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Record W4412123990 · doi:10.13031/aim.202500367

Simulating Climate Change Impacts and Management Strategies on Crop Yield and Soil Organic Carbon Dynamics in Eastern Canada

2025· article· en· W4412123990 on OpenAlexaboutno aff
Ruth C Sitienei, Zhiming Qi, Brian Grant, Andrew VanderZaag, Guillaume Jégo, Martin H. Chantigny, Marie-Élise Samson, Budong Qian, Ward Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityResilience (materials science)Climate changeTerm (time)Environmental sciencePsychological resilienceEnvironmental resource managementEnvironmental planningAgroforestryNatural resource economicsAgricultural engineeringEngineeringEconomicsGeologyEcologyMaterials scienceOceanography

Abstract

fetched live from OpenAlex

Highlights Warmer climate and enhanced CO2 levels are projected to generally increase crop yields and SOC levels in Eastern Canada. Average crop yields and SOC significantly increased under all future climate scenarios compared to the baseline. Managing the impacts of climate change on crop growth and SOC ensures long-term sustainability of the agriculture sector. Abstract. The agricultural sector in Eastern Canada is facing growing challenges from climate change, with regional warming occurring at twice the global average, necessitating the development of effective adaptation strategies. This study aimed to assess changes in crop growth and soil organic carbon (SOC) under current and projected future climate, exploring the potential implications of climate change on diverse crop rotations combined with long-term application of different manure and tillage types. The Denitrification-Decomposition (DNDC) model was calibrated and validated against historical data to simulate crop yields and SOC and was then used to project the impacts of climate change on these cropping systems from 1981 to 2100 with reporting for three future periods: Near-term (2030s), mid-term (2050s), and distant future (2070s). Simulations were conducted under three Shared Socio-economic Pathways (SSPs) scenarios (SSP 1-2.6, SSP 3-7.0 and SSP 5-8.5) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for Eastern Canada. Three cropping systems were considered including cereal monoculture, cereal-perennial legume hay rotation (1989-2010) on a silty clay soil near Normandin, Quebec and an annual crop rotation (wheat-corn-soybean) from 2009-2016 on a sandy loam soil near St-Augustin-de-Desmaures, Quebec. The DNDC model demonstrated satisfactory performance in simulating yields at the experimental sites for annual crops, legume hay, and SOC stock. Compared to the 2000-2029 baseline scenarios, the average yields and SOC significantly increased under all future climate scenarios at Normandin due to a longer growing season, and the beneficial effects of elevated atmospheric CO2. However, SOC showed minimal change, as increased carbon inputs from crop residues were offset by higher SOC mineralization rates under elevated temperatures. In contrast, diversified rotations, such as barley-hay and corn-soybean-wheat, demonstrated further yield increases and SOC gains. These improvements were linked to reduced crop nutrient stress and enhanced soil water-holding capacity associated with higher simulated SOC levels. As expected, increasing the proportion of perennials in rotation, particularly leguminous forages, enhances both yields and long-term soil organic carbon (SOC) accumulation. However, diverse annual cropping systems also show potential for improving soil health and resilience, which could, in turn, boost economic returns for farmers.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.028
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.242
Teacher spread0.212 · 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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