Yield Response to Variable Rate Irrigation in Corn
Bibliographic record
Abstract
To investigate the impact of variable rate irrigation on corn yield, twenty plots of corn were laid out under a center pivot variable rate irrigation (VRI) system in an experimental field near Stoneville, Mississippi. The VRI system is equipped with five VRI zone control units, a global positioning system (GPS) receiver, and computer software. Each zone control unit controls the duty cycle of the sprinklers in the zone to realize variable rate water application across the pivot lateral. The GPS receiver determines the pivot position for identification of the control zone in real time. Supplemental irrigation was scheduled based on evapotranspiration (ET) estimates. A randomized complete block design was used in this study, with five irrigation rate treatments (0, 50%, 75%, 100%, and 125% of the rate determined using the Arkansas Irrigation Scheduler) and four replications. During the growing seasons in 2012 and 2013, VRI prescriptions were created based on the experimental design, and wirelessly uploaded to the system to apply varying amounts of water to each plot. The corn was machine harvested for yield. Results indicated that effect of irrigation rate on yield was not significant in 2012 and was significant in 2013. The treatment of 125% irrigation rate had the highest yield for both years. No significant yield difference between treatments in the 2012 season could be due to the sufficient rainfall in that summer. The ET estimates used in the irrigation scheduling might be lower than actual water demand of the corn crops for a higher yield.
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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 | 0.001 |
| 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".