1 Mixed effect of soil erosion on the environmental performance of
2 wheat crops in a large agricultural watershed

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5 Célia Ruau¹, Victoria Naipal², Nathalie Gagnaire¹, Carlos Cantero-Martinez³, Bertrand

6 Guenet²,⁴, Benoît Gabrielle¹,*

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8 1. INRAE, AgroParisTech, Paris-Saclay University, UMR ECOSYS, F-91120, Palaiseau, France

9 2. Laboratoire de Géologie, Ecole normale supérieure/CNRS UMR8538, PSL Research University, F-75005

10 Paris, France

11 3. Crop and Forest Sciences Dpt., University of Lleida, 25198-Lleida, Spain

12 4. Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ-UPSCALAY, F-91191, Gif

13 sur Yvette, France

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15 *Corresponding author:

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18 E-mail: benoit.gabrielle@agroparistech.fr

19 Phone: +33 1 89 10 06 43

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This preprint has not undergone peer review or any post-submission improvements or corrections. The
Version of Record of this article is published in Agronomy for Sustainable Development, and is
available online at https://doi.org/10.1007/s13593-023-00942-4

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Soil erosion poses a major threat to agricultural production worldwide, with a still debated impact on the current build-up of atmospheric CO2. Whether erosion acts as a net carbon (C) source or sink, depends also on how it affects the greenhouse gas (GHG) emissions via its impact on crop yield and nutrient loss. Accounting for these effects on the environmental performance of agricultural crops remains to be done.

Here we combined watershed-scale erosion modelling with life-cycle assessment to evaluate the impact of soil erosion on the environmental impacts of wheat production in the Ebro valley in Spain under rain feed conditions. Two scenarios were simulated over the 1860-2005 time slice: an eroded water-shed corresponding to conventional agricultural practices, and a non-eroded control involving conservation practices such as no-till. Life-cycle assessment followed a cradle-to-farm-gate approach, with impacts expressed per kg of grains produced, and a focus on recent decades (1985 to 2005).

Soil erosion losses averaged to 2.6 tons ha⁻¹ yr⁻¹, resulting in a net annual loss of 7 kg N and 5.5 kg P2O5, respectively, compared to the non-eroded control. Soils in both watersheds simulations (eroded and non-eroded) lost organic C over time, with the eroded configuration emitting an extra 55 kg CO₂ ha⁻¹ yr⁻¹. This net C source in the non-eroded simulation only made up 3% of the overall life-cycle GHG emissions of wheat grains, while the compensation for nutrient losses contributed a similar fraction. Yield difference between the conventional and conservation practices proved to be the most sensitive parameter, reaching up to 60% increase when implementing conservation practices. Even at catchment scale, erosion induce by conventional practices did not emerge a net C sink, and increased the GHG emissions of wheat by 7 to 70%. Erosion control through soil conservation practices is still strongly recommended from the combined perspectives of soil preservation, crop yield enhancement and the mitigation of GHG emissions from agriculture.

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**Keywords:** Agricultural soils, Ebro valley, Greenhouse gas emissions, Life-cycle assessment, Soil erosion, Wheat, Erosion modelling, RUSLE, soil carbon dynamics

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# 1 Introduction

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Anthropogenic emissions of greenhouse gases (GHGs) need to be rapidly curbed to mitigate climate change and achieve the targets laid out in the 2015 Paris agreement (Smith et al. 2016). Carbon sequestration in soils has been identified as an important land based mitigation option, which is at the basis of the international “4 per 1000” initiative aiming to increase carbon storage in agricultural soils in order to partially compensate anthropogenic emissions (Minasny et al. 2017). It can be achieved through an increase in nutrient inputs to soils via organic matter amendment, the establishment of cover crops or agroforestry systems, among others (Guenet et al., 2021). However, a reduction in soil erosion is also necessary to preserve soil C and nutrient stocks. As a consequence, lateral fluxes due to soil erosion processes should be factored in when evaluating soil C sequestration potentials. According to the recent special report of IPCC on Climate Change and land (IPCC, 2019) a quarter of all lands are degraded and erosion is the most widespread cause for degradation. Currently, agricultural soils feature the highest soil erosion rates, which are accelerated by the removal of vegetation and ground cover, and by soil tillage practices (IPCC, 2019; Van Oost et al., 2007). Soil erosion on agricultural land has substantial impacts on soil fertility and food security (Lal 2009).

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The role of erosion (mainly due to water) as a sink or source of atmospheric CO₂ is currently highly debated in the literature (Van Oost et al. 2007; Lal 2009; Borrelli et al. 2017). Erosion causes a movement of material along the cascade of hillslopes, floodplains and rivers. This redistribution of soil material may lead, on the one hand, to C sequestration in deposition areas such as colluvial and alluvial soils due to (1) the replacement of eroded carbon by new photosynthetic substrate on eroded land, (2) burial of eroded C in deeper soil layers, hereby reducing soil carbon respiration. On the other hand, soil decomposition can be intensified by soil erosion during sediment transport and after deposition due to aggregate breakdown, and thus lead to higher CO₂ emissions. Despite this uncertainty in erosion being a C sink or source, the above-mentioned studies showed that the effect of erosion on the C emissions cannot be ignored in C cycle research.

