Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

[A physiological development time-based simulation model for cotton development stages and square- and boll

Fuyu Ma1, Weixing Cao, Lizhen Zhang

  • 1High-Tech Key Laboratory of Information Agriculture, Jiangsu Province, Nanjing Agricultural University, Nanjing 210095, China. mafuyu403@sohu.com

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|July 14, 2005
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Aperiodic activity modulation during visual oddball paradigm in clinical high-risk individuals and relationship with psychosis conversion.

Schizophrenia research·2026
Same author

Advances in oncolytic virus delivery strategies and challenges in clinical translation.

Materials today. Bio·2026
Same author

Exogenous melatonin mitigates cadmium stress on cotton seed germination: physiological, biochemical, and transcriptomic insights.

BMC plant biology·2026
Same author

Valence-specific oculomotor abnormalities induced by dynamic video stimuli as a robust biomarker for schizophrenia.

Asian journal of psychiatry·2026
Same author

Eye Movement Patterns as Robust Biomarkers for Schizophrenia Identification Using a Novel Data Transformation Approach.

Journal of eye movement research·2026
Same author

Dental pulp stem cell exosomes promote angiogenesis via the PI3K/Akt signaling pathway to treat androgenetic alopecia.

Stem cell research & therapy·2026

A new model accurately simulates cotton development stages and boll growth using physiological development time. It incorporates regional temperature variations and genetic factors, showing high accuracy across diverse conditions.

Area of Science:

  • Agronomy
  • Plant Physiology
  • Agricultural Meteorology

Context:

  • Cotton (Gossypium hirsutum) development is influenced by complex interactions between genetics and environmental factors.
  • Accurate prediction of cotton growth stages is crucial for optimizing agricultural management and yield.
  • Previous models often lack the integration of regional climatic variations and specific genetic traits.

Purpose:

  • To develop and validate a simulation model for cotton development stages and square-and boll development.
  • To incorporate physiological development time (PDT) and regional environmental factors, including diurnal temperature differences and plastic mulching effects.
  • To introduce key indices like initial fruiting node index (IFIN), sunlight duration factor (FSH), and solar radiation index on fruiting branch (IFBR) for enhanced simulation accuracy.

Related Experiment Videos

Summary:

  • Three cotton varieties were studied across multiple locations to analyze dynamic relationships between development and environmental factors.
  • A simulation model was constructed using physiological development time (PDT), integrating diurnal temperature variations, plastic mulching effects, and genetic factors (IFIN, FSH, IFBR).
  • Model validation across different years, ecological zones, genotypes, and cultivation practices demonstrated a high goodness of fitness, with low root mean square errors (RMSE) for key developmental stages.

Impact:

  • Provides a robust tool for predicting cotton growth and development under various environmental conditions.
  • Enhances the understanding of how regional climate and specific genetic traits influence cotton phenology.
  • Aims to improve cotton cultivation strategies, resource management, and ultimately, crop yield prediction.