# Unveiling on-farm anaerobic digestion systems diversity: A typology analysis of Northern French farms

Salma Amsaguine, Romain Gloaguen, Loïc Sauvée

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Salma Amsaguine, Romain Gloaguen, Loïc Sauvée. Unveiling on-farm anaerobic digestion systems diversity: A typology analysis of Northern French farms. Next Research, 2026, 7, pp.101486. 10.1016/j.nexres.2026.101486. hal-05517095

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# Unveiling on-farm anaerobic digestion systems diversity: A typology analysis of Northern French farms

Salma Amsaguine* ID, Romain Gloaguen ID, Loïc Sauvée ID

InTerACT UP 2018.C102, Institut Polytechnique UniLaSalle, 19 rue Pierre Waguet, 60000, Beauvais, France

## ARTICLE INFO

### Keywords:

- Anaerobic digestion

- Typology analysis

- Farming systems

- Practices modification

## ABSTRACT

On-farm anaerobic digestion (AD) has emerged in France as a pivotal strategy to align agricultural production with energy transition, converting livestock effluents, crop residues, and agro-industrial byproducts into biogas and digestate. Strategic farm-level choices, such as biomass sourcing through territorial cooperation or modifications to cropping systems, further shaped post-AD agricultural profiles. Understanding how farming systems change after AD implementation is therefore critical. This study thus aimed to develop a holistic typology of on-farm anaerobic digestion systems in the Hauts-de-France region (France). Field surveys (40 AD units) and multivariate analyses (Factorial Analysis of Mixed Data (FAMD) followed by Hierarchical Clustering on Principal Components (HCPC)) were used to assess structural and operational characteristics. Results revealed five distinct farm system clusters integrating AD. These groups are separated broadly into small-scale systems prioritizing manure management as the main input for AD process, and into larger, biomethane-oriented units heavily reliant on external substrates and energy crops. Clusters differed by scale in operating strategy, economics, digestate management, and key constraints (infrastructure, social acceptance). These findings provide an informative approach for analyzing the complex interactions within diverse on-farm AD systems. Additionally, they show that effective support and optimization strategies for AD must be adapted to specific farm system characteristics and their integrated operational and strategic choices, offering insights applicable to similar agricultural contexts.

## 1. Introduction

French agriculture plays a multifaceted role beyond food production. Farmers increasingly adopt agro-energy initiatives, such as anaerobic digestion (AD), which have demonstrable environmental and socio-economic impacts on local territories and social groups [9]. Anaerobic digestion is a biological process in which microorganisms convert organic matter (e.g., livestock manure, crop residues) into biogas and a nutrient-rich by-product, digestate, under oxygen-free conditions. At farm scale, AD represents a systemic innovation that couples renewable energy production with nutrient recycling, thereby supporting the transition to more sustainable agri-food systems. This growing commitment to shifting toward sustainable agro-ecosystems has positioned on-farm AD as both a key strategy for addressing the escalating challenge of organic waste accumulation in industrialized nations [12] and a strategic pillar of the European Union's renewable energy portfolio [4]. This technology's integration into regional systems advances transitional climate objectives and could help meet up to 25 % of future

bioenergy demand [65], a cornerstone for achieving the EU's 2050 carbon neutrality target [7].

Within this broader energy and policy context, different implementation models of AD have emerged. On-farm AD is principally distinct from industrial and territorial AD systems due to its focus on converting farm-generated substrates, such as livestock manure, crop residues, and agricultural byproducts, into biogas and digestate. This operational distinction directly aligns with the EU's two primary models for agricultural biogas production [37,65]. Centralized AD systems, as the first model, operate at industrial scales (hundreds to thousands of m³ per year), digesting manure pooled from multiple farms alongside external organic waste from municipalities or industries. The second model, i.e. farm-scale AD systems, treat substrates from a single farm or a small cluster of neighboring farms and has historically been often included in livestock farms, such as dairy or pig production. By prioritizing localized feedstock loops rather than regional waste aggregation, farm-scale AD highlights its unique capacity to embed circularity directly within agricultural production [129].

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: salma.amsaguine@unilasalle.fr (S. Amsaguine), romain.gloaguen@unilasalle.fr (R. Gloaguen), loic.Sauvee@unilasalle.fr (L. Sauvée).</footnote>
Over time, farmers increasingly perceive AD as a strategic mechanism to achieve resilience and long-term viability of their farming practices amidst persistent sector-wide financial pressures [14,58]. This perception is reflected in the adoption of digestate as a partial substitute for mineral fertilizers, with documented effects on soil fertility and crop performance [87,88]. At the same time, biogas production also represents an additional, though variable, income stream, and profitability depends strongly on the organizational and technological model, ranging from tightly controlled on-farm resource use to collective or partly outsourced arrangements with higher capital requirements [15]. In France, early AD development was facilitated by policy support through subsidies and feed-in tariffs, particularly for manure-based cogeneration [58].

Building on these economic and policy drivers, AD adoption also induces profound agronomic and logistical changes at farm level. Reported changes include adjustments in cropping strategies to secure biomass, including energy cover crops, i.e. short-cycle cover crops grown between main cash crops primarily to produce additional biomass for biogas (e.g., Italian ryegrass, forage sorghum, whole-crop rye or triticale) and shifts in fertilization practices as digestate replaces part of synthetic nitrogen inputs [30]. These shifts can also create new environmental and logistical constraints, for example ammonia losses when digestate storage is uncovered and nitrate leaching risks after spreading [58,89,112,79]. Increasing competition for substrates may further lead farmers to diversify feedstock sources, often via territorial cooperation and the use of agro-industrial co-products to stabilize biogas production [25,39]. Beyond agronomy, cooperative models can reduce investment barriers and generate local benefits, but projects may still face social opposition tied to the “Not in My BackYard” (NIMBY) syndrome, making stakeholder mediation and local anchoring important conditions for acceptance [22,24,20].

From a research perspective, however, these practical developments have not been matched by an equally comprehensive analytical framework. Existing studies have examined agronomic and biogeochemical impacts on specific types of French farms (e.g., mixed crop-livestock systems [19,86], field crops [30], livestock systems [48,53] and have explored socio-economic and organizational dimensions [25,40,54,57, 115,58], few adopted a holistic perspective linking technical practices, logistical constraints, and farm-level decision-making. Methodologically, the MethaLae project, a comprehensive French study [3], used socio-economic typologies to classify anaerobic digestion systems based on their degree of integration with associated farms and to analyze fertilization management practices. However, it did not systematically address the structural diversity of farms or the agronomic and logistical strategies adopted after methanization. In addition, the existing typologies of AD systems of French farms [14] proposed qualitative and subjective typologies to better understand the evolution of the sector, focusing on the strategies of actors, particularly farmers, and how they adapt to policy changes and the emergence of new industrial players, showing the variability and heterogeneity of the strategic adaptations of farmers and the sector's organizational diversity [15]. Despite these contributions, a critical gap persists; to the best of our knowledge, no studies have yet developed a typology of on-farm AD systems through statistical analyses that holistically combine: (i) a structural description of farms and their link to AD plants (e.g., size, feedstock type, energy output), (ii) an evaluation of adaptive agronomic-logistical strategies (e.g., changes in fertilization practices, biomass supply chains), and (iii) a socio-economic impact assessment, including farm revenue stability, employment effects, and community acceptance of AD projects

To address these gaps, this study aims to develop through field surveys and statistical analysis, a typology of on-farm AD in Hauts-de-France region, focusing on the technical, agronomic, logistical and socio-economic aspects of AD integration across farms with different orientation. Specifically, it seeks to answer the following questions: How do farmers' practice choices define typologies of on-farm AD in the Hauts-de-France region? On which criteria do the diversity of on-farm

AD systems structure itself?

We hypothesize that: H1: The typology will reveal distinct profiles of on-farm AD units. H2: Farms orientation influences the profitability and sustainability of AD units. H3: Post-AD fertilization management practices after AD implementation may influence farm agronomic performance. H4: The introduction of AD increases farm's incomes and modifies labor dynamics, creating new jobs or altering workload for farmers.

## 2. Materials and methods

### 2.1. Site description and farms selection

The study was conducted in the Hauts-de-France region. Among the 156 on-farm anaerobic digestion units identified in the region, contact information of 106 farmers were obtained from database records provided by the Head of the Research and Development Team at the Agricultural Chamber of Hauts-de-France. A screening process was implemented to eliminate duplicates, missing contacts details, and refusals to participate, therefore, initial contact was made with 82 farmers through mail, followed by phone calls. Ultimately, a total sample of 40 farms across the five case study departments (10 in Oise, 6 in Aisne, 8 in Nord, 9 in Nord-Pas-de-Calais, and 7 in Somme) was selected for in-depth semi-structured interviews as shown in Fig. 1. This corresponds to a non-probability, purposive sample based on voluntary participation: among all on-farm AD units in the region, we included only farms with available contact details whose managers agreed to be interviewed. The sample is therefore not statistically representative of all 156 units, but is intended to capture the main diversity of farm structures and AD configurations in the region.

### 2.2. Data collection

An expert-validated survey questionnaire was developed in collaboration with the regional biogas production expert from the Nord-Pas de Calais Agricultural Chamber. A simplified interview guide was employed to conduct semi-structured interviews with farmers managing on-farm anaerobic digesters, over a five-month period, from May to September 2024. All data were obtained through digitally recorded interviews which were next transcribed verbatim to facilitate the analysis. The survey questionnaire contained a total of 67 questions which were subsequently structured into a dataset, combining quantitative and qualitative information about farm size, agricultural practices, anaerobic digestion system characteristics, substrates composition, digestate management, and socio-economic indicators. The selection of variables was guided by their theoretical and empirical relevance, as identified through a comprehensive review of the existing literature. For clarity and consistency, a glossary defining all quantitative and qualitative variables used in this study is provided in Supplementary Table S7.

### 2.3. Data analysis

#### 2.3.1. Factorial analysis

All analyses were conducted using the FactoMineR package [80] in R statistical software. First, a descriptive analysis (mean, median, standard deviation, maximum, and minimum) was conducted to examine the distribution and variability of the quantitative variables. A Factorial Analysis of Mixed Data (FAMD) was then employed as a multivariate data analysis method on the dataset which included 31 categorical and 36 numerical variables. FAMD is defined as a combination of Principal Component Analysis (PCA) for quantitative variables and Multiple Correspondence Analysis (MCA) for qualitative variables [100].

A two-step data preprocessing approach was implemented to ensure balanced weighting between qualitative and quantitative variables. First, each numerical variable was initially standardized through centering and scaling for an equitable contribution given their varying
<figure><img src="image_2.png" /><figcaption>Fig. 1. Geographical distribution of surveyed on-farm anaerobic digestion systems in Hauts-de-France.</figcaption></figure>

units. Second, categorical variables were converted into factors by assigning numerical codes to represent different modalities, to facilitate their inclusion in the analysis

### 2.3.2. *Hierarchical clustering analysis*

Following the results of the Factorial Analysis of Mixed Data, a Hierarchical Clustering on Principal Components (HCPC) was conducted, using Ward's aggregation criterion [67], to classify the 40 surveyed on-farm AD systems into distinct groups. This ensures high intra-group homogeneity and clear inter-group heterogeneity among individuals. The hierarchical structure results were then visualized using a dendrogram, which facilitated the grouping of the 40 farms. Additionally, a spatial representation of the clusters onto a factor map was performed, to better display their distribution along the principal first and second dimensions.

### 2.3.3. *Statistical analysis*

Non-parametric tests were conducted on the resulting classification to assess which characteristics were more discriminant across the identified clusters. For continuous variables, Kruskal-Wallis test was employed to determine the inter-clusters statistical significant differences [120]. Subsequently, Dunn's test with Bonferroni correction was performed as a post-hoc analysis for variables showing a p-value<0.05 [62]. For categorical variables, Chi-square test of independence was applied [5] to determine the association between cluster groups and various characteristic modalities identifying each on-farm anaerobic digestion unit.

## 3. Results

### 3.1. *Descriptive analysis of the selected farms*

Table 1 shows the results of the descriptive analysis of quantitative variables considered in this study.

This analysis showed variability among the 40 sampled farms. The agricultural land averages around 526 ha, with a variability of approximately ± 265. Likewise, the spreading surface's mean is about 1392 ha, showing a comparable level of variability (± 512). Livestock integration within farms was highly variable, with a pronounced disparity; 50 % of the surveyed farms did not keep livestock, which directly affected

effluent production and management, with average yearly production volumes reaching 1083 m³.

