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Record W4285707555 · doi:10.5539/jas.v14n8p166

Water Deficit and Excess and the Main Physiological Disorders in Agricultural Crops

2022· article· en· W4285707555 on OpenAlexvenueno aff
Bruna de Villa, Mirta Teresinha Petry, Maicon Sérgio Nascimento dos Santos, Juliano Dalcin Martins, Isabel Lago, Murilo Brum de Moura, Henrique Schaf Eggers, Giane Lavarda Melo, Felipe Tonetto, Cassio Miguel Ferrazza, Andressa Fuzer Gonçalves, Ticiana François Magalhães, Isac Aires de Castro

Bibliographic record

VenueJournal of Agricultural Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureDeficit irrigationIrrigationAgronomyAgricultural productivityEnvironmental scienceBiologyIrrigation managementEcology

Abstract

fetched live from OpenAlex

The comprehension of the precise water consumption of agricultural crops is a valuable tool for establishing management programs and irrigation schedules. Appropriately, the purpose of this study was to promote a bibliographic review on the main reflexes of the inappropriate use of water and what this process can promote in the establishment and development of agricultural crops. Moreover, theoretical questions were raised regarding physiological responses triggered by soil water deficit and its effect on crop growth, critical periods for water deficit, physiological responses, and their effects on the growth of main agricultural crops. Information on the misuse of water resources and its effects have presented a series of manifestations to plants and, consequently, to agricultural production, such as a production depletion, reduction of carbon fixation, nutritional deficiency, reduction of plant height, reduction of thousand-grain weight, yellowing of leaves, reduction in germination percentage, among other factors. Correspondingly, water stress can cause a drastic reduction in leaf area, productivity decrease, stomatal closure, leaf senescence, reduced roots, reduced flowering, hampering crop emergence and stability, spikelet sterility, etc. Finally, studies aimed at the consequences of poor irrigation and/or inadequate precipitation values are of high importance, mainly due to the investigative improvement on the use of water in an effective and sustainable way.

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)0.000
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 score0.966
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.205
Teacher spread0.194 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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