS</td></tr><tr><td>N balance (without N fixation and atm. deposition)- kgN/ha</td><td>74</td><td>71</td><td>NS</td></tr></tbody></table>

<footnote n="1">¹ Institut de l'Elevage, France; ² INRA Agrocampus-Ouest UMR SAS, France</footnote>
Today, grazing systems (Maize < 10% forage area) have a moderate surplus of nitrogen compared to maize-based systems (Maize > 30% forage area), in mountain areas (52±23 kgN.ha⁻¹ and 101±34 kgN.ha⁻¹ respectively) as well as in lowlands (79±36 kgN.ha⁻¹ and 118±28 kgN.ha⁻¹ respectively). Intra-system diversity (figure 1) highlights the high variability of N surpluses at farm level, showing the large diversity of nitrogen management practices (feeding, fertilization, manure management, grass and grazing management...) within each class. To understand the determinants of these differences, also dependent on soil and climate potentials, data of the 20% lowest and 20% highest N surplus farms were compared. Results focus on lowland farms with >30% maize in fodder area.

<figure><img src="image_52.png" /><figcaption>Figure 1. Nitrogen Balance in kgN.ha⁻¹ for the five French dairy systems, Inosys Network data, 2009-2013 (Mountain 1 <10% maize; Mountain 2 > 10% maize, Lowlands 1 < 10% maize, Lowlands 2 10 to 30% maize, Lowlands 3 > 30% maize)</figcaption></figure>

## Maize based systems: high and low performance farms

The 20% most performant farms show an average N surplus of 79±10 kg N ha⁻¹ compared to 152±8 kg N ha⁻¹ for the 20% less performant. These performant farms are slightly less intensive than the less performant for milk production, expressed per ha (8030±2390 l ha⁻¹ vs 9520±2738 l ha⁻¹) or per cow (7360±994 cow vs 8040±1022 cow) (- 8% and - 15% respectively), but they have a higher ratio of gross operating surplus / gross product that reflects a better economic efficiency (36±9% vs 28±11%). The use of feed concentrates appears well managed considering the high milk production level, allowing a lower feeding cost 91±34 € vs 121±38 € (-25%). N fertilizer are used in a more parsimonious way within the most performant farms with a N mineral pressure of 64±28 kg N ha⁻¹ compared to 152 kg N ha⁻¹. Finally, their N efficiency at farm level (N output/N input) is 60% higher than the less performant (45±11% vs 28±6%).

## CONCLUSION

The intensification of the French dairy system that occurred in the last decade was mostly permitted by a more efficient use of resources, leading to a more important milk production with less N losses. Higher animal feed self-sufficiency, greater fertilizer self-sufficiency and a larger proportion of grasslands generally result in higher farm N efficiency in our sample. It is thus likely that this difference between efficient and less efficient farms would be even greater if N efficiency also considered soil N variations, input production and purchased feed production (Godinot et al., 2014). The optimization of practices within each system offers opportunities to reduce the use of nitrogen inputs. Thus, a good balance can be found by adapting the diet as closely as possible to the needs and the potential production of the animals and controlling the nitrogen fertilization of crops, forage and grasslands.

**Acknowledgements:** We would like to thank the French Agency for the Biodiversity for funding this study; and J-L. Peyraud, V. Manneville, A. Le Gall, M. El Jabri, C. Billy and M. Dourthe for technical support.
REFERENCES

Foray S., Billy C, Manneville V., Dolle J-B., Le Gall A., Vertès F., Godinot O., 2017. Gestion de l'azote dans les systèmes d'élevages herbivores. Evaluation et amélioration de l'efficience de l'azote, réduction des transferts vers les milieux aquatiques. Collection Résultats Idele, 97p.

Godinot O., Carof M., Vertès F., Leterme P. 2014. SyNE: an improved indicator to assess nitrogen use efficiency of farming systems. Agricultural Systems 127, 41–52.

Manneville V., El Jabri M., Divo C., Foray S., 2017. Contribution de l'élevage d'herbivores à l'amélioration de la teneur en nitrates de l'eau en France. Collection Résultats Idele.

Simon J-C., Grignani C, Jacquet A et al (2000) Typologie des bilans d'azote de divers types d'exploitation agricole: recherche d'indicateurs de fonctionnement. Agronomie 20, 2, 175-195.
# REGIONALIZED AMMONIA EMISSION REDUCTION POTENTIALS IN GERMANY

HÄUSSERMANN, U.¹, KLEMENT, L.¹, BACH, M.¹, BREUER, L.¹

¹ Justus-Liebig University Giessen, Institute for Landscape Ecology and Resource Management, Germany

## INTRODUCTION

The overall ammonia (NH₃) emissions in Germany account for 759 kt NH₃ in 2015 and have increased in recent times. 95% of all NH₃ emissions in Germany originate from agriculture. In accordance with international treaties, EU legislation and national regulations, Germany committed to reduce the NH₃ emissions significantly. According to the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) and the NEC directive (2016/2284/EU), the NH₃ emissions reduction targets for Germany are set at 5% and 29%, for 2020 and 2030, respectively, relative to NH₃ emissions in 2005 (678 kt) (Federal Environment Agency 2017).

Our aim is a) to evaluate mitigation options for agriculture with respect to their overall NH₃ emission reduction potential for Germany, b) their impact on net N surplus, and c) to identify regional "hot spots" and trends for NH₃ emissions from agriculture and its sources.

## MATERIAL AND METHODS

We developed a calculation scheme for gaseous emissions of nitrogen (N) species (NOₓ, N₂, N₂O, and NH₃) from agriculture and for net N balances on a regional scale (Häußermann and Bach 2017). This calculation scheme is based on the accounting method of the agricultural part of the National Emission Inventory Report (Rösemann et al. 2017), therefore the results from this calculation scheme correspond to those from the National Emission Inventory Report. It provides the possibility to calculate gaseous emissions of nitrogen species and net N balances for 402 NUTS-III regions (districts), 16 NUTS-I regions (federal states) and entire Germany on a yearly basis for 1995 to 2015. Furthermore, the calculation separates three categories of agricultural activities: animal husbandry, biogas and crop production, as well as different compartments within these categories: e.g. farmyard manure (FYM) storage, FYM spreading and animal housing systems. Activities in all of these compartments affect the amount of gaseous emissions of N species from agriculture.

In our study, we calculated the impact of six mitigation measures (MM) for the three major livestock categories (cattle, pigs and poultry) on their respective NH₃ emission reduction potential and net-N balance (see table 1). MM for digested FYM, which accounts for approximately 18% of all excreted N from cattle, pigs and poultry in 2015 were not considered.

<table><caption>Table 1. Ammonia emissions mitigation measure for animal husbandry (cattle, pigs and poultry).</caption><thead><tr><th>MM</th><th>Compartment</th><th>Species</th><th>Description</th></tr></thead><tbody><tr><td>MM1</td><td>grazing</td><td>cattle</td><td>time spent on pastures increased</td></tr><tr><td>MM2</td><td>FYM storage</td><td>cattle, pigs and poultry</td><td>solid covers for all undigested slurry for all outdoor storage systems</td></tr><tr><td>MM3</td><td>FYM storage</td><td>cattle, pigs and poultry</td><td>solid covers for all undigested slurry + no slurry storage underneath slatted floors</td></tr><tr><td>MM4</td><td>FYM spreading</td><td>cattle, pigs and poultry</td><td>grassland: all undigested slurry applied with trailing hoses<br>arable land: all undigested slurry applied with trailing hoses and incorporated within 1 hour<br>optimal application technique for all undigested leachate and solid manure</td></tr><tr><td>MM5</td><td>housing</td><td>pigs</td><td>all slurry based pig barns are equipped with air scrubbers</td></tr><tr><td>MM6</td><td></td><td>cattle, pigs and poultry</td><td>combination of measures MM1, MM3, MM4 and MM5</td></tr></tbody></table>
## RESULTS AND DISCUSSION

NH₃ emissions from cattle, pigs and poultry FMY management and application account for 463 kt NH₃ (61% of total NH₃ emissions) in 2015. Increasing the time spent on pastures for cattle (MM1) reduces NH₃ emissions by 2.0% (relative to NH₃ emissions from major livestock species in 2005). Equipping all outdoor storage systems with solid (concrete) covers (MM2) reduces NH₃ emissions by 1.9%; not storing any slurry underneath slatted floors (MM3) increases reduction potentials to 3.2%. High reduction potentials can be achieved with emission efficient FYM application techniques. Spreading FYM with trailing hoses and incorporation of slurry and solid manure within one hour on bare soil (MM4) reduces NH₃ emissions by 13.8%. Equipping all slurry based housing systems for pig husbandry has the potential to reduce NH₃ emissions by 11.7%. The combination of mitigation measures MM1, MM3, MM4 and MM5 has the potential of NH₃ emissions reductions of 26.9% (see figure 1).

<figure><img src="image_53.png" /><figcaption>Figure 1. Total NH₃ emissions reduction potentials for mitigation measures in animal husbandry of livestock species cattle, pigs and poultry compared to NH₃ emissions in 2005.</figcaption></figure>

## CONCLUSION

None of these mitigation measures suffices the emission reduction targets set by the CLRTAP and the EU-NEC directive (2016/2284/EU) for 2030. Implementing MM4, MM5 and MM6 might suffice to achieve the 2020 emission reduction targets for major livestock species, however might be difficult to implement within the short period until 2020. With respect to total NH₃ emissions, none of the described MM achieves even the 5% emission reduction target for 2020. Further steps should be considered to reduce total NH₃ emissions from German agriculture by 5% in 2020 and 29% in 2030.

## REFERENCES

* Häußermann U., Bach M. 2017. Berechnung von Stickstoff-Flächenbilanzen für Deutschland, Bundesländer und Kreise - Jahre 1995 bis 2015. Methoden und Ergebnisse. Stand Oktober 2017 (in preparation)

* Rösemann C., Haenel H.D., Dämmgen U., Freibauer A., Döring U., Wulf S., Eurich-Menden B., Döhler H., Schreiner C., Osterburg B. 2017. Calculation of gaseous and particulate emissions from German agriculture 1990 - 2015. Report on methods and data. Submission 2015. Thünen Report 46. Braunschweig. 372 p.

