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Record W1997397219 · doi:10.4141/s99-108

Tillage for soil fertility before fertilizers

2000· article· en· W1997397219 on OpenAlexvenueno aff
B. P. Warkentin

Bibliographic record

VenueCanadian Journal of Soil Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTillageAgronomyPloughEnvironmental scienceMineralization (soil science)ManureMulch-tillOrganic matterCrop rotationNo-till farmingSoil waterSoil fertilityAgroforestryCropBiologySoil scienceEcology

Abstract

fetched live from OpenAlex

The present concern for decreased tillage in crop production systems makes it interesting to look back 300 years to when Jethro Tull introduced his system of intensive tillage for cereals and fodder crops. His experiments in England, at a time when landowners started paying more attention to farming, established that frequent tillage with plows could substitute for manure or fallow in continuous cropping of wheat. The general understanding was that exposed soil absorbed plant nutrients from the atmosphere; therefore, it was important to turn over lower layers and to leave the soil in a loose condition where more surfaces were exposed. In addition to controlling weeds, a probably equally important factor was speeding up mineralization of organic matter through increased aeration. This would account for the increased yields even where weeds were not present, and the ability to substitute tillage for fallow. Tull's system of cultivating wheat grown in rows, sometimes on ridges, was vehemently debated in the 18th century and was not widely accepted. The benefits would have diminished as organic matter content of soils decreased, and was probably not effective on all soils. Key words: Horse-hoeing husbandry, mineralization, ploughing, Jethro Tull

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 score0.842
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.209
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 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

Citations5
Published2000
Admission routes1
Has abstractyes

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