### 1.2.5. Conservation de la biodiversité

Dans un souci de simplicité, nous n'avons pas décomposé le thème de la conservation de la biodiversité en plusieurs groupes taxonomiques comme dans MASC qui les distingue. Au minimum, il faudrait différencier les espèces liées au sol et peu mobiles des espèces mobiles (Le Roux et al., 2008). Ce thème intègre deux facteurs directs : le SE de l'habitat pour les espèces sauvages associé aux cultures (Sirami et al., 2019) et le PT de la gestion paysagère et des pratiques agricoles (Clough et al., 2020; Coudrain et al., 2016; van Capelle et al., 2012).

### 1.2.6. Qualité du paysage

Le paysage est un thème qui fournit un avantage à la société et à l'agriculteur en tant que citoyen mais aussi pour certaines de ses activités annexes (cf. discussion plus haut). Ce thème intègre le SE de potentiel récréatif (Junge et al., 2015; Schüpbach et al., 2016), les caractéristiques du milieu (présence de collines, rivières, étangs) (Clough et al., 2020) et les PT correspondant à la gestion des cultures (diversité, agencement) et des éléments semi-naturels (haie, bois, mares, etc.).

## 2. Évaluation conjointe des services écosystémiques et des impacts environnementaux

Plusieurs approches existent pour analyser un ensemble d'indicateurs, en commençant par une matrice de corrélation pour étudier les corrélations deux à deux (German et al., 2017; Sabourin, 2017), des approches multivariées, telle que l'analyse en composantes principales mais aussi des approches reliant des indicateurs à des variables explicatives tels que les arbres de régression (Dardonville et al., 2022).

Il nous a semblé intéressant d'aller plus loin que des approches corrélatives et multivariées travaillant sur l'ensemble des indicateurs d'IE et de SE en utilisant les relations décrites dans le cadre conceptuel. Celui-ci, décrit précédemment, montre que la plupart des thèmes de la durabilité peuvent être associés à un indicateur de service de régulation comme facteur explicatif. Dans cette section, nous présentons un cadre analytique pour étudier conjointement ces couples d'IE et SE qui ressortent du cadre conceptuel (Figure 14). Ainsi pour un couple d'indicateurs IE/SE, nous avons proposé de fixer un seuil qui, par défaut, a été choisi comme le milieu de la plage des valeurs, obtenues avec notre échantillon de 33 exploitations, pour un indicateur donné. Ce choix d'un seuil relatif peut se discuter mais dans tous les cas, il a permis de distinguer quatre situations. Deux situations fournissent une information évidente (Figure 14) :

- situation gagnant-gagnant, les deux indicateurs IE et SE sont dans l'intervalle favorable (impact faible et service élevé),

- situation perdant-perdant : les deux indicateurs IE et SE sont dans l'intervalle défavorable.

Les deux autres situations (Figure 14) nécessitent plus d'explications :
- La situation perdant-gagnant avec un SE faible (défavorable) et un impact faible (favorable) peut être due à une situation où l'impact environnemental n'est pas encore observé. Cela peut s'expliquer par un mécanisme tampon, un délai dans le temps de réponse ou les deux (ex. le stockage du nitrate dans un sol profond ; le contrôle des adventices). Un tel cas peut également être possible par la mise en place d'une pratique réduisant l'impact sans pour autant augmenter le service correspondant. Cela aura pour conséquence des impacts pour d'autres thèmes et se traduira par la dégradation d'un ou plusieurs autres indicateurs. Si on prend l'exemple de l'érosion, la pratique de non-travail du sol sans la mise en œuvre des deux autres piliers de l'agriculture de conservation des sols qui fournissent des SE (rotation, couverture du sol, Mondal et al., 2019) entraînera une utilisation plus importante d'herbicides et en particulier de glyphosate (Cordeau, 2022).

- La situation gagnant-perdant avec un SE élevé (favorable) et un impact élevé (défavorable) correspond à une situation où le potentiel SE n'est pas réellement utilisé par les agriculteurs (Dardonville et al., 2022). Un exemple peut être la non prise en compte des fournitures d'azote par la minéralisation du sol, l'apport des cultures dans le calcul de la dose d'azote.

<table><caption>Figure 14 : Cadre analytique de l'évaluation conjointe des impacts environnementaux et des services écosystémiques.</caption><tr><td rowspan="2">Service écosystémique</td><td>Elevé</td><td>Situation gagnant-gagnant : favorable à la durabilité</td><td>Situation gagnant-perdant : mitigée (forte vulnérabilité environnementale, pratiques à risques, etc.)</td></tr><tr><td>Faible</td><td>Situation perdant-gagnant : mitigée (effet tampon, temps de réponse de l'environnement, etc.)</td><td>Situation perdant-perdant : défavorable à la durabilité</td></tr><tr><td colspan="2"></td><td>Faible</td><td>Elevé</td></tr><tr><td colspan="2"></td><td colspan="2">Impact environnemental</td></tr></table>
## 3. Présentation des indicateurs

Une fois le cadre conceptuel de la méthode défini, nous avons associé à une majorité de thèmes un indicateur prédictif. Pour aider à la compréhension des résultats, nous avons ajouté à la liste les indicateurs de cause afin de voir les liens existants entre ces indicateurs simples et les indicateurs d'impacts et de service. Ces derniers peuvent intervenir dans différents thèmes. Pour certains thèmes, nous avons uniquement gardé l'indicateur de service correspondant (Tableau 8) car disponible et n'avons pas repris l'indicateur évaluant l'avantage pour simplifier le travail, avec une exception pour l'indicateur de la maîtrise de la structure du sol. Pour ce dernier peu traité dans les méthodes, 12 % d'après la revue de Soulé et al. (2021), il nous a paru pertinent de reprendre l'indicateur proposé par la méthode MASC. Enfin, nous n'avons pas ajouté d'indicateur à certains thèmes pour les raisons suivantes :

- Sujet peu pertinent pour le cas d'étude : exemple pression en eau. Les parcelles retenues pour notre échantillon n'étaient pas irriguées.

- Indicateur prédictif non disponible : pour la gestion des déchets

- Problème de connaissance scientifique disponible : pesticides dans le sol (cf. Encart 2)

- Pour la qualité du paysage, des facteurs ont été identifiés (Schüpbach et al. 2016) et même un indicateur prédictif (Weinstoeffer et Girardin, 2001) mais cela n'a pas été approfondi faute de temps.

L'ensemble des indicateurs ont été calculés à l'aide de divers outils : des calculateurs Excel, les logiciels QGIS et DEXi, le modèle AMG et l'outil SYSTERRE®. Dans les sections suivantes, nous présentons la méthode de calcul des indicateurs et les données mobilisées. Les indicateurs ont été regroupés par catégorie : indicateurs de services écosystémiques, indicateurs d'impacts environnementaux, indicateurs de cause, indicateurs socioéconomiques et les indicateurs nouvellement développés durant cette thèse.

### 3.1. Indicateurs de services écosystémiques

Quatorze indicateurs de services écosystémiques ont été calculés. Parmi eux, 12 indicateurs (Tableau 9) sont issus de l'indicateur IDRo pour « indicateur de diversité de la rotation » (Keichinger et al., 2021). Cet indicateur évalue l'ensemble des services écosystémiques associés à la rotation ou à la succession des cultures (en incluant les cultures intermédiaires). Pour chaque SE, on attribue des notes aux cultures en fonction de leurs caractéristiques. Prenons l'exemple du SE de régulation des maladies. Plusieurs travaux scientifiques ont montré que la rotation avait un effet sur certaines maladies en rompant leur cycle et en diminuant l'inoculum dans le sol (Charles et al., 2011; Krupinsky et al., 2002; Ratnadass et al., 2012). L'objectif est de limiter le temps de retour de la culture sensible en introduisant des cultures non hôtes, souvent de familles différentes (effet succession ou précédent) (Krupinsky et al., 2002). La diversité de la microflore et microfaune apportée par la diversité des résidus et des racines aide également à la régulation des pathogènes. C'est un des effets indirects de la diversification des cultures (Bailey and Lazarovits, 2003; Ghorbani et al., 2009; Ratnadass et al., 2012). Cependant, la diversification
des cultures a peu d'effet sur les maladies ayant un potentiel de dissémination important ou une durée de vie longue (Ratnadass et al., 2012).

Le calcul de l'indicateur de régulation des maladies est décrit ci-dessous :

$$I-SER\_maladies = (\sum (M_{Cin-1/Cin}))/n * \prod (Vref_i/Vréel_i)^2$$

Avec :

$M_{Cin-1/Cin}$ : note de l'effet sur la pression maladie de la culture précédente (Cin-1) sur la culture en cours (Cin) lors de la succession du couple « précédent/culture » (APCA, 2017 ; Jeangros & Courvoisier, 2019).

n : durée de la rotation en années

Vrefi : temps de retour conseillé de la culture ou de la famille i.

Vréeli : temps de retour réel de la culture i.

Remarque : Si Vréel < Vref alors le temps de retour est suffisamment long et l'indice est égal à 1, dans le cas contraire on retiendra la valeur Vref/Vréel.

Les critères retenus pour les 11 autres services écosystémiques et le mode de calcul sont synthétisés dans le tableau X ci-dessous. Le tableau de l'annexe C.2 donne plus de détails sur le calcul des indicateurs.
<table><caption>Tableau 9 : Critères retenus pour les SE et mode de calcul (adapté de Keichinger et al. 2021)</caption><thead><tr><th>Service écosystémique (exprimé entre [0 ; 1])</th><th>Principaux critères</th><th>Échelle temporelle du calcul</th><th>Mode de calcul</th><th>Références bibliographiques</th></tr></thead><tbody><tr><td>Régulation des adventices</td><td>Effet couvrant des cultures<br>Alternance des dates de semis</td><td>Précédent/culture</td><td>Indice = (max effet étouffement ; effet nettoyant précédent); changement date de semis)</td><td>Adeux et al. (2019), Gaba et al. (2014), Weisberger et al. (2019)</td></tr><tr><td>Régulation des maladies</td><td>Effet précédent sur la pression de la maladie (MCin-1/Cin)<br>Durée de la rotation (n)<br>Temps de retour de la culture conseillé (Vref)<br>Temps de retour de la culture réel (Vréel)</td><td>Précédent/culture</td><td>Indice = (Σ (MCin-1/Cin))/n * Π (Vrefi/Vréeli)<sup>2</sup></td><td>Bailey & Lazarovits (2003), Charles et al. (2011), Ghorbani et al. (2008), Krupinsky et al. (2002), Ratnadass et al. (2012)</td></tr><tr><td>Régulation des ravageurs</td><td>Effet précédent</td><td>Précédent/culture</td><td>Note attribuée au couple « précédent / culture »)</td><td>Ratnadass et al. (2012), Rusch et al. (2013, 2016)</td></tr><tr><td>Pollinisation</td><td>Potentiel à la floraison</td><td>Culture</td><td>Moyenne des notes attribuées à chaque culture</td><td>Davaine (2012), Hass et al. (2018)</td></tr><tr><td>Structuration du sol</td><td>Effet structurant du système racinaire de la culture i (SSCi)<br>Effet de chantier sur le tassement du sol lors de la récolte (ECCI)<br>Diversité des systèmes racinaires des cultures (DSR)</td><td>Culture</td><td>Indice = (Σ (SSCi * ECCi))/n * DSR</td><td>Boizard et al. (2004), Govindin (2014), Voisin et al. (2015), Therond et al. (2017)</td></tr><tr><td>Érosion des sols</td><td>C-factor (modèle RUSLE) intégrant l'effet travail du sol, la gestion des résidus et l'effet couvert végétal.</td><td>Culture</td><td>Indice = 1-2*C-factor</td><td>Panagos et al. (2015)</td></tr><tr><td>Stockage d'eau</td><td>Consommation (C)<br>Période de consommation (T)</td><td>Culture</td><td>Indice =(C+T)/2</td><td>Allen et al. (1998)</td></tr><tr><td>Fourniture en azote</td><td>Fourniture d'azote par la culture (NCp)<br>Fourniture d'azote du précédent (NP)<br>Durée de rotation (n)</td><td>Précédent/culture</td><td>Indice = (Σ (NCpi + NPi))/n N.B. La somme NCpi + NPi est plafonnée à 1 pour chaque année de la rotation.</td><td>Vertès et al. (2015), Guillier et al. (2020)</td></tr><tr><td>Lixiviation des nitrates</td><td>Minéralisation des résidus de culture et du sol<br>Absorption N avant hiver</td><td>Précédent/culture</td><td>Indice = (minéralisation résidus et sol - absorption N)</td><td>Beaudoin et al. (2005), Vertès et al. (2015), Voisin et al. (2015)</td></tr><tr><td>Contribution au stockage de carbone</td><td>Apport potentiel de la culture</td><td>Culture</td><td>Moyenne (apport cultures principales ; cultures intermédiaires)</td><td>Justes et al. (2012), Pellerin et al. (2019)</td></tr><tr><td>Habitat pour les espèces sauvages</td><td>Appétence (A)<br>Protection (P)<br>Temps de présence (T)</td><td>Culture</td><td>Indice = (P+A)/2*T</td><td>Keichinger (2001)</td></tr><tr><td>Esthétique du paysage</td><td>Ouverture (O)<br>Aspect floraison : épiaison (FE)<br>Aspect au cours du cycle (A)<br>Temps de présence (T)</td><td>Culture</td><td>Indice = (O+FE*A)*T/2</td><td>Junge et al. (2015), Schüpbach et al. (2016)</td></tr></tbody></table>

Les deux derniers SE, le stockage de C et l'albédo, correspondent au SE de régulation du climat global. Le stockage de C a été calculé en utilisant le modèle AMG (Clivot et al., 2019). Des simulations sur 30 ans ont été effectuées pour les 33 exploitations sur une rotation type. Le résultat obtenu par le modèle a ensuite été divisé par 30 afin de ramener le stockage ou déstockage de C pour une année.
Pour l'albédo, cet indicateur a été développé durant cette thèse et est présenté en détails dans la section « Indicateurs nouvellement développés ».

## 3.2. Indicateurs d'impacts environnementaux

Nous avons calculé 13 indicateurs d'impact en mobilisant de nouveau plusieurs outils.

Les émissions de gaz à effet de serre (GES), la pression en phosphore et la pression en énergie ont été calculés avec l'outil SYSTERRE®. Pour les émissions de GES (exprimées en kg éqCO₂/ha/an), les émissions de CO₂, CH₄ et N₂O ont été cumulées et pondérées par des coefficients propres à chaque gaz, fonctions de leur pouvoir de réchauffement global. Deux types d'émissions sont prises en compte : les émissions indirectes (en amont de l'exploitation, pour la production des intrants, leur production, transport, etc.) et les émissions directes (in situ). Les étapes de la production après sortie de la parcelle (transport, manutention, séchage, etc.) sont exclues des calculs.

Pour la pression en énergie, nous avons choisi d'utiliser la consommation d'énergie primaire non renouvelable exprimée en MJ/ha. « L'énergie primaire » correspond à l'énergie contenue dans les combustibles tirés de la nature (gaz naturel, charbon, pétrole, etc.). Cet indicateur est calculé selon l'approche de cycle de vie. La consommation « d'énergie primaire » non renouvelable liée à la consommation d'une ressource énergétique (électricité, carburant) correspond à la quantité d'énergie non renouvelable (i.e. énergies fossiles et énergie nucléaire) totale utilisée pour la production et la distribution. Par ailleurs, est également pris en compte l'ensemble des consommations d'énergie primaire utilisée pour produire les intrants (semences, engrais, produits phytosanitaires) » (ARVALIS Institut du végétal, 2013).

Pour la pression en phosphore, le phosphore minéral total apporté à la parcelle (kg P/ha) est la variable retenue comme indicateur de l'utilisation des ressources en phosphore.

Les indicateurs qui suivent ont été calculés à l'aide de divers calculateurs Excel. Les pertes azotées (NO₃, NH₃ et N₂O) ont été obtenues grâce au calculateur de l'indicateur I-N2 (v. 2.70) de la méthode INDIGO (Bockstaller and Girardin, 2010). L'indicateur est composé de trois modules : les pertes par lixiviation de NO₃, les pertes par émissions de N₂O et les pertes par volatilisation de NH₃. Les pertes sont estimées chronologiquement à l'aide d'un modèle empirique basé sur le système de culture (itinéraire technique et interculture suivante). Ainsi la volatilisation est prise en compte en premier, ce phénomène se produisant rapidement dans les heures qui suivent l'apport (Terman, 1979). La lixiviation liée aux apports d'azote est un phénomène plus lent. Ce risque est lié au décalage entre l'apport et la phase d'absorption par la culture. L'étape suivante est l'estimation du risque de lixiviation hivernale post-récolte dû à la gestion de l'interculture et à une éventuelle surfertilisation (Machet et al., 1997). Pour l'indicateur I-NO₃, on part sur l'hypothèse que la fertilisation est conforme aux recommandations en raison de toutes les actions de conseil de la SCARA. Ceci a facilité le calcul en évitant de devoir calculer ce paramètre. Enfin faute d'informations suffisantes sur leur chronologie (Germon et al., 1999 ; Conen et al., 2000), les pertes de N₂O sont estimées par rapport à la dose globale dont on a enlevé les pertes par volatilisation (Bouwman, 1996) et de lixiviation après apport. Une alternative pour les pertes en NH₃ utilisée jusqu'alors par la SCARA est présentée dans l'annexe C.3. Chaque module est exprimé sur une
échelle de la performance entre 0 et 10. Le détail du calcul par module est consultable dans l'annexe C.4.

Les risques de contamination des eaux souterraines (RCeso), des eaux de surface (Rcesu) et de l'air (RCair) sont calculés avec un indicateur issu de la méthode INDIGO, l'indicateur I-Phy3 (v. 1.70). L'indicateur I-Phy3 utilise la méthode CONTRA (Construction Transparente d'Arbre de décision) basée sur la logique floue pour construire des arbres de décision (Bockstaller et al., 2017). Cette méthode permet de s'affranchir des effets de seuil. Des variables sur les données pédoclimatiques, les pratiques agricoles et les propriétés physico-chimiques des substances actives (demi-vie, solubilité, etc.) sont renseignées dans le calculateur I-Phy. Ces variables sont ensuite agrégées pour un programme de traitement et permettent d'obtenir un risque de contamination globale à la parcelle (noté sur une échelle de la performance entre 0 et 10) des eaux souterraines, des eaux de surface et de l'air (Pierlot et al. soumis).

Le tableau 10 donne des détails sur les composantes des variables d'entrée et le mode de calcul des 9 indicateurs qui viennent d'être présentés.

Les derniers indicateurs d'impact (maîtrise de l'état structural du sol, maîtrise de l'érosion, pertes en phosphore et conservation de la biodiversité) ont été développés durant cette thèse et ont fait l'objet d'un travail important. Aussi, sont-ils décrits en détail dans la section « Indicateurs nouvellement développés ».
<table><caption>Tableau 10 : Mode de calcul des indicateurs d'impacts environnementaux non développés durant la thèse.</caption><thead><tr><th>Impact environnemental</th><th>Composantes variable entrée</th><th>Échelle temporelle du calcul</th><th>Mode de calcul</th><th>Outil de calcul</th></tr></thead><tbody><tr><td>Émissions de gaz à effet de serre (kg éqCO<sub>2</sub>/ha/an)</td><td>Émissions directes et indirectes de CO<sub>2</sub>, CH<sub>4</sub> et N<sub>2</sub>O</td><td>Année culturale</td><td>Calcul selon l'approche cycle de vie : Cumul des émissions de GES et pondération par des coefficients propres à chaque gaz.</td><td>SYSTERRE®</td></tr><tr><td>Pression en énergie (MJ/ha)</td><td>Consommation d'énergie primaire non renouvelable</td><td>Année culturale</td><td>Calculée selon l'approche de cycle de vie : consommation utilisée pour la production et la distribution et la production des intrants.</td><td>SYSTERRE®</td></tr><tr><td>Pression phosphore (kg P/ha)</td><td>Phosphore minéral apporté à la parcelle</td><td>Année culturale</td><td>Quantité totale de phosphore minéral</td><td>SYSTERRE®</td></tr><tr><td>Pertes en nitrate (I-NO3)</td><td>Données sol (texture, profondeur, ...)<br/>Climat (temp. moy., drainage hivernal printemps)<br/>Succession culture<br/>Gestion résidus récolte (mode, période)<br/>Date, quantité azote<br/>Dose recommandée</td><td>Année culturale</td><td>Voir annexe X</td><td>Calculateur I-N2 v2.70</td></tr><tr><td>Perte en ammoniac (I-NH3)</td><td>Sol (calcaire)<br/>Date, quantité azote<br/>Type d'engrais<br/>Mode d'apport</td><td>Année culturale</td><td>Voir annexe X</td><td>Calculateur I-N2 v2.70</td></tr><tr><td>Pertes en protoxyde d'azote (I-N2O)</td><td>Sol (hydromorphie, MO)<br/>Quantité azote<br/>Travail du sol<br/>Irrigation</td><td>Culture</td><td>Voir annexe X</td><td>Calculateur I-N2 v2.70</td></tr><tr><td>Contamination des eaux souterraines par les pesticides (Rceso)</td><td>GUS<br/>Période apport<br/>Potentiel de lessivage<br/>Solubilité<br/>Dose</td><td>Année culturale</td><td>Arbre de décision et logique floue</td><td>Calculateur I-Phy3 v.1.70</td></tr><tr><td>Contamination des eaux de surface par les pesticides (Rcesu_rd)</td><td>Potentiel de ruissellement DT50<br/>Écart date-ruissellement (= 3 jours)<br/>Mode apport<br/>Dose</td><td>Année culturale</td><td>Arbre de décision et logique floue</td><td>Calculateur I-Phy3 v.1.70</td></tr><tr><td>Contamination de l'air par les pesticides (Rcair_vol)</td><td>Pression vapeur<br/>KOC<br/>Solubilité<br/>Mode apport<br/>Dose<br/>Données pulvérisateur<br/>Données parcelles (bordures)</td><td>Année culturale</td><td>Arbre de décision et logique floue</td><td>Calculateur I-Phy3 v.1.70</td></tr></tbody></table>

### 3.3. Indicateurs de cause

Six indicateurs de cause ou indicateurs causals ont été calculés en complément pour expliquer les résultats mais aussi étudier des corrélations avec les indicateurs prédictifs : la
diversité des cultures, l'indice de fréquence de traitement (IFT), l'apport total d'azote minéral, la balance globale azotée (BGA), la balance en phosphore et le pourcentage de forêt autour de la parcelle. Le premier indicateur a été calculé selon la méthode décrite dans Keichinger et al. (2021). Les trois suivants ont été obtenus à l'aide de l'outil SYSTERRE®. L'IFT est calculé en faisant le ratio entre la dose appliquée par l'agriculteur et la dose de référence pour le bioagresseur visé (Hossard et al., 2017). L'apport total d'azote minéral est la somme des apports en azote minéral au cours de l'année culturale. La balance globale azotée (en kg N/ha) est calculée à l'échelle de l'exploitation et correspond à la différence entre la somme de toutes les unités d'azote apportées sur l'ensemble de la SAU (minéral et organique) et l'azote exportée par l'ensemble des cultures (grain, paille, tubercule, etc.).

