Effects of zinc application strategy on zinc content and productivity of chickpea grown under zinc deficient soils
Bibliographic record
Abstract
Field experiments were conducted at three locations with Zn deficient soils in southern Ethiopia during 2012 and 2013 cropping seasons to evaluate the effects of Zn fertilization strategies and varietal differences on Zn content and plant performance of chickpea (Cicer arietinum L.).Factorial combinations of three Zn fertilization strategies and five varieties were laid out in a randomized complete block design with three replications in each location and year.A combined analysis of variance was made using a mixed effects model.Zinc foliar application increased grain Zn content by 21 and 22% over Zn soil application and seed priming, respectively.The improvements were around four folds for straw Zn content for the same comparisons.Effects of Zn application strategies on gain and straw Zn contents were consistent across locations.The grain Zn concentration varied among the varieties ranging from 34 mg kg -1 for Mastewal to 42 mg kg -1 for the Landrace and variety Arerti.Zn application strategies did not affect the growth and yield parameters, except for pod bearing branches.Foliar Zn application and appropriate variety selection are potential approaches for Zn biofortification in chickpea.Further study aimed at identifying most effective spray timing for maximum grain quality response would be worthwhile to cut fertilizer and application costs.
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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) | 0.001 | 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 | 0.001 |
| 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".