# Impact of anaerobic digestion on n balance in a crop succession fertilized with treated or untreated manures: first results

Antoine Savoie, Catherine Pasquier, Adeline Ayzac, Polina Voylokov, Anastasiia Lemekhova, Sophie Génermont, Benjamin Loubet, Catherine Hénault, Sabine Houot

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## ► To cite this version:

Antoine Savoie, Catherine Pasquier, Adeline Ayzac, Polina Voylokov, Anastasiia Lemekhova, et al. Impact of anaerobic digestion on n balance in a crop succession fertilized with treated or untreated manures: first results. 20th N Workshop and Side event, Jun 2018, Rennes, France. , 2018. hal-01826831

HAL Id: hal-01826831

https://hal.science/hal-01826831

Submitted on 5 Jun 2020

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Proceedings
of the

20th
Nitrogen
WORKSHOP
“Coupling C - N - P - S cycles”

June 25-27, 2018

Rennes, France
Couvent des Jacobins
Conference Centre

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ORGANIZING PARTNERS

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INRA
SCIENCE & IMPACT

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Fertilisation
& Environnement

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Comité Français d'Etude et de Développement de la Fertilisation Rationnelle
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INRA
SCIENCE & IMPACT

AGRO
CAMPUS
OUEST

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Fertilisation
& Environnement

comifer
Comité Français d'Étude et de Développement
de la Fertilisation Raisonnée

20th
# Nitrogen WORKSHOP
## Coupling C - N - P - S Cycles

June 25-27, 2018 ■ Rennes, France

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BASF

We create chemistry

Poly
sulphate

BIO3G
L'AGRONOME

UNIVERSITE
BRETAGNE
LOIRE

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INRA
SCIENCE & IMPACT

Fertiberia
France

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MET
sante et intelligence

ROPOLE
rennes
## SCIENTIFIC COMMITTEE

Patrick Durand (INRA, France); Alberto Sanz Cobeña (Universidad Politecnica de Madrid, Espagne); Claudia Cordovil (Univ Lisboa, Portugal); Tommy Dalgaard (Aarhus University, Danemark); Sofia Delin (SLU Skara, Suède); Jan Willem Erisman (Louis Bolk Instituut, NL); Philippe Faverdin (INRA, France); Josette Garnier (UPMC Paris, France); Chantal Gascuel (INRA, France); Melynda Hassouna (INRA, France); Marie-Hélène Jeuffroy (INRA, France); Penny Johnes (Univ Bristol, UK); Édith Le Cadre (AGROCAMPUS OUEST, France); Tom Misselbrook (Rothamsted, UK); Maria Mooshammer (Univ Vienna, Autriche); Thierry Morvan (INRA, France); Stefaan de Neve (Univ Ghent, Belgique); Virginie Parnaudeau (INRA, France); Sylvie Recous (INRA, France); Karl Richards (Teagasc, Irlande); Zahra Thomas (AGROCAMPUS OUEST, France); Françoise Vertès (INRA, France)

## ORGANISING COMMITTEE
(INRA - AGROCAMPUS OUEST)

Patrick Durand; Virginie Parnaudeau; Laure Beff; Sylvaine Bitteur; Michael Corson; Monique Delabuis; Rémi Dupas; Cédric Durand; Karine Derrien; Olivier Godinot; Melynda Hassouna; Tiphaine Labbe; Lucie Lefèvre; Thierry Morvan; Maryvonne Pertue; Thierry Trochet
<table>
  <tbody>
    <tr>
      <td><strong>Session I: N cycle in landscapes - Oral presentations</strong></td>
      <td><strong>15</strong></td>
    </tr>
    <tr>
      <td>Keynote presentation: How Nitrogen changed different landscapes</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Jan Willem Erisman, James N. Galloway, Allison M. Leach, and Albert Bleeker</td>
      <td></td>
    </tr>
    <tr>
      <td>Rebalancing nutrient ratios in streams and rivers</td>
      <td>17</td>
    </tr>
    <tr>
      <td>Wade, AJ., Stutter, M., Jarvie, HP., Halliday, SJ., Cooper, DM.</td>
      <td></td>
    </tr>
    <tr>
      <td>Multi-decadal trajectory of riverine nitrogen, phosphorus and carbon dynamics in rural catchments</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Dupas, R, Minaudo, C., Gruau, G., Ruiz, L., Gascuel-Odoux, C.</td>
      <td></td>
    </tr>
    <tr>
      <td>The role of coupled macronutrient cycles for ecosystem service supply at the landscape scale</td>
      <td>21</td>
    </tr>
    <tr>
      <td>Reinsch, S., Blanes, M.A., Cosby, B., Glanville, H., Harmens, H., Jones, D., De Sosa, L.L., Marshal, M., Mercado, L., Smart, S., Winterbourn, J.B., Emmett, B.A.</td>
      <td></td>
    </tr>
    <tr>
      <td>Decadal trajectories of nitrate input and output in three central German catchments with differing land uses</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Ehrhardt, S., Musolff, A, Fleckenstein, J.H., Attinger, S., Kumar, R., Dissolved organic matter delivered to aquatic ecosystems</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Yates, C.A., Johnes, P.J., and the NERC DOMAINE team</td>
      <td></td>
    </tr>
    <tr>
      <td>Exploring the spatial variability of N<sub>2</sub>O emissions from a grazed upland and lowland site</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Cardenas, L.M., Charteris, A.F., Harris, P., Marsden, K.A., Harris, I., Guo, Z., Beaumont, D.A., Taylor, H., Sanfratello, G., Jones, D.L., Whelan, M.J, Chadwick, D.R.</td>
      <td></td>
    </tr>
    <tr>
      <td>Spatially differentiated scenarios to reduce agricultural nitrogen emissions</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Hashemi F., Olesen J.E., Dalgaard T., Børgesen C.D.</td>
      <td></td>
    </tr>
    <tr>
      <td><strong>Session I: N cycle in landscapes - Highlighted posters</strong></td>
      <td><strong>33</strong></td>
    </tr>
    <tr>
      <td>Better understanding nitrate sources and sinks in agricultural watersheds</td>
      <td>34</td>
    </tr>
    <tr>
      <td>XIA, Yongqiu, Weller Donald E, Yan Xiaoyuan</td>
      <td></td>
    </tr>
    <tr>
      <td>Identification of key atmospheric N sources at designated sites - a case study for special areas of conservation in Northern Ireland</td>
      <td>36</td>
    </tr>
    <tr>
      <td>Carnell, E.J., Dore, A.J., Misselbrook, T.H, Sutton M.A.1 & Dragosits, U.</td>
      <td></td>
    </tr>
    <tr>
      <td>Feasibility of using industrial anion exchange resin to remove nitrate from tile water</td>
      <td>38</td>
    </tr>
    <tr>
      <td>Wolf, K.A., Gupta, S.C.</td>
      <td></td>
    </tr>
    <tr>
      <td>Climate indicators explaining the variability of carbon, nitrogen and phosphorus emissions to streams in headwater agricultural catchment</td>
      <td>41</td>
    </tr>
    <tr>
      <td>Strohmenger, L., Fovet, O., Gascuel-Odoux, C.</td>
      <td></td>
    </tr>
    <tr>
      <td>Exposure time vs. residence time in upscaling nitrate mass Loss in the hyporheic zone</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Gilfedder, B.S., Durejka, S., Thomas, Z., Le lay, H., Frei, S.</td>
      <td></td>
    </tr>
    <tr>
      <td>Searching for the control mechanisms of nitrogen removal in a mediterranean riparian forest</td>
      <td>46</td>
    </tr>
    <tr>
      <td>Poblador, S., Thomas Z., Rousseau-Gueutin, P., Lupon A., Sabaté S., Sabater, F.</td>
      <td></td>
    </tr>
    <tr>
      <td>Modelling mitigation scenarios on a landscape in Central France</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Pasquier, c., Benhamou, C., Franqueville, D., Drouet, J.L., Henault, C.</td>
      <td></td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Session I: N cycle in landscapes - Posters</td>
      <td>50</td>
    </tr>
    <tr>
      <td>GIS-based estimation of shallow groundwater nitrate concentrations: from point measurements to the landscape scale</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Knoll, L., Breuer, L., Bach, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Tracing the fate of N-labelled animal manure in the environment</td>
      <td>53</td>
    </tr>
    <tr>
      <td>Frick, H., Oberson, A., Frossard, E., Wettstein, H.-R., Bünemann, E. K., High water tables during winter have no impact on winter wheat nitrogen uptake and yields under Danish conditions</td>
      <td>55</td>
    </tr>
    <tr>
      <td>Deichmann, M. M., Børgesen, C. D., Thomsen, I. K., Andersen, M. N.</td>
      <td></td>
    </tr>
    <tr>
      <td>Increasing the nitrogen use efficiency and consequently the groundwater quality in the Gäu region, Switzerland</td>
      <td>57</td>
    </tr>
    <tr>
      <td>Wey, H., Hunkeler, D., Buenemann, E.K.</td>
      <td></td>
    </tr>
    <tr>
      <td>Upscaling water and nutrient use efficiencies from field to catchment scale: a case study in the Selke catchment, Germany</td>
      <td>59</td>
    </tr>
    <tr>
      <td>Silva, J.V., Jomaa, S., Chukalla, A., Yang, X., Merbach, I., Rode, M., Anten, N.P.R., van Ittersum, M.K., Reidsma, P.</td>
      <td></td>
    </tr>
    <tr>
      <td>In-stream nitrate assimilatory uptake analysis based on high frequency measurements</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Yang, X., Jomaa, S., Rode, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Agricultural nitrogen budget and groundwater quality in a Portuguese vulnerable zone</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Faro, A.F., Cameira, M.R., Rolim, J. Cordovil, C., Dragosits, U.</td>
      <td></td>
    </tr>
    <tr>
      <td>Indirect emission of nitrous oxide from rivers: first results of a study on the Loir watershed</td>
      <td>66</td>
    </tr>
    <tr>
      <td>Grossel, A., Pasquier, C., Bourennane, H., Henault, C.</td>
      <td></td>
    </tr>
    <tr>
      <td>Plant growth indicates soil spatial variability related to water shortage and nitrate leaching vulnerability</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Haberle, J., Svoboda, P., Šimon, T., Lukáš, J., Kurešová, G., Křížová, K. Madaras, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Assessment of artificial wetland for nitrate removal from subsurface drained watershed</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Tournebize J., Chaumont C., Blandin M., Soosaar K., Hansen R., Muhel M., Teemusk A., Pärn J., Mander U.</td>
      <td></td>
    </tr>
    <tr>
      <td>Evaluating scenarios of land management practices in contrasted landscapes using a nitrogen landscape model</td>
      <td>72</td>
    </tr>
    <tr>
      <td>Casal L., Durand P., Akkal-Corfini N., Benhamou C., Salmon-Monviola J., Ferrant S., Probst A., Probst J.L., Sauvage S., Vertès F.</td>
      <td></td>
    </tr>
    <tr>
      <td>N<sub>2</sub>O emissions in response to the addition of nitrification inhibitors to digestate: a case study in the Po Valley (Northern Italy)</td>
      <td>75</td>
    </tr>
    <tr>
      <td>Chiodini, M.E., Perego, A., Sanz-Gomez, J., Tarlazzi, S. Acutis, M., Changes in land use and impact in water quality for water supply. Example of "Castelo de Bode" in Portugal</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Cordovil, C.M.d.s., Reis, R., Miranda, r. ; Vale, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Eutrophication: causes, mechanisms, consequences and predictability</td>
      <td>79</td>
    </tr>
    <tr>
      <td>Pinay, G., Gascuel-Odoux, C., Ménesguen, A., Souchon, Y, Le Moal, M., Durand, P., Levain, A., Pannard, A., Souchu, P., Etrillard, C., Moatar, F..</td>
      <td></td>
    </tr>
  </tbody>
</table>
## Session II: Regional studies – Oral presentations .................................................................... 81

Keynote presentation: Nitrogen dynamics in agricultural systems under Mediterranean climate...... 82

Luis Lassaletta

Potentials for simultaneous improvement of phosphorus and nitrogen management in Austria...... 84

Tanzer, J., Rechberger, H., Zessner, M., Zoboli, O.

Greenhouse gases Emissions from agriculture in the North of France (1852-2014): Consequence of specialisation and intensification .................................................................................................................. 86

Garnier J., Le Noë J, Marescaux A., Sanz-Cobena A., Thieu V., Billen G.

Driving forces of food system nitrogen flows in China, 1990 to 2012 ................................................................. 88

Gao Bing, Huang, Wei,, Cui Shenghui

Assessment of spatially explicit needed increase in nitrogen use efficiency in European agricultural soils in view of air and water quality.................................................................................................................. 90

de Vries, W., Kros, J., Voogd, J.C., Louwagie, G

Historic trends in N and S deposition in the UK: 1800 to present ................................................................ 93

Dragosits U., Tomlinson S.J., Carnell E.J., Dore A.J., Misselbrook T.H., Simpson D. Langford B., Mullinger N., Nemitz E.G., Sutton M.A.

The nitrogen operating space of agriculture: connecting nitrogen self-sufficiency to maximum net production.................................................................................................................................................. 95

Harchaoui, S., Chatzimpiros, P.

Impact of nitrogen management plans on winter soft wheat yield and nitrogen balance at European scale, simulated with the stics crop model .................................................................................................................. 98

Lecerf, R., Van Der Velde, M., Dentener, F.

## Session II: Regional studies - Highlighted posters................................................................ 100

Modelling biomass and nutrient flows in agro-food systems at the local scale. Scenario simulation and assessment in a French case-study .................................................................................................................. 101

Fernandez-Mena H., Nesme T., MacDonald GK, Pellerin S.

A comparison of disaggregated nitrogen budgets for Danish agriculture using Europe-wide and national approaches .................................................................................................................................................................................. 104

Kros, J., Hutchings, N.J., Toft Kristensen, I., Sillebak Kristensen, I., Duus Børgesen, C., Voogd, J.C.H., Dalgaard, T., de Vries, W.

Nitrogen management in French dairy systems: evaluation and enhancement of nitrogen efficiency and economic performance .................................................................................................................................................. 106

Foray, S., Vertes, F., Godinot, O.

Regionalized ammonia emission reduction potentials in Germany................................................................ 109

Häussermann, U., Klement, L., Bach, M., Breuer, L.

Production, nitrogen exportation and nitrate leaching from managed grasslands in France ............ 111

Graux, A.-I., Resmond, R., Casellas, E., Delaby, L., Faverdin, P., Le Bas, C., Meillet, A., Pomeon, T., Raynal, H., Ripoche, D., Ruget, F., Therond, o., Vertes, f., Peyraud, J.-L.

SYNERGY: a model to assess the economic and environmental impacts of increasing regional protein self-sufficiency.................................................................................................................................................. 113

Jouan, J., Carof, M., Ridier, A.
Mayer, J., Koeppe, P., Dubois, a., Hutter, M.

Modelling C-N-P-K soil dynamics in a context of repeated composts applications 183

Revallier, A., Orvain, M., Bisinella De Faria, A. B., Naves-Maschietto, G., Albuquerque, M., Houot, S., Hartman, M., Parton, B.

Impact of the urease inhibitor Limus® on agronomic and environmental parameters in temperate grassland 185

Krol, D.J., Forrestal, P.J., Wall, D., Lanigan, G.J., Sanz-Gomez, J., Knauer, M., Ford, I., Kelly, G., Richards, K.G.

Gas emissions during solid manure management at housing and storage stages from dairy cattle in contrasted feeding and climatic situations 187

Edouard, N., Robin, P., Almeida, J.G.R., Alves, T.P., Lamberton, P., Lorinquer, E.

Predicting C and N fate from mixture of sugarcane straw and organic fertilizers. Mechanistic approach by modeling 189

Kyulavski, V., Thuriès, L., Recous, S., Paillat, J.-M., Garnier, P.

Quality of carbon compounds of maize root and shoot litter controls short-term CO₂ and N₂O emissions from agricultural soils 191

Rummel, P.S., Pfeiffer, B., Pausch, J., Dittert, K.

Nitrification inhibitor N-Lock with Optinyte Technology – research on environmental and agricultural benefits in grain corn and oilseed rape 193

Dzikowski, M., Peterson, M., Lourdet, Y.

Maintaining soil nitrogen and carbon stocks in long-term grassland experiments in Norway 195

Sturite I., Opstad S.

Nitrogen leaching after solid manure application in autumn before spring sowing 197

Delin, s.

Human urine as a nitrogen fertilizer: a greenhouse experiment 199

Martin, T.M.P., Levavasseur, F., Esculier, F., Houot, S.

Nutrient cycling in grassland systems; N, P and C cycling in a plot experiment on the North Wyke farm platform 202

Le Cocq, K., McAuliffe, G., Horrocks, C., Darch, T., Takahashi, T., Blackwell, M., Misselbrook, T.1, Lee, M.R.F., Cardenas, L.

The impacts of soil incorporation of conventional and novel organic fertilizers on N availability and microbial parameters 204

Gebremikael, M., Ranasinghe, a., Salehi, P., Pipan, M., De Neve, s.

## Session III: Local process studies – Posters 206

Substitution of urea by calcium ammonium nitrate in a rainfed semi-arid crop: effect on N oxides emissions, yield and quality 207

Guardia, G., Sánchez-Martín, L., Sanz-Cobena, A., Rodríguez De Quijano, M., Fuertes-Mendizábal, T., González-Murua, C., Vallejo, A.

Nitrogen and phosphorous availability of organic fertilizers- a greenhouse study 209

Palmborg, C., Hahlin A.-H.
<table>
  <tbody>
    <tr>
      <td>Impact of conventional tillage and no-tillage cover crops on nitrous oxide emissions from vineyards in mediterranean portugal</td>
      <td>211</td>
    </tr>
    <tr>
      <td>Marques, F.J.M., Pedroso, V., Trindade, H., Pereira, J.L.S.</td>
      <td></td>
    </tr>
    <tr>
      <td>Ammonia and greenhouse gas emissions from a breeding hen building in Portugal</td>
      <td>213</td>
    </tr>
    <tr>
      <td>Pereira, J.L.S., Ferreira, S., Conde, A., Ferreira, P., Pinheiro, V., Trindade, H.</td>
      <td></td>
    </tr>
    <tr>
      <td>Residual effect and nitrogen use efficiency of N fertilizers in a maize/wheat rotation</td>
      <td>215</td>
    </tr>
    <tr>
      <td>Valentin, F., Calvo, M., Gabriel, J.L., Alonso-Ayuso, M., Quemada, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Long-term consequences of unbalanced fertilization with nitrogen and phosphorus</td>
      <td>217</td>
    </tr>
    <tr>
      <td>Rutkowska, A.</td>
      <td></td>
    </tr>
    <tr>
      <td>Nitrogen balance and crop nitrogen uptake in long-term lysimetric investigation</td>
      <td>220</td>
    </tr>
    <tr>
      <td>Miroslav F., Smatanová M., Hynšt J., Němec P.</td>
      <td></td>
    </tr>
    <tr>
      <td>The effect of nitrogen application methods on maize (zea mays L.) yield</td>
      <td>222</td>
    </tr>
    <tr>
      <td>Széles, A., Horváth, É., Kith, K., Ragán, P.</td>
      <td></td>
    </tr>
    <tr>
      <td>Agent based modelling of sheep movement and urine deposition to support N<sub>2</sub>O emission estimates in upland pastures</td>
      <td>224</td>
    </tr>
    <tr>
      <td>Johnson, S.C.M., Whelan, M.J., Balzter, H., Lush, L., King, A.J., Tucny, E., Wilson, R.P., Perroto-Baldivieso, H.L., Cardenas, L., Charteris, A.F., Marsden, K.A., Harris, I., Holmberg, J. Chadwick D.</td>
      <td></td>
    </tr>
    <tr>
      <td>Comparison between different stabilized nitrogen fertilizers in maize crop</td>
      <td>226</td>
    </tr>
    <tr>
      <td>Mateo-Marín, N., Quílez, D., Guillén, M., Isla, R.</td>
      <td></td>
    </tr>
    <tr>
      <td>N<sub>2</sub>O emissions during and after legume crops cultivation</td>
      <td>228</td>
    </tr>
    <tr>
      <td>Nicolardot, B., Bizouard, F., Coffin, A., Guinet, M., Henault, C., Lombard, F., Pauthenet, G., Pimet, E., Voisin, A.S.,</td>
      <td></td>
    </tr>
    <tr>
      <td>An incubation study about the potential of high organic carbon soil amendments to improve agricultural nitrogen retention capacity</td>
      <td>230</td>
    </tr>
    <tr>
      <td>Reichel R, Wei J, Islam M.S., Amelung W., Schmid C. Schröder P., Schloter M., Brüggemann N</td>
      <td></td>
    </tr>
    <tr>
      <td>Effects of long-term application of mineral nitrogen and manure on selected soil properties</td>
      <td>232</td>
    </tr>
    <tr>
      <td>Pikuła D., Martyniuk S.</td>
      <td></td>
    </tr>
    <tr>
      <td>Short-term fate of N in maize on tropical sandy soils as affected by N form, tillage and biochar addition</td>
      <td>234</td>
    </tr>
    <tr>
      <td>Munera-Echeverri, J.L., Martinsen, V., Obia, A., dörsch, P., Mulder, J.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effect of fertilizer N forms on root growth in winter wheat (Triticum aestivum L.)</td>
      <td>236</td>
    </tr>
    <tr>
      <td>Kirschke, T., Thiel, E., Christen, O.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effect of N-fertilizers on the abundance of nitrification and denitrification genes in bulk and rhizospheric soil of tomato and beans plants</td>
      <td>238</td>
    </tr>
    <tr>
      <td>Castellano-Hinojosa, A., González-López, J., Bedmar E.J.</td>
      <td></td>
    </tr>
    <tr>
      <td>Can N<sub>2</sub>O isotopocules help to improve our understanding of N<sub>2</sub>O processes during grassland conversion to maize cropping?</td>
      <td>240</td>
    </tr>
    <tr>
      <td>Buchen, C., Lewicka-Szczebak, D., Helfrich, M., Flessa, H., Well, R.</td>
      <td></td>
    </tr>
    <tr>
      <td>Is soybean yield limited by nitrogen supply?</td>
      <td>242</td>
    </tr>
    <tr>
      <td>Cafaro La Menza, N., Monzon, J.P., Specht, J. E., Grassini, P.</td>
      <td></td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Simulation of long term C &amp; N dynamics in a northern agro-ecosystem with DNDC</td>
      <td>244</td>
    </tr>
    <tr>
      <td>Jégo, G., Crépeau, M., Chantigny, M., D'Amours, E., Smith, W., Grant, B., Lafond, J., Angers, D.</td>
      <td></td>
    </tr>
    <tr>
      <td>Dimethyl pyrazol-based nitrification inhibitors effect on N cycle bacteria responsible of N<sub>2</sub>O emissions in grassland</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Fuertes-Mendizabal, T., Huerfano, X., Torralbo, F., Vega-Mas, I., Menendez, S., Gonzalez-Murua, C., Estavillo, J.M.</td>
      <td></td>
    </tr>
    <tr>
      <td>3,4-dimethylpyrazole-succinic acid (DMPSA) nitirification inhibitor effectively reduces N<sub>2</sub>O emissions in a winter wheat crop under no-tillage management</td>
      <td>248</td>
    </tr>
    <tr>
      <td>Corrochano-Monsalve, M., Huérfano, X., Fuertes-Mendizabal, T., Estavillo, J.M., González-Murua, C.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effect of N fertilization rate and soil tillage on n<sub>2</sub>O emissions from irrigated corn in a mediterranean agroecosystem</td>
      <td>251</td>
    </tr>
    <tr>
      <td>Pareja-Sánchez, E., Plaza-Bonilla, D., Álvaro-Fuentes, J., Cantero-Martínez C.</td>
      <td></td>
    </tr>
    <tr>
      <td>Evaluation of the sensitivity of portable chlorophyll meters to estimate leaf chlorophyll and N contents under excessive N conditions</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Padilla, F.M., Peña-Fleitas, M.T., De Souza, R., Giménez, C., Thompson, R.B., Gallardo, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Leaf nitrogen and δN fractionation during resorption in beech and larch trees in Japan</td>
      <td>255</td>
    </tr>
    <tr>
      <td>Lopez C, M.L., Enta, A., Fujiyoshi, L. Yamanaka, T., Oikawa, A.</td>
      <td></td>
    </tr>
    <tr>
      <td>Linking biochar properties with its ability to promote N<sub>2</sub>O reduction to N<sub>2</sub> in agricultural soils</td>
      <td>257</td>
    </tr>
    <tr>
      <td>Pascual De Vega, M.B., Sánchez-Monedero, M.A., Cayuela, M.L.</td>
      <td></td>
    </tr>
    <tr>
      <td>Monitoring rna induction to evaluate nitrogen absorption and assimilation following biostimulant application on wheat (triticum aestivum l.)</td>
      <td>259</td>
    </tr>
    <tr>
      <td>Kremer, L., Belval, L., Besson, M., Meyer, L., Haas, J., Benbrahim, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effects of a natural nitrogen-nutrional agent on winter wheat (triticum aestivum)</td>
      <td>262</td>
    </tr>
    <tr>
      <td>Bras, P.Y., Levivier, S. Klarzynski, O.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effects of organic waste application on N<sub>2</sub>O emissions and N leaching in sugarcane plantation soils in Reunion Island</td>
      <td>265</td>
    </tr>
    <tr>
      <td>Poultney, D.M, Versini, A., Thuries, L.</td>
      <td></td>
    </tr>
    <tr>
      <td>N<sub>2</sub>O mitigation potential of N-stabilizers applied with urea to winter oilseed rape</td>
      <td>267</td>
    </tr>
    <tr>
      <td>Thiel, E., Spott, O., Kreuter, T., Lietsch, A., Schuster, C.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effect of delayed N application on spring barley productivity</td>
      <td>270</td>
    </tr>
    <tr>
      <td>Hackett, R.</td>
      <td></td>
    </tr>
    <tr>
      <td>N<sub>2</sub>O soil emissions after biochar amendment: contrasting responses depending on the original feedstock</td>
      <td>272</td>
    </tr>
    <tr>
      <td>Sánchez-García, M., Sánchez-Monedero, M.A, Cayuela, M.L.</td>
      <td></td>
    </tr>
    <tr>
      <td>N<sub>2</sub>O emission during sugarcane cultivation with different levels of straw removal</td>
      <td>275</td>
    </tr>
    <tr>
      <td>Giacomini, S. J., Pinheiro, P. L., Dietrich, G., Recous, S., Pollet, C. S., Bick, R. A.</td>
      <td></td>
    </tr>
    <tr>
      <td>Ammonia loss from urea and calcium ammonium nitrate after application to winter wheat and winter oilseed rape</td>
      <td>277</td>
    </tr>
    <tr>
      <td>Spott, O., Kreuter, T., Schuster, C.</td>
      <td></td>
    </tr>
  </tbody>
</table>
A comparison of the nitrogen use efficiency and nitrogen losses attributed to three fertiliser types applied to an intensively managed silage crop 280