La balance en phosphore (en kg P/ha) est calculée en faisant la différence entre les apports et les exportations en phosphore.

Enfin, le pourcentage en forêt autour de la parcelle a été obtenu via le logiciel QGIS® en sommant (à l'aide du fichier Shapefile « Zone de végétation » de la BD Topo® fournie par l'IGN) les surfaces de végétation forestière (natures de végétation : bois, forêts fermées de conifères, forêts fermées de feuillus, forêts fermées mixtes, forêts ouvertes) dans une zone tampon de 1km de rayon autour de la parcelle. On divise ensuite la surface totale de forêt par la surface de la zone tampon.

## 3.4 Indicateurs socio-économiques

Sans être aussi exhaustifs que la méthode MASC pour des raisons opérationnelles, nous avons gardé quelques indicateurs socio-économiques, calculés avec l'outil SYSTERRE® qui paraissaient indispensables pour l'études des antagonismes et synergies (Ozerova et al., 2019; Tey and Brindal, 2015).

- **Marge brute :** comme indicateur de rentabilité

Marge brute (€/ha) = Produit brut – Charges opérationnelles (± Aides PAC couplées et découplées)

- **Coûts de production :** cet indicateur permet d'estimer la compétitivité. Il est absent de la méthode MASC.

Coût de production complet (€/t) = Somme des charges / Rendement

- **Charges de mécanisation :** indicateur d'investissement.

Elles intègrent l'amortissement technique du matériel, les frais financiers à moyen et long terme, l'entretien et la réparation du matériel, la consommation de fioul et la location du matériel (CUMA, ETA). Elles sont exprimées en €/ha.

- **Production d'énergie brute :** comme indicateur de fourniture de matières premières.

Énergie Brute (MJ/ha) = Σ(rendement * contenu énergétique)

- **Temps de travail :** indicateur des conditions de travail et de la qualité de vie pour l'agriculteur, des thèmes importants pour la durabilité sociale (Janker et Mann, 2020).

Temps de travail (h/ha) = Σ(Surface traitée x Débit de chantier (ha/h))/ Σ(Surface parcelles saisies)
## 3.5 Indicateurs nouvellement développés

### Maîtrise de l'érosion (MERO_v2)

L'érosion correspond à l'ablation des couches superficielles du sol et au déplacement des matériaux constituants, sous l'action de l'eau, du vent, des rivières, des glaciers, ou de l'homme. Ce phénomène naturel toucherait 26 millions d'hectares en Europe et près d'un million pour l'érosion éolienne. En France métropolitaine, on estime qu'environ 18% des sols présentent un aléa d'érosion des sols moyen à très fort (figure 15).

Les conséquences de l'érosion peuvent être rapides et visibles comme lors d'épisodes de coulées de boue mais elles peuvent également être moins visibles et sur le long terme via la dégradation des sols. L'érosion dépend fortement du contexte pédoclimatique (type de sol, topographie, régime de précipitation) mais elle peut être favorisée par l'action de l'homme. Ainsi, les changements d'occupation de sol et certaines pratiques agricoles (culture dans le sens de la pente et faible couverture du sol) accentuent le phénomène d'érosion (Auzet, 1987; Bissonnais et al., 2004).

<figure><img src="image_18.png" /><figcaption>Figure 15 : Carte du risque d'érosion en France (Gis Sol).</figcaption></figure>

### Construction de l'indicateur

L'indicateur sur la maîtrise de l'érosion (MERO_v2) est basé sur l'indicateur MERO de la méthode MASC 2.0 et s'inspire du modèle de prédiction de Le Bissonnais (2004). L'outil CONTRA (Bockstaller et al., 2017) a été utilisé pour construire et améliorer l'indicateur MERO_v2.
## Choix des variables

Pour le choix des variables, nous avons repris celles de l'indicateur MERO de la méthode MASC 2.0 excepté l'effet du travail du sol. Ainsi, 3 variables principales composent l'indicateur MERO_v2 :

- La sensibilité du milieu (SENS)

- Le défaut de couverture en période à risque (DCPR)

- La maîtrise de l'état structurale du sol (MESS)

## Calculateur Excel

Le calcul de MERO_v2 se fait sur un calculateur Excel et est divisé en 4 étapes : 1) Calcul de SENS ; 2) Calcul du DCPR ; 3) Calcul de MESS et 4) Calcul de MERO_v2.

### 1) Calcul de SENS

La sensibilité du milieu a été estimée en se basant sur le modèle de prédiction de Le Bissonnais (2004). Ce modèle donne une classe de la sensibilité du milieu selon 4 paramètres : la texture, la battance, l'érodibilité et la pente du sol. Pour les classes de texture, nous avons pris les classes de texture de la méthode INDIGO® (Bockstaller and Girardin, 2010) en vérifiant les correspondances entre le triangle des textures de l'Aisne utilisé par le modèle de le Bissonnais et le triangle GEPPA (Groupes d'études des problèmes de pédologie appliqué) utilisé dans le méthode INDIGO®. La pente a été calculée à l'aide du logiciel QGis® en utilisant la BD Alti 25 m et un modèle numérique de terrain.

### 2) Calcul du DCPR

Le défaut de couverture en période à risque est calculé pour une succession culturale de 4 années. Une note de défaut de couverture est attribuée aux cultures pour chaque période à risque « Automne-Hiver » et « Printemps-Eté ». Ainsi, la betterave sucrière et le colza de printemps ont un défaut de couverture durant la période « Printemps-Eté » alors que l'orge d'hiver a un défaut de couverture durant la période « Automne-Hiver ». Les notes sont ensuite divisées par le nombre d'année de la succession culturale qui est pour notre cas d'étude fixé à 4. La valeur de DCPR correspond à la valeur maximale entre le DCPR « Automne-Hiver » et le DCPR « Printemps-Eté ».

### 3) Calcul de MESS

L'indicateur MESS n'a pas fait l'objet d'amélioration particulière si ce n'est une adaptation du calcul de l'indicateur initialement sur le logiciel DEXi (Bohanec, 2009) à un calculateur Excel. Les variables et les règles de décision choisies dans la méthode MASC 2.0 ont été conservées.

La maîtrise de l'état structural du sol intègre deux processus : la dégradation de la structure du sol et la régénération de la structure du sol.

Le premier processus, la dégradation de la structure du sol, prend en compte la proportion de cultures récoltées en mauvaises conditions et l'effet des équipement limitant le tassement. Pour les cultures récoltées en mauvaises conditions (CRMC), on fait le ratio des CRMC sur le nombre total de cultures dans la rotation. L'équipement est caractérisé par deux classes : une classe défavorable correspondant à l'absence d'équipement limitant le tassement et une classe favorable avec utilisation de pneumatiques basse pression ou de roues jumelées. Les poids sont respectivement 40% pour la proportion de cultures récoltées en mauvaises conditions et 60% pour l'effet des équipements limitant le tassement du sol.
Le deuxième processus, la régénération du sol, intègre l'aptitude du sol à la fissuration et la régénération mécanique de la structure du sol. L'aptitude à la fissuration du sol est catégorisée en 4 classes qualitatives selon la texture du sol. La régénération mécanique est aussi renseignée en 4 classes qualitatives selon l'effet du travail du sol. La combinaison de ces deux critères est réalisée en appliquant un poids identique de 50%.

L'indicateur MESS est finalement obtenu en appliquant la pondération suivante : 40% pour la régénération mécanique du sol et 60% pour la dégradation de la structure du sol.

### 4) Calcul de MERO_v2

L'indicateur MERO_v2 est finalement calculé avec en utilisant le modèle CONTRA. Les trois variables principales sont agrégées (Figure 16) en respectant les pondérations suivantes : 40% pour la sensibilité du milieu, 40% pour le défaut de couverture et 20% pour la maîtrise de l'état structurale du sol. Certaines valeurs de MERO_v2 ont été corrigées. Cela correspond aux situations où la sensibilité du milieu est favorable. Pour ces cas-là, la note de MERO_v2 est fixée à 10.

<figure><img src="image_19.png" /><figcaption>Figure 16 : Schéma résumant la construction de l'indicateur MERO_v2. Les pourcentages correspondent à la pondération attribuée à chaque variable.</figcaption></figure>
# Albédo

L'albédo est la fraction de rayonnement solaire de courte longueur d'onde qui est réfléchie par la surface (sol et/ou couvert végétal). Ce phénomène physique intervient dans la régulation du climat de notre planète en réfléchissant une partie des rayons du soleil. Dans le milieu agricole, de nombreuses études se sont déjà penchées sur le sujet en essayant d'estimer le potentiel de l'effet albédo selon l'occupation des sols (Carrer et al., 2018; Ceschia et al., 2010; Sieber et al., 2019). L'albédo d'une parcelle agricole peut en effet être plus élevé lorsque l'on couvre le sol nu avec une culture intermédiaire. Les résultats de ces études montrent que le potentiel de régulation du climat lié à la variation d'albédo serait assez significatif allant jusqu'à une diminution de 30% à 40% du potentiel de réchauffement (Sieber et al., 2022). Or malgré ces estimations intéressantes, l'effet de la variation d'albédo n'est pas intégré dans les scénarios climatiques du GIEC (Groupe d'experts intergouvernemental sur l'évolution du climat).

Ayant choisi dans mon projet de thèse de travailler sur le service écosystémique de régulation du climat, nous avons estimé pertinent d'intégrer la variation d'albédo. En effet, cette dernière a un potentiel refroidissant intéressant comme expliqué précédemment et vue la SAU de la France, l'agriculture a un rôle à jouer. Une recherche bibliographique a confirmé qu'il n'existait pas d'indicateur pour estimer l'albédo au niveau d'une parcelle. De fait, ce phénomène physique est complexe à quantifier car il fait intervenir de nombreux paramètres (couleur du sol, rugosité du sol, humidité du sol, etc.). De plus, la mesure de l'albédo sur les sols agricoles est rare car elle nécessite un instrument coûteux, le pyranomètre. Ce type d'instrument est présent sur des plateformes d'expérimentations sur quelques sites en France. On a de plus en plus de données sur l'albédo mais elles sont encore peu nombreuses ou peu accessibles ce qui peut limiter le développement d'indicateurs ou de modèles. L'objectif ici est d'estimer la variation d'albédo d'une parcelle agricole pour ensuite calculer le forçage radiatif des ondes courtes associé et réaliser un bilan climatique net simplifié (figure 17).

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

Inspire de Ceschia et al. (2017)

<caption>Figure 17: Composantes prise en compte pour le calcul de l'effet climatique net simplifié.</caption>
# Construction de l'indicateur

## Choix des variables

Afin de sélectionner les variables pertinentes pour construire l'indicateur, un travail bibliographique préalable a été nécessaire. Un certain nombre de variables a été identifié et notamment des variables sur le sol (couleur, rugosité, humidité, taux de MO et taux d'éléments grossiers) et sur les pratiques agricoles (date et densité de semis, travail du sol, gestion des résidus). Certaines variables ont dû être éliminées faute de données disponible comme par exemple la couleur du sol ou le taux d'humidité. D'autres variables n'ont pas été retenues comme le travail du sol car c'est une variable très difficile à évaluer or labour profond. Il y a un effet significatif du travail du sol sur l'albédo avec le labour profond alors que d'autres effets transitoires interviennent avec le travail superficiel et le semis direct. Les variables finalement sélectionnées pour construire l'indicateur ont été validées par deux chercheurs du Cesbio (Centre d'Études Spatiales de la Biosphère), Éric Ceschia et Morgan Ferlicoq, travaillant sur l'albédo. Elles concernent pour le sol : la texture du sol, le taux de matière organique, le type d'éléments grossiers (EG) et le taux d'EG. Pour les pratiques agricoles, les données suivantes ont été nécessaires : les dates de semis et de récolte de la culture principale, la date de travail du sol après récolte, la date de semis de la culture intermédiaire, la date d'incorporation des résidus et la date de semis de la culture suivante.

## Calculateur excel

Nous avons encore une fois choisi le calculateur Excel pour créer l'indicateur. Le calcul de l'albédo de la parcelle est divisé en trois étapes : 1) calcul de l'albédo du sol ; 2) calcul de l'albédo du couvert végétal ; 3) calcul de l'albédo global.

### 1) Calcul de l'albédo du sol

L'albédo du sol est calculé à partir de la valeur d'albédo de la terre qui dépend de la texture du sol et du taux de MO, et de la valeur d'albédo des EG. Cette dernière varie en fonction du type d'EG et du taux d'EG. Concernant le taux d'éléments grossiers, un coefficient est appliqué pour augmenter l'albédo global du sol. Afin d'être cohérent avec les autres calculateurs Excel (I-Phy, I-N, MERO, etc.), nous avons repris les classes de texture définies dans la méthode INDIGO. Plusieurs classes d'albédo ont été définies en fonction de la texture du sol et du taux de MO de la parcelle. Pour ce faire, nous nous sommes appuyés sur le mémoire de fin d'études de Thibaut Chrétien (Chrétien, 2018). Il a mis au point une méthode basée sur des photos de sol afin d'estimer l'albédo du sol. Il s'est inspiré de l'équation de Post et al. (2000) qui permet d'estimer l'albédo d'un sol en fonction de sa couleur (« value » dans la charte de Munsell, Nickerson 1976). Cette équation sous-estime cependant l'albédo pour les valeurs en dessous de 2,5 et les suretime pour des valeurs supérieures à 4,5. Sa méthode s'avère robuste même si le modèle n'a pas pu être validé faute de données disponibles.

### 2) Calcul de l'albédo du couvert végétal

L'albédo du couvert végétal présent sur une parcelle fluctue significativement au cours d'une année culturale. Aussi, nous avons émis les hypothèses suivantes par expertise afin d'évaluer au mieux cette variation.

* L'albédo maximal du couvert végétal est atteint au bout de :
- 1 mois pour les cultures de printemps/été

- 2/3 mois pour les cultures d'hiver

* L'albédo est maximal jusqu'au travail du sol. On estime que les résidus en surface après la récolte conservent un albédo élevé.

* L'albédo du couvert végétal diminue linéairement en fonction de l'albédo du sol.

Les couvertures de sol maximales de l'indicateur I-Phy ont été appliquées afin de prendre en compte la précocité de la culture en fonction de la zone cultivée (Nord ou Sud). Dans notre cas d'étude, c'est la zone Nord qui a été retenue. Nous avons ainsi pu simuler l'augmentation de l'albédo du couvert végétal au cours des mois en l'interpolant avec l'albédo du sol.

### 3) Calcul de l'albédo global

L'albédo global est obtenu (figure 18) en calculant la moyenne pondérée par le temps de présence à l'année des valeurs d'albédo pour le sol nu et pour les couverts végétaux. L'annexe C.5 présente le détail du calcul de l'albédo.

<figure><img src="image_21.png" /><figcaption>Figure 18 : Schéma résumant la construction de l'indicateur Albédo.</figcaption></figure>

## Pertes en phosphore (PP)

Le phosphore est un élément essentiel à la croissance des plantes avec l'azote et le potassium. Ainsi un sol pauvre en phosphore peut engendrer des carences chez la plante et entraîner des pertes en rendement. Pour pallier ce problème, l'agriculteur va apporter du phosphore sous forme minérale et/ou organique (fumier, lisier, digestat). Si le phosphore est apporté en trop grandes quantités ou dans les mauvaises conditions, une partie du phosphore va être perdue par ruissellement ou lixiviation. Ce type de phénomène est à limiter au maximum pour premièrement éviter de polluer les eaux de surfaces ou de profondeur et ainsi accélérer l'eutrophisation de ces eaux, deuxièmement réduire le coût des intrants de l'agriculteur et son temps de travail et troisièmement, limiter la pression sur la ressource en phosphore qui est en grande majorité extrait de carrières. Les pertes en phosphore sont donc un impact environnemental important à prendre en compte dans l'évaluation de la durabilité d'une exploitation agricole (Parris, 2011; Ulén et al., 2007; Withers and Haygarth, 2007).
Construction de l'indicateur

Pour construire le calculateur sur les pertes en phosphore, nous nous sommes inspirés de l'indicateur Maîtrise des pertes de P de la méthode MASC 2.0 (Craheix et al., 2012) et de l'indice norvégien présenté dans Buczko et Kuchenbuch (2007) qui s'inspire du P index développé à l'origine aux États-Unis (Lemunyon and Gilbert, 1993).

Choix des variables

Concernant les variables retenues pour l'indicateur Pertes de Phosphore (PP), nous avons repris les variables de l'indicateur « Maîtrise des pertes de phosphore » de la méthode MASC 2.0 à savoir la maîtrise de l'érosion, la teneur en phosphore du sol, la quantité de phosphore apporté en moyenne et la méthode d'incorporation des amendements. Relatif à l'indice norvégien présenté dans Buczko et Kuchenbuch (2007), nous avons transformé et simplifié la variable « modified connectivity » en prenant en compte la présence de bandes enherbées adjacente à la parcelle étudiée et leur largeur. En effet, plusieurs études ont montré qu'une bande enherbée permet effectivement le contrôle des pertes en ruisselement de phosphore d'une parcelle (Dorioz et al., 2006; Haddaway et al., 2018; Mendes, 2020; Roberts et al., 2012). Duchemin et Hogue (2009) ont montré qu'une bande enherbée peut réduire de près des 50% les charges totales exportées en phosphore (drainage et ruissellement).

Calculateur Excel

Nous avons utilisé la méthode CONTRA (Bockstaller et al., 2017) pour construire l'indicateur PP. Le calcul des pertes de phosphore se divise en 5 étapes : 1) Calcul du risque d'érosion de la parcelle ; 2) Calcul de la teneur en phosphore du sol ; 3) Calcul de l'apport en phosphore ; 4) Renseignement de la présence de bande enherbée et 5) Calcul de l'indicateur PP.

1) Calcul de du risque d'érosion

Le risque d'érosion a été estimé avec l'indicateur MERO_v2 décrit précédemment.

2) Calcul de la teneur en phosphore du sol

La teneur en phosphore du sol correspond au taux de phosphore mesuré dans le sol modulé en fonction du travail du sol. On fait l'hypothèse que moins le sol est travaillé, plus le phosphore s'accumulera au niveau de la surface du sol, augmentant ainsi le risque de ruissellement du phosphore. Nous avons donc appliqué un coefficient multiplicateur à la teneur en phosphore dans le cas d'un travail superficiel ou d'un semis direct.

Les teneurs en phosphore (mg/kg) étant mesurées selon deux méthodes pour notre cas d'étude, la méthode d'Olsen et la méthode Joret-Hébert (Fardeau et al., 1988), la fonction de pédotransfert ci-dessous a été utilisée afin d'estimer toutes les teneurs en P selon la méthode d'Olsen.

$$
\text{Olsen } P_2\text{O}_5 = 218,385 + 0,263\text{Joret-Hébert } P_2\text{O}_5 - 29.419\text{pH}_{\text{eau}} + 0.079\text{Taux de limon}
$$

D'après Hu et al. (2021), R² de 0,61 et RMSE de 27,20.

3) Calcul de l'apport en phosphore

Pour l'apport en phosphore (kg P2O5/ha), cette variable a été calculée en fonction de l'apport
total en phosphore d'une année culturale, modulé selon la méthode d'incorporation des
amendements (Chien et al., 2010). Notre hypothèse est que le phosphore incorporé au sol réduira
les risques de ruissellement. Ainsi, si l'amendement apporté est incorporé dans les 7 jours suivant l'apport, un coefficient d'abattement de 50% est appliqué à la quantité de phosphore.

4) Renseignement de la bande enherbée

Si une bande enherbée est présente le long de la parcelle, sa largeur (en m) est renseignée dans le calculateur. Pour notre échantillon, la présence de bande enherbée autour des parcelles a été demandée aux agriculteurs lors des courtes enquêtes téléphoniques.

5) Calcul de l'indicateur PP

L'indicateur PP est calculé grâce à la méthode CONTRA. Ce dernier agrège les 4 variables principales (Figure 19) présentées dans les étapes précédentes en se basant sur leur fonction d'appartenance et leur pondération. La pondération choisie est la suivante : 30% pour le risque d'érosion, 20% pour la teneur en phosphore, 30% pour l'apport en phosphore et 10% pour la présence de bande enherbée. Les notes obtenues peuvent ensuite être corrigées, ajustées selon l'avis d'expert. Pour l'indicateur PP, nous avons décidé d'augmenter les notes lorsque le risque d'érosion est faible.

<figure><img src="image_22.png" /><figcaption>Figure 19 : Schéma résumant la construction de l'indicateur Pertes en phosphore. Les pourcentages correspondent à la pondération attribuée à chaque variable.</figcaption></figure>

## Atténuation des pesticides par les sols

Le service écosystémique d'atténuation des pesticides par les sols est un service peu étudié dans la littérature scientifique. De fait, nous n'avons pas proposé d'indicateur pour le quantifier parce qu'il manque encore des travaux sur ce sujet. Nous présentons néanmoins dans l'encart ci-dessous nos pistes de réflexion sur cet indicateur.
### Encart 2. Indicateur sur l'atténuation des pesticides par les sols : pistes de réflexion.

