Rate and frequency of fertilizer zinc application on Zn fractions in inceptisols of Gujarat, India
Bibliographic record
Abstract
A six year study (2012–18) was carried out to assess the effect of different application rate and frequencies of zinc (Zn) fertilizer on different forms of Zn in the Inceptisols of Gujarat, India in a maize-wheat cropping system. The efficiency of applied Zn was evaluated in combinations of four different rates (2.5, 5.0, 7.5 and 10.0 kg Zn ha-1) and at three different frequencies (first year only, alternate year and every year). Additionally, one Zn control plot which received only the recommended dose of fertilizers (RDF) was also included. The experiment was designed in a randomized block design with three replications. A sequential extraction method was adopted to fractionate water-soluble Zn (WS-Zn), exchangeable Zn (EX-Zn), Zn bound to carbonate (Car-Zn), Zn bound to iron (Fe) and manganese (Mn)-oxide (FeO/MnO-Zn), Zn bound to organic matter (OM-Zn), residual Zn (Res-Zn) and total soil Zn forms. The results showed that the continuous application of Zn for six years increased concentrations of all soil Zn fractions irrespective of Zn application rate and frequencies. Increasing Zn input increased the percentages of Ex-Zn, Carb-Zn, FeO/MnO-Zn and to soil total Zn, whereas reduced the OM-Zn and Res-Zn fraction. Apparent Zn recovery efficiency varied with the application rates from 1.46% in 2.5 kg Zn ha-1 applied every year to 0.17% in 10 kg Zn ha-1 applied during first year of study in maize crop and from 1.70% in 2.5 kg Zn ha-1 applied every year to 0.34% in 10 kg Zn ha-1 applied in the first year only for the wheat crop. Overall the use efficiency of Zn decreased with increased the rate of Zn application and its frequencies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".