Variability in Canada Western Red Spring wheat yield response to applied nitrogen in Manitoba soil landscapes
Bibliographic record
Abstract
Increasing economical and environmental pressures has sparked a great deal of interest in precision agriculture. Thus, a great deal of research has been initiated in order to gain a greater understanding of how existing technologies such as global positioning systems, geographic information systems, and equipment with variable rate capabilities can be used to manage agricultural amendments at a site specific level. In 1996 and 1997, small plot trials were established at six sites in Southern Manitoba. Four of these sites were located on glacial till landscapes of the Newdale Association and the other two were located on lacustrine landscapes of the Red River Association. A variety of soil and crop parameters were examined throughout the study. Replicated small plots with fertilizer N rates ranging from 0 to 200 kg N ha-1 were established in various positions in the landscape based on relative elevation, slope morphology, and slope aspect. The objective of the study was to determine if there were any significant differences in yield response to applied N in Canada Western Red Spring wheat in these landscapes. In the glacial till landscapes, a number of the soil parameters were found to be strongly associated with landscape position. Among these parameters, electrical conductivity, depth of A horizon, solum depth, NO3- -N, volumetric water content, and growing season N uptake tended to demonstrate the most consistent differences among landscape positions. However, yield and grain protein responses to applied nitrogen were extremely inconsistent throughout the study in these landscapes. The soil parameters studied in the lacustrine landscapes demonstrated very different trends than those observed at the glacial till landscapes... The use of landscape position as the only variable in determining differences in yield responses to applied N proved to be ineffective in the glacial till landscapes studied. In these landscapes, more comprehensive models with various other soil parameters may need to be developed in order to make variable rate nitrogen decisions. However, the use of landscape positions to make variable rate nitrogen decisions in lacustrine landscapes may be more promising.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".