L'atténuation des pesticides par les sols est un des services écosystémiques identifié par la classification CICES (Haines & Potschin 2013) et l'étude EFESE (Tibi & Therond 2017). Ce service n'a pas fait l'objet d'un développement d'un indicateur prédictif, parce qu'il repose sur de nombreux processus physiques et biologiques se déroulant dans le sol, avec des interactions possibles entre ces processus. Faute de littérature conséquente sur ce sujet, nous n'avons pas créé d'indicateur évaluant ce service au niveau d'une parcelle agricole. Ma recherche bibliographique me permet tout de même de proposer quelques pistes pour concevoir l'indicateur. Ainsi 4 variables semblent faire consensus au niveau de la communauté scientifique sur ce service :

* le pH : il a un rôle important dans le service d'atténuation des pesticides parce qu'il favorise ou non la métabolisation de la substance active par la communauté microbienne (Alletto et al., 2010, Constantin et al., 2015, Riedo et al., 2021);

* le taux de matière organique : influence la dynamique d'adsorption et de désorption des pesticides et module également la communauté microbienne (Hernandez-Guijarro et al., 2018; Pérez-Lucas et al., 2020);

* le KOC (le coefficient de partage carbone organique/eau) : il donne une indication sur l'aptitude d'une molécule d'être adsorbée ou désorbée sur la matière organique et détermine le niveau d'adsorption d'une substance active (SA) sur la matière organique qui peut échapper à la dégradation microbienne ou autrement dit, la quantité disponible pour la dégradation. Ce facteur interagit directement avec le taux de matière organique puisque si le taux de MO d'un sol est élevé et le KOC de la substance active l'est aussi les molécules de la SA s'adsorberont d'autant plus (Garrido et al. 2015);

* la quantité de pesticides appliquée à la parcelle conditionne directement la quantité de substances actives à métaboliser.

Parmi les variables étudiées mais non retenues figurent le travail du sol et le temps de demi-vie de la substance active. En ce qui concerne le travail du sol, cette pratique a été étudiée lors de plusieurs expérimentations sans pouvoir conclure sur son effet sur le service d'atténuation des pesticides par les sols. L'absence de labour diminue le brassage des diverses couches du sol pouvant entraîner une métabolisation des substances actives plus longues. Cet effet peut être en partie compensé par une activité microbienne et une communauté de microorganismes plus importante dans ce type de systèmes agricoles (Alletto et al. 2010).

## Indicateur I-BIO

L'indicateur I-BIO est un indicateur prédictif qui évalue les impacts des pratiques agricoles sur la biodiversité globale (multi-taxons) au niveau de la parcelle. L'indicateur I-BIO est le produit d'un travail de collaboration avec le James Hutton Institute (l'équivalent d'INRAE pour l'Écosse) et de plusieurs collaborations françaises. Nous sommes partis de l'arbre DEXi-CSC développé par les chercheurs écossais (Hawes et al., 2019) pour évaluer la durabilité économique (viabilité et rentabilité) et environnementale (biodiversité, utilisation et pertes de ressources) de la gestion intégrée des systèmes de culture. Un travail de reconstruction de sa branche dédiée à la
biodiversité a été effectué. Par rapport à la méthode MASC, le choix a été de retenir les principaux
groupes taxonomiques (microorganismes, plantes, invertébrés et vertébrés) et pas uniquement
ceux intéressant les agriculteurs. Le logiciel DEXi a été utilisé pour construire l'indicateur I-BIO
plutôt que la méthode CONTRA (Bockstaller et al., 2017) malgré ses limites (effet de seuil, manque
de transparence dans la construction automatique des règles de décision en fonction des
pondérations, explosion combinatoire du nombre de règles de décision). En effet, la méthode
CONTRA n'est pas encore outillé pour des arbres hiérarchisés complexes comme développé dans
I-BIO (voir figure 20 dans l'article). Pour atténuer le manque de transparence et la complexité des
arbres de décision de plus de 16 règles de décision, nous avons développé une méthode
spécifique pour le logiciel DEXi (annexe D).

Le développement de cet indicateur a fait l'objet d'un article scientifique soumis à Ecological
Indicators et est présenté dans la section suivante.
I-BIO : an indicator predictive evaluating the effect of the system of culture and the landscape environning
sur la biodiversité

A predictive indicator assessing effect of cropping system and surrounding landscape on
biodiversity

Emma Soulé<sup>a,b</sup>, Cathy Hawes<sup>c</sup>, Mark Young<sup>c</sup>, Laura Henckel<sup>e</sup>, Nadia Michel<sup>d</sup>, Philippe Michonneau<sup>b</sup>, Christian Bockstaller<sup>a</sup>

a Université de Lorraine, INRAE, LAE 68000 Colmar, France

b SCARA, 10700 Arcis sur Aube, France

c James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK

d Université de Lorraine, INRAE, LAE 54500 Nancy, France

e Agroécologie, INRAE, Université Bourgogne Franche-Comté, 21000 Dijon, France;

* Corresponding author: christian.bockstaller@inrae.fr, INRAE LAE, 28 rue de Herrlisheim, 68000 Colmar, France;

Abstract

Biodiversity is a key component of agricultural ecosystems and belongs to one of the exceeded planetary boundaries. In the quest for innovation to mitigate impact, it is essential to have tools to assess the anthropogenic impact on biodiversity. In this study, we developed a new indicator, I-BIO that aimed to predict the impacts of management practices and the influence of the landscape on overall biodiversity at species group level. I-BIO encompassed four groups: microorganisms, vegetation, invertebrates and vertebrates. This predictive indicator was built using DEXi software that facilitates the design of hierarchical decision trees, based on input variables in the form of qualitative classes, aggregated using utility functions. I-BIO was constructed using data from a long-term platform in Scotland and then applied to two data sets in France and one in Scotland. The results showed that the lowest class is predominant for all branches especially for invertebrates and soil invertebrates. Monte Carlo sampling was used to test for sensitivity. Then, a validation step was carried out to compare I-BIO outputs with real data using data on the four groups from Scottish and French datasets. I-BIO predictions of weed and soil invertebrate abundance conformed to field data. Due to low numbers of samples, validation of microorganisms, flying invertebrates and vertebrates predictions require more data. Future developments of I-BIO will focus on predictions specifically to assess the impact of management on species richness. Avenues for improvement include a better integration of landscape features and the history of agricultural practices as they both have a great influence on biodiversity.

Keywords

Biodiversity, agricultural system, impact, indicator, assessment.
# 1. Introduction

Conservation of biodiversity as an essential element of ecosystems has been on the agenda for decision makers since the Rio Conference in 1992 (Le Guyader, 2008). It is well established that loss of biodiversity is considered as a major issue regarding sustainability of the planet, having exceeded planetary boundaries (Campbell et al., 2017). A growing number of studies highlight its major role in the supply of many ecosystem services to mankind (Sandifer et al., 2015). Nevertheless, due to the complex relation between biodiversity and ecosystem services, this contribution remains subject to investigation (Duncan et al., 2015). In spite of the growing awareness of politicians, managers and civil society and the regulations put in place, recent publications show a continued dramatic decline in biodiversity, in terms of species abundance and diversity across many ecosystems: in protected areas (Hallmann et al., 2017), in grassland and forest (Seibold et al., 2019) as well as in cropped agroecosystems (Le Roux et al., 2008). These studies addressed decline of invertebrates while others focus on birds or plants (Boatman et al., 2004; Donald et al., 2001; Robinson and Sutherland, 2002; Stanton et al., 2018) and highlight the role of agricultural intensification as a cause of this decline. Intensification of agriculture has increased globally over the past 50 years through intensive use of synthetic inputs, mineral fertilizers and pesticides, and simplification of agricultural landscapes by removing semi natural habitat to increase field size, and reducing crop diversity to focus on more profitable crops (Mortensen and Smith, 2020).

In response to this tremendous transformation of agriculture, alternative approaches have been developed. Among them, organic farming has shown a clear potential to improve levels of biodiversity in farmland with an average gain of 30 % shown by several reviews and meta-analyses (Smith et al., 2020; Tuck et al., 2014). Nevertheless, other levers than minimising chemical inputs also promote biodiversity: reducing physical disturbances due to tillage (Christel et al., 2021), diversifying cropping systems (Viguier et al., 2021) and landscape (Tscharntke et al., 2021), and continuous cover cropping represent the three columns of conservation agriculture, which can also reduce deleterious impact of agriculture on biodiversity (Bitew and Abera, 2019). Beyond this categorization of well-established agricultural practices, the integration of best practice management options to achieve better sustainability are key drivers in the design of innovative cropping systems supporting biodiversity conservation (Hawes et al., 2021).

Besides the question of solution, development of assessment method is a prerequisite to any action in favour of biodiversity conservation or more generally to improve agroecological sustainability. Baseline assessments of biodiversity are essential for preliminary diagnosis to orient any action plan to mitigate or restore biodiversity in agroecosystems. These may also support the design of innovative systems that promote biodiversity for ecosystem service provision. Since it is not possible to "measure" biodiversity directly, any assessment will require biodiversity indicators, which is also true when considering sustainability. To gain insights in the "indicator zoo" developed over the past 30 years, Bockstaller et al. (2015) sets out a typology based on three types of indicators: causal effect (e.g. semi-natural habitat), measured effect (e.g. number of earthworms) and predictive effect indicators (e.g. operational model). Causal or indirect indicators and measured or direct indicators are most frequently used (Bockstaller et al., 2011; Clergue et al., 2005). Relative feasibility of the former and the direct link to biodiversity state of the latter explain
their popularity. Predictive effect indicators are derived from model outputs which require well
documented knowledge on causal-effect relationships or statistical relationships with consequent
datasets. Such indicators present a major advantage by making possible *ex ante* assessment to
test innovative solutions without heavy field experiments. This modelling approach broadens
remarkably the range of possibilities for exploring alternative solutions (Sadok et al., 2009).

With regard to biodiversity, predictive indicators have been developed using DEXi based models, i.e. the MASC method (Sadok et al., 2009: Craheix et al., 2012) the DEXiPM models (Angevin et al., 2017; Pelzer et al., 2012) and more recently with DEXi-CSC (Hawes et al., 2019). These methods assess the global sustainability of cropping systems integrating the biodiversity in their assessment process. However, some mechanisms are poorly considered. For example, the MASC method does not integrate the role of some vertebrates like birds, and neither the DEXiPM nor the DEXi-CSC model adequately cover soil microorganisms known for their central role in the food web and for its numerous biological functions (Bender et al., 2016; Singh, 2015; Tiemann et al., 2015). Other models mix ecosystem services and biodiversity such as in DEXiPM2 and DEXi-CSC that could bring greater complexity. However, in these examples, little account is taken of the impact of landscape management despite the key role that landscape plays in biodiversity conservation (Tscharntke et al. 2021). Connectivity between patches of semi-natural habitats, field size or crop diversity are landscape features that could greatly increase biodiversity.

The aim of this article is to present a new predictive indicator, called I-BIO, assessing the impact of cropping practices and the influence of landscape features on overall arable biodiversity. Actually, biodiversity encompasses different dimensions, structural, compositional and functional at different levels (Noss, 1990). Here we will focus mainly on the compositional, considering species diversity of organisms living above- (mammals, birds, insects, ...) and below-ground (earthworms, fungi, microorganisms, etc). Starting from the biodiversity branch of the DEXi-CSC model (Hawes et al., 2019) which is the most recent version, we redesigned this branch to obtain a predictive indicator based on simple to use accounting of the surrounding landscape and the main crop management practices in arable systems. We present first the construction of the I-BIO indicator with choices of variables, classes and aggregation rules. We tested then the newly developed indicator I-BIO and validated it by comparing its outputs to measurements of biodiversity for a set of different crop production and landscape situations.

## 2. Materials and Methods

The design of the I-BIO predictive indicator started with a preliminary step defining the situation in which the indicator can be applied, followed by the selection of input variables, their parametrization (class thresholds, weights) and aggregation rules, based on data from the Centre for Sustainable Cropping (Hawes et al., 2019)). Finally, the indicator was tested and validated against datasets from East of Scotland and French sites.

2.1. Preliminary choices guiding the design

Preliminary design choices are essential to clearly identify the purpose of the indicator, the
target users, the object assessed, the spatial and temporal scales, and the availability of data
(Bockstaller et al. 2015). These criteria define the context which should guide the design of the
indicator so that the tool is relevant to the requirements of end-users. The aim of the development of I-BIO is to help agronomists, advisers and famers working on innovative cropping systems, to gain insight into the impacts of management on biodiversity at the field level. I-BIO was developed to cover a large range of taxa covering the overall agroecosystem biodiversity, comprising four groups: microorganisms, vegetation, invertebrates and vertebrates (see section 2.2.1). While Sattler et al. (2010) addressed 3 species within 2 taxonomic groups, we worked, like most authors, on higher taxonomic groups encompassing a broader range of species. I-BIO seeks to balance the advantages of modelling specific taxonomic groups (e.g. the SALCA method (Jeanneret et al., 2014) which is based on 11 "indicator-species group") with the need to account for overall biodiversity which impacts on system functioning (e.g. the IBEA method (France Nature Environnement, 2013) which assesses the wild biodiversity as a whole). The data for model parametrization covers common agronomic practices, organic matter inputs and landscape data (composition and configuration of semi-natural habitats, crop diversity).

## 2.2. Design of the indicator I-BIO

The new indicator was designed with the DEXi software (Bohanec, 2009). This software very user friendly allows the construction of hierarchical decision trees with linguistic rules which facilitate communication among non-specialists (Babuška and Verbruggen, 2003). Input indicators used to build the decision tree should be defined as, or transformed into, qualitative classes. Those input indicators are then aggregated using utility functions and weightings to achieve a global, qualitative assessment of the scenario tested.

### 2.2.1. Selection and hierarchy of effect and causal indicators

The first step was to carry out a review to identify and select the effect and causal indicators to be integrated in the model and their hierarchy in the decision tree. Effects indicators result from the assessment of effect of causal indicators (Bockstaller et al., 2015). To assess the impacts of a management scenario, biodiversity was split in four main groups describing overall biodiversity: microorganism, vegetation, invertebrates and vertebrates (Bar-On et al., 2018). The invertebrate group is divided into soil and flying invertebrates as organisms in these groups have different lifespan and mobility capacity and are therefore likely to show different responses to management options. Soil invertebrates spend all their life cycle in or on the soil whereas flying invertebrate' habitats vary with life cycle stages. So, if we take the example of tillage practice, the level of impact will differ between these two sub-groups (Coudrain et al., 2016; van Capelle et al., 2012). The microorganism group, the vertebrate group and the two invertebrate subgroups were described by two branches: one related to anthropogenic impacts and the other one to trophic resources. The former tackles impacts of management directly, while the latter covers indirect impacts of management. For example, a strong impact of a cropping system on invertebrates or vegetation will decrease trophic resource for vertebrates resulting in a decline in abundance. These two sub-branches were characterized by relevant indicators in each group. For example, in the vertebrate group, we added the causal indicator "mowing" as this practice could have a great impact for ground nesting birds or young mammals like fawn (Buckingham et al., 2015; Jarnemo, 2002; Stanton et al., 2018). The vegetation group was split in two branches. The first one deals with the regional context including semi-natural flora surrounding fields and the second one describes the weed flora within the cropped area.
The whole biodiversity branch and its related effect-indicators are described by 19 causal indicators (Cf. figure 20). Eleven causal indicators characterize the cropping systems, such as crop sequences, crop diversity, tillage intensity and specificities of phytosanitary treatments (number of applications, pre or post emergence, weed target). Eight causal indicators are related to local context: trophic resources (soil organic matter, microorganism, vegetation and invertebrates), landscape features (field size, land cover diversity, percentage of semi-natural habitats, connectivity).

<figure><img src="image_23.png" /><figcaption>Figure 20 : Tree of I-BIO: a tool assessing the farming practices impacts on the overall biodiversity at field level.</figcaption></figure>
### 2.2.2. Choice of qualitative state for effect and causal indicators

The second step was to select qualitative classes for simple and aggregated indicators. The biodiversity branch of DEXi-CSC had only 3 classes for all branches. The aim was to increase the tree sensitivity and to be more precise in the assessment, following guidelines of Craheix et al. (2015). For I-BIO, two classes were added at the biodiversity indicator level, one class to each group i.e. microorganism, vegetation, invertebrates, vertebrates, as well as the two subgroups soil invertebrates and flying invertebrates (invertebrates) and the indicators "anthropogenic pressures" and "trophic resources" (vertebrates). Thus, I-BIO (the overall biodiversity) has 5 qualitative classes: very low, low, medium, high, very high; indicators with 4 qualitative classes are categorized in: low, low to medium, medium to high, high; other indicators (the basic one) have 3 qualitative classes: low, medium, high. All basic indicators except two (herbicide timing and tillage intensity) are assessed quantitatively and converted to a qualitative class based on references or expert knowledge. For herbicide timing and tillage intensity, they are directly assessed qualitatively. Some indicators required an adaptation to the context of a specific case study. An example was the treatment frequency index which enables easy comparison between farms or regions (Hossard et al., 2017) but is not used in Scotland. Without this index for the CSC case study, we have defined the indicator levels based on a threshold of number of applications. All aggregated indicators are in qualitative classes based on system of references and expertise.

### 2.2.3. Choice of aggregation rules

The third step was to define utility functions, to aggregate indicators, and their relative weight. Utility functions consist of "if – then" linguistic rules which means that the class of an aggregated indicator is set according to the class of input indicators from the lower level of aggregation. For instance, if the aggregated indicator Y has two descendant indicators X1 and X2 which have respectively low and high qualitative classes, then we apply the "if – then" rule that will be "if X1 is high and X2 low, then Y is high". Expert knowledge was used to apply the law of the maximum i.e. keep the class with the highest impact. Utility functions could be set to automatically weight the indicators. However, this can lead to compensation effects between indicators. For example, an indicator with a low class and an indicator with a high class yields the same average class as two indicators with medium class, leading to an information loss. Furthermore, the distribution of outputs may be unbalanced causing a lack of sensitivity. To prevent this, all the utility functions in I-BIO were manually fixed in the DEXi software (table 11). This was achieved using a function of the DEXi software to export the aggregation rules and transform the qualitative classes in quantitative ones. The mean of the input variables was then calculated and ranked. The aggregation rules were then checked and corrected if the output given automatically by DEXi did not match expert knowledge or the peer reviewed literature. DEXi then recalculated the weights according to these new qualitative classes.
<table><caption>Tableau 11 : Part of the adaptable utility function for the indicator "Regional intensification" with the relative weights of each basic indicators.</caption><thead><tr><th>Semi natural land cover<sup>a</sup></th><th>Field size<sup>a</sup></th><th>Crop diversity<sup>a</sup></th><th>Regional intensification<sup>b</sup></th></tr></thead><tbody><tr><td>Low</td><td>High</td><td>Low</td><td>High</td></tr><tr><td>Low</td><td>High</td><td>Medium</td><td>High</td></tr><tr><td>Low</td><td>High</td><td>High</td><td>Medium to high</td></tr><tr><td>Low</td><td>Medium</td><td>Medium</td><td>Medium to high</td></tr><tr><td>Medium</td><td>High</td><td>Low</td><td>High</td></tr><tr><td>Medium</td><td>High</td><td>Medium</td><td>Medium to high</td></tr><tr><td>Medium</td><td>High</td><td>High</td><td>Low to medium</td></tr><tr><td>Medium</td><td>Medium</td><td>High</td><td>Low</td></tr><tr><td>High</td><td>High</td><td>Low</td><td>Medium to high</td></tr><tr><td>High</td><td>High</td><td>Medium</td><td>Low to medium</td></tr><tr><td>High</td><td>Medium</td><td>Medium</td><td>Low to medium</td></tr><tr><td>High</td><td>Low</td><td>Medium</td><td>Low</td></tr><tr><td>Resulting weights (calculated by DEXi)</td><td></td><td></td><td></td></tr><tr><td>34%</td><td>23%</td><td>43%</td><td>-</td></tr></tbody></table>

## 2.3. Sensitivity analysis

Following recommendation of Bockstaller et al. (2008), we tested the sensitivity of I-BIO to assess its ability to discriminate between situation. As for quantitative models, we implemented a Monte Carlo sampling (MC sampling) approach as described by Carpani et al. (2012) for DEXi models to assess sensitivity of the main branches. This method is based on a random selection of combinations of input variables according to their probabilities of occurrence. In our study, 5000 samples were generated and simulated using I-BIO. By default, the same probability was attributed to each input variable, following Carpani et al. (2012).

## 2.4. Case studies assessed by the indicator

I-BIO was tested on several case studies in Scotland and France, to assess the reliability of the results and the ability of the model to discriminate situations for which different results are a priori expected. For some datasets, validation was carried out by comparing measurements of abundance and/or diversity against model outputs according to Bockstaller and Girardin (2003). I-BIO outputs were compared with observed data for the four groups (microorganisms, vegetation, invertebrates and vertebrates).

### 2.4.1. East of Scotland Farm (EOSF) dataset

This dataset was taken as part of a wider farm-scale survey of arable biodiversity, resilience and crop management conducted during 2007 at 57 farms across the east of Scotland from Mid-Lothian to Invernesshire. Farmers taking part in this survey were volunteers who followed a range of organic, integrated and conventional management practices. At each farm, two fields were

<footnote>a Three qualitative states: Low, Medium, High.</footnote>

<footnote>b Four qualitative states: Low, Low to medium, Medium to high, High.</footnote>
selected as representative of different stages in the crop rotation (generally a break crop and a
cereal crop). Information on the crop management and rotational history for each field was
analysed for 27 farms contributing between them 24 conventionally managed fields, 15 integrated
and 15 organic" (Hillier et al., 2009; Hawes et al., 2010). For this study, 42 fields were selected
where all the management data required to implement I-BIO scenarios were available to test the
vegetation branch.

2.4.2. Noé network in France

In France, I-BIO was tested on a network of wheat plots in the Noé association
(https://noe.org/). The network covers 3 regions in France (Charentes, Chalky Champagne region
and the Rhône valley) with 6 different departments. Biodiversity monitoring has been carried out
by the association on 50 fields since 2020. Several biodiversity indicators were evaluated: soil
organic matter, microorganism biomass, earthworm, pollinators, birds abundances, etc. In our
study, 36 fields were selected to validate the microorganism, flying invertebrates and vertebrates
branches.

2.4.3. ENI (nonintended effects) network database

The ENI network database (Andrade et al., 2021) is a French program launched in 2012 to assess side-effects of pesticide management practices on biodiversity. Several indicator groups were measured, covering abundance or/and diversity of taxonomic groups of interest for farmers (weed, earthworm, beetle and bird). 500 fields were selected across all the country to assure a gradient of landscape and pedoclimatic conditions. Selected crops represent the main crop types in France (field crops, vineyard, market gardening crops). For several taxonomic groups, the monitoring was done in field margins: plants and beetle (Coleoptera). For our study, 60 fields were randomly selected among the 500 fields of the ENI database. They were used to test the vegetation and soil invertebrates branches.

