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Record W2105839120 · doi:10.5539/jas.v5n8p182

Yield Responses of Maize to Organic and Mineral Fertilizers at Different Inclinations in Tropical Smallholder Farming Systems

2013· article· en· W2105839120 on OpenAlexvenueno aff
W. C. P. Egodawatta, P. Stamp, U. R. Sangakkara

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsGliricidiaGliricidia sepiumAgronomyHectareManureFertilizerCropping systemEnvironmental scienceAgricultureBiologyCropEcology

Abstract

fetched live from OpenAlex

A field study was conducted on the potential of Gliricidia (Gliricidia sepium (Jacq.) Kunth ex Walp.) to enhance productivity of degraded soils. Maize was cropped in a hilly region of Sri Lanka with and without the recommended mineral fertilization, in two major seasons, October-January in 2007/8 (Year 1) and in 2008/9 (Year 2) on 92 farms at two inclinations: Flat (0-10%) and Moderate (10-30%). On half the farms, green manure (Gliricidia leaves) was added (3 tonnes per hectare per season). NPK boosted production to a very respectable mean grain yield of 4.2 t/ha on Flat farms. At ZERO, the yield was lower by 60%, irrespective of the inclination. Gliricidia failed to replace the required nitrogen, even with an adequate supply of phosphorous and potassium (PK). In contrast, together with NPK, Gliricidia increased yields by 15-20% compared to NPK alone, while the gain was 35% at ZERO. Fields in the Moderate category were more responsive to green manure and mineral fertilizers. The high response to mineral fertilizers indicated that the degradation of the soils resulted to a greater extent in chemical rather than in physical deficits. But intensive cropping reduced the soil organic matter within two years, to some extent slowed down by Gliricidia green manure. Therefore an intense cropping for the sake of food security must be accompanied by soil conserving cropping systems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.235
Teacher spread0.208 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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