* Federal Environment Agency 2017. National Trend Tables for the German Atmospheric Emission Reporting 1990 - 2015. Dessau-Roßlau.

* https://www.umweltbundesamt.de/sites/default/files/medien/361/dokumente/2017_02_15_em_entwicklung_in_d_trendtabelle_luft_v1.0.xlsx
PRODUCTION, NITROGEN EXPORTATION AND NITRATE LEACHING FROM MANAGED GRASSLANDS IN FRANCE

GRAUX, A.-I.<fnref n="1" />, RESMOND, R.<fnref n="1" />, CASELLAS, E.², DELABY, L.<fnref n="1" />, FAVERDIN, P.<fnref n="1" />, LE BAS, C.³, MEILLET, A.⁴, POMEON, T⁴, RAYNAL, H.², RIPOCHE, D.⁵, RUGET, F.⁶, THEROND, O.⁷, VERTES, F.⁸, PEYRAUD, J.-L.<fnref n="1" />

INTRODUCTION

This study (Graux et al., 2017) aims to quantify and map French grassland yield and N exportation resulting from cutting and grazing practices, for the existing diversity of grassland types, managements, soil and climate conditions, and to assess the risk of nitrate leaching associated with increasing applications of organic N (that include N applications by grazing animals). Results are intended to provide scientific elements supporting a French request for derogation from the actual organic N spreading limit, set in the Nitrate Directive at 170 kg of organic N per year and per hectare of Utilised Agricultural Area (UAA).

MATERIAL AND METHODS

Observed data from regular grass measurements in four French regions were analysed to provide reference values of the grassland production and N exportation. This first assessment was extended to France by simulating grasslands with a research version of the STICS crop model (Brisson et al., 2003; Ruget et al., 2006), named PâturSTICS. This model simulates daily the dry matter (DM), the N and water fluxes involved in the functioning of grasslands and crops in response to management, soil and climate conditions. The planned cut and grazing events can be adapted in the course of the simulation according to predefined rules of management and effective simulated grass growth. STICS was improved to account for animal returns on grasslands during grazing, and to better simulate the DM production and protein content of grasses and legumes. Simulations were performed across France at a high resolution grid composed of pedoclimatic units (PCU) obtained by crossing the spatial resolutions of climate (SAFRAN data - 8 km grid) and soil (1/1,000,000-scale soil geographical database of France - Soil Mapping Units of variable size). Thanks to the French Land Parcel Identification System and to the French agricultural statistics, and based on previous work (Ruget et al., 2006), the main grassland types and associated managements were determined for each PCU, then simulated with pâturSTICS over 30 years (1984-2013). Annual simulation results were aggregated and analysed in the light of reference values. To go further, additional simulations testing the consequences of an intensification of grazing on nitrate leaching were performed in 6 French departments where conditions to obtain derogation would be potentially met.

RESULTS AND DISCUSSION

The simulated yield and N exportation are in agreement with available reference values, with the exception of mountain regions where the model tends to overestimate observations, probably due to the non-inclusion in present simulations of the effects of snow and night frosts on grass growth and of slope on soil water resources. Most French grasslands export more than 170 kg N ha⁻<fnref n="1" /> yr⁻<fnref n="1" /> up to more than 400 kg N ha⁻<fnref n="1" /> yr⁻<fnref n="1" />. The highest production (≥ 10 t DM ha⁻<fnref n="1" /> yr⁻<fnref n="1" />) and N exportation levels (≥ 200 kg N ha⁻<fnref n="1" /> yr⁻<fnref n="1" />) are observed in the following regions or departments: Bretagne, Normandie, Marne, Limousin, Vosges, Franche-Comté, the north-west part of Massif Central, French Alpes and the west of Pyrénées (Figure 1). Among these productive situations, Finistère, Morbihan, Vosges and the west of Pyrénées are also presenting the highest risks for nitrate leaching (Figure 1). These high-risk situations result from high simulated water drainage (≥ 500 mm yr⁻<fnref n="1" />) and soil N mineralisation (≥ 200 kg N ha⁻<fnref n="1" /> yr⁻<fnref n="1" />). The comparison of the observed (Vertès et al., 2007) and simulated responses of nitrate leaching to an increase of stocking rates showed that PâturSTICS tends to overestimate nitrate leaching, probably due to the

<footnote n="1">¹ INRA-Agrocampus Ouest, UMR 1348 PEGASE, France; ² INRA, UR 0875 UBIA, France; ³ INRA, US 1106 INFOSOL, France; ⁴ INRA, US 0685 ODR, France; ⁵ INRA, US 1116 AGROCLIM, France; ⁶ INRA, UMR EMMAH, France; ⁷ INRA, UR 1132 LAE, France; ⁸ INRA-Agrocampus Ouest, UMR 1069 SAS, France</footnote>
underestimation of soil N immobilisation and, to a lesser extent, to the possible slight overestimation of soil N mineralisation. However, the shape of the simulated response was in agreement with the observed curve, giving confidence in the analysis of the relative evolutions of risks in comparison with the Nitrates directive situation (170 kg organic N ha⁻¹ yr⁻¹). Increasing the organic N spreading limit up to 200 kg N ha UAA⁻¹ yr⁻¹ should lead to a moderate increase of nitrate leaching (≤ 20 kg of supplementary N ha⁻¹ yr⁻¹) from grasslands in most situations.

<figure><img src="image_54.png" /><figcaption>Figure 1. Simulated average annual N exportation and N leaching for French grasslands. Grey cells were not simulated as there are only few grassland surfaces at these locations. The variability of results linked to the diversity of grasslands, managements, soils and climate years is not presented here.</figcaption></figure>

## CONCLUSION

This work allowed providing a very detailed view of French grassland production and N exportation, as well as the associated N leaching risk, taking into account the diversity of situations, both in case of the actual grass management and of an organic N spreading increase on grasslands. Our results provide information to consider derogation from the actual organic N spreading limit in productive areas that are not exposed to high environmental risks. Nevertheless, the present results have to be treated with caution as they are subject to a degree of uncertainty due to the quality of inputs and to the current modelling limits.

## REFERENCES

* Brisson N., Gary C., et al., 2003. An overview of the crop model STICS. Eur. J. Agron. 18, 309-332.

* Graux A.-L., Delaby L., Peyraud J.-L. (coordinateurs) et al., 2017. Les prairies françaises : production, exportation d'azote et risques de lessivage. Rapport d'étude, INRA (France), 75 p.

* Ruget F., Novak S. et al., 2006. Du modèle STICS au système ISOP pour estimer la production fourragère. Adaptation à la prairie, application spatialisée. Fourrages, 186, 241-256.
# SYNERGY: A MODEL TO ASSESS THE ECONOMIC AND ENVIRONMENTAL IMPACTS OF INCREASING REGIONAL PROTEIN SELF-SUFFICIENCY

JOUAN, J.¹, CAROF, M.², RIDIER, A.¹

¹ SMART-LERECO, AGROCAMPUS OUEST, INRA, 35000 Rennes, France; ²SAS, AGROCAMPUS OUEST, INRA, 35000 Rennes, France

## INTRODUCTION

The European Union (EU) relies on imports to feed livestock. In particular, protein self-sufficiency in EU for feed is not reached. Most of imported protein rich feed consist of soybean meals, which raises questions in terms of deforestation in countries where soybean is grown, consumer expectations for GMO-free products and security of supply. In this context, the 2014 CAP aims at improving protein self-sufficiency in EU for feed by developing production of protein-rich crops, such as legumes. Nevertheless, the development of legumes still faces economic and environmental challenges (Watson et al., 2017), such as lower annual gross margins per hectare than those of major crops and regulatory constraints which prohibit the spreading of animal manure on most legumes.

The purpose of this paper is to implement an appropriate stylized framework to assess the impacts of increased protein self-sufficiency through legume development at the regional level. Both economic and environmental impacts have to be studied. Mathematical programming models offer a prospective analysis, which permits to assess agricultural practices even though they have not been introduced at large scale yet. Among mathematical programming, bio-economic models permits to assess both economic and environmental impacts. In the case of legume production, several bio-economic models have been conducted, at the field scale and at the farm scale (Schläfke et al., 2014). Such models are relevant because decision-making processes take place at the farm scale and because they help appraising farm's sustainability. However, they fail to give indicators at higher scales, while this may be useful to policy makers. Hybrid models (Britz et al., 2012) address this issue by aggregating results from the farm to the region. These models usually take into account the diversity of farms but they badly represent the heterogeneity of soil and climate conditions. In the case of legume production, conditions such as soil pH and water deficit have to be taken into account because they limit the possibilities of implanting legumes. Besides, one of the levers to increase the production of legumes has been very little studied: the complementarity of farms. On the one hand, livestock farms could export animal manure to crop farms, which are deficient in nitrogen for crop fertilization. On the other hand, crop farms could produce legumes and trade it in order to feed animals of livestock farms. Our hypothesis is that increasing protein self-sufficiency through legume exchanges between farms can have positive economic and environmental impacts.

## MATERIAL AND METHODS

The bio-economic model SYNERGY proposed here is in direct line with these considerations. First, it is a hybrid model implemented at farm scale and then, aggregated at the regional level. Second, it takes into account various types of farm (crop farm, dairy farm, hog farm) as well as the heterogeneity of soil and climatic conditions. Third, the complementarity of farms is highlighted by accounting for exchanges of legumes and animal manure between farms. SYNERGY optimizes the sum of each farm's expected income at the regional level. It is composed of five modules: four modules describe farm activities (i.e., the cropping module, the fertilization module, the livestock module and the feeding module). Thanks to farm activities, farmers produce commodities (i) to self-supply needs for their management systems (e.g., a livestock farmer can use crops grown on its farm to feed his animals) and, (ii) to sell them on markets. Depending on the commodity, commodities can be exchanged on either local markets (i.e., to other farms of the region), on worldwide market or on both markets. The fifth module permits to assess environmental impacts through nitrogen-related indicators: SyNE (System Nitrogen Efficiency) and SyNB (System N Balance) based on (Godinot et al., 2014) have been integrated.
SYNERGY is implemented on a stylized area inspired from a part of western France where livestock farms are dominant. Three scenarios are simulated: the baseline scenario (B), which should reproduce the observed data; the scenario (SC1) where local exchanges between farms are made possible; the scenario (SC2) where, in addition to these local exchanges, a GMO-free certification is implemented for animal commodities for produced from legume-based rations instead of soybean-based rations. SYNERGY generates three types of outputs: (i) an assessment of protein self-sufficiency in animal feed, (ii) an economic assessment by calculating incomes and, (iii) an environmental assessment by calculating the nitrogen-related indicators SyNE and SyNB. All these assessments are done at the farm scale, and at the regional level through a scaling process.