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To estimate the impacts of erosion and its drivers, mechanistic modelling is a promising way and large efforts were done in the recent years to develop soil erosion models (Barot et al. 2015). Nevertheless, these approaches are generally developed to represent processes occurring at a fine scale and might not be adapted to larger scales. Large scale soil erosion is usually estimated based on the Universal Soil Loss Equation Model (RUSLE) (Panagos et al. 2015; Naipal et al. 2018). The RUSLE model structure also facilitates the linkage of erosion processes with the C cycle processes on land. For example, different versions of the RUSLE model have been linked to soil organic matter models such as CENTURY (Lugato et al., 2018), and to land surface models (Naipal et al., 2020). However, these modelling approaches do not yet include the erosion-induced feedback on plant productivity, which may lead to an overestimation of the C input to the soil in eroding regions.
Life-cycle-assessment (LCA), is a widely used, holistic approach to estimate the environmental impacts of a given practice or product (Hellweg and Milà i Canals, 2014). It provides a suitable framework to understand the multi-faceted effects of soil erosion on GHG emissions and other environmental issues. It has been used to evaluate the impacts of land use on soil erosion (Núñez et al. 2013; van Zelm et al. 2017), or the effect of tillage practices and erosion control on the GHG balance of crops (Martin-Gorriz et al. 2020), but not on the reciprocal issue of how erosion affects the environmental performance of crops. This is understandable since most LCA of agricultural crops are done at the plot or farm scale (Poore and Nemecek 2018), whereas to estimate the GHG balance of erosion fluxes it must be captured at the catchment scale to estimate removal and redeposition, using for instance the modelling approach described above.

Here, we applied the LCA methodology to better understand the environmental impacts of wheat production as affected by soil erosion or not at the scale of the Ebro watershed in Spain. This large catchment (85 550 km² in size) was selected because it has been exposed to erosion (Cantero-Martínez et al. 2007), and cropland accounts for one third of its area. The main objective was to assess the impacts of soil erosion processes induced by rainfall and runoff on the environmental performance of the production of wheat (a common crop in the area), by combining plot-scale LCA with catchment-scale erosion modelling. The mechanistic model of soil and C erosion, CE-DYNAM, a combination of the soil erosion model RUSLE with a carbon emulator of a land surface model (Naipal et al., 2020) provided an estimate of net erosion (erosion losses minus deposition) from agricultural land on the hillslopes of the Ebro basin. Two crop management scenarios were compared: conservation agriculture (aimed to control erosion), referred to as CA, and conventional management (referred to as CT for conventional tillage). Yield differences were factored in using two main sources of information: plot-scale observations versus a model derived from a global meta-analysis comparing CA and CT. Historically (Cantero-Martínez et al. 2007; Su et al. 2021), and cropland accounts for one third of its area

# 2 Materials and Methods

## 2.1 The Ebro watershed

The watershed of the Ebro river basin extends over the autonomous communities of Cantabria, north of Castilla y Leon in the provinces of Burgos and Soria, south of País Vasco in the provinces of Alava, La Rioja, Navarra, Aragón, Cataluña and Castellón province in the Comunidad Valenciana. The climate is continental-Mediterranean with hot and dry summers followed by cold winters. Main rainfall events are observed in spring (April and May) and autumn (September and October), and summer droughts occur between these periods. The Ebro basin is a closed sedimentary basin where climate, tectonics and soil erosion played an important role in its formation. Currently, the Ebro basin
is characterized by tow mountain ranges, the Pyrennees in the north and Iberian System mountain ranges in the south, that surround a large alluvial plain. Therefore, soil erosion in this basin has been especially affected by the steep slopes, extensive agriculture and arid /temperate climate.

## 2.2 Erosion modelling

Carbon erosion and deposition flows over the Ebro watershed were estimated with the novel large-scale Carbon Erosion Dynamics model Version 1 (CE-DYNAM-v1) (Naipal et al. 2020). CE-DYNAM consists of a sediment budget scheme coupled to an emulator of the soil carbon scheme of the global Land Surface Model (LSM) ORCHIDEE. CE-DYNAM as a whole has been originally calibrated for the Rhine catchment, but its model structure and parameters are designed in a way to be applicable for other river catchments globally. The soil erosion module of CE-DYNAM is based on the Adjusted Revised Soil Loss Equation (RUSLE) model (Naipal et al. 2015), where the yearly average soil erosion removal is calculated as a product of the topographical factor (S), the rainfall erosivity factor (R), the land cover and management factor (Cm), and the soil erodibility factor (K). The Cm factor includes the effects of vegetation cover and crop residues on soil erosion for tilled soil (conventional agriculture). The effect of conservation practices on erosion are not taken into account here. The simulated yearly soil erosion rate (E) represents the potential gross removal of soil from eroding hillslopes.

$$E = S * R * K * Cm$$

The daily gross carbon removal rate by soil erosion (Ce) can then be calculated as following:

$$Ce = \frac{\frac{SOC * E}{365} * 100}{BD_{top} * dz * 10^6} * CER$$

where BDtop is the bulk density of the surface layer (g cm⁻³) and CER is the enrichment ratio, i.e. the ratio of the carbon content in the eroded soil to that of the source soil material, set equal to 1. dz is the soil layer thickness in meters. CE-DYNAM simulates the dynamics of soil organic C (SOC) down to the 2 m depth.

