MétaCan
Menu
Back to cohort
Record W2088122809 · doi:10.2134/agronj14.0253

Economic Effects of Preceding Crops and Nitrogen Application on Canola and Subsequent Barley

2014· article· en· W2088122809 on OpenAlexaffabout
Mohammad Khakbazan, Cynthia A. Grant, Jianzhong Huang, Elwin G. Smith, John T. O’Donovan, Robert E. Blackshaw, K. Neil Harker, G. P. Lafond, Eric N. Johnson, Yantai Gan, William E. May, T. Kelly Turkington, Newton Z. Lupwayi

Bibliographic record

VenueAgronomy Journal · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsMillar College of the BibleNational Association of Friendship CentresLethbridge CollegeAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCanolaAgronomyCrop rotationCropField peaHordeum vulgareLegumeBiologyGreen manureCrop yieldPoaceae

Abstract

fetched live from OpenAlex

The rising cost of N in western Canada has created interest in alternative sources of N fertilizer. Legumes have the ability to fix N supply for subsequent crops, but knowledge of the effects of legumes on subsequent canola or barley is limited. A multi‐location study was conducted from 2009 to 2011 in western Canada to evaluate the economic effects of various preceding crops (P) and N rate on subsequent canola and barley in a P–canola–barley rotation. Six preceding crops (field pea, lentil, faba bean, canola, wheat, and green manure [GRM] legume [faba bean]) were grown in factorial combination with five N rates (0, 30, 60, 90, and 120 kg ha −1 ) at seven sites in Alberta, Saskatchewan, and Manitoba. When the preceding crop was GRM, the net revenue (NR) of canola or canola–barley was highest but insufficient to compensate for negative NR during the GRM year (2009). Canola as a preceding crop yielded the least NR for the canola and canola–barley phases of the rotation. The quadratic responses of NR for canola and barley to optimal N indicated that N applied could be reduced below 120 kg ha −1 without diminishing yield at some locations in western Canada. Over the entire 3‐yr crop sequence, legume preceding crops (lentil or field pea) grown for seed provided the greatest returns. The GRM improved the yield of the following crops considerably but the increased canola and barley yields were not able to alleviate the lost NR during the preceding crop phase.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.359

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 head0.003
GPT teacher head0.205
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 designBench or experimental
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

Citations36
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueAgronomy JournalSame topicNitrogen and Sulfur Effects on BrassicaFrench-language works237,207