Cowan N., Levy P, Moring A., Loubet B, Polina Voylokov P., Skiba U

Nitrogen use efficiency of raw and anaerobically digested slurries: a field study with different N methods 282

Hutter, M., Krebs, R., Mayer, J.

Interactive effects of the factors controlling urine-N₂O emissions from an upland soil 285

Charteris, A.F., Marsden, K.A., Castellano-Hinojosa, A., Loick, N., Chadwick, D.R., Ravella, S.R., Mead, A., Field, N., Whelan, M., Cárdenas, L.M.

Optimizing nitrogen rate of pearl millet under arid and semi-arid environments 287

Ullah, A., Ahmad, A., Khaliq, T. Saeed, U. Ahmad, I. Hussain, J. Mahmood, A.

Evaluation of the efficiency of a new additive to UAN 32 on the improvement of the nitrogen assimilation by wheat 289

Maignan, VM., Geliot, PG.

Influence of pea, lentil and faba bean on soil microbial activities implicated in sulfur and nitrogen mineralization 291

Slezack-Deschaumes, S., Genestier J., Voisin, A.S., Piutti, S.

Soil nitrogen dynamics in intercropped wheat (triticum aestivum) and white lupin (lupinus albus). 294

De Oliveira, A. B.; Hinsinger, P.; Yvin, J. -C.; Arkoun, M.; Le Cadre, E.

Quantification of ammonia, nitrous oxide and methane emissions following biogas digestate application in an orchard field in the North China Plain 296

Deharde, A., Yuan, Y., Gao, Z., Ma, W., Roelcke, M., Nieder, R.

Effects of the nitrification inhibitor dmrsa on nutrient uptake, nitrate leaching and crop growth in barley and potato in pot and field trials 299

Pacholski, A., Goebel, M., Meyer, A., Mannheim, T.

Calibrating airborne multispectral data to autumnal fresh matter and N content of winter oilseed rape 301

Bukowiecki, J., Pahlmann, I., Kage, H.

Impact of band application at 2-5 cm of pelleted meat bone meal on winter oilseed rape yield 303

Engström, L., Delin, S.

First analysis of the nitrate leaching risk for different fertilisers in the persephone project 305

Daigneux, B., Molitor, N., Gennen, J., Debbaut, V., Tsachidou, B.

Biodiversity of indigenous rhizobia nodulating *Phaseolus vulgaris* in Croatia 307

Rajnovic, I., Ramírez-Bahena, M.H., Sánchez Juanes, F., González Buitrago, J.M., Kajic, S., Peix, A., Velázquez, E., Sikora, S.

Simulated nitrogen deposition in two italian beech forests exposed to different climate conditions: effect on soil nitrogen cycling 309

Marcolini, G., Gioacchini, P., Mazzenga, F., D'andrea, E., Ravaioli, D., Muzzi, E., Mezzini, E., Matteucci, G., Magnani, F.

Effects of N fertilizer forms and soil pH on N₂O emissions during nitrification 311

Tierling, J., Lebender, U., Brueck, H.
<table>
  <tbody>
    <tr>
      <td>Estimating wheat grain yield and N uptake by multispectral imaging</td>
      <td>313</td>
    </tr>
    <tr>
      <td>Corti, M., Cabassi, G., Cavalli, D., Vigoni, A., Ortuani, B., Degano, L., Pricca, N., Marino Gallina, P.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effect of modifying root distribution on growth of wheat and net nitrogen budgets</td>
      <td>315</td>
    </tr>
    <tr>
      <td>Buckingham S, Jones S, Crawford C, Bingham I J</td>
      <td></td>
    </tr>
    <tr>
      <td>Compound-specific N stable isotope probing to provide novel insights into the response of bacterial and fungal communities in soil to fertiliser</td>
      <td>317</td>
    </tr>
    <tr>
      <td>Reay, M.K., Jones, D.L., Evershed, R.P.</td>
      <td></td>
    </tr>
    <tr>
      <td>Long term effect of NPK and manure fertilisation and crop/grass rotations on soil C and N and plant N concentrations</td>
      <td>319</td>
    </tr>
    <tr>
      <td>Palmborg, C., Carlsson, G., Albertsson, J. Jensen, E.S.</td>
      <td></td>
    </tr>
    <tr>
      <td>Climate and management effects on N2O emissions from two crop sites in the southwestern France</td>
      <td>321</td>
    </tr>
    <tr>
      <td>Bigaignon, L., Le Dantec, V., Zawilski, B., Granouillac, F., Claverie, N., Brut, A., Ceschia, E., Mordelet, P., Tallec, T.</td>
      <td></td>
    </tr>
    <tr>
      <td>Analyzing and modeling the variability of soil organic nitrogen mineralization from five years of field measurements</td>
      <td>323</td>
    </tr>
    <tr>
      <td>Beff, L., Lambert, Y., Germain, P., Louis, B., Mary, B., Beaudoin, N., Morvan, T.</td>
      <td></td>
    </tr>
    <tr>
      <td>Estimation of delayed effect of some organic matter types on sugarcane</td>
      <td>325</td>
    </tr>
    <tr>
      <td>Fevrier A., Paillat J., Marion D.</td>
      <td></td>
    </tr>
    <tr>
      <td>A novel platform to provide services in the monitoring of greenhouse gases for agricultural systems</td>
      <td>328</td>
    </tr>
    <tr>
      <td>Decuq, C., Genermont, S., Loubet, B., Cellier, C. Gabrielle, B.</td>
      <td></td>
    </tr>
    <tr>
      <td>Comparing ammonia volatilisation of livestock effluents having undergone different treatments in field conditions</td>
      <td>330</td>
    </tr>
    <tr>
      <td>Voylokov, P., Loubet, B., Houot, S., Savoie, A., Genermont, S.</td>
      <td></td>
    </tr>
    <tr>
      <td>Effect of urease and nitrification inhibitors on nitric and nitrous oxide emissions, and yield in an irrigated maize crop</td>
      <td>332</td>
    </tr>
    <tr>
      <td>Recio, J., Montoya, M., Guardia, G., Gines, C., Sanz-Cobeña, A. Vallejo, A., Álvarez, JM.</td>
      <td></td>
    </tr>
    <tr>
      <td>Higher nitrogen use efficiency under reduced tillage</td>
      <td>334</td>
    </tr>
    <tr>
      <td>Ernfors, M., Jensen, ES.</td>
      <td></td>
    </tr>
    <tr>
      <td>N fertilizer forms affect nitrous oxide emissions in a fertigated potato system</td>
      <td>336</td>
    </tr>
    <tr>
      <td>Lebender, U., Tierling, J., Brueck, H.</td>
      <td></td>
    </tr>
    <tr>
      <td>Contribution of Escherichia coli for reducing greenhouse gas and ammonia emissions from treated cattle-slurry liquid fraction</td>
      <td>338</td>
    </tr>
    <tr>
      <td>Miranda, C., Soares, A.S., Teixeira, C.A., Coelho A.C., Trindade, H.</td>
      <td></td>
    </tr>
    <tr>
      <td>Changes of phosphorus availability in soils under different nitrogen fertilization levels in a long-term field experiment</td>
      <td>340</td>
    </tr>
    <tr>
      <td>Čermák, P., Mühlbachová, G., Káš, M., Marková, K., Pechová, M.</td>
      <td></td>
    </tr>
    <tr>
      <td>Changes of sulphur availability in soils under different nitrogen fertilization levels in a long-term field experiment</td>
      <td>342</td>
    </tr>
    <tr>
      <td>Mühlbachová, G., Čermák, P., Káš, M., Marková, K., Pechová, M.</td>
      <td></td>
    </tr>
  </tbody>
</table>
Effects of suppression of herbicides on vineyard nitrogen status, and soil microbial and chemical indicators.................................................................................................................................................. 344

Durocher E., Langenfeld, A., Ley L., Klein C., Thiollet-Scholtus M., Nassr, N.

Legume nutrients management for sustaining crop productivity and soil biodiversity ........................ 347

Toleikienė, M., Arlauskienė, A., Kadžiulienė, Ž., Supronienė, S.

Towards a better understanding of C and N cycling in agricultural soils .................................................. 349

Samson, M-E., Chantigny, M., Angers, D. Menasseri-Aubry, S. Vanasse, A.

Urea deep placement for transplanted rice: complementarity of N-labellingand ammoniacal N diffusion studies to undestand the fertilizer-N efficiency................................................................ 351

Gaudin, R., D'Onofrio, G.

A comprehensive analysis of N-losses from urine patches in dairy pasture systems .......................... 353

Carozzi, M., Bretscher D., Voglmeier K., Ammann, C.

Increasing Nutrient Use Efficiency by using controlled release fertilizers fitted to the crop needs... 355

Clemens, R.,Terrones, C.,Pardossi, A., Incrocci, L.

Polysulphate - a new multi nutrient fertilizer with sulphur, potassium, magnesium and calcium - for better nitrogen use efficiency .................................................................................................................. 357

Imas, P.

Impact of anaerobic digestion on N balance in a crop succession fertilized with treated or untreated manures: first results.................................................................................................................. 359

Savoie A., Pasquier C., Ayzac A., Voylokov P., Lemekhova A., Genermont S., Loubet B., Henault C., Houot S.

## Session IV: Studies and mitigation options at the farm level – Oral presentations............361

Keynote presentation: sources of nitrogen in cereal production and mitigation options .................... 362

Ladha, J.K.

Do agricultural practices impact carbon, nitrogen and phosphorus stoichiometry in plants and soils on the long-term?.................................................................................................................................................. 364

Ferchaud, F., Mary, B., Keuper, F., Mollier, A., Denoroy, P., Morel, C., Gallet-Budynek, A., Houot, S., Jouany, C., Hedde, M., Hinsinger, P., Jourdan, C., Bertrand, I.

Identifying NEMO: a model-based methodology to identify strategic N application rates for rainfed crop.................................................................................................................................................. 366

Pattey, E., Mesbah, M., Jego, G.

Ammonia emission from animal manure and mineral fertilizer measured with a cheap, reliable & simple to use method .................................................................................................................................................. 368

Sommer, S.G., Kure, J.L., Krabben, J., Duelund L., Pedersen, S.V.

Nitrogen use efficiency as an indicator of farm performance ................................................................ 371

Quemada, M., Brentrup, F., Rutkowska, L., Stoumann, L., Schulman, M., Oenema, O.

Aquaponics system, a solution to limit nutrient release by fish farming ? .................................................. 373

Jaeger, C., Foucard, P., Tocqueville, A., Nahon, S., Aubin, J.
A comparative nitrogen balance of novel cropping systems for feedstock production to future biorefineries: the role of perennial grasses and grass-legumes 375

Manevski, K., Lærke, P.E., Jørgensen, U.

Multi-model assessment of mitigation options for GHG emissions in croplands 377

Carozzi, M., Ehrhardt, F., Brilli, L., Bellocchi, G., Bhatia, A., De Antoni Migliorati, M., Doltra, J., Dorich, C., Doro, L., Fitton, N., Grace, P., Grant, B., Giacomini, S.J., Léonard, J., Loubet, B., Massad, R.S., Mula, L., Pattey, E., Sharp, J., Smith, P., Smith, W., Zhang, Q., Recous, S.

## Session IV: Studies and mitigation options at the farm level - Highlighted posters 379

Finetuning abatement of ammonia emissions from livestock housing according to impact on protected habitats 380

de Pue, D., Bral, A., Buysse, J.

Nitrate Leaching risk assessment after Incorporation of fertilized catch crops 385

de Waele, J., Radam, E.D., de Vliegher, A., Vandecasteele, B., de Neve, S.

Nitrous oxide-nitrogen emission factors for a biofuel crop 388

Cantarella, H., Carvalho, B.G.O., Lourenço, K., Carvalho, J.L.N., Gonzaga, L.C., Kuramae, E., Soares, J.R., Carmo, J.B.

Illustrative modelling of nitrate leaching from fertiliser and manure nitrogen applications 390

Hutchings, N.J., Sørensen, P.

Nitrogen supply by roller-crimped agro-ecological service crops in organic cabbage production 392

Hefner, M., Kristensen, H.L. 392

Acidification prior to drying of digestate solids as a bio-based fertiliser affects nitrogen and phosphorus uptake and fertiliser value when applied to maize 394

Liu, JN., Müller-Stöver, DS., Jensen, LS.

A novel platform to provide services in the monitoring of greenhouse gases for agricultural systems 396

Decuq, C., genermont, S., Loubet, B., Cellier, C. Gabrielle, B.

Yield of winter crops with legumes monocropped and intercropped with grasses: effects on a subsequent maize crop in a double cropping system 398

Báez, M.D., García, M.I., M. Mella, Gilsanz, C.

Implications of the cover crop termination date on N and water cycles 400

Alonso-Ayuso, M, Quemada, M, Vanblooster, M, Ruiz, M, Rodriguez, A, Gabriel, J.L.,

Fertilizer strategies to improve NUE in grazed dairy pastures 402

Smith, A. P., Christie, K. C., Rawnsley, R. P., Eckard, R. J.

Modelling the effect of wide ranging drip irrigation regimes and N-fertigation rates on potato growth and production on a coarse sand 404

Motarjemi, S.K., Zhou, Z., Andersen, M.N, Plauborg, F.

Nitrogen input mapping at the field scale using remotely piloted aircraft systems imagery 406

Maire J., Gibson-Poole S., Cowan N., Richards K., Skiba U., Rees R.M., Reay D.S., Lanigan G.

Manure N plays a dual role in C stabilization and N supply soil functions – evidence from long-term field studies 408
Chantigny, M.H., Angers, D.A.

Carbon and nitrogen sequestration and nitrate leaching mitigation by cover crops 410

García-González, I., Hontoria, C., Gabriel, J.L. Alonso-Ayuso, M., Quemada, M.

Grain yield and nitrogen use efficiency (NUE) response in old and new durum wheat genotypes to different fertilization timing 412

Gioacchini, P., Palumbo, M., Sciacca, F., Virzì, N., Allegra, M., Roccuzzo, G. Ciavatta, C.

Decision rules for environment-friendly wheat N fertilization: Combining crop model and viability theory 414

Jeuffroy, M.-H., Lenoir, A., Ravier, C., Sabatier, R., Meynard, J.-M.

Conserving carbon stocks and mitigating nitrous oxide emissions by using a forage crop rotation instead of continuous maize cultivation 416

Loges, R., Bunne, I., Hermann, A., Taube, F.

## Session IV: Studies and mitigation options at the farm level Posters 418

A spatial framework to facilitate testing and adoption of new technologies 419

Grassini, P, Rattalino Edreira, JI, Cassman, KG

Shade tree species impacts on soil fauna and C, N, P cycles in Costa Rican organic and conventional coffee agroforestry systems 422

Sauvadet, M., Van Den Meersche, K., Allinne, C., Gay, F., Virginio Filho, E.de.M., Chauvat, M., Becquer, T., Tixier, P., Harmand, J-M.

N fertilizer value of legume-based catch crops 424

de Notaris, C., Sørensen, P., Rasmussen, J., Olesen, J. E.

More profit from nitrogen in Australian agriculture 426

Smith, A. P., White, M.

Benchmarking on-farm N footprint using a simplified N-balance approach 428

Tenorio, F.A., Mclellan, E., Eagle, A., Grassini, P.

Effect of feeding the grass fibrous fraction obtained from biorefinery on N and P utilisation of dairy cows 431

Pijlman, J., Koopmans, S., De Haan, G., Lenssinck, F., Van Houwelingen, K.M., Sanders, J.P.M., Deru, J.G.C., Erisman, J.W.

Crop residues decomposition dynamics in farms practising conservation agriculture in the Grand Est region, France 434

Thiebeau, P., Recous, S.

An evaluation of nitrogen balances at the whole-farm and field scale on 21 Irish dairy farms 436

Murphy, P.M., Murphy, P.N.C., and Wall, D.P.

UMT ALTER'N: to strengthen the strategic farm advisory for cropping systems based on legume crops or organic fertilisers with low nitrogen losses and low dependency to synthetic fertilisers 438

Schneider A., Colnenne-David C., Cadoux S., Drouet J.-L., Houot S., Jeuffroy M.-H., Le Gall C., Reau R.

Comparative analysis of some ecosystem services components linked to nitrogen fluxes of cropping systems with grain legumes 440

Schneider A., Oddos L., Pelzer E., Jeuffroy M.-H.
<table>
  <tbody>
    <tr>
      <td>Optimising N-transfer in oilseed rape based crop rotations in Northern Germany</td>
      <td>443</td>
    </tr>
    <tr>
      <td>Rose, M., Pahlmann, I., Kage, H.</td>
      <td></td>
    </tr>
    <tr>
      <td>Towards nitrogen self sufficiency in cropping system of chalk soils in Champagne-Ardenne and Picardie (France)</td>
      <td>445</td>
    </tr>
    <tr>
      <td>Cros, C., Duparque, A., Reau, R.</td>
      <td></td>
    </tr>
    <tr>
      <td>N and S cycles in crucifer-legume cover crop mixtures</td>
      <td>447</td>
    </tr>
    <tr>
      <td>Couëdel, A., Alletto, L., Justes, E.</td>
      <td></td>
    </tr>
    <tr>
      <td>Autumn soil mineral nitrogen concentration as potential predictor of nitrogen leaching</td>
      <td>449</td>
    </tr>
    <tr>
      <td>Swartz, E., Børgesen, C.D., Blicher-Mathiesen, G., Piil, K., Vinther, F.P., Østergaard, H.S.</td>
      <td></td>
    </tr>
    <tr>
      <td>Living cover competition in banana plantation and nitrogen management after a legume cover crop rotation</td>
      <td>452</td>
    </tr>
    <tr>
      <td>Achard, R., Rosalie, E., Dorel M..</td>
      <td></td>
    </tr>
    <tr>
      <td>Landscape and national level solutions for a more sustainable N management in Denmark</td>
      <td>454</td>
    </tr>
    <tr>
      <td>Dalgaard, T., Brock, S., Cordovil, C.M.S., Graversgaard, M., Hansen, B., Hashemi, F., Hasler, B., Hutchings, N.J., Jacobsen, B., Jensen, L.S., Kjeldsen, C., Olesen J.E., Schjørring, J.K., Sigsgaard, T., Stubkjær Andersen, P., Termansen, M., Vejre, H., Vestergaard Odgaard, M., Wiborg, I.A.</td>
      <td></td>
    </tr>
    <tr>
      <td>Controlled traffic farming increases root growth, crop and soil nitrogen in vegetable cropping systems</td>
      <td>456</td>
    </tr>
    <tr>
      <td>Hefner, M., Labouriau, R., Nørremark, M., Kristensen, H.L.</td>
      <td></td>
    </tr>
    <tr>
      <td>Evaluating the potential of dietary crude protein manipulation in reducing ammonia emissions from cattle and pig manure</td>
      <td>458</td>
    </tr>
    <tr>
      <td>Amon, B., E.P.M, Sajeev., Zollitsch, W., Ammon, C., Winiwarter, W.</td>
      <td></td>
    </tr>
    <tr>
      <td>Supporting transition to low inputs production systems: economic and environmental assessment</td>
      <td>460</td>
    </tr>
    <tr>
      <td>Devienne S., Vertes F., Garambois N., Akkal-Corfini N., Durand P., Parnaureau V.</td>
      <td></td>
    </tr>
    <tr>
      <td>Organic N fertilization effects on pasture establishment and forest fire prevention</td>
      <td>462</td>
    </tr>
    <tr>
      <td>Ferreiro-Dominguez N, Rodriguez-Rigueiro J, Rigueiro-Rodriguez A, Mosquera-Losada MR</td>
      <td></td>
    </tr>
    <tr>
      <td>Pasture production in a silvopastoral system established with juglans regia l. and fertilized with sewage sludge</td>
      <td>464</td>
    </tr>
    <tr>
      <td>Mosquera-Losada, M.R., Arias-Martinez, D., Rigueiro-Rodríguez A, Ferreiro-Dominguez, N.</td>
      <td></td>
    </tr>
    <tr>
      <td>N<sub>2</sub>O emissions from crop residues vary greatly with residue quality and management</td>
      <td>466</td>
    </tr>
    <tr>
      <td>Olesen, J.E., Recous, S., Hansen, S., Jensen, E.S., Butterbach-Bahl, K., Rees, R.M., Bleken, M.A., Smith, K.E., Ernfors, M., Laville, P., Lashermes, G., Loubet, B., Massad, R., Petersen, S.O., Thorman, R.E., Taghizadeh-Toosi, A., Topp, C.F.E.</td>
      <td></td>
    </tr>
  </tbody>
</table>
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018
Le Couvent des Jacobins, Rennes – France

Session I: N cycle in landscapes - Oral presentations
KEYNOTE PRESENTATION : HOW NITROGEN CHANGED DIFFERENT LANDSCAPES

JAN WILLEM ERISMAN¹, JAMES N. GALLOWAY², ALLISON M. LEACH³, AND ALBERT BLEEKER⁴

¹Louis Bolk Institute, Kosterijland 3, 3981 AJ Bunnik, The Netherlands and VU Amsterdam, The Netherlands,
j.w.erisman@vu.nl
²University of Virginia, 291 McCormick Rd, Charlottesville, VA 22904, USA
³University of New Hampshire,
Durham, New Hampshire 03824, USA.
⁴PBL, Bezuidenhoutseweg 30, The Hague, The Netherlands.

