Copper fertilizer management for optimum seed yield and quality of crops in the Canadian great plains
Bibliographic record
Abstract
BACKGROUND• Management practices have long-term effects on In the Prairie Provinces, deficiency of Cu is not wide spread but when it occurs it can cause a serious reduction in seed yield (up to 50% or more) and quality of wheat.• Copper deficiency has been observed on coarse textured soils, and it usually occurs in irregular patches within fields.• Copper deficiency in cereals produces characteristic symptoms of yellowing and curling of young leaves, pigtailing of leaf tips, limpness or wilting, delay in heading, aborted heads and spikelets, head and stem bending, etc.• Crop species vary in their sensitivity to Cu deficiency, but cereals are more sensitive to Cu deficiency than other crops.• Lack of Cu in soil also has been associated with some cereal diseases and wheat is often cited as the most severely affected cereal and most sensitive to Cu deficiency. OBJECTIVE• The objective of this report is to summarize research information from various experiments conducted in the Prairie Provinces of Canada on various crops related to Cu fertilizer rate, time of application (one-time initial, annual, at sowing in spring and during the growing season), source, placement method (surfacebroadcast, broadcast-incorporation, sideband, seedrow-placement and in-crop foliar spray) and formulation (liquid, fine crystals/powder and granular), crop species/cultivar, balanced fertilization (interaction with other nutrients and herbicides), Cu deficiency and crop diseases, residual Cu in soil and yield response and soil/plant test issues in relation to crop yield and seed quality.• The indicators considered are seed yield, straw yield, seed quality (protein, hectolitre weight, thousand kernel weight, concentration of Cu in seed), Cu-and N-use efficiency (seed yield per unit of applied Cu or N), Cu uptake, recovery of applied Cu, residual DTPA-extractable Cu in soil. SUMMARY AND CONCLUSIONS• Prevention and/or correction of Cu deficiency on Cu-deficient soils have a dramatic effect on seed yield and quality of cereals.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".