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Record W7077078633 · doi:10.1016/j.agrcom.2025.100091

Impacts of global climate change on mushroom production: challenges and opportunities

2025· article· en· W7077078633 on OpenAlexafffund

Bibliographic record

VenueAgriculture Communications · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsMcMaster University
FundersChongqing Science and Technology CommissionMcMaster University
KeywordsClimate changeMushroomHabitatProductivityEcosystem servicesPrecipitationEffects of global warmingGlobal warmingTemperate climate

Abstract

fetched live from OpenAlex

Climate change has been linked to a wide range of adverse impacts on human health and socioeconomic well-being. While mushrooms have been suggested as potential agents for mitigating climate change, the effects of climate change on mushroom production remain largely unexplored. Here, we review and analyze the potential impacts of climate change on the production of both cultivated and wild edible and medicinal mushrooms, with a focus on the impacts of changing temperature and precipitation. First, we review global temperature and precipitation scenarios projected by 2100. Most climate scientists predict that global temperature will continue to rise gradually and precipitation distribution will become increasingly uneven in the future. Second, our analyses suggest that these environmental shifts will negatively impact the productivity of most cultivated mushrooms in multiple ways, including restricted substrate supplies, damage from pests and pathogens, and high electrical expenditures, etc. Third, some wild mushrooms may benefit from warmer and more humid conditions in certain regions, leading to increased mycelial growth and fruiting. However, hotter and drier environments in other regions, as projected for the future, will likely suffer decreasing yields for most mushrooms. For many wild gourmet mushroom species, their suitable habitats may change, with the majority predicted to experience habitat shrinkage, resulting in an overall decrease in productivity. Aside from challenges, we also discuss opportunities, including incorporating smart technologies for monitoring environmental factors, utilizing artificial intelligence for predictive analytics, and automating tasks such as irrigation and cooling/heating. We note that long-term monitoring across multiple ecological zones is needed to accurately quantify the impacts of global climate change on mushroom production and fine-tune strategies for sustainable mushroom cultivation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.095
GPT teacher head0.287
Teacher spread0.191 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations3
Published2025
Admission routes2
Has abstractyes

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