Yield Response to Variable Rate Irrigation in Corn
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it