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Record W1966178397 · doi:10.5539/jas.v2n3p26

Effects of Integrated Plant Nutrient Management (IPNM) Practices on the Sustainability of Maize-based Farming Systems in Nepal

2010· article· en· W1966178397 on OpenAlexaffvenue
Tejendra Chapagain, Gam Bahadur Gurung

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNutrient managementAgricultureAgronomySoil fertilitySustainabilityManureConservation agricultureAgricultural scienceAgroforestryNutrientCrop yieldEnvironmental scienceGeographyBiologySoil water

Abstract

fetched live from OpenAlex

Maize is a staple summer crop grown in the hilly areas of Nepal, where the soil is fragile and fertility is declining over years due primarily to degradation of natural resource base, high rates of soil erosion, increased cropping intensity and inadequate replenishment of soil nutrients. Forum for Rural Welfare and Agricultural Reform for Development (FORWARD) with the financial support from Hill Maize Research Program (HMRP/CYMMIT) conducted eight Integrated Plant Nutrient Systems (IPNS) trials, 16 Farm Yard Manure (FYM) improvement demonstrations and 16 conservation farming demonstrations each year through two women farmer groups since 2003 in Makawanpur District in order to raise the awareness of farmers on sustainable soil management practices through better utilization of locally available and external resources. The three years' trial results revealed that the maize crop with IPNS (15 t ha-1 FYM + 60:30:30 NPK kg ha-1) was better with respect to crop vigor and grain yields compared to the control treatment (farmers' practice with FYM and urea top dressing). The Improved cultivar with IPNS practices increased the grain yield by 64% (p

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 score0.906
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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

Citations24
Published2010
Admission routes2
Has abstractyes

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