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Record W4385541437 · doi:10.1016/j.fcr.2023.109048

Optimizing nitrogen fertilization for hybrid canola (Brassica napus L.) production across Canada

2023· article· en· W4385541437 on OpenAlex
Guoqi Wen, B. L., Mervin St. Luce, Kui Liu, Patrick S. Mooleki, Stephen Crittenden, Robert H. Gulden, Greg Semach, Paul Tiege, Prabhath Lokuruge

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueField Crops Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsOlds CollegeUniversity of ManitobaAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaChildhood Cancer CanadaCanola Council of CanadaUniversity of Manitoba
KeywordsCanolaHybridAgronomyHuman fertilizationAbiotic componentYield (engineering)BrassicaNitrogenBiologyCrop yieldEnvironmental scienceChemistryEcologyMaterials science

Abstract

fetched live from OpenAlex

Context or problem Hybrids are currently the dominant varieties in canola production, but their yield response to nitrogen (N) application across Canada has not been adequately updated. As a result, there is a lack of effective N management guidelines for modern hybrid canola to reach their full yield potential and cope with growing abiotic stresses caused by climate change . Objectives or research questions This study was designed to investigate the responses of yield and N use efficiency (NUE) of modern canola hybrids to N fertilization for determining site-specific economic optimum N rates (EONR). Additionally, the key driving factors of canola yields and N recommendations were identified. Methods A 32 site-year field study across Canada was conducted to test 8 combinations of N rates and application methods on 2 site-specific hybrids in each trial. Results Nitrogen fertilization greatly increased canola yield by an average of 41%, with significant responses in 19 out of 32 trials. Split-N strategy led to similar yield, NUE, and yield response index compared to preplant-only N application. However, these traits varied among hybrids due to different growing environments and hybrid-specific tolerance to abiotic stresses. The number of heat-stress days and heat-induced thermal accumulation surrounding the 4 weeks before and post-flowering stage directly determined the canola responses to N fertilization and EONR. Conclusions Our results suggest a site-specific EONR of 146–166 kg N ha −1 in the Black soil zone, 85–100 kg N ha −1 in the low-yielding Brown soil zone, and 140 kg N ha −1 in Ontario, preferably with a split-N strategy for hybrid canola production. The split-N fertilization is generally recommended, as it provides an opportunity to adjust the amount of topdressing N based on historical and early season weather conditions to achieve the dual goals of increasing canola productivity and reducing greenhouse gas (GHG) emissions from fertilizer use. The energy and time costs must be considered when making practical decisions. Environment-specific selection of canola hybrids also played an important role in the response to N, with ‘6074RR’ in favorable weather and ‘L233P’ in drought-prone conditions appearing to be good choices for specific ecoregions.

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.

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)0.001
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 score0.149
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.036
GPT teacher head0.356
Teacher spread0.320 · 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