## RESULTS AND DISCUSSION

SYNERGY model currently incorporates limited and highly constrained technical alternatives (i.e., soybean-based ration vs legume-based). Thus, the first results can only be interpreted in relation to the trends they present. When local exchanges between farms are possible (scenario SC1), protein self-sufficiency rises at the regional level (i.e., a part of western France), as do incomes. However, this greater self-sufficiency is not associated with an increase in the legumes area, but only with local exchanges of cereals. Thus, protein self-sufficiency is not only linked with protein rich materials but must be seen in a more comprehensive way by taking into account all sources of proteins. When a GMO-free certification is added (scenario SC2), the legumes area increases significantly and exchanges of legumes between crop farm and livestock farms happen. Protein self-sufficiency is improved thanks to a substitution of soybean-based rations for legume-based rations. However, the self-sufficiency is not strengthened compared to scenario SC2. One of the reasons is that the legume-based ration for pig is less effective than the soybean-based ration. Concerning the environmental assessment, in both SC1 and SC2 scenarios, SyNE indicator decreases and SyNB indicator increases in all types of farms. Thus, farms become less efficient in N and N losses become higher than in the baseline scenario (B).

## CONCLUSION

The purpose of this paper was to implement an appropriate stylized framework to assess the impacts of increased protein self-sufficiency through legume development at the regional level. The results show that protein self-sufficiency can initially be strengthened at the regional level, thanks to local exchanges of cereals. It can also be enhanced to the same extent by the development and exchanges of legumes, when a market for differentiated feeds such as GMO-free animal products, exists. Thus, SYNERGY model highlights that the complementarity between livestock farms and crop farms is a relevant lever for improving regional protein self-sufficiency.

## REFERENCES

- Britz W, van Ittersum M, Lansink AO, Heckelei T. Tools for Integrated Assessment in Agriculture. State of the Art and Challenges. Bio-Based and Applied Economics 2012;1:125–50. doi:10.13128/BAE-11232.

- Godinot O, Carof M, Vertès F, Leterme P. SyNE: An improved indicator to assess nitrogen efficiency of farming systems. Agricultural Systems 2014;127:41–52. doi:10.1016/j.agsy.2014.01.003.

- Schläfke N, Zander P, Reckling M, Bachinger J, Hecker J-M. Evaluation of legume-supported agriculture and policies at farm level. Legumes Futures Report 4.3., 2014.

- Watson CA, Reckling M, Preissel S, Bachinger J, Bergkvist G, Kuhlman T, et al. Chapter Four - Grain Legume Production and Use in European Agricultural Systems. In: Sparks DL, editor. Advances in Agronomy, vol. 144, Academic Press; 2017, p. 235–303. doi:10.1016/bs.agron.2017.03.003.
# MODEL ASSESSMENT AND VALIDATION OF AMMONIA CONCENTRATIONS AT HIGH SPATIAL AND TEMPORAL RESOLUTION OVER EUROPE

GE, X.¹, SCHAAP, M.², KROS, J.³, KRANENBURG, R.², SEGERS, A.J.², DE VRIES, W.¹,³

1 Environmental Systems Analysis Group, Wageningen University and Research, Wageningen, Netherlands; ² Department of Climate, Air and Sustainability, TNO, Utrecht, Netherlands; ³ Wageningen Environmental Research, Wageningen, Netherland

## INTRODUCTION

Ammonia emissions to the atmosphere have increased substantially in Europe since 1960, largely due to agricultural practices including intensive livestock production and the use of nitrogen fertilizers. These practices have enhanced deposition of ammonia and ammonium in the form of solutes, gases and particles, causing negative societal impacts on public health and terrestrial ecosystems. Due to the limited availability of ground-based measurements, models are used to assess large-scale ammonia emissions from agriculture. The modeled emissions are in turn used in chemistry transport models (CTMs) to assess ammonia concentration and deposition. One of the CTMs, LOTOS-EUROS, combined with the MACC-III emission model, is used to create grid maps of annual ammonia emissions over Europe at a spatial resolution of 7km x 7km (Kuenen et al., 2014). However, MACC-III does not distinguish differences in crop types, housing and storage systems and manure application techniques in its spatial allocation. Furthermore, it utilizes very simple parameterizations for the seasonal variation to allocate ammonia emissions over the year, not taking into account temporal variations in local agricultural management. This limits the capability to reproduce observed spatial and seasonal variations in the ammonia concentrations of rural regions with agricultural activities.

This paper describes the application of available novel ammonia emission approaches, that quantify emissions from agriculture at a higher spatial and temporal detail, within LOTOS-EUROS, to improve modeled ammonia emissions over Europe. The potential improvement was evaluated by comparing ammonia surface concentrations and total columns calculated by LOTOS-EUROS with ground-based measurements and IASI satellite observations.

## MATERIAL AND METHODS

### Improving the spatial resolution in annual ammonia emissions

The higher spatial detail in annual ammonia emissions was realized by embedding the ammonia emission module of INTEGRATOR into MACC-III. INTEGRATOR is a model that assesses greenhouse gas and nitrogen fluxes from agricultural sectors at high spatial resolution and accounts for differences in crop types and grass, grazing, manure application and housing and storage systems (De Vries et al., 2011; Kros et al., 2011). The emissions in INTEGRATOR are calculated for polygons with unique combinations of major land use and soil types (co-called NCUs) being multiples of 1km x 1km grid cells in the reference system of ETRS89, while MACC-III emissions are gridded at 7 km x 7km in World Geodetic System 1984 (WGS84). Therefore, emission estimates from INTEGRATOR were transformed into WGS84. By slicing and reallocating INTEGRATOR emissions onto regular MACC-III grids, a map of ammonia annual total emission over Europe was retrieved, with a higher spatial resolution and distinguishing different crop types, housing and storage systems.

### Improving the temporal resolution in ammonia emissions over the year

The much more detailed temporal distribution of ammonia emissions over the year was derived by the integration of the TIMELINES model (Hutchings et al., 2012), which provides predictions of timelines of key agricultural operations, such as fertilization, across Europe. The output used is the Julian day of fertilizer and/or manure application together with information about fertilizer type, amount of ammonium, nitrate or organic nitrogen, and application method, which all have impact on the ammonia emission factors over the course of one year. A
new algorithm was developed based on the above factors, as well as the difference between day and night, weekday and weekend.

## RESULTS AND DISCUSSION

The calculated total ammonia columns obtained from the renewed ammonia emission estimates showed a better correspondence with the IASI satellite observations than the ones retrieved with the original MACC-III results. The mean and standard deviation of difference between renewed estimates and remotely sensed data were 39.8% and 23.1% smaller than the original estimates, respectively. Furthermore, at selected ground stations from the European Monitoring and Evaluation Program (EMEP) framework, newly modeled surface ammonia concentrations over the year demonstrate closer temporal variations with in-situ measurements.

<figure><img src="image_55.png" /><figcaption>Figure 1. Comparison of ammonia surface concentrations from in-situ measurements and estimates obtained by LOTOS-EUROS with emission inputs from old MACC-III and renewed model.</figcaption></figure>

## CONCLUSION

The adapted LOTOS-Euros model, including high spatial and temporal resolution ammonia emissions by combining three models – MACC-III, INTEGRATOR and TIMELINES leads to improved 4D maps (in space and time) of ammonia concentrations over Europe. This is indicated by a closer comparison with satellite observations and in-situ measurements. At current stage, the temporal allocation needs more refinement to give better results.

**Acknowledgements:** This research has been supported by the NWO funded project AMARETTO (reference ALW-GO/16-02).

## REFERENCES

* De Vries W., Leip A., Reinds G.J., Kros J., Lesschen J.P., Bouwman A.F., 2011. Comparison of land nitrogen budgets for European agriculture by various modeling approaches, Environmental Pollution, 159-11, 3254-3268.

* Kros, J., Frumau K.F.A., Hensen A., De Vries W., 2011. Integrated analysis of the effects of agricultural management on nitrogen fluxes at landscape scale, Environ. Pollut., 159-11, 3171-3182.

* Hutchings, N.J., Reinds G.J., Leip A., Wattenbach M., Bienkowski J.F., Dalgaard T., Dragosits U., Drouet J.L., Durand P., Maury O., De Vries W., 2012. A model for simulating the timelines of field operations at a European scale for use in complex dynamic models. Biogeosciences, 9, 4487-4496.

* Kuenen J. J. P., Visschedijk A. J. H., Jozwicka M., Denier van der Gon H. A. C., 2014. TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963-10976.
# USING THE BOTTOM-UP INVENTORY METHOD CADASTRE_NH3 TO ASSESS THE EFFICIENCY OF MITIGATION TECHNIQUES TO REDUCE AMMONIA EMISSIONS IN FRANCE

DUFOSSE, K.¹, GILLIOT, J.-M. ¹, RAMANANTENASOA M. M. J.¹, VOYLOKOV P. ¹, GENERMONT, S. ¹, BESSAGNET, B.²

¹ UMR EcoSys; INRA; AgroParisTech, Université Paris-Saclay, France; ² INERIS, France

## INTRODUCTION

France is one of the higher ammonia (NH₃) emitting countries in Europe, and this gas comes mostly from the agricultural sector (97%) (CITEPA, 2017). It mainly derives from the livestock sector, but building and effluent storage emissions can easily be measured thus improving the estimates. However, due to their high nutrient content, animal effluents are used as fertilisers in arable fields and meadows. Due to different local pedoclimatic conditions, NH₃ emission varies highly at the national scale. When it reacts with industrial or vehicle emissions, NH₃ contributes to the formation of fine particle matter (PM₂.₅), a highly potent air pollutant having adverse impacts on human health. Mitigation to decrease NH₃ emissions measures have been proposed since 1999 through the Gothenburg protocol. However, due to the high variability in emissions from field-applied fertilizers and manures, the efficiency of these measures is difficult to assess at national scale.

