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Record W7161952516 · doi:10.82308/33390

Using old fields for new purposes: Modeling the impact of agricultural field restoration on ecosystem services as a nature-based solution in the Montérégie

2025· dissertation· en· W7161952516 on OpenAlexaboutno aff
Catherine Destrempes

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesAgricultureVulnerability (computing)Food securityRestoration ecologyEcosystemIntervention (counseling)Quality (philosophy)

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.292
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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