ABSTRACT

For a long time there was enough naturally occurring nitrogen (N) to provide food for the world's peoples. Then we decided to live in settlements and in cities and while the population started to grow nitrogen management became important. Still there was no shortage. Major breakthroughs in nitrogen science was more driven by warfare and market mechanisms than for food. When population increased further nitrogen became limited for our food. With the invention of the chemical fixation of N the scarcity problem was solved. But this transition from enough, to scarcity, to plenty has come with a tremendous environmental cost. It has changed the physical, societal, industrial and economic landscapes in the world.

The history of nitrogen (N) use by people is a field well-fertilized with materials detailing the evolution of knowledge about nitrogen, the development of the Haber-Bosch process and the extent and impact of N cycle alteration by people. This presentation provides an historical overview of the growth of knowledge about N and about its impacts, both positive and negative. It marks the major needs for nitrogen and the accumulation of N knowledge with respect to its benefits (feeding the wars) and its problems (specialization in agriculture and cascading and accumulating N impacts).

By focusing on the major drivers for managing nitrogen in the future we explore different potential solutions of how to feed the world's population while preserving the landscape. Furthermore, we will identify the need for continuous knowledge and technology development.
# REBALANCING NUTRIENT RATIOS IN STREAMS AND RIVERS

WADE, AJ.<fnref n="1" />, STUTTER, M.², JARVIE, HP.³, HALLIDAY, SJ.⁴, COOPER, DM.⁵

## INTRODUCTION

Eutrophication of fresh and coastal waters is a widely recognised problem and river-system primary production is known to depend on carbon (C), nitrogen (N) and phosphorus (P), light, temperature and residence time, with other factors becoming limiting under certain conditions such as silicon during diatom blooms (Bowes et al., 2016). A further consideration is that microbial growth and respiration can be limited by organic carbon availability in rivers and there may be potential within the heterotrophic cycling system for stream water N and P removal (Stanley et al. 2012). Whilst a good understanding of the links between the biochemical cycles of C, N and P in freshwaters has been developed through manipulation experiments and single site studies, there has been less focus on C, N and P variation in space and time at the catchment, national and global scales, and how instream inorganic and organic C:N:P ratios relate to catchment sources and within-channel processes including autotrophic and heterotrophic production. This work aims to quantify stream and river C:N:P ratio variation across spatial and temporal scales, comparing and contrasting inorganic and organic stoichiometry and how the re-balancing of each may aid a reduction in river-system eutrophication.

## MATERIAL AND METHODS

### Catchment scale

Atomic inorganic C:N:P ratios in eight catchments in Great Britain were plotted and related to key catchment characteristics. The catchments represent key land cover types found across Great Britain and the water quality measurements start in 1975 with a samples taken each week or fortnight typically. These data were supplemented by sub-hourly sampling from two catchments over a two year period. This was done to analyse the short-term dynamics to explore instream processing in addition to the flow-related, seasonal and long-term variations using the longer term, less frequent data.

### National scale

The Centre for Ecology and Hydrology's Countryside Survey (CS) was used to provide a snap shot of water samples (Reactive Phosphorus, Total Oxidised Nitrogen, pH and alkalinity) for 2007 across 591 1 km x 1 km sample squares outside major urban areas. The THINCARB model was used to estimate the dissolved inorganic carbon concentrations from pH, alkalinity and water temperature measurements (Jarvie et al., 2017). A key feature is the study of headwater streams in relation to the lower reaches. The atomic inorganic C:N:P ratios were again related to catchment characteristics and used to determine N and P depletion and the occurrence of N-limited, P-limited and N and P co-limited rivers amongst a broader analysis (Jarvie et al., 2018).

### Global scale

A global database of DOC and dissolved N and P forms, concentrations and ratios was assembled to describe global catchment nutrient sources and rivers, categorised by climate and land use. A second review assembled global evidence for the bioavailability of dissolved organic C, N and P (DOC, DON, DOP). This was necessary to scale the stoichiometric ratios of the first database to available resources, required to correctly evaluate constraints on microbial metabolism (Stutter et al., 2018).

## RESULTS AND DISCUSSION

<footnote n="1">¹Department of Geography and Environmental Science, University of Reading, UK; ²James Hutton Institute, Aberdeen, UK; ³Centre for Ecology and Hydrology, Wallingford, UK; ⁴Department of Geography and Environmental Science, University of Dundee, UK; ⁵Centre for Ecology and Hydrology, Bangor, UK.</footnote>
## Catchment and national scales

The combined results highlight that land use and point source inputs largely determine streamwater C:N:P ratios in Great Britain and, typically, the effect of instream processing on C:N:P concentrations is masked by the C, N and P inputs to the stream network. Stoichiometric P depletion is more commonly seen in lowland rivers, with a greater prevalence of N-limitation, and P and N co-limitation, in headwater streams.

## Global scale

Within the context of heterotrophic cycling systems, the atomic ratios of bioavailable C:N and C:P in rivers with urban and agricultural land use were often distant from the 'microbial optimum'. This C-deficiency relative to high availabilities of N and P likely appears to offer opportunity to retain N and P within the microbial biomass by active stoichiometric rebalancing.

## CONCLUSION

These studies indicate that, to reduce eutrophication of rivers, prioritisation of P concentration reduction in lowland, high alkalinity catchments and N concentration reduction in upland, low alkalinity catchments appears beneficial. In terms of heterotrophic cycling, alleviating organic-C depletion may lead to greater assimilation of nitrogen and phosphorus into the microbial biomass thereby aiding further reduction in instream N and P concentrations. Increased organic-C could be achieved through the reintegration of wetlands into river systems. Further work is needed to better integrate understanding derived from macro-scale data analysis with the more nuanced assessments of C:N:P interactions at local and short-time scales to provide the evidence on which to base eutrophication management and mitigation approaches on the ground. Of particular importance is development of an integrated understanding of the inorganic and organic C, N and P controls on autotrophic and heterotrophic production across a wide-range of river-system settings. Comparison with studies of C:N:P cycling in soil-vegetation systems may also help develop integrated soil and water strategies to better reduce eutrophication.

**Acknowledgements:** All authors acknowledge funding from the UK Natural Environment Research Council (UK NERC grant numbers: NE/J011908/1; NE/J011991/1; NE/J011967/1 under the Macronutrient Cycles Program) and MS additionally from Scottish Government's Rural Environment Science and Analytical Services, and HPJ from the UK NERC via the Strategic Importance of Headwaters for UK Water Resources project (NEC05966).

## REFERENCES

* Bowes MJ., Lowenthal M., Read DS., Hutchins MG., Prudhomme C., Armstrong LK., et al., 2016. Identifying multiple stressor controls on phytoplankton dynamics in the River Thames (UK) using high-frequency water quality data. Sci. Tot. Environ., 434, 201-212.

* Jarvie, HP, King SM, Neal, C., 2017. Inorganic carbon dominates total dissolved carbon concentrations and fluxes in British rivers: Application of the THINCARB model - Thermodynamic modelling of inorganic carbon in freshwaters. Sci. Tot. Environ., 575, 496-512.

* Jarvie HP., Smith DR., Norton LR., Edwards FK., Bowes MJ., King SM., Scarlett P., Davies S., Dils RM., Bachiller-Jareno N. 2018. Phosphorus and nitrogen limitation and impairment of headwater streams relative to rivers in Great Britain: a national perspective on eutrophication. Sci. Tot. Environ., 621, 849-862.

* Stanley EH., Powers S M., Lottig NR, Buffam I., Crawford JT., 2012. Contemporary changes in dissolved organic carbon (DOC) in human-dominated rivers: is there a role for DOC management? Freshwater Biol. 57 (suppl. 1), 26-42.

* Stutter M., Evans CD., Graeber D., Wade AJ., Withers PJA., 2018. Stoichiometric rebalancing allows microbial regulation of aquatic nutrient pollution. Sci. Tot. Environ., 634, 439-447.
# MULTI-DECADAL TRAJECTORY OF RIVERINE NITROGEN, PHOSPHORUS AND CARBON DYNAMICS IN RURAL CATCHMENTS

DUPAS, R¹, MINAUDO, C.², GRUAU, G.³, RUIZ, L.¹, GASCUEL-ODOUX, C.¹

¹ INRA, UMR1069 SAS, Rennes, France; ² University Francois Rabelais de Tours, E.A. 6293
GeoHydrosystemes Continentaux, Tours, France; ³ Observatoire des Sciences de l'Univers de Rennes, CNRS, UMR 6118
Geosciences Rennes, Rennes, France

## INTRODUCTION

Human activities have substantially altered riverine exports of nitrogen, phosphorus and carbon in the Anthropocene, causing major alterations of freshwater and marine ecosystems worldwide (Steffen et al., 2015). Documenting anthropogenic alterations of nutrient and carbon cycles in aquatic ecosystems is a challenge as industrialization in western countries began in the 18th century and the green revolution in the mid-20th century, while river monitoring became widespread only since the 1990's (Gascuel-Odoux et al., 2010). Brittany - France, is one of the few noticeable exceptions of a region with multi-decadal datasets of nitrate (N-NO₃⁻), soluble reactive phosphorus (SRP), total phosphorus (TP) and dissolved organic carbon (DOC) in meso-scale (< 5000 km²) rural catchments.

## MATERIAL AND METHODS

Here we analyze these multi-decadal and multi-parameter time series in seven rivers with the objective to investigate: i) long term trajectory of N, P, C concentrations in relation to the history of diffuse and point source pressures since the 1970's (Figure 1), ii) medium term variations in relation to inter-annual climate variability (during 5-year wet-dry cycles) and iii) seasonal variations (and their long term evolution) in relation to catchments properties and history. Statistical analyses include a seasonal-trend decomposition procedure based on Loess (STL), breakpoints detection (BFAST) and long term input-output hysteresis plots.

## RESULTS AND DISCUSSION

Results show that long term N-NO₃⁻ concentration is controlled by the agricultural N surplus with a hysteresis effect, indicating a time lag of approximately 10 years between decrease in N inputs and decrease in N-NO₃⁻ loads. Long term SRP and TP concentrations are mainly controlled by point source emissions that have decreased continuously since the 1980's. Long term DOC concentration shows little variation contrary to many catchments of the northern hemisphere (Monteith et al., 2007). Medium term inter-annual N-NO₃⁻ concentration is controlled by discharge, with an increase in concentrations during wet years and a hysteresis effect indicating the flushing of a pool of nitrate stored in the catchments during antecedent drier years (Dupas et al., 2016). Medium term SRP concentration is also controlled by discharge, with a dilution during wet years supporting the hypothesis of point-source dominated emissions. Medium term variations in TP and DOC concentrations are less clear, possibly because of opposite flushing and dilution effects during wet years. Seasonal variations exhibited winter maxima for N-NO₃⁻ and summer maxima for SRP, TP and DOC, confirming the dominant diffuse nature of N-NO₃⁻ emissions, the point-source dominated P emissions and possibly autochthonous DOC production during the summer period.
<figure><img src="image_23.png" /><figcaption>Figure 1: Long term trend of nitrate-N, Soluble Dissolved Organic Carbon (DOC), Soluble Reactive Phosphorus (SRP) and Total Phosphorus (TP) in seven Brittany rivers. Vertical bars indicate BFAST breakpoints.</figcaption></figure>

## CONCLUSION

Results show that multi-decadal N and P trends were mainly driven by N and P diffuse and point sources inputs, respectively, with response times for nitrate being controlled by the amount of legacy N stored in the catchment. At medium term inter-annual time scales, climate variability was the dominant control on concentration variability, which implies that evaluation of water quality mitigation programs should cover at least five years to decipher their effect from the effect of climate variations. Seasonally, N and P dynamics were typical of diffuse and point source dominated exports, respectively, similar to many catchments of the temperate zone. In-stream retention and remobilization processes, however, lead to difficulties in extracting the P land-to-river export signal from in-stream processes in these meso-scale catchments. Dominant controls on differences among catchments can often be inferred from catchment properties, but ambiguity issues (i.e. when the same observation can have several possible origins/interpretations) arose.

## REFERENCES

* Dupas R, S. J, Musolff A, Borchardt D, Rode M. Disentangling the influence of hydroclimatic patterns and agricultural management on river nitrate dynamics from sub-hourly to decadal time scales. Science of the Total Environment 2016: 791-800.

* Gascuel-Odoux C, Aurousseau P, Durand P, Ruiz L, Molenat J. The role of climate on inter-annual variation in stream nitrate fluxes and concentrations. Science of the Total Environment 2010; 408: 5657-5666.

* Monteith DT, Stoddard JL, Evans CD, de Wit HA, Forsius M, Hogasen T, et al. Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. Nature 2007; 450: 537-U9.

* Steffen W, Richardson K, Rockstrom J, Cornell SE, Fetzer I, Bennett EM, et al. Planetary boundaries: Guiding human development on a changing planet. Science 2015; 347
# THE ROLE OF COUPLED MACRONUTRIENT CYCLES FOR ECOSYSTEM SERVICE SUPPLY AT THE LANDSCAPE SCALE

REINSCH, S.<fnref n="1" />, BLANES, M.A.², COSBY, B.<fnref n="1" />, GLANVILLE, H³,⁴, HARMENS, H.<fnref n="1" />, JONES, D.³, DE SOSA, L.L.³, MARSHAL, M. <fnref n="1" />,³, MERCADO, L.⁵, SMART, S.<fnref n="1" />, WINTERBOURN, J.B.<fnref n="1" />, EMMETT, B.A.<fnref n="1" />

## INTRODUCTION

A major research programme in the UK has explored how the coupling of carbon (C), nitrogen (N) and phosphorus (P) within catchments influence the supply of ecosystem services on land, in rivers and across the land-marine interface. Service supply quantified included support of biodiversity, water quality, plant production, and C storage. We focussed our work around the Conwy catchment in North Wales, UK, which has a wide range of climate, topography, soil and land-use types (Emmett et al. 2016). The study approach included routine and campaign co-located soil, water and vegetation sampling, in-situ and laboratory manipulation experiments and modelling. Here we present results from the terrestrial C-N-P part of the project addressing the coupling of above-ground (plant) and below-ground (soil) C and nutrient dynamics across land-uses and with depth. We explore C and nutrient constraints on plant photosynthesis (PS), aboveground biomass productivity (aNPP) and soil C turnover.

## MATERIAL AND METHODS

In the Conwy catchment, 17 terrestrial sites were identified covering arable, improved and unimproved grasslands, woodlands and peatlands. At these sites, plant photosynthetic parameters (Jmax, Vcmax), foliar nutrients and aNPP were measured. Co-located soil chemical (total C, N, P, nitrate, plant-extractable P) and physical properties (bulk density, water content) were measured at 14 of the 17 sites. Soil measurements were carried out in 15 cm increments from 0 to 105 cm soil depth. Four of the 17 sites were chosen to study C-turnover and nutrient constraints in the topsoil (0-15 cm) and subsoil (15-100 cm). Therefore, C, N and P were added to intact soil cores in a multifactorial design. Cores were incubated at 15 °C and soil respiration rates were measured in campaign over two weeks.

## RESULTS AND DISCUSSION

Soil C, N and P stocks varied across the 17 sites. C stocks increased with decreasing land-use intensity (i.e. from arable to peatland), whereas soil N stocks decreased. The soil P stock was high in intensively managed sites and ~10 times lower in less managed sites. The distribution of soil C, N and P throughout the soil profile was different between intensively and less intensive managed sites (Figure 1). Arable and improved grassland showed a gradual change in soil C, N and P, whereas less managed sites showed decreased soil P (unimproved grassland) or decreased N (peatland) availability in the subsoil compared to the topsoil. These differences in soil C and nutrient stocks provide a great opportunity to understand mechanisms behind coupled above- and below-ground C and nutrient dynamics, and the their effects on ecosystem service supply.

PS across all land-uses correlated with foliar N, but not with foliar P. PS did not correlate with soil N or P content, but correlated weakly with soil available nitrate and plant available-P. This suggests that plant photosynthetic capacity and foliar nutrient contents are optimized under the prevailing environmental conditions and soil nutrient pools.

aNPP in managed systems was limited by available P. In contrast, aNPP was N and P co-limited in less managed systems (unimproved grassland and peatland). Land-management alleviated N constraints on aNPP, but not P. Across the land-use gradient we also observed a significant relationship between soil nitrate in the topsoil and aNPP (R²=0.93). These results suggest that although PS was not affected

<footnote n="1">¹ Centre for Ecology and Hydrology, United Kingdom; ² University of Jaen, Spain; ³ Bangor University, United Kingdom; ⁴ Keele University, United Kingdom; ⁵ Exeter University, United Kingdom</footnote>
by soil C and nutrient pools, optimized PS (and foliar nutrients) is not the exclusive determinant of aNPP.

**Soil C turnover** was different in topsoils and subsoils. Topsoils showed P limitation, whereas subsoils were only N limited. In the Topsoils, soil C turnover of intensively managed systems was co-limited by N and P; in less managed systems, N limitation was stronger than P limitation (unimproved grassland) and vice versa (peatland). These results suggest that topsoil N fertilization causes the development of microbial P limitation, which may translate into reduced aNPP.

<figure><img src="image_24.png" /><figcaption>Figure 1: Soil C:N and C:P ratios in the soil profile from topsoil 0-15 cm to subsoil 90-105 cm for a) Unimproved grassland (Unimp GL) and peatland, and b) arable and improved grassland (Imp GL). Dotted lines indicate soil C:N:P ratios of 186:13:1 after Cleveland and Liptzin (2007); resulting squares may be indicators of potential microbial C, N and P limitation.</figcaption></figure>

## CONCLUSION

The complex interplay of C and nutrient cycles, above- and below-ground, support the ecosystem service supply. Land-use may alter C, N and P availability which results in decoupled plant and soil nutrient pools with consequences for aNPP and C-turnover.

**Acknowledgements:** This data was collected for the NERC project 'The Multi-Scale Response of Water quality, Biodiversity and Carbon Sequestration to Coupled Macronutrient Cycling from Source to Sea' (NE/J011991/1). The project is also referred to as Turf2Surf. M.C. Blanes was funded by 'The Agrifood Campus of International Excellence' (ceiA3). Thanks to G. Dos Santos Pereira, E. Gozzard and S. Chesworth for nutrient analyses.

## REFERENCES

* Emmett et al. (2016) Spatial patterns and environmental constraints on ecosystem services at a catchment scale. Science of the Total Environment 572: 1586-1600.

* Cleveland and Liptzin (2007) C:N:P stoichiometry in soil: Is there a "Redfield ratio" for the microbial biomass? Biogeochemistry 85: 235-252.
# DECADAL TRAJECTORIES OF NITRATE INPUT AND OUTPUT IN THREE CENTRAL GERMAN CATCHMENTS WITH DIFFERING LAND USES

EHRHARDT, S.<fnref n="1" />, MUSOLFF, A.<fnref n="1" />, FLECKENSTEIN, J.H. <fnref n="1" />, ATTINGER, S. <fnref n="2" />, KUMAR, R. <fnref n="2" />,

## INTRODUCTION

In recent decades anthropogenic activities have doubled the input of nitrogen (N) to the biosphere (Galloway et al. 2004). Consequently, increased riverine concentrations of N (primarily as NO₃-N) cause exceeded drinking water limits and eutrophication as a global problem. A better knowledge about the fate of N after its agricultural application is indispensable to understand recent and to project future trends of stream NO₃-N concentrations. This study aims to compare the anthropogenic N-inputs with riverine N-outputs over three decades in three nested catchments with differing land uses. Specifically we pursue two following research questions: Are there considerable time lags between in- and output, and by that a built-up of an N-legacy? And, are the dynamics in the output on decadal and seasonal time scales? The study aims to provide a quantitative input-output assessment through a time-series analysis at both intra- and inter-annual scales; and using this we derive data-driven travel times for subsequent modeling work to project stream solute concentrations.

## MATERIAL AND METHODS

The Holtemme catchment (269 km<fnref n="2" />) is a sub-catchment of the Bode river basin, which is part of the TERENO Harz/Central German Lowland Observatory. This observatory is one of the meteorologically and hydrologically best-instrumented catchments in Central Germany (Zacharias et al. 2011), and provides long-term data for many environmental variables including water quantity (e.g. precipitation, discharge) and water quality at various locations. Three stations were selected to represent the characteristic land use and topographic gradient from the upstream pristine mountainous conditions to intensively agriculturally used lowland downstream conditions near the outlet. Agricultural N-surplus data (as N-input) were compared to biweekly time series (1970 – 2015) of discharge and NO₃ concentrations (as N-output) at the selected locations. The output-analyses were conducted using the EGRET-R-package (Hirsch & De Cicco 2015) to derive flow-weighted daily and annual concentrations.