The aim of this study is to show how the application of a bottom-up method (CADASTRE_NH3, Ramanantenasoa et al. in review) on the proposed mitigation scenarios is an asset for the evaluation of the efficiency of the NH₃ emission reduction.

## MATERIAL AND METHODS

Our approach relies on the use of three resources: a national survey, a modelling tool and a guidance document. Nitrogen fertilization management data were derived from the national AGRESTE survey of cultural practices for arable crops and grassland, conducted by the Department of Statistics and Forecasting of the French Ministry of Agriculture, during the crop years 2010/2011, for the 13 main crops and 21 regions (NUTS2) (AGRESTE, 2014). From this survey, statistical calculations were carried out to aggregate representative cultural practices at a regional scale [cf. abstract "An overview of nitrogen fertilisation practices in France" in the poster session].

CADASTRE_NH3 is a tool based on the Volt'Air model. The latter is a process-based 1D model predicting NH₃ emissions from N fertilizers on bare soils, from physical, chemical and biological processes. It takes into account the influence of soil, meteorological and agricultural variables and runs at an hourly time step at the field scale for a period of several weeks (Garcia et al. 2012). Simulation units (SU) were determined using a Geographical Information System, as the intersection of departments (NUTS3) and homogenous agricultural regions (AR), thus creating 713 SU. Local features were attributed to each SU: local pedoclimatic conditions, the area of each surveyed crop and the corresponding N fertilizing practices. All details about this method can be found in Ramanantenasoa et al. (in review).

Based on the guidance document for preventing and abating ammonia emissions from agricultural sources of the Gothenburg protocol (ECE/EB.AIR/113/Add.1, decision 2012/11), the two following scenarios will be applied to the N fertilisation practices and their efficiency will be evaluated:

- Scenario 1: Substitution of 60% of urea by ammonium nitrate

- Scenario 2: Abatement techniques for slurry and solid manure application (injection, incorporation within 24h of application, band-spreading)

Initial results have been calculated using (i) the AGRESTE survey data for the amount of mineral N applied in whole France in 2010-11, and (ii) the EMEP/EEA (2013) Tier 2 emission factors (EF) for NH₃ emissions in the field.
Abatement factors for Scenario 2 (organic fertilizers) come from the Gothenburg protocol. Simulations will be run through CADASTRE_NH3 to sharpen the efficiency of these measures, taking into account the local pedoclimatic conditions. The outcomes of these simulations will be presented at the workshop.

## RESULTS AND DISCUSSION

As an example of results obtained using EMEP EF, Scenario 1 (substitution of 60% of urea by ammonium nitrate) demonstrates a reduction of 14% in NH₃ emissions (from 204 to 176 kgNH₃) at the national scale (Table 1). However, even if urea represents 10% of the total mineral N applied in France, this ratio varies enormously amongst the regions (from 1% to 48%). Therefore, more local incentive could be implemented to help the implementation of the substitution where greater urea amounts should be substituted. Besides, this analysis did not consider the local pedoclimatic conditions that could impact greatly the NH₃ emissions rates and thus increase the regional disparity or affect the efficiency of this mitigation scenario.

<table><caption>Table 1. Amount of mineral N fertilizer applied (in tN) and distribution between the main forms (urea and ammonium nitrate, in % of N applied) from the survey, estimates of NH₃ emissions (in ktNH₃) from EF (EMEP/EEA, 2013) for the product distribution from the survey and for Scenario 1. The table presents a selection of 3 contrasted regions.</caption><thead><tr><th rowspan="2">Example of region (NUTS2)</th><th rowspan="2">Total N applied (tN)</th><th colspan="2">Distribution of N products</th><th colspan="2">Calculated total NH<sub>3</sub> emissions (EMEP method) (ktNH<sub>3</sub>)</th><th colspan="2">Simulated total NH<sub>3</sub> emissions (CADASTRE_NH3) (ktNH<sub>3</sub>)</th></tr><tr><th>Urea</th><th>Ammonium nitrate</th><th>Survey</th><th>Scenario 1</th><th>Survey</th><th>Scenario 1</th></tr></thead><tbody><tr><td>BOURGOGNE</td><td>111 424</td><td>9%</td><td>51%</td><td>10</td><td>9</td><td>12</td><td>11</td></tr><tr><td>PICARDIE</td><td>157 245</td><td>1%</td><td>48%</td><td>13</td><td>13</td><td>27</td><td>27</td></tr><tr><td>AQUITAINE</td><td>84 398</td><td>51%</td><td>31%</td><td>13</td><td>8</td><td>10</td><td>8</td></tr></tbody><tfoot><tr><td>Total</td><td>1 792 573</td><td>11%</td><td>51%</td><td>166</td><td>142</td><td>241</td><td>215</td></tr></tfoot></table>

## CONCLUSION

A complete bottom-up modelling will produce more accurate results of NH₃ emissions from local conditions than using fixed EF. The efficiency and pertinence of each proposed mitigation measure can be assessed for each French region individually. This will help to direct incentives or compensation measures depending on local contexts. Moreover, the effects of other mitigation measures could be tested, such as an application of closed restriction periods (to avoid peak pollution) or area limitations (to minimize environment contamination).

**Acknowledgements:** This work was supported by a public grant overseen by the French National Research Agency (ANR) as part of the « Investissements d'avenir » program (reference: ANR-10-EQPX-17 - Centre d'accès sécurisé aux données - CASD). It was conducted within the PolQA project (Mitigation policies to improve air quality through agricultural practices, agreement n°1662C0023), supported by the French PRIMEQUAL program (Programme de recherche interorganisme pour une meilleure qualité de l'air à l'échelle locale), by the French Environment and Energy Management Agency (ADEME) and by the French Ministry for an Ecological and Solidary Transition.

## REFERENCES

AGRESTE, 2014. Enquête Pratiques culturales 2011 - Principaux résultats. In Les Dossiers N° 21 - July 2014. pp.72.
CITEPA, 2017. "National inventories of air emissions in France: organisation and methodology.", OMINEA report - 14th ed. 838 p. https://www.citepa.org/en/activities/emission-inventories/ominea

EMEP/EEA, 2013. Air pollutant emission inventory guidebook 2013: Crop production and agricultural soils. Technical report N° 12/2013. pp. 43.

Garcia L., Génermont S., Bedos C., Simon N. N., Garnier P., Loubet B., and Cellier P., 2012. Accounting for Surface Cattle Slurry in Ammonia Volatilization Models: The Case of Volt'Air. Soil Sci. Soc. Am. J. 76(6), 2184-2194.
Ramanantenasoa M.M.J., Génermont S., Gilliot J.-M., Mignolet C., Bedos C., Mathias E., Eglin T., Makowski D., 2018. A process-based framework to estimate the spatio-temporal ammonia emissions due to nitrogen fertilization management. Sci. Total Environ. [in review].
# ESTIMATION OF THE GROWTH OF NH₃ EMISSIONS FROM ANAEROBIC DIGESTION IN THE UK

TOMLINSON, S.J.<fnref n="1" />, SCHMIDT-HANSEN, A.², SUTTON, M.<fnref n="1" />, TANG, Y.S.<fnref n="1" />, DRAGOSITS, U.<fnref n="1" />

## INTRODUCTION

Anaerobic Digestion (AD) is an organic treatment technology for biological materials to produce energy and heat from biogas and nitrogen-rich (N) fertiliser by-products known as digestates. The European Commission (EC, 2016) estimated biogas production to be in the region of 15 Mt of oil equivalent at the start of 2015, with over three quarters of the activity taking place in Germany, the United Kingdom (UK) and Italy. Digestate production through AD can act as a closed loop recycling system for mineral nutrients such as N, its application to fields can restore soil organic matter and production can be a sustainable practice (Tiwary et al., 2015). However, due to the higher fraction of ammoniacal N in digestates and their (usually) elevated pH, there is a higher potential for N losses through ammonia (NH₃) volatilisation, compared with undigested materials.

NH₃ emissions in Europe, which are responsible for the eutrophication and acidification of ecosystems, are dominated by the agricultural sector. In the UK, the AD industry has experienced rapid growth from around 32 plants in 2009 (processing circa 1 Mt of materials) to 400 plants at the end of 2016 (processing over 10 Mt of materials), with sectoral growth in the UK aided through government schemes. Given the rapid growth and large potential for NH₃ emissions, research has been carried out to quantify UK emissions from AD in detail, especially with regard to the impact of AD sources on the UK's National Emissions Ceilings. Emissions estimates are required to inform future policy surrounding the AD sector and are vital for developing effective mitigation strategies.

## MATERIAL AND METHODS

Information on AD sector activity in the UK was compiled regarding site throughput, materials processed and locations (Tomlinson et al., 2017). The data were processed to remove information not considered relevant to NH₃ emissions calculations due to their very large volumes and very low N content – primarily brewery/distillery effluents. NH₃ emissions were calculated for activity at the AD plant itself (site-based processes) and also for the subsequent landspreading of digestates using emission factors (EFs) as shown in Table 1 (Bell et al., 2016; Nicholson et al., 2017).