A fraction (f) of the eroded soil gets deposited at the foot of the hillslopes (colluvial deposits). This fraction is a function of the slope gradient and the vegetation type.

$$f = 1 - \left( a_f * e^{\left( \frac{b_f * \theta}{\theta_{max}} \right)} \right)$$

Where a_f and b_f are constant parameters that relate f to the average topographical slope (θ) of a grid cell depending on the type of land cover. These parameters have been derived from the study of Naipal et al. (2020). θmax is the maximum topographical slope of the watershed. The rest of the eroded flux is
transported to the floodplains and water network. In this study we will only consider the soil erosion and deposition, with resulting C dynamics on hillslopes.

CE-DYNAM has three SOC pools, labelled active, slow and passive, respectively. These pools differ by their C residence time, which is shortest for the active pool and longest for the passive one. Changes in each SOC pool on the hillslopes are a function of the C input to the soil by litter decomposition (I), the C decomposition rate (k), which is dependent on the soil physical parameters such as temperature, and the net erosion rate (kE), which is 1 minus the gross soil erosion (E) times the deposition rate (f). The I and k are landcover type specific and derived from the ORCHIDEE LSM on which forms the basis of the SOC scheme of CE-DYNAM.

$$ \frac{dSOC(t)}{dt} = I(t) - (k + k_E) * SOC(t) $$

For a more detailed description of the model see Naipal et al. (2020).

CE-DYNAM was run for the period 1850-2005, with and without soil erosion dynamics, with a daily time-step at a spatial resolution of 5 arcminutes (~ 8km). First the model was run to a steady state based on the environmental conditions of the period 1850-1860, and afterwards transient simulations were performed with changing climate and land use conditions. The overall change in SOC stocks over the period 1850-2005 is then a result of climatic changes, increasing atmospheric CO₂ concentrations, land use change and soil erosion. The resulting net soil C emissions due to erosion can be calculated as the difference in the cumulative SOC change over the period 1850-2005 between the erosion and no-erosion simulations. Here we consider the C fluxes provided by CE-DYNAM when erosion is activated as a surrogate of conventional agriculture practices whereas simulations performed when erosion is not activated are surrogate for conservation agriculture practices that limit erosion fluxes.

## 2.3 Input data for CE-DYNAM

The input datasets for CE-DYNAM as described below are the same as in Naipal et al., (2020). The land cover fractions are derived from the historical 0.25° maps of Peng et al., (2017). Daily precipitation data for the period 1850–2005 is derived from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), product ISIMIP2b Friel er et al., (2017). These data are based on model output of the Coupled Model Intercomparison Project. Phase 5 (CMIP5 output of IPSL-CM5A-LR (Taylor et al. 2012), which are bias corrected using observational datasets and the method of Hempel et al. (2013) and made available at a resolution of 0.5°. Data on soil bulk density and other soil parameters to calculate the soil erodibility factor (K), available at the resolution of 1 km, have been taken from the Global Soil Dataset for use in Earth System Models (GSDE) (Shangguan et al. 2014). Finally, the slope steepness factor (S), was originally estimated at the resolution of 1 km, based
196 on the GTOPO30 digital elevation model. All datasets that were not available at a 5 arcminute
197 resolution were regridded to this specific spatial model resolution before calculating the erosion rates.
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2.4 Life-Cycle Assessment
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2.3.1 Goals and scope

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203 The objectives of the LCA were two-fold: to appraise the contribution of soil erosion processes to the GHG balance of wheat, and to examine the effect of erosion control practices on the environmental performance of this crop in the context of the Ebro basin. To single out the effect of soil erosion, two sets of simulations were carried out with the catchment-scale model CE-DYNAM: a scenario with minimum erosion (reflecting erosion control practices) and a scenario with standard, default erosion (reflecting conventional crop management practices thus far). These simulations correspond to "eroded" and "non-eroded" watersheds, respectively, associated with CT and CA management practices.
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210 To compensate for the loss of crop productivity associated with the losses of soil material and
211 nutrients, fertilizer input rates were adjusted in the eroded case based on the estimated rates of N and P
212 losses. These were estimated from the net C losses output by the erosion model assuming C:N and C:P
213 ratios of 10 and 38, respectively, in eroded material (Xu et al., 2013). Nevertheless, we did not
214 represent the effect of soil erosion on physical and hydraulic parameters (e.g. porosity, water holding
capacity).
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216 We used cradle-to-farm-gate system boundaries and the functional unit for this system was the
217 production of one kilogram of wheat. The time-frame considered in the inventory extended from the
218 harvest of the previous crop to the harvest of wheat.
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2.3.2 Life-cycle inventory (LCI)
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221 LCA was carried out with the SimaPro software (SimaPro 8.0.3, Pré Sustainability, LE
222 Amersfoort, NL), and two LCI databases: AGRIBALYSE (Colomb et al., 2015), which focuses on
223 agricultural products in France, and EcoInvent (v3.6; Ecoinvent, Zurich) for background processes.
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225 Wheat production was adapted to the Ebro valley conditions based on the management and yield
226 data of Cantero-Martínez et al. (2007), which reports on various tillage systems at 3 field sites in the
227 Ebro river valley. The no-till system was used for the non-eroded watershed modelling while a
228 cropping system involving mouldboard plough was associated with the eroded watershed. The
229 "Agramunt" site was chosen since it involved wheat crops every other year from 2000 to 2005. Crops
229 received organic fertilizer inputs in this trial. However, since inorganic fertilizer types predominate in
the Ebro catchment, organic input rates were converted into equivalent mineral fertiliser inputs based on the fertilising values of manure and slurry (Table 1).

