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Record W2739280409 · doi:10.5539/jas.v9n8p106

Effect of Nitrification Inhibitor and Cutting Heights on Degradability of Pearl Millet

2017· article· en· W2739280409 on OpenAlexvenueno aff
Daniel Staciarini Corrêa, Aldi Fernandes de Souza França, Roberto Toledo de Magalhães, Emmanuel Arnhold, Adesvaldo José e Silva-Junior, Leonardo Guimarães de Oliveira

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyForageRandomized block designNitrificationNitrogenFertilizerNutrientCropDry matterPearlNitrateAmmoniumNeutral Detergent FiberFactorial experimentChemistryAnimal scienceBiologyMathematicsGeography

Abstract

fetched live from OpenAlex

Pearl millet crop has been increasingly growing in Brazilian Savanna and it is already being used as cover crop between annual crops. The plant has great forage potential, besides being a nutrient recycling plant due to its peculiar root system. This study was developed in order to assess the pearl millet nutritional value when submitted to nitrogen fertilizer. It was evaluated the effect of nitrogen fertilizer (ammonium sulfate nitrate) treated with the nitrification inhibitor 3,4-dimethyl pyrazole phosphate (DMPP) on the ruminal degradability of two pearl millets’ cultivars, under four nitrogen fertilization levels (0, 45, 90 and 180 kg ha-1) and pre-cutting heights (0.70, 0.80 and 0.90 m). The experimental design was a randomized block design in a factorial 3 × 4 (3 cutting heights × 4 nitrogen doses) with three replications. Data were submitted to analysis of variance and means were compared by Tukey test at 5% probability. The DMPP treated Nitrogen, in high doses, increased the dry matter, crude protein and neutral detergent insoluble fiber degradability in pearl millet handled at 0.90 m. The combination of fertilization with 45 or 90 kg ha-1 of nitrogen treated with DMPP, with the management of millet at 0.70 or 0.80 m did not favored the forage nutritional quality, indicating that in these treatments, the ratio between the availability of nitrogen in ammonium and nitrate forms may have been detrimental to the plants.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.717
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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