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Enregistrement W2957756763 · doi:10.2134/csa2019.64.0702

Agricultural Water Quality in Cold Environments

2019· article· en· W2957756763 sur OpenAlexaboutno aff
Tracy Hmielowski

Notice bibliographique

RevueCSA News · 2019
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueSmart Materials for Construction
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSnowmeltCold climateAgricultureCold winterJokeCold storageMeteorologyEnvironmental scienceSnowGeographyArchaeology

Résumé

récupéré en direct d'OpenAlex

Episode of snowmelt after a cold winter. Photo by Barbro Ulén. Do you live in a place where “mud season” is not a joke? It's that time of year when the ground is still frozen, but a rapid warm-up has melted the winter snow (or worse, it's already raining), resulting in a sloppy, messy, muddy layer that clings to shoes and pet paws. While this is a nuisance to those trying to keep their floors clean, this time of year poses unique challenges to farmers concerned with nutrient runoff from their fields. A collection of papers to be published in the July–August 2019 issue of the Journal of Environmental Quality tackle this issue of “Agricultural Water Quality in Cold Environments.” Jian Liu, Helen Baulch, Jane Elliott, Merrin Macrae, and Henry Wilson served as guest editors of this collection of papers. Liu answered questions from CSA News magazine about the need for a special section on this topic, goals, and what those in warmer climates can take away from this research. CSA News: Why did you decide to focus on cold climates with this collection of papers? Liu: The focus of this special collection on cold climates was primarily because cold agricultural regions are important for global food and water security and because there are large gaps in our knowledge of nutrient transport processes and management options across these regions. Cold climate regions are characterized by an average air temperature above 10°C in their warmest months and a mean temperature below −3°C in their coldest month. They cover a vast area in the northern hemisphere, spanning from the Great Plains, Great Lakes, and Maritime regions in North America to northern Europe and northern Asia (see inset image above). These regions constitute an important part of global crop and livestock production, but they also face a grand challenge of mitigating water pollution associated with nutrient losses from land to water. Agriculture has been identified as a critical contributor to the frequent cyanobacterial blooms in several large water bodies located in cold climate regions, such as Lake Winnipeg, Lake Erie, and the Baltic Sea. In addition to the challenges that also exist in warm regions such as soil legacy nutrients and nutrient stratification, cold agricultural regions have specific agronomic, biogeochemical, and hydrological characteristics that are different from warm regions. These characteristics include short growing seasons, limited water infiltration on frozen ground, vegetative nutrient release after freeze-thaw, and enhanced transport of late fall and winter nutrient applications. These factors create added challenges to nutrient mitigation efforts in cold agricultural regions, particularly because many of our insights around beneficial management practices (BMPs) have evolved from warmer regions. However, many BMPs don't work in cold conditions. The efficacy of BMPs has shown large interannual and cross-region variability, and some BMPs can even exacerbate issues. Coverage of cold climate regions (in blue; defined by Köppen-Geiger class D (Peel et al., 2007). To our best knowledge, this is the first special collection of papers with a focus on agricultural water quality in cold environments—helping to fill an important management and research gap. In light of the increasing attention on this topic (e.g., the Canada First Excellence Research Fund – Global Water Futures Program), and evidence of rapid change in cold regions globally, we have edited this special collection of more than 20 papers to improve our understanding of processes and BMP assessment and implementation across cold agricultural regions. CSA News: What were your specific goals for this special section or topics that you wanted to highlight? CSA News: Are there potential takeaways for those working in warmer or more arid environments? Liu: This special collection points to challenges in transferring research insights across regions. Many processes and BMP efficiencies regarded as knowns in warm regions become unknowns in cold regions. However, this work has also shown us that some BMPs seem to be applicable universally—such as the fundamental importance of nutrient management in soils, which controls the supply of nutrients to runoff. For example, a soil phosphorus drawdown approach has shown promise in reducing dissolved phosphorus concentrations in both snowmelt runoff and rainfall runoff. Likewise, much of the work in this special section reports on rainfall runoff processes, which are common across all regions. Cold regions are subject to extremes in terms of temperature and also hydrology. Warmer regions and arid regions can also experience extremes in temperatures and hydrology driving events such as drought, intense rainfall, and flooding. As climate change continues, greater extremes are expected across many regions, and a slow shift from cold-climate conditions to warmer-climate conditions are anticipated with many consequences to hydrology and nutrient transport. Ultimately, by building a stronger understanding of nutrient transport and BMPs across climate zones and landscape types, we are gaining insights into the suite of options that can work in varied situations. We're learning what lessons can be applied broadly and what the most cost-effective solutions to improve water quality might be. The Journal of Environmental Quality special section and “Agricultural Water Quality in Cold Environments and” will appear in the July–August 2019 issue at: https://dl.sciencesocieties.org/publications/jeq/tocs/48/4.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,383
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,014

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,212
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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