# Changes in cropping systems associated with biogas plants in French cereal-growing areas

Léa Boros, Marco Carozzi, Sophie Carton, Sabine Houot, Philippe Martin,

Florent Levavasseur

► To cite this version:

Léa Boros, Marco Carozzi, Sophie Carton, Sabine Houot, Philippe Martin, et al.. Changes in cropping systems associated with biogas plants in French cereal-growing areas. Agricultural Systems, 2025, 228, pp.104387. 10.1016/j.agsy.2025.104387. hal-05076549

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# Changes in cropping systems associated with biogas plants in French cereal-growing areas

Léa Boros<sup>a,b,*</sup>, Marco Carozzi<sup>b</sup>, Sophie Carton<sup>c</sup>, Sabine Houot<sup>a</sup>, Philippe Martin<sup>b</sup>, Florent Levavasseur<sup>a,*</sup>

## HIGHLIGHTS

* Farming practices on non-livestock farms producing biogas were described.

* These practices differed from those considered in anaerobic digestion evaluations.

* Energy cover crop production was associated with changes in main crop species.

* Energy cover crops were generally treated with pesticides and sometimes irrigated.

* Fertilizer savings correlated with the agro-industrial waste level in the digester.

## GRAPHICAL ABSTRACT

### Changes in cropping systems associated with biogas plants in French cereal-growing areas:

Results from semi-structured interviews with 33 farms associated with anaerobic digestion (AD)

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

Major gaps exist between the observed practices and those considered in environmental assessments of anaerobic digestion

## ARTICLE INFO

Editor: Kairsty Topp

### Keywords:

* Energy crop

* Anaerobic digestion

* Biogas plant

* Cover crop

* Cropping system

* Digestate

* Fertilizer savings

## ABSTRACT

**CONTEXT:** Anaerobic digestion (AD) is a process that produces a renewable gas (biogas) and an organic fertilizer (digestate). Energy cover crops, grown between two main crops, are expected to become the predominant AD feedstock in France and other European countries, potentially reshaping cropping systems and agricultural practices. However, farming systems linked to AD remain understudied, potentially leading to AD assessments disconnected from actual farming practices.

**OBJECTIVE:** Our aim was to address this gap by characterizing the cropping system changes in French farms associated with biogas plants mainly relying on energy cover crops in cereal-growing regions and to discuss the differences with agricultural practices usually considered in AD assessments.

**METHODS:** We conducted interviews on 33 farms in French cereal-growing regions between late 2020 and 2023 to characterize changes in cropping systems of non-livestock farms associated with AD. The interview guide covered general farm and biogas plant information, main crop and energy cover crop management, farm-scale fertilization and digestate management. Land cover changes before and after AD were considered, as well as the calculation of a simplified farm-scale nutrient (N, P and K) balance.

**RESULTS AND CONCLUSIONS:** Key findings revealed that energy cover crops were grown on an average of 30 % of farm surfaces each year. They were generally treated with pesticides, and irrigated on half of the surveyed

<footnote><sup>a</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, 91120 Palaiseau, France</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR SADAPT, 91120 Palaiseau, France</footnote>

<footnote><sup>c</sup> Ferme Expérimentale AgroParisTech, 78850 Thiverval-Grignon, France</footnote>

<footnote>* Corresponding authors at: Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, 22 place de l'Agronomie, 91120 Palaiseau, France.
E-mail addresses: lea.boros@inrae.fr (L. Boros), florent.levavasseur@inrae.fr (F. Levavasseur).</footnote>
farms when cultivated in summer. Their introduction implied significant reductions in surfaces of crops with longer growing periods, such as wheat and rapeseed, while the surface of barley, maize and other cereal crops increased. Yield losses of -10 % to -40 % in main crops following winter energy cover crops were reported by 39 % of respondents, who mainly attributed them to delayed sowing dates of summer crops, resulting in a shorter growth period. Potential fertilizer savings achievable through digestate application varied across farms and were positively correlated with the proportion of agro-industrial byproducts in the digester.

**SIGNIFICANCE:** This study provides the first description of agricultural practices of non-livestock farms with AD based mainly on energy cover crops in cereal-growing regions. It highlights discrepancies between observed practices and those considered in AD assessments. Future local or national assessments of AD should account for actual on-farm practices, evaluating the impacts of climate change and the potential water scarcity on cropping systems with AD. This is necessary to mitigate any adverse effects resulting from the energy transition.

# 1. Introduction

Anaerobic digestion (AD) is a process that converts organic matter into a renewable gas called biogas, and an organic fertilizer called digestate, contributing to both the energy transition and circular economy. The deployment of this technology has recently surged in France, increasing from 290 digesters in 2012 to 1308 digesters in 2022 (Observ'ER and ADEME, 2022), which may present new opportunities or challenges for farmers, as 90 % of the organic inputs for AD are projected to be sourced from agriculture by 2030 (ADEME and Solagro, 2013). Agricultural resources for AD include livestock manure, agro-industrial waste or byproducts such as beet pulp, and energy crops. Energy crops include both dedicated crops, which are main crops grown specifically for energy production, and energy cover crops (ECC), which are also cultivated for energy purposes during either summer or winter seasons, but between two main crops grown for food or feed (Fig. 1). When ECC are harvested, the entire aerial parts of the plants are collected without necessarily waiting for the grain to reach full maturity, as the primary objective is biomass production rather than grain yield.

Environmental and economic impacts of AD on farming systems have been observed in European countries with significant AD development such as Germany or Italy. These effects include conversion of grasslands to croplands, increase in regional or national biogas maize areas, and associated competition with feed or food sectors (Bartoli et al., 2016; Lüker-Jans et al., 2017; Mela and Canali, 2014; Yang et al., 2021). In France, the use of dedicated energy crops has been restricted to 15 % of the annual feedstock gross tonnage in the digester (Decree No 2016-929, 2016). Consequently, the majority of energy crops used for AD are ECC, positioning France as a valuable case study to inform Europe on the implementation of ECC as a sustainable approach to developing biomethane. The use of ECC is also expanding worldwide as they are promoted as a good opportunity to produce biomass for energy while limiting carbon emissions (Malone et al., 2023; Nilsson et al.,

2024), nitrogen losses (Malone et al., 2018; Malone et al., 2023) and land use changes (Magnolo et al., 2021; Selvaggi et al., 2018).

The 15 % limit on dedicated energy crops in AD has proven effective in limiting land cover changes at French national scale (Levavasseur et al., 2023b). However, regional disparities have emerged, especially in cereal-growing areas, where maize surfaces increased and wheat and rapeseed surfaces decreased. These areas are supposed to be the most impacted by the introduction of ECC, as AD mainly relies on this feedstock due to limited manure availability. These impacts may escalate in the next decades as these intermediate crops are widely promoted by the European Commission as a key feedstock for sustainable biogas production (Directorate General for Energy, 2023; European Commission, 2022; Gas for Climate, 2022). Biomethane scenarios suggest that biogas production will increase by 25-30 times at national scale, with ECC projected to constitute 24 to 48 % of total agricultural organic matter used as AD feedstock in 2050 in France (Beline et al., 2023). This share is even expected to reach 64 % at the European level according to Brémond et al. (2021).