<table><caption>Table 1. Emission Factors for Ammonia Emissions Estimates. Emission Factors (EFs) used (with ranges) in various stages of anaerobic digestion (AD) process to calculate ammonia emissions estimates.</caption><thead><tr><th colspan="2">Anaerobic Digestion: Stage and Activity</th><th>EF</th><th>Unit</th><th>Range</th></tr></thead><tbody><tr><td rowspan="3">Site-based</td><td>Pre-digestion storage</td><td>0.005</td><td>kg NH<sub>3</sub> t<sup>-1</sup></td><td>0.003 – 0.008</td></tr><tr><td>Digestion of materials</td><td>0.004</td><td>feedstock</td><td>0.002 – 0.006</td></tr><tr><td>Post-digestion storage</td><td>0.059</td><td>fresh weight</td><td>0 – 0.24</td></tr><tr><td rowspan="2">Landspreading</td><td>Food only digestates</td><td>0.83</td><td>kg NH<sub>3</sub> t<sup>-1</sup></td><td>-</td></tr><tr><td>Crop/slurry/mixed digestates</td><td>2.13</td><td>digestate</td><td>1.82 – 2.43</td></tr></tbody></table>

NH₃ emissions were subsequently spatially distributed in the UK onto suitable land use types (arable and improved grassland) within a varying distance of each AD plant, using a function of the quantity of digestate produced.

## RESULTS AND DISCUSSION

<footnote n="1">¹ Centre for Ecology & Hydrology, Edinburgh, UK; 2 University of Edinburgh, Edinburgh, UK</footnote>
Figure 1 shows the estimated total NH₃ emissions from AD in the UK from 1990 to 2016. NH₃ emissions estimates for 2016 are 9.8 kt (range 8.3 kt – 12.3 kt), roughly double the estimate for 2013. It is estimated that around 90% of NH₃ emissions from the AD sector are from the spreading of digestates to land, due to the higher ammoniacal N content. The strength of emissions varies, due to, for example, some plants specializing in processing large amounts of high-N food waste materials.

<figure><img src="image_56.png" /><figcaption>Figure 1. Estimated Emissions of Ammonia from Anaerobic Digestion in the UK. Estimated emissions of ammonia (NH₃) from anaerobic digestion (AD) in the United Kingdom from 1990 to 2016 in kilotons (both site-based process emissions and digestate field spreading emissions). Low and high estimates indicated by error bars (grey).</figcaption></figure>

There are a number of uncertainties that may effect NH₃ estimations, including robust quantification of the methods by which digestate is applied to land (e.g. shallow injection vs. band spreading), the wide ranging effects that co-digestion of differing materials may have on N-content and pH, and what type of soils and land cover digestate is applied to. Furthermore, mitigation strategies such as the acidification of digestates (to lower the volatilization rate of NH₃) and better soil incorporation techniques may have a large reduction effect in NH₃ emissions to the atmosphere. Work is being undertaken to explore the effect some of these mitigation strategies may have on current and future estimates.

## CONCLUSION

Emissions of NH₃ from AD in the UK are a growing trend, with potentially a further 5 Mt of materials going to digesters in 2018, in addition to the current 10 MT. This may be an issue for the UK's ability to meet its national emissions ceilings, but could be addressed with targeted mitigation strategies. The UK Clean Growth Strategy states a desire for the UK to develop its bioeconomy but this must be done with forethought to future NH₃ emissions.

## REFERENCES

* Bell, M. W., Tang, Y. S., Dragosits, U., Flechard, C. R., Ward, P. and Braban, C. F. (2016). Ammonia emissions from an anaerobic digestion plant estimated using atmospheric measurements and dispersion modelling, Waste Management, 56, 113–124.

* European Commission, 2016. Optimal use of biogas from waste streams. An assessment of the potential of biogas from digestion in the EU beyond 2020, viewed 26 Jan 2018, https://ec.europa.eu/energy/sites/ener/files/documents/ce_delft_3g84_biogas_beyond_2020_final_report.pdf.
Nicholson, F., Bhogal, A., Cardenas, L., Chadwick, D., Misselbrook, T., Rollett, A., Taylor, M., Thorman, R. and Williams, J. (2017). Nitrogen losses to the environment following food-based digestate and compost applications to agricultural land, *Environmental Pollution*, 228, 504-516. doi: 10.1016/j.envpol.2017.05.023

Tiwary, A., Williams, I. D., Pant, D. C. and Kishore, V. V. N. (2015). Assessment and mitigation of the environmental burdens to air from land applied food-based digestate, *Environmental Pollution*, 203, 62–270. doi: 10.1016/j.envpol.2015.02.001

Tomlinson, S.J., Carnell, E.J., Tang, Y.S., Sutton, M.A. and Dragosits, U., 2017, Ammonia emissions from UK non-agricultural sources in 2016: contribution to the National Atmospheric Emission Inventory. Edinburgh, NERC/Centre for Ecology & Hydrology, 23pp.
# GLOBAL NITROUS OXIDE DATABASE FOR IMPROVED ANALYSIS AND EXTRAPOLATION

DORICH, C.D<fnref n="1" />., CONANT, R.T<fnref n="1" />., GRACE, P²., SNOW, V³., VOGELER, I⁴., VAN DER WEERDEN, T³., ALBANITO, F⁵.

## INTRODUCTION

Nitrous oxide emissions are notoriously variable – through time, across space, and with management and environmental conditions. Time and budgetary constraints force compromises in N₂O emission sampling extent, frequency, and duration with field measurements of N₂O emissions typically carried out only during the growing season and at a roughly bi-weekly time scale. Given the episodic nature of N₂O emissions, spotty measurements can easily result in unreliable N₂O estimates, especially when field measurements are extrapolated to annual values (Reeves and Wang, 2015; Barton et al., 2015). Sampling and data analysis have therefore proven difficult and techniques used for gap filling missing data and extrapolating measurements have been largely inadequate. Further, previous analysis efforts primarily focus on examining a single site and research project being maintained by a research group, with methods not carrying over between sites. Proper extrapolation and gap filling techniques are needed in order to better estimate fluxes in lieu of missing measurement data. However, studies of ‘gap filling’ and timing of N₂O emissions are limited in the literature (Mishurov and Kiely, 2011; Reeves and Wang, 2015; Barton et al., 2015). This makes N₂O emissions an important research area as N₂O is a potent greenhouse gas (GHG) with agriculture representing the largest source of N₂O emissions. There is substantial interest in N₂O reduction from agriculture as a part of the global climate change mitigation strategy (Paustian et al., 2016), yet methods for measuring or estimating N₂O emissions remain highly uncertain. Intensification of crop production, at least in part through increased fertilizer inputs, is essential for feeding a growing and hungry planet. In order to address concerns around N₂O emissions, improve methods and estimation techniques, and set up mitigation pathways and potential markets, we need a more comprehensive research analysis to better understand N₂O emissions. We are creating the first global N₂O database in collaboration with researchers from around the world in order to use advanced statistics to analyze data across sites and improve the research communities understanding of N₂O.

## MATERIAL AND METHODS

Publically available data sets, like that found in Albanito et al (2017), will be combined with national level databases like GRACEnet to form the first global data set. The design and implementation procedures for this new, publically available database will be based on stakeholder feedback during a planning workshop held within the year. The database will be used for statistical analysis of auto chamber and flux tower data in order to develop gap-filling methods and to improve extrapolation techniques for making annual estimates of annual N₂O emissions. Analysis will be conducted on a calibration data set and then validated against independent data. The N₂O emission database will be housed on an interactive website created and hosted at Colorado State University. Using the global database we intend to pursue a variety of methods, including; gap filling, extrapolation, Bayesian, mixed models and more in order to fully utilize the available N₂O data and develop a better understanding of emissions.

## RESULTS AND DISCUSSION

Given the current practice of extrapolating measurement data to an annual value using a simple average or linear interpolation between measurements, the accuracy of field measurements is questionable (Reeves and Wang, 2015). Comparing empirical N₂O equation estimates to these field estimated annual N₂O fluxes extrapolated from limited field data may therefore be fundamentally flawed. Sampling around expected hot moments can lead to a

<footnote n="1">¹ Natural Resource Ecology Lab, Colorado State University, USA; ²Queensland University of Technology, Australia; ³AgResearch, New Zealand; ⁴Plant and Food Research, New Zealand; ⁵Institute of Biological and Environmental Science, University of Aberdeen, UK</footnote>
more reliable estimate of N₂O emissions if sampling and analysis is done properly (Reeves and Wang, 2015). If done improperly, using a simple average from field data or extrapolating from these emissions can potentially skew the result, either due to a high volume of sampling around peak emissions or the lack thereof. A global database will also allow for a thorough review of management practices and nitrogen inputs to further examine emission factors (EF) and our understanding of N₂O emissions. Reeves and Wang (2015) examined one site in Australia with three years of auto chamber data, comparing continuous data to various simulated sampling campaigns. Their analysis examined sampling from a tri-weekly basis up to fortnightly, with other scenarios also 'chasing peaks' by sampling around large (>20 mm) rainfall events on top of the routine sampling. As shown in **Erreur ! Source du renvoi introuvable.**1, triweekly sampling resulted in the lowest deviation from field emissions. This comes as no surprise as it is the closest sampling structure to the timing of auto chamber sampling, and thus the most complete dataset. However, weekly measurements with biweekly or triweekly measurements around large rainfall events resulted in reliable results as well (Reeves and Wang, 2015).

<figure><img src="image_57.png" /><figcaption>Figure 1. Static and auto chamber measured N₂O emissions compared at various typical sampling regime protocols. Taken from Reeves and Wang, 2015, Figure 5</figcaption></figure>

## CONCLUSION

This project will close the gap by examining global N₂O sites to determine better techniques for gap filling data, improved extrapolation techniques, provide more accurate methods for estimating annual emissions, and allowing proper comparison of N₂O methodologies to field emissions. We believe the database and resulting work will lead to a better understanding of N₂O emissions, new pathways for future research, and improved methodologies and estimation techniques. If you are interested in participating in this exercise or providing data please contact us.

## REFERENCES

- Albanito, F., Lebender, U., Cornulier, T., Sapkota, T.B., Brentrup, F., Stirling, C., and Hillier, J., 2017. Direct nitrous oxide emissions from tropical and sub-tropical agricultural systems – a review and modelling of emission factors. Nature, Scientific Reports, 7:44235.

- Barton, L., Wolf, B., Rowlings, D., Scheer, C., Kiese, R., Grace, P., Stefanova, K., and Butterbach-Bahl, K., 2015. Sampling frequency affects estimates of annual nitrous oxide fluxes. Nature Scientific Reports 5:15912.
Mishurov, M., and Kiely, G., 2011. Gap-filling techniques for the annual sums of nitrous oxide fluxes. Agricultural and Forest Meteorology, 151, 1763-1767.

Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G.P., and Smith, P., 2016. Climate-smart soils. Nature Perspectives 532, 49-57.

Reeves, S., and Wang, W., 2015. Optimum sampling time and frequency for measuring N₂O emissions from a rain-fed cereal cropping system.
# THE PROBLEM OF REGIONAL NUTRIENT IMBALANCES

BITTMAN, S¹. SHEPPARD, S.C. ² POON, D.³ HUNT D.E. ¹

¹ Agriculture and Agri-Food, Canada; 2 ECOMatters, Inc., Canada; 3 British Columbia Ministry of Agriculture, Canada;

## INTRODUCTION

Balancing nutrients on farms involves matching nutrient outputs with the least possible inputs, which for most farms is primarily fertilizer, feed, deposition, and in some cases biological nitrogen (N) fixation and manure from nearby livestock operations. On a national level, budgets must also account for the fate of nutrients that leave the farm. The contemporary problem is the increasing concentrations of nutrients in densely populated urban and peri-urban regions, the urban areas receiving nutrients as food and amenities, and the peri-urban areas typically as fertilizer and feedstuffs for intensive farming operations on expensive, limited and often very productive agricultural land. The peri-urban farmland is prized, and often protected by statutes, for providing fresh local food, economic activity, green space, and the sense of food security. But the contribution of intensive farming near cities to environmental sustainability is not clear in part because of its contribution to the influx of nutrients. Our study quantified the N budget and flows in a well delineated peri-urban region called Lower Fraser Valley (LFV) which includes the city of Vancouver and some of the most productive and expensive agricultural land in Canada. Our goal is to identify improvements in N cycling strategies.

## MATERIAL AND METHODS

The study area in south coastal British Columbia, Canada (1500 mm precipitation) is bounded by steep mountains, Pacific Ocean and the international border, and encompasses 2.6 million people along with 55,000 ha of expensive and intensively farmed land. The three dominant agricultural sectors are: dairy, occupying nearly half the land, horticultural (especially small fruits) on most of the rest, and a substantial, nearly landless poultry sector. The population, residing mainly in Metro Vancouver, is ethnically diverse and values its access to local, fresh food and supports the protected Agricultural Land Reserve. We estimated regional farm N influx from imported feeds, fertilizer and animals, and regional urban N influx from imported food, pet food, horse feed and fertilizer. We estimated regional efflux from farms as exported food and live animals, gaseous emissions (mainly ammonia, NH₃) from manure and fertilizer, and losses into above ground and below ground waterways. For cities, regional efflux were from waste products (sewage, food and garden) that were combusted, discharged into waterways, sequestered in landfills and in soils (as compost) and exported from the region. There was also gaseous emission from composting and incinerating waste. We considered N influx and emissions from fuels to be outside our system boundaries, but wet deposition of reduced and oxidized N was included. Internal flows were also determined including consumption of local food, and re-use of manure, rendering products and municipal wastes (food, yard and sewage). Data were mainly from government statistics, local surveys, industry and government experts and published literature. We also referred to a few local research studies.

## RESULTS AND DISCUSSION

LFV farms imported about 26.9 kt N for food production mostly as feed (19.5 kt) and fertilizer (4.5 kt) (Table 1). Most farm output (6.7 kt N) was consumed in the LFV but 3.7 kt N was exported from the region. Hence, regional farmgate N efficiency was 39%, but this does not account for N losses in production of imported feed crops. N was lost from farms mainly as ammonia emissions (6.7 kt), and the calculated soil N surplus of 8.3 kt was assumed to be lost mostly by leaching due to high wintertime precipitation. This was somewhat greater than our estimate of total LFV residual soil N after crop harvest (5.7 kt N), which was based on a recent field study of farm soils (Sullivan and Poon, 2016). It is also consistent with our estimates of N drainage (5.2 kt) into the Fraser River which collects the ample surplus water from the majority of the farmland in the LFV (from Voss et al. 2014). In the urban
ecosystem, N influx (14.2 kt) was mostly as imported food (9.2 kt), atmospheric deposition (2.9 kt), and smaller amounts of various amenity products such as horse feed, pet food and fertilizer for lawns, etc. Most N was lost from urban ecosystem by discharge from waste water treatment plants (10.3 kt) and as emissions from composting (2.7 kt) and combustion of solid waste (1.0 kt). Some N was used on urban soils or sequestered in landfills (3.7 kt) and a small amount exported mostly to low production rangeland outside the region (0.12 kt). Whereas most N was lost by emissions and leaching, a previous LFV study showed that P was lost by runoff and discharge, but also accumulated in rural and urban soils (Bittman et al. 2017).

The reactive N (all non-N₂ species) lost from the LFV (29 kt) has a commercial fertilizer value of almost $30 million. This reactive N may have additional environmental and human health costs. Some N was locally recycled (manure, rendering waste and atmospheric deposition) but very little of the 30 kt of imported N was returned to the sources of the imported feeds and foods. The recycling programs such as waste to energy (combustion), composting of solid waste and poultry litter, and biogas production are repurposing C but not N or P.

<table><caption>Table 1. Nitrogen budget (kt N/year) for Lower Fraser Valley (LFV) showing regional influx and efflux for the agricultural and urban ecosystems. Fuel combustion was not included except for deposition.</caption><thead><tr><th></th><th></th><th>Influx</th><th></th><th>Efflux</th></tr></thead><tbody><tr><td rowspan="5">Agriculture</td><td>Fertilizer</td><td>4.52</td><td>Food and animal export</td><td>3.73</td></tr><tr><td>Feedstuffs</td><td>19.5</td><td>Fish farms</td><td>1.52</td></tr><tr><td>Live animals</td><td>1.10</td><td>Emissions (NH<sub>3</sub>)</td><td>6.73</td></tr><tr><td>Deposition (NH<sub>4</sub> and NO<sub>x</sub>)</td><td>1.81</td><td>Surplus (leaching and denitrification)</td><td>8.28</td></tr><tr><td><strong>Total</strong></td><td><strong>26.91</strong></td><td>Food to urban ecosystem</td><td>6.71*</td></tr><tr><td rowspan="6">Urban</td><td>Imported food</td><td>9.21</td><td>Effluent from WWTP**</td><td>10.33</td></tr><tr><td>Fertilizer</td><td>0.89</td><td>Landfill</td><td>3.70</td></tr><tr><td>Pet food</td><td>0.31</td><td>Incineration</td><td>0.96</td></tr><tr><td>Horse feed</td><td>0.92</td><td>Emissions (NH<sub>3</sub>)</td><td>2.73</td></tr><tr><td>Deposition (NH<sub>4</sub> and NO<sub>x</sub>)</td><td>2.88</td><td>Export</td><td>0.12</td></tr><tr><td><strong>Total</strong></td><td><strong>14.21</strong></td><td></td><td><strong>17.84</strong></td></tr><tr><td>LFV</td><td><strong>Total</strong></td><td><strong>41.14</strong></td><td></td><td><strong>38.1</strong></td></tr></tbody></table>

## CONCLUSION

Waste recycling programs inadvertently focus on C and fail to improve N and P cycling, so much of imported N and P is wasted as gases and solutes which are harmful to the environment. There are end-of-pipe technologies for extracting nutrients that can be recycled to source farms, but these are expensive. Alternatively, restricting fertilizer imports may improve nutrient efficiencies by encouraging adoption of established and fledgling technologies. In cities, there is already a tendency to consume less animal products, and technology and education can be used to safely reduce waste.

## REFERENCES

* Bittman, S., Sheppard, S.C., Poon D. and Hunt, D.E. 2017. Phosphorus flows in a peri urban region with intensive food production: a case study. J. Env. Manag. 187, 286-297

* Sullivan, C.S., Poon, D., 2016. Fraser Valley Soil Nutrient Survey 2012. https://www2.gov.bc.ca/assets/.../soil-nutrients/.../fvsns2012_report-aug2016.pdf

* Voss, B.M., Peucker-Ehrenbrink, B. et al. 2014. Tracing river chemistry in space and time: Dissolved inorganic constituents of the Fraser River, Canada. Geochi. Cosmochim. Acta 124, 283-308.

<footnote>*internal flow from agriculture to urban, not included in efflux. ** WWTP Waste Water Treatment Plant</footnote>
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes – France

Session II: Regional studies - Posters
# NITROGEN USE EFFECTIVENESS OF TERRITORIAL AGRO-FOOD SYSTEMS: A LONG TERM PERSPECTIVE AT THE FRENCH REGIONAL SCALE

BILLEN, G., LE NOE, J., GARNIER, J.

UMR Metis, Sorbonne-Université/CNRS, France

## INTRODUCTION

While the definition of nitrogen use efficiency (NUE) of farming systems (ratio of outputs to inputs) might seem simple and straightforward, it is in fact very controversial because of the difficulties in defining the limits of the system, in estimating the inputs and in defining what to consider as output (see e.g. Godinot et al. 2014; Garnett et al., 2015). Here we address the issue of N effectiveness at the territorial scale through the analysis of the relationship between yield and the degree of N-intensification, defined as the total input of reactive N to the system. We want to analyze, over a long term historical perspective, how the design of nitrogen flows through territorial agro-food systems have evolved within their particular pedo-climatic and socio-technical contexts. In short, we want to link the structure of the regional agro-food systems to their fluxes of N inputs, outputs and losses, and to identify major transitions, with respect to nitrogen use effectiveness during the last centuries.

## MATERIAL AND METHODS

The estimation of N inputs and outputs of regional cropping and production systems at the scale of 33 regions and 22 dates from 1852 to 2014 is based on the data assembled according to the GRAFS approach (Generalized Representation of Agro-Food Systems) by Le Noë et al (subm). Our analysis first deals with cropping systems, for which total input consists of manure, symbiotic fixation, synthetic fertilizers and atmospheric deposition to the regional arable area, and output is the harvested and grazed crops. Then we consider agricultural production systems, for which input consists, not only of inputs to cropland and grassland, but also of imported forage and feed, while output is net crop production (i.e. production not locally used for feeding livestock) plus edible animal production.