2.5. Statistical analysis

ANOVA and Tukey tests were used to compare boxplot mean of classes and the Monte Carlo
sampling approach provided a sensitivity analysis of I-BIO i.e. a random sampling with
replacement among the values of each leaf variable of the model (5000 samples). Statistical
analyses were carried out using RStudio version 1.3.959 and R-statistical packages: Agricolae for
the ANOVA and Tukey test and XML, AlgDesign, ggtools, plotrix and genalg for the MC sampling.

3. Results

3.1. Distribution of the model outputs

Results for the overall biodiversity and main branches of I-BIO, the different taxonomic
groups, for the ENI and EOSF datasets are shown in Figure 21. Globally, the lowest class is largely
predominant for overall biodiversity and for three out four taxonomic groups (microorganism,
invertebrates and vertebrates). Only the vegetation and the flying invertebrates showed a more
even distribution across classes. The vertebrates branch stands out, especially for the ENI dataset,
as the medium to high class almost reaches 25% compared to less than 5% for the EOSF dataset. EOSF outputs were tighter around the lowest class than the ENI ones. Thus, the distribution of the invertebrates and soil invertebrates across classes was skewed to only the "Low" class. Last, soil invertebrates and flying invertebrates showed a marked difference - the lowest class of the former exceeds 75% with the ENI dataset, whereas the flying invertebrates had the first three classes around 30% and an additional fourth class "high". Flying invertebrates is the only branch with four classes although the highest remained largely underrepresented. Similarly, soil invertebrates from the EOSF dataset were all in the Low frequency category whereas the flying invertebrates have a more balanced distribution.

Figure 22 shows example of outputs of I-BIO evaluation for the ENI and EOSF datasets for some farms as the number of assessed farms were too low to show all the results together. For instance, field 311 yield a "medium" result for the overall biodiversity, being characterised by a very low level of farm inputs (fertiliser and pesticides) and no till, while fields 423 and 424 yielded a negative result "very low", due to more agrochemical inputs and tillage for the field 423. This representation makes possible direct comparison of the most aggregated indicator, overall biodiversity, alongside the main sub-indicators with an additional level of aggregation for invertebrates. Each calculation can be traced back to utility functions to understand each aggregation result. This ensures transparency of the method, a key feature, besides simplicity and flexibility to support the future implementation by external users (Craheix et al., 2015).

<figure><img src="image_24.png" /><figcaption>Figure 21: Illustration of the I-BIO classes distribution with the EOSF (n=42 fields) and the ENI (n=60 fields) datasets for the overall biodiversity and the different taxonomic groups. Sidebars correspond to the scenario outputs.</figcaption></figure>
<table><caption>Figure 22: Illustration of the I-BIO indicator outputs with the East of Scotland and ENI datasets (respectively on the left and right) and the input variables.</caption><thead><tr><th>Option</th><th>Id_30B</th><th>Id_33B</th><th>Id_48A</th><th>Option</th><th>Id_423</th><th>Id_424</th><th>Id_311</th></tr></thead><tbody><tr><td><strong>Biodiversity</strong></td><td>Very low</td><td>Very low</td><td>Low</td><td><strong>Biodiversity</strong></td><td>Very low</td><td>Low</td><td>Medium</td></tr><tr><td><strong>Microorganisms</strong></td><td>Low</td><td>Low to medium</td><td>Low to medium</td><td><strong>Microorganisms</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Anthropogenic pressures</strong></td><td>High</td><td>Medium to high</td><td>Medium to high</td><td><strong>Anthropogenic pressures</strong></td><td>High</td><td>Low to medium</td><td>Low</td></tr><tr><td><strong>Tillage intensity</strong></td><td>High</td><td>High</td><td>High</td><td><strong>Tillage intensity</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Chemical input</strong></td><td>High</td><td>Low</td><td>Low</td><td><strong>Chemical input</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Fertilizer</strong></td><td>High</td><td>Low</td><td>Low</td><td><strong>Fertilizer</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Pesticide</strong></td><td>Medium</td><td>Low</td><td>Low</td><td><strong>Pesticide</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Trophic resources</strong></td><td>Low to medium</td><td>Low to medium</td><td>Low to medium</td><td><strong>Trophic resources</strong></td><td>Low to medium</td><td>Low</td><td>Low to medium</td></tr><tr><td><strong>Soil OM</strong></td><td>Medium</td><td>Medium</td><td>Low</td><td><strong>Soil OM</strong></td><td>Medium</td><td>Medium</td><td>Medium</td></tr><tr><td><strong>Crop rotation</strong></td><td>Medium</td><td>Medium</td><td>Medium</td><td><strong>Crop rotation</strong></td><td>Medium</td><td>Low</td><td>Medium</td></tr><tr><td><strong>Vegetation</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Vegetation</strong></td><td>Medium to high</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Regional intensification</strong></td><td>High</td><td>High</td><td>Low to medium</td><td><strong>Regional intensification</strong></td><td>Low</td><td>Medium to high</td><td>Medium to high</td></tr><tr><td><strong>Semi natural land cover</strong></td><td>Medium</td><td>Low</td><td>High</td><td><strong>Semi natural land cover</strong></td><td>High</td><td>High</td><td>High</td></tr><tr><td><strong>Composition</strong></td><td>Medium</td><td>Low</td><td>Medium</td><td><strong>Composition</strong></td><td>High</td><td>High</td><td>High</td></tr><tr><td><strong>Land cover diversity</strong></td><td>Medium</td><td>Low</td><td>Medium</td><td><strong>Land cover diversity</strong></td><td>High</td><td>Medium</td><td>Medium</td></tr><tr><td><strong>Percentage Semi natural land cover</strong></td><td>Medium</td><td>Low</td><td>Medium</td><td><strong>Percentage Semi natural land cover</strong></td><td>High</td><td>High</td><td>High</td></tr><tr><td><strong>Configuration (Connectivity)</strong></td><td>Low</td><td>Low</td><td>High</td><td><strong>Configuration (Connectivity)</strong></td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td><strong>Field size</strong></td><td>High</td><td>High</td><td>Medium</td><td><strong>Field size</strong></td><td>High</td><td>High</td><td>Medium</td></tr><tr><td><strong>Crop diversity</strong></td><td>Medium</td><td>Medium</td><td>Medium</td><td><strong>Crop diversity</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Weeds</strong></td><td>Low</td><td>Low</td><td>Low to medium</td><td><strong>Weeds</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Weed abundance</strong></td><td>Low</td><td>Low</td><td>Medium</td><td><strong>Weed abundance</strong></td><td>Low</td><td>High</td><td>High</td></tr><tr><td><strong>Monocot abundance</strong></td><td>Low</td><td>Low</td><td>Medium</td><td><strong>Monocot abundance</strong></td><td>Low</td><td>High</td><td>High</td></tr><tr><td><strong>"Anti-monocot" herbicide quantity</strong></td><td>High</td><td>Medium</td><td>Low</td><td><strong>"Anti-monocot" herbicide quantity</strong></td><td>Medium</td><td>Medium</td><td>Medium</td></tr><tr><td><strong>Tillage intensity</strong></td><td>High</td><td>High</td><td>High</td><td><strong>Tillage intensity</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Dicot abundance</strong></td><td>Low</td><td>Low</td><td>Medium</td><td><strong>Dicot abundance</strong></td><td>Low</td><td>Medium</td><td>High</td></tr><tr><td><strong>"Anti-dicot" herbicide quantity</strong></td><td>Medium</td><td>Medium</td><td>Low</td><td><strong>"Anti-dicot" herbicide quantity</strong></td><td>Medium</td><td>High</td><td>Medium</td></tr><tr><td><strong>Tillage intensity</strong></td><td>High</td><td>High</td><td>High</td><td><strong>Tillage intensity</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Dicot diversity</strong></td><td>Low</td><td>Low</td><td>Medium</td><td><strong>Dicot diversity</strong></td><td>Low</td><td>Low</td><td>Medium</td></tr><tr><td><strong>Crop rotation</strong></td><td>Medium</td><td>Medium</td><td>Medium</td><td><strong>Crop rotation</strong></td><td>Medium</td><td>Low</td><td>Medium</td></tr><tr><td><strong>Herbicide timing</strong></td><td>Both or pre-emergence</td><td>Both or pre-emergence</td><td>Post-emergence</td><td><strong>Herbicide timing</strong></td><td>Both or pre-emergence</td><td>Both or pre-emergence</td><td>Post-emergence</td></tr><tr><td><strong>Invertebrates</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Invertebrates</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Soil invertebrates</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Soil invertebrates</strong></td><td>Low</td><td>Medium to high</td><td>Medium to high</td></tr><tr><td><strong>Anthropogenic pressures</strong></td><td>High</td><td>High</td><td>High</td><td><strong>Anthropogenic pressures</strong></td><td>High</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Tillage intensity</strong></td><td>High</td><td>High</td><td>High</td><td><strong>Tillage intensity</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Pesticide</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Pesticide</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Insecticide</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Insecticide</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Other</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Other</strong></td><td>Low</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Trophic resources</strong></td><td>Low</td><td>Low</td><td>Low to medium</td><td><strong>Trophic resources</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Microorganisms</strong></td><td>Low</td><td>Low to medium</td><td>Low to medium</td><td><strong>Microorganisms</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Vegetation</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Vegetation</strong></td><td>Medium to high</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Flying invertebrates</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Flying invertebrates</strong></td><td>Medium to high</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Anthropogenic pressures</strong></td><td>Medium to high</td><td>Medium to high</td><td>Low to medium</td><td><strong>Anthropogenic pressures</strong></td><td>Low to medium</td><td>Medium to high</td><td>Low to medium</td></tr><tr><td><strong>Pesticide</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Pesticide</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Insecticide</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Insecticide</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Other</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Other</strong></td><td>Low</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Trophic resources</strong></td><td>Low</td><td>Low</td><td>Low to medium</td><td><strong>Trophic resources</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Microorganisms</strong></td><td>Low</td><td>Low to medium</td><td>Low to medium</td><td><strong>Microorganisms</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Vegetation</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Vegetation</strong></td><td>Medium to high</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Flying invertebrates</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Flying invertebrates</strong></td><td>Medium to high</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Anthropogenic pressures</strong></td><td>Medium to high</td><td>Medium to high</td><td>Low to medium</td><td><strong>Anthropogenic pressures</strong></td><td>Low to medium</td><td>Medium to high</td><td>Low to medium</td></tr><tr><td><strong>Pesticide</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Pesticide</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Insecticide</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Insecticide</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Other</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Other</strong></td><td>Low</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Regional intensification</strong></td><td>High</td><td>High</td><td>Low to medium</td><td><strong>Regional intensification</strong></td><td>Low</td><td>Medium to high</td><td>Medium to high</td></tr><tr><td><strong>Trophic resources</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Trophic resources</strong></td><td>Medium to high</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Vegetation</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Vegetation</strong></td><td>Medium to high</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Invertebrates</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Invertebrates</strong></td><td>Medium to high</td><td>Low to medium</td><td>Medium to high</td></tr><tr><td><strong>Anthropogenic pressures</strong></td><td>High</td><td>Medium to high</td><td>Low to medium</td><td><strong>Anthropogenic pressures</strong></td><td>Low to medium</td><td>Medium to high</td><td>Low to medium</td></tr><tr><td><strong>Pesticide</strong></td><td>Medium</td><td>Low</td><td>Low</td><td><strong>Pesticide</strong></td><td>Medium</td><td>Medium</td><td>Low</td></tr><tr><td><strong>Machinery use</strong></td><td>Medium</td><td>Medium</td><td>Medium</td><td><strong>Machinery use</strong></td><td>Medium</td><td>Low</td><td>Low</td></tr><tr><td><strong>Tillage intensity</strong></td><td>High</td><td>High</td><td>High</td><td><strong>Tillage intensity</strong></td><td>High</td><td>Low</td><td>Low</td></tr><tr><td><strong>Mowing</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Mowing</strong></td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td><strong>Regional intensification</strong></td><td>High</td><td>High</td><td>Low to medium</td><td><strong>Regional intensification</strong></td><td>Low</td><td>Medium to high</td><td>Medium to high</td></tr><tr><td><strong>Trophic resources</strong></td><td>Low</td><td>Low</td><td>Low to medium</td><td><strong>Trophic resources</strong></td><td>Low to medium</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Vegetation</strong></td><td>Low</td><td>Low</td><td>Medium to high</td><td><strong>Vegetation</strong></td><td>Low to medium</td><td>Low to medium</td><td>Low to medium</td></tr><tr><td><strong>Invertebrates</strong></td><td>Low</td><td>Low</td><td>Low</td><td><strong>Invertebrates</strong></td><td>Low</td><td>Low to medium</td><td>Medium to high</td></tr></tbody></table>

## 3.2. Sensitivity analysis

The MC sampling showed an uneven frequency distribution for biodiversity indicators and the sub-indicators (figure 23). For overall biodiversity, the lowest category "very low" yielded the highest frequency. This unbalanced distribution in favour of "very low" can be observed for the "invertebrates" group and more precisely the soil invertebrates and to a less extent for microorganisms. However, for vertebrates and especially for vegetation, the distribution is more balanced. This uneven frequency distribution was not due to the parametrization of the final utility function of "overall biodiversity" which shows the following distribution: for "very low" 65 rules out of 256, for "low" 71 rules out of 256, for "medium" 54 rules out of 256, for "high" 35 rules out of 256, and for "very high" 31 rules out of 256. An explanation may lie in the integration of relation between taxonomic group. The distribution shape of the microorganisms group might have influenced the soil invertebrates group and exacerbate the effect on the lowest class. This can
also be observed to a lesser extent for the invertebrates group, influenced by the flying invertebrates group, which depends on vegetation. Yet this last group based on eutrophic organisms does not depend on another taxonomic group and shows a more balanced distribution, resulting in a difference in distribution between the flying invertebrates and the soil invertebrates group. Likewise, the vertebrates group depends on both the vegetation and invertebrates group, the former mitigating the effect of the latter. When combining both distributions of vegetation and invertebrates groups, the distribution of vertebrates may be understood. The uneven distribution with the overrepresentation of the lowest class in some groups seem to be amplified in the distribution of the overall biodiversity. Likewise, the slightly higher percentage in the third class relatively to the second comes from its advantage in the vegetation and vertebrates groups. However, the shape of the microorganisms group remains unexplained.

<figure><img src="image_25.png" /><figcaption>Figure 23 : Bar charts representing the distribution of the results of 5000 Monte Carlo simulations of I-BIO for the "Overall Biodiversity" and the four taxonomic groups (microorganisms, vegetation, invertebrates and vertebrates). The X-axis represents the classes of the studied variable (1 Very low, 2 Low, 3 Medium, 4 High, 5 Very high) and Y-axis gives the relative frequency of occurrence of the class (i.e. within the 5000 simulations).</figcaption></figure>

### 3.3. Validation of I-BIO

Validation of the microorganisms branch was limited as there were only 10 fields within the Noe database with microorganisms biomass data to use (Fig. 24). The difference between the two classes "low" and "low to medium" is significant (p value= 0.0050). Comparison of I-BIO with observed data met our expectation for monocotyledon and dicotyledon abundances for the EOSF site (Fig. 25a). The difference between boxplots is significant for monocotyledons (p value=0.0293) and for dicotyledons (p value=0.0227). For ENI data, the trend between I-BIO
outputs and observed data was the opposite to those predicted (Fig. 25b), as the high classes for both monocotyledon and dicotyledon abundances are below the other classes. For the EOSF weed richness, the boxplot corresponding to the "high" class is significantly higher (p value =0.0131) than "low to medium" class (Fig. 26a). The other boxplots "low" and "low to medium" showed lower value of richness than "high" although no clear relation existed between I-BIO output and field observations. For ENI, even if we focused only on the most abundant hemeromorphic species (Fried et al., 2018) to avoid biases linked to regional features or pedoclimatic context, there was no clear trend and substantial variability between classes. (Fig. 26b).

<figure><img src="image_26.png" /><figcaption>Figure 24 : Comparison of I-BIO outputs for the microorganisms branch with observed data of microorganisms biomass (µgDNA/gsoil) for the Noé dataset (n=10).</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>b</figcaption></figure>

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

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

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

Figure 26: Comparison of I-BIO outputs for monocotyledon abundance and dicotyledon abundance (number of individuals) with observed data: a) EOSF data (n=42), b) ENI data, the sub-list encompassed the hundred most abundant species (n=60). For the sake of readability, we removed two very high points for the EOSF data: the first one at 3284 for the Monocotyledon abundance and the second one at 3972 for the Dicotyledon abundance.

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

<figure><img src="image_32.png" /></figure>

Figure 25: Comparison of I-BIO outputs for weed richness with observed data: a) EOSF data for all the species (n=42), b) ENI data for the most abundant hemeromorphic species (n=60).
Fig. 27a shows a straightforward relation for the soil invertebrates branch between I-BIO outputs and field observations of earthworm abundance although no significant difference between boxplots could be found. The comparison of the flying invertebrates branch with coleoptera abundance from the ENI dataset showed a positive trend (Fig. 27b) although it was not so clear as for earthworms and not significant. The test with observation of butterfly abundance from the Noé dataset showed a slight positive trend for abundance which is a little more pronounced for butterfly richness (Fig. 28).

<figure><img src="image_33.png" /></figure>

Figure 28 : Comparison of I-BIO outputs for invertebrates branch with corresponding observed data a) the soil invertebrates branch with earthworm abundances for ENI data (n= 24) and b) the flying invertebrates branch with coleoptera abundance for ENI data (n= 60).

<figure><img src="image_34.png" /></figure>

Figure 27 : Comparison of I-BIO outputs for the flying invertebrates branch with observed data for the Noé dataset (n=16): a) Butterfly abundance, b) Butterfly richness.

Finally, it was not possible to validate the vertebrates branch, with the Farmland birds data (Fig. 29) from either the Noe or ENI datasets. This could be due to the very low number of observations and fields with "medium to high" or "high" classes (respectively 3 and 1 occurrences for the Noe dataset and 14 and 0 for the ENI dataset). Moreover, it's not surprising that birds are
less impacted by local management practices compared to earthworm or coleoptera as other factors such as landscape features have a great influence on this group.

<figure><img src="image_35.png" /><figcaption>Figure 29 : Comparison of I-BIO outputs for the vertebrates branch with observed data of farmland birds: a) Noé dataset (n=16) and b) ENI dataset (n=60).</figcaption></figure>

# 4. Discussion

## 4.1. Originality of the indicator

I-BIO was designed to give a prediction of the impacts of management practices on overall biodiversity at the field level. As for every indicator design, a compromise has to be found between feasibility and integration of processes (Bockstaller et al., 2015), the former depending especially on the amount and availability of data, the latter depending on sufficient coverage of all key biodiversity components in the system. Overall the I-BIO decision tree is more complex and requires more data than other predictive DEXI models: the IBEA (France Nature Environnement, 2013), the biodiversity branch of MASC 2.0 (Craheix et al., 2012) and DEXiPM1 (Pelzer et al., 2012) but less than this of DEXIPM (Demade, 2014). As a qualitative tool, it also requires less data than SALCAbd (Jeanneret et al., 2014). We did not select species groups for the different taxonomic groups like SALCA method but did not restrict to a global assessment of biodiversity as in IBEA. Like MASC 2.0 and DEXIPM 1, I-BIO addresses general taxonomic groups but, in our case, we tried to cover the whole range of groups from microorganisms to vertebrates, the former being only tackled by MASC2.0, and the latter only by SALCAbd. In comparison with DEXiPM2, I-BIO did not address ecosystem services linked to biodiversity for sake of simplicity because relation between both are complex (Duncan et al., 2015).

A major innovation in I-BIO is to consider direct impact of field and landscape management and indirect impact through trophic relation. Indeed, when a taxonomic group is impacted, for instance vegetation, higher groups in the trophic chain are also impacted by a reduction of their trophic resources, for example "flying invertebrates" (Bruun et al., 2022; Sohlenius, 1990). Another innovation in I-BIO is the integration of landscape variables. In order to better embed semi natural habitats, we created a branch for the semi natural flora which plays an important role in maintaining biodiversity (Bailey et al., 2010; Jeanneret et al., 2003; Martin et al., 2019; Sirami et al., 2019; Tscharntke et al., 2021). The IBEA method tackles some landscape variable like density, diversity, connectivity of landscape elements but also includes the quality of semi natural and
natural habitat particularly the combination of tree species, presence of dead wood or intra-forest
ponds. However, despite this example, functional aspects of landscape components remain
poorly integrated in biodiversity indicator assessment and would be a useful avenue for future
work (Fahrig et al., 2011; Santos et al., 2021).

4.2. Sensitivity

Qualitative methods assessing agricultural systems sustainability have shortcomings in terms
of sensitivity, which due to the inherent structure of the DEXi model, are limited by the threshold
effect. I-BIO attempts to mitigate this by adding more classes to some branches, but is still limited
by the total number of decision rules due to combinatory explosion. I-BIO resulted in a total of
256 decision rules and although we developed a method to overcome this inflation (see appendix
D), it is uncertain that the method can be implemented for a higher number of rules. In any case,
a trade-off has to be found between sensitivity of the tool and the problem of combinatory
explosion. One possibility to resolve this sensitivity problem is to implement a quantitative method
combining decision rules and fuzzy set like the CONTRA method which was tested to design an
assessment method of grassland management on biodiversity (Bockstaller et al., 2017).

The results of the MC sampling approach showed an uneven distribution of I-BIO outputs for
the overall biodiversity, skewed to the low class. This may be explained by the structure of the
tree, although the behaviour for one branch, microorganisms remain unexplained. The results
obtained may be compared with the outputs in Carpani et al. (2012) for the MASC1 model (Sadok
et al., 2009) for which extreme classes had a nil probability and always two mediums took the
majority of probability. For our indicators, no nil probability was observed. If we calculated a root
mean square deviation in relation to a balance distribution (probability = 0.20) it was slightly
smaller than for MASC 1, a value of 21.3 for I-BIO against 22.5 for MASC 1 but not enough to
conclude to a better sensitivity. Nevertheless the uneven distribution of I-BIO reflects the actual
sensitivity of biodiversity to perturbations due to arable management (Coudrain et al., 2016;
Martin et al., 2020).

As advised by Carpani et al. (2012), MC sampling may be completed by two other approaches: ANOVA and conditional probabilities. Unlike MC, the ANOVA makes possible the calculation of a sensitivity index for each input variable at each "node" of the tree, i.e., an output variable resulting from this aggregation of two or more variables. However, if the model is too large, there could be computational problems. The conditional probabilities method calculates a sensitivity index for all the variables along the tree i.e., at each aggregation level. Due to lack of time, it was not possible to implement both complementary approaches in this study. But for the conditional probabilities, taking into account similarity in the size of the tree and the number of aggregation level, some comparison can be made at least for basic variables.

