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Record W2106320055 · doi:10.1071/ea04136

Effect of raised beds, irrigation and nitrogen management on growth, water use and yield of rice in south-eastern Australia

2006· article· en· W2106320055 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

VenueAustralian Journal of Experimental Agriculture · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsOptech (Canada)
FundersAustralian Centre for International Agricultural ResearchRural Industries Research and Development Corporation
KeywordsIrrigationAgronomyEnvironmental scienceSurface irrigationProductivityDrip irrigationWater-use efficiencyWater useYield (engineering)CroppingLoamCropCropping systemSoil waterAgricultureBiologySoil scienceEcology

Abstract

fetched live from OpenAlex

To remain economically and environmentally sustainable, Australian rice growers need to be able to readily respond to market opportunities and increase cropping system productivity and water productivity. Water availability is decreasing whereas its price is increasing. Alternative irrigation layouts and water management approaches could contribute to reduced water use and increased irrigation efficiency. This paper reports results for the first crop (rice) in a cropping system experiment to compare permanent raised bed and conventional layouts on a transitional red-brown earth at Coleambally, New South Wales. The performance of conventional ponded rice grown on a flat layout was compared with rice grown on 1.84-m wide, raised beds with furrow and subsurface drip irrigation. In addition, deep and shallow ponded water depth treatments (15 and 5 cm water depth over the beds) were imposed on the rice on beds during the reproductive period. A range of nitrogen (N) fertiliser rates (0–180 kg N/ha) was applied to all treatments. The traditional flat flooded treatment (Flat) achieved the highest grain yield of 12.7 t/ha, followed by the deep (Bed 15) and shallow (Bed 5) ponded beds (10.2 and 10.1 t/ha, respectively). The furrow (Furrow) irrigated bed treatment yielded 9.4 t/ha and the furrow/drip (Furr/Drip) treatment yielded the lowest grain yield (8.3 t/ha). Grain yield from all bed treatments was reduced owing to the wide furrows (0.8 m between edge rows on adjacent beds), which were not planted to rice. Rice crop water use was significantly different between the layout–irrigation treatments. The Flat, Bed 5 and Bed 15 treatments had similar input (irrigation + rainfall – surface drainage) water use (mean of 18.3 ML/ha). The water use for the Furrow treatment was 17.2 ML/ha and for the Furr/Drip treatment, 15.1 ML/ha. Input WP of the Flat treatment (0.68 t/ML) was higher than the raised bed treatments, which were all similar (mean 0.55 t/ML). This single season experiment shows that high yielding rice crops can be successfully grown on raised beds, but when beds are ponded after panicle initiation, there is no water saving compared with rice grown on a conventional flat layout. Preliminary recommendations for the growing of rice on raised beds are that the crop be grown as a flooded crop in a bankless channel layout. This assists with weed control and allows flooding for cold temperature protection, which is necessary with current varieties. Until we find effective herbicides and other methods of weed control and N application that do not require ponding, there is little scope for saving water while maintaining yield on suitable rice soil through the use of beds.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.204

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 head0.022
GPT teacher head0.239
Teacher spread0.216 · 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