Digestate management strategies vary largely across farms. Raw digestate production is averaging around 19,055 m³, with a variability of ± 8261 m³. Upon phase separation, liquid digestate storage capacity reached a mean of 11,805 m³, showing a variability of ± 4647 m³, which aligns with liquid digestate volumes reaching 4600 m³ in average and a variability of ± 7624 m³. In contrast, solid digestate production is averaging 2075 t, with a variability of ± 3636 t

This variability in digestate management reflected differences in substrates composition: on average, livestock effluents accounted for 28.3 % of the total substrates, even though some farmers relied entirely (100 %) on this source for AD process. Energy cover crops represented an average of 25 %, showing a variability of ± 21 %, while agro-industrial and vegetal waste accounted for 8 % and 13 % of their respective average, with a variability of ±14 % and ±15 % respectively.

Biogas production varied among the surveyed farms. The installed power capacity averaged around 42kWe, with substantial variability across farms (± 103 kWe). Injection capacity i.e., the maximum bio-methane flow rate that can be injected into the gas grid, showed an average utilization of 198 Nm³/h, with significant inter-farm disparities (± 131 Nm³/h).

### 3.2. *Multivariate analysis*

#### 3.2.1. *Factorial analysis of mixed data*

The scree plot in Fig. 2 illustrates the distribution of explained variance across the first five dimensions of the analysis.

As shown in Table 2, the first five dimensions from the FAMD present the associated eigenvalues, the variance explained by each factor, and the cumulative percentage of variance explained. The highest eigenvalue (16.43) and a total variance of 13 % were shown in the first dimension, followed by the second dimension (eigenvalue = 8.76 and 6.95 % of variance). Dimensions 3, 4, and 5 explained progressively smaller proportions of variance (6.27 %, 5.44 %, and 4.66 %, respectively). The first two dimensions accounted cumulatively for 19.99 % of the variance, increasing to 36.38 % when all five dimensions were included. To provide an integrated overview of the variables structuring the five dimensions retained for clustering, we computed a contribution correlation plot in Fig. 3. This plot shows that variables related to farm
<table><caption>**Table 1**

Descriptive statistics of the quantitative variables.</caption><thead><tr><th>Variables (Units)</th><th>Mean±Sd<sup>a</sup></th><th>Median</th><th>Minimum</th><th>Maximum</th></tr></thead><tbody><tr><td><strong>Structural Farm Data</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Agricultural land (ha)<sup>**</sup></td><td>526 ± 265</td><td>465</td><td>158</td><td>1270</td></tr><tr><td>Spreading surface (ha)</td><td>1392 ± 512</td><td>1375</td><td>160</td><td>2440</td></tr><tr><td>Number of crops</td><td>6.9 ± 2.4</td><td>7</td><td>4</td><td>12</td></tr><tr><td>Grazing livestock density (Number)</td><td>116 ± 170</td><td>0</td><td>0</td><td>550</td></tr><tr><td>Livestock effluent volume (m<sup>3</sup>)</td><td>1083 ± 1511</td><td>0</td><td>0</td><td>5500</td></tr><tr><td>Full-time employees (FTE)</td><td>1 ± 1.02</td><td>1</td><td>0</td><td>4</td></tr><tr><td>Partners (Number)</td><td>2.5 ± 1.4</td><td>2</td><td>0</td><td>6</td></tr><tr><td>Share capital (Euro)</td><td>216,317 ± 352,751</td><td>99,000</td><td>1360</td><td>1776,400</td></tr><tr><td><strong>Operational Characteristics of AD System</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Years active</td><td>4.4 ± 1.9</td><td>5</td><td>1</td><td>12</td></tr><tr><td>Injection capacity (Nm<sup>3</sup>/h)<sup>***</sup></td><td>198 ± 131</td><td>210</td><td>0</td><td>500</td></tr><tr><td>Installed power (kWe)<sup>****</sup></td><td>42 ± 103</td><td>0</td><td>0</td><td>500</td></tr><tr><td>Daily substrates input (t/day)<sup>*****</sup></td><td>60 ± 26</td><td>59</td><td>15</td><td>100</td></tr><tr><td>Hydraulic retention time (days)</td><td>78 ± 29</td><td>80</td><td>22</td><td>125</td></tr><tr><td>Distance farm/digester (km)</td><td>2.3 ± 2.4</td><td>2</td><td>0</td><td>8</td></tr><tr><td>Transport distance of exogenous substrates (km)<sup>******</sup></td><td>15 ± 13</td><td>10</td><td>0</td><td>50</td></tr><tr><td><strong>Digestion Ration Mix Composition (%)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Silage corn</td><td>0.5 ± 3.1</td><td>0</td><td>0</td><td>20</td></tr><tr><td>Cereals byproducts</td><td>2 ± 4.7</td><td>0</td><td>0</td><td>25</td></tr><tr><td>Beet pulp</td><td>16 ± 14</td><td>14</td><td>0</td><td>45</td></tr><tr><td>Energy cover crops</td><td>25 ± 21</td><td>26</td><td>0</td><td>67</td></tr><tr><td>Livestock effluents</td><td>28 ± 32</td><td>14</td><td>0</td><td>100</td></tr><tr><td>Silo byproducts</td><td>0.32 ± 0.81</td><td>0</td><td>0</td><td>3</td></tr><tr><td>Vegetal waste</td><td>13 ± 15</td><td>8</td><td>0</td><td>50</td></tr><tr><td>Agri-food industry wastes</td><td>8 ± 14</td><td>0.5</td><td>0</td><td>70</td></tr><tr><td>Chicory roots</td><td>2.5 ± 14</td><td>0</td><td>0</td><td>90</td></tr><tr><td>Biowastes</td><td>0.5 ± 3</td><td>0</td><td>0</td><td>20</td></tr><tr><td><strong>Digestate Production per year</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Raw digestate (m<sup>3</sup>)<sup>******</sup></td><td>19,055 ± 8261</td><td>19,480</td><td>2000</td><td>32,700</td></tr><tr><td>Liquid digestate (m<sup>3</sup>)</td><td>4600 ± 7624</td><td>0</td><td>0</td><td>24,300</td></tr><tr><td>Solid digestate (t)<sup>******</sup></td><td>2075 ± 3636</td><td>0</td><td>0</td><td>15,500</td></tr><tr><td><strong>Digestate Management and Application</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Storage period (months)</td><td>8.1 ± 4.4</td><td>7</td><td>1</td><td>24</td></tr><tr><td>Liquid digestate storage capacity (m<sup>3</sup>)</td><td>11,805 ± 4647</td><td>10,276</td><td>2500</td><td>23,000</td></tr><tr><td>Solid digestate storage capacity (m<sup>2</sup>)<sup>******</sup></td><td>228 ± 330</td><td>0</td><td>0</td><td>1000</td></tr><tr><td>Digestate ration in fertilizer mix (%)</td><td>35 ± 13</td><td>30</td><td>15</td><td>75</td></tr><tr><td>Total nitrogen (kg ha<sup>-1</sup> yr<sup>-1</sup>)<sup>******</sup></td><td>4.3 ± 0.93</td><td>4</td><td>2</td><td>7</td></tr><tr><td>Phosphorus (kg ha<sup>-1</sup> yr<sup>-1</sup>)</td><td>1.9 ± 0.92</td><td>2</td><td>1</td><td>5</td></tr><tr><td>Potassium (kg ha<sup>-1</sup> yr<sup>-1</sup>)</td><td>4.1 ± 1.7</td><td>4</td><td>1</td><td>11</td></tr><tr><td>Spreading cost (€/t)<sup>******</sup></td><td>4.3 ± 1.3</td><td>5</td><td>2</td><td>7</td></tr></tbody></table>

size, substrates composition (e.g. livestock effluents, beet pulp, energy cover crops), biogas valorization mode and digestate management (storage capacity, phase separation, spreading practices) consistently exhibit the highest contributions across Dimensions 1-5.

Following the selection of dimensions based on eigenvalues, and upon analyzing the contributions of both quantitative and qualitative variables to the formation of the principal dimensions, key factors influencing the data structure were identified in Table 3.

For dimension 1, quantitative variables such as livestock effluents percentage (3.65 %), beet pulp percentage (3.51 %), and energy cover crops percentage (3.29 %) were prominent and central to this dimension. Among categorical variables, the biogas valorization technology (Injection vs. cogeneration) (4.12 %) and drivers for biogas systems adoption (4.07 %) were the most influential factors on dimension 1.

In dimension 2, the emphasis shifted towards energy recovery (through heat valorization) from cogeneration systems and digestate storage capacity. Solid digestate storage capacity (1.95 %), livestock effluents (1.11 %) and liquid digestate volume (0.72 %) were the primary drivers, reflecting variations in digestate management infrastructure, and compositional differences in substrates. The percentage of livestock effluents (1.11 %) showing a balanced contribution on both dimensions 1 and 2, indicates a dual influence across both dimensions. Categorical variables like heat valorization (5.14 %), had a substantial contribution, while storage type accounted for 2.97 %, crop type (3.04 %) and digestate phase separation (1.83 %) further contribute to the differentiation, indicating that the types of crops cultivated, and the methods employed for separating digestate phases, also defined farm profiles in the context of biogas production.

In order to link the observed influence of key quantitative and qualitative variables with the overall structural patterns in farm profiles, the factor map (Fig. 4) presents the spatial distribution of the surveyed farms across the first two dimensions, which together explain 20 % of the total variance. The position of each farm reflects its alignment with the most influential variables and highlights distinct clustering patterns. In addition, a FAMD biplot for Dim 1-2 (Fig. 5), overlays the main contributing quantitative variables as arrows, making explicit which variables drive separation along each axis. Complementary biplots for the remaining pairs of the first five dimensions (Dim 1-3, 2-3, 1-4, 2-4, 3-4, 1-5, 2-5, 3-5 and 4-5) are provided in Supplementary Material (Fig. S6, Fig. S7, Fig. S8, Fig. S9, Fig. S10, Fig. S11, Fig. S12, Fig. S13 and Fig. S14).

Farms showing high contribution to dimension 1, are primarily distinguished by substrates composition and biogas valorization strategies, as indicated by their alignment with high beet pulp percentage, livestock effluent use, and substantial biogas injection capacity. Conversely, farms contributing more to dimension 2 appear to be strongly associated with energy recovery strategies and digestate storage infrastructure, as reflected by their association with heat valorization, digestate storage capacity, and phase separation.

### 3.2.2. Hierarchical clustering on principal components (HCPC)

The “NbClust” [33] and “factoextra” R packages were used to define the optimal number of clusters, using the Elbow Method, which evaluates the within-cluster sum of squares (WSS) as a function of the number of clusters [72]. The Gap Statistic [124], using the “cluster” R package, was additionally employed to validate the optimal number of clusters. Initial analysis of the 40 farms revealed discordance between methods: the Elbow Method suggested 6 clusters (WSS reduction plateauing at $k = 6$), while the Gap Statistic indicated 2 clusters (maximal gap value at $k = 5$). Further exploration using hierarchical clustering dendrograms identified a single farm forming an isolated cluster, reflecting unique operational characteristics. Upon running the clustering methods again without the isolated farm ($n = 39$ farms), both methods converged to a consensus solution. The recalibrated analysis demonstrated a clear elbow point at 5 clusters ($k = 5$), and a maximal Gap Statistic value at the same $k$. The final 5-cluster configuration provided distinct,

<footnote>* mean and standard deviation for quantitative variables.</footnote>

<footnote>** hectare.</footnote>

<footnote>*** normal cubic meters per hour.</footnote>

<footnote>**** kilowatt-electric.</footnote>

<footnote>***** tonnes per day.</footnote>

<footnote>****** kilometers.</footnote>

<footnote>****** cubic metre.</footnote>

<footnote>****** tonnes.</footnote>

<footnote>****** square meter.</footnote>

<footnote>****** kilograms of nitrogen per hectare per year.</footnote>

<footnote>****** euro per tonne.</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Scree plot of the FAMD showing the percentage of total variance explained by each of the first five dimensions. Bars represent the individual contribution of each dimension to the explained variance, while the line indicates the decreasing marginal contribution across successive dimensions. The first dimension explains the largest share of variance, followed by a gradual decline, supporting the selection of the first five dimensions for subsequent clustering analyses.</figcaption></figure>

<table><caption>**Table 2**
Eigenvalues and explained variance for the first five dimensions.</caption><thead><tr><th>Dimensions</th><th>Eigenvalue</th><th>Percentage of variance (%)</th><th>Cumulative percentage of variance (%)</th></tr></thead><tbody><tr><td>Dim.1</td><td>16.43</td><td>13.04</td><td>13.04</td></tr><tr><td>Dim.2</td><td>8.76</td><td>6.95</td><td>19.99</td></tr><tr><td>Dim.3</td><td>7.90</td><td>6.27</td><td>26.27</td></tr><tr><td>Dim.4</td><td>6.86</td><td>5.44</td><td>31.72</td></tr><tr><td>Dim.5</td><td>5.87</td><td>4.66</td><td>36.38</td></tr></tbody></table>

interpretable profiles of on-farm AD systems, with no further outliers observed.