Incorporating ECC into cropping systems requires adjustments in both crop rotations and crop management practices, that are also impacted by the introduction of digestate fertilization. These changes may entail various impacts, including decreased greenhouse gas (GHG) emissions by substituting mineral fertilizers (Bacenetti et al., 2016; Esnouf et al., 2021; Hijazi et al., 2016), decreased nitrogen leaching thanks to ECC production (Heggenstaller et al., 2008; Malone et al., 2018), and enhanced soil carbon storage attributed to digestate spreading and the ECC root system (Launay et al., 2022; Levavasseur et al., 2023a). However, these changes may also lead to potential increases in water, fertilizer or pesticide usage (Launay et al., 2022), potential yield reduction in main crops following ECC due to delayed sowing date and the water and nutrient consumption of ECC (Marsac et al., 2019), or soil compaction resulting from the use of heavy machinery for digestate spreading (Lantz and Börjesson, 2014).

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Diagrams illustrating examples of how winter ECC (above) or summer ECC (below) can be integrated in rotations in France.</figcaption></figure>
Nevertheless, transformations in cropping systems associated with AD in France remain understudied. While Cadiou (2023) explored AD's impact on crop-livestock or livestock farming systems in Eastern France, there is a lack of literature on the impact of AD on farming systems in cereal-growing areas, despite the potential for these systems to be more affected by the introduction of ECC. Consequently, current environmental assessments of AD often rely on theoretical statements or field trials, ignoring real-life deployment conditions of AD and its systemic impact on farming systems (Cadiou, 2023). For instance, Esnouf et al. (2021) exclusively investigated winter ECC without the use of pesticides and with no impact on the subsequent crop yields. Similarly, Nilsson et al. (2024) studied summer ECC without pesticides or irrigation. These agricultural practices could differ from those actually employed by farmers associated with AD in French cereal-growing regions.

With this study, our objective is to characterize the cropping system changes associated with AD in French cereal-growing regions. We hypothesize that these farming practices may deviate from those often considered in AD environmental assessments, e.g., no impact on crop yield following ECC, no pesticides or irrigation on ECC, ECC fertilization only with digestate or no changes in main crop species and management (Berger and Esnouf, 2022; Esnouf et al., 2021; Nilsson et al., 2024). Knowing actual agricultural practices associated with AD will help in reassessing AD's impacts in future studies, particularly concerning water resources, food production, and GHG emissions.

## 2. Methods

### 2.1. General approach

We conducted semi-structured interviews with farmers selected from a population of interest defined in section 2.2. These interviews focused on assessing the management of ECC, and their impacts at the farm scale. Additionally, we explored the use and effects of digestate as fertilizer, and other changes observed at the farm scale following the biogas plant introduction (section 2.3.2). Subsequently, the interview data underwent quantitative and qualitative analysis, as detailed in section 2.3.3. To complement the interview data, land cover per year for each interviewed farm was retrieved from the Land Parcel Identification System (LPIS) (section 2.4.1), allowing to compute land cover changes before and after AD, as well as crop diversity evolution. The calculation of land cover evolution also allowed to estimate potential savings in plant protection products at the farm scale (section 2.4.3). Furthermore, simplified nutrient balances (nitrogen, phosphorus, and potassium) were calculated to assess changes in nutrient requirements before and after AD implementation (section 2.5).

### 2.2. Study area and population

Our study focused on non-livestock farms associated with biogas

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) Diagram illustrating the selection process identifying biogas plants associated with non-livestock farms in French cereal-growing regions. The number of biogas plants associated with each step are reported; (b) corresponding map highlighting biogas plants of interest in blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
plants in French cereal-growing regions. To identify these farms, we first retrieved data on the main type of farming for each French municipality (Agreste, 2020a) and a map of French small agricultural regions (SARs) (smaller than NUTS3 level, which is the third subdivision of countries according to the European Union nomenclature) (Agreste, 2017). These datasets were intersected using QGis (v. 3.30.2) to determine French cereal-growing regions. We classified SARs as 'cereal-growing regions' if their predominant type of farming were 'cereals and/or oilseed and protein crops' or 'other field crops' (Fig. 2b). Biogas plants located in these regions were identified by intersecting the previous datasets with the SINOE database (ADEME, 2023) containing information on biogas plants in France (location, start-up year, and biogas utilization). The locations of biogas plants from the SINOE database were manually verified with the help of aerial photography and corrected when needed, following Levavasseur et al. (2023b). This intersection resulted in a population of 198 digesters. To focus on digesters operational for at least one year at the time of the study, only biogas plants that started in 2021 or before were kept, resulting in a population of 146 digesters (Fig. 2a). To identify those linked with non-livestock farms among these 146 biogas plants, we consulted online databases providing insights on farm main activity, along with data on AD inputs when available (Supplementary Material 1). Ultimately, we identified 81 biogas plants of interest to our study (Fig. 2).

## 2.3. Semi-structured interviews of farms

### 2.3.1. Sampling of the biogas plants for interviews

The sampling strategy for selecting biogas plants for interviews among the total population of interest aimed to achieve geographical diversity across the study area (Fig. 2b) and to encompass a wide range of operational years and biogas production capacities. Despite limitations due to the online contact information availability and responsiveness to e-mails and phone calls, we conducted interviews with 35 farmers representing a total of 33 farms associated with 24 biogas plants with injection systems. To ensure the representativeness of our interview sample compared to the broader population of interest (section 2.2), we conducted a Wilcoxon test using R (v. 5.3.1) to compare the capacities of the interviewed and non-interviewed biogas plants. Capacity data were retrieved from the Open Data Réseaux-Energies database (Réseaux-Energies, 2023), which gathers information about all French digesters with grid injection systems, as all digesters in our population except two injected biogas into the network.

Furthermore, we conducted interviews with two agricultural advisers from departmental Chambers of Agriculture within our study regions to validate the consistency of obtained information with broader regional observations regarding changes following AD, and to ensure the comprehensive coverage of specific systems.

### 2.3.2. Interviewing methodology

Interviews with the 33 farms mentioned above were conducted between December 2020 and April 2023 by three interviewers. A semi-structured interview method was used to allow flexibility in questioning while relying on an interview guide to ensure all desired questions were asked (Supplementary Material 2). This flexibility facilitated following up on unexpected insights and obtaining in-depth information thanks to open-ended questions (Adams, 2015). The interview guide included a section about farm general information such as utilized agricultural area (UAA), soil type, production types, farm history, number of workers, etc. The subsequent section focused on the biogas plant associated with the interviewed farms, including its history, input types, number of associated farms, digestate production, number and characteristics of farms receiving digestate, and future plans. A third section centered on cropping system changes before and after AD, addressing ECC management (surface, fertilization, pesticide use, irrigation, sowing and harvesting date, tillage methods, yield), and their potential impact on main crops (changes in yield, crop species, tillage

methods, fertilization, weed and pest pressure, ...). The fourth section covered farm-scale fertilization before and after AD. The fifth and final section focused on digestate management, including field application and storage. When multiple farms were associated with the same biogas plant, and as it was not possible to interview them all, variations in farming practices between these farms were also addressed. When atypical quantitative data were reported for a specific year (e.g., unusually high or low yields), farmers were also asked to provide their usual yields to ensure more representative values. The reasons for these atypical data were systematically explored. This approach was consistently applied to all quantitative data.

Interviews were not audio recorded to foster an environment where participants could freely express themselves, avoiding potential stress or inhibition due to recording device presence (Adams, 2015). The interview guide was updated between 2020 and 2022, incorporating questions on fertilizer savings, perceived plant-available nitrogen in digestate, and frequency of digestate analysis. Updates for farmers interviewed in 2020 and 2021 were conducted by phone calls, for 5 out of 11 AD systems, as the remaining ones did not respond to phone calls.

