# An approach to provide maps of the N2O emission risks by soils at the regional scale: A case-study at the Haut-Loir watershed, France

Catherine Pasquier, Hocine Bourennane, Isabelle Cousin, Ghislain Girot, Agnès Grossel, Catherine Hénault

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## To cite this version:

Catherine Pasquier, Hocine Bourennane, Isabelle Cousin, Ghislain Girot, Agnès Grossel, et al. "An approach to provide maps of the N2O emission risks by soils at the regional scale: A case-study at the Haut-Loir watershed, France." Geoderma Régional, 2023, 33, pp.art. e00635. 10.1016/j.geodrs.2023.e00635. hal-04115030

HAL Id: hal-04115030

https://hal.inrae.fr/hal-04115030v1

Submitted on 19 Sep 2024

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1 **Title:**

2 An approach to provide maps of the N₂O emission risks by soils at the regional scale: a case-study at

3 the Haut-Loir watershed, France

4 **Authors:**

5 Pasquier Catherine¹, Bourennane Hocine¹, Cousin Isabelle¹, Ghislain Girot¹, Grossel Agnes ¹*, Hénault

6 Catherine ²

7 ¹INRAE, Info&Sols, F-45075, Orléans, France

8 ²INRAE, UMR Agroecologie, F-21078, Dijon, France

9

10 **\*: Corresponding author:**

11 agnes.grossel@inrae.fr

12 INRAE, Info&Sols, 2163 avenue de la Pomme de Pin, 45075 Orléans

13 Tel: France +33 2 38 41 80 48

14

15 **Highlights:**

16 A methodology is developed to map N₂O emission risk based on soil properties.

17 The hazard and the vulnerability allow inferring the risk of N₂O emissions

18 This methodology is relevant for all types of soil dedicated to agriculture

19 Areas where N₂O emissions mitigation actions are possible have been identified.

20

21 **Abstract:**

22 Nitrous oxide (N₂O) is often emitted by soils after nitrogen fertilization when the reduction of nitrate

23 into N₂ is incomplete and the soil is in hydromorphic condition. To take action to reduce N₂O

24 emissions, it is necessary to identify and locate areas that present a risk of N₂O emissions. In this

25 study, an approach to map N₂O emission risk by soils was therefore developed based on soil

26 properties. The risk of N₂O emission was assessed through two components linked to static properties,

27 independent on climate and agricultural practices; the Vulnerability: the ability of the soil to reduce

28 N₂O and the Hazard: the probability of soil water-logging. This approach was tested in the Haut-Loir

29 watershed (3600 km²), a highly cropped area in the French Center Region. Vulnerability and hazard

30 were estimated using French soil databases. The databases contain the drainage class information

31 which allowed inferring the hazard. They also have measurements of pH, CEC and clay content which
allowed estimating vulnerability through a pedotransfer function. In this watershed, contrasting risks were highlighted between different soil types and agricultural regions. High risk soils (~2% of the studied area) were generally found in valleys and were not under crop because of their hydromorphy and acidity. However, attention should be given to medium risk soils (~32% of the area) which were mainly found in the western region. Oppositely, soils of the eastern region present generally no risk of N₂O emissions. Some former field studies have been reported in the studied watershed: they generally supported this soil risk classification. For medium-risk soils, different actions of mitigation depending on the degree of risk were suggested: liming or adjusting nitrogen input periods. This risk mapping approach could be applied in other cropland regions to help mitigation strategy.

41

### Keywords:

Greenhouse gas; Risk assessment; Vulnerability; Hazard; Exposure; Soil mapping; Multiple soil classes.

45

### Abbreviations:

CEC, Cation Exchange Capacity; GHG, Greenhouse Gas; HV, High Vulnerability; HH, High Hazard; HR, High Risk; IPCC, Intergovernmental Panel on Climate Change; Km, Kilometers; LV, Low Vulnerability; LH, Low Hazard; LR, Low Risk; MM, Moderate Vulnerability; MH, Moderate Hazard; MR, Moderate Risk; RP (French), Pedological Referential; RRP, Pedological Regional Referential; STU, Soil Typological Units; SMU, Soil map Units; WRB, World Reference Base for soil resources.

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## 1. Introduction

Climate change induced by increased Greenhouse Gases (GHGs) emissions in the atmosphere results in global warming (Masson-Delmotte et al., 2018). Nitrous oxide (N₂O) is one of the three main anthropogenic GHGs in the atmosphere. N₂O has about 265 times the warming potential of CO₂ (Ciais et al., 2013), but its concentration is more than 1000 times lower than atmospheric CO₂ concentration, so its contribution to the greenhouse effect is evaluated for 2020 at about 7% against 66% for CO₂ and 16% for CH₄ (WMO, 2021). In France, agriculture is the main sector contributing to anthropogenic N₂O emissions and represented about 89% of these emissions in 2019 (Thompson et al., 2019; CITEPA, 2021). Agricultural emissions of N₂O are mainly due to N inputs of mineral and organic fertilizers in soils and a linear relation is assumed between N inputs and N₂O emissions (1% of N input, IPCC Tier-1, Hergoualc'h et al., 2019). In France, since 1990, N₂O emissions have been slightly decreased, from 65.4 to 38.2 Mt CO₂e, and emission due to agriculture decreased slowly from 38.2 to 34.5 Mt CO₂e (CITEPA, 2021), thanks to the use of more optimized and regulated mineral fertilizers. The "Centre - Val de Loire" region is a region of intensive agriculture where the contribution of agriculture to total N₂O emissions is estimated to be as high as 95% (2.6 Mt CO₂e) corresponding to about 14% of all GHGs in CO₂e (LIG'AIR inventory V2.4/2020).

