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Record W1910224911 · doi:10.1111/ejss.12164

Long‐term irrigation effects on soil organic matter under temperate grazed pasture

2014· article· en· W1910224911 on OpenAlexafffund
Leo M. Condron, D. W. Hopkins, E. G. Gregorich, Amanda Black, Steven A. Wakelin

Bibliographic record

VenueEuropean Journal of Soil Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAgricultural and Marketing Research and Development TrustAgResearch
KeywordsIrrigationPastureEnvironmental scienceAgronomySoil carbonSoil waterWater contentSurface irrigationSoil scienceBiologyGeology

Abstract

fetched live from OpenAlex

Summary Irrigation of grazed pasture significantly increases plant and animal production, which may in turn increase soil organic carbon ( SOC ), depending on the balance between primary production and below‐ground allocation of C on the one hand, and the decomposition and export of C from the soil on the other. To evaluate the effect of irrigation on SOC we sampled a grazed pasture field experiment maintained under different irrigation treatments for 62 years. The dry‐land treatment in this experiment only received rainfall at an average of 740 mm year −1 . The 10 and 20% irrigation treatments involved application of 100 mm of irrigation when the soil reached 10 and 20% gravimetric moisture content, respectively. The 10 and 20% irrigation treatments received average total annual irrigation inputs of 260 and 770 mm year −1 , respectively. The 10 and 20% irrigation treatments increased pasture production by 44 and 74%, respectively, compared with that from the dry‐land. Analysis of soils taken to 1‐m depth revealed that amounts of SOC were not significantly different between the dry‐land (125.5 Mg ha −1 ) and 10% irrigation (117.8 Mg ha −1 ) treatments, but these were significantly greater than the 20% irrigation treatment (93.0 Mg ha −1 ). At 50–100 cm, SOC was also less (34%) for the 20% irrigation treatment than for the 10% irrigation treatment. The relative quantities of carbon ( C ) and nitrogen ( N ) in the light fraction ( LF ) at all soil depths decreased successively from dry‐land to the 20% irrigation treatment, suggesting that wetter soil conditions accelerated decomposition of the LF fraction, a comparatively labile SOC fraction. The C ‐to‐ N ratio of the bulk soil was also less for the 20% irrigation treatment, indicating more decomposed SOM in the irrigated than in the dry‐land treatment. There were no significant differences in the microbial biomass between the three different irrigation treatments, but the respiration rate ( CO 2 production) of soil organisms in the 20% irrigation treatment was consistently greater than in the other two treatments. It was concluded that large increases in plant productivity as a result of irrigation had either no effect or significantly reduced SOC stocks under grazed pasture. The reduced SOC content observed in the 20% irrigation treatment was attributed to a combination of increased C losses in animal products and drainage associated with greater stocking, together with accelerated decomposition of organic C resulting from elevated soil moisture maintained throughout the growing season.

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 score0.004
Threshold uncertainty score0.007

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)0.0000.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations72
Published2014
Admission routes2
Has abstractyes

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