## RESULTS AND DISCUSSION

The comparison of N-input and -output at the outlet shows a time shift of around ten years regarding measured flow-weighted annual concentration (Fig. 1). Furthermore, the cumulated input of 60.322 t N over six decades faces a riverine output of 7.293 t N. Consequently we hypothesize that the significant storage of 88% N is either removed via denitrification or are still being stored within the terrestrial system as in the soil, groundwater and stream. Evaluating the catchment's potential for removal and storage of N is one of the subsequent tasks.

The seasonal differentiation of concentrations shows the expectable strong seasonality, but as well a changing seasonality over time (Fig. 1). The latter is attributable to vertical N-migration to different aquifer depths over time and their changing contribution to the seasonal discharge. Hence, additionally to the N-output changes on decadal time scales, also seasonal dynamics are abundant. This decadal and seasonal decomposition of the output signal and its explanation by vertical migration enables a better estimation of travel times within the study catchment. The vertical migration of N-input also results in changing C-Q-relationships over the years, evolving systematically from dilution over accretion to chemostatic behavior, which comes along with the observed changes in seasonality.

<footnote n="1">¹ Department of Hydrogeology, Helmholtz-Centre for Environmental Research – UFZ</footnote>

<footnote n="2">² Department Computational Hydrosystems, Helmholtz-Centre for Environmental Research – UFZ</footnote>
These new insights into solute transport processes at a catchment scale will be applied in a subsequent modeling work to predict N-solute concentrations.

<figure><img src="image_25.png" /><figcaption>Figure 1. Plot of the annual input of N-Surplus (referred to the whole catchment, 2^nd^ y-axis) to the catchment and measured median NO₃-N concentrations in the stream (1^st^ y-axis) over time at three different locations (upstream-Werbat, midstream-Derenburg, downstream-Nienhagen). The colors encode the season (winter-blue, spring-green, orange-summer, red-fall).</figcaption></figure>

## CONCLUSION

The study shows a strong changing chemical behavior over time and space that to a large extent is attributed to the agricultural input history and travel times to the stream, respectively. Hence, excessive anthropogenic N-input has on the one hand a long lasting effect on the catchment as a result of long travel times, and on the other hand a significant impact on the chemical conditions for the riverine ecosystem. Furthermore, up to now, at none of the three investigated stations, the strong decrease in N-input (after 80's) is followed by a comparable time shifted decrease in N-output. These constantly elevated concentrations in concert with the lack of 88% of the N-input account for a high legacy effect of the catchment. Hence, although excessive application of N-surplus is prevented nowadays, the legacy from the last decades will impact the water quality also in the near future. Overall our study underlines the need for long-term management approaches to achieve the goals concerning water quality and to handle global eutrophication problems.

## REFERENCES:

* Galloway J.N., Dentener F.J., Capone D.G., Boyer E.W., Howarth R.W., Seitzinger S.P., Asner G.P., Cleveland C.C., Green P.A., Holland E.A., Karl D.M., Michaels A.F., Porter J.H., Townsend A.R., Vörösmarty C.J. 2004: Nitrogen cycles: Past, present, and future. Biogeochemistry, 70, 153–226.

* Hirsch R.M., De Cicco L.A., 2015: User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data (version 2.0, February 2015): U.S. Geological Survey Techniques and Methods book 4, chap. A10, 93 p., https://dx.doi.org/10.3133/tm4A10.

* Zacharias S.S, Bogena H., Samaniego L., Mauder M., Fuß R., Pütz Th., Frenzel M., Schwank M., Baessler C., Butterbach-Bahl K., Bens O., Borg E., Brauer A., Dietrich P., Hajnsek I., Helle G., Kiese R., Kunstmann H., Klotz S., Munch J.C., Papen H., Priesack E., Schmid H.P., Steinbrecher R., Rosenbaum U., Teutsch G., Vereecken H., 2011: A network of terrestrial environmental observatories in Germany. Vadose Zone J., 10, 955–973.
# DISSOLVED ORGANIC MATTER DELIVERED TO AQUATIC ECOSYSTEMS

YATES, C.A.¹, JOHNES, P.J.¹, AND THE NERC DOMAINE TEAM

¹University of Bristol

## INTRODUCTION

The pool of dissolved organic matter (DOM) in aquatic ecosystems is dynamic, consisting of a wide range of compounds with differing chemical structures and reactivities. As a result, DOM has a multifaceted role in the biogeochemistry of aquatic ecosystems. Additionally, in both freshwater and marine systems DOM acts as a major global carbon reservoir (Battin et al., 2008) and is responsible for the delivery of significant quantities of nitrogen (N) and phosphorus (P) to the aquatic environment. This paper presents an analysis of the impact key environmental variables (land cover, modelled soil C:N and stream water nutrient chemistry) have on the concentration and composition of DOM exported to the aquatic environment across a gradient of C, N and P enrichment.

## MATERIAL AND METHODS

Surface water samples were collected weekly, from 47 sampling locations covering a broad range of land use classifications across a gradient of nutrient enrichment over a one-year period (01/10/2015 - 30/09/2016). Samples were collected across a representative range of flow conditions in acid washed (5% HCl) HDPE bottles, filtered on site through 0.45 µm pre-washed cellulose nitrate filters and stored in the dark at 4°C during transport to the University of Bristol for analysis.

### Carbon, nitrogen and phosphorus analysis

Inorganic nutrient analyses were conducted on a Skalar++ multi-channel continuous flow autoanalyser set up for simultaneous determination of total oxidised nitrogen (nitrate as NO₃-N, plus nitrite as NO₂-N) hereafter referred to as TON, total ammonium (NH₃-N + NH₄-N) and soluble reactive phosphorus (SRP, measured as PO₄-P) analyses. For details see Yates et al., 2016. Organic and particulate N and P fractions were determined by difference following persulphate oxidation. Concentrations of dissolved organic carbon (DOC) were determined by coupled high temperature catalytic oxidation using a Shimadzu TOC-L analyser measured as non-purgable organic carbon following sample acidification. The mean of three to five injections of 150 µl where the coefficient of variance (C.V) for the replicate injections was < 2% is presented here.

### Absorbance and fluorescence measurements

Absorbance spectra were scanned over the wavelength range 200 - 800 nm at 1 nm intervals and allowed to warm to a constant temperature (20°C) prior to analysis. SUVA₂₅₄ values were calculated by dividing the decadic absorbance at 254 nm by DOC concentration (mg l⁻¹) (Weishaar et al., 2003) with all absorption data presented in this manuscript expressed as absorption coefficients: a(λ) = 2.303A(λ)/l. Where a (λ) is the absorption coefficient in units of reciprocal length (m⁻¹), A(λ) is raw absorbance and l is the cuvette pathlength (m). Spectral slope (S) values were calculated using a non-linear fit of an exponential function to the absorption spectrum over the range 350 - 400 nm.

$$a_{\lambda} = a_{\lambda_{ref}} e^{-S(\lambda - \lambda_{ref})}$$

Where a, is the absorption coefficient, λ = wavelength (nm) and λ<sub>ref</sub> is a reference wavelength (nm). In addition, fluorescence excitation emission matrices were measured (excitation: 240-450 nm and emission: 300-600 nm) and used to generate proxies for DOM composition (e.g. Fluorescence index; Cory and McKnight, 2005). Statistical analyses were conducted using SPSS® Statistical software, version 23 (IBM®), with all plots generated using SigmaPlot version, 13.0 (Systat software). Analysis of
absorbance and fluorescence spectra including calculation of spectral slopes and generation of optical indices were conducted using R statistical software. Catchment reach structures and land cover were determined using ArcGIS (Esri) Hydrology toolbox based upon digital elevation models and land cover mapping (LCM 2007). Soil C:N estimates were generated using data provided by the Centre for Ecology and Hydrology (UK) following work conducted by Emmett et al., (2008).

## RESULTS AND DISCUSSION

Mean total N and P concentrations ranged between 0.35 - 9.12 mg N l⁻¹ and <10 - 380 µg P l⁻¹ across all sites. Dissolved organic nitrogen (DON) and phosphorus (DOP) concentrations (0.25 - 1.25 mg N l⁻¹ and <5 - 46 µg P l⁻¹) decreased as a % of TN and TP along a gradient of total nutrient concentration (92% - <10% and 70% - <10% respectively) while increasing in absolute concentrations. DOC ranged from 0.77 - 26.2 mg C l⁻¹ with the highest concentrations observed in catchments dominated by high % bog land cover, and the lowest concentrations observed in arable dominated, groundwater fed systems (Figure 1a). Instream DOC:DON ratio was shown to be a good predictor of SUVA₂₅₄ a common proxy for DOM aromatic content in aquatic systems (Weishaar et al., 2003) across a gradient of increasing modelled soil C:N (Figure 1b).

<figure><img src="image_26.png" /><figcaption>Figure 1. (a) Correlation bi-plot PCA showing sites (numbers) relative to key environmental variables and (b) relationship between instream DOC:DON and SUVA₂₅₄ across a gradient of soil C:N (mean values ± SEM).</figcaption></figure>

## CONCLUSION

Overall, differences observed across this gradient of land cover and nutrient enrichment status demonstrate the importance of landscape scale characteristics in the delivery of DOM to aquatic systems. Soil C:N plays an important role in the delivery of DOM to aquatic systems and impacts on the quality and composition of this material, which in turn will have implications for instream communities, catchment C, N and P budgets and efforts to reduce nutrient loading to freshwaters.

**Acknowledgements:** This work was carried out under the DOMAINE Project (NERC Large Grant NE/K010689/1).

## REFERENCES

- Battin TJ, Kaplan LA, Findlay S, Hopkinson CS, Marti E, Packman AI, Newbold D, Sabater F. (2008). Biophysical controls on organic carbon fluxes in fluvial networks. Nature Geoscience 2008; 1: 95-100.

- Cory RM, McKnight DM. Fluorescence spectroscopy reveals ubiquitous presence of oxidised and reduced quinones in dissolved organic matter. Environmental Science & Technology 2005; 39: 8142-8149.

- Emmett BA, Frogbrook ZL, Chamberlain PM, Griffiths R, Pickup R, Poskitt J, Reynolds B, Rowe E,
Rowland P, Spurgeon D, Wilson J, Wood CM. (2008). Countryside Survey Technical Report No.03/07: Soils Manual. Centre for Ecology & Hydrology.

Weishaar JL, Aiken GR, Bergamaschi BA, Fram MS, Fujii R, Mopper K. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Env. Sci & Tech 2003; 37: 4702-4708.

Yates CA, Johnes PJ, Spencer RGM. Assessing the drivers of dissolved organic matter export from two contrasting lowland catchments, UK. Science of the Total Environment, 2016; 569-570, 1330-1340.
# EXPLORING THE SPATIAL VARIABILITY OF N₂O EMISSIONS FROM A GRAZED UPLAND AND LOWLAND SITE

CARDENAS, L.M.<fnref n="1" />, CHARTERIS, A.F.<fnref n="1" />, HARRIS, P.<fnref n="1" />, MARSDEN, K.A.², HARRIS, I.², GUO, Z.<fnref n="1" />, BEAUMONT, D.A.<fnref n="1" />, TAYLOR, H.<fnref n="1" />, SANFRATELLO, G.²,³, JONES, D.L.², WHELAN, M.J.⁴, CHADWICK, D.R.²

## INTRODUCTION

Almost 25% of the Earth's land area is used for livestock grazing (Klein Goldewijk et al., 2017) and these ecosystems play an important role in global biogeochemical cycling. In the UK, grazing land occupies over 12 Mha (ONS, 2016), spanning improved lowland and unimproved upland pastures. These systems are managed to varying extents, under contrasting environmental and climatic conditions, with diverse forages, and result in differing degrees of environmental impact. The agricultural sector was responsible for 69% of total UK nitrous oxide (N₂O) emissions in 2015, produced mainly by microbial nitrogen (N) cycling in soils and the management of livestock manures (Brown et al., 2017). Improved understanding of the spatial and temporal variability of N₂O emissions from different grasslands will assist with the development of better aggregated emission estimates for different agro-climatic zones and management strategies. In this study, we explore spatial patterns of N₂O emission with respect to soil and topographic characteristics of contrasting upland and lowland sheep-grazed pastures.

## MATERIAL AND METHODS

### Sites, sampling and sample analyses

Gas and soil sampling campaigns were conducted over two sites: an 11.5 ha semi-improved, upland pasture (240-340 m above sea level) at the Henfaes Research Station, Abergwyngregyn, North Wales, and a 1.78 ha improved, lowland permanent pasture on the North Wyke Farm Platform, Rothamsted Research, Devon. At the upland site, 112 sampling points were identified on a regular 30 m grid. At the lowland site, a 25 m grid combined with an offset 15 m grid was used to give 99 sampling points, where the offset was chosen to capture small-scale spatial variation. Soil headspace sampling was conducted manually using airtight chambers, subsequently soil temperature was measured and soil samples (0-5 or 0-10 cm) collected from within the chamber area. The upland site has a strong east to west gradient, whilst the lowland site has slopes south to north. Soil headspace N₂O concentrations were determined by gas chromatography-electron capture detection (GC-ECD). Soil moisture content, bulk density, pH, electrical conductivity (EC), total carbon and N content (%TC and %TN), organic matter (OM) and extractable ammonium (NH₄⁺) and nitrate (NO₃⁻) concentrations were determined.

### Data processing and statistical analysis

In the first instance, the N₂O data were investigated for spatial dependence via variograms and, provided a reasonable spatial structure was observed, the N₂O data were kriged to provide a N₂O response surface. Second, N₂O linear regressions with and without spatial effects (in the error term) were assessed. The following N₂O predictors were investigated: soil %TC, %TN, C:N ratio, extractable NO₃⁻-N and NH₄⁺-N, and total inorganic N concentrations, pH, soil moisture, bulk density, percentage water filled pore space (% WFPS) and OM content; Eastings, Northings, aspect, slope, elevation, compound topographic index; and vegetation type (for upland site only); and inorganic fertilisation (for lowland site only). For instances of non-normality, the data were Box-Cox transformed.

## RESULTS AND DISCUSSION

<footnote n="1">¹ Rothamsted Research, North Wyke, Okehampton, Devon, EX20 2SB, UK; ² School of Environment, Natural Resources and Geography, Bangor University, Bangor, Gwynedd, LL57 2UW, UK; ³ Institute of Biological and Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DD, UK; ⁴ Department of Geography, University of Leicester, Leicester, LE1 7RH, UK.</footnote>
The variogram of N₂O emissions at the upland site showed little spatial structure (Fig. 1a). This suggested that either the N₂O fluxes were randomly distributed or the sample design resolution was too coarse, (i.e. N₂O spatial dependencies may occur at scales below 30 m). Given the ‘flat’ variogram and high nugget variance, data were not kriged (Fig. 1c). High N₂O fluxes were only observed at very few sampling points. Conversely, the N₂O variogram at the lowland site displayed clear spatial structure (Fig. 1b), so kriging was conducted (Fig. 1d). Nitrous oxide fluxes were highest in an east-west band across the lowland field. In both cases, the results of the regression analyses indicated significant soil predictors whilst topographic predictors were not significant. For the upland site, soil %TC, %TN, NO₃⁻-N, and vegetation type were significant; and for the lowland site, soil moisture was the only significant predictor (p < 0.05). The predictive fit of the upland regression was stronger (R² = 0.60) than that found for the lowland (R² = 0.32). The importance of spatial effects was captured by a significant lowering of the Akaike Information Criterion (AIC) from a regression without spatial effects to one with. Only the lowland site benefitted from spatial regression fit in this respect, as AIC reduced by 5 units.

<figure><img src="image_27.png" /><figcaption>Figure 1. Empirical and modelled variograms: a) upland and b) lowland, and maps: c) upland and d) lowland of N₂O.</figcaption></figure>

CONCLUSION

Our results suggest that N₂O fluxes are controlled by different factors at each site, but work is on-going to better characterise the nature of the interactions amongst these factors and the true extent of spatial heterogeneity. This will enable a better informed, site-specific strategy for the imposition of mitigation measures to reduce N₂O emissions.

**Acknowledgements:** Thanks to J.A. Holmberg, N.G. Williams, S. Rowlands, L. Greenfield, M. McMahon, D. Arnott, J. Liang, Y. Zhang and X. Wang for help with sample collection and processing. This work was funded by NERC UK, grant award NE/M015351/1/, BBSRC grant award BBS/E/C/00010320 and the National Capability ‘The North Wyke Farm Platform grant award’ BB/J004308/1. Rothamsted Research is sponsored by BBSRC UK.
REFERENCES

Brown P., Broomfield M., Cardenas L., Choudrie S., Kilroy E., Jones L., Passant N., Thomson A., Wakeling D., 2017. UK Greenhouse Gas Inventory, 1990 to 2015: Annual Report for submission under the Framework Convention on Climate Change. Didcot, Ricardo-AEA.

Klein Goldewijk K., Beusen A., Doelman J., Stehfest E., 2017. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data, 9, 927-953.

Office for National Statistics, 2016. UK Natural Capital: Ecosystem accounts for farmland (Experimental Statistics)
# SPATIALLY DIFFERENTIATED SCENARIOS TO REDUCE AGRICULTURAL NITROGEN EMISSIONS

HASHEMI F.¹, OLESEN J.E.¹, DALGAARD T.¹, BØRGESEN C.D.¹

¹ Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark

## INTRODUCTION

Denmark is one of the largest contributors of agricultural nitrogen (N) discharges to the Baltic Sea measured in terms of N-load per hectare. Many N mitigation measures have already been implemented in Denmark since the 1980s, but this has been insufficient to meet the environmental objectives without adversely affecting agricultural production. To this end, a combination of innovative methods to reduce discharges to recipient waters is needed. One such innovative strategy is spatially differentiated N mitigation in agriculture to replace the currently uniform regulation in Denmark and to exploit the fact that there is significant spatial variation in natural groundwater and surface water N-reduction (removal by biogeochemical processes or sedimentation).

## MATERIAL AND METHODS

A review of 130 published articles was carried out with scenario-based modelling of methods for reducing the nutrient load to the aquatic environment (Hashemi et al., 2016). Based on this overview, new methods developed for scenario analysis in two Danish catchments (Norsminde and Odense) based on spatially differentiated aspects of groundwater N-reduction considering spatial constraints (farm boundary and soil type) and two different scales for N-reduction maps (Hashemi et al., 2018a). Ten scenarios were defined using cover crops within the crop rotation, set-aside on high N-load areas and N-leaching relocation based on spatial variation in N-reduction to reach N-load reduction targets of 20% or 40%. Furthermore, the potential of farm-scale measures versus landscape measures for reducing N-loads was investigated in the Odense catchment through a combination of farm and landscape measures to reach the required N-load reduction target of 38% (Hashemi et al., 2018c). This resulted in 17 scenarios by reducing N-leaching at farm (changing to crop rotation and application of cover crops) and landscape scales (set-aside application on high N-load areas) and enhancing N-reduction at landscape scale (wetlands). Scenario analyses for were performed using a map-based N-load model to achieve N-load reduction targets with least effect on agricultural production. Using information on variation in groundwater N-reduction maps for construction of spatially differentiated scenarios in the Norsminde catchment provided a basis for exploring the uncertainty in the estimated results (Hashemi et al., 2018b). To reduce the uncertainty in the estimated N-load reductions, three different methods for targeting the set-aside were developed. These methods included application of set-aside based on each individual N-reduction map compared to a mean of 15 randomly chosen N-reduction maps, using the spatial frequency of high N-load and using the spatial frequency of low N-reduction. The efficiency of using different methods was compared in terms of set-aside area required for a 50 or 80% probability of achieving the 20% N-load reduction.

## RESULTS AND DISCUSSION

The scenario analysis showed that the highest reduction in catchment N-load and in set-aside area were achieved where there was the least constraint for N-leaching relocation. The greatest reductions in N-load and set-aside for the spatially differentiated method were achieved using the high-resolution rather than the low-resolution N-reduction maps. The results also showed that a combination of land use, soil type, spatial variation in N-reduction and cropping conditions within each catchment affect the reduction in excessive N-loadings and required set-aside area. Using a combination of measures showed that it was possible to achieve the N-load reduction target without need for set-aside. The potential gain of farm-scale measures was affected by farm type, soil type and the N-reductions possible, while the feasibility of using wetlands to reduce N-load depends on the availability of landscape features that can be used for the establishment of wetlands. In relation to uncertainty
analysis, the results showed that using ensembles of N-reduction maps rather than individual N-reduction maps could improve the efficiency of spatially differentiated strategies. Using a frequency map of high N-load areas compared to set-aside maps based on average N-reduction map and a spatial frequency of low N-reduction is more effective in terms of N-load and set-aside reduction.

## CONCLUSION

It was found that spatially differentiated strategies for N reduction and using combination of measures can significantly reduce the N load with low impact on agricultural production, and this potential gain varies considerably between catchments, depending on spatial variation in groundwater N reduction and land use. However, the potential benefit of the N-load reduction strategies resulting from a combination of measures at both farm and landscape scale is limited by farm type and the potential area available for the application of measures. In addition, it will be difficult to exploit the full potential in practice due to the current agricultural structure and the uncertainty associated with higher resolution of groundwater N-reduction maps. Despite the higher uncertainty of estimated N-load reductions based on fine-scale compared to coarse-scale N-reduction maps, we suggest using grid-scale N-reduction maps for spatially differentiated strategies since the benefits in terms of the smaller set-aside area required at this scale outweighs the higher uncertainty. Developing new methods for reducing the uncertainty in the estimated results of N-load reductions showed the benefit of using a frequency of high N-load areas for targeting set-aside compared to the other two methods.

**Acknowledgements:** This work was carried out in the BONUS SOILS2SEA project (www.Soils2Sea.eu), which received funding from BONUS (Art 185), funded jointly by the EU and Innovation Fund Denmark, The Swedish Environmental Protection Agency, The Polish National Centre for Research and Development, The German Ministry for Education and Research, and The Russian Foundation for Basic Research (RFBR). This study was further supported by The NitroPortugal Project and the Innovation Fund Denmark (contract number 0603-00517B) through the dNmark Research Alliance (http://dnmark.org) supported by the Danish Council for Strategic Research (Ref. 12-132421).

## REFERENCES

- Review of scenario analyses to reduce agricultural nitrogen and phosphorus loading to the aquatic environment. Hashemi, F., Olesen, J. E., Dalgaard, T., & Børgesen, C. D. Science of the Total Environment, 2016. 573, 608-626.

- Spatially differentiated strategies for reducing nitrate loads from agriculture in two Danish catchments. Hashemi, F., Olesen, J. E., Hansen, A. L., Børgesen, C. D., & Dalgaard, T. Journal of environmental management, 2018a. 208, 77-91.

- Reducing uncertainty of estimated nitrogen load reductions to aquatic systems through spatially targeting agricultural mitigation measures using groundwater nitrogen reduction. Hashemi, F., Olesen, J. E., Jabloun, M., & Hansen, A. L. Journal of environmental management special Issues, 2018b.218, 451-464.