Wheat yields are subject to large inter-annual and spatial variations across the Ebro basin (Cantero-Martinez et al., 2007; Plaza Bonilla et al., 2018). Since yield plays an important role in the outcome of LCAs, we used various sources of information and scenarios to determine crop yields for the conservation and standard tillage practices.

In the first yield scenario, the wheat yield in conventional agriculture (eroded watershed) was calculated from the average of the wheat yields of each province and autonomous community in the Ebro catchment area in Spain from 1996 to 2006. Yield data were taken from the annual report of the Ministry of Agriculture, Fisheries and Food of the Spanish Government. The average wheat yield in the eroded watershed analysis was 3283 kg grains ha⁻¹. Then, to assess the yield in conservation agriculture, we used the results of a model built from a meta-analysis comparing the yields of conventional (CT) and conservation agriculture (CA) wheat worldwide (Su et al., 2021), using machine-learning and a global data-base. The average yield change ratio between CA and CT (YieldCA-YieldCT/YieldCT) from 1996 to 2006 was estimated at -0.010 over the Ebro watershed area. This led to an estimate of 3250 kg grains ha⁻¹ for the yield of wheat under conservation agriculture practices. This scenario has the advantage of relying on catchment-scale data and a global model. Nevertheless, yields vary widely depending on climate, soil conditions and crop management characteristics. In some particular cases, conservation agriculture achieves higher yields than its conventional counterpart, as reported by Lampurlanés et al., (2016) in the Ebro basin. We therefore developed a second yield scenario based on these observations. One of the main reasons for this is that CA not only reduces erosion but also improves the water-holding capacity of the soil. The average wheat yield considered in this scenario was averaged over 7 years at the studied region and was 2314 kg ha⁻¹ for the no-till system (non-eroded watershed) and 1436 kg ha⁻¹ for the CT system (eroded watershed). Lampurlanés et al., (2016) in the Ebro basin. We therefore developed a second yield scenario based on these observations. One of the main reasons for this is that CA not only reduces erosion but also improves the water-holding capacity of the soil. The average wheat yield considered in this scenario was averaged over 7 years at the studied region and was 2314 kg ha⁻¹ for the no-till system (non-eroded watershed) and 1436 kg ha⁻¹ for the CT system (eroded watershed).

The net effect of erosion on soil C variations at the field scale was derived from the transient CE-DYNAM simulations, averaged over the 1985-2005 time slice. This erosion-induced C sink/source term for hillslopes compounded the following processes and fluxes: C replacement on eroding soils by new litter input (photosynthesis), C burial in deposition sites, increased decomposition of deposited SOC and the lateral C flux that leaves the hillslopes of the Ebro basin. The floodplains were excluded from the analysis, and it was assumed that the C flux leaving the hillslopes was entirely respired and lost.
### 2.3.3 Impact characterization

Environmental impact assessment was carried out with the IMPACT World+ characterization method (IMPact Assessment of Chemical Toxics), as integrated in the SimaPro software (Bulle et al. 2019). This method allows analysing the results with 15 midpoint categories which are linked to 4 damage categories. The following midpoint categories were included: human toxicity (cancer and non-cancer), respiratory inorganics, ionizing radiation, ozone layer depletion, respiratory organics, aquatic ecotoxicity, terrestrial acidification, land occupation, aquatic acidification, aquatic eutrophication, global warming, non-renewable energy and mineral extraction.

