Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

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

Sort by

Same author

Closing the Learning Gap in Arthroscopy: A Comparison of Conventional Training and Virtual Reality Simulation.

Journal of pharmacy & bioallied sciences·2026

Same author

Impact of meteorological parameters in fruit splitting of Daisy mandarin under subtropical conditions.

Frontiers in plant science·2025

Same author

Use of Field pathogenomics approach for Puccinia striiformis f. sp. tritici race identification and phylogenomic delineation in North India.

World journal of microbiology & biotechnology·2025

Same author

Perception of Indian Medical Students Towards University Exams and Proposed Licensure cum Post-Graduate Specialty (Residency) entrance examination (<b>National Exit Test</b>).

Medical science educator·2025

Same author

Viscotaxis of beating flagella.

Soft matter·2025

Same author

Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions.

International journal of biometeorology·2024

Search research articles

Related Experiment Video

Updated: May 12, 2026

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce (Lactuca sativa) Germplasm Collections

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce (Lactuca sativa) Germplasm Collections

Published on: April 17, 2015

A weather-informed decision tree framework for predicting stripe rust outbreaks in wheat.

Shubham Anand¹, Sarabjot Kaur Sandhu¹, Barun Biswas¹

¹Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana, India.

Frontiers in Plant Science

|May 11, 2026

Summary

Weather-based decision tree models accurately predict wheat stripe rust severity. These models use temperature and sunshine data to forecast disease risk, aiding in timely crop advisories and management strategies.

Keywords:

CART analysis decision tree principal component analysis real time disease assessment stripe rust severity

Related Experiment Videos

Last Updated: May 12, 2026

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce (Lactuca sativa) Germplasm Collections

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce (Lactuca sativa) Germplasm Collections

Published on: April 17, 2015

Area of Science:

Plant Pathology
Agricultural Meteorology
Computational Biology

Background:

Wheat stripe rust, caused by *Puccinia striiformis*, is significantly influenced by weather patterns.
Effective disease management relies on predictive modeling to anticipate outbreaks and severity.

Purpose of the Study:

To develop and evaluate a weather-based decision tree model for predicting wheat stripe rust severity.
To identify key meteorological variables influencing stripe rust development in Punjab, India.

Main Methods:

Collected meteorological data (2009-2023) and weekly yellow rust observations.
Calculated Area Under Disease Progression Curve (AUDPC) for severity assessment.
Applied Classification and Regression Tree (CART) analysis to build predictive models.

Main Results:

The 2012-13 season saw the highest rust severity (100%) and AUDPC (671.3).
Maximum stripe rust severity (79%) is predicted with minimum temperature ≥ 9.1°C and sunshine ≥ 9.2h, or mean temperature ≥ 15°C and humid thermal index ≥ 2.4.
All-weather variable decision tree models demonstrated superior performance compared to primary or derived variable models.

Conclusions:

Weather-based decision tree models are effective tools for real-time wheat stripe rust risk assessment.
These models can significantly enhance the development of targeted crop agro-advisories.
Understanding the relationship between weather variables and disease severity is crucial for disease management.