### 2.3.3. Analysis of the interviews

All interview responses were written up into detailed reports based on notes taken during the interviews. Using R (v. 5.3.1, with tidyverse, data.table, ggplot2, ggpubr, ggeasy, patchwork and superb packages) descriptive statistical analysis was conducted to analyze quantitative data on farms and digesters, including variables such as UAA, feedstock in the digester or variables about ECC or digestate management.

Interview reports were qualitatively analyzed using NVivo (v. 14.23.1) to understand and rank factors influencing farmers' preferences for specific ECC species. Employing an inductive coding approach, we systematically identified recurring themes in farmers' answers. This involved labelling similar categories of responses, known as 'codes', which capture key concepts, ideas, or patterns in the data. These codes were organized into themes and sub-themes, reported in Supplementary Material 3. By quantifying the number of farmers mentioning each theme and sub-theme, we ranked them based on their importance to the interviewees.

## 2.4. Land cover changes linked with AD

### 2.4.1. Main crop evolution following the implementation of AD

To study main crop evolution following AD implementation, farm land cover data before and after AD were collected during interviews. However, recalling specific crop surfaces from previous years proved challenging for farmers when describing land cover before AD. Thus, to ensure comprehensive data collection, the interviewed farms were identified in the French Land Parcel Identification System (LPIS) when farm identifier or parcel locations were provided during interviews. The LPIS is a geographic information system used for administering agricultural subsidies under the European Union's Common Agricultural Policy (CAP). It accurately identifies agricultural parcels, providing exhaustive crop area information per farm and per year. The European General Data Protection Regulation (GDPR) restricts the publication of data that could lead to the identification of surveyed farmers, thus crop areas per farm cannot be shared. However, the LPIS remains accessible upon request from the Secure Data Access Center (CASD). Identification data could not be collected for 5 farms out of 33 (associated with 2 different biogas plants).

LPIS data enabled comparison of mean land cover changes over three years before and after AD establishment. The "before AD" period encompassed the fourth, third, and second years prior to start-up, while the "after AD" period included the start-up year and subsequent two years, as proposed in Levavasseur et al. (2023b). For biogas plants initiated in 2020 or 2021, only the two- or one-year following start-up were considered due to the unavailability of LPIS data for 2022 and 2023 to date (4 biogas plants in 2020 linked to 7 farms, 6 biogas plants in
2021 linked to 7 farms). Between 2007 and 2014, LPIS categorized land cover into 28 groups of crops, such as the ‘barley’ group including winter and spring barley, but expanded to over 300 crops after 2015. Some interviewed farmers started AD in 2018 or earlier, meaning that their “before AD” period encompassed years before 2015 for which land cover description only included the 28 groups of crops. To ensure consistency across farms, land cover was analyzed only based on these 28 groups and not with the 300 crops available after 2015. Shapiro’s test, followed by t-tests or Wilcoxon tests, were performed in R (v. 5.3.1) to determine differences in mean main crop surfaces of interviewed farms before and after AD.

### 2.4.2. Crop diversification at the farm level before and after AD

To characterize crop diversification following the implementation of AD, two complementary crop diversity indexes were calculated: the crop biodiversity index (Chantrel-Valat et al., 2021) and the diversity index for crop families (DCF) (Hirschy et al., 2015). The crop biodiversity index provides information on the number of crops cultivated and their predominance at the farm scale, offering insights into the distribution level of cultivated species. This index simultaneously considers diversity and distribution. Indeed, a diverse range of crops with uneven distribution among species limits ecological functions when compared to a diverse range of crops with a uniform distribution among species (Chantrel-Valat et al., 2021). The DCF quantifies the variety of cultivated families in a year, considering crop distribution within each family. Higher diversity is indicated by a greater number of different cultivated families and a more equitable crop distribution within each family (Hirschy et al., 2015). By studying both indexes, we captured both the range of crops and their distribution across taxonomic families. Index calculations were limited to main crops and did not include ECC, as ECC species described during interviews were largely the same as main crop species reported in the LPIS database for most interviewed farms.

The crop biodiversity index is calculated using the following formula:

$$ \text{Crop biodiversity index} = \frac{1}{\sum_{i} (\text{crop weight}_{i})^{2}} \qquad (1) $$

where the crop weight is the yearly surface of crop i divided by total farm UAA excluding permanent grassland.

The DCF draws inspiration from the Simpson index and was adapted

plant protection products (PPP) on main crops (section 3.3.2). However, some mentioned potentially reduced PPP usage at the farm scale as they switched main crop species after AD, which required less PPP. To verify this assumption, theoretical changes in PPP usage for main crops at the farm scale were computed based on main crop surfaces before and after AD (section 2.4.1) and regional data on treatment frequency index (TFI) per crop (Agreste, French Ministry of Agriculture and Food, 2020b). For additional TFI description and calculation methods, see Supplementary Material 5.

Since TFI information was limited for some crops, our analysis focused on crops with significant surface changes in cultivated area before and after AD (see Results 3.3.1). Levels of PPP for selected crops before and after AD were calculated as follows:

$$ \text{Plant protection product level} = \sum_{i} (\text{crop weight}_{i} * \text{TFI}_{i}) \qquad (3) $$

where crop weight represents the surface of crop i divided by total UAA.

### 2.5. Nutrient balances (N, P and K) at the farm scale before and after AD

Simplified nitrogen, phosphorous and potassium balances (N, P and K) per hectare at the farm scale before and after AD were calculated, excluding all fertilizers except digestate. This allowed us to determine the potential level of theoretical chemical fertilizer savings achievable through AD. The calculations accounted for nutrient requirements of ECC and the additional nutrient supply from digestate, applicable to both ECC and main crops. For example, simplified N balance (ΔN) per hectare after AD was calculated for each farm and each year as follows:

$$ \Delta N_{\text{after AD}} = \frac{\sum (N \text{ requirements of main crops} + \text{ECC}) - N_{\text{supply from digestate}}}{\text{UAA without permanent grasslands}} \qquad (4) $$

with N requirements of main crops and ECC and Nsupply from digestate in kg N.ha⁻¹ and UAA in ha. During the ‘before AD’ period, no ECC surfaces or use of digestate were considered. The ‘before AD’ period was defined as detailed in 2.4.1. The ‘after AD’ period only covered one year, specifically 2021, due to the availability of ECC surfaces data gathered during interviews. The same calculations were conducted for P and K.

N requirements of main crops and ECC (simplified as ‘crops’ in the following formula) were calculated for each farm and year as follows:

$$ N \text{ requirements of crops} = \sum_{i} (\text{crop surface}_{i} * \text{mean crop yield}_{i} * \text{crop N requirements}_{i}) \qquad (5) $$

from Hirschy et al. (2015):

$$ DCF = \frac{1}{\sum_{i} \left(\frac{n_i}{N}\right)^2} \qquad (2) $$

where nᵢ represents the number of crops in crop family i and N is the total number of crops at the farm scale.

Additional methodological details for computing these indexes are provided in Supplementary Material 4. Shapiro’s test, followed by t-tests or Wilcoxon tests, were performed in R (v. 5.3.1) to determine differences in the means of those indexes before and after AD. Crop biodiversity index and DCF for each farm are available in the table provided in Supplementary Material 8.

