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Record W2565479472 · doi:10.5376/ijh.2016.06.0025

Soil fertility situation in potato producing Kenyan highlands Case of KALRO-Tigoni

2016· article· en· W2565479472 on OpenAlexvenueno aff
Jane Muthoni

Bibliographic record

VenueInternational Journal of Horticulture · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsSoil fertilityFertilizerAgronomyEnvironmental scienceNutrientKenyaAgriculturePhosphate fertilizerSoil nutrientsLimitingProduction (economics)Soil waterAgroforestryBiologyEconomicsEcology

Abstract

fetched live from OpenAlex

Low soil fertility is an important factor limiting potato production in the Kenyan highlands; the situation is no better at Kenya Agricultural and Livestock Research Organisation (KALRO) station at Tigoni, which is the national potato research centre. In addition to inherent low soil fertility, fertilizer use in potato production in the country is below the recommended rates. The situation is compounded by the low soil pH which in most cases result Soil fertility; Soil pH; Potato Production in nutrient imbalances. Though soils in most potato growing areas in the country have low pH due to acidic parent rock, the commonly used fertilizer for potato production di-ammonium phosphate (DAP) (18:46:0) has been lowering the soil pH even further over time. In order to alleviate the situation, there is need to change the fertilizers used in potato production in Kenya.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2016
Admission routes1
Has abstractyes

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