## 3 Results and discussion

### 3.1 Evaluation of the erosion model

We show that erosion has an important impact on the total SOC stock averaged for the period 1995-2005 in crop fields at 30cm depth (Fig. 2a) with a total SOC stock of 86.5 t C ha⁻¹ when erosion was set to zero and 11.4 t C ha⁻¹ when erosion was considered. It must be noted here that the big differences between the two simulations is partially due to the fact that maximized erosion is considered during the entire simulations (1860-2005) whereas it was calibrated on modern agriculture techniques. Thus, we probably overestimate the erosion fluxes for the first decades of the simulation since mechanization of agriculture responsible for higher erosion rates started in this region in the 1940’s. To evaluate the model we extracted data on SOC stocks from the GSDE product (Shangguan et al. 2014) when plant cover was at least 30% crop using the land cover maps from Peng et al., (2017). We calculated over the studied crop regions an average SOC stock at 30cm depth of 67.3 t C ha⁻¹. This lies in between the values predicted by the model with or without erosion, knowing that the model simulations are extreme cases considering no erosion at all or erosion rates calibrated over conventional agriculture without taking into account the erosion control measures that can be locally deployed in the fields. We also compared our simulated erosion rates over the Ebro basin with findings of other studies (Quine et al. 1994; Lizaga et al. 2018). In Fig. 2b, we compared the gross erosion rate predicted by the CE-DYNAM model and the rate estimated by Lizaga et al., (2018) using ¹³⁷Cs tracer. Our model underestimates the gross erosion rate compared to the rate derived from Lizaga et al., (2018). The model predicts a gross soil erosion rate ranging from 6.6 to 18.2 t ha⁻¹ yr⁻¹ (equivalent to a range from 167.8 to 371.7 kg C ha⁻¹ yr⁻¹), whereas Lizaga et al., (2018) estimated a gross soil erosion rate over their studied region to be 25.4 t ha⁻¹ yr⁻¹. It is important to note that Lizaga et al., (2018) did not estimate the soil gross erosion rate over the full Ebro basin region but over a sub basin of 23 km² located in Navarra province that is a Mediterranean-continental climate suffering of Atlantic influence receiving more rainfall that in the central Ebro valley area. This sub basin is also generally steeper than the central Ebro valley area. The model-observation errors may therefore be
explained the differences between the two studied regions. However, also the fact that we assume a
model steady state in the period 1850-1860 for CE-DYNAM may contribute to the underestimation in
erosion. It is important to take into account the past changes in climate and land use as possible in
order to be able to better predict the current state of soil erosion but the absence of precise knowledge
on previous land use and land management makes this task very complex. Furthermore, the SOC is
derived from a simulation performed by the coarse resolution ORCHIDEE LSM and the plant cover is
derived from a global map, which may also contribute to the discrepancies. Finally, net soil erosion
rate from CE-DYNAM can also be compared to observations derived from Lizaga et al., (2018) and
from Quine et al. (1994). In this case the model predicts a net soil erosion rate ranging from 1.7 to 4.7
t ha⁻¹ yr⁻¹ (equivalent to a range from 43 to 95.5 kg C ha⁻¹ yr⁻¹) which is lower than the rate derived
from Lizaga et al., (2018) who estimated a net soil erosion rate of 8.3 t ha⁻¹ yr⁻¹. This could be
expected as the gross erosion rate is already underestimated by the CE-DYNAM model. Quine et al.
(1994), also using the ¹³⁷Cs tracer method, estimated a rate ranging from 16 to 25 t ha⁻¹ yr⁻¹ for a small
sub basin of 0.52 km². The representativeness of such sub basins compared to the entire Ebro basin is
therefore not guaranteed. Finally, since conservation agriculture practices are not implemented in the
CE-DYNAM model that can run with no erosion or with maximized erosion for every grid cells, the
performances of the model are reasonable. Net erosion rates might be under estimated by CE-
DYNAM but the absence of data over the entire Ebro basin makes the evaluation of the model not
straightforward.

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322 **3.2 Contribution of soil erosion to the GHG balance of wheat**

Based on the CE-DYNAM simulations of soil organic C variations over the recent (1985-2005) time slice, cropland soils emerged as net sources of CO2, with an estimated magnitude of 215 and 249 kg CO2 ha⁻¹ yr⁻¹ for the non-eroded and eroded watersheds, respectively. It is notable that despite the large effect of erosion on soil C stocks (Fig. 2), the losses experienced since the 1860 starting point in the eroded watershed were still more than compensated by the rates of C burial in deposition sites. Indeed, erosion drastically affect the spatial distribution of SOC with higher SOC stocks in floodplain and lower SOC stocks in hillslopes. Ultimately, this sink effect was no longer active in the non-eroded configuration.

Figure 3 puts these soil C sources in perspective by reporting the life-cycle GHG emissions of wheat production for yield scenario #1, per kg of wheat grains harvested. Overall, they only accounted for 5% of life-cycle GHG emissions. Their magnitude fits with the 0 to 1700 kg CO2 ha⁻¹ yr⁻¹ range reported by Goglio et al. (2018) in their survey of the life-cycle GHG balances of wheat crops worldwide, although some studies based on direct field observations of SOC variations pointed to larger sink or source strengths (from -3.5 to 5 Mg CO2 ha⁻¹ yr⁻¹). Conservation agriculture in general is assumed to favour soil C sequestration, and Alvaro-Fuentes et al. (2011) estimated it could store
between 200 and 500 kg C ha⁻¹ yr⁻¹ (730 to 1 830 kg CO₂ ha⁻¹ yr⁻¹) whereas conventional systems were simply neutral. The fact that erosion simulations started prior to 1985 (the first year of the 20-year sequence used here for SOC variation calculations) and that CA and CT systems had largely different SOC contents at that time (reflecting the “legacy” of past erosion processes) certainly mitigated the sink capacity of CA. Factoring in the past (1860 to 1985) differences in SOC simulated with CE-DYNAM between the two systems would amount to attributing an extra 480 kg C ha⁻¹ yr⁻¹ sink term to the CA system. This would offset 40% of the life-cycle GHG emissions of the CA systems, which are equivalent to 1210 kg C ha⁻¹ yr⁻¹. Along with potential soil C sink terms, Figure 4 highlights the importance of N₂O emissions (which made up 85% of direct field emissions of GHG), and the use of chemical fertilizers on the GHG balance of wheat. Nitrogen fertilizers bore the largest impact, followed by phosphorus and potassium fertilizers. The 35% share of these inputs in the overall carbon footprint was similar to the 30% ratio reported by Poore and Nemecek (2018). From a fertilizer use perspective, another consequence of erosion is the need to compensate for nutrient losses. This adjustment has a two-fold impact: a rise in off-farm emissions due to the manufacturing and transportation of extra inputs, and an increase in the field emissions of N₂O derived from fertilizer N. Compared to the non-eroded scenario, the eroded case resulted in a 5% relative increase for both terms, and thus a 10% relative increase overall (Figure 3).

