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Record W3042440578 · doi:10.3390/agronomy10071035

Sulfur Uptake from Fertilizer Fortified with Sulfate and Elemental S in Three Contrasting Climatic Zones

2020· article· en· W3042440578 on OpenAlexaboutno aff
Fien Degryse, Roslyn Baird, Rodrigo C. da Silva, Christopher B. Holzapfel, Claudinei Kappes, Mónica Beatriz Tysko

Bibliographic record

VenueAgronomy · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsnot available
FundersMosaic Company
KeywordsFertilizerSulfateLeaching (pedology)δ34SPhosphate fertilizerSulfurNutrientPhosphateEnvironmental scienceAgronomyChemistryAnimal scienceBiologySoil waterSoil science

Abstract

fetched live from OpenAlex

Field trials with labeled fertilizers can be used to provide information on fertilizer efficiency, residual value, and nutrient fate. We assessed the uptake from elemental S (ES) and sulfate S (SO4-S) in S-fortified monoammonium phosphate fertilizers by various crops at three sites in Argentina, Brazil, and Canada. The S sources were labeled with 34S, and the 34S abundance in the plant tissue was analyzed at an early stage and at maturity over two consecutive years. At the sites in Argentina and Canada, the recovery of ES in the crop was smaller than that of SO4-S in the first year, while the opposite was true in the second year. At the Brazilian site, the recovery of ES was similar to that of SO4-S in the first year, but higher in the second year. In the Argentina and Canada sites, the cumulative recovery of SO4-S was >65% and that of ES was 20−25%. In the Brazilian site, the cumulative recovery of SO4-S was 9% and that of ES 16%. The higher recovery of ES than of SO4-S in the Brazilian site was attributed to leaching of added SO4-S and relatively fast oxidation of ES due to the warm climate. These results suggest that ES may be more suitable than SO4-S as a fertilizer S source in warm humid climates, while inclusion of SO4-S in the fertilizer is recommended in colder climates as slow oxidation limits the initial availability of ES.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.213
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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