Agricultural N₂O emissions exhibit a very large spatial and temporal variability because they are highly dependent on pedoclimatic conditions (e.g., soil concentration of mineral nitrogen, soil moisture, temperature). The N₂O is indeed produced by several microbial processes resulting from the activity of different species of microorganisms (e.g., nitrification, denitrification, nitrifier denitrification...; Butterbach-Bahl et al., 2013) using soil nitrogen as substrate. Denitrification (the reduction of nitrate in several steps, to N₂O and lastly to N₂) is considered as one of the most important processes of N₂O production in soils (Dobbie and Smith, 2001). Denitrification occurs in anoxic soils and is therefore favored by soil moisture. Large peaks of N₂O emissions occur in wet soils with high nitrogen (N) levels, usually after input of nitrogen fertilizers (Stehfest and Bouwmann, 2006; Ito et al., 2018). Complete denitrification leads to N₂ emission, which is not of environmental concern since N₂ is a non-reactive molecule and is not absorbing in the infrared spectrum. The last
step of denitrification, the reduction of N₂O in N₂, is inhibited in certain soil conditions or in some soils. Therefore, some soils do not present a capacity to reduce N₂O to N₂. For example, pH controls the ability of the denitrifying bacteria to express N₂O reductase early and efficiently (Russenes et al., 2016), which probably explains why soil pH influences N₂O emissions when denitrification is the main source of N₂O. Previous studies also revealed soil pH as one of the main factors governing regional variability of N₂O emission on a global meta-analysis (Wang et al., 2018). Many studies have been conducted to understand the determinism of N₂O production and reduction, bringing more and more insight into these processes and their controlling factors (Stehfest and Bouwman, 2006; Cui et al., 2021) and the effect of different agricultural practices. This enables to propose some technical solutions to limit N₂O emissions. Avoiding excess soil nitrate, particularly when soils are too wet, can help minimize N₂O emissions from agriculturally managed soils. Other solutions are being studied, like bacterial seeding, draining moist soil, nitrification-inhibiting (e.g. Henault et al., 1998; Rochette, 2008; Ruser and Schulz, 2015; Hinton et al., 2015). pH management, i.e. soil liming to raise the pH, is also an important track to reduce N₂O emissions (Henault et al., 2019; Shaaban et al., 2020).

However, most studies have been made at field scale, so the representativeness of observations when upscaling (e.g. regional scale) is unclear. N₂O is indeed produced at soil microsite scale and this production is controlled by a complex interaction of factors. Therefore, similar agricultural practices can have different effect on N₂O emission in different soil types (e.g. Rochette et al., 2008). The use of methods to reduce N₂O emissions involve identifying soils which are likely to emit N₂O. This is consistent with the findings of Cui et al. (2021), who recently provided a global map of N₂O emission factors (i.e. N₂O emission taking into account nitrogen inputs) based on a data-driven meta-analysis. They outlined that the most policy-relevant question is to identify where emissions can be mitigated more efficiently.

It is therefore useful to provide maps of N₂O emissions or emission risk which can indicate where actions have to be taken. Few studies provide spatial assessment of emissions and they generally consider agricultural practices, which can be difficult to obtain. Mapping N₂O emission can be done by applying predictive models: this approach was used by the European Soil Data Centre, who
published a map of N₂O emissions from agricultural soil in Europe taking soil properties into account, based on LUCAS soil sampling program and using DayCent model combined with random forest approach (Lugato et al., 2017). The resulting map depends on climatic data inputs and information on managements practices. As N₂O emissions are extremely dependent on climatic conditions, they averaged 5 years of data to smooth the temporal variability. Another method was proposed by Kritee et al. (2018) who mapped risk of large N₂O emissions from rice production taking into account two components: water management regimes and regional N fertilizer rates, but ignoring soil properties. In both cases, the maps are a snapshot of a given situation as climate and management practices are subjected to change. Providing soil N₂O emission risk, based on soil properties, i.e. static or more or less static properties, is a valuable approach for policy-makers. This is consistent with the approach of Cui et al. (2021): they provided a global map of emission factors rather than N₂O emissions. Global information may however have a too coarse resolution for defining mitigation strategies; the best scale would be regional or national, which is also the scale of policy-making. However, Cui et al. (2021) also observed that controlling factors are scale-dependent, so it is very important to further provide regional maps of N₂O emission risk.

The objective of this paper was therefore to develop an approach to map at regional scale N₂O emission risk based on soil properties. IPCC approach defined risk as the likelihood of harmful alterations due to hazardous physical events interacting with vulnerable conditions (Lavell et al., 2012). They proposed to cross hazard, vulnerability and exposure to fully assess risk, and defined these three notions. Hazard is the potential occurrence of a physical event having adverse effect; vulnerability is the predisposition of an element to be affected due to internal characteristics; and exposure referred to the presence of resources in places that could be affected by the physical events. A similar approach is then developed here for N₂O emission risk. Hazard was considered as soil water excess probability, which leads to conditions favorable to denitrification. Vulnerability was considered to be due to the soil capacity to reduce N₂O in N₂, which, as already mentioned, depends on soil properties. Exposure corresponds to soil nitrogen inputs. In this study, vulnerability and hazard were considered simultaneously to provide maps considering only static risk in relation to soil properties.
Soil capacity to reduce N₂O to N₂ can be evaluated in the laboratory, using incubated soil samples according to ISO/TS 20131-2 method (Le Gall et al., 2014; Henault et al., 2019). An index, called r-max value, is calculated, which corresponds to the ratio between the amount of N₂O emitted without and with acetylene during incubation because acetylene inhibits the last step of denitrification. The higher is the r-max, the lower is the soil's ability to reduce N₂O to N₂. The r-max value can also be estimated by a soil function depending of pH, Cation Exchange Capacity (CEC) and clay content, where pH is the more relevant variable of function (Henault et al., 2019). This function was applied on soil databases. Hazard was assessed by the natural soil hydromorphic class.

Providing N₂O emission risk assessment is especially important in intensive cropland areas receiving large fertilizations. The present approach was thus applied in an intensive cropland region of France. The first step was to map the r-max index using the pedotransfer function and soil variables from a French Soil database. In a second step, the risk approach was tested to assess graduated soil N₂O emissions risk based on the combination of vulnerability and hazard. A former dataset was also used to assess the validity of the soil risk classification. Therefore, this study aims to contribute and improve existing tools and approaches to provide at decision-scale N₂O emission maps from existing soil databases.

## 2. Materials and methods

### 2.1. The study area

The study area is a watershed in the upper valley of Loir River "Haut-Loir", located in French "Centre Val-de-Loire" region France, that has already been the support of several studies on direct and indirect N₂O emissions (Gu et al., 2013; Grossel et al., 2016; Billen et al., 2018, 2020). It extends over 3600 km² and includes seven agricultural regions: Perche, Faux Perche, Beauce, Beauce Dunoise, Orléanais, Val de Loire and Vallée de Loir (Figure 1). The four main regions (Perche, Faux Perche, Beauce, Beauce Dunoise) represent 90% of the study area and are covered by intensive croplands – usually wheat, barley, maize and rapeseed - that are subjected to high nitrogen inputs.
<figure><img src="image_3.png" /><figcaption>Figure 1: Haut-Loir extension, study area</figcaption></figure>

The Loir River consists in a natural limit between the Eastern Beauce/Beauce Dunoise regions and the Western Perche/Faux Perche regions. For the purposes of this regional study, the soil classification was kept in its original repository (RP2008, Baize et al., 2009) described in the French database (Figure 2). The correspondence between the RP2008 and the WRB soil classifications is given in the Appendix A. The match is not easy between the two systems of classification because a soil described in RP2008 can have several matchings in WRB and vice versa. The correspondence depends on many possibilities of qualifying soils that have not been further analysed in this study due to the lack of representativeness at this regional scale.