- Potential benefits of farm scale measures versus landscape measures for reducing nitrate loads in a Danish catchment. Hashemi, F., Olesen, J. E., Børgesen, C. D., Tornbjerg, H., Thodsen, H., & Dalgaard, T. Science of the Total Environment, 2018c.637-638, 318-335.
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes – France

Session I: N cycle in landscapes - Highlighted posters
# BETTER UNDERSTANDING NITRATE SOURCES AND SINKS IN AGRICULTURAL WATERSHEDS

XIA, YONGQIU<fnref n="1" />, WELLER DONALD E<fnref n="2" />, YAN XIAOYUAN<fnref n="1" />

## INTRODUCTION

Nitrate sources and sinks in agricultural watersheds are poorly understood because of the complexity of N pathways. Human activities have greatly increased nitrogen loading to surface waters resulting from fertiliser human and animal waste applications to agricultural land, and burning of fossil fuels. With the increasing amounts of N loading, biotic N demand can become exceeded, potentially resulting in excess N delivered to surface waters from land-based sources in the watershed. The resulting N exports from aquatic systems is poorly quantified as terrestrial and instream retention can temporary or permanently reduce downstream transport of nitrogen through physical, biological, or chemical processes.

The export coefficient model is an attractive tool due to its flexibility to incorporate processes and minimum data requirements. However, this method does not account for the extensive variability in space and time because of the great variability of land characteristics, weather, and management practices. This weakness potentially results in two most important uncertainties: structural and parameter uncertainty. Structural uncertainty is defined here as the difference between the true outputs and the model output using lumped and simplified representation of coefficient model. Parameter uncertainty is difference between true parameters and output parameters, reflecting the inability to specify exact values of model parameters due to finite length and uncertainties in the calibration data, imperfect process understanding, model approximation, etc., and showing high sensitivity in space and time.

## MATERIAL AND METHODS

We selected the Qinhuai River watersheds as a case study because they have different proportions of agricultural and non-agricultural land, varied hydraulic residence time, no sewage outfalls, reservoirs, or lakes. A mixed linear model was first used to test the effects of land use and rainfall on water quality. We included the percentages of the impervious surface, dry land, paddy fields, and water categories because the former three are potential sources of N, while the latter is a potential sink for N.

To better quantify nitrate sources and sinks in agricultural watersheds, three improved candidate formulations were developed and compared using a flexible Bayesian framework to explore and quantify the uncertainties of model parameters, to determine their correlation structure, and to use the calibrated model to predict stream nitrate concentration dynamics. The first statistical formulation explicitly assumes that the simulation errors mainly come from the imperfect model structure, and temporal and spatial viability terms should be included to account for the discrepancy between the simulation and the observation. The second statistical formulation explicitly recognizes that the simulation errors mainly come from the static parameters, and parameters should vary temporally and spatially based on the prior distribution. The third formulation is identical to the assumption of model 3, but now allows the parameter distributions to vary temporally and spatially with the input conditions.

## RESULTS AND DISCUSSION

A study over three successive years was conducted to examine the spatial and temporal variations of nutrient concentrations in a paddy agricultural-urban gradient watershed in southeast China. The

<footnote n="1">¹ Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China;</footnote>

<footnote n="2">² Smithsonian Environmental Research Center, P.O. Box 28, 647 Contees Wharf Road, Edgewater, MD 21037-0028, USA</footnote>
results showed that monthly average nutrient concentrations in the river were much higher in the dry seasons of spring and winter than those in the rainy seasons of summer and autumn. The increases in nutrient concentrations with the increase in the proportion of impervious surfaces in the watershed probably results from anthropogenic inputs of fertilizer. We constructed a statistical model to link the variation of nutrient concentrations to land uses and rainfall in the paddy agricultural-urban gradient watershed, and the model predictions from leave-one-out cross-validation provided satisfactory estimates of the contribution of nutrients from different land cover types for the study of the watershed. In terms of the average nitrate concentration of 2.83 mg N L⁻¹ in the studied river, impervious land contributed the largest amount at 1.45 mg N L⁻¹, followed by paddies (0.58 mg N L⁻¹), while water bodies removed 0.89 mg N L⁻¹.

Model skill is assessed by examining the plots of observed versus predicted values, and determining the percent of the observation variance explained by the predictions. The original mixed linear model has poor explanations of observation with an R² value of 61%. Strategy 1 explains the majority of the variability (R²=0.77), and the 95% prediction range captures most of the observations. However, strategy 2 and strategy 3 results were strongly correlated with the observation data, with R² value of 0.97 and 0.99, respectively. Observations fall in all the intervals of the 95% prediction. Compared to strategy 2, The 95% prediction range of strategy 3 is much narrower and the plot of strategy 3 is more close to 1:1 line (with coefficient of 0.9501 in strategy 3 vs. 0.8672 in strategy 2) and has smaller interception. Therefore, the preferred strategy 3 is used to present spatial and temporal series of posterior values of river nitrate sources and sinks (Fig.1).

<figure><img src="image_28.png" /><figcaption>Fig. 1 Spatial and temporal variations of the components of predicted nitrate sources and sinks</figcaption></figure>

CONCLUSION

To better quantify nitrate sources and sinks in agricultural watersheds, we examined three popular methodologies of export coefficient models based on a Bayesian hierarchical framework along with Markov Chain Monte Carlo (MCMC) simulations. Strategy 1 improved the model predictive capacity by the addition of structure and unstructured error terms, but the resulting structural model errors do not give clear ideas about the dynamic parameter values. While strategy 2 included the variability of parameters, it showed low efficiency and much of the error variability. The inclusion of time and space varying parameters in strategy 3 is found to have high predictive skill, realistic uncertainty quantification, and reasonable complexity, provides a promising framework for quantifying nitrate sources and sinks. By parameterizing the spatial and temporal variability of coefficients, we believe that the framework of strategy 3 can meaningfully assist watershed nutrient management.
# IDENTIFICATION OF KEY ATMOSPHERIC N SOURCES AT DESIGNATED SITES - A CASE STUDY FOR SPECIAL AREAS OF CONSERVATION IN NORTHERN IRELAND

CARNELL, E.J.¹, DORE, A.J.¹, MISSELBROOK, T.H.², SUTTON M.A.¹ & DRAGOSITS, U.¹

¹Centre for Ecology & Hydrology, Edinburgh, UK; 2 Rothamsted Research, North Wyke, UK

## INTRODUCTION

The effects of atmospheric nitrogen (N) deposition are evident in terrestrial ecosystems worldwide, with eutrophication and acidification leading to significant changes in species composition. In the UK, 62% of sensitive habitats are estimated to receive N input in excess of critical loads (2012-14 data). Substantial reductions in N deposition from nitrogen oxides (NOx) emissions have been achieved in recent decades. By contrast, UK ammonia (NH3) emissions from agriculture have not decreased substantially and are typically highly spatially variable, potentially making efficient mitigation more challenging. Current exceedance levels mean that the UK will struggle to meet national and international biodiversity commitments, in particular for sites designated under the Habitats Directive. The work here presents an approach for identifying the main atmospheric N sources contributing to elevated NH3 concentrations and N deposition at sensitive sites (adapted from Dragosits et al. 2015 and Carnell et al. 2017). The approach has been applied to all Special Areas of Conservation (SACs) in Northern Ireland (NI), but the approach is generally applicable to other regions and natural habitat designations. Ammonia concentrations throughout NI are estimated to exceed >1 µg m⁻³ and therefore exceed the critical level for lichens, mosses and other lower plants. Approximately 50% of the area of NI has NH3 concentrations above 3 µg m⁻³, which is in exceedance of the critical level for higher-plants. In addition to the national scale analysis, five sites were also assessed in more detail using a local database to supplement the national assessment.

## MATERIAL AND METHODS

The main emission sources contributing to N deposition at each SAC were estimated using modelled source attribution data. Source attribution data are derived by performing multiple model runs of an atmospheric transport and deposition model, with each source type removed in turn. N deposition attributed to individual emission source categories (such as agriculture, road transport etc.) or individual large point sources (such as power stations) can then be calculated as a proportion of total deposition to each model grid square. Using these data it is possible to estimate the main contributors to the total N deposition at each designated site.

Agricultural NH3 emissions were also estimated for a 2 km area surrounding each of the designated sites, using 2015 agricultural census data. Buffer zones around SACs were created to estimate the agricultural NH3 emission density for the immediate area surrounding the SACs, indicating the average intensity of the N-emitting agricultural activities, and to determine all major agricultural sectors contributing to emissions within these zones. UK average NH3 emission factors (EFs) from the agricultural emission inventory (Misselbrook et al., 2015) were applied at the holding level data to estimate emission densities surrounding each SAC.

## RESULTS AND DISCUSSION

The analysis of the UK source attribution dataset for SACs in Northern Ireland shows that diffuse agricultural activities pose a significant threat to most sites in Northern Ireland. On average, agriculture contributes ~59% of total N deposition (to low-growing semi-natural features) received by SACs, and contributes ~90% of the N deposition from locally depositing species. A substantial proportion of SACs in NI are estimated to be subject to high concentrations of agricultural NH3 from emission sources close to their site boundary. The dominant emission source of NH3 (i.e. the largest contribution to estimated NH3 emissions within the 2 km (or larger) buffer zone) for most SACs is cattle farming (~73%), and in
particular activities associated with beef farming (Figure 1). The refined methodology applied to the five example sites enabled a reliable distinction of the main threats from atmospheric N (e.g. diffuse agriculture, point sources, roads, etc.) to sensitive habitats and species.

<figure><img src="image_29.png" /><figcaption>**Figure 1** — Estimated contributions from main agricultural sectors to emissions in areas immediately surrounding SACs in Northern Ireland, which have terrestrial designated features (56 sites). The size of each pie chart is proportional to the estimated local NH₃ emission density surrounding each site (< 2 km where data licensing conditions are met, and up to 5 km in extensive agricultural regions). The category 'other' refers to fertiliser emissions and all livestock sectors which are disclosive (i.e. data points from less than five agricultural holdings) or a category that contributes less than 5% of the total.</figcaption></figure>

## CONCLUSION

The biggest threat to Northern Ireland's SACs is estimated to come from diffuse agriculture and, in particular, activities associated with cattle farming. Sites with high agricultural emission densities of > 30 kg N ha⁻¹ yr⁻¹ across the surrounding area are predominantly affected by emissions from dairy, beef and poultry farming. The more detailed analysis applied to the five example sites enabled a relatively clear assessment of whether local mitigation measures were likely to be worth considering for targeted mitigation at a site, and/or whether wider regional or national/international efforts would be required to benefit the site.

**Acknowledgements:** This work was financially supported by the Department of Agriculture, Environment and Rural Affairs (DAERA), Northern Ireland.

## REFERENCES

Carnell E.J., Misselbrook T.H., Dore A.J., Sutton M.A. and Dragosits U. (2017) A methodology to link national and local information for spatial targeting of ammonia mitigation measures. Atmospheric Environment, 163, pp 195-204. doi: 10.1016/j.atmosenv.2017.05.051

Dragosits U., Carnell E.J., Jones L., Rowe E., Hall J.R., Dise N., Dore A.J., Tomlinson S.J., Sheppard L., Reis S., Bealey W., Braban C.F., Misselbrook T.H., Stevens C., O'Shea L., Smyntek P. and Sutton M.A. (2015) Identification of Potential “Remedies” for Air Pollution (nitrogen) Impacts on Designated Sites (R.A.P.I.D.S.). Defra project AQ0834. 59pp (main report) + 11 appendices

Misselbrook T.H., Gilhespy S.L., Cardenas L.M., Williams J. and Dragosits U. (2015) Inventory of Ammonia Emissions from UK Agriculture 2014. Inventory submission report. Defra Contract SCF0102. Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB. 38 pp
# FEASIBILITY OF USING INDUSTRIAL ANION EXCHANGE RESIN TO REMOVE NITRATE FROM TILE WATER

WOLF, K.A.<fnref n="1" />, GUPTA, S.C.²

## INTRODUCTION

The northern Gulf of Mexico is the largest body of hypoxic water (the Dead Zone) in the western Atlantic Ocean. Scientists believe the Dead Zone is caused by nitrate (NO₃-N) discharged by the Mississippi River into the Gulf of Mexico. The Dead Zone has been ever-increasing since its monitoring began in 1970's. In 2017, it span an area of 22,730 km². The presence of high nitrate levels in the Gulf waters causes increased algal production, which on decay leads to low dissolved oxygen (<2 mg N L⁻<fnref n="1" />) in the water at the bottom of the Gulf (Rabalais et al., 2001). Much of the NO₃-N in the Mississippi River originate from agricultural lands in the Midwestern United States. The subsurface drain tiles are the main pathway for nitrate losses, which farmers have installed to carry excess water from their lands, especially during spring when soils are excessively wet for field operations and optimum crop growth (Goolsby et al. 2001).

Recently, large efforts have been underway to reduce NO₃-N losses from agricultural lands to the US midwestern rivers and then to the Mississippi River. These efforts include recommending optium timing and amount of N fertilizer applications as well as the use of remediation technologies such as woodchip bioreactors, saturated buffer strips, controlled drainage, and cover crops. However, there has been a varied success especially in the use of above technologies because of the requirement for high residence time of drainage water in these set-ups to mitigate NO₃-N. The objectives of this research was to evaluate the feasibility of using an industrial anion-exchange resin (trapping selected ions while simultaneously releasing another ion) to remediate NO₃-N from tile drain water at the edge of agricultural fields. Remediation set-up was similar to water softener set-up used in most households in the United States. The advantage of using an industrial anion-exchange resin was the instantaneous removal of NO₃-N, as well as reusability of the resin thus making the technology more cost effective. For regeneration of the resin, we also envisioned the use of potash (KCl) instead of common salt (NaCl) such that the waste (KNO₃) can be recycled back to land as fertilizer.

## MATERIAL AND METHODS

The testing of anion-resin to remediate NO₃-N in tile drain water from agricultural lands involved a series of field and laboratory experiments in 2015 and 2016 (Wolf, 2017). In 2015, the field set-up involved passing tile drain water through two PVC columns filled with 11 liters of two different types of resins at the edge of a 0.5 ha soybean field near Vernon Center, Minnesota (Fig. 1a). Two resins from separate manufacturers were tested to compare each resins' efficiency. In 2016, the field testing was with one resin and two tile waters from two different corn fields where hog manure had been applied (Fig. 1b, c). Starting each spring, a small portion of the tile drain water was passed through the resin columns and samples were taken once or twice a day from both the tile and the column outlets. These samples were then analyzed for NO₃-N concentrations to estimate NO₃-N retention. Each week the field columns were exchanged with columns that had been recharged with a 13% KCl solution.

<footnote n="1">¹ Department of Climate and Water, Institute of Agroecology, Aarhus University, Denmark; ²University of Minnesota, U.S.A.</footnote>
<figure><img src="image_30.png" /></figure>

Figure 1a-c: Field set-ups for testing of anion exchange resin to remediate nitrate in tile water. (a) 2015 set-up with two columns containing two different resins and the tile water from a soybean field, and (b) & (c) 2016 set-ups with both columns containing resin #1 but remediating tile water from two different hog manure applied corn fields.

# RESULTS AND DISCUSSION

## 2015 Study

In 2015, resin testing was conducted for a total of 50 days for both types of resin. NO₃-N concentration in tile drain water generally corresponded to about 15 mg N L⁻¹ during the study period. Comparatively, NO₃-N concentrations coming from the resin were around 2-3 mg N L⁻¹ right after recharged columns were installed. During the test period, a total of 664 g of NO₃-N passed through resin column #1 and 313 g was retained. Comparatively, 1,031 g of NO₃-N passed through the resin column #2 and 271 g of NO₃-N was retained by the resin. This corresponded to NO₃-N retention efficiency of 46% for resin #1 and 26% for resin #2. The difference in the amount of NO₃-N passing through different resin columns is likely due to differences in flow rate between the resins.

## 2016 Study

In 2016, only the resin #1 was tested since it was more efficient than resin #2. Differences in the resin were resin #1 had a total exchange capacity of 18.2 mg L⁻¹ whereas resin #2 had a total exchange capacity of 11.89 mg L⁻¹. Resin testing was done for a total of 55 days at location #1 (L1) and 31 days at location #2 (L2). Nitrate leaching behavior through the resin was similar to that observed in 2015. On average, NO₃-N concentrations in the tile water varied around 45 mg N L⁻¹ at L1 and around 30 mg N L⁻¹ at L2. The higher NO₃-N concentration at these locations, relative to 2015 location, was likely due to the application of hog manure at these sites. Over the period of 55 days, a total of 1,125 g of NO₃-N pass through the resin at L1 out of which 593 g (34.5% retention) was retained. Comparatively at L2, a total of 608 g of NO₃-N passed through the resin over 31 days and 48 g (7.3% retention) was retained. The difference in NO₃-N retention efficiency between the two sites is likely due to differences in recharging of the columns as well as due to differences in sulfate replacing the adsorbed nitrate on the resin (Wolf, 2017).

# CONCLUSION

Overall, the resin was very efficient at removing NO₃-N from tile water in the field because of its instantaneous retention properties. Results showed no presence of heavy metals in the leachate thus a strong potential for its recycling as KNO₃ fertilizer back on the land. Although very effective, we also encountered some challenges in field testing. These include (1) possibility of resin contamination from sediments in the tile drain, (2) interference of other anions such as sulfate, (3) high volume of tile drain water compared to the retention capacity of the resin in small columns, and (4) labor-intensive nature of this technology and our set-ups (Wolf, 2017). In spite of the above challenges, the results of this study do demonstrate the potential use of this resin in remediating water in individual homes in rural settings where groundwater may be high in NO₃-N concentrations. In relation to Danish agriculture, this method could prove as an efficient alternative to removing NO₃-N from drainage water.
REFERENCES

Goolsby, D.A., W. A. Battaglin, B.T. Aulenbach, and R.P. Hooper. 2001. Nitrogen input to the Gulf of Mexico. J. Environ. Qual. 30:329-336.

Rabalais, Nancy N., R. Eugene Turner, and William J. Wiseman. 2001. "Hypoxia in the Gulf of Mexico." J. Environ. Qual. 30(2):320-329.

Wolf, K.A. 2017. Climate and land use change impacts on N-loads in Iowa Rivers and remediation of tile water with an anion-exchange resin. M.S. Thesis, University of Minnesota, St. Paul, MN, USA, pp 144.
# CLIMATE INDICATORS EXPLAINING THE VARIABILITY OF CARBON, NITROGEN AND PHOSPHORUS EMISSIONS TO STREAMS IN HEADWATER AGRICULTURAL CATCHMENT

STROHMENGER, L.¹, FOVET, O.¹, GASCUEL-ODOUX, C¹.

¹ UMR SAS, INRA, AGROCAMPUS OUEST, 35000 Rennes, France

## INTRODUCTION

Agriculture intensification during the 20th century led to the increase of nutrient inputs in agricultural areas. Water quality deterioration because of these fertilization practices has been acknowledged worldwide (Sun et al. 2018) with increased loads of e.g. nitrate, phosphorus or organic carbon. When used in excess, these elements accumulate within the soil and/or groundwater and are eventually exported at the catchment outlet. Local climate conditions and hydrology control hydro-chemical processes and thus need investigation in order to understand the temporal responses of agro-ecosystem to agricultural activities.

The analysis of long-term hydrological and chemical data series allowed to classify several elements in agricultural headwater catchments depending on their source, based on their 10 years mean concentration and annual seasonality (Aubert et al. 2013; Aubert, Gascuel-Odoux, and Merot 2013): i) anthropogenic origin and storage in upland groundwater (nitrate NO₃⁻ and chloride Cl⁻), ii) biogeochemical processes in the wetland and downslope (dissolved organic and inorganic carbon - DOC, DIC- and phosphorus -P-) (Humbert et al. 2015; Dupas et al. 2015).

Climate conditions affect production and/or accumulation of DOC, DIC and P in summer and their export in winter (Humbert et al. 2015). Climate also affects the connectivity between hillslope groundwater - which is the main source of NO₃⁻- and stream during the wet period. The investigation of DOC, DIC and P concentrations during flood events suggests a progressive depleting of stock throughout the hydrological year (Humbert et al. 2015; Dupas et al. 2015).

Questions remain on how climate affects the carbon (C), nitrogen (N) and P emissions relatively to each other. This study aims to assess how climate controls, at different temporal scales (seasonal and inter-annual), the nutrients exported to surface waters in headwater catchments. It focuses on C, N, P concentrations, the synchrony/asynchrony of their cycles and their ratio dynamics with respect to climate variability.

## MATERIAL AND METHODS

### Study site and data collection

The study site is the Kervidy-Naizin (Brittany, France) catchment, part of the AgrHyS environmental research observatory (http://www6.inra.fr/ore_agrhys_eng) since 2002. It is a 5km² headwater catchment with oceanic temperate climate. Land use is mostly agricultural: dominated by maize, cereal crops and grassland.

Data were collected at the minute time step for outlet flow, 15 minutes for water table level in 10 locations along the hillslope, and every hour for the rainfall and air temperature. Daily samples provide the base flow concentrations of NO₃⁻-DOC and phosphate (PO₄³⁻) completed by analysis with higher frequency during some flood events.

### Method

The first step of this work consists in the description of each year with selected indices, for characterizing climate, hydrology and water quality dynamics. These indices describe the water year with precipitation amount and distribution, total runoff; the dry and wet period durations and
intensities (regarding mean temperature, cumulative rain, water table levels, flood occurrence, water quantity); and the water quality dynamics in particular relatively to variations in discharge or catchment storage.

The cross-comparison of these key values with multivariate statistics (Principal Component Analysis, Heatmaps, Decision trees) is used to identify the relationships between contrasted years in terms of chemical signals with the associated climate conditions. This global analysis is then completed by bi-variate analyzes with the aim of identifying the nutrients production and mobilization mechanisms.

## RESULTS AND DISCUSSION

Many indicators can be derived from the long term data series and reflect the specific characteristics of each hydrologic year in term of climate, hydrology and chemistry. The major ones that influence water concentration dynamics are the cumulative rainfall, dry period duration, water table levels and the flood event occurrence.

Dry season characteristics (duration and intensity) impact the initial state of the sources of C and P while wet season characteristics (water table levels, water volume, flood events) show different relationships with the export depending on the element. These results highlight the crossed and possibly antagonist effects of climate on nutrient emissions because it controls: i) biogeochemical production/accumulation mechanisms via soil saturation dynamics ii) transfer mechanisms by connecting or not different compartments and modifying their relative contributions.

The multi and bi-variate analysis enabled the formulation of assumptions to build a coupled C-N-P conceptual catchment hydro-chemical model by establishing how each hydro-biogeochemical compartment should respond to hydro-climatic indices.

## REFERENCES

* Aubert, A. H., C. Gascuel-Odoux, G. Gruau, N. Akkal, M. Faucheux, Y. Fauvel, C. Grimaldi, Y. Hamon, A. Jaffrézic, M. Lecoz-Boutnik, J. Molénat, P. Petitjean, L. Ruiz, and P. Merot. 2013. 'Solute transport dynamics in small, shallow groundwater-dominated agricultural catchments: insights from a high-frequency, multisolute 10 yr-long monitoring study', *Hydrology and Earth System Sciences*, 17: 1379-91.

