Effect of overhead irrigation on corn yield and quality under shallow water table conditions.
Bibliographic record
Abstract
Corn is a moisture sensitive crop and drought conditions during critical growth stages affect kernel yield and quality. The objective of this field research was to determine the impact of water contribution from shallow water table under overhead irrigation and no irrigation treatments on corn yield, in a fine sandy loam soil in Southern Manitoba. The study was conducted at two different sites (Canada Manitoba Crop Diversification Centre (CMCDC), and Hespler Farms). Compared to no irrigation treatment, the overhead-irrigated plots had a 16% (p = 0.021) and 9% (p = 0.025) significantly higher yield at CMCDC, and Hespler sites, respectively. The kernel quality, based on kernels passing through 14/64-mesh size, in overhead-irrigated plots was found to be significantly better in overhead-irrigated plots at CMCDC (p = 0.011) and Hespler (p = 0.003) sites compared to the non-irrigated treatment. The increased unsaturated hydraulic conductivity due to increased water content of the soil beneath the root zone in the irrigated treatment led to an increased upward migration of water from the shallow water table compared to the upward migration in the non-irrigated treatment. In the irrigated treatment, the irrigation water quality was better than the quality of the water supplied from the water table because groundwater had high concentration of nitrate (55 ppm). However, in the non-irrigated treatment, the precipitation alone was not sufficient to dilute the poor quality water from the water table leading to lower yield and poor kernel quality.
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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".