4.3. Validation of the model

Following Bockstaller and Girardin (2003), we assessed the predictive quality of I-BIO in which
is rarely shown in in publications presenting a new indicator. Jeanneret et al. (2014) is one
exception, where a validation test was presented for two taxonomic groups for grasslands but not
for arable plots. I-BIO covered a larger range of taxonomic groups, for all except the "microorganisms" group where insufficient data were available. Overall, I-BIO performed well for microorganism and earthworm data. I-BIO performed better with abundance data than richness data, although even there, the predictive power remained weak which is not surprising regarding the simplified qualitative design of I-BIO. The validation results are somewhat similar to SALCAbd for which they found significant, positive relations for a slightly larger sample (n=77) even though the correlations were weak (Spearman coefficient = 0.58 for plants and 0.39 for grasshoppers). In the case of SALCAbd, results concerned species richness and not abundance.

The poor performance of I-BIO for the vegetation branch may be due to the fact that these data were observed at the field margin and not in the centre of the field, so less influenced by agronomic practices. Landscape data are typically heterogeneous, especially for the percentage of semi natural habitats and their connectivity. Indeed, some datasets included detailed landscape features i.e. percentage of crops and semi-natural habitats within a radius of 1 km whereas others had little or no data on landscape context. Thus, I-BIO was restricted, in some instances to using the medium class or using expert opinion in the absence of appropriate data. This was especially the case for the East of Scotland farm dataset.

Improvements to the causal indicators for predicting impact on the soil invertebrates include additional information on tillage intensity and other pesticides. For the tillage intensity, the "high" class should include animated agricultural equipment such as rotative or disc harrow and lafforge spade. For the pesticides, a distinction should be made between fungicide, molluscicide and herbicide as the latter will have less impact on soil invertebrate abundance and diversity.

As recommended by Bockstaller and Girardin (2003), we compare sub-indicators dealing with one taxonomic group. For very broad groups like invertebrates, the comparison with one specific taxonomic group (e.g. earthworms) may be questionable. Ideally, it should be compared to a set of observations (earthworms, carabidae, etc.). A multispecies indicator like this proposed by Sirami et al. (2019) could be put forward. This would even be appropriate for I-BIO at the level of overall biodiversity. But in any case, this would require on one site multi-taxonomic observations for a very large sample.

## 4.4. Implementation and utility

In their methodological framework on the validation of indicators, Bockstaller and Girardin also pointed out the need for end-user validation. At this stage of development of I-BIO, it was not possible to test the utility among potential users but some points may be discussed. According to Craheix et al. (2015), simplicity, flexibility and transparency are key characteristics to foster implementation of a tool by other users than the designer of the tool.

Simplicity is ensured by predictive indicators in form of decision tree based on linguistic rules which are easier to understand for non-specialists (Phillis and Andriantiatsaholiniaina, 2001) than quantitative models. However, some utility functions like this for the overall biodiversity with 256 rules are not so easy to understand and require some explanation. In spite of some points, the methodology and the tool are simple to use. Input data may remain qualitative, based on expertise or expert assumption. Many input data are available in farmers' traceability information system. An effort has to be made, without this being an issue for advisers and their farmers, to
assess the intensity of herbicide use which is separated by target between herbicide for the control
of monocotyledons (grass species) or dicotyledons. However, a bottleneck lies in data collection
of landscape variables, proportion of semi-natural elements and their connectivity. But here again,
expertise may replace real data, especially in arable land where in many situations these variables
may be set to the lowest category.

The tool is also very flexible since it is very easy to change a value and to get a result. This
may support discussion between stakeholders by providing quick answers to question like what
can happen if I change this management practice. However, the drawbacks are the lack of
sensitivity due to its qualitative form and the uneven frequency distribution on the side of the
"low class". The design of a quantitative indicator based on fuzzy decision like the CONTRA
method would mitigate this issue. Lastly, transparency is supported by the form in which the
results are presented by the tool, where basic indicators and all the aggregated results are
available and can be traced. Aggregation functions (utility functions) are also transparent even if
they are not always easy to understand. But here again DEXi offers synthesis of decision rules to
facilitate an overview of the rules.

5. Conclusions

With the improvements described above, I-BIO not only provides a framework for
understanding arable ecosystems and comparing management practices but can be developed
as a predictive tool and decision aid for farmers, based on a greater level of detail and complexity
than previously available. Indeed, I-BIO was built to assess the impact of management practices
on four taxonomic groups, covering the whole range of diversity from microorganisms to
vertebrates, and encompasses both field management and landscape variables (composition and
configuration). A key novelty of this indicator lies in the assessment of direct impact and indirect
impact on the trophic chain. The use of decision tree ensures simplicity and feasibility but the
drawback is a certain lack of sensitivity for some groups. Predictive quality assessment delivered
mitigated results but, in some cases, satisfactory for species abundance. This work should be
continued with other datasets and for microorganisms. Last, the tool should be tested by end-
users.

Acknowledgements

This study is part of a PhD research project supported by a grant (n°2019/0020) overseen by the French National Research Technology Agency (ANRT). The authors would like to thank Enzo Armaroli, Daniel Cluzeau, Victor Dupuy, Isis Poinas, Benoît Ricci and Laura Schlosser for the provided datasets and, Jacques-Eric Bergez for statistical support.

CRediT authorship contribution statement

Christian Bockstaller and Emma Soulé: Conceptualization, Investigation, Formal analysis, Writing - original draft, Writing - review & editing, Visualization. Cathy Hawes and Mark Young: Conceptualization, Investigation, Formal analysis, Writing - review & editing, Visualization. Laura Henckel, Nadia Michel and Philippe Michonneau: Conceptualization, Writing - review & editing, Visualization.
# Chapitre 3 : Caractérisation des exploitations agricoles via l'évaluation conjointe impacts/services

Nous avons présenté dans le second chapitre le cadre conceptuel et le développement de la méthode d'évaluation. Le chapitre 3 est l'application de cette méthode à notre cas d'étude dans la Champagne crayeuse. Ce travail comprend l'analyse conjointe IE/SE, l'étude de corrélations entre indicateurs et à la recherche du meilleur compromis. La partie A présente l'article qui rappelle la méthode d'évaluation et analyse les résultats obtenus sur les 33 exploitations qui constituent notre échantillon, sans tenir compte spécifiquement des cahiers de charge liés aux exploitations. La partie B se penche sur l'étude de différences entre cahiers des charge publics (AB et HVE) afin de répondre aux attentes de la coopérative.
# Résumé du chapitre 3 : Une nouvelle méthode pour évaluer la durabilité des systèmes agricoles en intégrant les services écosystémiques et les impacts environnementaux

## Intérêt et position du chapitre dans la thèse

Ce chapitre décrit la mise en œuvre de la nouvelle méthode d'évaluation de la durabilité (chapitre 2) sur notre cas d'étude dans la Champagne crayeuse. Cette partie présente les résultats de l'ensemble des indicateurs calculés et les analyses réalisées afin d'estimer la pertinence des indicateurs sélectionnées et la complémentarité des indicateurs de services écosystémiques et des indicateurs environnementaux. En outre, une analyse comparative a été effectuée sur deux certifications environnementales : le référentiel HVE de niveau 3 (voie A) et l'agriculture biologique.

## Contexte

L'agriculture a fortement évolué en France après la seconde guerre mondiale. En effet, il fallait que la France recouvre sa souveraineté alimentaire et produise suffisamment pour être autosuffisante. L'agriculture s'est donc industrialisée en recourant à la mécanisation et aux intrants de synthèse. Cette industrialisation n'a pas été sans effet sur la santé, l'environnement et l'économie. Pour limiter ces impacts deux voies existent. La première repose sur la diminution des intrants de synthèse comme en agriculture biologique et la seconde sur la reconception des systèmes agricoles en s'appuyant sur les services écosystémiques (SE). Plusieurs travaux ont proposé des cadres conceptuels afin d'évaluer ces SE en milieu agricole fortement anthropisé, mais des confusions persistent notamment sur la notion de disservice. Par ailleurs depuis les années 90, de nombreuses méthodes évaluant la durabilité en milieu agricole sont apparues. Cependant, elles se limitent souvent à une liste d'indicateurs plus ou moins organisée et intègrent très peu de SE. Il existe ainsi un front de science relatif à l'analyse des synergies et des antagonismes entre les services écosystémiques et les impacts environnementaux.

## Questions de recherche

1. Comment intégrer les concepts d'avantage et d'impact dans un cadre conceptuel ?

2. Quels sont les antagonismes et synergies entre indicateur d'impacts, de services écosystémiques et socio-économiques ?

3. Existe-t'il des exploitations, et si oui lesquelles, qui présentent un bon compromis entre indicateurs ?

4. Est-ce que les cahiers des charges ont un effet sur les résultats ?

## Méthodologie

La méthode d'évaluation de la durabilité repose sur un cadre conceptuel qui intègre 22 thèmes dont 7 avantages, 15 impacts environnementaux avec 14 indicateurs de SE. Un cadre analytique pour évaluer conjointement les services écosystémiques et les impacts environnementaux est également proposé. Les indicateurs de la nouvelle méthode sont issus de la méthode INDIGO et de la méthode MASC 2.0. Ils ont été calculés à l'aide de l'outil SYSTERRE® d'Arvalis, du modèle AMG et de plusieurs calculateurs Excel. La méthode a ensuite été appliquée sur 33 exploitations en Champagne crayeuse. Ces exploitations suivent divers cahiers des charges (HVE, AB, etc.) et ont des itinéraires techniques variés. Pour représenter au mieux l'assolement des exploitations, 6 à 10 parcelles par exploitation, 250 au total, ont été sélectionnées avec la majorité en grandes cultures (céréales, oléagineux, légumes industriels). Concernant l'analyse des résultats, des boîtes à moustache ont été utilisées pour étudier la distribution des indicateurs et comparer plusieurs certifications environnementales. Une matrice des corrélations a permis de sélectionner les indicateurs les plus intéressants et de repérer des synergies et antagonismes entre eux. Puis, une ACP suivie d'une classification ascendante hiérarchique ont été réalisées afin de caractériser les divers types d'exploitations agricoles. Enfin, l'analyse des déviants positifs via la recherche du front de Pareto a été appliquée sur une liste réduite d'indicateurs afin d'identifier les exploitations présentant le meilleur compromis.
## Résultats principaux

L'analyse conjointe des services écosystémiques et des impacts environnementaux a mis en évidence qu'une grande partie des exploitations agricoles ont un SE potentiel correct voire élevé mais qu'elles ne l'utilisent pas suffisamment pour diminuer leurs impacts environnementaux. C'est notamment le cas des exploitations en HVE. La matrice de corrélations a confirmé la complémentarité des indicateurs de l'outil SYSTERRE® et de la méthode INDIGO. La classification ascendante hiérarchique a distingué 5 groupes d'exploitations avec deux groupes en agriculture intensive, un groupe en agriculture biologique et deux autres groupes qui ont des SE potentiels intéressants mais avec peu de retombés positives sur les performances environnementales. En outre, les résultats ne montrent pas d'antagonisme entre les performances environnementales et socio-économiques. Concernant l'analyse des déviants positifs, une exploitation agricole en HVE présentant le meilleur compromis entre les volets environnementaux, économiques et sociaux a été identifiée. Enfin relatif aux certifications environnementales, le référentiel HVE a de bons résultats pour certains indicateurs liés à la stratégie phytosanitaire mais doit encore progresser sur les thèmes de la fertilisation et de la biodiversité. L'agriculture biologique a de très bons résultats sur les thèmes de la biodiversité, de la stratégie phytosanitaires et de la gestion de la fertilisation mais peut s'améliorer sur la gestion du phosphore et les services écosystémiques.

## Apport pour la thèse et la recherche en général

Ce chapitre a permis d'appliquer la nouvelle méthode d'évaluation de la durabilité développée dans cette thèse et de tester la pertinence des indicateurs utilisés ainsi que leur complémentarité vis-à-vis des indicateurs employés par la SCARA. Ce travail a aussi montré une nouvelle approche pour analyser conjointement les services écosystémiques et les impacts environnementaux et ainsi identifier des synergies et antagonismes entre les indicateurs. Finalement, ce chapitre a permis de comparer plusieurs certifications environnementales en soulignant les thématiques à améliorer et fournit des pistes de réflexion pour tendre vers une agriculture durable. Les résultats sont à confirmer ou à comparer dans d'autres situations de production.

## Productions scientifiques

Un article scientifique soumis dans *Journal of Cleaner Production*.

Une présentation orale en septembre 2021 à la conférence internationale *Landscape 2021*.

Une présentation au congrès Phloème en novembre 2022.

Un poster présenté à l'International conference in Ecology & Evolution (SFE², GfÖ, EEF) en novembre 2022.

## Partenariats et stage

Ce travail a bénéficié de l'appui de certains collègues de l'équipe AGISEM pour le choix des méthodes statistiques et le renseignement de calculateurs Excel. Les collègues de la SCARA ont aussi contribué à ce travail par la validation des données dans l'outil SYSTERRE® et en fournissant des données techniques complémentaires.

Enfin, ce travail a permis l'encadrement d'un stage de fin d'études d'école ingénieur de 6 mois pour la collecte des données, le calcul de certains indicateurs et les premières analyses des indicateurs calculés.
# Partie A. Une nouvelle méthode pour évaluer la durabilité des systèmes agricoles en intégrant les services écosystémiques et les impacts environnementaux

## A new method to assess sustainability of agricultural systems by integrating ecosystem services and environmental impacts

Emma Soulé<sup>a,b</sup>, Rémi Charbonnier<sup>a</sup>, Laura Schlosser<sup>b</sup>, Philippe Michonneau<sup>b</sup>, Nadia Michel<sup>c</sup>, Christian Bockstaller<sup>a</sup>

## Abstract

The intensification of agriculture after the Second World War was a success in terms of substantially increasing yields to meet the demand of the growing population, but the trade-off was a rise in negative environmental, economic and social impacts. Despite disagreement on how to put the general concept of sustainability into practice, most stakeholders recognize the need for assessment methods to identify and implement new measures to characterize the current situation and to gauge progress that must be made to reach the end goal. Many sustainability assessment methods for agricultural systems have been published, but most are simply a list of indicators and very few integrated ecosystem services (ES). Yet, ecosystem service indicators are essential for better integrating biological processes in order to support the agroecological transition. Based on the MASC method (which stands for Multi-attribute Assessment of the Sustainability of Cropping systems) and a recent conceptual ES framework, we developed a conceptual indicator framework embedding environmental impacts (EI) and ecosystem service indicators. This framework covers 22 environmental themes and integrates few basic economic and social indicators. The method was implemented on 33 farms with different certifications and production specifications. All were members of an agricultural cooperative in the Champagne crayeuse region in north-eastern France. We selected 250 plots to cover the farms' crop rotations. The correlation analysis showed no systemic trade-off was identified between EI or ES and economic indicators. Meanwhile, the joint assessment of ES and EI, like the multivariate analysis, delivered results with more nuanced results than a classical opposition between intensive and organic farming. Some farms have a moderate or even good level of ES but often do not leverage them, as shown by their high level of EI. The principle component analysis and subsequent hierarchical ascendant classification allowed us to distinguish five clusters: two clusters included intensive farms, one showed enhanced performances for some indicators without trade-offs, one had reduced pesticide use and more diversified crops, and one included an organic farm. These last two clusters showed some trade-offs for economic indicators, energy production and production costs, but no trade-offs for profitability, the most important performance indicator. Finally, the positive deviance analysis revealed that economic, environmental and social considerations can be balanced in a way that seems to be more sustainable than, for example, an

<footnote>a Université de Lorraine, INRAE, LAE 68000 Colmar, France</footnote>

<footnote>b SCARA, 10700 Arcis-sur-Aube, France</footnote>

<footnote>c Université de Lorraine, INRAE, LAE 54500 Nancy, France</footnote>

<footnote>* Corresponding author: christian.bockstaller@inrae.fr, INRAE LAE, 28 rue de Herrlisheim, 68000 Colmar, France</footnote>
organic farm that has very successfully reduced EI but at a cost of several economic and social trade-offs.

Keywords

Agroecological transition, sustainability assessment method, arable farm, indicator framework, joint assessment, diversified crop rotation

# 1. Introduction

After the Second World War, efforts to ensure food security led to a major intensification of agriculture in many Global North countries as well as in the Global South through the Green Revolution. While this evolution resulted in a tremendous increase in production, it has also brought about negative impacts of an economic (e.g., falling prices), environmental (e.g., degradation of natural resources) and social (e.g., health) nature. These problems give rise to major questions about the sustainability of agriculture, which is highly implicated in exceeding planetary boundaries (Campbell et al., 2017). Mitigation of these impacts can occur through two major pathways. The first is based on improving input use efficiency with, for example, precision farming, while the second relies on input substitution (chemical to organic) and the redesign of the system to replace chemical inputs with ecosystem services (Therond et al., 2017a)). Ecosystem services (ES) are the biophysical components of the ecosystem from which humans derive benefits (Haines-Young and Potschin-Young, 2018; Tibi and Therond, 2017). A huge body of literature has been produced on this matter since the introduction of the concept by Costanza et al. (1997) and Daily (1997), covering natural as well as anthropized ecosystems like agriculture (Bennett et al., 2009; Palomo-Campesino et al., 2018). In spite of this effort, Barot et al. (2017) noted that ES had not been clearly defined in anthropized systems like cropland, perhaps because the concept was supported by natural ecosystem specialists. Barot et al. (2017) thus proposed a conceptual framework to assess ecosystem services in anthropized ecosystems emphasizing the need to distinguish what is truly an ES resulting from an ecological process and the impact caused by human activity. But in their framework, the concept of disservices overlaps with EI linked to human interventions. Like ES, a disservice results from an ecological mechanism. The same confusion between EI and disservices can be found in the framework proposed by Power (2010). For example, 'loss of biodiversity' and 'loss of wildlife habitat' can suggest that ecosystems may be comprised of self-destroying mechanisms. In any case, what is at stake is not a simple theoretical question of terminology. Moreover, such losses may lead to attempts to disguise the results of human activity on the outcome of ecosystem functioning. Clarification is therefore needed to avoid any association of positive or negative impacts with ecosystem processes in order to mitigate any 'greenwashing'.

Despite disagreement on how to put the general concept of sustainability into practice, most stakeholders recognize the need for assessment methods to identify and implement new measures (Pope et al., 2004) to characterize the current situation (Wang et al., 2009) and to gauge progress that must be made to reach the end goal (Simon Bell and Morse, 2001). Developing a conceptual framework should be the initial step in creating any sustainability assessment method (J Alkan Olsson et al., 2009). Since the 1990s, many methods evaluating the environmental sustainability of agricultural systems have been created (Coteur et al., 2019). In their review of 262 environmental sustainability assessment methods, Soulé et al. (2021) found that only a minority have an explicit framework and that only three tackle ES. Until now, environmental impact assessment and ecosystem service assessment have been two parallel research areas because
most methods focus on evaluating either EI or ES, not both. This separation is illustrated by the
original and comprehensive work of Dardonville et al. (2022) on the development of an ES
assessment method without any reference to EI. One exception is the work of Dumont et al. (2019),
who studied both the EI and ES provided by livestock farming. Nevertheless, to our knowledge
no approach combining ES and EI concerned arable crops.

According to Soulé et al. (2021), many assessment methods include causal indicators like the number of treatments or the percentage of semi-natural habitats, and only a few methods include predictive indicators, such as the INDIGO, DEXi-PM, MASC 2.0 or SALSA methods (Bockstaller and Girardin, 2010; Craheix et al., 2011; Eriksson et al., 2005; Pelzer et al., 2012). Indeed, predictive indicators are based on model output (operational or complex) and require more data than causal indicators (based on a causal variable or a simple combination of variables) or measured indicators (based on field assessment or observation). Additionally, a main advantage of predictive indicators is their ability to trace cause-effect relationship (Bockstaller et al., 2015). Regarding the assessment of ES specifically, Eigenbrod et al. (2010) pointed out that proxies based on land-use data weighted by coefficient to provide a level of a given ES are poor predictors of ES. Furthermore, Barot et al. (2017) argued that disentangling the share of ES really due to ecological processes from those due to human activities is complicated, although some have made attempts to do so (Tibi and Therond, 2017). The latter authors stated that it was advisable to separately assess EI and ES and to visualize ES and EI results on figures like radars, the most widely used visualization method to represent outcomes of an indicator set (Albo et al., 2016). Nevertheless, on such figures the indicators are juxtaposed without any information on how they interrelate, so that at this stage, the integration between indicators is quite low. Thus the question of integrating of ES and EI indicators remains to be addressed.

Many assessment methods of environmental sustainability are based on a list of non-
aggregated indicators (Soulé et al., 2021), which makes it possible to avoid the pitfalls of
aggregation (Nardo et al., 2005). This debate on aggregation may be futile because an
aggregated composite indicator may be useful to compare systems, while system analysis must
be performed with help of non-aggregated indicators (Bockstaller et al., 2017). Non-aggregated
indicators may be used to analyse synergies and trade-offs to provide insights on the relationships
between farm and cropping system performance, etc. to help indicator end-users in their
decision-making. Yet such studies are rare according to Schader et al. (2016), who carried out this
type of an analysis with the Sustainability Monitoring and Assessment Routine (SMART) method
using mainly causal indicators. German et al. (2017) performed a meta-analysis on the relationship
between different ‘sustainability aspects’, which included real EI and ES. The impacts and services
were listed together and plotted on a radar diagram without any effort to really integrate them.
Different aggregation methodologies from operational research on multicriteria decision aid have
been implemented to assess agricultural system sustainability (Sadok et al., 2008). The DEXi
decision support system (Sadok et al., 2008) and the fuzzy decision tree (Bockstaller et al. 2007)
both based on mixed models allow users to combine different advantages and avoid some of the
downsides to classical calculation of a weighted mean. In particular, users can avoid indicators
offsetting each other. For example, two situations with medium values may produce the same
result as one with very high and another with very low values. However, the implementation of
these methods requires a parametrization phase with a weighting. One way to avoid this time-
consuming stage and discussions linked to subjectivity in weighting is to identify the positive
deviant, i.e. systems with the best combination of indicator values with help from the Pareto
ranking (Dardonville et al., 2022; Steinke et al., 2019).
The aim of this study was first to develop a new assessment method based on a clear conceptual framework explicitly integrating (i.e. going further than a simple list of indicators) ES and EI using predictive indicators. Some new indicators, especially for ES, were developed to better integrate processes without overly impairing their feasibility (Bockstaller et al., 2015). The idea was to do better than using a simple coefficient of service, which would multiply a farm's cropping surface to an inaccurately high level. Second, this new method aims to gain knowledge on trade-offs and synergies between environmental indicators, ecosystem service indicators, and social and economic indicators to go a step further in trade-off analysis than authors focusing on ES alone (Dardonville et al., 2022; Kanter et al., 2018; Obiang Ndong et al., 2021; Rosa-Schleich et al., 2019). We conducted a case study in a very agriculture-intensive region with high yields in a typical arable landscape with large field sizes that was poor in semi-natural habitats. A sample of farms was selected from a small region that was homogeneous in terms of soil and climate to mitigate variations among them, which can strongly influence ecological mechanisms. We worked on groups of plots at the farm level (basic calculation at the plot level). We first present our conceptual framework and some original indicators to cover a large range of environmental themes. We then analyse the trade-offs and synergies between environmental impact indicators and ecosystem service indicators for the main environmental themes. Lastly, we characterized farms in our sample, identifying positive deviants presenting the best compromise between environmental indicators, ecosystem service indicators, and social and economic indicators. For both social and economic indicators, some widely used basic indicators like gross margin were implemented without further consideration due to the context and constraints of the study.