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Dominant Soil Typological Unit (RP 2008)

Mineral soils

RANKOSOLS

Undeveloped soils

BRUNISOLS

Valley soils

COLLUVIOSOLS

FLUVIOSOLS

Developed soils

NEOLUVISOLS

LUVISOLS

Soils developed on limestone

RENDISOLS

CALCISOLS

RENDOSOLS

CALCOSOLS

Stagnic soils

REDUCTISOLS

REDOXISOLS

PLANOSOLS

PELOSOLS

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10

Km
Figure 2: Haut-Loir soils map. Soils are given in regional repository and the correspondence to WRB is given in Appendix A.

The Western Perche and the Vallée du Loir Region are dominated by hydromorphic LUVISOLS and PLANOSOLS, which are usually drained and limed for agriculture practices (Figure 2). REDUCTISOLS are usually located near streams. In the Beauce/Beauce Dunoise regions, soils are developed on limestone (CALCOSOLS, CALCISOLS and NEOLUVISOLS ...): they are more clayey and exhibit a higher pH value. Agricultural soils in these Eastern areas are usually irrigated, which allows maize plant in crop rotation. Orléanais forest region, in the Southwest part of the study area, and the Val-de-Loire Region, are dominated by PLANOSOLS and REDOXISOLS. The white areas in the soils map (Figure 2) correspond to undefined soil (urban areas) where there is no data in DoneSol.

## 2.2. The soil database

We have used a French Soil Geographical database on a scale of 1:250,000: "Référentiels Régionaux Pédologiques", RRPs (Richer-de-Forges et al., 2019). RRPs are regional geographic databases established from field surveys and observations by soil scientists. The data from RRPs are available to users through a national standardized soil information storage system (DoneSol).

Donesol contains a list of Soil Typological Units (STU). In RP2008, STU are described by variables specifying the soil type and their properties (soil texture, CEC, pH, soil drainage, etc.). Soil natural drainage is coded from 1 (well-drained soils) to 9 (submerged soils) (Richer-de-Forges et al., 2019; cf. Table 1, supplementary material). This indicator provides information on the frequency of excess water in the soil.

At the scale of 1:250,000, it is not possible to delineate the STUs. Therefore, they are grouped into Soil Mapping Units (SMU) to form soil associations and to illustrate the functioning of pedological systems in landscapes. Each SMU corresponds to soil-landscape, i.e. a part of the mapped territory defined by specific pedology, hydrogeology, topology and / or land use. It has a known shape and location and is represented by one or more polygons in a geometrical dataset. Oppositely, STU cannot
be precisely located within SMU. Soil type (STU) can be identified within SMU and is specified as a percentage of SMU area. As a result, RRPs consists of both a geometrical dataset defining SMU and a semantic dataset which links attribute values, including STU and soil variables, to the SMUs. It is the same principle than for Soil Geographical Database of Europe, well-illustrated in the supplementary Figure 1.

For display purpose, the SMU properties are represented with either the dominant STU, or a weighted average of STU areas. Both methods were tested in this study. Database structure is explained in the following link:

(https://esdac.jrc.ec.europa.eu/ESDB_Archive/ESDBv2/esdb/sgdbe/metadata/purity Maps/purity.htm.

208

209 2.3. R-max index

The r-max index, indicative of the capacity of a soil to reduce N₂O to N₂, was calculated for each STU by using soil variables (CEC, pH and clay content) from the DoneSol database according to the following function (Henault et al., 2019 and ISO/TS 20131-2 norm):

$$r-max = -0.4 pH + 0.026 CEC - 0.001 clay + 3.13 (r = 0.88)$$

where pH is evaluated on an air-dried sample suspended in water according to the NF ISO 10390 norm, CEC is evaluated on a soil sample extract using a cobalt-hexamine solution (Orsini and Rémi, 1976) according to the NF X 31-130 norm, and clay represents the clay content (g.g⁻¹) measured on a soil sample without decarbonation by using the Robinson pipette method according to the NF X 31-107 norm. The r-max values are limited to 1.2.

According to Hénault et al., 2019, soils with an *r-max* > 0.8 have a very low capacity to reduce N₂O, soils with *r-max* < 0.4 are able to reduce N₂O and soils with an *r-max* value between 0.4 and 0.8 have an intermediate capacity to reduce N₂O. Soil pH explains most of the *r-max* index variability (61%): soils with pH < 6.4 have usually *r-max* > 0.8, and soils with pH > 6.8 have *r-max* < 0.4, (Hénault et al., 2019).
## 2.4. Definition of N₂O emission risk

Risk of N₂O emission was based on Hazard, Vulnerability and Exposure as suggested in literature (Crichton, 1999; Wolf, 2012; Lavell et al., 2012).

<figure><img src="image_6.png" /><figcaption>Figure 3: representation of N₂O emission risk, inspired by core concept of SREX IPCC, 2012</figcaption></figure>

In our case (Figure 3) Vulnerability corresponds to the soil inability to reduce N₂O, natural Hazard is the probability that a situation of excess water occurs, and Exposure corresponds to N fertilization by farmers, leading to an increase in available mineral N into the soil. Hazard can be predicted thanks to precipitations or soil water content measured by in situ sensors. Exposure could be limited by decreasing N inputs. Exposure depends on land use: croplands were considered as the only ones that are subject to nitrogen inputs related to fertilization. For urban area or forest, Exposure is zero.

<figure><img src="image_7.png" /></figure>
Three classes of Vulnerability were defined: Low Vulnerability (LV) for *r-max* < 0.4, Moderate Vulnerability (MV) for 0.4<r-max<0.8 and High Vulnerability (HV) for *r-max* >0.8. Hazard, i.e. probability of excess water in soils, depends not only on precipitations but also on soil type. Soil drainage class was thus considered to identify areas where excess water may occur. Three classes of Hazards were similarly defined: Low Hazard (LH) for DoneSol soil drain classes 1, 2 and 3, Moderate Hazard (MH) for classes 4, 5 and 6 and High Hazard (HH) for classes 7, 8 and 9. Finally, we defined risk by crossing vulnerability with hazard (Figure 4). However, hazard and vulnerability, as defined, are not controlling N₂O emission risk in the same way. Excess water controls the occurrence of denitrification, i.e. N₂O production in soils. The soil capacity to reduce N₂O in N₂ is important only if denitrification and N₂O production can occur, i.e. if there is significant hazard. This is why hazard was considered to have a higher control on risk than vulnerability and the figure 4 is not fully symmetrical. In other words, moderate Risk (MR) corresponds at situations of Moderate Hazard with Moderate or High Vulnerabilities and High Risk (HR) corresponds at situations of High Hazard with Moderate or High Vulnerabilities.

