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Record W3214832865 · doi:10.7163/przg.2021.3.4

Rola ekosystemów nadrzecznych w krajobrazach rolniczych w kontekście ostatnich zmian klimatu = The role of riparian ecosystems within agricultural landscapes in the climate change context

2021· article· en· W3214832865 on OpenAlexaboutno aff
Ewa Kołaczkowska

Bibliographic record

VenuePrzegląd Geograficzny · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneEnvironmental scienceContext (archaeology)Climate changeEcosystemGeographyAgroforestryHydrology (agriculture)EcologyHabitatGeology

Abstract

fetched live from OpenAlex

Average global temperatures have been rising extremely rapidly over recent decades, with all the side-effects that may denote, including increased risks of both drought and flood, prolongation of the growing season, intensification of other extreme weather events, potentially enhanced erosion and sediment transport through river basins, and even invasions of pests and diseases. Against that factual background, this paper presents a review, and in essence a summary, of existing scientific literature as it pertains to the functioning of the riparian ecosystems present within agricultural landscapes, as well as the former’s potential role in mitigating climate change. Riparian ecosystems of course constitute areas of transition between the aquatic and terrestrial environments, and are in a position to serve as buffers, as they filter and neutralise nutrients and pesticides descending from areas at higher elevations, provide shade (that may limit the spread of light-demanding alien species), moderate stream temperatures, and work to sequester atmospheric CO2 in both plant biomass and soil. They also support water retention in river valleys, and protect banks against erosion. Zoned buffer strips consisting of one strip of trees and one of grassy or herbaceous vegetation are shown to be among the most-effective measures deployable in the mitigation of diffuse pollution. A search through 2 bibliographical databases (the Web of Science Core Collection and Scopus) was undertaken in respect of the terms: ” riparian buffer” OR” riparian corridor” OR” riparian zone” OR” riparian strip” AND” agricultural” AND” climate change”. Such a procedure allowed for the identification of 76 separate scientific papers, albeit with 12 of these warranting exclusion from further analyses on account of their actual irrelevance. The largest body of literature on this topic is seen to concentrate on highly-developed countries of North America and Europe, notably the USA, Canada and France. Deeper analysis of the papers found points to a growing interest in mathematical modelling of the effects of agricultural best-mangement practices (BMPs), in regard to future streamflow, supply of water, the transport of sediment through a basin, rates of export of nitrogen and phosphorus, etc. – with both current climatic conditions and various future scenarios for climate being taken account of. The results of all this modelling tend to show how riparian buffers may serve in a basin-based strategy for climate adaptation, by which change may actually be mitigated more effectively than it can through other BMPs, even as no full offsetting of impacts is likely to prove achievable. Many of the authors in publications selected also choose to underline the multifunctional nature of riparian ecosystems, and the specific nature of the services they have to offer. 69% of the publications analysed address implications for practice, e.g. by offering guidelines as regards conservation strategies, and/or recommendations for managers of basins or other key decision-makers when it comes to restoring or improving both the ecological health of rivers, and levels of human well-being in general.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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