## 2. Materials and methods

### 2.1. Conceptual indicator framework

Following Alkan Olsson et al. (2009), we developed a conceptual indicator framework to organize indicators assessing both EI and ES (Table. 12). We took as a basis the MASC 2.0 framework (Craheix et al., 2012), which was the most comprehensive in the review by Soulé et al. (2021), keeping only the biophysical themes tackling the whole environmental dimension and the 'productive capacity' of the economic dimension in relationship to the provision of ES, and the 'landscape quality' linked to physical and experiential interaction ES. The conceptual framework of the French national assessment ecosystems and services – EFESE (Tibi and Therond, 2017) was used to guide the integration of the themes. Starting from the aforementioned definition of ES, we introduced benefits as positive impacts due to human activity as opposed to negative impacts. We considered negative impacts at the first level as an outcome of the agricultural system (cropping, farming, etc.). The impacts and benefits were aggregated at a higher level following the MASC 2.0 structure. Nevertheless, we enhanced the MASC framework in several ways. We separated the productive capacity into two aggregation sublevels, abiotic and biotic factors. We added the water factor and kept only one soil fertility factor (nitrogen provision) that can be linked to an ES. For the sake of simplicity, we did not disaggregate the biodiversity theme into several taxonomic groups (Table. 12). For each impact or benefit, using literature knowledge and our expertise, we first identified as direct drivers two groups of drivers on one level: the first linked to management and the second to an ES. We then followed the suggestion of Tibi and Therond (2017) and Barot et al. (2017) to separate what is due to ecosystem processes and what is due to human activity in interaction with environmental characteristics (e.g. soil depth). Indirect drivers were added at a second level, but here we restricted them to ES, the idea being not to develop an exhaustive causal framework but to combine and integrate EI and ES indicators. For example, the theme 'pesticide contamination' (groundwater) is aggregated with three other themes in the
higher-level theme of 'water quality'. Pesticide contamination depends on three main drivers (level 1): ES pesticide regulation, which makes it possible to reduce pesticide use rates, environmental characteristics (e.g. soil depth, organic matter content, etc.) and pesticide management (Lindahl and Bockstaller, 2012). At level 2, the amount of pesticide available for leaching may be reduced by the ES of natural attenuation of pesticides by soils (Monard et al., 2013), while the soil organic matter content depends on the climatic regulation (carbon storage) service.
<table><caption>Tableau 12 : Conceptual indicator framework of the method assessing sustainability of agricultural systems</caption><thead><tr><th rowspan="2">Aggregation level 2</th><th rowspan="2">Aggregation level 1</th><th rowspan="2">Impact/benefit assessment</th><th rowspan="2">Theme</th><th rowspan="2"></th><th rowspan="2">Key drivers</th><th rowspan="2">Level 2 (only ES)</th><th rowspan="2">References</th></tr><tr><th>Level 1</th><th>Level 2 (only ES)</th></tr></thead><tbody><tr><td rowspan="10">Long term<br>production capacity</td><td rowspan="5">Control of abiotic factors</td><td>Soil compaction control</td><td>Soil tillage</td><td>Climate regulation (C storage)</td><td></td><td>Abixen et al. (2009), Liu et al. (2014), Khaleel et al. (1981), Diacono et al. (2019)</td></tr><tr><td>Water supply</td><td>Water storage and restitution</td><td>Climate regulation (C storage)</td><td></td><td>Martin (1997)</td></tr><tr><td>Nitrogen supply</td><td>irrigation + tillage</td><td></td><td></td><td>Khaleel et al. (1981), Boyle et al. (1989), Lado et al. (2004), Aguilera et al. (2020)</td></tr><tr><td>Weed control</td><td>Weed regulation</td><td></td><td></td><td>Spoane et al. (2012), Bianco-Cantiqui and Ruis (2018), Aguilera et al. (2020)</td></tr><tr><td>Pest control</td><td>Management practices</td><td></td><td></td><td>COMIFER (2013)</td></tr><tr><td rowspan="5">Control of biotic factors</td><td>Disease regulation</td><td>Pest regulation</td><td></td><td></td><td>Duchene et al. (2017), Palomo-Campesino et al. (2018)</td></tr><tr><td>Disease control</td><td>Management practices</td><td></td><td></td><td>Adoux et al. (2020), Gaba et al. (2014), Welberger et al. (2019)</td></tr><tr><td>Nitrate losses</td><td>Water quality regulation (NQ3)</td><td>Habitat for wild species</td><td></td><td>Scholberg et al. (2010), Kleijn et al. (2019)</td></tr><tr><td>Phosphorus losses</td><td>Environmental characteristics</td><td></td><td></td><td>Bianchi et al. (2006), Tschernykke et al. (2007), Bartual et al. (2019)</td></tr><tr><td>Pesticide contamination (surface water)</td><td>Fertilization practices and intercultural management</td><td></td><td></td><td>Dassou and Tixier (2016), Kleijn et al. (2019)</td></tr><tr><td rowspan="10">Environment quality</td><td rowspan="5">Water quality</td><td>Pesticide contamination (groundwater)</td><td>Erosion/runoff</td><td>Soil stabilization and erosion control</td><td></td><td>Buckto (2007)</td></tr><tr><td rowspan="5">Air quality</td><td>Phosphorus content</td><td>Soil phosphorus supply</td><td></td><td>Buckto (2007), COMIFER (2019)</td></tr><tr><td>Management practices (fertilization)</td><td>Regulation services (disease, pest, weed)</td><td>Climate regulation (C storage)</td><td></td><td>Duchemin and Hogue (2009), Uusitalo et al. (2017), Yuan et al. (2020), Bender et al. (2016), El Muhtar et al. (2019)</td></tr><tr><td>Management practices (crop, treatment)</td><td>Regulation services (disease, pest, weed)</td><td>Natural attenuation of pesticides by soils</td><td></td><td>Van der Werf (1996), Strassenmayer et al. (2003), Lindahl and Bockstaller (2012), Monard et al. (2013)</td></tr><tr><td>Management practices (crop, treatment)</td><td>Regulation services (disease, pest, weed)</td><td>Climate regulation (C storage)</td><td></td><td>Van der Werf (1996), Strassenmayer et al. (2003), Trevisan et al. (2009)</td></tr><tr><td>Organic matter losses</td><td>Management practices (crop, treatment)</td><td>Soil stabilization and erosion control</td><td></td><td>Van der Werf (1996), Strassenmayer et al. (2003), Trevisan et al. (2009)</td></tr><tr><td rowspan="5">Soil quality</td><td>Phosphorus pressure</td><td>Nitrogen supply</td><td></td><td></td><td>COMIFER (2013)</td></tr><tr><td>Water</td><td>Ammonia volatilization</td><td>Environmental characteristics (limestone)</td><td></td><td>Hutchings et al. (2019)</td></tr><tr><td>Energy</td><td>Pesticide contamination (air)</td><td>Management practices (fertilization, etc.)</td><td></td><td>Ucar and Hall (2001), Zhu et al. (2012), Kleij et al. (2013)</td></tr><tr><td>Waste impact</td><td>Climate regulation (greenhouse effect)</td><td>Regulation services (disease, pest, weed)</td><td></td><td>Lichihem et al. (2016), Guiral et al. (2016)</td></tr><tr><td>Biodiversity conservation</td><td>Erosion/runoff</td><td>Management practices (crop, treatment)</td><td></td><td>Campbell et al. (2017)</td></tr><tr><td rowspan="10">Resource consumption</td><td rowspan="5">Control of abiotic factors</td><td>Soil compaction control</td><td>Climate regulation (C storage)</td><td></td><td>Tibi and Therond (2017)</td></tr><tr><td>Water supply</td><td>Climate regulation (Δ albedo)</td><td></td><td>Ceschia et al. (2017), Carrer et al. (2018)</td></tr><tr><td>Nitrogen supply</td><td>Soil stabilization and erosion control</td><td></td><td>Le Bissonnais et al. (2002), Liu et al. (2014), Khaleel et al. (1981), Aguilera et al. (2020)</td></tr><tr><td>Weed control</td><td>Environmental characteristics (slope, etc.)</td><td>Management practices (crop, tillage)</td><td></td><td>Martin (1997), Aguilera et al. (2020)</td></tr><tr><td>Pest control</td><td>Management practices (crop, tillage)</td><td>Climate regulation (C storage)</td><td></td><td>Martin (2021), Clivot et al. (2019)</td></tr><tr><td rowspan="5">Control of biotic factors</td><td>Soil compaction control</td><td>Environmental characteristics (lay rate, etc.)</td><td></td><td>Launay et al. (2021), Clivot et al. (2019)</td></tr><tr><td>Water supply</td><td>Management practices (crop residue, organic matter)</td><td></td><td>Bailey and White (1970), Aletto et al. (2010)</td></tr><tr><td>Nitrogen supply</td><td>Natural attenuation of pesticides by soils</td><td></td><td>Aletto et al. (2010), Riedo et al. (2021)</td></tr><tr><td>Weed control</td><td>Management practices (treatment, fertilization)</td><td></td><td>Gurstone et al. (2021)</td></tr><tr><td>Pest control</td><td>Management practices</td><td></td><td>Cordell et al. (2009)</td></tr><tr><td rowspan="10">Long term<br>production capacity</td><td rowspan="5">Control of abiotic factors</td><td>Soil compaction control</td><td>Water storage and restitution</td><td>Climate regulation (C storage)</td><td></td><td>Khaleel et al. (1981), Boyle et al. (1989), Lado et al. (2004), Aguilera et al. (2020)</td></tr><tr><td>Water supply</td><td>irrigation</td><td>Nitrogen supply</td><td></td><td>Beckman (1997), Pervanchon et al. (2002)</td></tr><tr><td>Nitrogen supply</td><td>Energy consumption</td><td>Renewable energy production</td><td></td><td>DAE-G (2009)</td></tr><tr><td>Weed control</td><td>Waste production</td><td>Recycling</td><td></td><td>DAE-G (2009)</td></tr><tr><td>Pest control</td><td>Habitat for wild species</td><td>Waste impact</td><td></td><td>Sirami et al. (2019)</td></tr><tr><td rowspan="5">Control of biotic factors</td><td>Soil compaction control</td><td>Management practices (crop residue, organic matter)</td><td></td><td>Van Cappelle et al. (2012), Coudrain et al. (2016), Clough et al. (2020)</td></tr><tr><td>Water supply</td><td>Soil pollution - Disruption of microbial activity</td><td></td><td>Junge et al. (2015), Schlapbach et al. (2016)</td></tr><tr><td>Nitrogen supply</td><td>Climate regulation (C storage)</td><td></td><td>Clough et al. (2020)</td></tr><tr><td>Weed control</td><td>Organic matter losses</td><td></td><td></td><td></td></tr><tr><td>Pest control</td><td>Soil compaction control</td><td></td><td></td><td></td></tr><tr><td rowspan="10">Long term<br>production capacity</td><td rowspan="5">Control of abiotic factors</td><td>Phosphorus pressure</td><td>Phosphorus pressure</td><td>Climate regulation (C storage)</td><td></td><td></td></tr><tr><td>Water</td><td>Water pressure</td><td></td><td></td><td></td></tr><tr><td>Energy</td><td>Energy pressure</td><td></td><td></td><td></td></tr><tr><td>Waste impact</td><td>Waste impact</td><td></td><td></td><td></td></tr><tr><td>Biodiversity conservation</td><td>Biodiversity conservation</td><td></td><td></td><td></td></tr><tr><td rowspan="5">Control of biotic factors</td><td>Soil compaction control</td><td>Energy consumption</td><td></td><td></td><td></td></tr><tr><td>Water supply</td><td>Energy pressure</td><td></td><td></td><td></td></tr><tr><td>Nitrogen supply</td><td>Recycling</td><td></td><td></td><td></td></tr><tr><td>Weed control</td><td>Habitat for wild species</td><td></td><td></td><td></td></tr><tr><td>Pest control</td><td>Soil pollution - Disruption of microbial activity</td><td></td><td></td><td></td></tr><tr><td rowspan="10">Long term<br>production capacity</td><td rowspan="5">Control of abiotic factors</td><td>Phosphorus pressure</td><td>Phosphorus pressure</td><td>Climate regulation (C storage)</td><td></td><td></td></tr><tr><td>Water</td><td>Water pressure</td><td></td><td></td><td></td></tr><tr><td>Energy</td><td>Energy pressure</td><td></td><td></td><td></td></tr><tr><td>Waste impact</td><td>Waste impact</td><td></td><td></td><td></td></tr><tr><td>Biodiversity conservation</td><td>Biodiversity conservation</td><td></td><td></td><td></td></tr><tr><td rowspan="5">Control of biotic factors</td><td>Soil compaction control</td><td>Energy consumption</td><td></td><td></td><td></td></tr><tr><td>Water supply</td><td>Energy pressure</td><td></td><td></td><td></td></tr><tr><td>Nitrogen supply</td><td>Recycling</td><td></td><td></td><td></td></tr><tr><td>Weed control</td><td>Habitat for wild species</td><td></td><td></td><td></td></tr><tr><td>Pest control</td><td>Soil pollution - Disruption of microbial activity</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_36.png" /></figure>
## 2.2. Studied indicators

To assess themes of the framework, we selected indicators used in MASC (Craheix et al., 2011). As shown in Table 1, only a subset of themes was worked out due to time constraints. Instead of some of the benefits, we included the corresponding ES indicators, except for soil compaction control. For several themes, we transformed the qualitative indicator of MASC 2.0 into a quantitative one using the CONTRA aggregation method (Bockstaller et al., 2017). This was done for erosion and P loss indicators. Themes related to water management were removed since irrigation is not a major issue in the case study region. Regarding soil pollution, the task was daunting when only addressing pesticide contamination (Martin-Laurent, personal communication, 6 January 2021). Last, the biodiversity indicator was totally new to integrate new knowledge since the release of the MASC model more than 10 years ago. To this list we added some economic indicators: gross margin, working time and energy production as a proxy for the MASC themes of farm revenue, farmers' expectations, societal expectations (food supply), respectively. We added an additional economic indicator, production cost, to assess farm competitiveness (Jarnemo, 2002; Ozerova et al., 2019), a theme which was not in the MASC model. Finally, some causal indicators used by the cooperative as a proxy for environmental indicators (e.g. global nitrogen balance, treatment frequency index) and other causal indicators that may explain the EI and ES indicators, like landscape variables, amounts of inputs (N, P, use of digestate from the cooperative methanization unit), a component of the gross margin and mechanization costs.

Like in other multicriteria assessment work (locola et al., 2020; Viguier et al., 2021), indicators were calculated with a set of different tools depending on the origin of the indicators (see appendix E.1 for an overview). Most indicators – the new EI and ES developed for this study (see SM2), the ES indicators of Keichinger et al. (2021) and INDIGO indicators (Bockstaller and Girardin, 2010) – were calculated in an Excel spreadsheet. Organic carbon storage was calculated with the AMG model (Clivot et al., 2019), while the rest of the environmental impacts, causal, economic and social indicators were calculated with the SYSTERRE® tool (locola et al., 2020; Toqué et al., 2019; Viguier et al., 2021). The management practices entered into the SMAG-Farmer® traceability software (used by the cooperative) or other competing software are automatically transferred to SYSTERRE® by a standard AGRO-EDI exchange. For farms that are not computerized, the field notebook is entered into SMAG-Farmer® by the SCARA cooperative teams, and the data is then automatically transferred to SYSTERRE®. Corrections are made manually after proofreading if necessary.

To analyse indicators at farm level, we calculated the weighted average of the plot indicators by weighting them by the surface area of the crop in the farm's crop rotation for the 2020 crop year.

## 2.3. Joint assessment of environmental impacts and ecosystem services for one sustainability theme

The conceptual indicator framework shows that most sustainability themes can be linked to a regulating ES indicator as a driver. We proposed an analytical framework for a joint analysis as shown in Fig. 30. For a couple of EI/ES indicators, we proposed setting a threshold which by default was chosen as the centre of the value range for a given indicator. By setting a relative threshold according to the typology, we were able to distinguish four situations. Two situations provide obvious information: win-win, where both indicators are in the favourable interval, and
lose-lose, where both indicators are in the unfavourable interval. The other two situations require further explanation.

- Lose-win situations with low ES (unfavourable) and low EI (favourable) may be due to situations where the EI is not yet expressed. This can be explained by a buffer mechanism, delayed response time, or both (e.g. nitrate storage in deep soil). Such situations may also be possible when mitigating management is implemented without increasing the corresponding service that will impact other indicators. In the case of erosion, no-tillage without the two other pillars of conservation agriculture providing ES – rotation and soil cover (Mondal et al., 2019) – will lead to higher herbicide use, and especially glyphosate (Cordeau, 2022).

- Win-lose situations with high ES (favourable) and high EI (unfavourable) may be due to situations when the ES potential is not actually used by farmers (Dardonville et al., 2022).

<table><caption>Figure 30 : Conceptual illustration of the joint assessment of environmental impacts and ecosystem services.</caption><thead><tr><th>Ecosystem service level</th><th>High</th><th>Win-Win situations: favourable to sustainability</th><th>Win-Lose situations: mitigated (high environmental vulnerability, risky management, etc.)</th></tr></thead><tbody><tr><th>Low</th><td></td><td>Lose-Win situations: mitigated (buffer effect, delayed response of environment, etc.)</td><td>Lose-Lose situations: unfavourable to sustainability</td></tr><tr><th></th><td></td><td>Low</td><td>High</td></tr><tr><th></th><td></td><td colspan="2">Environmental impact level</td></tr></tbody></table>

## 2.4. Case study

The conceptual indicator framework was implemented on farms located in the Champagne crayeuse area of the Grand Est region in north-eastern France. Crayeuse means "chalky" in French and refers to the chalky soil that is typical of the region. This is a region of intensive agriculture with the main crop sequence: winter rape/wheat/barley. The permeable, extremely calcareous and superficial silty soils (grey rendzina, 25 cm deep on average) have an average available water storage capacity of 180 mm. The region is characterized by a 'humid oceanic temperate' climate. Farmers in the sample all belonged to the SCARA, a cooperative with 623 members around Arcis-sur-Aube, between Châlons-en-Champagne and Troyes. Its management is very proactive and committed to supporting farmers who want to adopt new practices. It collects mainly field crops: cereals (wheat, barley and maize), oilseed crops (rapeseed, sunflower) and one protein crop (pea). A large part of the production meets certain certification standards (organic farming, High Environmental Value) or specifications (baby food, LU'Harmony, Harrys, among others). For
example, more than 75% of the cereals meet at least one type of specification. The cooperative recently installed a methanization unit for farmers who want to fertilize their fields with digestate for plots located on the cooperative's spreading plan (10,000 ha). Finally, the cooperative also collaborates with an environmental association (Noé) to monitor biodiversity on farms.

## 2.5. Sample composition

We selected 33 farms with a variety of certifications and specifications for our study. Among the selected farms, 2 are certified organic and 3 have started or plan to start the organic conversion process, 19 have High Environmental Value certification, 5 spread methanization digestate, and 4 have a livestock activity (dairy cows, sheep farming). The other specifications (baby food, LU'Harmony, SAPPORO, CRC) are found among the different farms. All farms grow at least one certified crop.

We then selected 4 to 8 plots by farm according to their crop rotation in order to cover at least 75% of all rotations (Appendix E.3). Rotation length varied between 4 and 10 years with 75% between 5 and 9 years (Appendix E.5). In total, the farms grow 18 crops, the majority (58%) of which are field crops (cereals, winter oilseed rape, maize) and 27% industrial vegetables (sugar beet and potato). The last 15% includes legumes, medicinal poppy, hemp, flax and dactyl. Finally, to see if there was a year effect, we selected one plot of wheat for each of the 33 farms for the 2018 and 2019 crop years. In all, we obtained a total of 250 plots (Appendix E.4).

## 2.6. Data collection

Most of the data on agricultural practices were collected with the SMAG-Farmer® information system, which is filled in by farmers each year. Data are then checked by a member of the SCARA cooperative's own staff. Thus, the database used to calculate the indicators and run the analyses needed very few corrections. To complete this large database, we conducted two short phone interviews with farmers to collect additional, very specific information on farm equipment (speed sprayer, nozzle type, etc.) or observed phenomena in the field (such as erosion issues).

## 2.7. Statistical analyses

We characterized the indicator distribution with boxplots. We then systemically calculated the correlation coefficient between pairs of indicators and synthesized the results in a matrix to identify redundancy between indicators as well as synergies and trade-offs. We used the threshold of $r = ±0.70$ proposed by Dormann et al. (2013). This redundancy analysis made it possible to reduce the set of indicators for the multivariate analyses. Then, we carried out a principle component analysis (PCA) and a hierarchical ascendant classification (HAC) to created groups of farms. Finally, we selected a reduced set of non-redundant indicators covering the main framework themes in relation to the area to analyse for positive devians in order to identify the Pareto optimal front (Adelhart Toorop et al., 2020; Steinke et al., 2019). Statistical analyses were carried out using RStudio version 1.3.959 and R-statistical packages: corrplot for the correlation matrix; FactoMineR, factoextra and Factoshiny for the PCA and the HCA; and emoa for Pareto optimal identification.