Last four risk categories were defined to suggest different mitigation strategies. Vulnerability can be reduced by actions of mitigation. For example, liming soils raises the pH and increases the soil's ability to reduce N₂O (Henault et al., 2019). Categories 1 (moderate risk) and 3 (high risk) correspond to situations of moderate vulnerability that can mostly be mitigated by liming soil when pH < 6.8. Categories 2 and 4 correspond respectively to Moderate Risk and High Risk that can be reduced by liming soil when pH < 6.4 and that further requires special precautions when supplying nitrogen in soil (dose reduction or taking account soil water condition).

## 2.5 Validation data

N₂O emission measurements from previous studies (Hénault et al., 2005; Franqueville et al., 2018 and other unpublished studies) were used to validate the present approach. These measurements were carried out over thirteen study sites included in our study area “Haut-Loir”.
Thus, N₂O emissions and soil properties are available from direct measurements for one CALCISOLS, six LUVISOLS, two BRUNISOLS and four COLLUVIOSOLS. N₂O measurements were done by static chamber with a frequency varying from once per week to once per month.

All plots were cropped with winter cereals and fertilized with mineral N but at different timing, splitting and amount. Therefore, to compare sites, N₂O emissions were cumulated from the last date before first fertilization to one month after the last fertilization. N₂O peaks generally occur in the weeks following N inputs so this may encompass most of the fertilization effect. The ratio of cumulative emission during post-fertilization period to the N input amount was then calculated for all sites.

Measured r-max values following the protocol of Hénault et al. (2019) were reported when available (measured r-max index). A Calc. r-max index was calculated from soil properties measured on in situ soil samples during the studies and STU r-max index was calculated from the Donesol values of the map soil STU. Drainage classes were inferred from Donesol database. This allowed to assess hazard and vulnerability and to calculate a risk class for each site.

## 3. Results and discussion

### 3.1. Application of the *r-max* function at STU and SMU resolution

#### 3.1.1. *r-max* index computed by STU

DoneSol semantic database was used to infer the *r-max* values for all STU with the values of clay. CEC and pH (85% of STU). This allowed to assess an *r-max* value (Table 1) for each soil type of the study site and thus infer its vulnerability typology (ability to reduce N₂O to N₂).

<table><caption>Table 1: *r-max* mean value by soil type and associated standard deviation (std), number (nb) of values in each STU and SMU, representativeness in study site (% area) and vulnerability typology.</caption><thead><tr><th>Soil type (RP2008)</th><th>nb STU</th><th>r-max mean</th><th>r-max std</th><th>% area</th><th>nb SMU</th><th>Vulnerability</th></tr></thead></table>
<table><tr><td>CALCOSOLS (CALCO)</td><td>23</td><td>0.11</td><td>0.13</td><td>10</td><td>25</td><td>LV</td></tr><tr><td>RENDOSOLS (RENDO)</td><td>11</td><td>0.14</td><td>0.14</td><td>3</td><td>22</td><td>LV</td></tr><tr><td>RENDISOLS (RENDI)</td><td>5</td><td>0.17</td><td>0.16</td><td>1</td><td>10</td><td>LV</td></tr><tr><td>HISTOSOLS* (HIST)</td><td>1</td><td>0.25</td><td></td><td>&lt;1</td><td>1</td><td>LV</td></tr><tr><td>CALCISOLS (CALCI)</td><td>18</td><td>0.29</td><td>0.19</td><td>6</td><td>26</td><td>LV</td></tr><tr><td>FLUVIOSOLS (FLUV)</td><td>19</td><td>0.42</td><td>0.26</td><td>&lt;1</td><td>9</td><td>MV</td></tr><tr><td>PEYROSOLS (PEYR)</td><td>1</td><td>0.44</td><td></td><td>&lt;1</td><td>1</td><td>MV</td></tr><tr><td>COLLUVIOSOLS (COLL)</td><td>10</td><td>0.51</td><td>0.36</td><td>1</td><td>7</td><td>MV</td></tr><tr><td>NEOLUVISOLS (NEO)</td><td>18</td><td>0.58</td><td>0.21</td><td>12</td><td>20</td><td>MV</td></tr><tr><td>BRUNISOLS* (BRUN)</td><td>51</td><td>0.61</td><td>0.31</td><td>14</td><td>45</td><td>MV</td></tr><tr><td>ARENOSOLS (ARE)</td><td>1</td><td>0.62</td><td></td><td>&lt;1</td><td>2</td><td>MV</td></tr><tr><td>PELOSOLS (PEL)</td><td>3</td><td>0.63</td><td>0.10</td><td>&lt;1</td><td>2</td><td>MV</td></tr><tr><td>LUVISOLS (LUV)</td><td>50</td><td>0.64</td><td>0.22</td><td>28</td><td>32</td><td>MV</td></tr><tr><td>FERSIALSOLS (FER)</td><td>1</td><td>0.74</td><td></td><td>&lt;1</td><td>1</td><td>MV</td></tr><tr><td>PLANOSOLS (PLANO)</td><td>10</td><td>0.78</td><td>0.09</td><td>8</td><td>15</td><td>MV</td></tr><tr><td>REDOXISOLS (REDOX)</td><td>6</td><td>0.80</td><td>0.37</td><td>2</td><td>11</td><td>HV</td></tr><tr><td>VERTISOLS (VERT)</td><td>1</td><td>0.86</td><td></td><td>&lt;1</td><td>1</td><td>HV</td></tr><tr><td>REDUCTISOLS (REDUCT)</td><td>5</td><td>0.88</td><td>0.28</td><td>1</td><td>3</td><td>HV</td></tr><tr><td>ALOCRISOLS (ALO)</td><td>1</td><td>1.20</td><td></td><td>&lt;1</td><td>1</td><td>HV</td></tr><tr><td>PODZOSOLS (PODZ)</td><td>2</td><td>1.20</td><td>0.00</td><td>&lt;1</td><td>2</td><td>HV</td></tr></table>

286 The *r-max* mean values vary from 0.11 (CALCOSOLS, carbonated soil from the surface) to 1.20 (PODZOSOLS, acidic soils). Soil types FERSIALSOLS, HISTOSOLS, ARENOSOLS, VERTISOLS and ALOCRISOLS are less encountered in the study area, thus their *r-max* values must be further confirmed.

