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Record W6903452243 · doi:10.1139/cjps-2014-356

Yield and profitability of fallow and fertilizer inputs in long-term wheat rotation plots at Lethbridge, Alberta

2015· article· en· W6903452243 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsFertilizerCrop rotationRotation systemYield (engineering)StrawRotation (mathematics)PhosphorusCrop yield

Abstract

fetched live from OpenAlex

Smith, E. G., Janzen, H. H. and Kröbel, R. 2015. Yield and profitability of fallow and fertilizer inputs in long-term wheat rotation plots at Lethbridge, Alberta. Can. J. Plant Sci. 95: 579-587. A long-term, 42-yr study was used to determine the impacts of crop rotation and fertility management on wheat yield and profitability. Crop rotations included continuous wheat (W), fallow-wheat (FW), and fallow-wheat-wheat (FWW). Original plots were split for nitrogen (N) and phosphorus (P) fertility treatments, a two-factor factorial for N (0 and 45 kg ha-1) and P (0 and 20 kg ha-1). Phosphorus increased yield during the first half of the period, but had little impact during the last half. Nitrogen had no yield impact on fallow crops during the first half of the period, but had a positive impact during the last half, and throughout for wheat after wheat. The soil became incapable of releasing adequate N for wheat after fallow. Simulated distributions of net returns determined the W rotation with N and P fertilizer had, on average, the highest but also the most variable net return. Net return was higher during the last half of the study, and the fertilized continuous wheat rotation had the highest average net return. The continuous wheat rotation was more risky, and producers averse to risk would prefer the less risky fertilized fallow-wheat rotation.

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 score0.180
Threshold uncertainty score0.983

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 head0.254
GPT teacher head0.246
Teacher spread0.008 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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