# 3. Results

## 3.1. Indicators variability

Fig. 31 shows that some indicators, such as the ES indicators linked to crop rotation (disease, weed, N loss and pollination), present a high degree of variability with a large interquartile range for disease and pollination (Fig. 31a) and an impact indicator RCesu_rd illustrated in Fig. 31b (see appendix E.1 for an overview of indicators). The causal indicators linked to input use – Nmin, Pmin
and TFItotal – also show considerable variation (Fig. 31b and Fig. 31c). At the other extreme, the albedo indicator fluctuates very little, despite variations in crop rotation length (between 4 and 10 years), because it is an average of the crop albedo. The N provision indicator also has a small range with 6 points that stand out (Fig. 31a). These correspond to rotations that include leguminous plants (Fig. 31a). Seven indicators show a slight variation: I-BIO, MESS, MERO, the phosphorus losses, RCeso, N2O and Perforest (Fig. 31b, Fig. 31c). Among them, three indicators have a very tight range of variation (I-BIO, MESS and MERO). MESS does not have a large distribution because the soil conditions, proportion of risky crops in rotation (harvested in autumn) and soil management (tillage) are quite similar. MESS is one of the variables used to calculate MERO, which partly explains the low variation of this indicator, such as the relative soil and topographic conditions. Regarding I-BIO, the only farms that stand out are the organic farms or farms with very low chemical input levels. However, few farms are concerned because our case study focuses on an intensive agricultural region. Moreover, the low variation of I-BIO could also be explained by the low proportion and homogeneity of semi-natural habitats. Such habitats have quite a heavy weighting in the indicator, yet they were not very discriminating in the studied region as there are very few agroecological structures such as hedgerows, tree lines or groves. This is consistent with the percentage of forest, which did not exceed 17%.

Regarding the variability of indicators among the 2018, 2019 and 2020 crop years, there is no significant variation except for the pesticide contamination of air and groundwater (Appendix E.6). For air contamination, the increase in RCair_vol during the last crop year was due to the prohibition of a very volatile active ingredient, chlorothalonil (Appendix E.6d). For groundwater contamination, the improvement in the RCeso indicator could not be explained by a single cause (Appendix E.6c).

a

<figure><img src="image_37.png" /></figure>

b

<figure><img src="image_38.png" /></figure>
<figure><img src="image_39.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_41.png" /></figure>

Figure 31: Boxplots of indicator values within the ranges of a) 0 and 1, b) 1 and 10, c) -50 and 200, d) 0 and 3500 and e) 0 and 200,000. For simplicity's sake and in order to properly represent the variation in energy pressure, this indicator has been added to the energy production graph. The energy pressure varies between 0 and 25,000 and the energy production between 75,000 and 200,000. The indicator units are as follows: kg/ha for Nmin, Pmin, GNB, P balance, NO₃, NH₃ and N₂O; h/ha for working time; kg CO₂-eq/ha for GHG; €/ha for mechanization costs, production costs and gross margin; and MJ/ha for energy pressure and energy production.

## 3.2. Joint assessment of ecosystem services and environmental impacts

The joint assessment of ES and EI based on the method described in Fig. 30 showed that most of the farms (79%) are not sustainable with regard to nitrogen supply (Fig. 32a) as they add a high amount of mineral nitrogen to their crops and while the agricultural system itself provides only a low level of nitrogen. Only three farms are in the win-win situation for this theme. The need for high amounts of mineral nitrogen is due to the low soil supply by mineralization, the lack of organic resources across the region and the breeding varieties of wheat, which require a substantial nitrogen supply to achieve the required protein rate (14.5% compared to 11.5% for
classic varieties). For the ecosystem service, the weak presence of legumes in the crop rotation, potentially linked to the absence of a market, could explain the low scores. If we look at nitrate losses (Fig. 32b), the results are better: 45% of the farms are in the win-win situation with a high level of ecosystem services. However, lixiviation is high for 50% of the sample. This could be due to environmental characteristics, shallow soil or low available water storage capacity. For the themes linked to pesticides (Fig. 32c, Fig. 32d), the situation is mitigated for sustainability. Regarding weeds, most farms are in the lose-win situation with a medium-high service level but high herbicide use. Only five farms are in the win-win situation with a low herbicide TFI and a high service level, while two are in a lose-win and one in a lose-lose situation. These high herbicide TFI values could be explained by the high proportion of sugar beet in the farm rotations. This crop is quite sensitive to weeds and very often has a high herbicide TFI. For the risk of pesticide contamination of surface water (Fig. 32d), more than 70% of the farms are in the win-lose situation. They have a high impact but a medium-high service level to mitigate this EI. There are nine farms that are in a win-win situation, meaning they are favourable in terms of sustainability with a moderate or even high level of performance for the pesticide contamination risk. Here, the risk factor is the number of treatments and the fact that the indicator was parametrized for a worst-case situation. The variable interval between date of the treatment and the date of the runoff was set to three days just like for other indicators (Strassemeyer et al., 2017, 2003). All the farms are in the win-win situation for the soil erosion theme (Fig. 32e). This environmental impact is no longer an issue as farmers adapted their practices when it was required.

Finally, for the climate mitigation (Fig. 32f), only two farms are in the win-win situation as they have low GHG emissions and store carbon, although they have not reached equilibrium as they emitted more CO2 than they sequestered. Just over 25% of the farms are in the lose-lose situation as they emitted substantial amounts of GHG but did not store carbon. The majority of the farms (66%) are in the win-lose situation with a medium-high service level but a high impact. The main explanatory factor is the use of mineral nitrogen, which explains the high rate of CO2 emissions. As the calculation of these emissions integrates the energy required to produce the mineral nitrogen used by farmers and the intensity of machinery use, especially for organic plots. With regard to the albedo, there was no major difference between crop rotations when we calculated the variation of albedo required to calculate the radiative forcing. This is probably due to the choice of reference, as it has an albedo value close to the other crop rotations. It would be interesting to improve the reference value as the albedo effect could significantly mitigate the greenhouse effect when cover crops are planted (Carrer et al., 2018; Sieber et al., 2022).
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

Figure 32: Joint assessment of six main environmental themes: a) Nitrogen inputs; b) Nitrate losses; c) Weed control; d) Pesticide losses; e) Soil erosion and f) Global climate regulation. The dots represent each farm situation. The red lines correspond to the centre range value. This centre value is relative to the case study. The coloured zones refer to the different win/lose situations (see Fig. 30): green for win-win, red for lose-lose, light grey for win-lose and dark grey for lose-win.
### 3.3. Study of correlations between indicators

#### 3.3.1. Farm level

Unsurprising, the correlation matrix (Fig. 33) shows that GHG emissions are positively correlated with energy pressures and the global nitrogen balance (|r| > 0.7). This is also the case for MERO with phosphorus losses and mineral phosphorus with the phosphorus balance. For the latter, MERO is one of the variables of the phosphorus losses indicator and has a heavier weighting, which explains the positive correlation. Similarly, the total TFI is negatively correlated with RCair_vol. This correlation was not expected and is not observed for other the components of I-Phy, RCESO and RCESU_rd; RCair_vol is based on performances, so the higher the total TFI, the lower the RCair_vol.

<figure><img src="image_48.png" /><figcaption>Figure 33: Correlation matrix of all the indicators calculated at farm level. Blue dots are positively correlated and red dots are negatively correlated. Dot size is proportional to the level of correlation. All correlations shown in this figure are significative (p-value < 0.05).</figcaption></figure>

### 3.4. Hierarchical ascendant classification of farms

A first hierarchical ascendant classification with the data set for the 2020 crop year revealed that some farms had very low yields caused by meteorological conditions, which biased the classification. Thus, we replaced some of the low yields of impacted crops (e.g. winter oilseed rape, maize) with average values in order to obtain the energy production and production costs within a normal range of variation. As shown in Fig. 34, cluster 1 is intensive with more erosion and N₂O emissions. Four ES are lower and two are higher, but the services related to pesticides were only slightly affected. Cluster 2 includes the most intensive farms of the Champagne crayeuse region with higher yields. Indeed, there is more energy production but also more EI.
Mineral phosphorus inputs are much higher as are pesticide treatments (total TFI). There are more emissions of pesticides (RCair_vol) and GHG, but NO₃ emissions are lower and there is less erosion. The presence of winter oilseed rape and cover crops could explain the reduced NO₃ losses. The lower water ES level could be linked to the presence of cover crops, as confirmed by the literature (Blanco-Canqui, 2018). Cluster 2 presents a trade-off for the water storage ES, which can be explained by the increase in water consumption, although this has a real impact in the case of cover crops with a long cycle (Tribouillois et al., 2022). This was the case here with cover crops sown after harvest and destroyed four months later. Cluster 3 stands apart from the others as it has improvements at all levels: lower NO₂ emissions for the EI, three improved ES (pollination, soil structure and landscape), lower mechanization costs for the economic performances and less working time for the social performances. A higher percentage of flowering crops (winter oilseed rape and medicinal poppy) explains the better pollination ES. The lower mechanization costs may be attributed to the higher percentage of medicinal poppy, which is harvested by the pharmaceutical company that commissions the crop. Moreover, the first farm with the positive deviants belongs to this cluster. Cluster 4 included one organic and two conventional farms. It performed better for the EI with a lower treatment frequency of pesticides and less pesticide contamination of air, but yielded lower carbon storage, weed regulation, production costs and energy production. The lower energy production may have induced lower carbon storage, while the high proportion of cereals in the crop rotation may have had a negative effect on the weed control ES. The presence of an organic farm and the diversified crop rotations could explain the low level of pesticides applied. The calculation of the rotation taxonomic diversity by means of Simpson's reciprocal index (Keichinger et al., 2021) yielded an average diversity of 3.9 for cluster 2 with high pesticide use and 4.8 for cluster 4 with low pesticide use, respectively, i.e. nearly one crop more in cluster 4. Cluster 5 is the typical organic farm with long and diversified crop rotations and the presence of agroecological features like forests. Indeed, this farm yielded very good environmental performances (impact on biodiversity, nitrogen losses, pesticide pollution and GHG emissions) and a high level of some ES (weed regulation and albedo). But it performed worse on the economic side with higher production costs.

Overall, for these clusters, crop patterns alone could not explain the differences among the farms (see Appendix E.7). For example, clusters 1, 2 and 3 present similar proportions of winter wheat, spring barley and sugar beet. Clusters 4 and 5 were totally different. While the presence of some crops like rapeseed could explain certain results in cluster 2, in general, no clear trend was found. This means that crop management also plays an important role.
<table><thead><tr><th></th><th>Environmental impacts</th><th>Ecosystem services</th><th>Economic performances</th><th>Social performances</th><th>Farm characteristics</th></tr></thead><tbody><tr><td>Cluster 1<br>(n=12)</td><td>N2O emissions (+12%)<br>Soil erosion control (-7%)</td><td>Pollination (-19%)<br>Soil structure (-10%)<br>Landscape (-8%)<br>Reduction N loss (-4%)<br>Water (+9%)<br>Pest (+2%)</td><td></td><td></td><td>Use digestate</td></tr><tr><td>Cluster 2<br>(n=10)</td><td>Mineral phosphorus (+45%)<br>Total TFI (+22%)<br>Pesticide contamination of air (+14%)<br>GHG (+10%)<br>NO3 (-23%)<br>Erosion control (+7%)</td><td>Water (-12%)</td><td>Energy production (+10%)</td><td></td><td></td></tr><tr><td>Cluster 3<br>(n=7)</td><td>NO2 (-18%)</td><td>Pollination (+37%)<br>Soil structure (+10%)<br>Landscape (+8%)</td><td>Mechanisation cost (-25%)</td><td>Working time (-25%)</td><td></td></tr><tr><td>Cluster 4<br>(n=3)</td><td>Total TFI (-50%)<br>Pesticide contamination of air (-36%)</td><td>C storage (-297%)<br>Weed (-25%)</td><td>Production cost (+73%)<br>Energy production (-31%)</td><td></td><td>1 farm in organic farming</td></tr><tr><td>Cluster 5<br>(n=1)</td><td>Impact on biodiversity (-170%)<br>Pesticide contamination of surface water (-144%)<br>Total TFI (-100%)<br>Mineral nitrogen (-100%)<br>NH3 (-87%)<br>Pesticide contamination of ground water (-85%)<br>N2O (-70%)<br>GHG (-54%)<br>Pesticide contamination of air (-54%)</td><td>Weed (+44%)<br>Albedo (+14%)</td><td>Production cost (+103%)</td><td></td><td>Full organic farming<br>Forest (+145%)</td></tr></tbody></table>

Figure 34: Characterization of the clusters given by the hierarchical ascendant classification (HAC) for the 33 farms. Indicators within each cluster are considered as discriminant by the HAC. The farm characteristics column specifies only variables that significantly discriminated farms. Percentages in brackets correspond to the difference in the percent of the cluster mean to the sample mean. The indicators in green have a positive effect on agroecosystem sustainability, while those in red have a negative effect.

### 3.5. Establishing farm profiles analysing positive deviants

The analysis of positive deviants with the Pareto-optimal front (Steinke et al., 2019) showed that one farm (02-2H0 in Fig. 35) presents a good synergy between environmental performances (EI and ES) and socio-economic performances (Fig. 35). It has a good supply of ecosystem services with the best score for the landscape value. Indeed, the crop rotation of this farm integrates several family crops all along the rotation. For the environmental performance, it performs well for the nitrate losses and the pesticide contamination of the air but less for the contamination of groundwater. Scores for the economic performances are in the average. Lastly, the farm has a good social performance with a low working time.
<table><caption>Figure 35: Classification of the 33 farms with 10 indicators following the positive deviance analysis with the Pareto-optimal front. The colour gradient thematizes the indicator value according to its position.</caption><thead><tr><th>Farm</th><th>RCeso</th><th>Rcair vol</th><th>NO3</th><th>C balance</th><th>Soil structure</th><th>Landscape</th><th>Energy production</th><th>Gross margin</th><th>Work time</th><th>Rank</th></tr></thead><tbody><tr><td>02-2H0</td><td>5.8</td><td>8.5</td><td>-19</td><td>-1654</td><td>0.67</td><td>0.60</td><td>142,304</td><td>1053</td><td>-3.0</td><td>1</td></tr><tr><td>01-100*</td><td>4.4</td><td>6.7</td><td>-22</td><td>-1063</td><td>0.65</td><td>0.55</td><td>156,263</td><td>1262</td><td>-1.8</td><td>2</td></tr><tr><td>01-2M0</td><td>5.7</td><td>5.9</td><td>-50</td><td>-2768</td><td>0.52</td><td>0.41</td><td>151,244</td><td>2142</td><td>-2.9</td><td>2</td></tr><tr><td>01-3M0</td><td>4.9</td><td>7.6</td><td>-17</td><td>-2125</td><td>0.57</td><td>0.49</td><td>146,534</td><td>447</td><td>-4.1</td><td>2</td></tr><tr><td>01-4HC</td><td>5.2</td><td>7.5</td><td>-34</td><td>-3394</td><td>0.62</td><td>0.52</td><td>105,388</td><td>738</td><td>-2.9</td><td>2</td></tr><tr><td>02-1H0</td><td>5.2</td><td>4.5</td><td>-9</td><td>-2003</td><td>0.69</td><td>0.57</td><td>161,252</td><td>714</td><td>-3.0</td><td>2</td></tr><tr><td>02-300*</td><td>6.5</td><td>6.9</td><td>-24</td><td>-2392</td><td>0.60</td><td>0.50</td><td>143,804</td><td>1447</td><td>-4.0</td><td>2</td></tr><tr><td>02-40B</td><td>6.3</td><td>7.0</td><td>-34</td><td>-749</td><td>0.59</td><td>0.56</td><td>134,622</td><td>1082</td><td>-1.6</td><td>2</td></tr><tr><td>03-1HB*</td><td>5.8</td><td>8.1</td><td>-15</td><td>-2336</td><td>0.65</td><td>0.49</td><td>137,007</td><td>1253</td><td>-5.2</td><td>2</td></tr><tr><td>03-20B*</td><td>5.3</td><td>5.3</td><td>-18</td><td>-3558</td><td>0.60</td><td>0.56</td><td>169,148</td><td>1225</td><td>-4.5</td><td>2</td></tr><tr><td>03-3H0</td><td>5.1</td><td>7.0</td><td>-19</td><td>-3344</td><td>0.61</td><td>0.55</td><td>185,912</td><td>1225</td><td>-3.5</td><td>2</td></tr><tr><td>03-4H6</td><td>5.7</td><td>5.9</td><td>-15</td><td>-2624</td><td>0.56</td><td>0.49</td><td>161,054</td><td>592</td><td>-3.8</td><td>2</td></tr><tr><td>04-10B</td><td>5.4</td><td>4.1</td><td>-23</td><td>-2238</td><td>0.56</td><td>0.55</td><td>159,430</td><td>2356</td><td>-5.9</td><td>2</td></tr><tr><td>04-20B*</td><td>5.9</td><td>3.8</td><td>-21</td><td>-1675</td><td>0.59</td><td>0.55</td><td>184,174</td><td>890</td><td>-3.9</td><td>2</td></tr><tr><td>04-320</td><td>10.0</td><td>10.0</td><td>-11</td><td>-628</td><td>0.72</td><td>0.52</td><td>104,063</td><td>1054</td><td>-5.7</td><td>2</td></tr><tr><td>04-406*</td><td>4.5</td><td>6.1</td><td>-33</td><td>-2179</td><td>0.71</td><td>0.56</td><td>179,083</td><td>1443</td><td>-2.7</td><td>2</td></tr><tr><td>05-130*</td><td>6.2</td><td>9.2</td><td>-36</td><td>-2871</td><td>0.60</td><td>0.51</td><td>90,758</td><td>17</td><td>-3.4</td><td>2</td></tr><tr><td>05-2H7</td><td>5.2</td><td>5.9</td><td>-39</td><td>-2794</td><td>0.70</td><td>0.56</td><td>161,633</td><td>949</td><td>-2.8</td><td>2</td></tr><tr><td>05-4V0</td><td>4.6</td><td>4.7</td><td>-23</td><td>-2255</td><td>0.59</td><td>0.58</td><td>162,654</td><td>752</td><td>-3.7</td><td>2</td></tr><tr><td>06-200</td><td>5.3</td><td>5.7</td><td>-33</td><td>-1854</td><td>0.70</td><td>0.56</td><td>135,442</td><td>963</td><td>-3.4</td><td>2</td></tr><tr><td>06-4H5</td><td>4.4</td><td>6.1</td><td>-29</td><td>-2631</td><td>0.53</td><td>0.49</td><td>155,263</td><td>1041</td><td>-2.8</td><td>2</td></tr><tr><td>07-2MY*</td><td>5.1</td><td>4.5</td><td>-43</td><td>-3146</td><td>0.54</td><td>0.45</td><td>143,187</td><td>2179</td><td>-3.3</td><td>2</td></tr><tr><td>07-4H5</td><td>5.1</td><td>7.0</td><td>-28</td><td>-2719</td><td>0.62</td><td>0.55</td><td>147,387</td><td>1031</td><td>-3.4</td><td>2</td></tr><tr><td>08-2HY</td><td>4.9</td><td>7.1</td><td>-36</td><td>-2696</td><td>0.59</td><td>0.55</td><td>148,213</td><td>1003</td><td>-4.1</td><td>2</td></tr><tr><td>08-410</td><td>4.8</td><td>5.4</td><td>-29</td><td>-2114</td><td>0.66</td><td>0.55</td><td>148,359</td><td>1</td><td>-5.2</td><td>2</td></tr><tr><td>09-2HY</td><td>5.5</td><td>7.0</td><td>-24</td><td>-2641</td><td>0.57</td><td>0.47</td><td>178,718</td><td>980</td><td>-4.5</td><td>2</td></tr><tr><td>09-4H5</td><td>4.0</td><td>6.6</td><td>-37</td><td>-1708</td><td>0.66</td><td>0.53</td><td>143,550</td><td>1339</td><td>-2.1</td><td>2</td></tr><tr><td>10-2H7</td><td>4.8</td><td>6.3</td><td>-21</td><td>-3634</td><td>0.47</td><td>0.43</td><td>193,938</td><td>1412</td><td>-3.1</td><td>2</td></tr><tr><td>10-4HC</td><td>4.3</td><td>5.7</td><td>-42</td><td>-2965</td><td>0.65</td><td>0.56</td><td>147,463</td><td>1661</td><td>-2.9</td><td>2</td></tr><tr><td>11-220</td><td>7.3</td><td>9.7</td><td>-44</td><td>-2555</td><td>0.53</td><td>0.49</td><td>87,105</td><td>691</td><td>-5.1</td><td>2</td></tr><tr><td>11-446*</td><td>4.7</td><td>8.3</td><td>-21</td><td>-2245</td><td>0.59</td><td>0.55</td><td>111,931</td><td>117</td><td>-4.4</td><td>2</td></tr><tr><td>12-2MB</td><td>4.1</td><td>4.2</td><td>-46</td><td>-1905</td><td>0.54</td><td>0.49</td><td>112,477</td><td>356</td><td>-2.5</td><td>2</td></tr><tr><td>13-210</td><td>6.6</td><td>6.3</td><td>-26</td><td>-3323</td><td>0.54</td><td>0.43</td><td>151,620</td><td>393</td><td>-6.3</td><td>2</td></tr></tbody></table>

# 4. Discussion

## 4.1. An original indicator framework

Integrating both environmental impacts (EI) and ecosystem services (ES) in sustainability assessments of agricultural systems is, to our knowledge, a novel approach. Previous studies propose a list of juxtaposed indicators which are then plotted on a radar diagram like in Barot et al. (2017) or in synthetic and symbolic figures representing how livestock farming interacts with its physical, economic and social environments (Dumont et al., 2019). In many studies, a list of regulating services (carbon storage, pest control, etc.) are proposed and implicitly linked to provisioning services (Haines-Young and Potschin, 2018; Millennium Ecosystem Assessment, 2005), whereas in our framework we systematically connected benefits or impacts (i.e. the sustainability themes with components due to human activity [management] interacting with environmental characteristics [soil, climate, etc.]) and ES. Our aim was to clearly separate the share due to ES from the share due to management, following Duru et al. (2015), Tibi and Therond (2017) and Barot et al. (2017). In this case, we not only applied it to the main provisioning ES, namely production of goods, as in Duru et al. (2015) and Siciliano (2009), but we also linked regulation ES to EI as in Tibi and Therond (2017). The difference between provisioning and regulating ES is that for benefits driven by provisioning ES, the share due to ES and the share due to management may be additive, while for environmental impacts, in many cases ES play a
mitigating role (e.g. carbon storage vs. greenhouse gas emissions, nutrient uptake vs. accumulation and nutrient losses in soil).

