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Record W2609151187 · doi:10.2489/jswc.72.3.191

Modeling soil organic carbon in corn ( <i>Zea mays</i> L.)-based systems in Ohio under climate change

2017· article· en· W2609151187 on OpenAlexaboutno aff
Ellen D.v.L. Maas, Rattan Lal, K. Coleman, Abelardo Antônio de Assunção Montenegro, Warren A. Dick

Bibliographic record

VenueJournal of Soil and Water Conservation · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureOhio State UniversityGarden Club of AmericaU.S. Department of Agriculture
KeywordsEnvironmental scienceSoil carbonTopsoilSoil waterClimate changeSoil qualityLand managementAgricultureLand useLand use, land-use change and forestrySoil managementGreenhouse gasAgronomySoil scienceGeographyEcology

Abstract

fetched live from OpenAlex

Soil organic carbon (SOC) is a key indicator of soil quality. Knowledge of the effects of land management and climate change on SOC stocks is of vital importance in creating future sustainable land use systems. This study presents both the promise and current challenges of modeling SOC in mineral soils under climate change. Soils data from two long-term agricultural research sites in Ohio under no-till (NT) and plow-till (PT) management, the RothC soil C model, and climate data from the Canadian Regional Climate Model were used to project future SOC content in agricultural soils using low-emissions (LE) and high-emissions (HE) climate change scenarios. It was hypothesized that from 2015 to 2070, SOC levels in soils under NT management in Ohio will show increasing trends under the LE scenario, decreasing trends in NT under the HE scenario, and decreasing trends in PT under both scenarios, with lower levels of SOC for both treatments under the HE scenario. The results of this study projected total SOC content in the topsoil layers (0 to 25 cm [0 to 10 in] at Wooster and 0 to 23 cm [0 to 9 in] at Hoytville) to decrease at all sites and under all management and climate projections, with the exception of NT at Wooster and Hoytville and PT at Wooster under the LE scenario. Starting at 32.4 Mg C ha<sup>−1</sup> (14.5 tn C ac<sup>−1</sup>) in 1962 at Wooster, by 2070, soil under NT management is projected to have 45.4 and 32.1 Mg C ha<sup>−1</sup> (20.3 and 14.3 tn C ac<sup>−1</sup>) for LE and HE scenarios, respectively, while PT management starting at 31.5 Mg C ha<sup>−1</sup> (14.1 tn C ac<sup>−1</sup>) would have 29.4 and 21 Mg C ha<sup>−1</sup> (13.1 and 9.4 tn C ac<sup>−1</sup>) for LE and HE scenarios, respectively. Starting at 65.2 Mg C ha<sup>−1</sup> (29.1 tn C ac<sup>−1</sup>) in 1963 at Hoytville, by 2070, soil under NT management would have 65.9 and 51 Mg C ha<sup>−1</sup> (29.4 and 22.8 tn C ac<sup>−1</sup>) for LE and HE scenarios, respectively, and PT starting at 63.5 Mg C ha<sup>−1</sup> (28.3 tn C ac<sup>−1</sup>) would have 36.9 and 28.7 Mg C ha<sup>−1</sup> (16.5 and 12.8 tn C ac<sup>−1</sup>) for LE and HE scenarios, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.225
Teacher spread0.181 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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