### 2.4.3. Plant protection product usage at the farm scale before and after AD

Most interviewed farmers did not observe changes in their use of

with i being the crop species, crop surfaceᵢ being in ha, mean crop yieldᵢ in t.ha⁻¹ and crop N requirementsᵢ in kg N.t⁻¹. Main crop surfaces were determined as in section 2.4.1 and crop categories were expanded when sufficient information was available (Supplementary material 6). ECC surfaces were determined through interviews and were cross-referenced with LPIS main crop surfaces to ensure consistency. Minor adjustments were made to align main crop surfaces with ECC data. Crop N, P and K requirements sources and calculation methods are described in Supplementary material 7. Local mean yields at the NUTS3 level from 2010 to 2021 were obtained from French annual agricultural statistics (Agreste, 2023), except for cup plant (Silphium perfoliatum L.) yield sourced from Ferrand (2021). To mitigate year-to-year variations, mean crop yields between 2010 and 2021 were considered. In this simplified nutrient balance, we assumed that there were no yield differences before and after AD to study an optimal scenario. Mean summer and winter ECC yields were obtained from interviews (Fig. 5). The same
calculations were conducted for P and K.

$N_{supply}$ from digestate was calculated for each farm and each year per hectare as follows:

calculations, a method consistently applied throughout the analysis. The additional calculations using the LPIS and simplified nutrient balances were carried out to further support the robustness of our conclusions.

$$N_{supply} from digestate = NUE * N_{tot} in digestate * Digestate quantity applied on the farm \quad (6)$$

with $N_{tot}$ in kg N.t⁻¹ ($N_{tot} = N-NH_4^+ + N_{org}$) and digestate quantity in t. Nitrogen use efficiency (NUE) was defined as follows:

The details of missing data by variable and farm are provided in Supplementary Material 9.

$$NUE = \frac{Nitrogen uptake by fertilized crop - Nitrogen uptake by unfertilized control crop}{Nitrogen added by fertilizer} \times 100 \quad (7)$$

NUE was either 100 % or 50 %. While the former represents an idealized condition, serving as a benchmark for maximum NUE under perfect circumstances, the latter reflects a more practical scenario aligned with real-world conditions. This 50 % threshold was chosen as a reasonable assumption based on the limited and heterogeneous data in the literature, which reports NUE values of up to 90 % for liquid digestate and below 50 % for solid digestate, with these variable varying considerably depending on application conditions (Grillo et al., 2021; Jin et al., 2022). The same calculation was performed for P and K, assuming 100 % availability in the digestate (Morel, 2020; Trochard, 2016). In these calculations, all other nutrient fluxes were considered equal before and after AD. When possible, nutrient levels in digestate from each farm were obtained through digestate analyses (9 out of 24 digesters). In cases where digestate analysis during the interviews was not possible, farmers provided their own routine analyses, typically performed at least once a year, often more frequently (n = 13 and data missing for 2 digesters). Nutrient levels in these analyses were consistent with both our measurements and literature (Bareha et al., 2021; Michaud et al., 2023). Digestate quantity applied on the farm was determined through interviews. These data are detailed in the table provided in Supplementary Material 8 and missing data are mentioned in Supplementary Material 9.

We compared $\Delta N_{before AD}$ and $\Delta N_{after AD}$: farms with lower $\Delta N$ during the ‘after AD’ period could theoretically achieve chemical nitrogen fertilizer savings, as it would indicate that their farming system required less nitrogen than before AD, even after ECC cultivation and with the use of digestate. Conversely, farms with a higher $\Delta N$ during the ‘after AD’ period may not achieve expected fertilizer savings from AD, as this would suggest increased nitrogen requirements despite digestate application. The difference between $\Delta N_{before AD}$ and $\Delta N_{after AD}$ was notated as $\Delta N_{(before-after)}$. To further analyze $\Delta N_{(before-after)}$ variation among farms, we conducted linear regressions correlating $\Delta N_{(before-after)}$ with the amount of agro-industrial waste and byproducts in the digester or with the ECC proportion at the farm scale. Agro-industrial waste and byproducts refer to the substances produced as secondary outputs during the processing of agricultural products into food, feed, or other industrial goods, such as peels, pulps, oilseed cakes, etc. Same method was applied for P and K.

Finally, some variables used throughout the manuscript had missing data. To address this, we excluded incomplete data from the

# 3. Results and discussion

## 3.1. Description of interviewed farms and associated biogas plant

Median production capacity of interviewed and non-interviewed AD within the total population (Fig. 2) showed no significant difference (p-value = 0.9), suggesting our sample’s representativeness in terms of digester size. Follow-up phone calls in 2023 with farms interviewed at the beginning of the study revealed relatively stable farming practices, including consistent ECC proportions and management, with the exception of some mentions of sunflower cultivation following ECC. This observed stability reinforces the validity of the results presented below.

The interviewed farms had been involved in AD operation for 1 to 8 years, with a median experience of 3 years. Between 1 and 5 farmers (median = 3) were involved in operating each of the interviewed biogas plants. The 33 surveyed farms were made up of 48 separate agricultural holdings, as farmers could own and manage multiple holdings as a single entity, referred to as a ‘farm’. The size of these farms and their agricultural holdings is described in Fig. 3a. The average size of French cereal farms, determined based on the administrative size of the agricultural holdings, cannot be directly compared with the average size of the 33 farm entities. Instead, it can be compared with the average size of the 48 agricultural holdings (258 ha), which largely exceeds the average size of cereal farms in cereal-growing region like Ile-de-France (about 150 ha) and France overall (about 96 ha) (Agreste, French Ministry of Agriculture and Food, 2021, 2022). This significant size of cereal farms associated with AD suggests that investing in a biogas plant may require a substantial land area to cultivate enough ECC to supply the plant without impacting other farm activities, especially without livestock. Another hypothesis is that farms with larger land holdings have the necessary capital to invest in injection AD infrastructure, while still requiring collaboration with other farmers for capital and ECC supply (Berthe et al., 2022).

ECC covered 17 % to 50 % of total crop surfaces per year (Fig. 3b). Details per farm and per ECC type are presented in the table of Supplementary Material 8. On average, primary feedstock for biogas plants was silage from dedicated energy crops or ECC sourced either from the farm or outside sources, followed by beet pulp, and then by other agro-industrial wastes or byproducts such as whey, onion or potato peels, downgraded vegetables, etc. (Fig. 3c). Among the 24 studied biogas plants, 9 reported using dedicated energy crops as AD input, while the others declared growing only ECC. Farms associated with these nine
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Characteristics of the interviewed systems with farms and biogas plants:</figcaption></figure>

a) Utilized agricultural area of interviewed farms (and their agricultural holdings) associated with AD (one farm may encompass multiple agricultural holdings, managed as one entity under the designation of 'farm'; *n* = 33 farms, *n* = 48 agricultural holdings), b) Percentage of UAA with ECC per year (*n* = 30 farms) and c) Share of various feedstocks used in the interviewed biogas plants (*n* = 24 digesters). The mean is reported in the central points in the boxplots.

<caption>**Table 1**</caption>

Number of users among the interviewed farms (*n* = 33 farms) of crop species grown as ECC or dedicated energy crop, as main crop following or prior to an energy crop, or as perennial crop for AD (one crop species can be grown as an ECC, a dedicated energy crop and also as a main crop following or prior to an ECC on the same farm).