## RESULTS AND DISCUSSION

### Cropping systems

A robust, one parameter (Ymax) hyperbolic relationship links output from cropping systems (Y) and total soil N input (F), averaged over a whole crop rotation cycle (Lassaletta et al., 2014). The distance of the relationship to the 1:1 curve is the surplus, an indicator of N losses. Within the same technical-pedo-climatic context, the same relationship holds whatever the rotation is based on organic or mineral fertilization (Anglade et al, 2015) (Fig.1a). This relationship also describes the trajectory of the yield response to long term N-intensification of particular regions (Fig. 1b), with however a distinct shift in Ymax occurring after the 1980's, reflecting either better (e.g., Picardy, Burgundy) or worse (e.g. Brittany) N-effectiveness.

### Production systems including livestock farming

The preceding relationship for cropland can be generalized to the entire territorial production system including livestock, by taking into account several other parameters related to livestock feed conversion efficiency, and permanent grassland role in livestock nutrition. The predicted relationship between total system production and its degree of N-intensification levels off at much lower total yield when the part of animal products increases in the regional production. This theoretical framework of analysis allows modeling and understanding the pattern of the trajectory followed in terms of production of harvested food and N-intensification since the mid-19th century by the different French regions.
<figure><img src="image_58.png" /><figcaption>Figure 1.a. Currently observed relationship between harvested crop from arable land and total N soil inputs for conventional (red) and organic (green) rotations documented through surveys in the central Paris basin and in the Plateau "petites terres" in Burgundy (Anglade et al., 2015). b. Long term trajectories in terms of cropland yield and N-intensification in Picardy, Burgundy and Brittany, based on territorial statistical data (Le Noë et al, 2018) (blue points for post 1995).</figcaption></figure>

## CONCLUSION

Studying the relationship between production (in terms of embedded protein) and nitrogen input for either cropping or whole production systems at the territorial scale allows to avoid several drawbacks of a simple NUE approach. NUE cannot be considered as an intrinsic property of a single cropping or production system: it primarily depends on the degree of N-intensification and decreases with increasing N inputs. Thus, N-intensification, more than agronomical techniques as reflected by Ymax changes, is the primary factor explaining the long term trends of N-losses from cropping systems. Similarly for territorial production systems involving livestock, N-intensification, as well as the share of animal products in the total agricultural production and the share of grass in animal nutrition, are the most important factors controlling total yield and N losses. The trends of specialization and intensification observed in French regional agricultural systems since the mid-19th century have strong and sometimes counteracting consequences in terms of N effectiveness, that can only been disentangled using the analytical framework we are proposing here.

## REFERENCES

* Anglade J, Billen G, Makridis T, Garnier J, Puech T et Tittel C., 2015. Nitrogen soil surface balance of organic vs conventional cash crop farming in the Seine watershed. Agricultural Systems 139:82-92.

* Garnett T., Röös E., Little D., 2015. Lean, green, mean, obscene...? What is efficiency? And is it sustainable? Food Climate Research Network. Environmental Change Institute. University of Oxford. UK.

* Godinot O., Carof M., Vertès F., Leterme P., 2014. SyNE: An improved indicator to assess nitrogen efficiency of farming systems. Agricultural Systems. 127:41-52

* Lassaletta L., Billen G., Grizzetti B., Anglade J., Garnier J., 2014. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environ. Res. Lett. 9. DOI:10.1088/1748-9326/9/10/105011

* Le Noë J., Billen, G., Esculier, F., Garnier, J., 2018. Long term socio-ecological trajectories of agro-food systems revealed by N and P flows: the case of French regions from 1852 to 2014. Agr. Ecosyst. Env. (submitted).
# REGIONAL N AND P CYCLING IN AGRO-FOOD SYSTEMS: LONG TERM HISTORICAL AND FUTURE TRAJECTORIES IN FRANCE

LE NOË, J.¹, BILLEN, G.¹, GARNIER, J.¹

¹Sorbonne Université, UPMC, CNRS, EPHE, UMR 7619 METIS, 4 place Jussieu, Paris, France

## INTRODUCTION

Modern agricultural practices are responsible for a considerable disturbance of the N and P cycles. To engage agriculture on more sustainable paths, lessons learnt from the history are needed, because current agro-ecosystems result from historical process which may vary widely from one place to another. In this context, we first analyzed the trajectories of agricultural systems in French regions across the 1850-2014 period. Then we explored two contrasted scenarios for French agriculture in the future, either pursuing or reverting the past observed trajectories toward specialization and opening.

## MATERIAL AND METHODS

We used the Generalized Representation of Agro-Food System (GRAFS) approach, a model which provides a biogeochemical description of agro-food system in a delimited area. It traces the N and P flows between four main compartments: cropland, grassland, livestock biomass, the local population. The GRAFS provides some key indicators for analyzing agro-food systems from both the environmental and agronomic performances such as N surplus and P balance over grassland and arable land. For retrospective analysis, this approach was used at the scale of the 33 French regions as defined by Le Noë et al. (2016) for 22 dates between 1852 and 2014. For prospective analysis, we used the GRAFS approach to explore possible scenarios for 2050, accounting for official demographic forecasts and on hypothesis regarding cropping techniques, their connection with livestock and the composition of human diet.

## RESULTS AND DISCUSSION

### Typology of the different kinds of agro-food systems

To systematize the analysis of our results and to obtain an objective assessment of the trajectories of the 33 French regions, we have chosen to aggregate regions according to similarities in their production pattern. For this purpose, we have defined biogeochemical criteria to check the level of interaction between arable land, livestock and grassland, based on nutrient fluxes or flux ratios and identified five different types of agro-food systems in French regions: (i) "specialized arable crop"; (ii) "Intensive livestock farming"; (iii) "Extensive integrated crop and livestock farming"; (iv) "Intensive integrated crop and livestock farming" and (v) "disconnected Intensive crop and extensive livestock farming".

### Historical trajectories

In the mid-19th century, integrated crop and livestock farming systems were the rule everywhere in France (Figure 1) and manure recycling was the main source of agricultural land fertilization. However, at the beginning of the 20th century, the sustainability of this farming system began to be jeopardized by the mining of soil nutrient stocks, especially P. Over the course of the 20th century, this situation shifted towards more contrasted production patterns. This became particularly true after the Second World War: modernization of agriculture contributed to the emergence of specialized cereal cropping systems in the most fertile plains of the North of France (Figure 1), exporting a large fraction of their cereal production. The significant rupture after 1946, which accentuated the openness of regional agro-food system, was promoted by the increased use of mineral fertilizers and the subsequent disconnection of vegetal and animal production in regions on a path to specialization. Overall, regions of intensive cropping revealed the highest degree of openness through crop exportation, while the region of
intensive livestock farming was characterized by a growing reliance on feed import and over-fertilization of arable land through manure inputs. However, all regions intensified their production during the second half of the 20th century by increasing rates of mineral fertilizer inputs. As a result, increasing arable yields, N surpluses and P balances rose too (Figure 1). The rise of fertilizer prices together with the implementation of agro-environmental measures over the last two decades resulted in the lowering of N surpluses and P balances, which were even negative in regions specialized in crop production (Figure 1). However, as long as the agro-food system remains specialized, possible changes through improved fertilization will be limited by the inefficiency of nutrient recycling at the regional level.

<figure><img src="image_59.png" /><figcaption>Figure 1. Evolution of regional specialization in France and of livestock density, N surplus (kg N.ha⁻¹.yr⁻¹) and P balances (kgP.ha⁻¹.yr⁻¹) over arable land from 1852 to now</figcaption></figure>

## Possible future scenarios

Overall, it was notable that greater specialization went parallel with decreasing environmental performances, while the increasing demand for animal proteins appeared as a driver of an increased dependency on feed import for livestock production. Therefore, the trend scenario assuming the pursuit of agriculture specialization, a human diet rich in animal products and an intensive use of chemical fertilization would exacerbate the poor recycling of nutrient, the robbing of soil P in feed exporting country and the losses of N and P to hydrosystems. By contrast, the scenario assuming reconnection of crop and livestock production and a better use of soil P reserves would enable to progressively eliminate the use of chemical fertilizer. Switching back to a Mediterranean diet would facilitate to decrease the dependency of livestock upon feed importation and would instead promote C storage through extension of grassland surfaces, a sink of C.

## CONCLUSION

This study demonstrates the value of adopting a long-term perspective to articulate a vision of how environmental and agronomic performances are related to production pattern changes. Based on our results, we argued that sustainable agriculture at the regional scale could only be achieved through a complete overhaul of the structure of agricultural systems and a shift toward less animal protein in the human diet.

## REFERENCES

* Le Noë J., Billen G., Lassaletta L., Silvestre M., Garnier J., 2016. La place du transport de denrées agricoles dans le cycle biogéochimique de l'azote en France : un aspect de la spécialisation des territoires Cah. Agric. 25, 15004.
# NITROGEN AND CARBON FOOTPRINTS OF CONTRASTING DAIRY FARM SYSTEMS IN CHINA AND NEW ZEALAND

LEDGARD, S.F.¹, WEI, S.², WANG, X.³, FALCONER, S.¹, ZHANG, N.², ZHANG, X.², MA, L.²,

¹ AgResearch, New Zealand; ² The Chinese Academy of Sciences, China; ³ Northwest Agriculture and Forestry University, China

## INTRODUCTION

Dairy farm systems are important for global food protein production. However, they can contribute significantly to environmental impacts, including through nitrogen (N) emissions to water and the atmosphere, as well as to global greenhouse gas (GHG) emissions. There have been numerous studies of the carbon (C) footprint (i.e. total GHG emissions) of milk, but few studies have provided detailed results of the total reactive N emissions (N footprint; e.g. Leip et al. 2014) of different dairy production systems to understand the main drivers and reduction opportunities for the N footprint of milk. Such studies need to account for all reactive N emissions and this is best achieved using a life cycle assessment (LCA) approach to ensure that all N emissions are accounted for. The aim of this study was to evaluate the N and C footprints of milk production (using LCA) in contrasting systems in China (full housed cow systems) and New Zealand (NZ; year-round outdoor grazing systems), and the effects of intensification achieved through increased production per cow by increased level of feeding of crops.

