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Record W2763806517 · doi:10.5539/sar.v7n1p7

The Role of Soil Amendments on Population Dynamics of Insect Pests, Growth Parameters and Yield of Eggplant, Solanum melongena (L.) Moench

2017· article· en· W2763806517 on OpenAlexvenueno aff
Philip Kweku Baidoo, M. B. Mochiah, D. K. Asare, A. A. Sefah

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyBiologyMelongenaAphis gossypiiSoil fertilityPopulationSolanumCow dungCropFertilizerLeucinodes orbonalisRandomized block designCrop yieldSoil waterHorticulturePEST analysisHomopteraInfestationAphididae

Abstract

fetched live from OpenAlex

Loss of soil fertility as a result of continuous cropping on the same piece of land has necessitated the need to improve soil fertility for better crop yields. Inorganic and organic fertilizers have been used to improve soil fertility, however, excessive use of soil amendments improve vegetative growth of plants thereby attracting large numbers of insect pests. Cow dung and poultry droppings were used as soil amendments in a field experiment using eggplant Solanum melongena. The effects of these organic manures were compared with inorganic fertilizer (NPK) and a control where there was no application of soil amendment in a randomized complete block design with 3 replicates. Parameters studied were pests’ and their numbers, plant height, number of leaves per plant, leaf area, stem girth and yield. The major insect pests identified on the plant were Bemisia tabaci, Aphis gossypii, Leucinodes orbonalis and Eublemma olivacea. Bemisia tabaci and Aphis gossypii scores were significantly larger on cow dung and poultry manure plots. Leucinodes orbonalis and Eublemma olivacea numbers were not significantly different on the treated and control plots. Mean plant height, number of leaves and yield differed significantly among the soil amended plots. Even though soil amendments improve the nutrient content of the soil and the yield of crops it could lead to increase in pests numbers and damage caused to 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.037
GPT teacher head0.287
Teacher spread0.250 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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