## RESULTS AND DISCUSSION

Practical applications of this new framework are yet available for France for crop years 2005-06 and 2010-11. A set of about 80 000 situations are available for each crop-year (see Dufosse, this workshop): they can be considered as representing the realistic combinations of the main factors known to influence ammonia volatilization encountered in French agricultural conditions. CADASTRE_NH₃ accounts for both the spatial and temporal variabilities of both N fertiliser use and NH₃ emission rate.

<figure><img src="image_40.png" /><figcaption>Figure 1. Regional distributions of (a) N fertiliser use (kt ammoniacal-N) and NH₃ emissions (kt NH₃) in France estimated using CADASTRE_NH₃ for the crop-year 2005-06</figcaption></figure>

## CONCLUSION

One of the most valuable interests of the CADASTRE_NH₃ framework for decision making relies on the fact that it does not use mean data estimated from a general knowledge of fertilization practices in France. Indeed, real cultural practices data on specific crop years are used: specific fertiliser application practices which are coherent with specific weather conditions encountered during the crop year described. Robust prospective studies can thus be undertaken to evaluate the effect of the implementation of policies aiming at mitigating ammonia emissions (e.g. Gothenburg Protocol, 1999). This tool is also being used to generate simple ammonia volatilization response curves for inclusion in N fertilization decision support models.

## Acknowledgements

This work was conducted within the CADASTRE_NH₃ (Spatial and temporal high-resolution inventory of ammonia emissions from agricultural soils over France at regional and national scales, agreement n°1081C0031) project, supported by ADEME. It was supported by a public grant overseen by ANR as part of the «Investissements d'avenir» program (reference: ANR-10-EQPX-17 – Centre d'accès sécurisé aux données – CASD).

## REFERENCES

* Mignolet, C et al., 2007. Spatial dynamics of farming practices in the Seine basin: methods for agronomic approaches on a regional scale. Sci Total Environ 375, 13-32. DOI:10.1016/j.scitotenv.2006.12.004

* Ramanantenasoa M.M.J. et al., 2018. A process-based framework to estimate the spatio-temporal ammonia emissions due to nitrogen fertilization management. Sci. Total Environ. [in review].
# A DECISION SUPPORT FRAMEWORK FOR THE INTEGRATED EVALUATION OF AGRICULTURAL MANAGEMENT IMPACTS ON CROP YIELD, SOIL QUALITY AND ENVIRONMENT

Young, M.¹, Ros, G.H.¹,³, De Vries, W.¹,²

¹Wageningen University and Research, Netherlands; ²Wageningen Environmental Research (Alterra), Netherlands, ³Nutrient Management Institute, Netherlands

## INTRODUCTION

Current agricultural management practices significantly affect crop growth and environmental quality in varying ways based on local agro-ecosystem properties. Integrated and optimal combinations of farm management are needed for agriculture to intensify sustainably. We develop a decision support system (DSS) to evaluate the overall benefits and trade-offs that management has on crop yield, soil quality and environment, using indicators for soil organic carbon (SOC), phosphorus and nitrogen cycles. The DSS integrates a range of soil, crop, and nutrient management practices along with an assessment of the influence of local agro-ecosystem properties (AEPs).

## TOOL DESCRIPTION

At the current stage, an initial model framework is developed into a prototype, consisting of: (1) management and AEP input data, (2) model calculations, and (3) output of changes in response variables (Figure 1). Empirical relationships between management and impacts on crop yield, nitrogen use efficiency (NUE), and SOC changes are assessed using meta-analysis. Process-based modelling estimates changes in nutrient losses to air and water.

<figure><img src="image_41.png" /><figcaption>Figure 1. Conceptual overview of DSS (SOC = soil organic carbon; NUE = nitrogen use efficiency; PUE = phosphorus use efficiency)</figcaption></figure>

At a later stage, phosphorous use efficiency (PUE) or soil P status will be integrated, and process-based models can supplement the medium-term meta-analytical models for long-term SOC, N, and P changes. Furthermore, the results of these models will be integrated into a DSS framework by means of multi-criteria analysis (MCA), since the selection of best management options is a multi-objective goal. The aim is to maximize agricultural intensification (e.g., fertilizer use efficiency, crop yield) and minimize negative environmental externalities (e.g., N and P losses to air and water), while relating (multiple) measures to (multiple) outcomes. Using the indicators mentioned as response variables, evaluation and weighting will be based on (1) various user goals (farmer, policy) and (2) the distance that current levels in the system are
from target levels (crop yield, soil quality) or critical levels (N and P losses). Other impacts, such as soil compaction (by a meta-analytical approach) or heavy metals (by existing model algorithms), will be integrated as well.

