Growth, chlorophyll content and productivity responses of maize to magnesium sulphate application in calcareous soil
Bibliographic record
Abstract
Abstract Magnesium (Mg) is an essential plant macronutrient responsible for modulating many physiological or biochemical processes such as photosynthetic activity, amino acid synthesis and nucleotide metabolism. Agricultural soils with a more-than-adequate availability of calcium (Ca) have inherent Mg deficiency, potentially resulting in overall reduced soil productivity and crop yield potential. We conducted a field experiment to investigate the optimum soil application of Mg to increase crop growth and productivity under calcareous soil conditions. In addition to recommended soil application of mineral fertilizers, we applied the following four levels of Mg to the soil in the form of anhydrous MgSO4: control, 4 kg Mg ha−1 (Mg4), 8 kg Mg ha−1 (Mg8) and 16 kg Mg ha−1 (Mg16). Results showed that Mg16 application enhanced the plant height (21%), number of grains (18%), 1,000 grains weight (20%), grain yield (20%) and biological yield (9%) over control (p ≤ 0.05). Chlorophyll a, chlorophyll b and total chlorophyll were generally higher at the Mg8 and Mg16 levels than at the control level. Contrasting to increases in growth traits, the concentration of K significantly decreased in grains, leaves and shoots of maize along the soil’s Mg gradient (p ≤ 0.05). We suggest that Mg16 overcomes the deficiency of soil Mg and can increase the crop yield traits in calcareous soils. More investigations of the effect of soil Mg on various crops grown in calcareous soils may add to our knowledge related to the stressing impact of soil Mg on plant K concentration.
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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) | 0.000 | 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 | 0.001 |
| 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".