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Enregistrement W2592002208 · doi:10.2134/csa2017.62.0318

Modeling Change in Soil Organic Carbon under Future Climate Conditions

2017· article· en· W2592002208 sur OpenAlexaboutno aff
Tracy Hmielowski

Notice bibliographique

RevueCSA News · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueSoil Carbon and Nitrogen Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSoil carbonClimate changeSoil waterEnvironmental scienceGray (unit)AgricultureSoil fertilityEnvironmental resource managementSoil scienceGeographyEcology

Résumé

récupéré en direct d'OpenAlex

Soil carbon is expected to decline over agricultural soils such as these in Australia's New Sourth Wales Central West region. Source: B. Murphy, OEH. Climate change is expected to alter regional temperature and precipitation patterns and will subsequently impact soils and the distribution of plants and animals. Understanding how soils might vary with climate change will allow us to better prepare for and adapt to the altered soil conditions, according to Jonathan Gray, Senior Scientist with the New South Wales (NSW) Government Office of Environment and Heritage. And this, he says should ultimately improve our management of agricultural lands and native ecosystems into the future. Gray describes a novel approach to modeling potential changes in soil organic carbon (SOC) as the lead author on a recent paper in the Soil Science Society of America Journal titled, “Change in Soil Organic Carbon Stocks under Twelve Climate Change Projections over New South Wales, Australia.” The researchers used digital soil mapping (DSM) in combination with space-for-time substitution (SFTS) to model changes in SOC. Gray says this method “was an alternative to the dynamic process modeling that is normally applied in similar studies.” The authors believed this was a conceptually simple but robust approach, suitable for providing predictions at a finer scale, and one of the objectives of the study was to demonstrate the validity of this method. According to Gray, this analysis focused on SOC because “it is a major determinant of soil health,” influencing many chemical, physical, and biological properties such as fertility, water-holding capacity, and biological activity, and it is also important for carbon sequestration and potential climate change mitigation programs. Gray says this method could be used to model changes in other soil properties, including pH and major nutrient content. Gray and his fellow researchers selected four global climate models, CSIRO_MK30, CCCMA31, ECHAM5, and MIROC32 (developed by research groups in Australia, Canada, Germany, and Japan, respectively). Each global model was downscaled with three regional climate models, resulting in a total of 12 climate change projections. These models were selected on the basis that they reflected a full and varied range of projected future climate outcomes. The authors mapped SOC for three time periods: current conditions (1999–2009), near future (2020–2039), and far future (2060-2079) at five soil depths. This resulted in 180 maps. To determine the change in SOC, the two future projections were compared with the current conditions. The final results were presented at just two depth intervals: 0–30 cm and 30–100 cm. There was much variation in SOC predictions among the 12 climate projections, but Gray points out that some broad trends were apparent at both the state and regional levels. For example, he says all 12 climate projections predicted a loss in SOC over the alpine areas in the southeast of the state. The researchers also observed the “wetter” models (CCCMA31, MIROC32) consistently predicted an increase in SOC over time while the “drier” models (CSIRO_MK30, ECHAM5) predicted a loss in SOC. When comparing the different projections for different soil types, the authors noted systematic variation for soil type, current climate, and land use regimes. “The projected average decline of SOC across NSW to 2070 was less than 1 Mg/ha for sandy, low-fertility soils in dry conditions under cropping regimes but over 15 Mg/ha for clay-rich, fertile soils in wet conditions under native vegetation regimes,” Gray says. Understanding how different soil types are likely to respond to changes in climate could help in planning efforts to reduce SOC loss or maximize SOC gains. The differences in predicted SOC among models were also important as they point to the variation in climate models. “We need more consistency and reliability in climate change models,” Gray says, “especially with respect to changing rainfall, in order to reliably predict soil property change due to climate change.” View the open access Soil Science Society of America article, “Change in Soil Organic Carbon Stocks under Twelve Climate Change Projections over New South Wales, Australia,” online at https://doi.org/10.2136/sssaj2016.02.0038 Average change in soil organic carbon stock across New South Wales to approximately 2070 (0-30 cm, Mg ha–1). The authors were able to demonstrate the usefulness of this method, but also see room for improvement in terms of reducing some of the uncertainty and incorporating other soil properties and updated climate models in future analyses. The series of maps produced in this analysis are of interest to many stakeholders. Economic modelers are interested in SOC for future carbon trading, nature reserve managers could predict changes in plant distribution based on changes in soil properties, and agricultural land managers are interested in future soil conditions with consideration of soil amendments or changes in crop suitability. Although there are limitations to these models, Gray says recognizing that a change in SOC and other soil properties is coming, along with the likely direction of change, will be important for these groups and other individuals interested in planning for future climate and soil conditions.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,320
Score d'incertitude au seuil0,912

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)0,0000,000

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 Opus0,045
Tête enseignante GPT0,265
Écart entre enseignants0,221 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

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