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Record W2163570277 · doi:10.1071/cp13004

Can summer-active perennial species improve pasture nutritive value and sward stability?

2013· article· en· W2163570277 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrop and Pasture Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsKimberly-Clark (Canada)
FundersAustralian Wool InnovationFuture Farm Industries Cooperative Research CentreDepartment of Environment and Primary Industries
KeywordsPasturePerennial plantAgronomyLolium perenneBiologyCichoriumLolium rigidumRuminantGrazingWeed

Abstract

fetched live from OpenAlex

Although generally well adapted and productive, the summer-dormant perennial pastures of southern Australia do not provide a year-round, high nutritive value feed base, they fail to respond to summer rainfall, and they are inefficient in using stored soil water, which can contribute to dryland salinity. An experiment was conducted to test the hypothesis that deep-rooted, summer-active perennial pasture species, matched to soil type, can be grown successfully in southern Australia to increase pasture and animal productivity and to provide high quality feed in summer–autumn. Specifically, the experiment compared a traditional perennial ryegrass (Lolium perenne L.) pasture system with two systems based on summer-active species: the triple system with lucerne (Medicago sativa L.) and tall fescue (Lolium arundinaceum (Schreb) Darbysh), and the novel system with chicory (Cichorium intybus L.) and kikuyu (Pennisetum clandestinum Hochst. ex Chiov). The experiment incorporated three livestock systems (two sheep and one cattle) and took into account the three main soil types occurring on the DPI Hamilton research farm. After 4 years the perennial ryegrass, lucerne, and tall fescue components were all persisting well and providing feed with high nutritive value (all with frequency scores >70% in the last year of the experiment). The chicory and kikuyu pastures declined over the life of the experiment and were contributing little at the end (frequency scores <15% in the final year). Lucerne, tall fescue, and perennial ryegrass cv. Banquet were able to respond to summer rainfall events to provide valuable, high-quality feed at a time when the quality of perennial ryegrass pasture is normally at its lowest; April 2007 crude protein per cent dry matter values were Avalon perennial ryegrass 16.6, Fitzroy perennial ryegrass 15.6, kikuyu 24.2, lucerne 25.8, and tall fescue 20.3 following a 98 mm rainfall event in late January 2007. This study has shown that the triple and ryegrass systems were persistent and of high nutritive value, with the sown perennial species contributing the majority of the sward dry matter during the growing season.

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.

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 score0.939
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.018
GPT teacher head0.211
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