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Record W2531727748 · doi:10.5539/sar.v5n4p71

Timed Strategy for Control of Bollworm for Sustainable Sorghum Crop Yield under Varied Regimes of Rainfall, Temperature and Soil Fertility

2016· article· en· W2531727748 on OpenAlexvenueno aff
Daniel L. Mutisya, C. P. M. Khamala, Jacob JO Konyango, C. K. Kamau, Lawrence K Matolo

Bibliographic record

VenueSustainable Agriculture Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
FundersEuropean CommissionGovernment of the Republic of Kenya
KeywordsSorghumAgronomyCropBiologyHelicoverpa armigeraYield (engineering)BotanyMaterials science

Abstract

fetched live from OpenAlex

<p class="sar-body"><span lang="EN-US">Various environmental factors influence yield of sorghum grain, <em>Sorghum bicolor</em> (L) in Sub-Sahara Africa. Various production conditions of rainfall amount, temperature regimes, soil fertility levels and bollworm <em>Helicoverpa armigera</em> density at specific sorghum grain stage were evaluated for effect to sorghum grain yield. High rainfall amount, high temperature and soil fertility levels were positively correlated to sorghum grain yield at three test sites at Ithookwe, Katumani and Kampi of eastern Kenya. The warmest Kampi site achieved the highest seed viability on germination test at 43, 87 and 99% for grain stage of light-green, cream-dough and hard dough, respectively. High <em>H. armigera</em> density was inversely correlated to grain yield. Comparatively, yield loss of < 10% was observed when grain was at early soft dough and > 35% as the grain ripened to early hard dough stage. Thus initial <em>H. armigera</em> damage occurred at late soft dough stage and increased exponentially as the grain ripened to early hard dough stage. The right time to spray against <em>H. armigera</em> was determined as at soft dough stage of sorghum grain to prevent economic damage of the crop. Thus fertility level, rainfall amount and time of bollworm pest attack were deemed worth considerations towards sustainable yield of sorghum. </span></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)0.002
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 score0.568
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.290
Teacher spread0.253 · 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
Published2016
Admission routes1
Has abstractyes

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