As shown in Table 12, it should be noted that the causal chain cannot be reduced to one impact as it is driven by three factors: one linked to ES, one to management and one to environmental characteristics. Reality is always more complex than a linear chain (Niemeijer and de Groot, 2008). For some impacts, several ES are implied, such as for climate change, with carbon storage and albedo. In some cases, a so-called ES may be the result of several processes, which for some are 'services' and other 'disservices'. This is the case for the ES reduction of nitrate leaching, which results from the balance between a service (nitrate uptake by crop) and a disservice (mineralization of nitrogen in crop residue and soil organic matter) (see our indicator in appendix E.2). Both are based on biological processes (see Barot et al., 2017), although mineralization is amplified by human activity (tillage, fertilization, residue type, keeping the soil bare during the winter drainage period, etc.). Mineralization would not be significant if the crop cover were not removed.

## 4.2. A method based on predictive indicators

Indicators selected for the assessment methods used in the literature until now were predictive effect indicators based on operational models that combined management, soil and climate data. Such models present several advantages (Bockstaller et al., 2015) and are easier to implement than more mechanistic models, but they are less feasible than causal indicators using management variables (e.g. amount of mineral fertilizer) or a simple combination of these variables (e.g. TFI; Hossard et al., 2017). One drawback was the need for soil and climate data, which may add uncertainty to the calculation. This may explain why in the review of 262 assessment methods by Soulé et al. (2021), only a third used such indicators (30%), a figure that is even lower when considering only the methods using these indicators for all themes. For carbon storage, the AMG model does not simulate the whole plant cycle like in the STICS model (Brisson et al., 2009). For the assessment of ES, we remained at the level of potential capacity service according to the framework of Dardonville et al. (2022), who distinguished three levels: the potential capacity of ES, the real capacity of ES and the actual use. Here we stick to the potential capacity of ES, which is related to the spatiotemporal configuration of plant cover. We focused on crop rotation but used original indicators (habitat associated with crops, landscape value of crops) that integrated more processes than Dardonville et al. (2022).

New indicators were also designed for some themes like erosion, phosphorus losses, biodiversity (I-BIO). Except for biodiversity, we tried to move away from indicators based on a qualitative scale designed with DEXi to a semi-quantitative continuous approach with the CONTRA approach to avoid a threshold effect (Bockstaller et al., 2017). Although they did not reach the degree of complexity of a crop model like STICS, our indicators required more details than other indicators in some cases. Assessing the risk of pesticide transfer requires nitrogen emissions, data on application date, rate and type of input, among others. The SCARA information system was very useful in collecting a considerable amount of input data. Nevertheless, such data collection requires a verification procedure because farmers' data often contain mistakes. Furthermore, the abovementioned indicators and several others require data on soil and even climate (i.e. nitrate). Soil description may be a bottleneck when no precise database and map are available. We collected as much data as possible from diverse sources, but some uncertainties do remain.
For the climate change theme, we performed a quantitative assessment that included the calculation of the GHG balance (carbon balance) resulting from EI and ES indicators. For other themes, we remained at a qualitative level, using ES indicators based on semi-quantitative scores that could be combined with an indicator assessing the effect due to human activity. We did not aggregate those indicators, as this was done in the MASC 2.0 method with the DEXi tool (Craheix et al., 2012). It should be noted that these authors did not explicitly separate the share due to ES because they did not manipulate the concept of ES. We made this choice to save time and discussion on the aggregation model. The main goal of this work was not to achieve a comprehensive method but to lay the foundations of joint assessment of ES and EI indicators. This was also the reason for limiting the number of indicators for the assessment of the economic and social dimensions. For some themes, we could have performed a quantitative assessment, such as for nitrate leaching like in Tibi and Therond (2017). But those authors implemented the STICS model, which exceeded the possibility of this study, especially if such a model were to be implemented for a set of environmental themes. Following their work, we could have taken part of the nitrate INDIGO indicator to calculate what was due to the ES, N uptake and mineralization. But here again, it was faster to implement the indicator used by Keichinger et al. (2021).

### 4.3. Results obtained with the assessment method

The joint assessment of ES and EI, like the multivariate analysis, delivered results that showed more nuances than a classical opposition between intensive and organic farming (Ponisio et al., 2015; Tuomisto et al., 2012). For several themes, results were mitigated regarding sustainability. The intensive systems presented some drawbacks regarding EI despite their rather good level of services due to much more diversified rotations compared to the two most frequent crop sequences in France: winter oilseed rape/wheat/barley and maize/winter wheat (Meynard et al., 2013). This high level of diversification is specific to this region of Champagne and northern France, where there are a lot of market opportunities for industrial crops, sugar beet, potatoes, alfalfa (in Champagne only), etc. This win-lose situation (see Fig. 30, Fig.32b, Fig.32c, Fig.32d, Fig.32f) can also be seen in the study of Dardonville (2022), who identified a group of famers with quite a high unused ES potential. We found several reasons for this specific case which are linked to the high level of intensification and crops sensitive to weed competition. Furthermore, clusters 1 to 3 were less diversified than cluster 4, which showed a lower impact of transfer to the atmosphere and a substantial reduction in pesticide use that met the French national reduction plan goals (Hossard et al., 2017). This shows that rotations could still potentially be enhanced in the region to further lower pesticide use. To go one step further, more legumes could be introduced into the rotation, thus leveraging a major approach to reduce mineral fertilizer use, the main contributor to GHG emissions. While doing so would certainly improve several indicators, it could also lead to a trade-off regarding pesticide-related themes due to the use of some active ingredients (Viguier et al., 2021).

The correlation study and the multivariate analysis did not show a clear trade-off between environmental and socio-economic indicators, at least regarding farm profitability. Cluster 4 yielded better results for pesticide reduction and risk but did not perform as well when it came to energy production due to lower yield, crops with lower energy content and production costs. But the analysis did not indicate an impact on the gross margin, which is a profitability indicator of major concern for most farmers (Tey and Brindal, 2015). Results produced over more than 20 years have shown that integrated cropping systems based on a system approach to reduce input use perform as well as or even better than the conventional system (Butault et al., 2010; Holland et al., 1994). Production costs reflect weaker competitiveness on the world market (Ozerova et al.,
2019), but when products are certified with a specification on input use, the higher competitiveness of the conventional system is lost. In any case, the positive deviance analysis showed one farm with average values for all indicators, while the organic farm in cluster 5 performed better for environmental indicators but more poorly for energy production and working time and at the same level for the gross margin than the positive deviant.

These interesting results were obtained on a limited sample of farms in a specific region, which is reflected in the small cluster size (especially clusters 4 and 5, n = 3 and 1), including the most different farms from among the conventional ones. This thus shows the need to have a more diversified and larger sample of farms from different production contexts, such as regions with lower yield potentials and with other or lower diversification possibilities. It would be especially interesting to add farms with grassland or/and hay in their rotations. Another limitation of the study was that ES indicators were calculated for one rotation selected on the farm while EI indicators were calculated for one year for a sample of plots on the farm. We did not precisely control the representativity of the plots within each farm. Ideally the study should be repeated on all plots on the farms for the years of the whole rotation, but data were not available and time was lacking. It should be noted that our study assessed the effect of interannual variability for one crop (wheat for three years). Furthermore, we mitigated the effect of extreme low yields of the studied year by replacing some very low yields for certain crops with average yield values. This discussion on temporal aspects leads us to a general limit of the study: the assessment remains based on static indicators without tackling dynamic aspects related to sustainability like resilience or vulnerability (Dardonville et al., 2021; Hercher-Pasteur et al., 2020). Such dynamic assessment requires a time series, which was beyond the possibility of our study. One alternative would be to assess resilience using a set of system features (Urruty et al., 2016) as proposed by Zahm et al. (2018) in the IDEA v.4. This required a specific effort again beyond the scope and limits of our study.

Another shortcoming of the analysis of our results concerns the definition of the reference values in the joint assessment of ES and EI. We opted to use the relative threshold, rather than the absolute, based on statistics taken from the sample due to the lack of an absolute threshold for many indicators (Acosta-Alba and Van der Werf, 2011). For some of them, the absolute value could have been proposed, such as by using the water quality threshold (50 mg NO3/L) for nitrate leaching or the negative balance showing carbon storage for the carbon balance. But to maintain consistency between indicators, we chose to use the same type of reference value. Doing so enabled us to compare farms to each other and determine which farm was the 'most sustainable' according to our sample. However, this does not mean that this farm is really sustainable from an absolute point of view. Relative references are more frequently used than absolute. In the review by Soulé et al. (2021), 33% of the methods used relative references (e.g. Acosta-Alba et al. (2019); Paracchini et al. (2015); Ripoll-Bosch et al. (2012)), while 52% of them used absolute values, 21% used none at all and two methods used both relative and absolute references. This issue of absolute reference values remains a research gap with recent development with planetary boundaries (Vanham et al., 2019).

## 5. Conclusion

Our original indicator framework makes it possible to integrate both ecosystem services and environmental impacts in an explicit way. Using this framework, we showed that ES are drivers of benefits and impacts that interact with effects due to management and environmental characteristics. To obtain a list of environmental themes, we performed a joint analysis of EI and
ES indicators for some single themes as well as a multivariate analysis. Both the joint assessment of ES and EI and the multivariate analysis showed more nuanced results than a classical opposition between intensive and organic farming. Some farms have a moderate or even good level of ES but often do not leverage them, as shown by their high level of impacts. We performed a PCA analysis followed by a hierarchical ascendant classification to distinguish five clusters with different profiles of impacts and services, but without systematic trade-offs with economic indicators, and especially profitability. Finally, the positive deviance analysis revealed that it is possible to strike a balance between the economic, environmental and social considerations. A conventional farm could be more sustainable than an organic farm, which may perform well when it comes to reducing EI but at the cost of some economic and social trade-offs. The results we obtained based on a limited number of farms in one region should be confirmed by similar studies in other climatic, soil and crop diversification contexts. Likewise, for sake of simplicity and resource availability, the indicator framework was simplified for some themes, while economic and social dimensions were assessed with a reduced number of indicators. A more comprehensive indicator framework could be developed for sustainability assessment integrating dynamic indicators, such as to tackle resilience.

## Acknowledgements

This study is part of a PhD research project supported by a grant (no. 2019/0020) overseen by the French National Research Technology Agency (ANRT). The authors would like to thank Olivier Therond and Manon Dardonville for the interesting discussion regarding the indicator framework, and Olivier Keichinger and Hélène Huin for the help in calculating some of the indicators.

## CRediT authorship contribution statement

Christian Bockstaller and Emma Soulé: Conceptualization, Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Rémi Charbonnier, Nadia Michel, Philippe Michonneau and Laura Schlosser: Conceptualization, Writing – review & editing, Visualization.
# Parte B. Influence des cahiers des charges sur les performances des exploitations agricoles : focus sur l'agriculture biologique et le référentiel HVE

## 1. Introduction

La première référence ou norme imposée à un produit agricole est apparue aux États-Unis au milieu du 19ème siècle pour le commerce des graines de céréales (blé et maïs). Les céréales jusqu'alors transportées en sac vont avec le développement du chemin de fer être mélangées dans des bacs. Il n'est alors plus possible de distinguer les différents propriétaires des graines. Cette perte de traçabilité et de maîtrise de la qualité qui en découlait a conduit le Chicago Board of Trade à créer une norme pour les graines basée sur 3 notes (critère à 3 modalités) : « white, red and spring prime quality » (Cronon 1991 dans Daviron and Vagneron, 2011). On voit ensuite émerger dans les années 60 des cahiers des charges pour l'agriculture biologique et le commerce équitable faisant écho aux alertes de plusieurs associations sur les limites de l'agriculture industrielle. Suite à la conférence de Rio en 1992, on assiste à une multiplication des certifications durables internationales avec la création de quatorze certifications en moins de 20 ans (figure 36, Daviron and Vagneron, 2011). Les entreprises agroalimentaires vont également s'emparer de cette problématique et proposer leurs propres références (Thorlakson et al., 2018). En effet, d'après (Green, 2013), 90% des références environnementales privées des secteurs alimentaire et textile ont été créées depuis les années 90.

<figure><img src="image_49.png" /><figcaption>Figure 36: Chronologie des initiatives de normes de durabilité (Daviron and Vagneron, 2011).</figcaption></figure>

L'inondation des marchés agricoles par les certifications durables depuis les années 90 a pour effet d'initier la marchandisation des produits certifiées augmentant l'écart de prix entre les producteurs et les consommateurs pour certaines certifications. Dans certains organismes de labélisation non vertueux, les prix des produits labélisés sont plus élevés sans qu'une prime ne soit versée aux producteurs (Daviron and Vagneron, 2011). En effet, une des caractéristiques des
certifications durables est l'effet de « watering down » (Green, 2005) c'est-à-dire la dilution des principes portés par les pionniers en la matière, l'agriculture biologique et le commerce équitable. Afin d'impliquer le plus d'acteurs possible aux intérêts divers et parfois conflictuels, le « watering down » semble incontournable, or une des conséquences est l'adoption de principes et critères moins strictes tels que les dispositions sur les prix. Un autre exemple présenté dans l'étude d'Englund and Berndes (2015) est l'intégration de la biodiversité aux divers cahiers des charges. Leur étude montre que pour la thématique de la biodiversité, on observe de grandes divergences sur les exigences des cahiers des charges. Si on prend le cas de l'agriculture biologique, le référentiel de l'Union Européenne pour l'agriculture biologique (EU organic) prend peu en compte les critères de destruction et fragmentation de l'habitat, ou de dégradation et de modification de l'habitat alors que c'est le cas de l'IFOAM (International Federation of Organic Agriculture Movements) qui couvre le monde entier. Par ailleurs, la certification « Sustainable Agriculture Network/Rainforest Alliance » intègre bien les critères d'utilisation de l'énergie et des émissions de gaz à effet de serre contrairement à l'EU organic et à l'IFOAM. On retrouve ces divergences au niveau national pour la France. Le volet Biodiversité pour la certification Haute Valeur Environnementale de niveau 3 comporte plusieurs critères dont notamment le pourcentage d'infrastructures agroécologiques, la diversité des espèces végétales cultivées ou la présence de ruche alors que le volet Biodiversité du cahier des charges LU'HARMONY n'impose qu'une action obligatoire parmi quatre options.

Ces certifications environnementales multiples viennent donc compliquer le choix des consommateurs lors de l'achat. La certification en agriculture biologique reste cependant une valeur sûre pour une grande partie de la population de par l'effet halo associé aux aliments biologiques qui en plus d'avoir un faible impact sur l'environnement sont réputés plus sains (Muller et al., 2019). Les consommateurs vont ainsi acheter des produits labellisés AB parce qu'ils font attention à leur santé sans obligatoirement songer aux effets bénéfiques de l'AB sur l'environnement. Cette dernière, dont le cahier des charges est principalement basé sur une liste d'intrants interdits plus que sur des principes, ne permet pas d'atteindre tous les objectifs de durabilité. En effet, si on compare les principes énoncés par la méthode SAFA (Sustainability Assessment of Food and Agriculture systems, FAO 2013), développée par la FAO pour évaluer la durabilité des chaînes de valeur de l'alimentation et de l'agriculture, aux critères du cahier des charges de l'IFOAM, certains thèmes ne sont pas entièrement traités comme la fertilité des sols à long terme ou les techniques de sélection (Merfield et al., 2015). Aussi, on oppose souvent classiquement l'agriculture biologique et l'agriculture conventionnelle, or il existe une multitude de pratiques agricoles au sein de ces deux groupes associées à un large gradient de performances environnementales qui dépend des décisions des agriculteurs et des prises d'initiative locales (Therond et al., 2017).

Ainsi, afin d'aller plus loin sur l'évaluation des performances environnementales des certifications, nous avons décidé de les comparer en nous focalisant sur deux certifications : la certification Agriculture biologique et la certification HVE qui depuis récemment est encouragée par le gouvernement français pour répondre aux exigences environnementales de la PAC (Lassalas, 2020).
## 2. Matériel et méthode

Pour ce sous-chapitre, nous avons repris les valeurs des indicateurs calculés précédemment sur les 33 exploitations et analysé ces valeurs en distinguant 3 groupes d'exploitations : les exploitations en agriculture conventionnelle (AC), (n=14), les exploitations certifiées HVE de niveau 3 (n=17) et les exploitations certifiées en agriculture biologique (AB), (n=2). Il est à noter que les exploitations en AC, servant de référence pour comparer les deux certifications, sont toutes certifiées Agriconfiance volet vert ce qui correspond au niveau 2 de la certification HVE (voir encart 3). Les exploitations détenant la certification HVE à la SCARA sont toutes certifiées par la voie A. Cette dernière se décline en 4 groupes thématiques : la biodiversité, la stratégie phytosanitaire, la gestion de la fertilisation et de l'irrigation alors que la voie B exige que le poids des intrants dans le chiffre d'affaire soit inférieur à 30% et que la part des infrastructures agroécologiques (IAE) soit supérieure à 10%. Cette voie B a fait l'objet de nombreuses critiques car les exploitations des filières à forte valeur ajoutée (viticulture) ou à fort coût de main d'œuvre (maraîchage) peuvent obtenir la certification HVE via cette voie sans aucune amélioration de leur performance environnementale (Aubert et al., 2021). Pour obtenir la certification HVE niveau 3 par la voie A, il faut obtenir un nombre de points égal ou supérieur à 10 pour chaque thématique. Nous allons ci-après nous intéresser aux 3 premières thématiques afin de regarder si les exploitations en HVE et/ou en AB se distinguent des exploitations en AC. L'irrigation n'est pas étudiée parce qu'il n'y a pas d'irrigation sur les parcelles retenues dans notre échantillon (sauf certaines cultures industrielles comme la pomme de terre). Nous compléterons l'analyse avec d'autres thématiques environnementales qui ne sont pas prises en compte par le référentiel HVE. Enfin, nous ferons une analyse conjointe des IE et des SE afin d'étudier si les exploitations en HVE ou en AB s'appuient sur les SE pour diminuer leurs IE.

Pour analyser les performances des exploitations selon leurs certifications, nous avons réalisé des boites à moustache pour chaque indicateur calculé. Une ANOVA suivi d'un test de Tukey ont été ensuite effectués afin de vérifier si les différences entre les groupes (AC, HVE et AB) étaient significatives.

### Encart 3. Les différents niveaux de la certification environnementale de la SCARA

* Niveau 1 : Maîtrise de la réglementation environnementale (Agri-Confiance®)

* ✓ Bilan de conditionnalité

* ✓ Autodiagnostic de l'exploitation au regard du niveau de certification visé

* Niveau 2 : Obligations de moyens (Agri-Confiance® Volet Vert)

* ✓ Respect des exigences du référentiel de niveau 2

OU

* ✓ Engagement dans une démarche reconnue équivalente

* Niveau 3 : Obligations de résultats (Agri-Confiance® Volet Vert + HVE)

* ✓ Respect des indicateurs du référentiel HVE par la voie A<fnref n="1" />

Obtenir au minimum 10 points dans les 4 thématiques suivantes :

* ❖ Biodiversité

* ❖ Stratégie phytosanitaire

* ❖ Gestion de la fertilisation

* ❖ Gestion de l'irrigation

<footnote n="1">¹Voie B (non pratiquée) : poids intrant du chiffre/chiffre affaires <30 % & Infrastructure agroécologique > 10 %</footnote>
# 3. Résultats

## 3.1. Les volets thématiques du référentiel HVE (hors gestion de l'irrigation)

### ❖ Biodiversité

Les exploitations en HVE ne se démarquent pas des exploitations en AC sur la thématique de la biodiversité (figure 37). En effet, elles obtiennent des notes pour l'indicateur I-BIO très proches des exploitations en AC. Seules les exploitations en AB ont des notes significativement plus élevées (p-value = 3,610⁻⁴). Le même constat est observé pour l'indicateur « Habitat associé aux cultures ». La différence des moyennes n'est toutefois pas significative pour cet indicateur.

<figure><img src="image_50.png" /></figure>

Figure 37 : Comparaison des indicateurs relatif à la biodiversité pour les 3 groupes d'exploitations agricoles : a) Indicateur I-BIO : les valeurs qualitatives (obtenues par parcelle) ont été transformées en valeurs quantitatives afin de calculer la moyenne d'I-BIO par exploitation. Si la valeur d'I-BIO est faible, l'impact des pratiques agricoles sur la biodiversité est élevé et si la valeur d'I-BIO est élevée, l'impact est faible, et b) Indicateur de SE de l'habitat associé aux cultures (AC : n=14, HVE : n=17, AB : n=2). Les lettres au-dessus des boîtes à moustache indiquent la significativité des comparaisons réalisées par le test de Tukey.

### ❖ Stratégie phytosanitaire

Les exploitations en HVE diffèrent significativement des exploitations en AC pour l'IFT hors herbicide (p-value = 7,3810⁻³) et pour l'IFT total (p-value = 0,01) mais ce n'est pas le cas pour l'IFT herbicide (figure 38). Les exploitations en AB se distinguent des deux autres groupes pour l'IFT herbicide et pour l'IFT total. L'IFT fait partie des critères permettant d'obtenir des points dans le volet Stratégie phytosanitaire du référentiel HVE. Aussi, on s'attend à ce que les exploitations en HVE aient des meilleures performances sur ce critère ce qui est le cas pour l'IFT hors herbicide et l'IFT total mais pas pour l'IFT herbicide. Une des raisons est la présence de betterave sucrière dans les rotations qui est sensible aux adventices. Concernant les indicateurs complémentaires à l'IFT, à savoir les risques de contamination RCeso (eaux souterraines), RCesu_rd (eaux de surface) et RCair_vol (air), les exploitations en HVE obtiennent de meilleurs résultats essentiellement pour le risque de contamination de l'air (p-value = 0,01). Les exploitations en AB se démarquent très largement pour ces trois indicateurs. En revanche, si on regarde les services écosystémiques de régulation des adventices, des maladies et des ravageurs, aucun groupe n'a un niveau de service plus élevé que les autres.
a

<figure><img src="image_51.png" /></figure>

c

<figure><img src="image_52.png" /></figure>

e

<figure><img src="image_53.png" /></figure>

g

<figure><img src="image_54.png" /></figure>

b

<figure><img src="image_55.png" /></figure>

d

<figure><img src="image_56.png" /></figure>

f

<figure><img src="image_57.png" /></figure>

h

<figure><img src="image_58.png" /></figure>