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Record W2069412759 · doi:10.4141/s99-028

Organic C accumulation in soil over 30 years in semiarid southwestern Saskatchewan – Effect of crop rotations and fertilizers

2000· article· en· W2069412759 on OpenAlexaffvenueabout
C. A. Campbell, R.P. Zentner, Biqing Liang, G. Roloff, E. G. Gregorich, B. Blomert

Bibliographic record

VenueCanadian Journal of Soil Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCrop rotationChernozemSoil carbonCrop residueHumusStrawAgronomySecaleTotal organic carbonEnvironmental scienceCropSoil scienceSoil waterChemistryAgricultureGeographyEnvironmental chemistry

Abstract

fetched live from OpenAlex

Because crop management has a strong influence on soil C, we analyzed results of a 30-yr crop rotation experiment, initiated in 1967 on a medium textured Orthic Brown Chernozem at Swift Current, Saskatchewan, to determine the influence of cropping frequency, fertilizers and crop types on soil organic C (SOC) changes in the 0- to 15-cm depth. Soil organic C in the 0- to 15-cm and 15- to 30-cm depths were measured in 1976, 1981, 1984, 1990, 1993, and 1996, but results are only presented for the 0- to 15-cm depth since changes in the 15- to 30-cm depth were not significant. We developed an empirical equation to estimate SOC dynamics in the rotations. This equation uses two first order kinetic expressions, one to estimate crop residue decomposition and the other to estimate soil humus C mineralization. Crop residues (including roots) were estimated from straw yields, either measured or calculated from grain yields. The parameter values in our equation were obtained from the scientific literature or were based on various assumptions. Carbon lost by wind and water erosion was estimated using the EPIC model. We found that (i) SOC was increased most by annual cropping with application of adequate fertilizer N and P; (ii) that frequent fallowing resulted in lowest SOC except when fall-seeded crops, such as fall rye (Secale cereale L.), that reduce erosion were included in the rotation, and (iii) the fallow effects are exacerbated when low residue yielding flax (Linum usitatissimum L.) was included in the rotation. Some of the imprecision in SOC values we speculated to be related to variations in soil texture at the test site. In the first 10 yr of the experiment, SOC was low and constant for fallow-spring wheat (Triticum aestivum L.) (F-W) and F-W-W rotations because this land was managed in this manner for the previous 50 yr. However, in rotations that received N + P fertilizer and were cropped annually [continuous wheat (Cont W) and wheat-lentil (Lens culinaris L.)], or that included fall-seeded crops (e.g., F-Rye-W), SOC appeared to increase sharply in this period. In the drought period (1984–1988) SOC was generally constant, but large increases occurred in the wet period (1990 to 1996) in response to high residue inputs. The efficiency of conversion of residue C to SOC for the 30-yr experimental period was about 10–12% for F-W, F-W-W and Cont W (+P) systems, and it was about 17–18% for the well fertilized F-Rye-W, Cont W, and W-Lent systems. The average annual SOC gains (Mg ha −1 yr −1 ) between 1967 and 1996 were 0.11 for F-W (N + P), 0.09 for the mean of the three F-W-W rotations (N + P, + N, + P), 0.23 for F-Rye-W (N + P), 0.32 for Cont W (N + P), 0.12 for Cont W (+P), and 0.28 for W-Lent (N + P). The corresponding mean estimated (by our equation) annual SOC gains for these rotations, were 0.06, 0.10, 0.16, 0.22, 0.14, and 0.22 Mg ha −1 yr −1 , respectively. Because soil C measurements are usually so variable, we recommend that calculations such as ours may be employed to assist in the interpretation of measured C trends and to test if they seem reasonable. Key words: Carbon sequestration, carbon conversion efficiency, eroded carbon, crop residue carbon, cropping frequency, wheat, fall rye, flax

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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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations230
Published2000
Admission routes3
Has abstractyes

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