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Record W4236947075 · doi:10.2489/63.5.322

Effect of tillage regimens on soil erosion, nematodes, and carrot yield in Prince Edward Island

2008· article· en· W4236947075 on OpenAlexaboutno aff
D. Holmstrom, K. Sanderson, J. Kimpinski

Bibliographic record

VenueJournal of Soil and Water Conservation · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsChiselMulchSowingTillageAgronomyEnvironmental scienceErosionSurface runoffSoil waterErosion controlBiologyGeographySoil scienceEcology

Abstract

fetched live from OpenAlex

Prince Edward Island soils have rooting depths of 25 to 40 cm (10 to 16 in), and soil erosion is critical on these shallow soils. Soil erosion has been identified as a problem by local carrot producers. The objective of this project was to examine the impact of pre- and post-planting soil conservation methods on the reduction of soil sediment loss and relative water runoff using a rainfall simulator. The effect of these methods on carrot yield and change in nematode populations were also assessed. Pre-planting treatments consisted of fall mold-board plowed, fall disced, fall chisel plowed, and no fall tillage with or without fall-applied mulch. Post-planting treatments were established between-row as mulch, dyking, fall rye, barley, and a control (no treatment). The pre-planting experiments show that fall disced, fall chisel plowed and no tillage with or without mulch are unacceptable to producers because they hinder bed formation and seeding operations. Root-lesion nematodes in the carrot root at harvest were four times higher in the mulched than the non-mulched plots. The post-planting results show that between-row mulching or dyking in combination with the spring chisel plowed significantly reduced relative soil sediment loss overall by 68% and 48%, respectively. We conclude that soil erosion prevention in a carrot crop can be achieved with spring chisel plowed combined with post planting mulching.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.110

Codex and Gemma teacher scores by category

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 head0.019
GPT teacher head0.220
Teacher spread0.202 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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