287 The soils with the lowest *r-max* are soils developed on limestone (RENDOSOLS, CALCOSOLS, RENDISOLS and CALCISOLS). These soils have an *r-max* under 0.4, so they are able to reduce N₂O. They cover 20% of the Haut-Loir surface and they are mostly to the Eastern of the site (Beauce/Beauce dunoise). For the HISTOSOLS the *r-max* value is 0.25. As HISTOSOLS correspond to peat soils (organic soils) and are often acidic, farmed organic soils appear to emit exceptionally large amounts of N₂O (Kasimir-Klemedtsson et al., 1997). However, in this study the only HISTOSOLS is a eutrophic-peat with pH > 8.
299

All the other soil types present a mean of *r-max* over 0.4, and some of them are over 0.8
(REDUCTISOLS, REDOXISOLS, VERTISOLS, PODZOSOLS and ALOCRISOLS, for 5% of the
study site) and therefore not able to reduce N₂O (HV). Over 60 % of soil surface are classed in MV.

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The BRUNISOLS (14 % of the study area) show *r-max* values ranging from 0 to 1.2. In fact, there are two large categories of BRUNISOLS: Eutric BRUNISOLS and Dystric BRUNISOLS Dystric soils have a base saturation (S/CEC: S being the sum of exchangeable cations (Ca²⁺, Mg²⁺, K⁺ and Na⁺)) at pH=7 of less than 50 % whereas Eutric soil have a base saturation at pH=7 of 50 % or more (Baize et al., 2009). This soil attribute depends on the CEC and therefore has an influence on the *r-max* value. Dystric BRUNISOLS have an *r-max* mean value upper than 0.8 and Eutric BRUNISOLS have a *r-max* value about 0.59, i.e. in the intermediate class.

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There is also a large variability in *r-max* values of hydromorphic soils (COLLUVIOSOLS, FLUVIOSOLS, REDOXISOLS and REDUCTISOLS), because these soils could be more or less clayey and more or less acidic.

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The value of the *r-max* seems to follow a growth in the direction of soil evolution through the lateralization processes of clay lixiviation and acidification. Low-evolved soils (RENDOSOLS, RENDISOLS, CALCOSOLS and CALCISOLS) have the ability to reduce N₂O to N₂, then soils that move towards BRUNISOLS, LUVISOLS, PLANOSOLS lose their ability to reduce N₂O and finally REDUCTISOLS and PODZOSOLS no longer reduce N₂O at all in N₂.

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This soil evolution is described in soil Atlas of Europe in WRB classification. Cambisols degrade because of vertical water erosion. The continuous leaching moves the calcium carbonate front further downwards, the pH drops to about 6 and clay illuviation starts to become rich Luvisol. However, the leaching will continuously remove the base elements from the soil. This will make the profile so acid that it will be classified as an Alisol. At this stage the soil is so acid that the clay in the illuviated horizon will disintegrate or be redistributed to other parts of the profile and tongues of silt and sand will cut into the clay illuviated horizon. This is referred to as an Albeluvisol. Finally, the leaching will enable an iron pan to develop and the soil turns into a Podzol.

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324
325 3.1.2.r-max aggregation by SMU

326 SMU *r-max* maps joining data from RRPs and Donesol were carried out with the r-max mean of all
327 STU contained in the SMU weighted by their area (Fig. 5a) and using the soil variables of dominant
328 STU (Fig. 5b). There are many similarities between the two maps. The difference between Eastern and
329 Western regions can be explained by the presence of soils developed on limestone in Beauce/ Beauce
330 Dunoise (CALCOSOLS, RENDOSOLS, RENDISOLS, CALCISOLS) and the more acidic soils in
331 Western regions. Overall Western soils are not able to reduce N<sub>2</sub>O. The highest *r-max* values are
332 located near the streams and in watershed heads of the Western-Perche/Faux Perche region
333 (REDUCTISOLS and LUVISOLS).

334 Figures 5c and 5d correspond, respectively, to the standard deviation calculated in the SMU and to the
335 representativeness of the dominant soil in the SMU. The maps show SMU with an *r-max* values > 0.8
336 in northeast (Beauce) and in southeast (Orléanais). In these areas, the *r-max* standard deviation is > 0.4
337 (Figure 5d). This suggests a large variation of *r-max* within the SMU due to differences between *r-
338 max* from grouped STU.

<figure><img src="image_8.png" /></figure>

339

340 Figure 5: *r-max* maps and related standard deviation at study site (Haut-Loir) (respectively with mean
341 and standard-deviation of all STU per SMU: 5a and 5c; and dominant STU values, 5b and 5d). See
342 text for more details.
<figure><img src="image_9.png" /></figure>

SMU aggregation allows to represent spatially the r-max at the expense of accuracy. The mean value tends to smooth out extreme values and the dominant STU value is not always representative.

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

## 3.2. Risk assessment

<figure><img src="image_12.png" /></figure>

### 3.2.1. Vulnerability and Hazard

<figure><img src="image_13.png" /></figure>

Figure 6 exhibits the percentage of area of each SMU corresponding to the different Vulnerability and

<figure><img src="image_14.png" /></figure>

Hazard typologies. This area was estimated based on vulnerability and hazard of the STUs forming the

<figure><img src="image_15.png" /></figure>

SMU.

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>Figure 6: Vulnerability and Hazard maps estimated at STU scale and expressed in percentage of SMU area</figcaption></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

These maps allow to detect where the most vulnerable soils are located and those subject to the greatest hazards. 15% of soil surface is not classified because of missing values. 29% of soil surface from study site are classified in Low Vulnerability, 44% in Moderate Vulnerability and 12% in High Vulnerability. 49% of soil surface are classified in Low Hazard, 35% in Moderate Hazard and 1% in High Hazard.

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

Soils with Low Vulnerability and Hazard are mostly located in Eastern Beauce/Beauce Dunoise. Soils with Moderate Vulnerability and Hazard are mostly located in Western Perce/Faux-Perche and Orléanais and soils with High Vulnerability and Hazard are located in valleys.

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>
362

Hazard and Vulnerability areas are quite similar. Non-vulnerable soils, therefore able to reduce N₂O to N₂, seems not to have water excess characteristics. Conversely, vulnerable soils (MV and HV) are generally in a situation of excess soil water (MH and HH). Indeed, excess water in the soil and leaching induce redox processes and increase the soil acidity (Van Breemen and Buurman, 2002), so these that two components are not always independent.

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There are still a few special cases: the heads of watersheds on the Western side are classified in HV and LH. There is also an area in North of the Beauce region that is classified in HV (because of low pH value) and which is classified largely in LH.

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370

### 3.2.2. Risk assessment by soil type

371

The risk of N₂O emission was assessed according to the soil type. The results are shown in Figure 7.

372

<figure><img src="image_28.png" /></figure>

373

<caption>Figure 7: risk by soil type based on their Vulnerability (mean r-max in x-axis) and Hazard ranges (mean drainage class in y-axis)). Bars indicate the min-max values of both r-max values and drainage</caption>

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375

codes for each soil type. For clarity only the first letters of the name of soil types is given; see Table 2
and text for the full name.