<table><thead><tr><th></th><th>Summer ECC</th><th>Winter ECC</th><th>Summer dedicated energy crop</th><th>Winter dedicated energy crop</th><th>Crop following winter energy crop</th><th>Crop prior to summer energy crop</th><th>Perennial crop for AD</th></tr></thead><tbody><tr><td>Maize</td><td>29</td><td></td><td>7</td><td></td><td>33</td><td></td><td></td></tr><tr><td>Winter barley</td><td></td><td>25</td><td></td><td>5</td><td></td><td>28</td><td></td></tr><tr><td>Winter rye</td><td></td><td>17</td><td></td><td>1</td><td></td><td>6</td><td></td></tr><tr><td>Sunflower</td><td></td><td></td><td></td><td></td><td>9</td><td></td><td></td></tr><tr><td>Peas</td><td></td><td></td><td></td><td></td><td></td><td>8</td><td></td></tr><tr><td>Sorghum</td><td>7</td><td></td><td>2</td><td></td><td></td><td></td><td></td></tr><tr><td>Triticale</td><td></td><td>6</td><td></td><td>1</td><td></td><td>4</td><td></td></tr><tr><td>Cereals and legumes mix</td><td></td><td>3</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Green bean</td><td></td><td></td><td></td><td></td><td>2</td><td></td><td></td></tr><tr><td>Cup plant</td><td></td><td></td><td></td><td></td><td></td><td></td><td>2</td></tr><tr><td>Oat</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grass</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>Spring barley</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Soy</td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td></tr><tr><td>Camelina</td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td></tr><tr><td>Buckwheat</td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td></tr></tbody></table>

biogas plants cultivated dedicated energy crops on 5 % to 57.5 % of their crop surfaces, with a median of 16 %. Only one interviewed farm managed to cultivate a large amount of dedicated energy crops, as it was associated with a biogas plant built before 2017 and thus was exempted from the 15 % limit on dedicated crops set for AD feedstock gross tonnage by Decree No 2016-929. As farmers did not distinguish the source of crops in their silage (whether energy cover crops or dedicated energy crops), it was not possible to gather precise information about the dedicated energy crop proportion in biogas plants during interviews.

## 3.2. Energy cover crop management on farms associated with AD

Among interviewed farms associated with AD, maize emerged as the predominant summer ECC in terms of number of users, followed by

sorghum, while winter barley and winter rye were the predominant winter ECC (Table 1). Interestingly, only three farms cultivated a mix of cereals and legumes as winter ECC (Table 1), explaining that diversified crop species on the same plot presented challenges in weed management due to limited herbicide options, and that legumes were particularly susceptible to drought, making summer cultivation challenging. Additionally, farmers commented that the total biomass production of legumes and cereals combined was not higher than when only ECC cereals were cultivated, but with a higher seed price associated with the mix. These factors contributed to the limited interest in cultivating ECC combining legumes and cereals, although these mixed crops with legumes bring nitrogen to the cropping system, thus potentially improving farmers' nitrogen fertilizer autonomy (Launay et al., 2022).

Among the 33 farms interviewed, 25 used a strip-till system to sow
<figure><img src="image_7.png" /><figcaption>Fig. 4. Usage of plant protection products on predominant ECC: number of users and applied dose comparison with main crop of the equivalent species.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 5. Average crop management practices on the most encountered energy cover crops among interviewed farmers.</figcaption></figure>

summer ECC or summer crop following winter ECC, with 3 missing data. Additionally, 9 farms mentioned adopting this system after ECC introduction, enabling rapid sowing of summer crops while preserving soil moisture, which is crucial in systems with two crops grown in a year, as the cultivation period is shorter compared to cases without ECC.

Figure 4 details PPP types and doses used on ECC and the number of users, compared to applications on main crops of the same species. Most farms used PPP on ECC, especially herbicides. Out of 29 farms growing maize as summer ECC, 20 used PPP, 4 reported using none, and data was missing for 5 farms. Similarly, of 25 farms cultivating winter barley as winter ECC, 21 used PPP, 3 reported no use, and data was missing for 1 farm. Generally, PPP quantities applied on ECC were lower compared to those typically applied to the equivalent main crop. Details per farm are

available in Supplementary Material 8.

The ECC received nitrogen fertilization at levels either lower or similar to when the same species was cultivated as main crop, depending on the farm. On average, winter ECC received 133 kg N<sub>eff.ha<sup>-1</sup></sub>, while summer ECC received 106 kg N<sub>eff.ha<sup>-1</sup></sub> (Fig. 5 and Supplementary Material 8). This fertilization was sourced from both digestate and mineral fertilizers, depending on the application period. More information about digestate application on ECC is available in 3.4.1. The application rates of N fertilizer and PPP on winter barley ECC were positively correlated (R<sup>2</sup> = 0.37 and *p* < 0.01), indicating that farmers who applied high levels of fertilizer on ECC were also prone to using significant amounts of PPP on ECC.

Farmers mentioned increasingly prioritizing winter ECC cultivation
to secure AD feedstock without irrigation. Despite this trend, 17 farms reported irrigating summer ECC, especially maize, while sorghum was generally irrigated in smaller volumes or not irrigated at all. Water application levels varied across farms and years, from 0 mm during years with significant rainfall, and up to 3 × 40 mm.

Summer ECC yields varied considerably from year to year, depend-
ing on climate conditions, ranging from 0 to 13.5 t DM.ha⁻¹. Median
yields for irrigated maize and non-irrigated maize as ECC were 8.1 t DM.
ha⁻¹, and 6.3 t DM.ha⁻¹, respectively. Winter ECC yields were more
stable due to regular winter rainfall, ranging from 6.6 t to 15 t DM.ha⁻¹,
with a median yield of 10 t DM.ha⁻¹.

Figure 5 illustrates typical crop management practices observed for the two predominant ECC, including sowing and harvesting dates and fertilization rates.

In Cadiou's (2023) study on AD's impact on crop-livestock and livestock systems in Eastern France, ECC management practices were less intensive compared to those observed in French cereal-growing regions. Fertilization levels ranged between 80 and 100 kg N<sub>eff.</sub>·ha<sup>-1</sup>, with limited PPP use and irrigation on ECC in livestock and crop-livestock farms. This could be attributed to manure availability for AD, reducing the need for high ECC yields. Lower revenue and less intensive farming practices in Eastern France (Agreste, French Ministry of Agriculture and Food, 2020b) may also influence ECC production. These findings suggest broader cultural or regional differences in agricultural approaches, contributing to variations in ECC management. This underscores the need for assessments at more localized scales, as AD systems are not uniform.

The qualitative analysis (Supplementary Material 3) revealed that the primary factor influencing the choice of ECC species was its potential biomass productivity, ensuring sufficient input for biogas production and economic returns. Following closely behind this factor was the ECC sensitivity to drought, influenced by its agronomic characteristics and growth period. Indeed, winter ECC were increasingly favored over maize ECC by interviewed farmers for reduced risk of water scarcity in winter. Additionally, sorghum was perceived as a good drought-resistant alternative to maize. Factors such as presence of irrigation system and soil characteristics also influenced choices, with maize often avoided on soils with low water holding capacity.

Environmental concerns related to ECC such as the level of pesticide
usage or insect species biodiversity were mentioned by only a few
farmers (Supplementary Material 3). Limited mention of environmental
impacts may be attributed to high AD investment costs, which push
farmers to prioritize economic considerations over environmental con-
cerns. This finding aligns with results from interviews conducted in
Eastern France regarding cogeneration biogas plants, despite their lower

investment costs compared to injection biogas plants, as reported by
Cadiou (2023).

