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Record W7068159602

Nitrogen Acquisition of Pea-Oat and Pea-Canola Intercrops and Their Impact on Subsequent Wheat Crops

2024· dissertation· en· W7068159602 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaGeneral Mills
KeywordsMonocroppingIntercroppingFertilizerNitrogenLegumeNitrogen fixationProductivityField experiment
DOInot available

Abstract

fetched live from OpenAlex

The increased diversity provided by intercropping systems can provide many benefits, including increased nitrogen (N) use efficiency (NUE). The ability of legume crops to fix atmospheric N can increase in an intercrop, leading to increased productivity compared to monocrops. However, limited research has tested the efficiency of intercropping systems in western Canada and their effect on subsequent crops. Therefore, pea-oat (PO) and pea-canola (PC) intercrops (mixed row) were grown at Swift Current, Melfort, and Redvers, Saskatchewan followed by wheat in the subsequent year. Each of the intercrops were supplied with three N fertilizer rates (0, ¼, ½ of their recommended rate), while the monocrops (pea, oat, canola) received their full recommendation of N fertilizer, except pea which received no N fertilizer. In the following year, wheat was supplied with its full recommendation of N fertilizer. The 15N dilution method was used to measure the percentage of N derived from the atmosphere (%Ndfa), %N transfer, N fertilizer recovery, and residue recovery in the succeeding wheat crop. The %Ndfa measured the percentage of N in pea that was derived from the atmosphere. In contrast, biological N fixation (BNF) determined the amount of N uptake that was from the atmosphere. The %Ndfa increased in pea intercrops compared to the pea monocrop but due to reduced pea biomass in PO, it decreased the amount of N derived from fixation by 8% compared to the pea monocrop; however, PC increased this amount by 23%, on a per-plant basis. These differences in BNF did not influence productivity, where the intercrops were similar to monocrops. The intercrops produced grain land equivalent ratios (LER) of 0.94 (PO) and 0.98 (PC). In contrast, PC accumulated more above-ground dry matter N than PO and had a 3% advantage over its respective monocrops. This enabled wheat to produce 5% more yield following PC compared to PO but no differences were found between intercropping and monocropping systems. The recovery by wheat of above-ground residue N was higher in monocrops than the intercrops but the low recovery (4%) did not have any impact on wheat yield between the two systems. Overall, PC intercrops appear to be a viable option for producers as they increased BNF, N uptake, and produced slightly higher yields of the following wheat crop than PO. This intercrop also provided comparable intercrop and wheat yields to its monocropping systems; therefore, its ability to utilize N more efficiently made up for reduced fertilizer applications.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.004
GPT teacher head0.172
Teacher spread0.167 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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