376

377

378 Only REDUCTISOLS are in risk situation “4” and some of the FLUVIOSOLS which may be in a
379 significant situation of excess water are in risk situation “3”. These two soil types are located in
380 wetlands that are currently protected from excessive nitrogen inputs. They are mostly occupied by
381 grasslands because the excess water does not allow cultivation.

382 Soils developed on limestone (CALCISOLS, CALCOSOLS, RENDISOLS and RENDOSOLS) do
383 not present any risk of N₂O emissions. HISTOSOLS, PEYROSOLS, VERTISOLS and
384 ALOCRISOLS have no risk either, but there is only one value available to characterize them.
385 PODZOSOLS have a High Vulnerability but the mean value of soil drain class is 3, thus the risk is
386 between Low and Moderate. Podzosols are very poor and very acidic soils that are not conducive to
387 agriculture.

388 All the others soils have a mid point in risk situation class “1” or “2”. As soil types are generally
389 composed of several STU, some STU may be at risk of N₂O emission and some others not, which is
390 illustrated in the Figure 7 by bars crossing the dashed risk line. Some of BRUNISOLS,
391 COLLUVIOSOLS, LUVISOLS, NEOLUVISOLS, PLANOSOLS and REDOXISOLS can be in
392 moderate risk category (situations 1 or 2) depending on their r-max values and usually of acidity.
393 High-risk situations of N₂O emissions are not necessarily found in cultivated areas and are therefore
394 not subject to high nitrogen Exposure. Indeed, very moist and acidic soils are not favourable to
395 agriculture.

396

3.2.3. Validation of the approach using a former dataset

397

398
Table 2 shows a very good consistency between N₂O emission and Calc. *r-max* index. However, some discrepancy exists between Calc. r-max index and STU r-max index (Table 2). This is owing to soil map resolution. Nevertheless, all soil types are within the same risk class as shown in Figure 7.
<table><caption>Table 2: Sites used for the validation and the measured properties during the field campaigns. The 3 first sites were taken from Hénault et al. (2005). SP sites were taken from Franqueville et al. (2018). ND4 is the same site as presented in Grossel et al. (2016) but flux data are unpublished. See these studies for more details. Note that SP5, SP6 and ND4 were sampled in both 2014 and 2015. SP7, SP8 and INRAE data are unpublished. Last column indicates the r-max index value calculated with the pedotransfer function of Hénault et al. (2019). Class risk estimated with the map soil typology.</caption><thead><tr><th rowspan="2">Site</th><th rowspan="2">Region</th><th rowspan="2">Period</th><th colspan="8">Field measurements</th><th colspan="7">Donesol database</th></tr><tr><th>N<sub>2</sub>O emission (g-N/ha)</th><th>N input (kg N/ha)</th><th>Measured r-max index</th><th>pH</th><th>CEC (cmol +.kg<sup>-1</sup>)</th><th>Clay (g.kg<sup>-1</sup>)</th><th>Calculated r-max index</th><th>STU name</th><th>pH</th><th>CEC (cmol +.kg<sup>-1</sup>)</th><th>Clay (g.kg<sup>-1</sup>)</th><th>STU r-max index</th><th>Drain class</th><th>class risk</th></tr></thead><tbody><tr><td>Villamblain (1999)</td><td>Beauce Dunoise</td><td>Feb - June 1999</td><td>517</td><td>230</td><td>0.2</td><td>7.9</td><td>22.8</td><td>334</td><td>0.23</td><td>CALCISOLS</td><td>8</td><td>20.8</td><td>341</td><td>0.13</td><td>2</td><td>0</td></tr><tr><td>La Saussaye (1999)</td><td>Beauce</td><td>Feb - June 1999</td><td>376</td><td>164</td><td>0.2</td><td>7.8</td><td>16.5</td><td>242</td><td>0.2</td><td>BRUNISOLS</td><td>6.6</td><td>9.6</td><td>175</td><td>0.56</td><td>2</td><td>0</td></tr><tr><td>Arrou (1999)</td><td>Faux-Perche</td><td>Feb - June 1999</td><td>2855</td><td>173</td><td>0.6</td><td>6.23</td><td>2.9</td><td>50</td><td>0.66</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>SP1 (2014)</td><td>Faux-Perche</td><td>Feb - May 2014</td><td>696</td><td>142</td><td></td><td>7.1</td><td>7.24</td><td>125</td><td>0.35</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>SP3 (2014)</td><td>Faux-Perche</td><td>Feb - May 2014</td><td>950</td><td>140</td><td></td><td>6.8</td><td>9.82</td><td>181</td><td>0.48</td><td>COLLUVIOSOLS</td><td>6.4</td><td>12.7</td><td>250</td><td>0.65</td><td>5</td><td>1</td></tr><tr><td>SP5 (2014)</td><td>Faux-Perche</td><td>Feb - April 2014</td><td>986</td><td>170</td><td></td><td>7</td><td>9.11</td><td>145</td><td>0.42</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>SP6 (2014)</td><td>Faux-Perche</td><td>Feb - April 2014</td><td>2034</td><td>175</td><td></td><td>6.3</td><td>8.06</td><td>237</td><td>0.58</td><td>COLLUVIOSOLS</td><td>6.3</td><td>8.1</td><td>230</td><td>0.59</td><td>5</td><td>1</td></tr><tr><td>ND4 (2014)</td><td>Faux-Perche</td><td>Feb - April 2014</td><td>2781</td><td>175</td><td>0.66</td><td>6.3</td><td>6.81</td><td>141</td><td>0.65</td><td>COLLUVIOSOLS</td><td>6.4</td><td>12.7</td><td>250</td><td>0.65</td><td>5</td><td>1</td></tr><tr><td>SP2 (2015)</td><td>Faux-Perche</td><td>Feb - May 2015</td><td>740</td><td>231</td><td></td><td>6.4</td><td>6.82</td><td>140</td><td>0.61</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>SP4 (2015)</td><td>Faux-Perche</td><td>Feb - May 2015</td><td>933</td><td>220</td><td></td><td>6.5</td><td>9.82</td><td>137</td><td>0.65</td><td>COLLUVIOSOLS</td><td>6.4</td><td>12.7</td><td>250</td><td>0.65</td><td>5</td><td>1</td></tr><tr><td>SP5 (2015)</td><td>Faux-Perche</td><td>Feb - May 2015</td><td>314</td><td>230</td><td></td><td>7</td><td>9.11</td><td>145</td><td>0.42</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>SP6 (2015)</td><td>Faux-Perche</td><td>Feb - May 2015</td><td>293</td><td>230</td><td></td><td>6.3</td><td>8.06</td><td>237</td><td>0.58</td><td>COLLUVIOSOLS</td><td>6.3</td><td>8.1</td><td>230</td><td>0.59</td><td>5</td><td>1</td></tr><tr><td>ND4 (2015)</td><td>Faux-Perche</td><td>Feb - May 2015</td><td>2118</td><td>230</td><td>0.66</td><td>6.3</td><td>6.81</td><td>141</td><td>0.65</td><td>COLLUVIOSOLS</td><td>6.4</td><td>12.7</td><td>250</td><td>0.65</td><td>5</td><td>1</td></tr><tr><td>SP7 (2018)</td><td>Faux-Perche</td><td>Feb - May 2018</td><td>1550</td><td>240</td><td></td><td>7.2</td><td>9.7</td><td>118</td><td>0.4</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>SP8 (2018)</td><td>Faux-Perche</td><td>Feb - May 2018</td><td>1126</td><td>234</td><td></td><td>6.6</td><td>8.8</td><td>118</td><td>0.58</td><td>LUVISOLS</td><td>6.7</td><td>9</td><td>140</td><td>0.54</td><td>4</td><td>1</td></tr><tr><td>INRAE (2018)</td><td>Val de Loire</td><td>May - June 2018</td><td>200</td><td>100</td><td>0.61</td><td>6.23</td><td>2.9</td><td>50</td><td>0.66</td><td>BRUNISOLS</td><td>4.2</td><td>6.1</td><td>48</td><td>1.2</td><td>2</td><td>0</td></tr></tbody></table>
406