Water resource concerns were prevalent, potentially partly influ-
enced by some interviews conducted after the severe water scarcity of
the summer of 2022. It is not surprising that this concern is predomi-
nant, given that cropping systems with ECC are sensitive to water
scarcity, potentially impacting the yield of summer crops (Krueger et al.,
2011). Diminished ECC yields would necessitate farmers to purchase
more AD inputs from outside sources, posing economic challenges. With
water availability and irrigation capacity being crucial factors in
choosing ECC species, there are concerns about the viability of these
cropping systems with ECC in the face of climate change, with rising
temperatures and altered precipitation patterns.

3.3. Impact of anaerobic digestion on cropping systems at the farm scale

3.3.1. Impact of AD on main crop surfaces at the farm scale

On average, at the farm scale, rapeseed and wheat areas decreased significantly (−5.89 % of total UAA and − 11.13 % respectively) after the introduction of AD, while barley, maize, and ‘other cereals’ (such as rye, oat, or sorghum) which are characterized by shorter growth periods, increased (+8.71 %, +3.67 % and + 3.36 % respectively) (Fig. 6). This shift enables farmers to grow two crops in a year (Fig. 1). ‘Other industrial crops’ areas remained stable, sugar beet being the predominant crop of this group in our sample. Selling beets to sugar refineries facilitates farmers’ access to beet pulp, a major AD input. Additionally, sunflower areas increased (+2.88 %) but this was not significant (p-value = 0.078). Sunflower was mentioned by interviewed farmers as an interesting crop following winter ECC due to its short growth period, its limited water requirements, and its favorable price in the oil market at the time of the interview. Some farms introduced peas before summer ECC or green beans after winter ECC, due to their shorter cultivation periods compared to maize or winter barley, thereby diversifying crops at the farm scale. However, these crops, especially green beans, are known for their high water demand, and involve using irrigation systems. The changes in crop proportions for the most cultivated crops per farm are presented in the table provided in Supplementary Material 8.

Observed average land cover changes before and after AD align with some findings in Levavasseur et al. (2023b), who investigated similar changes at the French national scale for specific groups of farms (cluster 4 and 5). However, the changes quantified in our study remain low compared to the significant increases in maize surfaces observed in German or Italian regions with extensive biogas development, where dedicated energy crops were widely cultivated (Mela and Canali, 2014; Yang et al., 2021). Although changes linked with ECC introduction are less pronounced, they cannot be ignored as the agricultural output not produced as a result of ECC cultivation would need to be generated elsewhere, potentially offsetting some environmental gains achieved through AD (Styles et al., 2015). It is interesting to note that among crops experiencing reduced surfaces, rapeseed is frequently cultivated for bioenergy purposes such as biodiesel in France (Direction Générale de l'Energie et du Climat, 2020), while crops with expanding surfaces, namely barley and other cereals, are primarily cultivated for feed and food purposes.

In terms of cultivated biodiversity, the crop biodiversity index for main crop species slightly increased after the start of AD (from 3.06 ± 1.27 to 3.57 ± 1.13), while the DCF slightly decreased (from 3.37 ± 0.87 to 2.86 ± 1.15), with both changes being statistically significant. Out of 33 surveyed farms, 22 experienced an increase in the crop biodiversity index, 6 observed a decrease, and data was missing for 5. Additionally, 12 observed an increase of DCF while 16 experienced a decrease, with 5 missing data. This confirms that, as highlighted in arguments supporting AD, the introduction of ECC in rotation helps diversify main crop species at the farm scale, potentially enhancing ecosystem services provided by the crop system. This is particularly beneficial in cases where crop rotations was initially simpler (Beillouin

<figure><img src="image_9.png" /><figcaption>Fig. 6. Average land cover before and after anaerobic digestion of interviewed farms (main crops only) (significant changes are specified by stars (*) *p* < 0.05; (***) *p* < 0.001).</figcaption></figure>
et al., 2021; Beaumelle et al., 2021). However, this diversification primarily involved crops from the same family, “Poaceae”, which may reduce the environmental benefits of enhanced cultivated diversity (Vialatte et al., 2022).

### 3.3.2. Impact of AD on main crop management

No significant impact on main crop cultivation practices was reported, except for changes in sowing and harvesting dates due to the introduction of ECC. The adoption of a cropping system with ECC affected main crop yields of 13 out of 33 farms, with reported losses in summer crops following winter ECC. Respondents indicated losses ranging from 10 % to 40 %, attributed to factors including delayed sowing dates of summer crops, resulting in a shorter growth period. Additionally, summer crops following winter ECC may grow in soil with reduced water and nutrient resources (Alvarez et al., 2017; Krueger et al., 2011; Meyer et al., 2020). It is worth noting that some farmers observed that winter rye ECC depleted soil water content more than winter barley ECC, although confirmation through experimental trials is needed.

While two farmers mentioned increased tillage following AD due to greater soil compaction from digestate spreading and ECC silage, two others noted that AD had enabled them to transition to no-till farming. This shift was facilitated by the financial stability provided by AD, allowing them to invest in suitable machinery.

Considering the significant changes in land cover at the farm scale presented in section 3.3.1 (i.e. maize, barley, wheat, rapeseed and other cereals), along with the regional mean TFI for each crop, interviewed farms could theoretically decrease their TFI on main crops by 0.35 on average. This reduction in TFI comes from replacing wheat and rapeseed surfaces with high mean TFI by barley, maize and other cereals with lower mean TFI. Consequently, a slight decrease in pesticide pressure on farms associated with AD without livestock could be expected if pesticide application on ECC is limited. This suggests that TFI on ECC should not exceed 0.35 on average to maintain the TFI of the system at its pre-AD level. However, it should be noted that this expectation may not always align with reality, as illustrated in Figs. 4 and 5. Furthermore, these results only consider average changes in crops and farmers may individually achieve higher or lower reductions in TFI on main crops.

## 3.4. Anaerobic digestion and its impact on fertilization at the farm scale

### 3.4.1. Digestate management

All interviewed farms, except one, received digestate from the biogas plant they supplied. The exception was due to the farm's distance from the biogas plant, but the farmer planned to construct suitable storage facilities in the future. Digestate phase separation allowed 6 digestate users out of 32 to use both liquid and solid digestate on their farms. Quantities of raw or liquid digestate applied varied from 20 to 45 m³. $ha^{-1}$ across farms and across plots, depending on the nitrogen content per m³ differing across digestate and the crop to which it was applied. Umbilical systems with trailing hoses or trailing shoes were used by all farmers to spread raw or liquid digestate, aiming to minimize soil compaction and $NH_3$ volatilization (Lantz and Börjesson, 2014; Wulf et al., 2002). However, 15 out of 32 mentioned the occasional need to use tankers for spreading on small or distant parcels. This contrasts with findings by Cadiou (2023) in Eastern France, where crop-livestock and livestock farms associated with AD more frequently relied on tanker spreading, leading to increased soil compaction. Indeed, the smaller plots common in Eastern France are less suited to umbilical systems with trailing hoses, compared with the contiguous and larger parcels in cereal-growing areas. Additionally, most surveyed cereal farmers had access to farm machinery cooperatives offering this spreading method, or had personally invested in spreading equipment, which was not as accessible to smaller livestock farms for economic reasons.

