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Record W2016141187 · doi:10.4141/s00-041

Changes in soil carbon under long-term maize in monoculture and legume-based rotation

2001· article· en· W2016141187 on OpenAlexaffvenue
E. G. Gregorich, C. F. Drury, Jeff Baldock

Bibliographic record

VenueCanadian Journal of Soil Science · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersCurtin University of Technology
KeywordsMonocultureAgronomyCrop rotationSoil carbonSoil organic matterLegumeSoil waterCropping systemOrganic matterChemistryEnvironmental scienceBiologyCropSoil science

Abstract

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Legume-based cropping systems could help to increase crop productivity and soil organic matter levels, thereby enhancing soil quality, as well as having the additional benefit of sequestering atmospheric C. To evaluate the effects of 35 yr of maize monoculture and legume-based cropping on soil C levels and residue retention, we measured organic C and 13C natural abundance in soils under: fertilized and unfertilized maize (Zea mays L.), both in monoculture and legume-based [maize-oat (Avena sativa L.)-alfalfa (Medicago sativa L.)-alfalfa] rotations; fertilized and unfertilized systems of continuous grass (Poa pratensis L.); and under forest. Solid state 13C nuclear magnetic resonance (NMR) was used to chemically characterize the organic matter in plant residues and soils. Soils (70-cm depth) under maize cropping had about 30-40% less C, and those under continuous grass had about 16% less C, than those under adjacent forest. Qualitative differences in crop residues were important in these systems, because quantitative differences in net primary productivity and C inputs in the different agroecosystems did not account for observed differences in total soil C. Cropping sequence (i.e., rotation or monoculture) had a greater effect on soil C levels than application of fertilizer. The difference in soil C levels between rotation and monoculture maize systems was about 20 Mg C ha-1. The effects of fertilization on soil C were small (~6 Mg C ha-1), and differences were observed only in the monoculture system. The NMR results suggest that the chemical composition of organic matter was little affected by the nature of crop residues returned to the soil. The total quantity of maize-derived soil C was different in each system, because the quantity of maize residue returned to the soil was different; hence the maize-derived soil C ranged from 23 Mg ha-1 in the fertilized and 14 Mg ha-1 in the unfertilized monoculture soils (i.e., after 35 maize crops) to 6-7 Mg ha-1 in both the fertilized and unfertilized legume-based rotation soils (i.e., after eight maize crops). The proportion of maize residue C returned to the soil and retained as soil organic C (i.e., Mg maize-derived soil C/Mg maize residue) was about 14% for all maize cropping systems. The quantity of C3-C below the plow layer in legume-based rotation was 40% greater than that in monoculture and about the same as that under either continuous grass or forest. The soil organic matter below the plow layer in soil under the legume-based rotation appeared to be in a more biologically resistant form (i.e., higher aromatic C content) compared with that under monoculture. The retention of maize residue C as soil organic matter was four to five times greater below the plow layer than that within the plow layer. We conclude that residue quality plays a key role in increasing the retention of soil C in agroecosystems and that soils under legume-based rotation tend to be more “preservative” of residue C inputs, particularly from root inputs, than soils under monoculture. Key words: Soil carbon, 13C natural abundance, 13C nuclear magnetic resonance, maize cropping, legumes, root carbon

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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 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

Citations312
Published2001
Admission routes2
Has abstractyes

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