## MATERIAL AND METHODS

The three Chinese farms, categorized as low, medium and high (Table 1) according to milk production per cow, were based on averages from survey data for farms in the Shaanxi, Hebei and Beijing regions, respectively. All cows and replacement animals were housed year-round with all feeds produced off-farm. For the NZ farms, the three farm systems from the Waikato region were summarized from farm survey data. All NZ farms relied largely on grazing of perennial ryegrass/white clover pastures and were classified into low, medium and high according to the level of brought-in supplementary feeds (average of 0.3, 0.8 and 1.7 t dry matter/cow, respectively).

Crop yields and inputs for production of brought-in feeds in China and NZ were based on published data. For NZ, the total feed intake from grazed pasture was calculated using a national energy requirement model applying animal live-weight and production data. The amount of N excreted was calculated by subtracting the output of N (in milk and live-weight sold) from the total feed N intake. In China, manure collected from housing was assumed to be subjected to passive composting for up to 3 months, after which it was transported and applied to cropland, except for 20% which was discharged to water based on national data. In NZ, manure collected from milking sheds was assumed to be sprayed onto the pasture. Emissions of N and GHGs were calculated using a combination of default IPCC emission factors and the NUFER model (Ma et al. 2010) for Chinese farm systems, while for NZ farm systems it was based on national inventory methods and the OVERSEER® model (Ledgard et al. 2004). LCA models were used to calculate emissions from the production and use of inputs (e.g. fertilizers, fuel, electricity, off-farm feeds), including NOx from energy sources.

## RESULTS AND DISCUSSION

Most N loss occurred as ammonia in all systems, with ammonia-N per kg fat- and protein-corrected milk (FPCM) decreasing from low to high farm systems in both countries. For NZ, this can be attributed to the low N concentration in brought-in feeds compared to that in pasture, resulting in less surplus N per kg feed-N consumed and a lower proportion excreted in urine-N which is the main source of the ammonia emissions. However, in China there was little difference in average dietary N concentration and the decrease in ammonia-N/kg FPCM was associated with higher milk production per cow (with lower associated feed use for animal maintenance) and with lower manure emissions. For China, there was little difference between farm systems in other forms of N emissions, so that overall the high farm system had a 47% lower N footprint than the low farm system.
corresponding difference for NZ was a 13% lower N footprint in the high feed-input farm system. Manure and fertilizers were dominant contributors to total N emissions in China, whereas in NZ the total N emissions were dominated by urine from grazing animals. In both countries, there was a large variation in the N emissions per kg dry matter from the production of feeds, illustrating that there is potential to reduce the N footprint of milk by selecting low N-emission feeds. The effects of different feed options were evaluated using scenario analysis.

Methane (largely from animal feed digestion) and N₂O emissions (mainly from manure and fertilizer use) per kg FPCM decreased from low to high farms in both countries. In NZ, these decreases were offset by increased CO₂ emissions associated with the production and feeding of crops in the high feed-input farm system. In contrast, the CO₂ emissions were a significant component of the total carbon footprint in China, but all GHGs decreased between low and high systems, with greater system efficiency through higher milk production per cow.

<table><caption>Table 1. Comparison of farm systems in China and New Zealand varying in level of milk production per cow based on increased use of brought-in crop feeds. Data was derived from averages of survey farms, while nitrogen and carbon emissions were estimated using country-specific models, including life cycle assessment for background emissions.</caption><thead><tr><th rowspan="2"></th><th colspan="3">China</th><th colspan="3">New Zealand</th></tr><tr><th>Low</th><th>Medium</th><th>High</th><th>Low</th><th>Medium</th><th>High</th></tr></thead><tbody><tr><td>Milk per cow (t FPCM/cow)</td><td>5.8</td><td>7.3</td><td>8.5</td><td>4.6</td><td>5.0</td><td>6.1</td></tr><tr><td>Nitrogen footprint (g N/kg FPCM):</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nitrate to water</td><td>3.66</td><td>2.97</td><td>3.29</td><td>2.52</td><td>2.55</td><td>2.67</td></tr><tr><td>Ammonia</td><td>11.12</td><td>8.31</td><td>4.74</td><td>3.81</td><td>3.51</td><td>2.77</td></tr><tr><td>Nitrous oxide (N₂O)</td><td>0.45</td><td>0.38</td><td>0.37</td><td>0.26</td><td>0.24</td><td>0.19</td></tr><tr><td>NOx</td><td>0.19</td><td>0.21</td><td>0.26</td><td>0.12</td><td>0.14</td><td>0.18</td></tr><tr><td>TOTAL</td><td>15.43</td><td>11.87</td><td>8.67</td><td>6.71</td><td>6.44</td><td>5.81</td></tr><tr><td>Carbon footprint (kg CO₂-eq/kg FPCM):</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Methane</td><td>0.80</td><td>0.60</td><td>0.53</td><td>0.54</td><td>0.51</td><td>0.44</td></tr><tr><td>Nitrous oxide</td><td>0.27</td><td>0.22</td><td>0.23</td><td>0.12</td><td>0.11</td><td>0.09</td></tr><tr><td>Carbon dioxide (CO₂)</td><td>0.28</td><td>0.20</td><td>0.22</td><td>0.08</td><td>0.09</td><td>0.22</td></tr><tr><td>TOTAL</td><td>1.35</td><td>1.02</td><td>0.97</td><td>0.74</td><td>0.71</td><td>0.75</td></tr></tbody></table>

## CONCLUSION

In housed dairy systems in China, the N and C footprints of milk decreased with increased system intensity through greater use of quality feeds and increased milk production per cow. Similarly, in the grazed pasture systems of NZ, the N footprint of milk decreased with increased milk production per cow through brought-in crop feeds. However, there was no change in the C footprint of milk in NZ due to the greater CO₂ emissions from the production and feeding of crops compared to that from the grazing systems with low GHG emissions.

## REFERENCES

Ledgard S.F., Wheeler D.M., de Klein C.A.M., Monaghan R.M., Johns K., 2004. Development of the OVERSEER® nutrient budget model to examine implications of pastoral management practices on nitrogen flows and losses, in: Hatch, D.J., Chadwick, D.R., Jarvis, S.C., Roker, J.A. (Eds.), Controlling Nitrogen Flows and Losses. Wageningen Academic Publishers, The Netherlands. pp. 571-572.

Leip A., Weiss F., Lesschen J.P., Westhoek, H., 2014. The nitrogen footprint of food products in the European Union. J. Agric. Sci. 152, S20-33.

Ma L., Ma W.Q., Velthof G.L., Wang F.H, Qin W., Zhang F.S., Oenema O., 2010. Modeling nutrient flows in the food chain of China. J. Env. Qual., 39, 1279-1289.
# AN OVERVIEW OF NITROGEN FERTILISATION PRACTICES IN FRANCE

DUFOSSE, K.¹, RAMANANTENASOA M. M. J.¹, MIGNOLET C. ², TROCHARD R. ³, GILLIOT, J.-M. ¹, BESSAGNET, B.⁴,
GENERMONT, S. ¹

¹ UMR EcoSys, INRA; AgroParisTech, Université Paris-Saclay, France; ²UR SAD, INRA, France; ³ARVALIS, France; ⁴ INERIS, France

## INTRODUCTION

Nitrogen (N) fertilisation is a key element to ensure crop yield and thus food security. Amongst numerous other factors, the amount of N applied in fields is driven by the type of culture and soil, as well as by economics and temporal factors. However, this fertilisation is also a source of multiple environmental impacts (soil acidification, water eutrophication, global warming and air quality depletion). In order to mitigate the impacts, politics are developed at large scale, usually national, whereas the impacts happen at a finer scale. Besides, the range of products available for farmers is always wider. Detailed information on the N fertilisation is thus crucial to develop mathematical models to evaluate yields and/or environmental impacts. This study is aiming at presenting an overview of the organic and mineral N fertilisation in France for the crop years 2005-06 and 2010-11. A focus is carried on the latter as the available information is more precise.

## MATERIAL AND METHODS

N fertilisation management data are issued from national survey of cultural practices for arable crops and grassland, conducted by the Department of Statistics and Forecasting of the French Ministry of Agriculture during the crop years 2005-2006 and 2010-2011, for 13 main crops and 21 regions (NUTS2) (AGRESTE, 2014). From this survey, statistical calculations were carried out following the methodology of Mignolet et al. (2007) to aggregate representative cultural practices at regional scale, including amount of organic and mineral fertilizer applied and distribution of these applications (fragmentation and period of application, types of products), as described in Génermont et al. (2015).

Mineral products were aggregated into the three main categories used in France: urea, nitrogen solution and ammonium nitrate. Organic fertilisation, described within 22 different categories in the survey, was aggregated into 8 main categories after a literature review and based on their origin and their chemical (pH, total and mineral N content) and physical (density, dry matter content and state) properties. The aggregated forms of organic fertilisers comprise 3 farmyard manures (FYM), 2 slurries, composts, industrial effluent and sludge.

## RESULTS AND DISCUSSION

The first sequence of results present the distribution of different forms of mineral (Figure 1) N fertilization per region in France for the crop year 2010-2011. The average repartition of mineral forms in France in 2010-2011 demonstrates a preference on ammonium nitrate (59% of N applied) over nitrogen solution (30%) and urea (11%). Major differences are observed between northern and southern parts as the share of ammonium nitrate is dominant in Southern France (Provence-Alpes-Côte-d'Azur and Limousin, over 85%) but nitrogen solution seems to be preferred in most of Northern regions (77% in Champagne-Ardenne). Urea, the greater NH₃ emission source amongst mineral N fertilizers, presents an unequal distribution over the country. Indeed, it shows large share of N applied in Aquitaine (48%) and Alsace (39%), but it is nearly not applied in Normandy and Picardie (less than 1%).

Similar outputs were produced for organic N fertilization. They express a large preference for bovine FYM application in France. However, the spatial distribution of organic of these organic products demonstrates huge discrepancies between regions (results not shown).