Respondents estimated nitrogen available from digestate, when not incorporated, to be around 60 % on average, with some farmers suggesting it could reach 80 %, while others were more conservative, estimating it not to exceed 50 %. Indeed, the NUE of digestate is fluctuating mainly because of ammonia volatilization, largely influenced by spreading method, weather conditions, the characteristics of digestate and the specific crop and season to which it is applied (Grillo et al., 2021; Jin et al., 2022). Farmers mentioned that they typically relied on values provided by agricultural chambers or their own experience, while acknowledging a knowledge gap in this area, which they hope future research will address.

All users primarily spread digestate on winter cereals in February and March, on both main crops and ECC. Digestate was applied in April on spring crops such as sugar beet and potatoes by 7 users, with two

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Difference in $\Delta N$, P and K per hectare at the farm scale before and after anaerobic digestion (notated as $\Delta N_{(before-after)}$) as a function of agro-industrial waste and byproduct levels in the biogas plant, with $\Delta N_{after} = \Sigma (N$ requirements of main crops + ECC) - N_{supply}$ from digestate (same for P and K). The equation before AD does not consider ECC and digestate (n = 24). a) $\Delta N_{(before-after)}$ in kg N.ha$^{-1}$ for two levels of digestate NUE. Results of the same farm are connected with dots b) $\Delta P_{(before-after)}$ in kg P.ha$^{-1}$ and c) $\Delta K_{(before-after)}$ in kg K.ha$^{-1}$.</figcaption></figure>
others planning to do the same in the future. 9 users applied digestate to summer crops like maize following a winter ECC. Additionally, 6 users applied digestate before sowing rapeseed in August or September. 14 farmers with limited digestate storage capacity had to spread digestate during summer and autumn on cover crops or winter cereals, when the nitrogen uptake by plants is generally less efficient, potentially leading to more reactive nitrogen losses and diminishing the potential nitrogen fertilizer savings achievable thanks to digestate spreading (Matsunaka et al., 2006; Schwager et al., 2016). Out of 33 farms interviewed, 7 had digestate storage for over a year, while 10 had storage for less than 9 months. While this exceeds the four-month requirement stipulated by French regulation (Article 34, ICPE n°2781, 2010), it may still lead to suboptimal periods for digestate spreading for those with less than nine months of storage. Finally, in most cases, digestate was stored in uncovered lagoons. Respondents argued that covering these lagoons would require significant investments and would be impractical due to the large size of the required covers. They highlighted that a crust forms on the surface of the digestate, limiting ammonia volatilization. However, these limitations in volatilization are not always achieved as observed in Baldé et al. (2018).

### 3.4.2. Evolution of nutrient balances at the farm scale before and after AD

Comparison of theoretical nitrogen balances per hectare at the farm scale before and after AD ($\Delta N_{(before-after)}$) revealed that, out of 24 studied farms, only 1 exhibited higher $\Delta N$ per hectare after AD when considering a digestate NUE of 100 % (as depicted by $\Delta N_{(before-after)}$ < 0 on Fig. 7a). This suggests that, even under maximum theoretical conditions, this farm could not achieve the expected fertilizer savings from AD, as its system required more nitrogen after AD, which was not surprising as this farm was the only one not receiving digestate.

On adjusting theoretical NUE of the digestate to a more realistic level of 50 %, the number of farms exhibiting higher $\Delta N$ per hectare than before AD increased to 4 farms out of 24 studied farms. The 20 remaining farms demonstrated reduced $\Delta N$ following AD implementation, suggesting potential nitrogen fertilizer savings after AD along with the adoption of effective digestate management practices. Additionally, regarding P and K balances, 6 farms exhibited theoretical increases in $\Delta P$ and 7 in $\Delta K$ after AD, with these farms not necessarily being the same for both P and K (Fig. 7b and c). These results confirm that significant fertilizer savings are achievable even when cultivating ECC on non-livestock farms, however this is not the case for all farms. Explanatory variables were explored to better understand the factors driving the variability in results across farms.

The $\Delta N_{(before-after)}$ was significantly correlated with the amount of agro-industrial waste and byproducts in the digester ($p < 0.001$ for both NUE = 50 % and 100 %) (Fig. 7a). The R-squared values of 0.50 and 0.55 for NUE = 100 % and NUE = 50 % respectively suggested that half of the variance of $\Delta N_{(before-after)}$ was explained by the level of agro-industrial waste in the digester. Similar regressions were performed for P and K and were also statistically significant but with R-squared being lower (0.38 and 0.22 respectively). No significant correlations were found between $\Delta N_{(before-after)}$ and the share of ECC in the farm UAA. It is worth noting that when the extreme data point, representing a farm with a high proportion of external inputs, is removed from the $\Delta N_{(before-after)}$ regression (Fig. 7), the relationship becomes less pronounced but remains statistically significant ($R^2 = 0.24$ and 32 for NUE = 100 % and 50 % respectively). This suggests that the observed trend is not solely driven by this extreme point, but reflects a broader underlying pattern.

These results highlight that significant fertilizer savings are achievable on non-livestock farms with ECC when high-levels of external agro-industrial waste and byproducts are used as AD input. Agro-industrial waste and byproducts are typically rich in nutrients, the nutrient levels varying depending on the type of waste or byproducts. What makes these inputs especially effective in achieving fertilizer savings on farms is that they come from outside the farm system, unlike ECC. As such, they can provide external nutrient sources that can reduce the

need for synthetic fertilizers. While this practice can help closing the nutrient loop by recycling nutrient from waste that might otherwise be incinerated, it also introduces certain drawbacks, such as increased dependence on resources with fluctuating prices and growing competition (Britz and Delzeit, 2013; De Groot et al., 2022), which is contrary to one of the objectives of interviewed farmers. With the growing number of biogas plants, the increased competition for agro-industrial resources could undermine the anticipated savings. However, this dependence could be lowered by cultivating more legumes as ECC, but several barriers have been mentioned, as discussed in section 3.2.

Nitrogen fertilizer savings effectively achieved were assessed during interviews conducted in 2022 and 2023 (17 respondents). Twelve farmers reported saving nitrogen fertilizer, although some encountered challenges in accurately quantifying the extent of these savings. Among those who could provide measurements, saved quantities varied considerably across farms, ranging from -20 % to -60 %. Notably, among the 17 respondents, farmers unable to achieve these savings had only one or two years of experience in AD, which is not surprising, as at this early stage they often rely heavily on ECC but receive little digestate. These findings present a more optimistic outlook compared to Launay's (2023) study, which demonstrated, through modeling, an increase in synthetic nitrogen fertilizer consumption in farms in a French cereal-growing region upon the introduction of ECC for AD.

### 3.5. Variability in farming and AD supply strategies among interviewed farms

Surveyed agricultural practices linked to AD were relatively homogeneous among respondents, with only minor variations observed such as ECC species, digestate storage and application managements, ECC proportion in the digester, PPP and irrigation practices on ECC, and the degree of fertilizer savings achieved. Despite the homogeneity in the studied agricultural systems, two distinct farmer behaviors emerged from our surveys regarding their management of biogas plant. The first type of farmers sought to use significant amounts of agro-industrial waste or byproducts from external sources to supplement their digester, in order to reduce ECC cultivation and maximize biogas yield and revenue. Conversely, other farmers preferred to minimize reliance on external inputs, prioritizing self-sufficiency. However, seeking greater autonomy in inputs often resulted in lower nitrogen levels in digestate, leading to reduced fertilizer savings and increased dependence on chemical fertilizers and their volatile prices. Thus, both types of farmers, those prioritizing revenue and those emphasizing autonomy, faced fluctuating input prices, whether from agro-industrial waste or from fertilizers. All interviewed farmers expressed satisfaction with their investment in biogas plants, despite the increased workload post-AD, including on-call work during weekends or at night.

