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Record W2128694150 · doi:10.6000/1927-5129.2014.10.07

Effect of Biofertilizer Addition on Nitrous Oxide Emission

2014· article· en· W2128694150 on OpenAlexvenueno aff
Endah Retnaningrum, Irfan Dwidya Prijambada, Sukarti Moeljopawiro, B.S. Daryono

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsnot available
FundersUniversitas Gadjah Mada
KeywordsBiofertilizerUreaManureChemistryAgronomySowingNitrogenFertilizerBiology

Abstract

fetched live from OpenAlex

Application of nitrogen fixing biofertilizer, such as Azotobacter, has a potential for reducing nitrous oxide(N2O) emission. The aim of this study was to examine the effect of nitrogen fixing biofertilizer addition to common practices of urea and fresh cattle manure usages for maize (Zea mays L.) growing on N2O emission. The field experiment was conducted at GunungKidul, Yogyakarta, Indonesia. The treatments were addition of fresh cattle manure (M), fresh cattle manure added with nitrogen fixing biofertilizer (MB), urea (U), urea added with nitrogen fixing biofertilizer (UB), and control (no N fertilizer added). Nitrogen contents of the added urea and fresh cattle manure were adjusted to be equal. Urea and fresh cattle manure were given three times throughout the experiment period, i.e. 12, 30, and 48 days after planting (DAP). Urea was given at a rate of 44, 29, and 15 kg.ha-1, respectively while fresh cattle manure was given at a rate of 6000, 4000, and 2000 kg.ha-1, respectively. The emitted N2O was collected using a closed-chamber method at 24, 42, 60, and 72 DAP and were determined using Gas Chromatograph. Soil properties including available N (NH4+-N and NO3–-N) and organic C contents were also analyzed. On the harvesting time, the harvest index and the grain yield were determined. Biofertilizer addition influence decomposition process of cattle manure and urea that led to mineralization and nitrification of residual organic matterand hence to cause soil NH4+N in the order concentration of M treatment > MB > U > UB>C, and soil NO3–-N of MB treatment > M > U > UB>C. Reduction of NO3–N was resulted in the highest N2O emission of M >U>MB>UB>C(P < 0.01). The grain yield, and harvest index of maize wereresulted in the order value of MB> UB > U > M>C treatments.Available mineral N and soil organic C contents strongly affected N2O emission (P < 0.01).The results suggested that biofertilizer addition to common agricultural practices reduce N2O emission and simultaneusly increased grain yield, and harvest index of maize.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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
Published2014
Admission routes1
Has abstractyes

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