Consequently, these clusters were further validated and visualized using HCPC. Fig. 6 presents a hierarchical clustering dendrogram of surveyed farms distinct clusters. Complementary, the factor map shows the projection of clusters onto a two-dimensional space. Each cluster occupies a distinct position showing specific characteristics that discriminate one group from another, showing that the sampled farms can be categorized into 5 types. To these 5 types is added Farm 40 representing a cluster of its own, bearing the total number of clusters to 6.

### 3.2.3. Statistical validation of clusters differences

Table 4 summarizes the 21 quantitative variables identified from the Kruskal-Wallis test, as significantly discriminating the five clusters selected, based on their p-value and chi-squared values. Variables related to farm size, presence of livestock and associated effluents production, emerged as the strongest discriminators (p < 0.005). Substrates composition variables further distinguished clusters, while logistical parameters (Solid digestate storage capacity $\chi^2$ = 23.18; Substrates transport distance $\chi^2$ = 15.00) highlighted infrastructural contrasts. These statistical differentiations align with the geometric separation observed in the HCPC factor map in Fig. 4. Dunn post-hoc tests (including the atypical farm as a sixth group) revealed statistically significant differences between cluster, confirming cluster heterogeneity and supporting the validity of the five-cluster solution; for readability, comparisons involving the single-farm group are omitted from Table S1, while adjusted p-values are reported in the Supplementary Material (Table S1).

The results of Chi-square test of independence on categorical

variables (Table 5) show a total number of 17 qualitative variables, revealing statistically significant differences across clusters. The most pronounced associations were observed for biogas valorization strategies (p-value = 3.71 × 10⁻⁵), farm typology (p-value = 5.56 × 10⁻⁶), and heat valorization practices (p-value = 1.38 × 10⁻⁹). Significant differences were also identified for spreading period variation, farmer's motivation for AD adoption, and reliance on external substrates for the AD process. Moderately significant associations were noted for farmer's income modifications after AD units' integration, sowing and harvesting period modification. Variables such as departments (p-value = 0.0045), soil type (p-value = 0.0055), and AD process (solid vs liquid AD) (p-value = 0.0435) exhibited weaker yet statistically significant differences. A detailed numerical overview of these discriminant variables by cluster is provided in Supplementary Material (Table S2, Table S3, Table S4, Table S5 and Table S6).

### 3.3. On-farm AD systems typology construction

The nomenclature and farms profiles description of these clusters were derived from the integration of both qualitative and quantitative variables significantly associated to each group. Each cluster is defined by a unique combination of variables. Key indicators were used to discriminate clusters, by quantifying the proportion of farms within each cluster that shared specific traits, to statistically assess the significance of these associations, and to provide an insight of the variability within a cluster and across all surveyed farms.

In addition to the main clusters identified, the atypical group, characterized by the integration of on-farm anaerobic digestion systems specialized in industrial cropping, consists of a single farm primarily utilizing chicory root (Cichorium intybus L.) residues, which make up 90 % of the AD substrates input. The digestate exhibits a relatively low total nitrogen content, averaging 2 nitrogen units, indicative of the specific feedstock profile. In operation for 12 years, the farm operates a 500 kWe AD unit, with energy, both electricity and heat, being largely used on-site, notably for forcing chamber systems essential to chicory cultivation. Notably, substrates transport distance is zero, since all inputs are sourced from the farm itself, making the process highly integrated and efficient.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Contribution correlation plot showing the contributions of quantitative and qualitative variables to the first five dimensions retained from the Factorial Analysis of Mixed Data (FAMD). Each row corresponds to a variable, and each column to a factorial dimension (Dim 1-Dim 5). Color intensity reflects the strength and direction of the contribution (correlation coefficient) of each variable to a given dimension. Variables related to farm size, substrate composition, anaerobic digestion configuration, energy valorization pathways, and digestate management exhibit the highest contributions across dimensions, justifying the retention of five dimensions for subsequent hierarchical clustering.</figcaption></figure>

### 3.3.1. Cluster 1: Predominantly mixed crop-livestock systems with on-farm substrates self-sufficiency for electricity production only

Cluster analysis identified 3 farms in this group. Farmers rely exclusively on their on-farm livestock effluents as substrates for AD process. The farms in this cluster have an average of 450 heads of cattle, resulting in annual effluent production of approximately 2772 m³, these

<caption>**Table 3**</caption>

Contribution (%) of quantitative and qualitative variables to the first two dimensions of the analysis.

<table><thead><tr><th>Data Type</th><th>Variables</th><th>Contrib. (Dim.1 %)</th><th>Contrib. (Dim.2 %)</th></tr></thead><tbody><tr><td>Continuous</td><td>Livestock effluents (%)</td><td>3.65</td><td>1.11</td></tr><tr><td>Continuous</td><td>Beet pulp (%)</td><td>3.51</td><td>0.05</td></tr><tr><td>Continuous</td><td>Energy cover crops (%)</td><td>3.29</td><td>0.22</td></tr><tr><td>Continuous</td><td>Livestock effluents volume (m³)</td><td>3.22</td><td>0.65</td></tr><tr><td>Continuous</td><td>Injection capacity (Nm³/h)</td><td>2.94</td><td>0.53</td></tr><tr><td>Continuous</td><td>Solid digestate storage capacity (m²)</td><td>2.68</td><td>1.95</td></tr><tr><td>Continuous</td><td>Agricultural land (ha)</td><td>2.38</td><td>0.40</td></tr><tr><td>Continuous</td><td>Liquid digestate (m³)</td><td>2.18</td><td>0.72</td></tr><tr><td>Continuous</td><td>Grazing livestock density (Number)</td><td>2.05</td><td>0.58</td></tr><tr><td>Continuous</td><td>Installed power (kWe)</td><td>1.88</td><td>0.4</td></tr><tr><td>Categorical</td><td>Biogas valorization</td><td>4.12</td><td>0.63</td></tr><tr><td>Categorical</td><td>Farmer's motivation</td><td>4.07</td><td>2.56</td></tr><tr><td>Categorical</td><td>Livestock type</td><td>3.59</td><td>1.46</td></tr><tr><td>Categorical</td><td>Farm typology</td><td>3.51</td><td>0.76</td></tr><tr><td>Categorical</td><td>Energy cover crops type</td><td>3.26</td><td>0.88</td></tr><tr><td>Categorical</td><td>Departments</td><td>3.2</td><td>1.48</td></tr><tr><td>Categorical</td><td>Crop type</td><td>2.94</td><td>3.04</td></tr><tr><td>Categorical</td><td>Storage type</td><td>2.87</td><td>2.97</td></tr><tr><td>Categorical</td><td>Heat valorization</td><td>2.62</td><td>5.14</td></tr><tr><td>Categorical</td><td>Phase separation</td><td>2.56</td><td>1.83</td></tr></tbody></table>

effluents are entirely used in AD, with a daily input quantity of 19 tons. The digester operates with an average hydraulic retention time of 25 days, and the average storage capacity for raw digestate is 3000 m³. The biogas produced is systematically valorized through co-generation, enabling the simultaneous production of electricity and heat. However, the heat generated through this process remains unutilized, and none of these farms currently possess the infrastructure to valorize it. The available land for digestate applications averages 173 ha. In terms of crop management, these farms do not cultivate nor use energy cover crops for AD process and focus solely on livestock effluents. 67 % of the farms utilize open-air lagoons for digestate storage, while the remaining 33 % use covered storage systems. All farmers schedule digestate applications during spring season. Economically, these farms have not reported direct revenue changes attributable to AD activities.

### 3.3.2. Cluster 2: Mixed crop-livestock farming with local energy recovery

This cluster comprises 2 livestock farms integrating anaerobic digestion as a strategy for both energy valorization and effluent management, as a complementary strategy to traditional agricultural practices. The installed power capacity of these units averages 207 kWe, reflecting a moderate energy production potential compared to the first cluster. The biogas produced is entirely valorized in co-generation system. Unlike fully self-sufficient livestock farms, these on-farm AD units incorporate external substrates into the digestion process to enhance biogas yields. The daily quantity of treated organic matter averages 19 tons, a moderate volume characteristic of small to medium-sized livestock operations. The production of raw digestate reaches an average of 4775 m³ per year, consistent with relatively low input volumes. Digestate management follows an extended spreading calendar, covering summer, winter, and post-pasture application periods. Half of the farms in this cluster employ trailing shoe slurry tanks. The economic approach to digestate management is optimized, with a spreading cost averaging 2 euros per ton, mainly applied on the farmer's own agricultural lands. These farms are located in the Nord department. The area also provides access to agro-industrial residues, which are integrated as complementary substrates for AD processes. AD systems implementation on farms is primarily driven by a dual objective: diversifying revenue streams while improving the management of livestock effluents.
<figure><img src="image_5.png" /><figcaption>Fig. 4. FAMD factor map of farms (Dim 1-2). Factor map of individual farms from the FAMD, showing the first two dimensions (Dim 1: 13 % of total variance; Dim 2: 7 %). Each point represents one farm and is coloured according to its cluster grouping (Clusters 1-5, as identified by HCPC). Convex hulls delineate the dispersion of farms within each cluster.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. FAMD biplot for Dimensions 1 (13 %) and 2 (7 %), showing the projection of the surveyed farms and a subset of quantitative variables on the Dim1-Dim2 plane. Farms are colored according to HCPC cluster grouping (Clusters 1-5), with shaded polygons illustrating within-cluster dispersion. Variable vectors indicate the direction of increasing values and the strength of association with the factorial axes. On this plane, Dim1 is positively associated with daily substrate input, raw digestate production and spreading surface, and negatively associated with livestock effluents (percentage and volume), whereas Dim2 is mainly associated with solid digestate storage capacity.</figcaption></figure>

### 3.3.3. Cluster 3: Mixed crop-livestock farming with territorial AD systems and biomethane injection

This cluster comprises 10 farms integrating AD into territorial-scale agricultural systems, where mixed crop-livestock farming remains predominant (90 %), 80 % of farms specialized in dairy cattle and only one

farm did not keep cattle. Covering an average of 300 ha of agricultural land, with a dominance of forage crops, maize, and cereals, for both feed autonomy and a continuous supply of substrates for AD, which is further supplemented by exogenous inputs. 80 % of farms in this cluster prioritize biomethane injection into the gas grid. High volume of raw
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Hierarchical clustering dendrogram of on-farm AD systems. Dendrogram from the hierarchical clustering on principal components (HCPC, Ward's method) based on the FAMD results for the 40 on-farm AD units. Branch height represents dissimilarity between farms. The coloured rectangles delimit the five clusters retained for the typology (Clusters 1-5).</figcaption></figure>

<table><caption>**Table 4**

Results of the Kruskal-Wallis Test quantitative variables across clusters.</caption><thead><tr><th>Variables</th><th>Chi²</th><th>p-value</th></tr></thead><tbody><tr><td>Agricultural land (ha)</td><td>28.23</td><td>&lt; 0.001</td></tr><tr><td>Spreading surface (ha)</td><td>18.17</td><td>&lt; 0.01</td></tr><tr><td>Grazing livestock density (Number)</td><td>32.51</td><td>&lt; 0.001</td></tr><tr><td>Livestock effluent volume (m³)</td><td>32.96</td><td>&lt; 0.001</td></tr><tr><td>Share capital (Euro)</td><td>14.75</td><td>&lt; 0.05</td></tr><tr><td>Injection capacity (Nm³/h)</td><td>13.84</td><td>&lt; 0.05</td></tr><tr><td>Installed power (kWe)</td><td>25.79</td><td>&lt; 0.001</td></tr><tr><td>Beet pulp (%)</td><td>22.62</td><td>&lt; 0.001</td></tr><tr><td>Energy cover crops (%)</td><td>20.08</td><td>&lt; 0.01</td></tr><tr><td>Livestock effluents (%)</td><td>21.95</td><td>&lt; 0.001</td></tr><tr><td>Vegetal waste (%)</td><td>14.59</td><td>&lt; 0.05</td></tr><tr><td>Chicory roots (%)</td><td>14.45</td><td>&lt; 0.05</td></tr><tr><td>Daily substrates input (t/day)</td><td>12.75</td><td>&lt; 0.05</td></tr><tr><td>Hydraulic retention time (days)</td><td>11.71</td><td>&lt; 0.05</td></tr><tr><td>Transport distance of exogenous substrates (km)</td><td>15</td><td>&lt; 0.05</td></tr><tr><td>Raw digestate (m³)</td><td>14.97</td><td>&lt; 0.05</td></tr><tr><td>Liquid digestate (m³)</td><td>18.84</td><td>&lt; 0.01</td></tr><tr><td>Solid digestate (t)</td><td>15.07</td><td>&lt; 0.05</td></tr><tr><td>Storage period (months)</td><td>21.25</td><td>&lt; 0.001</td></tr><tr><td>Liquid digestate storage capacity (m³)</td><td>17.97</td><td>&lt; 0.01</td></tr><tr><td>Solid digestate storage capacity (m²)</td><td>23.18</td><td>&lt; 0.001</td></tr></tbody></table>

digestate is produced, reaching 21,953 m³ per year. The management of digestate is primarily in raw form, with no phase separation. Storage and spreading practices to optimize digestate management show that 50 % of farms utilize covered tanks for liquid digestate storage. Additionally, 70 % of farms apply digestate directly to field, facilitated by appropriate spreading equipment and manageable digestate volumes, maintaining relatively stable crop rotations, with little modification to existing practices.

