Using old fields for new purposes: Modeling the impact of agricultural field restoration on ecosystem services as a nature-based solution in the Montérégie
Notice bibliographique
Résumé
Human activities are increasingly straining the environment, posing significant ecological and social challenges. Technological approaches have struggled to effectively address these problems. For instance, while intensive agricultural practices have boosted food security, they have also reduced landscape diversity and increased vulnerability to pest outbreaks, sometimes surpassing the capabilities of technologies like insecticides. This has catalyzed a shift towards nature-based solutions (NbS), such as restoration, which utilizes natural processes to address environmental problems while generating multiple benefits for people and nature. In fact, the 2023 COP15 agreement promoted the use of NbS to meet its targets of restoring 30% of degraded land by 2030. However, despite the growing body of research on the site-specific benefits of NbS there remains limited understanding of their broader landscape-scale impact. Significant gaps persist in our knowledge regarding how different amounts of NbS influence desired outcomes, the role of underlying site conditions in shaping the success of NbS, the extent of effects beyond implementation sites (spillover), and the configurations of NbS intervention that can optimize benefits.To address these questions, I investigated the outcomes for multiple ecosystem services (ES), defined as the benefits people receive from ecosystems, across different scenarios that could be used to meet the COP15 targets. I selected the Montérégie, an agricultural landscape in south-eastern Canada, as our case study area. I explored scenarios ranging from no restoration to full restoration of unproductive lands, modeling the outcomes for seven ES: crop production, maple syrup production, white-tailed deer hunting, water quality regulation, above and below-ground carbon storage, pollination and outdoor recreation. My scenarios included different proportions of land restoration (3.3%, 10.8%, and 30%) across two types of sites (abandoned and degraded fields), evaluated against randomly selected fields. I generated 70 maps (one baseline + nine different restoration patterns for each of seven ES), illustrating the supply of ES following diverse levels of COP15 target achievement using different types of sites. My findings indicate that increasing the restored area generally significantly enhances ES supply, though the rate of increase varies by service. The type of land restored—whether random, abandoned, or degraded—has limited impact at the landscape scale, although restoring abandoned fields typically yields lower ES supply. However, unlike degraded or random fields, restoring abandoned fields below 10.8% maintains baseline crop production. Certain ES, such as hunting and water quality, are more sensitive to land type whereas others, like carbon storage and pollination, show minimal variation. My results also suggest that ES supply from restored sites has off-site impacts up to 500 meters away. My study advances our understanding of the advantages and challenges of large-scale NbS restoration in agricultural landscapes, highlighting the importance of carefully considering the placement of these sites for maximum compounded ES benefits
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».