## MODELLING APPROACH AND RESULTS

The first set up of the DSS has been focused on (1) assessing the impacts of measures on soil organic carbon (SOC) content and crop yields by meta-analytical approaches and (2) related uptake and losses of N by process-based modelling. Furthermore, site factors such as local AEPs are included in both to assess their influence on each management-impact relationship. As an illustrative example of the meta-regression for yield and SOC, consider the following equation set-up:

$$ \Delta \text{Yield, SOC} = a * \text{MP} + b * \text{soil properties} + c * \text{climate properties} + d * \text{crop type} + \text{interactions} $$

where: MP = management practices; see Figure 1 for soil, climate, and crop properties. Coefficients are estimated by meta-regression.

We currently quantify medium-term impacts on SOC (a minimum of 5 years). So far focus has been on the effects of soil management (tillage) practices on soil carbon inputs and SOC changes. Existing meta-analytical approaches in literature will be improved by assessing the influence of AEPs (e.g. initial SOC status) and their interactions, and by extending the model as a function of time. In a second meta-analytical study, we focus on short-term effect sizes of yield changes due to management using yield data for at least one season. From yield changes, changes in N uptake can be estimated based on a fixed N content of crop types, assuming no changes in those contents in response to the management measures. Based on additional data of N added to the soil, this gives an initial idea of NUE changes as a function of management as well as soil status. In later stages of the research, a meta-analytical approach can be taken to improve these estimations. Where relevant, results of similar meta-analytical studies related to SOC and yield changes in response to management practices will be included (e.g. Haddaway et al., 2017; Han et al., 2016; Hijbeek et al., 2017). Based on results of changes in nutrient uptake from the above-mentioned approaches, changes in N (and later P) balance (nutrient input minus nutrient uptake) will be assessed using the MITERRA/INTEGRATOR model approach (Velthof et al., 2009). Related losses to the environment will be estimated in response to predicted changes in N and P surplus. At the conference, results of (1) meta-analytical techniques for modelling SOC, crop yields, and nutrient uptake changes and (2) N losses will be presented.

**Acknowledgements:** Modelling results are in part obtained from the work of M.Sc. thesis students Susana Lopez Rodriguez, Shan Zhang, and Malte Lessmann. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 675120.

## REFERENCES

* Haddaway, N.R et al. (2017). How does tillage intensity affect soil organic carbon? A systematic review. Environmental Evidence, 6:1-48.

* Han, P et al. (2016). Changes in soil organic carbon in croplands subjected to fertilizer management: a global meta-analysis. Scientific Reports, 6:27199.

* Hijbeek, R et al. (2016). Do organic inputs matter – a meta-analysis of additional yield effects for arable crops in Europe. Plant and Soil, 411: 293-303.

* Velthof, G.L. et al. (2009). Integrated assessment of nitrogen losses from agriculture in EU-27 using MITERRA-EUROPE. Journal of Environmental Quality, 38:402-417.
Authors Index

