Irrigation treatments for corn with limited water supply in the loess plateau, China
Bibliographic record
Abstract
Huang, H., Zhong, L. and Gallichand, J. 2002. Irrigation treatments for corn with limited water supply in the Loess Plateau, China. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 44:1.29-1.34. The lack of available growing-season water in the middle reaches of the Yellow River watershed has caused the development of deficit irrigation in the Loess Plateau of China. This research was conducted to evaluate the effect of different irrigation frequencies and timings on water use and corn yield and to determine optimum water management practices with limited water supply. Irrigation treatments were zero (I0), one (I1), two (I2), three (I3), and four (I4) irrigations per growing season with each irrigation consisting of a 87.5 mm depth of water. For treatment I1, the irrigation water was applied at tasselling, whereas I2 consisted of I1 plus an irrigation during the vegetative stage. Treatment I3 consisted of I2 plus an irrigation at silking, while for I4 an additional irrigation was provided at the grain filling stage. Irrigation quantities ranged from deficit to excess irrigation. The study was conducted at the Changwu Agri-ecological Station of the Loess Plateau from 1991 to 1995 on a moderately permeable silty clay loam soil. A single irrigation increased yield by an average of 20%. On average, two, three and four irrigations increased yields by an additional 16.9, 6.7, and 4.3%, respectively. There were no significant differences in corn grain yield and water use efficiency (WUE) among treatments I2, I3, and I4, but I1 yield was significantly lower. We concluded that substantial water savings can be achieved by applying two irrigations, one at tasselling and one during the vegetative stage. This should increase yield significantly compared to the no irrigation treatment, without significantly reducing yield compared to four irrigations per growing season.
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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) | 0.001 | 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".