Wheat production with CT practices required more frequent use of agricultural machinery (ploughing, fertilization) inducing a slight increase in CO₂ emissions. Indeed, diesel consumption was 3-times higher with CT than with CA, but contributed only a minor fraction (under 10%) of the emissions. Also, erosion per se lead to higher impacts for some of the impact categories due to the pollution of surface water through runoff of nutrients, organic matter and chemicals. This was particularly apparent for freshwater eutrophication and ecotoxicity, which were 25% lower under CA (Figure 3 and Table 2). Conversely, the loss of soil with CT mitigated to a minor extent (5%) the impact of CA wheat on human toxicity (non cancer) because a reduced exposure of human consumers to contaminated soils.

Here, we considered that only mineral forms of fertilizers inputs whereas organic ones may impact the productivity and the SOC dynamics of cropland. For instance, Larney & Janzen, (1997) showed that organic fertilizers were more effective at supplying P to crops in eroded areas than the input of phosphates, which were immobilized by calcium carbonates. Comparing inorganic and organic fertilizers to maintain crop productivity on eroded lands could bring interesting insight into the environmental performance of cereals in eroded catchments.

## 3.3 Erosion and crop yields

Figure 3 compares the environmental impacts of wheat production under CA and CT using a first scenario in which yields differed by only 1% between both managements. Under this assumption of
similar yields, soil erosion *per se* caused higher emissions to the air and water compartments in general, and resulted in 10 to 25% larger impacts for all categories except human toxicity. With the second scenario based on cropping systems trials comparing CA and CT, differences between the two managements and their associated erosion configurations were much larger, reaching 60 to 80% (Fig. 3b), again with the exception of human toxicity (non cancer). This magnitude could be expected from the 61% relative yield difference between CA and CT wheat (Table 1), and the differences in environmental impacts reported in the previous sections, whereby CA outperformed CT because of soil erosion processes.

Overall, experimental trials on the effect of soil erosion rates on crop yields are inconsistent, but a recent meta-analysis pointed out that they were negative when erosion depth dropped under 10 cm (Zhang et al. 2021). In the Ebro valley, the soil losses simulated by CE-DYNAM in the eroded watershed (averaging $1.5 \ t \ ha^{-1} \ yr^{-1}$) indicate a rapid depletion of the topsoil and point at an erosion depth larger than this threshold (see also picture on Figure 1). This adverse effect on crop productivity is backed by the field data used for yield scenario 2, with the additional benefits of soil conservation practices for the CA/non eroded watershed configuration. These benefits are known to be larger under semi-arid conditions (Su et al., 2021), even though the model we used only pointed to a marginal effect. The CA system also featured less intensive management practices than its conventional counterpart, related to the absence of tillage and reduced fertilizer inputs. No-till is known to trade-off with other practices such as weed management, and collecting management data representative of the area is paramount to produce reliable results.

Finally, an important point to mention is that our estimation of erosion from CE-DYNAM does not consider the feedbacks between erosion and primary productivity. CE-DYNAM is a C-only model and the loss of soil due to erosion only remove C whereas in reality soil erosion also remove N and P and affect soil physical structure, water infiltration etc. Therefore, the higher SOC loss due to erosion over the 1985-2005 period given by CE-DYNAM can be underestimated because primary production and therefore C inputs into the soil is not affected by erosion. Our estimation of erosion effect on SOC should be therefore considered as an optimistic estimation likely underestimated.

## 3.4 The full GHG balance of erosion

Here we demonstrated that soil erosion increased the GHG emissions of wheat through its impact on SOC dynamics, nutrient losses, chemical fertilizer use and crop management practices and, most importantly, crop yields. The difference ranged between 0.1 and 2.5 kg eq. $CO_2 \ kg^{-1}$ of wheat grains produced depending on the yield scenario. This is quite significant considering the world average of 1.1 kg eq. $CO_2 \ kg^{-1}$ reported by Poore and Nemecek (2018) for this crop, and the average value of 0.89 1.1 kg eq. $CO_2 \ kg^{-1}$ reported by the AgriBalyse database in France (Colomb et al., 2015). Compared to
the control watershed, erosion on its own increased on-field emissions of CO₂ (related to SOC losses), indirect emissions from chemical fertilizer use while diesel consumption was higher with conventional crop management than with soil conservation practices. This resulted in a compounded increase of 7% in the GHG emissions of wheat production, yields effects notwithstanding. On the other hand, erosion leads to the burying of soil organic carbon and represents a net sink (Van Oost et al. 2007). Using the simulations done by CE-DYNAM we calculated this sink to be 6.7 g C m⁻² yr⁻¹, which corresponds to 5.5% of the wheat's life-cycle emissions. Factoring in this sink to obtain the “full” GHG balance of the eroded scenario, we find that the effect of C burying soil is commensurate with the GHG emissions due to other erosion-related processes (which a share of 7%).