### 3.3.4. Cluster 4: Cropping system and mixed crop-livestock farms with territorial AD systems and biomethane injection

This cluster is comprised of 17 farms, the vast majority (95 %) being strictly crop-based and only one having a mixed crop-livestock system. Their anaerobic digestion (AD) units focus on biomethane injection into the gas grid. All these farms (100 %) rely on exogenous biomass substrates (e.g., agro-industrial residues, vegetal wastes) instead of on-farm

<table><caption>**Table 5**

Results of the Chi-Square Test for independence for qualitative variables.</caption><thead><tr><th>Variables</th><th>p-value</th><th>df</th></tr></thead><tbody><tr><td>Heat valorization</td><td>&lt; 0.001</td><td>20</td></tr><tr><td>Farm typology</td><td>&lt; 0.001</td><td>5</td></tr><tr><td>Biogas valorization</td><td>&lt; 0.001</td><td>5</td></tr><tr><td>Farmer's motivation</td><td>&lt; 0.001</td><td>35</td></tr><tr><td>Livestock type</td><td>&lt; 0.001</td><td>10</td></tr><tr><td>Spreading period</td><td>&lt; 0.001</td><td>25</td></tr><tr><td>External substrates</td><td>&lt; 0.001</td><td>5</td></tr><tr><td>Income modification</td><td>&lt; 0.01</td><td>10</td></tr><tr><td>Sowing period modification</td><td>&lt; 0.01</td><td>5</td></tr><tr><td>Harvest period modification</td><td>&lt; 0.01</td><td>5</td></tr><tr><td>Phase separation</td><td>&lt; 0.01</td><td>5</td></tr><tr><td>Departments</td><td>&lt; 0.01</td><td>20</td></tr><tr><td>Digestate distribution</td><td>&lt; 0.01</td><td>5</td></tr><tr><td>Soil type</td><td>&lt; 0.05</td><td>15</td></tr><tr><td>Agricultural fertilization services</td><td>&lt; 0.05</td><td>5</td></tr><tr><td>AD process</td><td>&lt; 0.05</td><td>5</td></tr></tbody></table>

effluents. Wheat is cultivated on every farm (100 %), and 90 % of them incorporate energy cover crops to ensure a stable substrates supply for the AD process. Digestate management is organized through territorial cooperation: all farms, 83 % of which are located in the Aisne department, redistribute digestate to neighboring growers, integrating it into broader regional fertilization plans. Finally, AD integration in this cluster is largely driven by economic diversification, with 77 % of farms seeking to stabilize long-term revenue streams.

### 3.3.5. Cluster 5: Territorial innovative on-farm AD system in extensive cropping systems

A total of 7 farms were grouped into Cluster 5, oriented toward crop production, with a mean agricultural land of 989 ha. None of these farms were engaged in livestock farming; the reported effluent volume from livestock was 0. Farms in this cluster introduced energy cover crops with high methanogenic potential into their production systems and produced substantial volumes of digestates, which were stored over extended periods prior to field application. All farms are primarily oriented toward biomethane injection into the gas grid. All of the seven farms showed a high solid-digestate storage capacity (775 m² on
average) and an extended storage duration of up to 14 months. The volume of liquid digestate produced by these farms reached 14,174 m³ per year, and 85 % of the farms were in Oise department. The substrates incorporated in this cluster were mainly characterized by 32 % beet pulp and 51 % of energy cover crops.100 % of the farms in this cluster implemented a systematic phase-separation process for digestate management. They adapted their harvest and sowing schedules to accommodate the operation of anaerobic digesters, and 57 % of them performed digestate spreading across multiple seasons. External contracting services were employed for digestate spreading by 100 % of the farms in this cluster, in part due to the specialized equipment required. Among these external services, 30 % involved the use of non-tanker spreading system.

## 4. Discussion

This study, through a multivariate analysis of 40 on-farm AD systems in northern France, unveils five distinct profiles, characterized by a pronounced variability in structural and operational characteristics. Statistical analysis confirmed the significant differentiation between clusters, reinforcing the relevance of the classification approach.

To support the cluster-wise interpretation developed in the following subsections, Table 5, Table 6 presents, for each of the 5 identified clusters, the mean values of key quantitative variables and the percentage frequencies of the corresponding categorical variables.

### 4.1. Substrates management and energy valorization

Mixed crop-livestock systems where anaerobic digestion is deeply embedded within existing agricultural practices vary markedly in scale, energy utilization and feedstock strategy. The dichotomy between cluster 1 and cluster 2 shows a fundamental tension in on-farm AD between waste management and energy production objectives. Cluster 1

(micro-scale) prioritizes manure valorization over energy output, as a practical solution for waste management for smallholders [96], operating with short retention times (≈20-25 days) and low cogeneration capacity. Heat recovery is underutilized despite constituting 40-50 % of biogas energy potential, as logistical constraints and low economic returns deter investment [27,64]. In contrast, cluster 2 farms optimize digestion with >60 day retention time for higher biogas yield, and integrate heat valorization for a local use, exemplifying circular economy principles [49]. Cluster 3 (mid-scale) prioritize biomethane injection despite being implemented on livestock farms, using advanced infrastructure, including hygienization units that thermally pre-treat selected exogenous substrates (sourced within a 20-km radius) to reduce pathogen loads in line with EU sanitary requirements, prior to co-digestion with on-farm manure to enhance methane yield and process stability [52] and are co-digested with farm's own manure to improve biogas yield and more stable digestion performance [29,74]. However, reliance on external substrates represents supply-chain risks such as cost volatility and transportation expenses [56]. Therefore, farmers mitigate these risks by maintaining high biogas output to offset capital costs, but fluctuating substrates prices and unreliable contracts threaten profitability. As one farmer noted: "Scaling to 350 Nm³ required 25 % more feedstock, risking 1 million € for uncertain returns... We chose stability staying at 250 Nm³ avoids financial gambles". Many maintain on-farm substrates reserves to buffer market volatility, and prioritize opportunistic sourcing: "If an agro-industrial stream becomes available, we adapt immediately... Our plant prioritizes agro-waste valorization". Nonetheless, prolonged storage risks significant energy losses, and fugitive gas emissions [50,51]. In contrast, ensiling as a storage method, preserves methane yield over long storage periods [18,101].

Cluster 4 and 5 as a large-scale systems prioritize biomethane injection into gas network, ensuring stable revenue through guaranteed prices and reduced exposure to energy market volatility [78]. Farmers reported treating AD systems as a stand-alone firm, one interviewee

<table><caption>**Table 6**
Key variables by cluster: mean (quantitative variables) and Modality/Class (%) (categorical variables).</caption><thead><tr><th>Variables / Modality</th><th>Type</th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th><th>Cluster 5</th></tr></thead><tbody><tr><td>Agricultural land (ha)</td><td>Continuous</td><td>166</td><td>155</td><td>300</td><td>560</td><td>989</td></tr><tr><td>Spreading surface (ha)</td><td>Continuous</td><td>173</td><td>142</td><td>1198</td><td>1697</td><td>1501</td></tr><tr><td>Grazing livestock density (Number)</td><td>Continuous</td><td>450</td><td>210</td><td>258</td><td>14</td><td>0</td></tr><tr><td>Livestock effluents (%)</td><td>Continuous</td><td>100</td><td>68</td><td>39</td><td>16</td><td>0</td></tr><tr><td>Beet pulp (%)</td><td>Continuous</td><td>0</td><td>4</td><td>4.2</td><td>21</td><td>32</td></tr><tr><td>Energy cover crops (%)</td><td>Continuous</td><td>0</td><td>9</td><td>13</td><td>30</td><td>51</td></tr><tr><td>Transport distance of exogenous substrates (km)</td><td>Continuous</td><td>0</td><td>12</td><td>15</td><td>15</td><td>12</td></tr><tr><td>Installed power (kWe)</td><td>Continuous</td><td>36</td><td>207</td><td>66</td><td>0</td><td>0</td></tr><tr><td>Daily substrates input (t/day)</td><td>Continuous</td><td>19</td><td>19</td><td>69</td><td>66</td><td>70</td></tr><tr><td>Hydraulic retention time (days)</td><td>Continuous</td><td>25</td><td>77</td><td>75</td><td>84</td><td>89</td></tr><tr><td>Raw digestate (m³)</td><td>Continuous</td><td>3000</td><td>4775</td><td>21,953</td><td>20,408</td><td>19,671</td></tr><tr><td>Liquid digestate (m³)</td><td>Continuous</td><td>0</td><td>0</td><td>0</td><td>4811</td><td>14,174</td></tr><tr><td>Solid digestate (t)</td><td>Continuous</td><td>0</td><td>0</td><td>0</td><td>2933</td><td>4369</td></tr><tr><td>Liquid digestate storage capacity (m³)</td><td>Continuous</td><td>2666</td><td>10,000</td><td>10,663</td><td>11,293</td><td>16,715</td></tr><tr><td>Solid digestate storage capacity (m²)</td><td>Continuous</td><td>0</td><td>0</td><td>0</td><td>164</td><td>775</td></tr><tr><td>Total nitrogen (kg ha⁻¹ yr⁻¹)</td><td>Continuous</td><td>3</td><td>4.5</td><td>4</td><td>4</td><td>4</td></tr><tr><td>Storage = lagoon</td><td>Categorical</td><td>67 %</td><td>50 %</td><td>50 %</td><td>30 %</td><td>72 %</td></tr><tr><td>Storage = covered tank</td><td>Categorical</td><td>33 %</td><td>50 %</td><td>50 %</td><td>12 %</td><td>0 %</td></tr><tr><td>Spreading equipment = trailing hose</td><td>Categorical</td><td>100 %</td><td>50 %</td><td>30 %</td><td>41 %</td><td>28 %</td></tr><tr><td>Spreading equipment = injection / boom tank</td><td>Categorical</td><td>0 %</td><td>50 %</td><td>70 %</td><td>12 %</td><td>28 %</td></tr><tr><td>Spreading period = spring</td><td>Categorical</td><td>100 %</td><td>50 %</td><td>20 %</td><td>12 %</td><td>28 %</td></tr><tr><td>Digestate distribution = shared</td><td>Categorical</td><td>0 %</td><td>0 %</td><td>90 %</td><td>100 %</td><td>100 %</td></tr><tr><td>Farm typology = mixed-crop livestock</td><td>Categorical</td><td>100 %</td><td>100 %</td><td>90 %</td><td>5 %</td><td>0 %</td></tr><tr><td>Farm typology = cropping system</td><td>Categorical</td><td>0 %</td><td>0 %</td><td>10 %</td><td>95 %</td><td>100 %</td></tr><tr><td>Phase separation = done</td><td>Categorical</td><td>0 %</td><td>0 %</td><td>0 %</td><td>100 %</td><td>100 %</td></tr><tr><td>Income modification = none</td><td>Categorical</td><td>100 %</td><td>50 %</td><td>60 %</td><td>35 %</td><td>30 %</td></tr><tr><td>Farmer motivation = slurry/waste management</td><td>Categorical</td><td>66 %</td><td>50 %</td><td>40 %</td><td>11 %</td><td>0 %</td></tr><tr><td>Farmer motivation = income diversification</td><td>Categorical</td><td>33 %</td><td>50 %</td><td>30 %</td><td>76 %</td><td>72 %</td></tr><tr><td>External substrates = present</td><td>Categorical</td><td>0 %</td><td>50 %</td><td>80 %</td><td>100 %</td><td>100 %</td></tr><tr><td>Biogas valorization = cogeneration</td><td>Categorical</td><td>100 %</td><td>100 %</td><td>20 %</td><td>0 %</td><td>0 %</td></tr><tr><td>Biogas valorization = injection</td><td>Categorical</td><td>0 %</td><td>0 %</td><td>80 %</td><td>100 %</td><td>100 %</td></tr><tr><td>Heat valorization = none</td><td>Categorical</td><td>100 %</td><td>0 %</td><td>0 %</td><td>0 %</td><td>100 %</td></tr><tr><td>Storage = platform</td><td>Categorical</td><td>0 %</td><td>50 %</td><td>0 %</td><td>30 %</td><td>28 %</td></tr></tbody></table>
from cluster 5 explained: "We wanted the AD plant to invest in our farms when a change is needed, not the other way around", a strategy viewed as essential amid declining crop incomes [13,128]. Feedstock strategies focus on high-yield energy cover crops (43-80 % carbon to methane efficiency) [79], outperforming manure [106]. Co-digestion with agro-industrial residues like sugar beet pulp [68], cereal dust, and other biowastes helps optimize carbon-to-nitrogen (C/N) ratios, boost microbial biomass [116,130] and enhance biogas production efficiency [69]. Cluster 4 farmers strategically include imported manure in the substrates mix: "Choosing manure is about biological stability it captures nitrogen, enhances microbial stability in digesters ..." to enhance process stability, for a higher biogas yield, and greater economic viability of biogas plants [102,125,70,83,84,118]