### 3.6. Differences between observed farming practices and those considered in AD assessments

With this study, it is the first time that comprehensive on-farm agricultural practices of non-livestock farms associated with biogas plants located in French cereal-growing regions are being described. It highlights the divergence between the observed farming practices and those considered in AD assessments (Bacenetti et al., 2016; Berger and Esnouf, 2022; Esnouf et al., 2021; Malet et al., 2023; Nilsson et al., 2024; Riau et al., 2021; Styles et al., 2015).

Our observations align more closely with practices typically considered for dedicated energy crops in the Renewable Energy Directive II (Red II) evaluation methodology, that assesses the sustainability of renewable energy such as anaerobic digestion (Berger and Esnouf, 2022; European Parliament, 2018). Indeed, unlike described in AD assessments, ECC in our observations were very often treated with pesticides and irrigated (Fig. 5). Fertilization levels on ECC in assessments, ranging from 0 kg $N_{eff}$·ha⁻¹ to 140 kg $N_{eff}$·ha⁻¹, did not always match
our findings (Nilsson et al., 2024; Riau et al., 2021), with some assess-
ments only accounting for digestate spreading on ECC (Berger and
Esnouf, 2022; Nilsson et al., 2024), neglecting mineral fertilizer use
observed in French cereal farms. ECC yields considered in assessments
ranged from 6 t DM.ha⁻¹ to 9 t DM.ha⁻¹, which is consistent with our
findings. However, the majority of assessments did not consider the
impact on subsequent main crop yields (Bacenetti et al., 2016; Berger
and Esnouf, 2022; Esnouf et al., 2021; Malet et al., 2023; Nilsson et al.,
2024; Riau et al., 2021), whereas several farmers reported yield losses
on main summer crops. Moreover, the increasing preference for winter
ECC shifts the risk of water scarcity to summer main crops. This shift
may potentially reinforce yield losses, particularly in the context of
climate change, with expected higher temperatures and altered rainfall
patterns in the coming years.

The recent AD assessments have not considered the observed indirect
land cover changes associated with the introduction of ECC (section
3.3.1) (Bacenetti et al., 2016; Berger and Esnouf, 2022; Esnouf et al.,
2021; Malet et al., 2023; Nilsson et al., 2024; Riau et al., 2021). How-
ever, these changes entail the need to produce elsewhere the agricultural
outputs not generated, potentially reducing the environmental benefits
of anaerobic digestion (Styles et al., 2015).

A comprehensive environmental reassessment of AD considering
real-world farming practices observed in cereal-growing regions,
including management of ECC, changes in land cover, and their impli-
cations for food production and TFI at the farm scale, as well as
achievable fertilizer savings, should be performed. While the semi-
structured interviews conducted in this study provide valuable
context-specific insights, they also offer an empirical foundation that
can support future modeling efforts aimed at exploring the broader
implications of AD under diverse pedoclimatic systems and scenarios.
These interviews capture farming practices related to ECC imple-
mentation that are not yet reflected in large-scale databases, either in
France, Europe, or globally. Combining empirical data from this study
with crop modeling techniques could help re-evaluate if AD, as a
renewable energy production method, is being implemented sustain-
ably, especially concerning mitigating GHG emissions, preserving water
resources, and maintaining food production. Furthermore, it will facil-
itate the examination of AD development scenarios proposed for 2050
(ADEME, 2021; Couturier et al., 2016; Mourjane and Fosse, 2021) in
comparison to actual farming practices, prompting a reconsideration of
the anticipated proportion of ECC in future AD systems.

4. Conclusion

To the best of our knowledge, no scientific paper has yet investigated
changes in farming systems associated with AD in cereal-growing re-
gions. Our research offers a novel contribution by documenting these
systems which predominantly rely on ECC as AD feedstock, and by
providing unique data that were previously unavailable in this context.

Our research revealed that maize was the most produced summer
ECC and winter barley the preferred winter ECC in French cereal-
growing areas. We found that plant protection products were
frequently used on ECC, but in lower quantities than those applied on
the equivalent main crops. Additionally, summer ECC were irrigated on
half of the interviewed farms while fertilization levels varied from
approximately half to the same level of what is applied on main crops. At
the farm scale, changes in main crop areas were observed, leading to
changes in pesticide use and food production. Although these changes
are less pronounced compared to those of dedicated energy crops, they
can still significantly impact AD sustainability. Potential fertilizer sav-
ings allowed by digestate use were positively correlated with agro-
industrial waste and byproduct levels in the biogas plants. This reli-
ance implies a vulnerability, as the availability of these inputs could
limit savings, especially in cereal regions with fewer agro-industrial
resources or an increasing number of biogas plants.

These findings provide a solid basis for reevaluating anaerobic

digestion based on ECC, focusing on real-world farming practices rather
than the theoretical systems previously considered. ECC production is
increasingly promoted across Europe as a means to support sustainable
biomethane production and enhance energy autonomy. However, our
research revealed notable deviations between the practices considered
in AD sustainability assessments and those actually observed in French
cereal-growing areas, which were more intensive. Given these differ-
ences and the projected development of ECC-based agricultural systems,
reassessing these systems under real-world conditions is critical. Our
results also emphasise the heavy reliance of cropping systems with ECC
on water resources. Therefore, future assessments of AD should also
focus on the impact of climate change on systems with ECC. Addressing
these issues will help ensure that the large-scale development of this
energy supply chain is carried out in a sustainable manner. Such reas-
sessments could inform public policies, facilitating the virtuous devel-
opment of this renewable energy, enabling the energy transition to be
associated with a sustainable agricultural transition.

CRediT authorship contribution statement

**Léa Boros:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. **Marco Carozzi:** Writing – review & editing, Visualization, Supervision, Methodology. **Sophie Carton:** Writing – review & editing, Validation, Methodology, Investigation, Formal analysis, Data curation. **Sabine Houot:** Writing – review & editing, Supervision, Methodology. **Philippe Martin:** Writing – review & editing, Supervision, Methodology. **Florent Levavasseur:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Ethics approval

The study received approval from the Data Protection Officer of INRAE and was certified compliant with GDPR requirements (EU Regulation 2016/679). Informed consent was obtained from all individual participants included in the study, along with their permission for the publication of aggregated results derived from their interviews.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgments

We extend our thanks to all the farmers for generously offering their
time during the interviews and for sharing valuable information. Addi-
tionally, we express our appreciation to the agricultural advisers from
the Chamber of Agriculture for their willingness to share insights with
us. This work received funding from the French Research Agency within
the CLAND project under the "Investissements d'avenir" programme
with the reference ANR-16-CONV-0003. It was also funded by the
metaprogramme "Agriculture and forestry in the face of climate change:
adaptation and mitigation" (CLIMAE) of the French National Research
Institute for Agriculture, Food and Environment (INRAE). It was also
partly supported by the Center for Interdisciplinary Studies on Biodi-
versity, Agroecology, Society and Climate (C-BASC).

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.org/10.1016/j.agsy.2025.104387.
# Data availability

The datasets generated and analyzed during the current study contain personal data and are therefore not publicly available (EU Regulation 2016/679), but aggregated data can be obtained from the corresponding author upon reasonable request.

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