Whether erosion is a net sink or a source of C is still debated (see Doetterl et al. 2015 for review) but its effects on the life-cycle impacts of crops had hitherto not been taken into account. We show that in the Ebro watershed, even though a fraction of soil C ends up buried in river sediments through erosion, this fraction is on a part with the GHG emissions directly caused by erosion, and small compared to the overall life-cycle emissions of wheat, making erosion carbon-neutral at best.

## 4 Conclusion

Soil erosion had mixed effects on agricultural GHG emissions over the Ebro watershed. From a life-cycle perspective, taking into account all the processes involved in wheat production in this area, erosion was both a source of emissions and a sink through the burial of C in river sediments. These two terms being of similar magnitude, erosion *per se* appeared as a carbon-neutral process. The major differences between the eroded and non-eroded watersheds occurred with the scenario assuming a large yield gap between the conventional and conservation-orientated crop management practices. In this case the zero-erosion control clearly outperformed its eroded counterpart, with 60 to 80% lower life-cycle impacts per kg of wheat produced. Notwithstanding yield differences, soils in the eroded watershed had lost a large amount of carbon by the beginning of the time period investigated here (1985 to 2005), leading to an overall loss of c. 0.5 t C ha⁻¹ yr⁻¹ relative to the un-eroded control, potentially offsetting 40% of the wheat’s C footprint. Factoring in this legacy of past land use changes and management practices is not an easy task in retrospect. However, our modelling and assessment shows that it is important to control erosion and to adopt conservation practices to preserve soil structure, crop productivity, and mitigate the environmental impacts of crops in the context of climate change.

## Declarations
444 • Funding: this work received support from the project ERANETMED2-72-209 ASSESS funded by
445 the European Commission. French government under the ANR "Investissements d'avenir" program
446 with the reference CLAND ANR-16-CONV-0003 also financially supported this study.

447 • Conflicts of interest/Competing interests: the authors declare that they have no conflict of interest.

448 • Ethics approval: not applicable.

449 • Consent to participate: not applicable.

450 • Consent for publication: not applicable.

451 • Availability of data and material: erosion simulation data and LCA input and output data are
452 available upon request from the authors.

453 • Code availability (software application or custom code): Model data can be accessed from the
454 Zenodo repository under the following link: https://doi.org/10.5281/zenodo.2642452

455 • Authors' contributions:

456 C.R., B.Ga and N.G. ran the LCA models, V.N. ran the CE-DYNAM model, B.Ga, B. Gu and N. G.
457 designed the study, C.C-M provided data. All the authors contributed to the model analysis and to the
458 writing of the paper.

459

460 Acknowledgements

461

462 The authors would like to thank Dominique Desbois (INRAE) for supplying data on wheat yield
463 statistics in the Ebro basin, and Yang Su (AgroParisTech/INRAE) for running his yield model for
464 conservation agriculture on the same area.

465
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Table 1. Crop management data for wheat production on the non-eroded and eroded watersheds (corresponding to conventional tillage – CT – and conservation agriculture – CA – practices, respectively), for yield scenario #1. The extra N and P inputs for CT compared to CA compensate for the net loss of organic matter through erosion.

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<table><thead><tr><th></th><th>Non-eroded watershed (CA)</th><th>Eroded watershed (CT)</th></tr></thead><tbody><tr><td><strong>Crop rotation</strong></td><td colspan="2">Barley from 1990 to 2000 then wheat one year in two until 2005</td></tr><tr><td><strong>Yield in scenario #1</strong></td><td>3250 kg grain DM ha<sup>-1</sup></td><td>3283 kg grain DM ha<sup>-1</sup></td></tr><tr><td><strong>Yield in scenario #2</strong></td><td>2314 kg grain DM ha<sup>-1</sup></td><td>1436 kg grain DM ha<sup>-1</sup></td></tr><tr><td><strong>Tillage</strong></td><td>No-till</td><td>1 mouldboard ploughing (30-35 cm)<br>1/2 cultivator passes (15cm)<br>1 cultivator pass before sowing</td></tr><tr><td><strong>Sowing</strong></td><td colspan="2">No-till disc drill : 500 seeds m<sup>-2</sup></td></tr><tr><td><strong>Herbicide inputs</strong></td><td>Glyphosate (0.685 kg a.i. ha<sup>-1</sup>)<br>MCPA (0.644 kg a.i. ha<sup>-1</sup>)<br>Prosulfocarb (4 kg a.i. ha<sup>-1</sup>)</td><td>Prosulfocarb (4 kg a.i. ha<sup>-1</sup>)</td></tr><tr><td><strong>Fertilizer inputs (inorganic)</strong></td><td>134 kg N ha<sup>-1</sup><br>81.5 kg P2O5 ha<sup>-1</sup><br>142 kg K2O ha<sup>-1</sup></td><td>(134 + 6) kg N ha<sup>-1</sup><br>(81.5 + 4) kg P2O5 ha<sup>-1</sup><br>142 kg K2O ha<sup>-1</sup></td></tr><tr><td><strong>Disease and pest management</strong></td><td colspan="2">No treatment</td></tr></tbody></table>