## 4.2. Digestate application and management

Digestate management strategies vary across farm clusters based on scale, infrastructure, and regulatory constraints. Cluster 1 is characterized by a closed-loop system, relying on existing on-farm pits for digestate storage, enabling seasonal flexibility: "we work with the weather and not with fixed dates", as one farmer emphasized. However, prolonged storage of manure-based digestate during restricted spreading periods risks nitrogen losses due to ammonia volatilization, especially under warm and uncovered conditions reaching up to 40 %, and up to 38 % for unseparated digestate during summer [104,105]. Cluster 1 aligns spreading schedules with crop needs (e.g., post-harvest or before spring cereals [10,119], to synchronize nutrient release, avoiding transport regulations through full on-farm use but requiring careful internal storage and compliance with EU waste directive [44]. In contrast, cluster 2 and 3 apply digestate in springtime to synchronize nutrient release with crop demand [88]. Cluster 1 employs trailing-hose systems for digestate spreading, though this method may increase ammonia losses compared to injection methods [114,103], contrasting with the nutrient loss and odor reduction achieved through injection systems [99, 113]. In contrast, cluster 2 optimize digestate use through injection and immediate soil incorporation, though some exceed nitrogen application limits (170 kg N/ha/year) [73], to reduce synthetic fertilizer dependency, risking long-term soil imbalances and localized environmental risks. All clusters report the agronomic efficacy of digestate in improves or maintains crop yields due to higher nutrient availability [31,88,111], however, its efficacy varies with crop type and application rate [126]. Digestate fertilization has improved pasture quality and growth, despite ammonia volatilization remains a concern [46], its efficacy depends primarily on application rate and frequency, rather than substrates type or co-digestion practices [123,127], reinforcing its viability as a slurry alternative for small-scale on-farm AD systems. Cluster 2 and 3 face infrastructure cost (e.g., underground pipelines) especially for small-scale farms [6,117,110], and regulatory constraints. Cluster 3 farmers report tensions between crop nutrient requirements and French legal application limits [76], even when digestate allows high mineral fertilizer substitution rates (up to 80 %). By contrast, cluster 2 mitigates over-application risks through digestate exchange networks, supporting a mutually beneficial closed-loop system.

As for micro and mid-scale clusters, despite regulatory and operational challenges, digestate was acknowledged for its nutrients for large-scale clusters, though timing issues arose, as one farmer from cluster 4 noted "It would be preferable to spread the digestate before sowing wheat, but we are not allowed to do that!". In cluster 5, digestate was primarily applied in March to align with early crop growth stages, enhancing nutrient uptake [75,92], often complemented by supplemental mineral nitrogen [8]. This practice reduced mineral nitrogen use by 25-50 %, with full substitution in sugar beet cultivation without yield loss. Both clusters face logistical costs (3-6 €/m³) for digestate transport, driving interest in innovations like buried pipelines to reduce expenses and road traffic [55,107], with long-term benefits including reduced fertilizer use and potential income gains [38]

## 4.3. Farm practices modifications

Cluster 1's small-scale AD systems integrated smoothly into existing mixed crop-livestock farms with minimal modifications, primarily through cover crop incorporation, to enhance cattle feed, slurry quality, biogas and digestate performance [108,43,63,108,43,63]. In contrast, cluster 2 underwent a significant shift in agricultural practices following AD implementation, such as double cropping and Italian ryegrass (Lolium multiflorum Lam.) cultivation to maximize biomass for the digester, as one farmer stated: "Shifting 10 hectares of maize after ryegrass". However, it is crucial to consider the environmental impacts of cropping system redesigns for AD. Reviews by Möller et al. [90] highlight an increased risk of nitrate leaching associated with autumn-sown green manures, which can accumulate large amounts of nitrogen that are later mineralized and released during periods of low crop uptake [36,77,91]. In parallel, Cherubin et al. [34] emphasize broader effects on soil nutrient dynamics and soil organic carbon (SOC) storage [16,109,121,122], although a recent study by Chaplot & Smith [32] challenges the assumed SOC gains from cover crops due to potential methodological biases. Cluster 3 prioritized livestock feed autonomy by phasing out rapeseed and intensifying maize silage, aligning AD systems with livestock density [82]. Despite theoretical incentives, energy cover crops saw limited adoption here due to late regional harvest cycles conflicting with recommendations for early planting/late harvesting [17], likely influenced by local conditions like increased winter precipitation [98,85].

The introduction of AD systems in large-scale farms reshaped cropping systems, driving farmers in clusters 4 and 5 to adopt energy cover crops (e.g., forage maize, winter cereals) and double cropping. This intensified year-round cultivation, often displacing traditional crops like winter rapeseed with shorter-cycle cereals. Cluster 4 farmers relied more on external substrates, minimizing on-farm energy crop cultivation due to economic and operational constraints. In contrast, cluster 5 farmers reported benefits such as weed suppression [26,81] and improved soil health from digestate application. Wheat yields reached 10 t/ha post-digestate application [47] as stated by farmers from cluster 5, while cluster 4 observed elevated soil potassium levels, suggesting digestate can act as a near-complete macronutrient fertilizer, providing almost full autonomy in phosphorus and potassium and improving nutrient availability, soil health and microbiological activity [1,61,66,71]. However, its nutrient composition depends on the substrates and digestion process, so its fertilizing value can vary substantially between systems [60, 61,66,71,88]. However, its nutrient composition depends on the substrates and digestion process, so its fertilizing value can vary substantially between systems [60,88].

## 4.4. Economic and socio-organizational aspects

This study revealed the scale-dependent impacts of AD systems with micro-scale AD systems prioritizing slurry valorization as one farmer from cluster 1 stated: "We wanted to valorize our slurry rather than just storing it...", though limited electricity generation hindered significant income gains, "It doesn't bring much additional revenue...it's a micro-plant", aligning with technical-economic analyses of small-scale systems [97], while self-financing challenges [45,58] shift focus to fertilizer savings and environmental benefits over financial returns. Mid-scale AD systems diversify farm income through biomethane injection for cluster 3, and heat valorization for cluster 2 [11] beyond electricity sales, "It adds another income stream...", as exemplified by a farmer's use of excess heat for spirulina production. Social acceptance favors micro-scale digesters due to minimal footprint, contrasting mid and larger systems' neighbor concerns [21], which face opposition requiring community dialogue, as stated one farmer: "One neighbor riled the others... so we had to really communicate the project" [42]. Internally, On-farm labor demands are minimal for micro-digesters, while mid-scale systems require a modest increase of one to two employees.
For large-scale clusters, multi-partner ownership (e.g., cluster 5) enables risk-sharing but complicates decision-making, potentially reducing project effectiveness [35]. Long-term biomethane contracts, (e. g., 15-year feed-in agreements for French producers under 25 GWh/year [59] stabilize income, with long-running units (>5 years), validating revenue diversification. Cluster 4 farms sometimes integrate solar PV to offset energy costs, though grid compatibility issues persist. Operationally, AD necessitates logistical and labor adjustments (new hires, outsourcing), to manage the "significant daily commitment", that can slow down biogas project implementation [28,93]. Despite these burdens, farmers express overall satisfaction; the nationwide PROdige study [2] revealed over 80 % of farmers report net benefits, despite persistent labor and logistical demands in this new farming paradigm. Initial community resistance in both clusters linked to odor, traffic, and perceived ecological limits from unequal farmer-community benefits [23], mitigated by a proactive communication and transparency [94] to foster social acceptance.

### 4.5. Autonomy of fertilization

Farmers highlighted the multifaceted challenges of achieving fertilization autonomy via digestate, spanning agronomic, economic, and regulatory dimensions. Agronomically, precision in application methods is constrained by difficulties replicating mineral fertilizer accuracy, heterogeneous soil conditions, and climatic variability, which collectively impede efficient and timely field application [95]. Economically, the high costs of specialized spreading equipment and digestate post-treatment technologies reduce feasibility, particularly for smaller farming systems [41]. In addition, stringent regulatory frameworks aimed at mitigating nitrogen leaching restrict alignment between application timing and crop nutrient demand. Nevertheless, farmers recognize the potential of digestate to substantially reduce reliance on synthetic fertilizers (by up to 90 %). Achieving fertilization autonomy necessitates a multifaceted approach integrating adaptive agronomic strategies, scalable and cost-effective technological innovations, and policy frameworks that balance environmental safeguards with operational flexibility.

Overall, the findings allow us to reassess the initial hypotheses. H1, stating that the multivariate analysis would reveal distinct profiles of on-farm AD units, is clearly supported: the clustering identified five significantly differentiated configurations in terms of farm structure, feedstock strategy and energy valorization patterns. H2, which proposed that farm orientation influences the profitability and sustainability of AD units, is broadly supported, as micro-scale mixed crop-livestock farms in cluster 1 primarily use AD for slurry management with limited additional income, whereas mid- and large-scale, more crop-oriented or multi-partner systems (clusters 3-5) rely on biomethane injection, external substrates and risk-sharing arrangements to stabilize revenues. H3, hypothesizing that post-AD fertilization management practices affect agronomic performance, is supported by the contrasted digestate strategies observed across clusters, with reported yield maintenance or gains and high mineral fertilizer substitution where storage capacity and spreading windows allow close alignment with crop demand; however, regulatory constraints and potential nitrogen losses indicate that these benefits are not uniform across all situations. H4, expecting that the introduction of AD increases farm incomes and modifies labour dynamics, is only partially supported: most clusters report income diversification, long-term contracts or reduced fertilizer expenditure and the creation or reallocation of jobs, but micro-scale units mainly prioritize environmental services over monetary returns, and the additional labour burden is perceived as a constraint in some larger units. Overall, the hypotheses are thus validated to differing degrees depending on farm type and AD configuration, rather than uniformly across all clusters.

## 5. Conclusion and perspectives

Integrating on-farm AD systems into Northern France's agricultural systems offers opportunities for energy transition and more sustainable farming. Using field survey data and multivariate analyses of 40 on-farm AD systems, this study provides a statistically derived typology that jointly considers structural, agronomic, logistical and socio-economic variables. In doing so, it complements previous qualitative or single-dimension typologies of French biogas plants and refines understanding of how AD reshapes farming systems rather than acting as a stand-alone energy technology.

The typology revealed five distinct profiles of on-farm AD integration. Micro-scale, manure-based cogeneration units (clusters 1-2) account for 5 of the 40 farms (12.5 %) and mainly serve slurry management with limited income effects. A further 10 farms (25 %; cluster 3) correspond to mixed crop-livestock systems with territorial biomethane injection, combining feed autonomy with external substrates. Large, crop-based biomethane plants relying heavily on external substrates and energy cover crops (clusters 4-5) represent 24 farms (60 %), while one atypical chicory-processing farm forms a sixth profile. These profiles clarify the scale-dependent trade-offs between energy valorization, digestate management, cropping-system adjustments and income diversification pathways.

The findings have several practical implications. For micro-scale units, targeted incentives for heat recovery and low-cost digestate logistics could enhance both energy efficiency and nutrient recycling. Territorial biomethane systems require support to secure substrates contracts, manage nutrient surpluses and coordinate digestate redistribution. Large crop-based plants raise specific governance challenges related to land use, competition for biomass and regional nutrient cycling, calling for territorial cooperation and advisory services tailored to each profile.