<table><thead><tr><td>Author</td><td>Page</td></tr></thead><tbody><tr><td>AGASSE S.</td><td>40</td></tr><tr><td>ALBRECHT A.</td><td>24</td></tr><tr><td>ALBUQUERQUE M.</td><td>48 61</td></tr><tr><td>ARRABAL F.</td><td>19 46</td></tr><tr><td>BAEZ D.</td><td>44</td></tr><tr><td>BARATTE C.</td><td>13</td></tr><tr><td>BEDU M.</td><td>7 30</td></tr><tr><td>BEFF L.</td><td>5 34</td></tr><tr><td>BENOIST T.</td><td>48 61</td></tr><tr><td>BHOGAL A.</td><td>11 36 63</td></tr><tr><td>BISINELLA DE FARIA A. B.</td><td>48 61</td></tr><tr><td>BONNARD A.</td><td>9 32</td></tr><tr><td>BRAME C.</td><td>42</td></tr><tr><td>BRAVIN M.</td><td>24</td></tr><tr><td>BRUN-LAFLEUR L.</td><td>28</td></tr><tr><td>CAHUREL J.-Y.</td><td>15 50</td></tr><tr><td>CASTRO J.</td><td>44</td></tr><tr><td>CHADWICK D.R.</td><td>11 36 63</td></tr><tr><td>CLOSSET M.</td><td>9 32</td></tr><tr><td>COLLINET M.</td><td>24</td></tr><tr><td>DAMAY N.</td><td>17</td></tr><tr><td>DE BANDT M.</td><td>40</td></tr><tr><td>DE VRIES W.</td><td>54 67</td></tr><tr><td>DIZIEN C.</td><td>40</td></tr><tr><td>DUBRULLE P.</td><td>7 17 30 38 40 50</td></tr><tr><td>DUFOSSE K.</td><td>52 65</td></tr><tr><td>DUMERCY L.</td><td>28</td></tr><tr><td>DUPAS R.</td><td>7</td></tr><tr><td>DUPONT A.</td><td>7 30</td></tr><tr><td>DUVAL R.</td><td>17</td></tr><tr><td>EDOUARD N.</td><td>28 42</td></tr><tr><td>EGLIN T.</td><td>42</td></tr><tr><td>EL AZHARI A.</td><td>5 34</td></tr><tr><td>ENGSTRÖM L.</td><td>26</td></tr><tr><td>ESPAGNOL S.</td><td>42</td></tr><tr><td>EUGENE M.</td><td>42</td></tr><tr><td>EVEILLARD P.</td><td>40</td></tr><tr><td>FAVERDIN P.</td><td>13 28</td></tr><tr><td>GALLARDO M.</td><td>19 46</td></tr><tr><td>GARCIA M.I.</td><td>44</td></tr><tr><td>GENERMONT S.</td><td>42 52 65</td></tr><tr><td>GILLIOT J.-M.</td><td>52 65</td></tr></tbody></table>
<table>
  <tr>
    <td>GOUVIN G.</td>
    <td>48</td>
    <td>61</td>
  </tr>
  <tr>
    <td>GUINGAND N.</td>
    <td>42</td>
    <td></td>
  </tr>
  <tr>
    <td>HASSOUNA M.</td>
    <td>42</td>
    <td></td>
  </tr>
  <tr>
    <td>HERVE M.,</td>
    <td>40</td>
    <td></td>
  </tr>
  <tr>
    <td>HEURTAUX M.,</td>
    <td>38</td>
    <td>40</td>
  </tr>
  <tr>
    <td>HUTCHINGS, N.J.</td>
    <td>59</td>
    <td></td>
  </tr>
  <tr>
    <td>JEUFFROY, M.H.,</td>
    <td>7</td>
    <td></td>
  </tr>
  <tr>
    <td>KAMMOUN, B.</td>
    <td>9</td>
    <td>32</td>
  </tr>
  <tr>
    <td>KLUMPP, K.,</td>
    <td>42</td>
    <td></td>
  </tr>
  <tr>
    <td>LAFERTE S.</td>
    <td>21</td>
    <td></td>
  </tr>
  <tr>
    <td>LAGADEC S.</td>
    <td>42</td>
    <td></td>
  </tr>
  <tr>
    <td>LAMBERT M.</td>
    <td>40</td>
    <td></td>
  </tr>
  <tr>
    <td>LAMBERT Y.</td>
    <td>5</td>
    <td>34</td>
  </tr>
  <tr>
    <td>LAURENT F.</td>
    <td>38</td>
    <td>40</td>
  </tr>
  <tr>
    <td>LE BRIS X.</td>
    <td>32</td>
    <td></td>
  </tr>
  <tr>
    <td>LE GALL C.</td>
    <td>38</td>
    <td></td>
  </tr>
  <tr>
    <td>LE ROUX C.</td>
    <td>15</td>
    <td>17</td>
    <td>40</td>
    <td>50</td>
  </tr>
  <tr>
    <td>LE SOUDER C.</td>
    <td>21</td>
    <td>40</td>
    <td></td>
  </tr>
  <tr>
    <td>LECONTE L.</td>
    <td>40</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>LEDUC D.</td>
    <td>40</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>LEFEVRE L.</td>
    <td>7</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>LEMERCIER B.</td>
    <td>5</td>
    <td>34</td>
