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Record W2163758602 · doi:10.5539/sar.v2n1p15

Yields and Quality of Italian Ryegrass (Lolium multiflorum) and Common Vetch (Vicia sativa) Grown in Monocultures and Mixed Cultures under Irrigated Conditions in the Highlands of Madagascar

2012· article· en· W2163758602 on OpenAlexvenueno aff
Volatsara Baholy Rahetlah, J. M. Randrianaivoarivony, Blandine Andrianarisoa, Lucile H. Razafimpamoa, V.L. Ramalanjaona

Bibliographic record

VenueSustainable Agriculture Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyLolium multiflorumVicia sativaMonocultureIntercroppingHectareSowingDry matterBiologyForageVicia villosaCover cropAgriculture

Abstract

fetched live from OpenAlex

<p>A field experiment was conducted under irrigated conditions in the highlands of Madagascar to assess the potential of intercropping Italian ryegrass with common vetch for improving yield and quality of forage. Seed proportions studied were ryegrass-vetch 100:0; 0:100; 50:33; 50:50; 50:66; 75:33 and 75:66. Mixtures were sown in alternate rows and the sowing rates of pure stands of ryegrass and vetch were 20 and 60 kg per hectare, respectively. The results showed that all mixtures achieved yield advantage over pure stands with the highest land equivalent ratio value for dry matter yield obtained from the mixture of 75:66 (1.47) followed by 50:50 (1.35). Slight increase of crude protein content and protein digested in the small intestine when rumen-fermentable nitrogen is limiting (PDIN) content were obtained from mixtures compared with pure stand of ryegrass. Vetch rate in dry matter yield of mixtures increased with the increase of vetch seed proportion and ranged from 31 to 44%. Agressivity and competitive ratio indices showed that ryegrass was slightly competitive than vetch. Intercropping Italian ryegrass with common vetch at the seed proportions of 75:66 or 50:50 could be a more sustainable alternative cropping to alleviate dry season feed shortages of dairy livestock in the highlands of Madagascar.</p>

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)0.003
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 score0.220
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.067
GPT teacher head0.356
Teacher spread0.289 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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