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Record W2007692483 · doi:10.2134/agronj2005.0079

Influence of Nitrogen Fertilization on Multi‐Cut Forage Sorghum–Sudangrass Yield and Nitrogen Use

2005· article· en· W2007692483 on OpenAlexafffundabout
R. C. Roy

Bibliographic record

VenueAgronomy Journal · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaAgence Nationale de la Recherche
KeywordsSorghumForageAgronomyNitrogenLoamFertilizerYield (engineering)Nitrogen fertilizerHuman fertilizationMathematicsHayAnimal scienceEnvironmental scienceChemistryBiologySoil waterSoil science

Abstract

fetched live from OpenAlex

Forage sorghum–sudangrass [ Sorghum bicolor (L.) Moench] is a relatively new crop to eastern Canada and the effects of additions of fertilizer N on yield, N accumulation, and N use efficiency are not available for this region. In 1998, 1999, and 2000 the response of forage sorghum–sudangrass to additions of fertilizer N rates (0, 50, 100, 150, 200, and 250 kg N ha −1 ) either applied as a single sidedress application or split into two sidedress applications was evaluated on a Fox loamy sand (Psammentic Hapludalf). Although timing of N application had little effect on DM production, splitting the N application into two equal applications may be of benefit by enhancing the NUE and ANR of individual cuts. Maximum yield was estimated at 5.95 Mg ha −1 at an N rate of 125 kg N ha −1 and the most economic rates of N ranged from 83 to 107 kg N ha −1 dependent on the cost of N fertilizer and value of hay. Nitrogen concentration increased linearly with increasing N application and the maximum N accumulation was 161 kg N ha −1 at an N rate of 196 kg N ha −1 . Total N use efficiency and apparent N recovery decreased with increasing N rates ranging from 36 to 11 kg DM kg −1 and 90 to 24%, respectively. Optimum yield and N efficiency occurred when 100 kg N ha −1 was applied as a split application. Producers in southern Ontario require N fertilizer additions to optimize sorghum–sudangrass yields but need to avoid overfertilization with N to maximize N use efficiency and apparent N recovery.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.174

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 head0.027
GPT teacher head0.221
Teacher spread0.194 · 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

Citations56
Published2005
Admission routes3
Has abstractyes

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