<figure><img src="image_29.png" /></figure>

407

Figure 9: r-max index calculated based on measured soil properties versus emission ratio during post-fertilization period.

408

409

The risk class for each site was estimated (table 2). Only three sites were classified as risk class 0 (no risk). One of them presented both low hazard and low vulnerability (Villamblain site) while the two others presented medium vulnerability but low hazard (good drainage class) resulting in low emission risk. For one of these sites, (La Saussaye) this was not consistent with direct observations because a small r-max index was measured.

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412

413

414

A risk class 1 was found for ten sites, corresponding to thirteen N₂O emission values (three sites were sampled in both 2024 and 2015). This could be explained by the fact that an area known for its emissions is usually selected for such studies. There may therefore be a bias in favor of soils at risk when choosing sites. However, no risk class greater than 1 was found.

415

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417

418

To assess if soil classified as "at risk" indeed present largest N₂O emission, Figure 10 shows the ratio of cumulated N₂O emission on N inputs during fertilization and post-fertilization period as a function of class risk. During wet seasons, the ratio of N₂O emission on N input was smaller on sites presenting no risk (about 0.2%) than on sites presenting a risk class 1.

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422
423

showed a large variability of the emission ratio, but it was always larger than 0.4%. In 2015, which was a rather dry year, only sites having a risk class 1 were studied. The emission ratio also presented a large variability but it was smaller than during wet seasons and some sites even presented emission ratio as low as the no risk sites during wet years. This is consistent with the hypothesis that risk is associated to hazard more than vulnerability because in dry conditions, even in hydromorphic soils, there is little denitrification.

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<figure><img src="image_30.png" /></figure>

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436

Figure 10: N₂O emission ratio versus soil risk class. Emission ratio is calculated as the ratio of cumulated emission of N₂O during fertilization and post-fertilization period on N inputs and soil risk class is assessed through Donesol variables of the corresponding STU (see methodology part for more details). Left: observations made during wet climatic years, right: observations during normal to a dry climatic year. Emissions were cumulated over the fertilization periods, thus corresponding to different intervals (see Table 2).

437

### 3.2.4. Risk mapping
438

Figure 8 (top) presents the percentage of area that present a risk of N₂O emissions, and on the other maps (Figure 8, bottom), the risk is declined in the 4 situations described in Figure 4.

439

<figure><img src="image_31.png" /></figure>

440

<caption>Figure 8: Top. map of the relative area presenting N₂O emission risk; bottom: maps of relative area of each emission risk categories 1, 2, 3 and 4. For risk category definition, see Figure 5.</caption>

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The risk situation is undefined for 15% of the total area. 53% of the surface present no risk of N₂O emissions conversely to 32% of the total area. These 32% can be split into 4 situations: 24% in 1, 6%

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445

in 2, less than 1% in 3 and 1% in 4. Most of these risky soils are located in the Western Perche/Faux
Perche region, where 12% of the surface is covered by forest. Part of risky soils is also located in the
Orleans forest (Orléanais region), but these soils are not exposed to nitrogen inputs, risk does not exist
without exposure.

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449

3.2.5. Uncertainties and strengths of the present approach

450

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470

This approach of mapping emission risk can present several limitations. Firstly, as already written for hazard and vulnerability maps, SMU aggregation entails uncertainties on the final risk map. To check how reliable the two aggregation methods were, the risk for validation site was also estimated based on SMU and not true STU (see supplementary material). Both SMU aggregation methods gave the same risk class than the true STU corresponding to the sites. The methodology used for soil classification also present uncertainties. The protocol for the *r-max* index measurements is based on incubation with acetylene. The acetylene method can underestimate denitrification and ratio N₂O/(N₂O+N₂) because of several reasons, e.g. low diffusion of acetylene in intact soils and inhibition of nitrification in field conditions (Groffman et al., 2006) so field assessment may be biased. The present index is measured in artificial conditions: soil slurry amended with nitrate under agitation: these conditions provide an index of the soil capacity of reduction to N₂ and not a direct measurement. Reduction of N₂O to N₂ can also be influenced in the fields by the soil nitrate and carbon availability (Senbayram et al., 2012). However, the *r-max* index measured in the laboratory has been shown to relate well with field N₂O emissions (Hénault et al., 2005; Hénault et al., 2019). Last, only natural drainage class from Donesol database was considered to define the hazard. Soil hydromorphy effect on N₂O emission can however be highly impacted by management practices: e.g. tile-drainage (Grossel et al., 2016), tillage (Rochette et al., 2008), soil compaction (Pulido-Moncada et al., 2022).

