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Record W3133932331 · doi:10.3390/agronomy11030459

Effect of Harvesting Corn after Frost in Alberta (Canada) on Whole-Plant Yield, Nutritive Value, and Kernel Properties

2021· article· en· W3133932331 on OpenAlexafffundabout
Jessie Guyader, V. S. Baron, K. A. Beauchemin

Bibliographic record

VenueAgronomy · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaBeef Cattle Research Council
KeywordsFrost (temperature)Dry matterAgronomyNeutral Detergent FiberGrowing seasonStarchForageHybridBiologyHorticultureFood scienceGeography

Abstract

fetched live from OpenAlex

This study compares yield, nutritive value, and kernel properties of whole plant corn (WPC) harvested before and after a light frost in short growing season areas. Six corn hybrids grown in two years at three locations within Alberta (Canada) were harvested before or after the first frost. Samples of WPC were analyzed for dry matter (DM) content, neutral detergent fiber (NDF) concentration, starch concentration, and 48-h in vitro DM and NDF digestibility (DMD and NDFD, respectively). Cob samples were analyzed for DM, and kernels were analyzed for DM, hardness, particle size distribution, density, and stage of maturity. Delaying harvest to after frost increased DM content of WPC at all locations but exceeded the recommended range (32–38%) in the two warmest locations. Whatever the year and hybrid, DM yield was either not affected or decreased after frost. Postfrost harvest increased starch concentration and modified kernel characteristics only if these were less than expected before frost. Fiber concentration was not affected by harvesting time. Frost had either no impact or increased DMD or NDFD of WPC. We conclude that delaying harvest until after frost in short growing season areas can be beneficial when whole-plant DM content is low before frost.

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 score0.362
Threshold uncertainty score0.731

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 head0.012
GPT teacher head0.181
Teacher spread0.170 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

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