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Record W2916473741

Novel forage legumes for sustainable summer pasture mixtures in Saskatchewan

2018· dissertation· en· W2916473741 on OpenAlexaboutno aff
Samuel Peprah

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPastureForageAgroforestryAgronomyGeographyForestryEnvironmental scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Two experiments were conducted (2016 and 2017) to determine the potential of perennial binary legume-grass mixtures. Experiment 1 evaluated effect of July and September harvest dates on stockpiled forage dry matter yield, quality and botanical composition of legume and grass species at AAFC SCRDC-Swift Current (Brown soil site) and AAFC Saskatoon (Dark Brown soil site), Saskatchewan. Experiment 2 evaluated forage dry matter yield (DMY), quality, grazing animal preference and performance, and cost-benefits analysis of binary legume-grass mixtures at AAFC SCRDC-Swift Current (Brown soil site) and WBDC-Lanigan (Thin Black soil zone) Saskatchewan. Experiment 1 forage varieties included AC Yellowhead alfalfa (ALF), AC Mountainview sainfoin (MSF), Nova sainfoin (NSF), Shoshone sainfoin (SSF), Delaney sainfoin (DSF), AC Veldt cicer milkvetch (CMV), Great Plains Ecovar Canadian milkvetch (GCM), AC Lamour purple prairie clover (PPC) and Antelope white prairie clover (WPC) in binary mixtures with either Tom Russian wildrye (RWR), AC Success hybrid bromegrass (HBG) or Admiral meadow bromegrass (MBG) in a split-plot arrangement of a randomized complete block design (RCBD). Forage DMY of tame binary mixtures were greater (P = 0.01) at AAFC Saskatoon than AAFC SCRDC site. Forage DMY differed (P = 0.01) among tame binary mixtures ranging from 1,828 (NSF-RWR) to 4,826 kg ha-1 (ALF-HBG) and 4,511 (NSF-RWR) to 10,113 kg ha-1 (CMV-HBG) mixtures at AAFC SCRDC and AAFC Saskatoon, respectively. Forage DMY also differed (P = 0.01) among native binary mixtures ranging from 884 (WPC-RWR) to 3,582 kg ha-1 (PPC-HBG) mixtures at AAFC SCRDC site. Year of harvesting forages at both sites were greater (P < 0.05) in 2016 than 2017. Dry matter yield of mixtures harvested in September were 15 to 20% lower (P = 0.01) than July harvest yields in yr 2 at both study sites. Nutritive value of binary mixtures was 26 to 46% greater (P < 0.05) at AAFC Saskatoon compared to AAFC SCRDC-Swift Current site because of precipitation. Among the binary mixtures, CMV-RWR and GCM-RWR mixtures (native binary mixture) ranked the highest in nutritive value at both sites. RWR was more compatible with forage legumes (> 50 % legumes) compared to MBG and HBG in mixtures with legumes. Most native binary mixtures may not be recommended as fall stockpiled forage due to low CP level not meeting CP requirement of grazing animals and yielding less than 2,000 kilogram per hectare. Based on the 2 yr study, it is not recommended to seed either HBG or MBG in a binary mixtures with a legume in a mixed-row seeding pattern. Experiment 2 evaluated alfalfa (ALF) (Medicago falcata L.; cv. AC Yellowhead) and sainfoin (SF) (Onobrychis viciifolia Scop; cv. AC Mountainview) in binary mixtures with either Russian wildrye (RWR) (Psathyrostachys junceus [Fisch.]; cv. Tom) or hybrid bromegrass (HBG) (B. riparius Rehm × B. inermis Leyss; cv. AC Success) in a randomized complete block design at WBDC-Lanigan and AAFC SCRDC-Swift Current sites. Yearling steers (yr 1, n = 40, BW = 404 kg ± 18 kg; yr 2, n = 48, BW = 400 kg ± 16 kg) at AAFC SCRDC, and heifers (yr 1, n = 64, 364 kg ± 51) and steers (yr 2, n = 48, BW = 338 kg ± 23 kg) at WBDC were randomly allocated to 1 of 4 replicated (n=4) pasture types, (i) ALF-RWR; (ii) ALF-HBG; (iii) SF-RWR; and (iv) SF-HBG mixtures. Forage DMY were greater (P = 0.01) for HBG + legume mixtures at WBDC than at AAFC SCRDC and similar (P > 0.05) for RWR+ legumes mixtures at both sites. Forage DMY differed (P = 0.01) among binary mixtures ranging from 3,638 (SF-RWR) to 5,901 kg ha-1 (ALF-HBG) at WBDC site. In contrast, DMY was similar (P = 0.84) among binary mixtures ranging from 3,931 (ALF-HBG) to 4,140 kg ha-1 (ALF-RWR) at AAFC SCRDC site. Hand plucked samples had greater (P < 0.05) nutritive value in yr 2 at WBDC compared to clipped forage samples. However, at AAFC SCRDC, nutritive values from hand plucked samples were similar (P > 0.05) to clipped samples. Estimated forage dry matter intake (kg d-1) and forage utilization (%) were similar (P > 0.05) among binary mixtures at both sites. Average daily gain (ADG) was similar (P = 0.32) among binary mixtures at AAFC SCRDC. However at WBDC, ADG differed (P = 0.02) among binary mixtures in yr 2 ranging from 0.64 to 1.1 kg d-1 for ALF-HBG and SF-RWR mixtures, respectively. Animal grazing days (AGD) (P = 0.26) and total beef production (TBP) (P = 0.59) at WBDC were similar in both yrs, for all pasture mixtures ranging from 78 to 116 AU ha-1 AGD and 58 to 78 kg ha-1 TBP, respectively. However, at AAFC SCRDC in yr 2, AGD and TBP differed (P = 0.01) with steers grazing ALF-HBG mixtures having greater AGD (121 vs 74 AU ha-1) and TBP (120 vs 67 kg ha-1) compared to steers grazing the SF-RWR (74 AU ha-1) pasture. Despite the late summer and fall grazing, stocker performance was improved at both sites. Costs to seed perennial mixtures differed (P= 0.01) in both yrs and at both sites ranging from $58.78 (ALF-RWR) to 82.06 (SF-HBG) per ha at AAFC SCRDC and $29.00 to 49.09 and 75.95 to 96.03 per ha in yr 1 and yr 2 at WBDC, respectively. Value of gain ($ ha-1) (P = 0.66, yr 1; P = 0.27, yr 2) and net returns (P = 0.42, yr 1; P = 0.47, yr 2) were similar among mixtures at WBDC site. However, value of gain and net returns differed (P = 0.01 vs. P = 0.02) among mixtures in yr 2 at AAFC SCRDC site. These results suggest that beef producers can adopt placing a value on forages for higher profit compared to compensation rates for custom grazing and animal grazing days. Despite differences in agro-climatic condition, all binary mixtures were profitable for late summer and fall grazing in southwest and central Saskatchewan.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.240
Teacher spread0.227 · 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 designNot applicable
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".

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Citations2
Published2018
Admission routes1
Has abstractyes

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