The approach was evaluated by comparison with a limited database from in situ measurements and it
would be interesting to have further field data for validation. Moreover, field data also present
uncertainties : emissions were measured by manual static chambers and cumulative emissions has an
uncertainty linked to the frequency of measurements (Smith and Dobbie, 2001). Observed variability
471 is linked to the soil moisture dynamics, which is controlled not only by the precipitation regime but
472 also the vegetation, tillage, and soil hydromorphy itself which can be due to the presence of a deep
473 clay layer, position in topography (foot slopes) or contact with a water table. This can be illustrated by
474 the site 5 (Franqueville et al., 2018) which was measured in 2014 and 2015. It was close to an
475 intermittent river and soil hydromorphy is due to this vicinity (river water table). In spring 2015 the
476 river dried out and N₂O emissions ratio on N input was very low, while it was large in spring 2014.
477 Last the variability of emissions is also controlled by the timing of fertilization and precipitations. The
478 classification does not discriminate all field conditions but observations are consistent with the
479 assessed soil classification for N₂O emissions risk, suggesting that it can help distinguishing between
480 risk level 0 and 1.

481 N₂O emissions are controlled by a complex interplay of many factors, resulting in large variations both
482 in time and space. N substrate is the main factor so many mitigation solutions have focused on
483 applying N at optimal rate to limit surplus, using enhanced efficiency fertilizers, including nitrification
484 inhibitors or introducing legumes in rotations (Luo et al., 2019; Kanter et al., 2020; Wagner Riddle et
485 al., 2020). Although some solutions will benefit to any soil or climate type (such as health diet habit,
486 Luo et al., 2019), identifying the riskiest areas for N₂O emissions is also needed to target mitigation
487 efforts (Cui et al., 2021). Site-specific management is proposed to mitigate local hotspots of N₂O
488 emissions but it has yet to be tested how this approach can apply at regional scale (Wagner-Riddle et
489 al., 2020). This study proposes thus an approach to identify possible risk areas. This approach is based
490 especially on a risk classification by soil types; soil type has indeed been long recognized as a control
491 factor of N₂O emission (Robertson 1989).

492 Identifying the risk factors could help proposing mitigation strategies. For 3/4 of the risky situations
493 (risk classes 1 + 3), N₂O emissions could be mitigated by regularly liming soil to maintain a pH
494 around 6.8, as suggested by Hénault et al. (2019). Liming can have beneficial effects by reducing N₂O
495 emissions, while it can also increase agronomic risk (defficiency) and CO₂ emissions, but a recent
496 meta-analysis suggested that, because of general yield increase, it may be beneficial (Zhang et al.,
497 2022). Management based on liming should therefore still be assessed by local studies (Hénault et al.,
2019) and the present approach can also serve to guide where to conduct such studies. A third of other soils (risks 2 and 4), require a liming so-called "redress" action to avoid any risk. A soil drainage action does not prevent temporary water excess, but just limit this duration. It is best to avoid exposing soils to risk in too wet soil situations by not providing nitrogen. Farmers can predict these situations through the weather forecast or using soil moisture sensors. Theses situations are located at the edge of streams (The Loir and its tributaries), in areas with REDUCTISOLS and FLUVIOSOLS. It is not advisable to fertilize these soils areas, which are often classified as wetlands and which also cause problems with nitrate pollution.

The patterns of the risk map (Figure 8) are consistent with those of Lugato et al. (2017), who mapped mean N₂O emission simulated on five years (supplementary material). Their map also showed two contrasting N₂O emission areas with values from 1.09 kg N ha⁻¹ yr⁻¹ to 3.8 kg N ha⁻¹ yr⁻¹ for 2010 to 2014 at Haut-Loir. The added value of current paper is that it proposes maps of emission risk provided by soil types, rather than N₂O emission, which are dependent on temporal variables such as precipitation regimes and agricultural practices. This enables also to propose action to be taken according to the risk situation. The *r-max* value (computed from soil clay content, CEC and pH) associated with a soil water excess indicator, available in French soil database, seems to be an efficient approach to define soil situations that require special care and help farmer to identify risky situation.

## Conclusion

A methodology to map N₂O emission risk at regional scale based on soil properties was developed. Risk was defined by crossing a "vulnerability", defined by the low capacity of soil to reduce N₂O in N₂ during denitrification, and "hazard", defined by the probability to have water excess and directly linked to soil drainage class. In the Haut-Loir watershed, 32% of soils presented N₂O emission risks (when exposed to nitrogen fertilization), of which 75% could be mitigated by liming. Some soils (mainly the REDUCTISOLS in the valleys and some hydromorphic LUVISOLS), covering 7% of the watershed area, would require moreover special attention in nitrogen inputs. As the method is based
on knowledge of well-known factors controlling N₂O production and reduction by denitrification and as these data are accessible in soil databases, i.e. drainage class, pH, CEC and clay content, it could be also applied to other regions. These risk maps can allow decision-makers to identify agricultural areas that require special precautions to reduce agricultural N₂O emissions.

Acknowledgements

We gratefully acknowledge N. Canal. This work was supported by the Region Centre, by the ADEME (Agence de la Transition Ecologique, former Agence de l'Environnement et de la Maîtrise de l'Energie, France) through the HydroGES project (grant 1660C0003) and by the Labex Voltaire (ANR-10-LABX-100-01).

## Appendix A : soil correspondance RP 2008 to WRB 2006

<table><thead><tr><th>RP 2008</th><th>WRB 2006</th></tr></thead><tbody><tr><td>ALOCRISOLS</td><td>Cambisols Hyperdystric</td></tr><tr><td>ARENOSOLS</td><td>Arenosols</td></tr><tr><td>BRUNISOLS</td><td>Cambisols Eutric or Dystric</td></tr><tr><td>CALCISOLS</td><td>Cambisols Hypereutric</td></tr><tr><td>CALCOSOLS</td><td>Cambisols Calcaric</td></tr><tr><td>COLLUVIOSOLS</td><td>Colluvic Regosols</td></tr><tr><td>FERSIALSOLS</td><td>Haplic Luvisols</td></tr><tr><td>FLUVIOSOLS</td><td>Fluvisols</td></tr><tr><td>HISTOSOLS</td><td>Histosols</td></tr><tr><td>LUVISOLS</td><td>Haplic Luvisols or Haplic Albeluvisols or Luvisols</td></tr><tr><td>NEOLUVISOLS</td><td>Luvic Cambisols</td></tr><tr><td>PELOSOLS</td><td>Epistagnic regosols or Vertic Cambisols</td></tr><tr><td>PEYROSOLS</td><td>Hyperskelectic Leptosols or Hyperskelectic Podzols</td></tr><tr><td>PLANOSOLS</td><td>Planosols</td></tr><tr><td>PODZOSOLS</td><td>Podzols</td></tr><tr><td>REDOXISOLS</td><td>Stagnosols</td></tr><tr><td>REDUCTISOLS</td><td>Gleysols</td></tr><tr><td>RENDISOLS</td><td>Epileptic Cambisols Calcaric</td></tr><tr><td>RENDOSOLS</td><td>Epileptic Cambisols Hypereutric</td></tr><tr><td>VERTISOLS</td><td>Vertisols</td></tr></tbody></table>
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