Effect of raised beds, irrigation and nitrogen management on growth, water use and yield of rice in south-eastern Australia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".