### CRediT authorship contribution statement

**Salma Amsaguine:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Romain Gloaguen:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Formal analysis, Conceptualization. **Loïc Sauvée:** Writing – review & editing, Validation, Supervision.

### Declaration of competing interest

The authors declare that there is no conflict of interest.

### Acknowledgments

We gratefully acknowledge Mr. Arnauld Etienne and Ms. Elena Fourcroy from the Hauts-de-France Chamber of Agriculture for their assistance in providing contact details of participating farmers. We also thank all the farmers who generously shared their time and expertise for this study. This research was supported by the Hauts-de-France region, Chaire Méthanisation Agricole et Transition, and the research project METHABIOM (ADEME GRAINE 2021 N°2303D0285).

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.nextres.2026.101486.

### References

[1] J. Abubaker, K. Risberg, M. Pell, Biogas residues as fertilisers – Effects on wheat growth and soil microbial activities, Appl. Energy 99 (2012) 126-134, https://doi.org/10.1016/j.apenergy.2012.04.050.
[2] ADEME & APCA. (2022). Analyse Technico-économique de 84 unités de méthanisation agricole (p. 84) [Synthèse des résultats du programme PROdigel et 2]. https://librairie.ademe/agriculture-alimentation-foret-bioeconomie/5632-analyse-technico-economique-de-84-unites-de-methanisation-agricole.htm

[3] ADEME, & Solagro. (2018). La méthanisation, levier de l'agroécologie ? (p. 14) [Rapport de synthèse des résultats du programme MéthaLAE]. www.ademe.fr/me diatheque.

[4] ADEME, GRDF, & GRTgaz. (2018). Un mix de gaz 100% renouvelable en France en 2050? (p. 283) [Rapport de synthèse]. https://librairie.ademe/ennergies/1548-mix-de-gaz-100-renouvelable-en-2050-9791029710476.html.

[5] O.M.F.R. Adjobo, J.A. Yabi, J.Y. Gouwakinnou, Typologie des exploitations agricoles productrices d'anacarde au Nord et au Centre du Bénin, Glazoué Tchaourou Djougou 16 (5) (2020) 303-316. https://hal.science/hal-05012267v1.

[6] T. Al Seadi, B. Drosg, W. Fuchs, D. Rutz, R. Janssen, Biogas digestate quality and utilization. The Biogas Handbook, Elsevier, 2013, p. 267-301, https://doi.org/10.1533/9780857097415.2.267.

[7] S. Alberici, G. Toop, B. Monchen, S. Peeters, J. Peterse, Biogases Towards 2040 and Beyond : A realistic and Resilient Path to Climate Neutrality. (p. 35) [Publication], European Biogas Association, 2024. https://www.europeanbiogas.eu/biogases-towards-2040-and-beyond/.

[8] J.A. Albuquerque, C. De La Fuente, A. Ferrer-Costa, L. Carrasco, J. Cegarra, M. Abad, M.P. Bernal, Assessment of the fertiliser potential of digestates from farm and agroindustrial residues, BioMass BioEnergy 40 (2012) 181-189, https://doi.org/10.1016/j.biombioe.2012.02.018.

[9] G. Anzalone, C. Mazaud, Agriculture, territoire et industrie en interface. Le cas de la méthanisation Agriculture, territoire et industry interfacing with each other, case anaerob. dig. 0 (2) (2023) 226-233. https://www.cairn.info/revue-regards-croises-sur-l-economie-2023-2-page-226.htm?ref=doi.

[10] P. Bartóg, L. Hlisnikovský, E. Kunzová, Yield, content and nutrient uptake by winter wheat and spring barley in response to applications of digestate, cattle slurry and NPK mineral fertilizers, Arch. Agron. Soil Sci. 66 (11) (2020) 1481-1496, https://doi.org/10.1080/03650340.2019.1676890.

[11] J.R. Bautista Angeli, T. LeFloc'h, A. Lakel, B. Lacarrière, Y. Andres, Anaerobic digestion of urban wastes : integration and benefits of a small-scale system, Env. Technol 43 (22) (2022) 3414-3425, https://doi.org/10.1080/09593330.2021.1921857.

[12] F. Béline, R. Girault, P. Peu, A. Trémier, C. Téglia, P. Dabert, Enjeux et perspectives pour le développement de la méthanisation agricole en France, Sci. Eaux Territ. Numéro 7 (2) (2012) 34-43, https://doi.org/10.3917/set.007.0034.

[13] D. Bentivoglio, G. Chiaraluce, A. Finco, Economic assessment for vegetable waste valorization through the biogas-biomethane chain in Italy with a circular economy approach, Front. Sustain. Food Syst. 6 (2022) 1035357, https://doi.org/10.3389/fsufs.2022.1035357.

[14] Berthe, A., Fautras, M., Grouiez, P., & Issehnane, S. (2020). Les formes d'unités de méthanisation en France : typologies et scénarios d'avenir de la filière. Agronomie, environnement et sociétés. https://doi.org/10.54800/utm105.

[15] A. Berthe, P. Grouiez, M. Fautras, Heterogeneity of agricultural biogas plants in France, Sect. Syst. Innov. Perspect.: J. Innov. Econ. Manag. 38 (2) (2022) 11-34, https://doi.org/10.3917/jie.038.0011. N°.

[16] S. Bhartiya, U. Jan, S.M. Barman, T.N. Singh, M. Kumar, H.S.D. Kalisetty, Soil organic carbon : dynamics, storage, and the path to climate resilience, Int. J. Res. Agron. 7 (12) (2024) 974-978, https://doi.org/10.3354/2618060X.2024.v7.i121.2406.

[17] H. Blanco-Canqui, S.J. Ruis, C.A. Proctor, C.F. Creech, M.E. Drewnoski, D. Redfearn, Harvesting cover crops for biofuel and livestock production : another ecosystem service? Agron. J. 112 (4) (2020) 2373-2400, https://doi.org/10.1002/agi2.20165.

[18] G. Boehmann, L.F.R. Montgomery, Storage and pre-treatment of substrates for biogas production. The Biogas Handbook, Elsevier, 2013, p. 85-103, https://doi.org/10.1533/9780857097415.1.85.

[19] Bodilis, A.-M., Trochard, R., Lechat, G., Airiaud, A., Lambert, L., & Hruschka, S. (2015). Impacts of agricultural biogas plants on soil organic matter : an analysis of 10 farms in the Pays de la Loire region of France. (p. 8). https://inis.iaea.org/search/search.aspx?orig_q=RN:51096081.

[20] S. Bourdin, Le NIMBY ne suffit plus ! étude de l'acceptabilité sociale des projets de méthanisation, Espace Polit. (38) (2020), https://doi.org/10.4000/espacepolitique.6619.

[21] S. Bourdin, H. Delcayre, Does size matter? The effects of biomethane project size on social acceptability, Energy Policy 195 (2024) 114363, https://doi.org/10.1016/j.enpol.2024.114363.

[22] S. Bourdin, F. Nadou, The role of a local authority as a stakeholder encouraging the development of biogas : a study on territorial intermediation, J. Env., Manage 258 (2020) 110009, https://doi.org/10.1016/j.jenvman.2019.110009.

[23] S. Bourdin, M. Colas, F. Raulin, Understanding the problems of biogas production deployment in different regions : territorial governance matters too, J. Environ. Plan. Manag. 63 (9) (2020) 1655-1673, https://doi.org/10.1080/09640568.2019.1680158.

[24] V. Burg, K.G. Troitszch, D. Akyol, U. Baier, S. Hellweg, O. Thees, Farmer's willingness to adopt private and collective biogas facilities : an agent-based modeling approach, Resour. Conserv. Recycl. 167 (2021) 105400, https://doi.org/10.1016/j.resconrec.2021.105400.

[25] J. Cadiou, Le Déploiement de La Politique De Méthanisation Agricole En France : Implications Pour La Transition Agroécologique [Thèse De Doctorat En Sciences Agronomiques], Université Paris-Saclay, 2023. http://www.theses.fr/2023UPASB029/document.

[26] G. Camargo Silva, M. Bagavathiannan, Mechanisms of weed suppression by cereal rye cover crop : a review, Agron. J. 115 (4) (2023) 1571-1585, https://doi.org/10.1002/jagj.2.21347.

[27] N. Camus, S. LY, T. PAULY, Chaleur Issue de La Méthanisation de Réelles Opportunités, ADEME, 2016, p. 38 [Guide Pratique], https://www.ademe.fr/sites/default/files/assets/documents/8840_chaleur-issue_de_la_methanisation.pdf.

[28] A. Capodaglio, A. Callegari, M. Lopez, European Framework for the diffusion of biogas uses : emerging technologies, acceptance, incentive strategies, and institutional-regulatory support, Sustainability 8 (4) (2016) 298, https://doi.org/10.3390/su8040298.

[29] M. Carlsson, A. Lagerkvist, F. Morgan-Sagastume, The effects of substrate pre-treatment on anaerobic digestion systems : a review, Waste Manag. 32 (9) (2012) 1634-1650, https://doi.org/10.1016/j.wasman.2012.04.016.

[30] S. Carton, F. Levavasseur, Performances Agronomiques Et Environnementales De La Méthanisation Agricole Dans Un Contexte De Grandes Cultures Céréalières (Sans élevage) Et Recommandations De Bonnes Pratiques, AgroParisTech et Inrae, 2022, p. 84. https://hal.inrae.fr/hal-03714590v1/.

[31] D. Cavalli, G. Cabassi, L. Borrelli, G. Geromel, L. Bechini, L. Degano, P. Marino Gallina, Nitrogen fertilizer replacement value of undigested liquid cattle manure and digestates, Eur. J. Agron. 73 (2016) 34-41, https://doi.org/10.1016/j.eja.2015.10.007.

[32] V. Chaplot, P. Smith, Cover crops do not increase soil organic carbon stocks as much as has been claimed : what is the way forward? Glob. Chang. Biol. 29 (22) (2023) 6163-6169, https://doi.org/10.1111/gcb.16917.

[33] M. Charrad, N. Gazzali, V. Boiteau, A. Niknafs, NbClust : an R package for determining the relevant number of clusters in a data set, J. Stat. Softw. 61 (6) (2014), https://doi.org/10.18637/jss.v06i06.

[34] M.R. Cherubin, D.M.D.S. Oliveira, B.J. Feigl, L.G. Pimentel, I.P. Lisboa, M. R. Mach, L.L. Varanda, M.C. Morais, L.S. Satiro, G.V. Popin, S.R.D. Paiva, A.K.B. D. Santos, A.L.S.D. Vasconcelos, P.L.A.D. Melo, C.E.P. Cerri, C.C. Cerri, Crop residue harvest for bioenergy production and its implications on soil functioning and plant growth : a review, Sci Agric 75 (3) (2018) 255-272, https://doi.org/10.1590/1678-992x-2016-0459.

[35] R. Condor, L'entrepreneuriat collectif dans la méthanisation agricole : motivations et challenges1 collective entrepreneurship in agricultural biogas : motivations and challenges, Syst. Aliment./Food Syst. 4 (2019) 71-91.

[36] P. Cottney, L. Black, P. Williams, E. White, How cover crop sowing date impacts upon their growth, nutrient assimilation and the yield of the subsequent commercial crop, Agronomy 12 (2) (2022) 369, https://doi.org/10.3390/agronomy12020369.

[37] C. Couturier, Méthanisation agricole : quelle rentabilité selon les projets ? Sci. Eaux Territ. Numéro 12 (3) (2013) 72-77, https://doi.org/10.3917/set.012.0072.

[38] W. Czekala, A. Lewicki, P. Pochwatka, A. Czekala, D. Wojcieszak, K. Józwiakowski, H. Waliszewska, Digestate management in polish farms as an element of the nutrient cycle, J. Clean. Prod. 242 (2020) 118454, https://doi.org/10.1016/j.jclepro.2019.118454.

[39] R. Debref, P. Grouiez, F.-D. Vivien, La filière de la méthanisation agricole, 2023, https://doi.org/10.3917/lcb.dziez.2023.01.0023.

[40] Depoudent, C., Anzalone, G., Boudes, P., & Mazaud, C. (2020). Methanisation territoriale : facteurs sociaux de reussite (p. 19). https://librairie.ademe.fr/societ-e-et-politiques-publiques/5771-7315-methanisation-territoriale-facteurs-sociaux-de-reussite.html.

[41] P. Dhull, V. Moziharasi, S. Kumar, P.M. Benish Rose, R.K. Lohchab, Current and prognostic overview of digestate management and processing practices, regulations and standards, Biocatal. Agric. Biotechnol. 61 (2024) 103345, https://doi.org/10.1016/j.bbcat.2024.103345.