* Aubert, Alice Helene, Chantal Gascuel-Odoux, and Philippe Merot. 2013. 'Annual hysteresis of water quality: A method to analyse the effect of intra- and inter-annual climatic conditions', *Journal of Hydrology*, 478: 29-39.

* Dupas, R., G. Gruau, S. Gu, G. Humbert, A. Jaffrezic, and C. Gascuel-Odoux. 2015. 'Groundwater control of biogeochemical processes causing phosphorus release from riparian wetlands', *Water Res*, 84: 307-14.

* Humbert, G., A. Jaffrezic, O. Fovet, G. Gruau, and P. Durand. 2015. 'Dry-season length and runoff control annual variability in stream DOC dynamics in a small, shallow groundwater-dominated agricultural watershed', *Water Resources Research*, 51: 7860-77.

* Sun, M., Z. L. Huo, Y. X. Zheng, X. Q. Dai, S. Y. Feng, and X. M. Mao. 2018. 'Quantifying long-term responses of crop yield and nitrate leaching in an intensive farmland using agro-eco-environmental model', *Science of the Total Environment*, 613: 1003-12.
EXPOSURE TIME VS. RESIDENCE TIME IN UPSCALING NITRATE MASS LOSS IN THE HYPORHEIC ZONE

GILFEDDER, B.S.<fnref n="1" />,², DUREJKA, S.², THOMAS, Z.³, LE LAY, H.³, FREI, S.²

INTRODUCTION

Nitrate transport in streams and rivers is one of the primary vectors moving nitrate from terrestrial
source areas to sensitive coastal zones. Streams and rivers integrate the runoff and nitrate flux from
various land uses and thus give a broad picture of hydrological and biogeochemical coupling at the
catchment scale. However, rivers are not simply a transport vector, but a living ecosystems in their
own right, with a multitude of chemical, biological and hydrological processes occurring across scales
from centimetre (e.g. exchange with ripples, microbial microsites) to continental (whole basins,
Gomez-Velez et al., 2015). The hyporheic zone (HZ) is located at the interface between surface water
and the groundwater system and encompasses the active exchange between these two reservoirs. The
time that the surface waters spends in this interface is known as the residence time (RT). The HZ is
considered a biogeochemical 'hot-spot' as it receives chemical species from both the stream (e.g. DOC,
nitrate) and from the regional groundwater (e.g. reduced species such as Fe²⁺). This drives a multitude
of redox processes ranging from respiration to methane production. The time surface water spends in
the HZ that is conducive to reaction is known as the exposure time. In contrast to the RT, which is
wholly dependent on hydrological factors, the exposure time is dependent on both hydrology and the
biogeochemistry of the rivers subsurface. There has been a keen interest recently in the HZ due to its
potential to reduce nitrate loads and function as a natural filter for excess nitrate runoff from
agricultural areas. Some estimates have suggested that up to 25 % of nitrate can be removed in the HZ
(Kiel and Cardenas, 2014). The majority of experiments have been conducted on small scales (e.g. few
tens of meters) using e.g. tracer addition experiments. The means of upscaling nitrate mass loss to tens
of kilometres is difficult, and often based solely on numerical modelling (Gomez-Velez et al., 2015). It
is also often assumed that the exposure time equals the RT. However, it is well documented that nitrate
reduction first begins once oxygen levels are low enough for denitrification and thus previous efforts
may have overestimated the nitrate loss in the HZ.

In this communication we present methods to both upscale nitrate loss at catchment scales (using radon as a tracer) as well as calculate loss using both residence times and exposure times. We show clearly that the 'lag' in nitrate reduction due to oxygen in the upper parts of the HZ is critical in assessing the ability of streams to naturally reduce nitrate loads.

MATERIAL AND METHODS

Study site and sampling

Measurements were conducted at the Long Term Ecosystem Research Catchment (LTER) near the town of Pleine-Fougères, about 20 km from Rennes, France. The 2.5 km stream studied here drained a small agricultural sub-catchment of the LTER, known locally as *Vilqué*. It is surrounded by mostly agricultural land including dairy and low intensity crops. Fourteen 1L samples for radon and nitrate analysis were taken from the headwater areas to its confluence with *l'Hermitage* at intervals between 100 m and 500 m. Radon was measured in the field using a RAD7 radon in air detector, with each sample being purged for 5 minutes and then counted for 1 h. Nitrate was measured by ion chromatography.

Calculating nitrate mass loss and groundwater fluxes

<footnote n="1">¹ Limnological Research Station University of Bayreuth, Universitätsstrasse 30, 95447 Bayreuth, Germany, ² Department of Hydrology, Bayreuth Center of Ecology and Environmental Research (Bayceer) University of Bayreuth, Universitätsstrasse 30, 95447 Bayreuth, Germany, ³ UMR SAS, AGROCAMPUS OUEST, INRA, 35000 Rennes, France</footnote>
The groundwater flux, hyporheic depth and mean residence times in the HZ was calculated using the FINIFLUX model (Frei and Gilfedder, 2015). This is a 1D finite element mass-balance model that includes terms such as radon loss to the atmosphere, radioactive decay, groundwater discharge and the hyporheic parameters. The hyporheic parameters and groundwater discharge are fitting variables in the mass-balance. The newest version of FINIFLUX uses three different residence time distributions (RTDs, exponential, gamma and power law distributions) to represent storage in the HZ.

The nitrate loss is based on the UPERflux mass-balance equations for the HZ (Pittroff et al., 2017). In its most basic form the mass-balance encompasses the concentration of nitrate in the stream, water flux through the sediment from the stream, RTD and the kinetics for nitrate reduction in the sediments. It does not include the groundwater nitrate flux.

$$T_{NO3} = q_h \Delta x(C_{in} - C_{out})$$

(Eq. 1)

With $T_{NO3}$ is the nitrate loss, $q_h$ the water flux through the hyporheic sediments, $x$ is the reach length, and $C_{in}$ and $C_{out}$ are the concentrations of nitrate entering and leaving the HZ respectively. $C_{in}$ is measured in stream water, while $C_{out}$ is calculated by convolving $C_{in}$, reaction kinetics (first order, or Monod equation) and the RTD:

$$C_{out}(\tau) = \int_{0}^{\infty} C_{in} e^{-\lambda z} RTD dz$$

(Eq. 2)

The new model has been extended Eq 2 to include a factor $a$ which describes the RT of surface water in the oxic part of the HZ. This 'lag' essentially cuts away a part of the RTD, ranging from 0 to $a$ hours, and simulates explicitly the exposure time of nitrate to conditions suitable for reduction. We have varied $a$ from 0 to 100 h to capture a range of possible values, but based on kinetics of oxygen reduction $a$ is likely to range between 2-6 hours.

## RESULTS AND DISCUSSION

The radon results show clearly that there is a hot-spot for groundwater discharge to the Vilqué stream at around 500 m from the first sampling location, with activities increasing from 200 Bq m⁻³ to 15 kBq m⁻³. This was also observed in the field as spring entering the stream from the bank. The FINIFLUX model fitted the measured data very well (r=0.9), with a total groundwater discharge of 430 m³ d⁻¹ over 2.5 km. The mean residence time varied depending on the RTD, but in a rather narrow range from 2.3 to 2.48 hours. Over the length of the river measured nitrate concentrations decreased from 44 mg/l to 32 mg/l, due to a combination of dilution by groundwater and reduction in the HZ. The loss of nitrate attributed to reduction in the HZ using UPERflux with $a=0$, exponential RTD and first order kinetics was 4.8 kg d⁻¹. Implementing an $a$ with a value of 2 h reduced loss to 2 kg d⁻¹ due to the inhibitory effect of the oxic zone. To systematically test the influence of RT and $a$, we varied these parameters between published values for 1-3rd order streams. We found that the loss of nitrate is highly sensitive to both the RT and $a$, with only a small combination of values (RT 1-20 h, $a<2$) leading to significant nitrate loss (Figure 1). It is also clear that the nitrate loss is inversely related to residence time. Thus to maximise nitrate mass loss in stream networks a combination of short residence times and low values of $a$ are needed. This work shows that it is vital to consider the exposure of chemicals such as nitrate to conditions conducive to reaction in the HZ, rather than only relying on the residence time in the subsurface.
<figure><img src="image_31.png" /><figcaption>Figure 1: Nitrate mass loss in the hyporheic zone as a function of lag time (portion of RTD allocated to the oxic zone) and mean residence time.</figcaption></figure>

REFERENCES

Frei, S., Gilfedder, B.S., 2015. Technical Note: FINIFLUX an implicit Finite Element model for quantification of groundwater fluxes and hyporheic exchange in streams and rivers using Radon. Water Resources Research 51, 6776-6786.

Gomez-Velez, J.D., Harvey, J.W., Cardenas, M.B., Kiel, B., 2015. Denitrification in the Mississippi River network controlled by flow through river bedforms. Nature Geoscience 8, 941–945; doi:910.1038/ngeo2567.

Kiel, B., Cardenas, M.B., 2014. Lateral hyporheic exchange throughout the Mississippi River network. Nature Geoscience, 413–417; doi:410.1038/ngeo2157.

Pittroff, M., Frei, S., Gilfedder, B.S., 2017. Quantifying nitrate and oxygen reduction rates in the hyporheic zone using $^{222}$Rn to upscale biogeochemical turnover in rivers. Water Resources Research 53, 563-579; doi: 10.1002/2016WR018917.
# SEARCHING FOR THE CONTROL MECHANISMS OF NITROGEN REMOVAL IN A MEDITERRANEAN RIPARIAN FOREST

Poblador, S. <fnref n="1" />, Thomas Z. ², Rousseau-Gueutin, P. ³, Lupon A. ⁴, Sabaté S. <fnref n="1" />,⁵, Sabater, F. <fnref n="1" />,⁵

## INTRODUCTION

Riparian zones are often considered ecotone ecosystems between terrestrial and aquatic environments, playing a key role in controlling stream discharge and nutrient exchange. Its unique position within catchments allows a high water availability at the area and the establishment of tree species that support large transpiration rates. Moreover, riparian zones are well-known to be biogeochemical hot-spots that can reduce large amounts of nitrogen (N) loads arriving from hillslopes to the stream, mainly via denitrification and vegetation uptake. However, in arid and semiarid regions, where shallow organic soil layers get disconnected from the groundwater, changes in this biogeochemical control may arise. Mediterranean systems are a unique natural laboratory to understand the close link between riparian hydrology and biogeochemistry due to their marked temporal and spatial variations in soil water content, which can affect not only the stream hydrology, but also the microbial activity in the riparian soils. Here, we summarize results from different studies in order to examine the efficiency of plant uptake and denitrification on reducing N exports from Mediterranean catchments.

## MATERIAL AND METHODS

The study plot (30 m x 25 m) is a riparian forest located at the valley bottom of a forested headwater catchment (14.2 km², 500-1500m a.s.l.), NE Spain. The climate is sub-humid Mediterranean (Precipitation = 925 ± 151 mm; Temperature = 12.1 ± 2.5 °C). The plot exhibits a strong horizontal gradient in terms of groundwater level, soil water content, and riparian tree distribution (Table 1). Therefore, we defined the riparian area in three zones: near-stream, intermediate, and hillslope.

### Soil N processes

We evaluated the main soil microbial N processes (mineralization, nitrification, and denitrification) and how changes in soil properties can modify them over time and space (detailed information: Poblador et al., 2017).

### Vegetation water sources

We determined water sources of our riparian tree species by analysing isotopic composition (δ¹⁸O and δ²H) of trees' sap and the main water sources (shallow soil water, deep soil water, and groundwater). We estimated the most likely proportion of water taken up by plants from each water source using Bayesian mixing models following Barbeta et al. (2015).

### Water and N fluxes modelling

Following our denitrification capacity and vegetation water sources results, we used a 1D model (HYDRUS) to simulate water flow and N-NO₃⁻ transport in the vadose zone of the three riparian zones. We simulated the water and N-NO₃⁻ fluxes for the actual period (2012-2014) based on field data, as well as future scenarios (2015-2100) based on the IPCC projections for the Mediterranean zone (Poblador et al., submitted).

<footnote n="1">¹ Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Universitat de Barcelona. Av. Diagonal 643, 08028 Barcelona, Spain. ² UMR SAS, AGROCAMPUS OUEST, INRA, 35000 Rennes, France. ³ EHESP Rennes, Sorbonne Paris Cité, Paris, France. ⁴ Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU).</footnote>

<footnote>Skogsmarksgränd, 901 83 Umeå, Sweden. ⁵ Centre for Ecological Research and Forestry Applications (CREAF). Campus de Bellaterra Edifici C, 08193, Cerdanyola del Vallès, Spain.</footnote>
## RESULTS AND DISCUSSION

Findings obtained from soil experiments showed high rates of net N mineralization and nitrification as well as large pools of inorganic N. Conversely, denitrification rates in our study plot were really low (Table 1), especially compared with those reported for temperate riparian zones (up to 2mg N kg soil$^1$ d$^{-1}$; Hefting et al., 2004). These results suggest that vegetation uptake, rather than denitrification, may be the major sink of N-removal in Mediterranean riparian zones.

The isotopic water signal revealed that riparian trees took up >80% of water from the vadose zone, except during spring, when phreatophic species (*Alnus glutinosa* and *Populus nigra*) used groundwater as a main source of water at the near-stream zone. Moreover, the model simulations suggested a higher hydraulic connectivity between vadose zone and groundwater at the near-stream zone. This higher water supply allowed greater transpiration rates at the near-stream (80% of potential evapotranspiration, PET) than at the hillslope zone (60% of PET) (Table 1), where riparian transpiration might be water limited.

Vegetation N uptake was the main N-removal process at the riparian forest, and decreased from the near-stream to the hillslope (Table 1). However, N uptake represented only from 7-13% of N fluxes at the vadose zone, questioning its real N-removal capacity. Climate change projections suggested that a decrease in groundwater level at the area, together with the consequent transpiration restriction, may increase the soil N pools, and thus reduce the effective N-removal area in Mediterranean riparian forests.

<table><caption>Table 1. For each riparian zone (near-stream, intermediate, and hillslope): distance from the stream channel, tree species, groundwater level, soil water content, transpiration, denitrification, and nitrate uptake rates. Values are expressed in annual mean ± standard deviation.</caption><thead><tr><th></th><th>Near-stream</th><th>Intermediate</th><th>Hillslope</th></tr></thead><tbody><tr><td>Distance from the stream (m)</td><td>0 – 4</td><td>4 – 7</td><td>7 – 25</td></tr><tr><td>Tree species</td><td><em>Alnus glutinosa</em><br><em>Populus nigra</em><br><em>Robinia pseudoacacia</em></td><td><em>P. nigra</em>,<br><em>R.pseudoacacia</em></td><td><em>Fraxinus excelsior</em>,<br><em>R.pseudoacacia</em></td></tr><tr><td>Groundwater level (m below soil surface)</td><td>0.41 – 0.6</td><td>1.20 – 1.35</td><td>&gt; 2.20</td></tr><tr><td>Soil water content (cm$^3$cm$^{-3}$)</td><td>0.38 ± 0.01</td><td>0.25 ± 0.04</td><td>0.17 ± 0.04</td></tr><tr><td>Transpiration (mm)</td><td>666 ± 75</td><td>536 ± 46</td><td>406 ± 26</td></tr><tr><td>Denitrification (g N-NO$_3$ m$^{-2}$)</td><td>1.05 ± 0.66</td><td>0.56 ± 0.26</td><td>0.42 ± 0.21</td></tr><tr><td>Nitrate Uptake (g N-NO$_3$ m$^{-2}$)</td><td>4.48 ± 0.50</td><td>3.43 ± 0.26</td><td>2.77 ± 0.14</td></tr></tbody></table>

## CONCLUSION

Our findings highlight that the hydrological and biogeochemical processes occurring in the vadose zone are key to understand the capacity of N-removal of Mediterranean riparian forests, as well as question the capacity of these systems to reduce N loads reaching streams.

## REFERENCES

* Barbeta A., Mejía-Chang M., Ogaya R., Voltas J., Dawson T.D., Peñuelas J., 2015. The combined effects of a long-term experimental drought and an extreme drought on the use of plant-water sources in a Mediterranean forest. Global Change Biology, 21 (3), 1213-1225.

* Hefting, M. M., Clément, J.-C., Dowrick, D., Cosandey, A. C., Bernal, S., Cimpian, C., Tatur, A., Burt, T. P., Pinay, G., 2004. Water table elevations controls on soil nitrogen cycling in riparian wetlands along a European climatic gradient, Biogeochemistry, 67, 113–134.

* Poblador S., Lupon A., Sabaté S., Sabater F., 2017. Soil water content drives spatio-temporal patterns of CO$_2$ and N$_2$O emissions from a Mediterranean riparian forest soil. Biogeosciences, 14,4195-4208.
# MODELLING MITIGATION SCENARIOS ON A LANDSCAPE IN CENTRAL FRANCE

PASQUIER, C.<fnref n="1" />, BENHAMOU, C. <fnref n="2" />, FRANQUEVILLE, D. <fnref n="2" />, DROUET, J.L.<fnref n="2" />, HENAULT, C.<fnref n="1" />

## INTRODUCTION

Excess of nitrogen, produced by intensive agriculture, is diffused into the environment in reactive forms – such as nitrate (NO₃), ammonia (NH₃), and nitrogen oxides (NOx and N₂O). These losses may adversely affect impacts on agroecosystems: soil, water or air pollution, greenhouse gas emission, biodiversity loss. They result from a cascade of numerous processes, which interact spatially and temporally, in agroecosystems. Integrated models are very useful to investigate such complex systems. The aims of this work are to assess the reliability of model predictions on an experimental landscape located in Central France, and to compare the nitrogen budget of actual practices with two nitrogen emission mitigation scenarios, (Pellerin et al., 2017).

## MATERIAL AND METHODS

### Study site

The study site is a watershed, representative in arable crops in Central France, (Gu et al., 2014). This watershed has an area of 427 ha, it includes 63 fields owned by 9 different farms. The whole crop area is drained. The most common crop rotations is rapeseed-wheat-wheat. The dominant crop is winter common wheat which occupied around 50% of the agricultural land. Pea crop is also present in a few crop rotations.

### Dataset

Three datasets were implemented (S0, S1 and S2). In S0, based on actual farming practices, data were collected through farmer surveys (fertilization type, date, amount, crop type, sowing, tillage and harvest date, and livestock management). S1 is a scenario including the same main crops as S0 and we added the requirements of the European Nitrate Directive : grass strips along waterway, catch crops, sowing rapeseed crop. To calculate fertilization management we used N balance method calculated with Azolis © and staggered nitrogen inputs. In the S2 scenario, a unique crop rotation was applied in all fields: rapeseed-wheat-pea-rapeseed-wheat-barley.

### Model assessment

All datasets were implemented in the NitroScape model (Drouet, J.-L. et al., 2012). The NitroScape model was developed by linking four elementary models (CERES-EGC, FARM-EF, FIDES, SURFATM,) representing reactive nitrogen (Nr) transformations and transfers within four compartments of agricultural landscapes: farms and livestock systems, terrestrial agroecosystems, atmosphere and hydrological network. To assess model reliability, we used the S0 dataset and compared output variables with available observations. In years 2014 and 2015, field data were measured in 5 sites, such as soil properties, meteorological data, biomass, N content in vegetation, N₂O emissions, NH₃ concentrations, nitrate and ammonium concentrations in soil and soil water content.

## RESULTS AND DISCUSSION

The model provided a satisfactory estimate of dry biomass, nitrogen content in biomass and nitrate content in soil. Nitrate content peaks clearly corresponded to fertilization inputs (figure 1). There was some lack of measurements for soil N₂O emissions during emission peaks to assess the all quality of simulations.

<footnote n="1">¹ URSOLS, INRA, 45075 Orléans, France</footnote>

<footnote n="2">² UMR ECOSYS, INRA, 78850 Thiverval-Grignon, France</footnote>
<figure><img src="image_32.png" /><figcaption>Figure 1. NO₃⁻ soil content of surface layer (mg N kg soil-1 or ppm).</figcaption></figure>

The European Nitrate Directive implementation (S1) reduced environmental Nr losses by 15% (Table 1) and increased nitrogen returned to soil with crop residues by 12.8 kg N ha⁻¹ yr⁻¹. The global nitrate leaching was decreased by 5 kg N ha⁻¹ yr⁻¹. Even though mineral fertilization was reduced by an average of 40 kg N ha⁻¹ yr⁻¹ in comparison with actual practices (S0), nitrate leaching was higher of about 2 kg N ha⁻¹ yr⁻¹ in the S2 scenario than in S1. This difference was due to an overestimation of symbiotic fixation of nitrogen by pea by the model.

<table><caption>Table 1. Global mineral nitrogen budget (kg N ha⁻¹ yr⁻¹ and %) at the end of the simulation period for the three scenarios.</caption><thead><tr><th>Scenario</th><th>S0</th><th>S1</th><th>S2</th></tr><tr><th>Unit</th><th colspan="3">kg N ha<sup>-1</sup> yr<sup>-1</sup></th></tr></thead><tbody><tr><td>Mineral fertilization</td><td>164.61</td><td>162.65</td><td>123.89</td></tr><tr><td>Symbiotic fixation</td><td>2.48</td><td>2.47</td><td>102.99</td></tr><tr><td>Harvest exportation</td><td>137.01</td><td>135.43</td><td>195.99</td></tr><tr><td>NO<sub>3</sub><sup>-</sup> leaching</td><td>29.75</td><td>24.65</td><td>27.17</td></tr></tbody></table>

## CONCLUSION

The NitroScape model predicted fluxes of different forms of Nr in the study site with a consistent order of magnitude in comparison with observed fluxes. Implementation of nitrates directive and new crop rotation provided better performance with a reduction of nitrate losses and an increase of nitrogen use efficiency. These NitroScape simulations demonstrate some ways to reduce Nr losses in environment by applying agricultural management scenarios at the landscape scale.

**Acknowledgements:** we thank ANR for funding this study through the ANR Escapade project (ANR-12-AGRO-0003).

## REFERENCES

* Drouet, J.-L., Duretz, S., Durand, P., Cellier, P., 2012. Modelling the contribution of short-range atmospheric and hydrological transfers to nitrogen fluxes, budgets and indirect emissions in rural landscapes. Biogeosciences 9, 1647–1660.

* Gu, J., Loustau, D., Hénault, C., Rochette, P., Cellier, P., Nicoullaud, B., Grossel, A., Richard, G. (2014). Modeling nitrous oxide emissions from tile-drained winter wheat fields in Central France. Nutrient Cycling in Agroecosystems, 98 (1), 27-40.

