Magnesium supply is vital for improving fruit yield, fruit quality and magnesium balance in citrus orchards with increasingly acidic soil
Bibliographic record
Abstract
• Mg leaching results in soil Mg imbalance and depletion in citrus orchards. • Mg fertilization alleviates the soil Mg imbalance in citrus plantation systems. • Mg leaching accounted for 12.1–42.4% of Mg fertilizer application in a citrus orchard. Magnesium (Mg) deficiency is becoming a limiting factor for citrus production in acid soils of subtropical and tropical zones. It is speculated that soil Mg leaching and thereby its imbalance may be a major cause of yield decline, yet Mg deficiency in citrus receives little attention. A two-year field experiment was therefore conducted to quantify soil Mg leaching in a typical citrus orchard in China fertilized with varying levels of Mg (Mg 0 , no Mg fertilizer; Mg 45 , 45 kg MgO ha –1 yr –1 ; Mg 90 , 90 kg MgO ha –1 yr –1 ; Mg18 0 , 180 kg MgO ha –1 yr –1 ). Results showed that Mg application significantly increased citrus fruit yield by 4.1–16.4% compared with where MgO was not added. The average amount of soil Mg leaching was 65.7 kg ha –1 yr –1 where no Mg fertilizer was added, while it reached up to 91.3 kg Mg ha –1 yr –1 where MgO was added at the rate of 180 kg ha –1 . Over the 4 treatments, Mg leaching accounted for 12.1–42.4% of the applied Mg fertilizer. Mg leaching and its removal through harvested fruits resulted in an orchard soil Mg balance of –69.9, –51.1, –27.4 and 10.9 kg ha –1 in the Mg 0 , Mg 45 , Mg 90 and Mg 180 , treatments, respectively. The pH values of leachate from the acid soil were alkaline and it contained higher amounts of calcium and potassium than that of Mg. Considering the high leaching of Mg from the acid soils of citrus orchards, applications of Mg fertilizer or Mg-fortified soil conditioner are vital to sustain soil Mg balance, high fruit yield and fruit quality in citrus production systems in humid subtropical regions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".