[42] G.M. Dobers, Acceptance of biogas plants taking into account space and place, Energy Policy 135 (2019) 110987, https://doi.org/10.1016/j.enpol.2019.110987.

[43] Duzy, L.M., Arriaga, F., & Balkcom, K.S. (2010). Rye cover crop AS a source of Biomass feedstock: an economic perspective.

[44] J. Edwards, M. Othman, S. Burn, A review of policy drivers and barriers for the use of anaerobic digestion in Europe, the United States and Australia, Renew. Sustain. Energy Rev. 52 (2015) 815-828, https://doi.org/10.1016/j.rser.2015.07.112.

[45] A. Eftaxias, I. Kolokotroni, C. Michailidis, P. Charitidis, V. Diamantis, Techno-economic assessment of Anaerobic digestion technology for small- and medium-sized animal husbandry enterprises, Appl. Sci. 14 (11) (2024) 4957, https://doi.org/10.3390/app14114957.

[46] T. Eickenscheidt, A. Freibauer, J. Heinichen, J. Augustin, M. Drösler, Short-term effects of biogas digestate and cattle slurry application on greenhouse gas emissions affected by N availability from grasslands on drained fen peatlands and associated organic soils, Biogeosciences. 11 (22) (2014) 6187-6207, https://doi.org/10.5194/bg-11-6187-2014.

[47] I. Erdal, I. Erdal, C. Yaylacı, Effect of biogas digestate, incubation, and NPK fertilization on some soil properties and growth, mineral nutrition, and nutrient uptake of wheat, J. Elem. (2021), https://doi.org/10.5601/jelem.2021.26.2.2165 (3/2021).

[48] S. Espagnol, A. Wilfart, A. Gac, L. Aissani, P. Levasseur, Analyse Environnementale d'installations De Méthanisation Agricole En élevage Porcin, 51èmes Journées de la Recherche Porcine, Paris, France, 2019. https://hal.inrae.fr/hal-03220548v1.

[49] European Commission (Éd.). (2018). A sustainable bioeconomy for Europe : strengthening the connection between economy, society and the environment:
updated bioeconomy strategy. Publications Office. https://doi.org/10.2777/47
8385.

[50] R.T. Franco, Optimisation Des Pratiques De Gestion Des Déchets Agricoles En Lien Avec Leur Valorisation Par Méthanisation [Thèse De Doctorat En Sciences Agricoles], Université de Lyon, 2017. http://www.theses.fr/2017LYSEI092 /document.

[51] R.T. Franco, R. Bayard, P. Buffière, Mathematical modelling of the ensiling process before biogas production : strengthening the links between biomass storage and anaerobic digestion, Chem. Eng. J. 350 (2018) 872-882, https://doi.org/10.1016/j.cej.2018.06.009.

[52] H.L.C. Frenandez, Prétraitement Des Déchets Agricoles Pour L'optimisation De Leur Valorisation Par Méthanisation [Environnement Et Société, Université de Lyon, 2021. http://www.theses.fr/2021LYSEI024/document.

[53] Gac, A., Lorinquer, E., Wilfart, A., & Espagnol, S. (2018). Analyse Environnementale D'installations De Méthanisation Agricole En élevages Bovins. 24èmes Rencontres Recherche Ruminant, Paris, France.

[54] N. Garambois, I. Reguer, N. Pirard, F. Schruijer, Transition énergétique et durabilité de l'agriculture : les limites et paradoxes du développement de la méthanisation agricole Etude comparée en Bretagne et Grand-Est, Territ. mouv. (55) (2022), https://doi.org/10.4000/tem.9710.

[55] E. Ghafoori, P. Flynn, J. Feddes, Pipeline vs. Truck transport of beef cattle manure, BioMass BioEnergy 31 (2-3) (2007) 168-175, https://doi.org/10.1016/j.biombioe.2006.07.007.

[56] S. Ghose, M.J. Franchetti, Economic aspects of food waste-to-energy system deployment. Sustainable Food Waste-To-Energy Systems, Elsevier, 2018, p. 203-229, https://doi.org/10.1016/B978-0-12-811157-4.00011-5.

[57] P. Grouiez, Une analyse de filière des dynamiques de revenus de la méthanisation agricole, Notes Etudes Socio-Econ. 49 (2021) 41-61.

[58] P. Grouiez, A. Berthe, M. Fautras, S. Issehnane, Déterminants et mesure des revenus agricoles de la méthanisation et positionnement des agriculteurs dans la chaîne de valeur "biomasse-énergie" (p. 84) [Rapport de recherche], Minist. Agric. Aliment. (2020). https://shhal.science/halsh-02886217v1.

[59] GRTgaz. (2025). You sell your biomethane. https://www.natrangroupe.com/e/
n/you-are/customer/producer/sell-biomethane.

[60] F. Guilayn, J. Jimenez, J.-L. Martel, M. Rouez, M. Crest, D. Patureau, First fertilizing-value typology of digestates : a decision-making tool for regulation, Waste Manag. 86 (2019) 67-79, https://doi.org/10.1016/j.wasman.2019.01.032.

[61] D. Gurmessa, S. Cocco, A.J. Ashworth, R.P. Udawatta, V. Cardelli, A. Ilari, D. Serrani, F. Fornasier, A. Del Gatto, E.F. Pedretti, G. Corti, Short term effects of digestate and composted digestate on soil health and crop yield : implications for sustainable biowaste management in the bioenergy sector, Sci. Total Environ. 906 (2024) 167208, https://doi.org/10.1016/j.scitotenv.2023.167208.

[62] D. Hauswirth, T.S. Pham, J. Wery, P. Titonnell, D. Jourdain, F. Affholder, Exploiting farm typologies for designing conservation agriculture systems : a case study in northern Vietnam, Cah. Agric. 24 (2) (2015) 102-112, https://doi.org/
10.1684/agr.2015.07444.

[63] S. Herbstritt, T.L. Richard, S.H. Lence, H. Wu, P.L. O'Brien, B.D. Emmett, T. C. Kaspar, D.L. Karlen, K. Kohler, R.W. Malone, Rye as an energy cover crop : management, forage quality, and revenue opportunities for feed and bioenergy, Agriculture 12 (10) (2022) 1691, https://doi.org/10.3390/agriculture12101691.

[64] O. Hijazi, S. Munro, B. Zerhusen, M. Effenberger, Review of life cycle assessment for biogas production in Europe, Renew. Sustain. Energy Rev. 54 (2016) 1291-1300, https://doi.org/10.1016/j.rser.2015.10.013.

[65] J.B. Holm-Nielsen, T. Al Seadi, P. Oleskowicz-Popiel, The future of anaerobic digestion and biogas utilization, Bioresour, Technol 100 (22) (2009) 5478-5484, https://doi.org/10.1016/j.biortech.2008.12.046.

[66] S. Hupfauf, S. Bachmann, M. Fernández-Delgado Juárez, H. Insam, B. Eichler-Löbermann, Biogas digestates affect crop P uptake and soil microbial community composition, Sci. Total Environ. 542 (2016) 1144-1154, https://doi.org/
10.1016/j.scitotenv.2015.09.025.

[67] Husson, F., Josse, J., & Pages, J. (2010). Principal component methods—Hierarchical clustering—Partitional clustering : why would we need to choose for visualizing data?. http://www.sthda.com/english/upload/hcpc_h usson_josse.pdf.

[68] M. Hutnan, M. Dril, L. Mrafkova, Anaerobic biodegradation of sugar beet pulp, Biodegradation 11 (4) (2000) 203-211, https://doi.org/10.1023/A: 1011139621329.

[69] S.C. Iweka, K.C. Owuama, J.L. Chukwuneke, O.A. Falowo, Optimization of biogas yield from anaerobic co-digestion of corn-chaff and cow dung digestate : RSM and python approach, Heliyon. 7 (11) (2021) e08255, https://doi.org/10.1016/j. heliyon.2021.e08255.

[70] A. Kacprzak, L. Krzystek, S. Ledakowicz, Co-digestion of agricultural and industrial wastes, Chem. Pap. 64 (2) (2010), https://doi.org/10.2478/s11696-009-0108-5.

[71] B. Karimi, S. Sadet-Bourgeteau, M. Cannavaciuolo, C. Chauvin, C. Flamin, A. Haumont, V. Jean-Baptiste, A. Reibel, G. Vrignaud, L. Ranjard, Impact of biogas digestates on soil microbiota in agriculture : a review, Env. Chem Lett 20 (5) (2022) 3265-3288, https://doi.org/10.1007/s10311-022-01451-8.

[72] A. Kassambara, Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning, 1, STHDA, 2017. https://www.sthda.com/english/download/index.php?uri=/3-ebooks/9-practical-guide-to-cluster-analysis-in-r&utm_content=buffer762eb&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer

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

[73] Knoden, D., Vertès, F., & Foray, S. (2015). La directive nitrates dans quelques régions d'Europe. https://afpf-asso.fr/_objects/afpf_revues/F224-Knoden.pdf.

[74] D. Kondusamy, A.S. Kalamhad, Pre-treatment and anaerobic digestion of food waste for high rate methane production - A review, J. Environ. Eng. 2 (3) (2014) 1821-1830, https://doi.org/10.1016/j.jece.2014.07.024.

[75] M. Koszel, S. Parafiniuk, A. Szparaga, A. Bochniak, S. Kocira, A.Z. Atanasov, S. Kovalyshyn, Impact of digestate application as a fertilizer on the yield and quality of Winter rape seed, Agronomy 10 (6) (2020) 878, https://doi.org/10.3390/agronomy10060878.

[76] D. Kovačić, Z. Lončarić, J. Jović, D. Samac, B. Popović, M. Tišma, Digestate management and Processing practices : a review, Appl. Sci. 12 (18) (2022) 9216, https://doi.org/10.3390/app12189216.

[77] U. Kumar, I.K. Thomsen, J. Eriksen, I. Vogeler, M. Mäenpää, E.M. Hansen, Delaying sowing of cover crops decreases the ability to reduce nitrate leaching, Agric Ecosyst Env. 355 (2023) 108598, https://doi.org/10.1016/j. agee.2023.108598.

[78] H. Laing, C. O'Malley, A. Browne, T. Rutherford, T. Baines, A. Moore, K. Black, M. J. Willis, Optimisation of energy usage and carbon emissions monitoring using MILP for an advanced anaerobic digester plant, Energy 256 (2022) 124577, https://doi.org/10.1016/j.energy.2022.124577.

[79] C. Launay, S. Houot, S. Frédéric, R. Girault, F. Levavasseur, S. Marsac, J. Constantin, Incorporating energy cover crops for biogas production into agricultural systems : benefits and environmental impacts. A review, Agron. Sustain. Dev. 42 (4) (2022) 57, https://doi.org/10.1007/s13593-022-00790-8.

[80] S. Lè, J. Josse, F. Husson, FactoMineR : an R package for multivariate analysis, J. Stat. Softw. 25 (1) (2008), https://doi.org/10.18637/jss.v025.i01.

[81] R. Leskovšek, K. Eler, S.A. Zamljen, Weed suppression and maize yield influenced by cover crop mixture diversity and tillage, Agric Ecosyst Env. 383 (2025) 109530, https://doi.org/10.1016/j.agee.2025.109530.

[82] N. Lüker-Jans, D. Simmering, A. Otte, The impact of biogas plants on regional dynamics of permanent grassland and maize area—The example of Hesse, Germany (2005-2010), Agric Ecosyst Env. 241 (2017) 24-38, https://doi.org/
10.1016/j.agee.2017.02.023.

[83] J. Mata-Alvarez, J. Dosta, M.S. Romero-Güiza, X. Fonoll, M. Peces, S. Astals, A critical review on anaerobic co-digestion achievements between 2010 and 2013, Renew. Sustain. Energy Rev. 36 (2014) 412-427, https://doi.org/10.1016/
j.rser.2014.04.039.

[84] J. Mata-Alvarez, Joan Dosta, Sandra Macé, S. Astals, Codigestion of solid wastes : a review of its uses and perspectives including modeling, Crit, Rev, Biotechnol 31 (2) (2011) 99-111, https://doi.org/10.3109/073885510.2010.525496.

[85] S.C. McClelland, K. Paustian, M.E. Schipanski, Carbon management of cover crops in temperate climates influences soil organic carbon stocks : a meta-analysis, Ecol. Appl. 31 (3) (2021) e02278, https://doi.org/10.1002/eap.2278.

[86] Moinard, V. (2021). Consequences de l'introduction de la méthanisation dans une exploitation de polyculture-élevage sur les cycles du carbone et de l'azote. Combinaison de l'expérimentation et de la modélisation à l'échelle de la ferme [Thèse de doctorat]. http://www.theses.fr/2021UPASB049/document.