* Pellerin, S. & al., 2017. Identifying cost-competitive greenhouse gas mitigation potential of French agriculture. Environmental Science and Policy, 77, 130-139.
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes – France

**Session I: N cycle in landscapes - Posters**
# GIS-BASED ESTIMATION OF SHALLOW GROUNDWATER NITRATE CONCENTRATIONS: FROM POINT MEASUREMENTS TO THE LANDSCAPE SCALE

KNOLL, L.¹, BREUER, L.¹, BACH, M.¹

1 Research Centre for BioSystems, Land Use and Nutrition (IFZ)
Justus Liebig University Giessen, Germany

## INTRODUCTION

In the context of the implementation of the EU Water Framework Directive, the estimation of nitrogen (N) inputs into surface waters and groundwater reached increasing awareness. Particularly nitrate loads from agricultural land use contribute to the high nitrate concentrations observed in the groundwater. To develop effective management plans, a better representation of N flow paths, retention times and denitrification processes in the groundwater body are needed for hydrobiogeochemical models. However, data required for meaningful large-scale applications of these models is often not at hand. In Germany, the river basin management system MoRE (Modeling of Regionalized Emissions) is used to estimate nitrogen loads into surface waters on the national scale. This work aims at developing a new component within the model instrument MoRE to simulate the entry pathways for nitrate from non-point sources through the groundwater.

## MATERIAL AND METHODS

Recently, the federal state of Hesse (Germany) provided measurements from about 2.500 monitoring sites (springs, wells and monitoring wells) for nitrate concentrations in groundwater bodies. In addition, a wide range of spatial data on land use, nitrogen surplus on agricultural land, groundwater recharge, hydrogeological conditions and potential denitrification rates in soils is available. Based on this data, we developed a multiple linear regression (MLR) approach for statewide spatial predictions of nitrate concentration in shallow groundwater.

### Geodata analysis

The groundwater data and coordinates were stored as point information in a geodatabase. Different types of contributing area surrogates (CAS) such as circles and wedges were developed to quantify the spatial data in relation to the point measurements. We performed correlation analysis to evaluate dependencies between nitrate and the particular spatial data and identified the most influential parameters.

### Multiple linear regression analysis

With a MLR approach, the relationship between a response variable and multiple significant explanatory variables is modelled. MLR was applied in this work to predict nitrate concentration from the most influential spatial variables. The MLR was performed for different parameter sets of the particular CAS. Furthermore, we tested the performance of the MLR for several hydrogeological subunits.

## RESULTS AND DISCUSSION

### Geodata analysis

We identified a high positive correlation between nitrate concentration and percentage of arable land use, a high negative correlation between nitrate and percolation rate and a low positive correlation between nitrate and N-surplus on agricultural land. The performance of the CAS appeared to be lower for smaller CAS designs, commonly the 1.000 m circular CAS showed the highest correlations.
**Multiple linear regression analysis**

We generated spatially differentiated MLR models from the percentage of arable land use as the most influential explanatory variable and additional variables like percolation rates, groundwater recharge rates, residence times, N-surplus on agricultural land and potential denitrification rates in soils to predict nitrate as the dependent variable. A spatial subdivision in unconsolidated rocks and consolidated rocks characterized as aquifer, aquitard, or a mixture of both, show an efficient improvement of the MLR models. Our MLR models reveals that shallow groundwater nitrate concentrations of the state of Hesse can be explained with an adj. r² from 0.37 to 0.54 for different hydrogeological regions. The coefficients of the explanatory variables describe the observed dependencies regarding nitrate concentrations in a good way, but the MLR model does not explain the entire range of the measured nitrate distribution. Keeping in mind that we used only large-scale spatial input data as explanatory variables, our model provides sufficient results and is in line with other similar models based on higher resolution input data and additional point measurements as explanatory variables.

**CONCLUSION**

The use of statewide, commonly available spatial data as independent variables makes the model applicable to other comparable regions or even on a national scale of Germany. We show preliminary results and discuss how to apply our GIS-based statistical approach to estimate groundwater nitrate concentrations in regions where less field observations are available.

**Acknowledgements:** We thankfully acknowledge the funding by the German Federal Environmental Agency (FKZ 37 1522 2200, UBA). We further would like to thank the Hessian Agency for Nature Conservation, Environment and Geology Tasks and Targets (HLNUG) for providing the data.
# TRACING THE FATE OF ¹⁵N-LABELLED ANIMAL MANURE IN THE ENVIRONMENT

FRICK, H.<fnref n="1" />,², OBERSON, A.², FROSSARD, E.², WETTSTEIN, H.-R.³, BÜNEMANN, E. K.<fnref n="1" />,

## INTRODUCTION

Animal manure is suspected to be a major source of nitrate leaching due to its variable N concentration and availability and the resulting difficulties in synchronizing crop N-demand and N-supply. In order to gain a better understanding of the fate of N from fertilizers in the soil-plant-system, N-uptake by the crop, residual N in the soil as well as losses via nitrate leaching and ammonia volatilization after the application of mineral fertilizer or animal slurry will be assessed and compared in a field study over a period of 2.5 years.

Thereby, we will use ¹⁵N-labelling approaches to address the following questions: i) how much N is lost from animal manure by leaching, and ii) how to improve N use efficiency of animal manure and reduce leaching risk?

## MATERIAL AND METHODS

### Production of ¹⁵N-labelled cattle manure

¹⁵N-labelled manure will be produced shortly by feeding a young cattle with ¹⁵N-labelled ryegrass hay (*Lolium multiflorum*) over 10 days. Thereby, urine and faeces will be collected separately allowing for detailed analysis of ¹⁵N enrichment in the different components and their respective mineralization potential in a column trial.

For the further experiments, urine and faeces will be mixed relative to their respective excretion amounts. Parts of the resulting slurry will be anaerobically digested following standard procedures for biogas production, or mixed with amendments such as biochar or nitrification inhibitors, respectively. Potential differences in leaching losses and nitrogen use efficiency of the processed manures will be assessed in column experiments.

### Microplot field study

During the cropping season of 2018, we will apply the ¹⁵N-labelled cattle slurry to microplots in a similar design as suggested by Jokela and Randall (1987), in amount and timing according to common agricultural practice. Thereby, our microplot study will be integrated with an on-farm trial in the canton Solothurn, Switzerland, focusing on different management strategies to prevent nitrate leaching.

Furthermore, we will establish microplots with either ¹⁵N-labelled mineral fertilizer, or without any N fertilization as a control treatment with four replicates and on two different field sites, respectively (i.e. 24 microplots in total). In the subsequent years, microplots will be fertilized together with the surrounding fields with non-labelled fertilizers according to farmer's practice.

Plant and soil (at least up to 90 cm depth) will be sampled upon harvest of each crop. Nitrate leaching will be assessed via so-called self-integrating accumulators (SIAs) (Bischoff, 2007), a method which allows for measuring cumulative NO₃-losses per area per cropping season. It is based on cylinders filled with an ion exchange resin that will be installed underneath the undisturbed soil profile of the microplots in 100 cm depth and exchanged after the harvest of each crop. Additionally, losses via ammonia volatilization upon manure application in the field will be measured via acid traps for at least 48 hours after manure application. Assessment of ¹⁵N-enrichment in all the mentioned compartments

<footnote n="1">¹ Department of Soil Science, Research Institute of Organic Agriculture (FiBL), Switzerland; ² Institute of Agricultural Sciences, ETH Zurich, Switzerland; ³ Agrovet-Strickhof, ETH Zurich, Switzerland</footnote>
will allow to trace the fate of the labelled fertilizer components and to establish a soil-system N-balance including leaching losses.

**Source identification of nitrate in drainage water via natural abundance of ¹⁸O/¹⁵N**

In addition to the above described microplot study, the natural abundance of ¹⁸O/¹⁵N in drainage water, sampled with both suction cups and SIAs under five fields in the same region, will be assessed in order to identify sources of nitrate in agricultural drainage water (Stoewer, 2016).

**RESULTS AND DISCUSSION**

The ¹⁵N-labelled ryegrass hay has been produced both, in a greenhouse and on a field site by fertilizing ryegrass with ¹⁵N-ammoniumnitrate and will be fed to a young cattle in February 2018. By June 2018, we expect to have first results on the manure properties. For the field and column studies we hypothesize that i) nitrate in the groundwater has variable sources, ii) cattle slurry contributes considerably to nitrate leaching, but that iii) anaerobic digestion or manure amendments will increase N use efficiency, thus, reduce nitrate leaching from cattle slurry.

With this study we expect to gain valuable insights into the sources of nitrate in agricultural drainage water as well as the fate of nitrogen from cattle manure in the environment over several years, helping to develop strategies to optimize N use efficiency.

**Acknowledgements** The research project "NitroGäu" receives financial support from the Swiss Federal Office of Agriculture and the canton of Solothurn.

**REFERENCES**

BISCHOFF, W.-A. 2007. Development and applications of the self-integrating accumulators: A method to quantify the leaching losses of environmentally relevant substances. PhD thesis, TU Berlin.

JOKELA, W. & RANDALL, G. 1987. A nitrogen-15 microplot design for measuring plant and soil recovery of fertilizer nitrogen applied to corn. Agronomy journal, 79, 322-325.

STOEWER, M. M. 2016. Vulnerability assessment of nitrate leaching on the regional scale using isotope techniques. Dissertation, Albert-Ludwigs-Universität Freiburg, 2015.
# HIGH WATER TABLES DURING WINTER HAVE NO IMPACT ON WINTER WHEAT NITROGEN UPTAKE AND YIELDS UNDER DANISH CONDITIONS

DEICHMANN, M. M.<fnref n="1" />, BØRGESEN, C. D.<fnref n="1" />, THOMSEN, I. K.², ANDERSEN, M. N.<fnref n="1" />

## INTRODUCTION

Denmark is currently testing new management tools for lowering nitrogen (N) leaching from agricultural fields and among the tools tested is controlled drainage (CD). It has been shown that CD can lower N-leaching under Nordic conditions by 60-95% (Weström et al. 2007). It has also been reported that CD under Nordic conditions can improve yields by 2-18% when applied during the spring and summer season by raising the water table to 70-20 cm below the soil surface (Weström et al. 2007). However, if CD is used to lower N-leaching in Denmark, it must work during the winter where Denmark typically receives surplus precipitation and N-leaching is highest on average (SEGES 2017). Accordingly, CD might result in waterlogging conditions. Waterlogging is known to lower yields and N-uptake in plants (Herzog, Striker et al. 2016), but the intensity of waterlogging stress is depending on the wheat variety (Dickin et al. 2008). This study focuses on the negative effect of CD on wheat yields and wheat N-uptake when applied during winter and whether there are differences in the tolerance of typical Danish wheat varieties to CD.

## MATERIAL AND METHODS

A lysimeter experiment was set up in the semifield of Aarhus University Foulum, Denmark during the growing season 2015/2016. It consisted of 54 lysimeters containing a sandy loam soil. Sowing took place on September 28 with winter wheat of the varieties Belgrade (BG), Benchmark (BM) and Substance (SUB) in random order. The lysimeters treatments consisted of water levels of 5, 10, 20, 30, 40 cm below the soil surface as well as a free drainage control. Treatment started on January 26 and end on April 6, where drainage began. Drainage had finished on April 28. Each treatment had nine replicates with three lysimeters of each wheat variety. Before completely filling the lysimeters, they were fertilized with 50 kg N ha⁻<fnref n="1" />. The height of the water was measured every third day from January 26 until drainage on April 6. However, these measurements showed technical challenges in controlling the water levels at the wanted heights and the intended water heights had to be adjusted to 10, 20, 25, 35 and 45 cm below soil surface. The lysimeters all received 50 kg N ha⁻<fnref n="1" /> on April 28 and 140 kg N ha⁻<fnref n="1" /> on May 2. Mildew treatment was conducted on May 11. Harvest took place on August 12, where after the plants were sorted into grain and residuals (stem+leafs).

### Chlorophyll-fluorescence

Chlorophyll fluorescence (Fv/Fm) measurements started on April 6 just before drainage started and continued until May 11. Measurements were taken using the Mini-PAM fluorimeter (Walz, Germany). Before measuring, the leaves were dark-adapted with Dark Leaf Clips for 30 min.

### Yield

After sorting the harvested plants into grain and residuals (the samples were dried at 60°C until stable weight was obtained. After drying the total dry matter (DM) Yield, Grain DM Yield and the Residual DM Yield was calculated for each lysimeter.

### Nitrogen-measurements

The N-concentration was determined in the Grain DM and Residual DM in accordance with the Dumas method. The N-uptake for each lysimeter was calculated by multiplying the N-concentration of the Grain DM and Residual DM with the respective Grain DM Yield and Residual DM Yield.

## Statistics

<footnote n="1">¹ Department of Climate and Water, Institute of Agroecology, Aarhus University, Denmark; ² Department of Soil Fertility, Institute of Agroecology, Aarhus University, Denmark</footnote>
All normally distributed data was subjected to a two ANOVA while data not following a normal distribution was subjected to Kruskal-Wallis test. Both tests were made using R-Studio software (Version 1.1.383).

## RESULTS AND DISCUSSION

Fv/Fm showed no significant difference among treatment and varieties at any point. However, the treatments at 10cm and 20cm had the lowest Fv/Fm values and BM was the variety that performed the worst at the end of treatment. The treatment of 10 cm continued to have the lowest Fv/Fm values thought they were not significantly lower than the other treatments. Hence, a longer treatment period or higher water level could induced significant stress on the plants. Neither Total DM yield or Grain DM Yield was affected by treatment, but both showed significant variety effects. In both cases, BG had the lowest average yields while BM had the highest yields (Figure 1a, 1b).

<figure><img src="image_33.png" /><figcaption>Figure 1: The relation between yields and N-uptake, depending on wheat varieties. Where a shows the grain yield and grain N-uptake and b shows total DM yields and total N-uptake.</figcaption></figure>

At harvest, no effects of either treatment or variety were measured for the grain N-concentration indicating that CD had not impaired the grain quality. However, the total N-concentration was depending on the variety where BG had significantly lower N-concentration compared to both BM and SUB. N-uptake at harvest was not affected by treatments, but BG had the lowest N-uptake while BM had the highest N-uptake (figure 1). This indicated once again that the high-stress levels measured at April 6 on BM before drainage started had no effect on the final N-uptake.

## CONCLUSION

The highest stress levels were measured in the treatments at 10cm and 20cm, and especially at the last day of treatment, though the differences were not significant. This indicated that high water levels for 3-4 month did effect the development and uptake of N in any of the three varieties of winter wheat. Variety however were determining for DM yields and N-uptake at harvest, where BG performed worst. Hence, the stress induced by the treatments had no effect on final DM yields or N-uptake in any of the varieties. Our result indicates that CD could be applied in Denmark during the winter without damaging the N-uptake or yields of winter wheat.

## REFERENCES

- Dickin E., Bennett S., Wright D., 2009. Growth and yield of UK wheat cultivars to winter waterlogging. Jour. Agri. Sci. 147, 127-140

- Herzog M., Striker G. G., Colmer T. D., Pedersen O., 2016. Mechanisms of waterlogging tolerance in wheat--a review of root and shoot physiology. Plat. Cell. Env. 39, 1068-1086

- SEGES, 2017. Kontrolleret dræning. SEGES. https://www.seges.dk/innovation-og-udvikling/projekter/kontrolleret-draening

- Wesström I., Messing I., 2007. Effects of controlled drainage on N and P losses and N-dynamics in a loamy sand with spring crops. Agri. Water. Manag. 87, 229-240
# INCREASING THE NITROGEN USE EFFICIENCY AND CONSEQUENTLY THE GROUNDWATER QUALITY IN THE GÄU REGION, SWITZERLAND

WEY, H.¹, HUNKELER, D.¹, BUENEMANN, E.K.²

¹ Centre d'Hydrogéologie et Géothermie (CHYN), Université de Neuchâtel, Switzerland ² Research Institute of Organic Agriculture (FiBL), Switzerland

## INTRODUCTION

The loss of nitrogen (N) is one of the biggest unsolved environmental problems of our time: on one hand, nitrogen is a critical yield-limiting factor and therefore N supply in agriculture in the form of fertilizer is crucial to ensure food quality and security. On the other hand, part of this fertilizer is lost to other environmental compartments and leads to the contamination of groundwater, among other issues. Drinking water limits for nitrate are now exceeded in many regions including Switzerland, despite the introduction of measures such as cover crops in winter, adjusted fertilizing strategies and adapted crop rotation systems.

Studies on nitrate leaching frequently use lysimeters or small plots to control experimental conditions, establish mass balance and, in general, achieve statistically sound conclusions. However, these methods do not fully represent real agricultural practices. With this experimental study in the Gäu region in the Swiss Plateau, we aim at bridging the gap between highly controlled small-scale experiments and plot-scale studies on real agricultural fields. In this presentation, the experimental design is detailed, consisting of various monitoring devices, as conclusions on the nitrate leaching itself are not yet available by Summer 2018.

## MATERIAL AND METHODS

When studying nitrate leaching under field conditions, there are two challenges: first, both the high spatial variability ("hot spots") and the temporal dynamics have to be considered to identify "hot moments" of nitrate leaching, potentially leading to an excessive number of samples. Second, the "true" leaching losses have to be determined, which is not identical with nitrate in the root zone as it might be subject to later plant uptake. Our experimental design addresses these two challenges as follows:

* Sampling strategies able to efficiently capture the spatial variability of nitrate leaching (nitrate passive samplers, soil coring) are combined with those that yield a high temporal resolution (suction cups) for selected locations (Figure 1).

* To identify the complete downward transport of nitrate to the groundwater table, not only the root zone, but also the entire unsaturated zone is covered (vadose zone monitoring, Figure 1).

* Agricultural management practices can be carried out unrestrictedly as all installations are completely buried.

The study is multidisciplinary, as knowledge from soil science is connected with agricultural science and hydrogeology.

On eleven fields, managed by the farmers according to their usual management, nitrate leaching under status quo conditions is assessed (Figure 1, Table 1). In addition, two improved agricultural practices, implemented on a strip in each of six fields (annotated with "HYD" in Table 1), will be directly compared to the baseline scenario. Current options for improved practices include measuring all fluxes necessary for a full N budget on the field scale, subsequently allowing for a better adjustment of the fertilizer and manure amount. Moreover, the reduction of the amount of fertilizer applied and the application of a
controlled release fertilizer offer great promise in reducing leaching. It will be decided in Summer 2018 which measures to implement.

<figure><img src="image_34.png" /><figcaption>Figure 1: The measurement strategies and the instruments.</figcaption></figure>

<table><caption>Table 1. The experimental design with the specification of the 11 monitored fields.</caption><thead><tr><th rowspan="2">field code</th><th colspan="2">system</th><th colspan="6">crop rotation</th><th colspan="4">measurements</th></tr><tr><th>conventional</th><th>organic</th><th colspan="3">2018</th><th colspan="3">2019</th><th colspan="3">2020</th><th>soil coring</th><th>passive samplers</th><th>suction cups</th><th>vadose zone monitoring</th></tr></thead><tbody><tr><td>BIK1</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr><tr><td>BIK2</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr><tr><td>BIK3</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr><tr><td>BIK4</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr><tr><td>BIK5</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr><tr><td>BIK6 / HYD1</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td></tr><tr><td>HYD2</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td></tr><tr><td>HYD3</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr><tr><td>HYD4</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td></tr><tr><td>HYD5</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td></tr><tr><td>HYD6</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td></tr></tbody><tfoot><tr><td colspan="14">pasture corn crop canola catch crop</td></tr></tfoot></table>

## RESULTS

By June 2018, it is expected that the first results for Nmin soil coring and soil water analysis from the suction cups will be available for a first comparison of methodology.

**Acknowledgement.** The research project "NitroGäu" receives financial support from the Swiss Federal Office of Agriculture and the canton of Solothurn.
# UPSCALING WATER AND NUTRIENT USE EFFICIENCIES FROM FIELD TO CATCHMENT SCALE: A CASE STUDY IN THE SELKE CATCHMENT, GERMANY

SILVA, J.V.¹, JOMAA, S.², CHUKALLA, A.¹, YANG, X.², MERBACH, I.², RODE, M.², ANTEN, N.P.R.¹, VAN ITTERSUM, M.K.¹, REIDSMA, P.¹

1 Wageningen University (WU), Wageningen, The Netherlands;

2 Helmholtz Centre for Environmental Research (UFZ), Magdeburg, Germany

## INTRODUCTION

Sustainable intensification aims to reconcile food production and environmental quality of agricultural systems through its focus on yield gap closure and high resource use efficiency. In agronomy, this has been assessed at field and farm levels (e.g., Silva et al., 2017), but its environmental impacts on water quality at larger spatial scales are poorly understood. Bridging this knowledge gap requires an upscaling protocol to integrate field/crop level approaches in methodologies able to explore water and nutrient dynamics at catchment scale (e.g., Jomaa et al., 2016). The objective of this research is to develop and test an upscaling protocol from field to catchment scale able to reconcile food production, nutrient management and water quality dimensions of agricultural systems, using the Selke catchment (Germany) as a case study.

## MATERIAL AND METHODS

The Selke catchment is a tributary of the Bode River and can be described as a mesoscale, lower mountain range catchment which drains an area of ca. 456 km². The elevation in the catchment varies between 605 m at the headwater and 53 m at the catchment outlet. Forest is the dominant land use in the mountain areas, covering ca. 35% of the catchment area, and agriculture is the dominant land use in the lowland areas and with a share of 58% of the catchment area. Two main soil types can be distinguished throughout the catchment, namely cambisols in the mountain areas and chernozems in the lowland areas.

Long-term data on crop phenology, crop yield and N uptake from the Static Fertilization Experiment of Bad Lauchstädt (1978 - 2016) were analyzed for the main crops in the region, namely ware potato, sugar beet, winter wheat, spring barley and silage maize. These data were used to assess empirically the relationships between N applied and N uptake and between N uptake and crop yield using the three quadrant diagram of de Wit (1992). As a next step, the crop model WOFOST is evaluated against these field data and applied to describe the temporal dynamics of N uptake by the different crops and to simulate potential, water-limited and nitrogen-limited yields throughout the Selke catchment.

Long-term daily discharge and water quality data (1993-2016) for the Selke catchment has been recorded in three gauging stations, representing the spatial heterogeneity of the catchment. The process-based, semi-distributed HYPE (HYdrological Predictions for the Environment) model was used to represent the hydrological response and nitrogen concentration in the Selke catchment. The crop-specific N uptake processes and their parameters' identification in the HYPE model were refined based on the insights gained from the Static Fertilization Experiment and based on the crop model WOFOST. Then, the effect of different sustainable crop intensification and resource use efficiency measures were tested and compared to the baseline situation.

## RESULTS AND DISCUSSION

Descriptive statistics of the empirical data used to parameterize WOFOST are presented in Table 1. Crops yields between 2.1 and 12.6 t DM/ha for spring barley and silage maize, respectively, were observed when no fertilizer was applied. The N uptake associated with these yields varied between 37.5 kg N/ha for spring barley and 83.5 kg N/ha for silage maize. Application of N as mineral fertilizer
increased yields of all crops except sugar beet, which can be explained by considerable increases in N uptake compared to the ON treatment. These data provide preliminary insights into the interaction between N use efficiency and crop yield in the region and further efforts will focus on refining this across the Selke catchment using WOFOST.