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Table 2: Life-cycle impacts of the production of one kg of wheat in the non-eroded (CA) and eroded (CT) Ebro watersheds, for yield scenario #1, expressed at mid-point level.

Key to units (see Bulle et al. 2019 for more details): CTUh, CTUe: comparative toxic units for humans and ecosystems, respectively; NMVOC: non-methane volatile organic compounds.

<table><thead><tr><th>Impact category</th><th>Unit</th><th>Non-eroded watershed (CA)</th><th>Eroded (CT)</th><th>watershed</th></tr></thead><tbody><tr><td>Human toxicity (cancer)</td><td>CTUh</td><td>2.87 10<sup>-9</sup></td><td>2.76 10<sup>-9</sup></td><td></td></tr><tr><td>Human toxicity (non-cancer)</td><td>CTUh</td><td>3.23 10<sup>-8</sup></td><td>3.40 10<sup>-8</sup></td><td></td></tr><tr><td>Particulate matter formation</td><td>kg PM2.5 eq.</td><td>4.12 10<sup>-4</sup></td><td>4.19 10<sup>-4</sup></td><td></td></tr><tr><td>Ionizing radiation</td><td>Bq C-14 eq.</td><td>1.8</td><td>1.9</td><td></td></tr><tr><td>Ozone layer depletion</td><td>kg CFC-11 eq.</td><td>5.37 10<sup>-8</sup></td><td>6.08 10<sup>-8</sup></td><td></td></tr><tr><td>Photochemical oxidants formation</td><td>kg NMVOC eq.</td><td>3.10 10<sup>-3</sup></td><td>3.63 10<sup>-3</sup></td><td></td></tr><tr><td>Freshwater ecotoxicity</td><td>CTUe</td><td>59.2</td><td>95.8</td><td></td></tr><tr><td>Terrestrial acidification</td><td>kg SO<sub>2</sub> eq.</td><td>1.18 10<sup>-5</sup></td><td>1.25 10<sup>-5</sup></td><td></td></tr><tr><td>Freshwater acidification</td><td>kg SO<sub>2</sub> eq.</td><td>1.09 10<sup>-8</sup></td><td>1.25 10<sup>-8</sup></td><td></td></tr><tr><td>Freshwater eutrophication</td><td>kg PO<sub>4</sub> P-lim eq.</td><td>1.13 10<sup>-4</sup></td><td>3.20 10<sup>-4</sup></td><td></td></tr><tr><td>Marine eutrophication</td><td>kg N eq.</td><td>1.63 10<sup>-4</sup></td><td>1.74 10<sup>-4</sup></td><td></td></tr><tr><td>Global warming</td><td>kg CO<sub>2</sub> eq.</td><td>1.42</td><td>1.51</td><td></td></tr><tr><td>Non-renewable energy</td><td>MJ primary</td><td>5.56</td><td>6.29</td><td></td></tr><tr><td>Land occupation</td><td>m2org.arable</td><td>0.206</td><td>0.204</td><td></td></tr><tr><td>Mineral extraction</td><td>kg deprived resource</td><td>1.00 10<sup>-2</sup></td><td>1.13 10<sup>-2</sup></td><td></td></tr><tr><td>Water scarcity</td><td>m3 world eq.</td><td>24.4</td><td>26.1</td><td></td></tr></tbody></table>
587

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<figure><img src="image_1.png" /><figcaption>**Figure 1:** Evidence of soil erosion processes on agricultural land in the Ebro valley (credit: Julien Tournebize, INRAE).</figcaption></figure>

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<figure><img src="image_2.png" /><figcaption>**Figure 2:** Evaluation of the erosion model for SOC stocks at 30 cm against data from the GSDE product (Shangguan et al. 2014) (a), for the gross soil erosion rate against observations from Lizaga *et al.*, (2018) (b), for the net soil erosion rate against observations from observations from Lizaga *et al.*, (2018) in light gray and from Quine *et al.*, (1994) in deep gray (c).</figcaption></figure>

8

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595

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<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3:** Relative life-cycle impacts of the production of 1 kg of wheat on the non-eroded (dashed line) and eroded (solid line) Ebro watersheds, for yield scenarios #1 (top) and #2 (bottom). The system with the highest impact is given the 100% value for the various impact categories.</figcaption></figure>
<figure><img src="image_5.png" /></figure>

**Figure 4:** Contribution analysis for the global warming impact of the production of one kg of wheat in the non-eroded (CA) and eroded (CT) Ebro watershed for yield scenario #1. Direct emissions encompass field exchanges of CO₂ and N₂O.