[87] K. Möller, Effects of anaerobic digestion on soil carbon and nitrogen turnover, N emissions, and soil biological activity. A review, Agron. Sustain. Dev. 35 (3) (2015) 1021-1041, https://doi.org/10.1007/s13593-015-0284-3.

[88] K. Möller, T. Müller, Effects of anaerobic digestion on digestate nutrient availability and crop growth : a review, Eng, Life Sci 12 (3) (2012) 242-257, https://doi.org/10.1002/elsc.201100085.

[89] K. Möller, W. Stinner, Effects of different manuring systems with and without biogas digestion on soil mineral nitrogen content and on gaseous nitrogen losses (ammonia, nitrous oxides), Eur. J. Agron. 30 (1) (2009) 1-16, https://doi.org/
10.1016/j.eja.2008.06.003.

[90] K. Möller, R. Schulz, T. Müller, Effects of setup of centralized biogas plants on crop acreage and balances of nutrients and soil humus, Nutr. Cycl. Agroecosystems 89 (2) (2011) 303-312, https://doi.org/10.1007/s10705-010-9395-z.

[91] K. Möller, W. Stinner, G. Leithold, Growth, composition, biological N2 fixation and nutrient uptake of a leguminous cover crop mixture and the effect of their removal on field nitrogen balances and nitrate leaching risk, Nutr. Cycl. Agroecosystems 82 (3) (2008) 233-249, https://doi.org/10.1007/s10705-008-9182-2.

[92] G. Nascimento, D. Villegas, C. Cantero-Martínez, Crop diversification and digestate application effect on the productivity and efficiency of irrigated winter crop systems, Eur. J. Agron. 148 (2023) 126873, https://doi.org/10.1016/j.eja.2023.126873.

[93] T. Nevzorova, V. Kutcherov, Barriers to the wider implementation of biogas as a source of energy : a state-of-the-art review, Energy Strategy Rev. 26 (2019) 100414, https://doi.org/10.1016/j.esr.2019.100414.

[94] A. Niang, A. Torre, S. Bourdin, How do local actors coordinate to implement a successful biogas project? Env. Sci Policy 136 (2022) 337-347, https://doi.org/
10.1016/j.envsci.2022.06.019.

[95] R. Nkoa, Agricultural benefits and environmental risks of soil fertilization with anaerobic digestates : a review, Agron. Sustain. Dev. 34 (2) (2014) 473-492, https://doi.org/10.1007/s13593-013-0196-z.

[96] S. O'Connor, E. Ehimen, S.C. Pillai, A. Black, D. Tormey, J. Bartlett, Biogas production from small-scale anaerobic digestion plants on European farms, Renew. Sustain. Energy Rev. 139 (2021) 110580, https://doi.org/10.1016/
j.rser.2020.110580.

[97] S. O'Connor, E. Ehimen, S.C. Pillai, G. Lyons, J. Bartlett, Economic and environmental analysis of small-scale Anaerobic digestion plants on Irish dairy farms, Energies 13 (3) (2020) 637, https://doi.org/10.3390/en13030637.

[98] Observatoire Régional sur l'Agriculture et le Changement Climatique. (2020). Etat des lieux sur le changement climatique et ses incidences agricoles en Hauts-
de-France. (p. 64). Chambre régionale d'agriculture Hauts-de-France. https://hautsdefrance.chambres-agriculture.fr/.

[99] V. Orzi, C. Riva, B. Scaglia, G. D'Imporzano, F. Tambone, F. Adani, Anaerobic digestion coupled with digestate injection reduced odour emissions from soil during manure distribution, Sci. Total Environ. 621 (2018) 168-176, https://doi.org/10.1016/j.scitotenv.2017.11.249.

[100] J. Pagès, Analyse factorielle de données mixtes, Rev. Stat. Appl. 52 (4) (2004) 93-111.

[101] O. Pakarinen, A. Lehtomäki, S. Rissanen, J. Rintala, Storing energy crops for methane production : effects of solids content and biological additive, Bioresour, Technol 99 (15) (2008) 7074-7082, https://doi.org/10.1016/j.biortech.2008.01.007.

[102] S.-Y. Pan, C.-Y. Tsai, C.-W. Liu, S.-W. Wang, H. Kim, C. Fan, Anaerobic co-digestion of agricultural wastes toward circular bioeconomy, iScience 24 (7) (2021) 102704, https://doi.org/10.1016/j.isci.2021.102704.

[103] J. Pedersen, K. Andersson, A. Feilberg, S. Delin, S. Hafner, T. Nyord, Effect of exposed surface area on ammonia emissions from untreated, separated, and digested cattle manure, Biosyst. Eng. 202 (2021) 66-78, https://doi.org/10.1016/j.biosystemseng.2020.12.005.

[104] F. Perazzolo, G. Mattachini, E. Riva, G. Provolo, Nutrient losses during winter and summer storage of separated and unseparated digested cattle slurry, J. Env., Qual 46 (4) (2017) 879-888, https://doi.org/10.2134/jeq2016.07.0274.

[105] Perazzolo, F., Mattachini, G., Tambone, F., Calcante, A., & Provolo, G. (2016). Nutrient losses from cattle co-digestate slurry during storage. 47(2), 94. https://doi.org/10.4081/jae.2016.500.

[106] Pislor, E. (2015). Étude au champ des potentiels agronomiques, méthanogènes et environnementaux de cultures intermédiaires. (p. 61). https://www.bioeconomie-hautsdefrance.fr/wp-content/uploads/2024/08/expecive_2013_ademe.pdf.

[107] Plana, P.V., & Noche, B. (2016). A review of the current digestate distribution models : storage and transport. 345-357. https://doi.org/10.2495/WM160311.

[108] A. Planisich, S.A. Utsumi, M. Larripa, J.R. Galli, Grazing of cover crops in integrated crop-livestock systems, Animal. 15 (1) (2021) 100054, https://doi.org/10.1016/j.animal.2020.100054.

[109] C. Poeplau, A. Don, Carbon sequestration in agricultural soils via cultivation of cover crops - A meta-analysis, Agric Ecosyst Env. 200 (2015) 33-41, https://doi.org/10.1016/j.agee.2014.10.024.

[110] P. Quideau, P. Levasseur, A. Charpiot, T. Lendormi, F. Guiziou, Intérêts conjugués d'une évacuation rapide des déjections animales et de leur méthanisation, Innov. Agron. 34 (2014) 309-320, https://doi.org/10.17180/JE83-V412.

[111] X. Ran, N.S.T. Uppuluri, Y. Deng, Y. Zheng, R. Dong, J. Müller, H. Oechsner, B. Li, J. Guo, Comparison of phosphorus species in livestock manure and digestate by different detection techniques, Sci. Total Environ. 874 (2023) 162547, https://doi.org/10.1016/j.scitotenv.2023.162547.

[112] T. Rehl, J. Müller, Life cycle assessment of biogas digestate processing technologies, Resour. Conserv. Recycl. 56 (1) (2011) 92-104, https://doi.org/10.1016/j.resconrec.2011.08.007.

[113] C. Riva, V. Orzi, M. Carozzi, M. Acutis, G. Boccasile, S. Lonati, F. Tambone, G. D'Imporzano, F. Adani, Short-term experiments in using digestate products as substitutes for mineral (N) fertilizer : agronomic performance, odours, and ammonia emission impacts, Sci. Total Environ. 547 (2016) 206-214, https://doi.org/10.1016/j.scitotenv.2015.12.156.

[114] G.H. Rubæk, K. Henriksen, J. Petersen, B. Rasmussen, S.G. Sommer, Effects of application technique and anaerobic digestion on gaseous nitrogen loss from animal slurry applied to ryegrass (Lolium perenne), J. Agric. Sci. 126 (4) (1996) 481-492, https://doi.org/10.1017/S0021859600075572.

[115] Rui, S., Bouzin, A., & Samson, A. (2023). Étude sociologique des conflits relatifs aux projets de méthanisation agricole en Nouvelle-Aquitaine. 71.

[116] M.C.D. Salangsang, M. Sekine, S. Akizuki, H.D. Sakai, N. Kurosawa, T. Toda, Effect of carbon to nitrogen ratio of food waste and short resting period on microbial accumulation during anaerobic digestion, BioMass BioEnergy 162 (2022) 106481, https://doi.org/10.1016/j.biombioe.2022.106481.

[117] M. Samoraj, M. Mironiuk, G. Izydorczyk, A. Witek-Krowiak, D. Szopa, K. Moustakas, K. Chojnacka, The challenges and perspectives for anaerobic digestion of animal waste and fertilizer application of the digestate, Chemosphere 295 (2022) 133799, https://doi.org/10.1016/j.chemosphere.2022.133799.

[118] S. Shrestha, R. Pandey, N. Aryal, S.P. Lohani, Recent advances in co-digestion conjugates for anaerobic digestion of food waste, J. Env., Manage 345 (2023) 118785, https://doi.org/10.1016/j.jenvman.2023.118785.

[119] T. Simon, E. Kunzová, M. Friedlová, The effect of digestate, cattle slurry and mineral fertilization on the winter wheat yield and soil quality parameters, Plant Soil Environ. 61 (11) (2015) 522-527, https://doi.org/10.17221/530/2015-PSE.

[120] B. Soukaradji, A. Abdou, S. Lawali, I. Aboubacar, A. Mahamane, M. Saadou, Typologie des exploitations agricoles familiales : cas de la périphérie de la forêt protégée de Baban Rafi du Niger, Int. J. Biol. Chem. Sci. 11 (3) (2017) 1096, https://doi.org/10.4314/ijbcs.v11i3.14.

[121] K. Steenwerth, K.M. Belina, Cover crops enhance soil organic matter, carbon dynamics and microbiological function in a vineyard agroecosystem, Appl. Soil Ecol. 40 (2) (2008) 359-369, https://doi.org/10.1016/j.apsoil.2008.06.006.

[122] W. Stinner, K. Möller, G. Leithold, Effects of biogas digestion of clover/grass-leys, cover crops and crop residues on nitrogen cycle and crop yield in organic stockless farming systems, Eur. J. Agron. 29 (2-3) (2008) 125-134, https://doi.org/10.1016/j.eja.2008.04.006.

[123] Tampere, M., & Viiralt, R. (2014). The efficiency of Biogas digestate on Grassland compared to mineral fertilizer and cattle slurry. 1.

[124] R. Tibshirani, G. Walther, T. Hastie, Estimating the number of clusters in a data set via the gap statistic, J. R. Stat. Soc. B: Stat. Methodol. 63 (2) (2001) 411-423, https://doi.org/10.1111/1467-9868.00293.

[125] F. Valenti, Y. Zhong, M. Sun, S.M.C. Porto, A. Toscano, B.E. Dale, F. Sibilla, W. Liao, Anaerobic co-digestion of multiple agricultural residues to enhance biogas production in southern Italy, Waste Manag. 78 (2018) 151-157, https://doi.org/10.1016/j.wasman.2018.05.037.

[126] F. Valentinuzzi, L. Cavani, C. Porfido, R. Terzano, Y. Pii, S. Cesco, C. Marzadori, T. Mimmo, The fertilising potential of manure-based biogas fermentation residues : pelleted vs. liquid digestate, Heliyon. 6 (2) (2020) e03325, https://doi.org/10.1016/j.heliyon.2020.e03325.

[127] J.J. Walsh, D.L. Jones, D.R. Chadwick, A.P. Williams, Repeated application of anaerobic digestate, undigested cattle slurry and inorganic fertilizer N : impacts on pasture yield and quality, Grass Forage Sci. 73 (3) (2018) 758-763, https://doi.org/10.1111/gfs.12354.

[128] K.G. Wilkinson, A comparison of the drivers influencing adoption of on-farm anaerobic digestion in Germany and Australia, BioMass BioEnergy 35 (5) (2011) 1613-1622, https://doi.org/10.1016/j.biombioe.2011.01.013.

[129] Q. Yue, P. Guo, H. Wu, Y. Wang, C. Zhang, Towards sustainable circular agriculture : an integrated optimization framework for crop-livestock-biogas-crop recycling system management under uncertainty, Agric Syst 196 (2022) 103347, https://doi.org/10.1016/j.agsy.2021.103347.

[130] Y. Zhong, R. Chen, J.-P. Rojas-Sossa, C. Isaguirre, A. Mashburn, T. Marsh, Y. Liu, W. Liao, Anaerobic co-digestion of energy crop and agricultural wastes to prepare uniform-format cellulosic feedstock for biorefining, Renew. Energy 147 (2020) 1358-1370, https://doi.org/10.1016/j.renene.2019.09.106.