<table><caption>Table 1. Descriptive statistics of crop yield and N uptake in harvestable organs and biomass for arable crops in two N treatments of the Static Fertilizer Experiment, Bad Lauchstädt (Germany).</caption><thead><tr><th>Crop</th><th>Years</th><th>N applied (kg N/ha)</th><th>N uptake (kg N/ha)</th><th>Yield (t DM/ha)</th></tr></thead><tbody><tr><td rowspan="2">Winter wheat</td><td rowspan="2">2000 – 2016</td><td>0</td><td>53.5 ± 14.8</td><td>3.5 ± 1.1</td></tr><tr><td>100</td><td>154.3 ± 30.2</td><td>7.3 ± 1.6</td></tr><tr><td rowspan="2">Spring barley</td><td rowspan="2">2000 – 2016</td><td>0</td><td>37.5 ± 10.2</td><td>2.1 ± 0.7</td></tr><tr><td>80</td><td>90.5 ± 34.1</td><td>3.9 ± 1.3</td></tr><tr><td rowspan="2">Ware potato</td><td rowspan="2">2000 – 2014</td><td>0</td><td>49.0† ± 12.0</td><td>4.1 ± 1.3</td></tr><tr><td>140</td><td>85.5† ± 23.2</td><td>5.7 ± 1.9</td></tr><tr><td rowspan="2">Sugar beet</td><td rowspan="2">2000 – 2014</td><td>0</td><td>64.5 ± 19.0</td><td>7.5 ± 3.0</td></tr><tr><td>170</td><td>161.3 ± 63.6</td><td>6.8 ± 3.9</td></tr><tr><td rowspan="2">Silage maize</td><td rowspan="2">2015 – 2016</td><td>0</td><td>83.5 ± 27.2</td><td>12.6‡ ± 2.7</td></tr><tr><td>140</td><td>157.1 ± 27.5</td><td>16.4‡ ± 3.7</td></tr></tbody></table>

The HYPE model was calibrated and validated successfully at the three gauging stations for discharge (lowest Nash-Sutcliffe Efficiency, NSE, was 0.81) and nitrogen concentration (lowest NSE was 0.65). The simulations of nitrogen concentration improved, particularly, when the N uptake parameters of the HYPE model were refined based on empirical field data (we expect to achieve similar results based on WOFOST simulations). Then different sustainable crop intensification scenarios, suggested by the stakeholders, were evaluated using the HYPE model at the Selke catchment. For instance, preliminary results showed that 20% reduction of nitrogen fertilizer application reduces the nitrogen loads by 6% compared to baseline conditions. Detailed results of the impacts of different sustainable crop intensification will be presented and discussed.

## CONCLUSION

Improving the description of the processes governing crop growth and nutrient uptake in a spatially explicit semi-distributed hydrological model allows future assessments of yield gaps, resource use efficiencies and water quality at catchment scale. Insights in these different dimensions of agricultural systems can be used in *ex-ante* analyses of environmental policies within the EU, particularly in the context of the water and nitrates framework directives.

**Acknowledgements:** This research was funded by Netherlands Organization for Scientific Research (NWO) via the WaterFARMING project (Grant Agreement No: 689271).

## REFERENCES

* de Wit, C.T. 1992. Resource use efficiency in agriculture. Agricultural Systems, 40:125-151.

* Jomaa, S., Jiang, S., Thraen, D., Rode, M. 2016. Modelling the effect of different agricultural practices on stream nitrogen load in central Germany. Energy, Sustainability and Society, 6:11.

* Silva, J.V., Reidsma, P., van Ittersum, M.K. 2017. Yield gaps in Dutch arable farming systems: Analysis at crop and crop rotation level. Agricultural Systems, 158: 78-92.

<footnote>†Data refers to potato tubers only. ‡Data refers to aboveground biomass.</footnote>
# IN-STREAM NITRATE ASSIMILATORY UPTAKE ANALYSIS BASED ON HIGH FREQUENCY MEASUREMENTS

YANG, X., JOMAA, S., RODE, M.

Helmholtz Centre for Environmental Research - UFZ, Germany

## INTRODUCTION

Streams receive nutrients from terrestrial exports and point sources contribution. They do not only deliver nutrients at the outlet, but also transform and remove them through various physical and biogeochemical processes, which strongly modify the surface water quality. Therefore, understanding in-stream nutrient processes is essential for quantifying nutrient removal and environmental conservation.

Field experiments using isotope tracers have been numerously conducted to quantify in-stream nutrient uptake, especially nitrate uptake. However, traditional field studies could not represent the spatiotemporal variability. Benefiting from the extension of in-situ sensor technology development high frequency environmental information is becoming increasingly available. Spatial and temporal representations are covered by multi-site and continuous sensor deployments, respectively. In this study, we (i) analyzed five years' continuous data from two nested multi-parameter sensor monitoring deployments, (ii) determined the most influential factors for in-stream nitrate assimilatory uptake spatiotemporally, and (iii) developed a parsimonious calculating method and compared model results with calculations derived from high frequency measurements.

## MATERIAL AND METHODS

### Study site

The Selke catchment (456 km2) is a subcatchment of the Bode River, one of the TERENO observatories in Central Germany. The land-use conditions of upper and lower parts of the Selke catchment are in striking contrast, with about 73% dominated by forests and 80% dominated by arable lands, respectively. Two multi-parameter sensors were deployed in the nested catchment. Station Meisdorf is located at the outlet of the forested sub-basin, representing forested streams. While, the station Hausneindorf is the catchment outlet, representing the typical open-canopy agricultural stream condition.

### High-frequency measurements

The YSI 610 multi-parameter probes measured temperature, dissolved oxygen (DO), pH and electric conductivity at 15 min interval. High frequency nitrate-N concentrations (15 min interval) were measured by a TRIOS proPS-UV sensor. 15-min discharge and meteorological data (i.e., air temperature, air pressure, precipitation and global radiation) were collected from the State Agency for Flood Protection and Water Management of Saxony-Anhalt (LHW) and German Weather Service (DWD). Leaf Area Index (LAI) data were collected from remote sensing data (NEO). All data were collected continuously from 1st January, 2011 to 31st December, 2015 at both stations. After the methodology of Rode et al. (2016), five-year's daily nitrate assimilatory uptake data were calculated based on Net Ecosystem production, i.e., gross primary production (GPP; $gO_2 m^{-2} d^{-1}$) minus ecosystem respiration (ER; $gO_2 m^{-2} d^{-1}$). Potential uptake rate (a universal parameter introduced in this study) was then determined.

### Factor analysis

The unique five-year's continuous N uptake data offers the opportunity of further analyzing influential factors on in-stream nitrate processes spatially and temporally. Based on previous understanding, the potentially influential factors can be identified (e.g., Photosynthetic Active Radiation - PAR, water
temperature, riparian shading). The new data are used to quantify effects of these factors on assimilatory nitrogen uptake according to different seasons and stream conditions. We specified a parsimonious approach to calculate assimilatory nitrate uptake based on LAI and PAR.

# RESULTS AND DISCUSSION

## Daily N uptake data

Based on the correlation between gross primary production (GPP, $gO_2 m^{-2} d^{-1}$) and assimilatory N uptake ($U_a-NO_3^-$, $mgN m^{-2} d^{-1}$), the five-year time series of daily $U_a-NO_3^-$ were calculated in both forested and agricultural reaches. They showed different seasonal patterns. In the forested reach, the uptake increased considerably in spring (reached to the highest value right before riparian leaves coming out) but decreased sharply and maintained at a low level until winter (Fig. 1, left). While in the agricultural reach, the uptake kept increase after spring and reached peaks in summer (Fig. 1., right).

## Influential factors and simulation results

According to linear regression analysis between N uptake and different parameters in the agricultural reach, the most influential factor was identified as PAR. While water temperature presumably explained the unexpected high values in springs of last three years. The sharp reduction of N uptake in the forest reach is mostly due to the shading effect of riparian vegetation. We considered LAI information to represent the riparian shading. Results showed that the parsimonious approach could explain well the seasonal patterns in both forested and agricultural reaches (Fig 1.).

<figure><img src="image_35.png" /><figcaption>Figure 1. Five-year continuous Nitrate assimilatory uptake and simulation results of the forested and agricultural reaches, Selke catchment.</figcaption></figure>

# CONCLUSION

Benefiting from the high-frequency sensor measurements, continuous time series of in-stream nitrate assimilatory uptake were calculated for different stream conditions. Moreover, those high-frequency data were used to formulate and validate a parsimonious modelling approach. The new approach can be used to upscale the in-situ insights from reach investigations to whole stream networks of a given catchment.

# REFERENCES

* Rode, M., S. Halbedel née Angelstein, M. R. Anis, D. Borchardt, M. Weitere (2016), Continuous In-Stream Assimilatory Nitrate Uptake from High-Frequency Sensor Measurements, *Environ. Sci. Technol.*, 50(11), 5685-5694.
# AGRICULTURAL NITROGEN BUDGET AND GROUNDWATER QUALITY IN A PORTUGUESE VULNERABLE ZONE

FARO, A.F.¹, CAMEIRA, M.R.², ROLIM, J.³, CORDOVIL, C.⁴, DRAGOSITS, U.⁵

1 Universidade de Lisboa, Instituto Superior de Agronomia MSc student, Portugal; 2,3,4 Universidade de Lisboa, Instituto Superior de Agronomia, LEAF Research Centre, Portugal; 5 NERC Centre for Ecology & Hydrology, Edinburgh, UK

## INTRODUCTION

The aim of the Nitrates Directive (EC, 1991) is to reduce water pollution induced by nitrates from agricultural sources in the designated Nitrate Vulnerable Zones (NVZ). In these areas, Member States must establish programmes of measures to be adopted by farmers. They are also required to report the status of water quality and trends to the EC. This study contributes with a methodology to identify and quantify groundwater contamination sources associated with agricultural practices. A case study is presented for the Tagus Vulnerable Zone to Nitrates (TVZ) to evaluate the impacts of the Nitrates Directive (ND) implementation in 2004.

## MATERIAL AND METHODS

### Study area and data sources

The TVZ comprises 241,686 ha (60 % total NVZ area in Portugal), comprising 20 municipalities and 72 parishes. In this region, agriculture is of major economic importance, with the most important crops being irrigated grain corn and horticulture for industry, followed by vineyards, olive groves and permanent pastures. About 48 % of the soils have high permeability (Haplic Podzols and Regosols). The climate is Mediterranean with hot dry summers and mild wet winters. The TVZ extends over a hydro-geologically complex area, comprising two layered aquifers separated by an aquitard with a reduced permeability. The study was carried out using decadal information for years with detailed data availability (1989 to 2009) from the National Agricultural Census (INE, 2017) regarding crop areas and yields, livestock populations and fertilization. Other information includes data series regarding nitrate concentrations in the groundwater (SNIRH, 2017) and N deposition (EMEP, 2017). To assess the spatial distribution of ground water contamination and its relation with agricultural practices over the TVZ several maps were used e.g. administrative limits, soil classification and Corine Land Cover. The base data and the calculation procedures were integrated in a Geographical Information System using the QGIS software.

### N gross balance, groundwater quality and global risk index

The nitrogen gross balance (GNB) was calculated for representative groups of crops, at the highest spatial resolution (parish level), applying the OECD methodology (Eurostat, 2013). The inputs considered were the N in mineral fertilizers and animal manures, N deposition, N in the irrigation water, biological N fixation and N from crop residues. The output was the crop N uptake. Thus, the N surplus represents the total potential loading from N. In order to assess the groundwater contamination with nitrates, the risks associated with the N surplus, given the soil permeability and groundwater depth were combined in a global risk index (GRI). This index was analyzed together with the nitrate concentration [NO₃⁻] time series from the monitoring wells.

## RESULTS AND DISCUSSION

### N surplus

As a general rule, the municipalities with the highest N surplus were dominated by grain maize and vegetable crops, with lower N surplus in areas of legume crops and permanent pastures. Results show a reduction of 46% in the N surplus between 1989 and 2009, which can be directly linked to the
designation of the NVZ in 2004, when the measures affecting the calculation of the fertilization plans started to be implemented.

## Global Risk Index and groundwater quality

Figure 1a shows a considerable decrease of the GRI in almost all municipalities. This trend is reflected in Fig 1b showing two example wells in the upper aquifer where the [NO₃⁻] has been slowly decreasing. Nevertheless, in the deeper semi confined aquifer (Fig 1c), concentrations have been slowly increasing over time while remaining well below the maximum permitted value of 50 mg L⁻¹.

<figure><img src="image_36.png" /><figcaption>Figure 1. a) Evolution of the risk index for groundwater contamination with nitrates with the location of the three example wells; b) monitoring data from two wells in the upper aquifer; c) monitoring data from one well in the deeper aquifer.</figcaption></figure>

## CONCLUSION

Overall, an improvement is observed in the groundwater quality after the implementation of the ND measures. This improvement is substantial in the irrigated areas in the middle and upper part of the TVZ, where the highest input of N in the balance comes from mineral fertilizers. Nevertheless, in the Southern part there is an inverse trend, particularly in the deeper aquifer, indicating that nitrate is passing through the low permeable aquitard that connects the aquifers, or in some cases travelling through the vadose zone. This trend is not explained by the changes in the N surplus nor the GRI, and is probably related to increased application of livestock manures, and also increasing sewage associated with the urban development this part of the TVZ over recent years.

**Acknowledgements:** Authors thank the NitroPortugal, H2020-TWINN-2015, a Coordination & support action n. 692331 project for funding.
REFERENCES

European Commission, 1991. Council Directive 91/676/EEC of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources. Official Journal 1991;L 375 [31/12/1991]

Eurostat, 2013. Methodology Handbook: Nutrient Budgets. Version 1.02. Eurostat; OECD, Luxembourg.

EMEP, 2017. http://www.emep.int/

INE, 2017. https://www.ine.pt

SNIRH, 2017. http://snirh.apambiente.pt
# INDIRECT EMISSION OF NITROUS OXIDE FROM RIVERS: FIRST RESULTS OF A STUDY ON THE LOIR WATERSHED

GROSSEL, A.¹, PASQUIER, C.¹, BOURENNANE, H.¹, HENAULT, C.¹

¹ URSOLS, INRA, 45075 Orléans, France

## INTRODUCTION

The greenhouse gas (GHG) N₂O is mainly produced in cropped soils (direct emissions) or in water after transfer of nitrogen used in agriculture by leaching or run-off (indirect emissions). According to CITEPA (2014), direct and indirect emissions from agricultural soils contribute to 4.2 and 3.4% respectively, of GHG emissions in France. These values are calculated by the Tier 1 methodology: the N₂O loss is given by N - N₂O = EF₅ × (total N input × Fracₗleach), where Fracₗleach is the fraction of the original total fertilizer N input into the system that is lost to waterbodies and EF₅ is the emission factor for freshwater. IPCC (De Klein et al., 2006) provides a factor EF₅= 0.0075 [0.0005 - 0.025]. This value presents a large uncertainty and is still debated. In this paper, we investigate the nitrogen spatial distribution in the Haut-Loir watershed (France), with a special focus on dissolved N₂O concentrations to improve quantification of N₂O emission by rivers to the atmosphere.

## MATERIAL AND METHODS

### Study watershed

The studied area is the Haut-Loir watershed (137 km long, draining 3453 km²), including 15 smaller watersheds. Hydrological processes are different east and west of the Loir. The eastern part (Beauce) overlays the Beauce limestones; precipitations infiltrate down to the Beauce aquifer through this permeable layer. This explains why there are only 2 tributaries, the Conie and Aigre rivers, fed by the Beauce aquifer. The western part (Perche area) presents a dense tributary network with surface hydrology (Fig. 1).

### OSUR database

Nitrate (NO₃⁻) and ancillary data of 16 sites in 2013-2014 in the Haut-Loir were taken from the OSUR database (http://osur.eau-loire-bretagne.fr/exportosur/Accueil). This database was combined with other databases (IGN, AGRESTE, RPG, RRP) to give information on the spatial environment (cropped areas, drainage, wetlands...).

### Dissolved nitrous oxide and ancillary measurements

We propose to enrich the previous database with dissolved N₂O values collected during 2017. Water samples of the Loir and its tributaries were taken monthly at 5 sites and twice a year at 22 points (including the 16 OSUR sites, Fig. 1). Dissolved N₂O contents were measured by the headspace equilibration method with N₂O concentration determined by gas chromatography. NO₃⁻, nitrite (NO₂⁻) and ammonium (NH₄⁺) were measured by colorimetry. Water pH, redox potential Eh and water and air temperature were directly measured in situ.

### Data analysis

Mean concentrations were compared by t-test. A factorial discriminant analysis (FDA) was applied on the 2013-2014 OSUR database to discriminate various biogeochemical regions inside the Haut-Loir watershed based on the measured variables.

## RESULTS AND DISCUSSION

### OSUR database (2013-2014)
The FDA enabled to discriminate very clearly 3 groups corresponding to the regions: (1) Perche, (2) Beauce and (3) areas having contributions of both (mixt) with a 6% probability of mis-classification. NO₃⁻ concentration appears the most discriminant variable with higher and almost constant weights over time in the Conie and Aigre waters (Beauce area). Seasonal variations were observed in waters from the Perche area. Mean NO₃⁻ concentrations in water were significantly (p<0.01) different between the geographical areas with values of 11.1 mg N.L⁻¹ of NO₃⁻ (n=30) in Beauce, 8.2 mg N.L⁻¹ (n=22) in mixt areas and 5.0 mg N.L⁻¹ (n=118) in Perche. These differences can probably be related to the watershed hydrology directly from the aquifer in the Beauce area while the rivers of Perche are fed by superficial and drainage waters.

## Dissolved N₂O database (2017)

Dissolved N₂O concentrations in the studied river water (Fig. 1) were consistent to those previously observed in the Seine watershed (Garnier et al., 2009) with mean values of 2.8 µg N.L⁻¹ of N₂O, 1.4 µg N.L⁻¹ of N₂O and 0.78 µg N.L⁻¹ of N₂O in the regions: (1) Perche, (2) Beauce and (3) mixt areas. As for NO₃⁻, the differences were significant (p<0.01) between areas.

<figure><img src="image_37.png" /></figure>

Figure. 1. Dissolved N₂O measured in a snapshot campaign in Oct-2017. The furthest north point is the Loir headwater and the river uphill this point is non-permanent.

## CONCLUSION

The spatial pattern of nitrogen both as NO₃⁻ and dissolved N₂O seems be conditioned by the geographical areas of the Haut Loir Watershed. Higher concentrations were measured in the water from the Beauce area, directly fed by the aquifer. A new campaign will be conducted in 2018 to strengthen these observations and to investigate deeply temporal variabilities of nitrogen in water that are different between the 3 investigated geographical areas.

**Acknowledgements:** we thank ADEME for funding this study through the HydroGES project. We also thank Adeline Ayzac, Lionel Cottenot, Christian Lelay, Didier Laloua for the help in field and lab work.

## REFERENCES

* CITEPA, 2014. Inventaire des émissions de polluants atmosphériques et de gaz à effet de serre en France – format SECTEN.

* Garnier, J., Billen, G., Vilain, G., Martinez, A., Silvestre, M., Mounier, E., Toche, F., 2009 Nitrous oxide (N₂O) in the Seine river and basin: Observations and budgets, Agriculture, Ecosystems and Environment 133, 223-233.

* De Klein, C., Novoa, R. S. A., Ogle, S., Smith, K. A., Rochette, et al. 2006. N₂O Emissions from Managed Soils, and CO₂ Emissions from Lime and Urea Application. Volume 4: Agriculture, Forestry and Other Land Use, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Intergovernmental Panel on Climate Change. Institute for Global Environmental Strategies: Hayama, Japan.
# PLANT GROWTH INDICATES SOIL SPATIAL VARIABILITY RELATED TO WATER SHORTAGE AND NITRATE LEACHING VULNARABILITY

HABERLE, J., SVOBODA, P., ŠIMON, T., LUKÁŠ, J., KUREŠOVÁ, G., KŘÍŽOVÁ, K. MADARAS, M.

Crop Research Institute, Prague, Czech Republic

## INTRODUCTION

The production of vegetables and potatoes shows low nitrogen use efficiency and it may be the source of excessive residual nitrate that is prone to leaching, especially under irrigation (Cameira, Mota 2017). Early potatoes and many vegetables demand great amount of readily available water and nitrogen in soil due to a shallow root system and lower absorption capacity. Main regions of irrigated early potatoes and vegetable production in the Czech Republic are situated along rivers, especially Labe, Jizera, and Morava. Fields are mostly light and medium soils with sandy, often stony subsoil, with a low water capacity and fast infiltration. The regions are also utilized for drinking water accumulation and extraction (Bruthans et al., 2015). The coexistence of the intensive agricultural production and water quality requirements is not easy. A typical example of resulting problems is found in the area along lower Jizera river. Water is extracted by over six hundreds of bore wells supplied by water seeping from the river and the percolation from near and farer fields (water works Káraný). Monitoring of vegetable and potatoes production and Nmin (NO3-N + NH4-N) content down to 120 cm in the area showed high nitrate contents in deep subsoil layers (under) 60 cm, not accessible to the roots of many crops (Klíř et al., 2017). This can be linked to the observed trend of increasing nitrate concentration in extracted water in the last 10 - 20 years. Soil vulnerability to leaching is amplified by a high spatial variability of soil properties which is manifested visually, by water stress impacts, when not irrigated crops are grown in the fields (Duffková et al. 2012).

The objective of research was to gather data on soil and plant spatial variability with standard methods and distant or indirect methods.

## MATERIAL AND METHODS

The four experimental fields, denoted as So1, So2, So3 and Ko1, near Sojovice (50.2139350N, 14.7571592E) and Kochánky villages (50.2757078N, 14.7926503E) were monitored in year 2017. Experimental fields are adjacent to lines of collection wells. Grain pea (Ko1) and winter wheat (So1-3) were grown without irrigation. Soil and plants were sampled in 21 (Ko) and 32 (So) points selected according aerial photos from 2015 and/or 2016 dry years, to represent areas of different impact of drought on crops. Nmin and soil moisture was determined to depth of 90 cm in early Spring. The soil was also analyzed for nutrient content (K, P, Ca, Mg), Cox and Ntot contents in top soil (0-30 cm) and shallow subsoil (30-60 cm) layers. Pedological survey of the fields was performed, soil samples taken to 90 cm were analyzed for soil texture classes, used for calculation of soil water capacity (FWC) with simple pedotransfer functions (Váša, 1960). Above-ground parts of pea and wheat were sampled during vegetative growth and before maturity. The contents of N, 13C and 12C in plants were determined with elementary analyzer EA 3200 (Eurovector, Italy) connected with isotope mass spectrometer Isoprime (GV Instruments, UK). RGB and thermal images were obtained during growth with drones.

## RESULTS AND DISCUSSION

The dry weather of year 2017 contributed to pronounced impact of soil variability on plants. Strong differences in plant height, growth, the time of onset and the severity of water stress, withering and drying were distinguishable at the scale of one meter and less. The results showed significant relationships between 13C discrimination, the indicator of water conditions during growth (e.g. Raimanová et al., 2016), and the impact of soil variability on wheat and pea growth and yield (Fig. 1).