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Record W7005654316

Short-season high-moisture corn products in western Canadian beef cattle diets

2023· dissertation· en· W7005654316 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
FundersBeef Cattle Research CouncilUniversity of Saskatchewan
KeywordsCorn stoverGrazingSilageBeef cattleForageStoverLivestockNutrient
DOInot available

Abstract

fetched live from OpenAlex

As low-heat unit corn becomes more abundant in western Canada, alternative utilization methods to corn silage and standing corn for grazing should be investigated. The objectives of this dissertation were to 1) evaluate yield and production costs of low-heat unit, high-moisture shelled corn (HSC) and snaplage; 2) evaluate the potential to utilize low-heat unit, HSC stover in a winter grazing system for pregnant beef cows relative to grazing whole-plant barley in a swath grazing system including system economic costs; 3) characterize low-heat unit HSC stover quality based on each plant component at the appropriate time of harvest and the digestibility of each component; and 4) evaluate the use of low-heat unit, high-moisture corn products (HSC and snaplage) in finishing diets compared to barley grain and silage. Over two consecutive years, high-moisture corn products (HSC, snaplage, and HSC stover) and barley greenfeed (BAR) were produced at the Livestock and Forage Centre of Excellence (LFCE; Clavet, SK, Canada). These products were utilized in five experiments: two in vivo winter feeding studies examining the use of HSC stover compared to BAR as forage for dry pregnant beef cows; one in vitro batch culture study comparing the nutrient composition and disappearance of anatomical components of HSC stover and BAR; one in vivo, metabolism study comparing the previously mentioned diets in site and extent of nutrient digestibility; and one in vivo small pen study comparing performance of steers finished on a traditional barley-based diet and those that substituted barley products with high-moisture corn products. An economic summary based on the results of these studies and crop production data was prepared. Both HSC and snaplage, along with their stovers, yielded less dry matter (DM) than BAR in 2020, but HSC and its stover yielded more DM than BAR in 2021. Production costs ($/ha) were greatest for snaplage, then HSC, then BAR. When HSC stover was swathed, cows performed similarly to those fed swathed BAR, but BAR was more economically favorable. When baled, cows fed BAR had greater final body weights (BW) and body condition scores (BCS), but HSC stover was more economically favorable. In vitro batch culture revealed that BAR leaves had the highest forage quality based on nutritional composition and in vitro fiber disappearance, while HSC stover cobs had the lowest forage quality. In the metabolism study, the barley grain finishing diet was comparable in starch digestion and live performance to a diet substituting 50% of barley grain with HSC (DM basis), but the diet containing HSC yielded heavier carcasses in the performance study. Additionally, when barley silage and approximately 10% of barley grain were replaced with snaplage, there were no differences in live performance of finishing steers, but ruminal starch digestion was more complete in the metabolism study, although severe liver abscesses were less common in the performance study. Substituting snaplage in finishing diets produced lower costs of gain than buying all feeding ingredients, and substituting HSC was more expensive than the traditional barley finishing diet. These results indicate that short-season high-moisture products are acceptable replacements for barley products in western Canadian beef cattle diets, but HSC was not as economically favorable at the time of the current study.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.263
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.161
Teacher spread0.153 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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