    <td></td>
  </tr>
  <tr>
    <td>LORINQUER E.</td>
    <td>42</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>LOYON L.</td>
    <td>42</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>MACHET J.-M.</td>
    <td>17</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>MALLAVAN B.</td>
    <td>9</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>MAURY O.</td>
    <td>52</td>
    <td>65</td>
    <td></td>
  </tr>
  <tr>
    <td>MEGEL R.</td>
    <td>48</td>
    <td>61</td>
    <td></td>
  </tr>
  <tr>
    <td>MISSELBROOK T.H.</td>
    <td>11</td>
    <td>36</td>
    <td>63</td>
  </tr>
  <tr>
    <td>MORVAN T.</td>
    <td>5</td>
    <td>34</td>
    <td></td>
  </tr>
  <tr>
    <td>NAVES-MASCHIETTO G.</td>
    <td>48</td>
    <td>61</td>
    <td></td>
  </tr>
  <tr>
    <td>NICHOLSON F.A.</td>
    <td>11</td>
    <td>36</td>
    <td>63</td>
  </tr>
  <tr>
    <td>OBRIOT F.</td>
    <td>17</td>
    <td>38</td>
    <td>50</td>
  </tr>
  <tr>
    <td>ORVAIN M.</td>
    <td>48</td>
    <td>61</td>
    <td></td>
  </tr>
  <tr>
    <td>PADILLA F.M.</td>
    <td>19</td>
    <td>46</td>
    <td></td>
  </tr>
  <tr>
    <td>PAGANELLI E.</td>
    <td>9</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>PAILLAT J.M.</td>
    <td>38</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>PAJEAN C.</td>
    <td>48</td>
    <td>61</td>
    <td></td>
  </tr>
  <tr>
    <td>PARNAUDEAU V.</td>
    <td>7</td>
    <td>30</td>
    <td></td>
  </tr>
  <tr>
    <td>PEÑA-FLEITAS M.T.</td>
    <td>19</td>
    <td>46</td>
    <td></td>
  </tr>
  <tr>
    <td>PERBOST R.</td>
    <td>13</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>PEYRAUD J-L.</td>
    <td>13</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>PICHELIN P.</td>
    <td>5</td>
    <td>34</td>
    <td></td>
  </tr>
  <tr>
    <td>PIIKKIK.</td>
    <td>26</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>PONCHANT P.</td>
    <td>42</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>RAMANANTENASOA M.M.J.</td>
    <td>52</td>
    <td>65</td>
    <td></td>
  </tr>
  <tr>
    <td>RAMAT E.</td>
    <td>13</td>
    <td></td>
    <td></td>
  </tr>
</table>
<table><tr><td>RAMOS M.</td><td>24</td></tr><tr><td>RAMOS-CASTILLO M.</td><td>48 61</td></tr><tr><td>REAU R.</td><td>7 30</td></tr><tr><td>RECOUS S.</td><td>7 17 38 40 50</td></tr><tr><td>REVALLIER A.</td><td>48 61</td></tr><tr><td>ROBIN P.</td><td>42</td></tr><tr><td>ROS G.H.</td><td>54 67</td></tr><tr><td>ROUILLE B.</td><td>28</td></tr><tr><td>SAGOO E.</td><td>11 36 63</td></tr><tr><td>SÖDERSTRÖM M.</td><td>26</td></tr><tr><td>SOENEN B.</td><td>21 32</td></tr><tr><td>SQUIVIDANT H.</td><td>32 34</td></tr><tr><td>STENBERG B.</td><td>26</td></tr><tr><td>THOMAS S.</td><td>13</td></tr><tr><td>THOMPSON R.B.</td><td>19 46</td></tr><tr><td>THORMAN R.E.</td><td>11 36 63</td></tr><tr><td>THURIES L.</td><td>24</td></tr><tr><td>TODOROFF P.</td><td>24</td></tr><tr><td>VALE M.</td><td>21</td></tr><tr><td>VAN DOORSLAER B.</td><td>57</td></tr><tr><td>VELOSO A.</td><td>9</td></tr><tr><td>VERBEQUE B.</td><td>38 40</td></tr><tr><td>VERSINI A.</td><td>24</td></tr><tr><td>VIGAN A.</td><td>42</td></tr><tr><td>VINCENT S.</td><td>34 42</td></tr><tr><td>WETTERLIND J.</td><td>26</td></tr><tr><td>WILLIAMS J.R.</td><td>11 36 63</td></tr><tr><td>YOUNG M.</td><td>54 67</td></tr></table>
<figure><img src="image_42.png" /></figure>

CNPS

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