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 Concept Videos

Multiple Regression01:25

Multiple Regression

4.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.4K
Prediction Intervals01:03

Prediction Intervals

3.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.6K
Methods of Medium Optimization01:28

Methods of Medium Optimization

63
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
63
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
342
Response Surface Methodology01:16

Response Surface Methodology

880
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
880
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

15.3K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
15.3K

You might also read

Related Articles

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

Sort by
Same author

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data.

BMC genomics·2020
Same author

A comparison of the wild food plant use knowledge of ethnic minorities in Naban River Watershed National Nature Reserve, Yunnan, SW China.

Journal of ethnobiology and ethnomedicine·2012
Same author

Ethnobotanical study of medicinal plants utilised by Hani ethnicity in Naban River Watershed National Nature Reserve, Yunnan, China.

Journal of ethnopharmacology·2011
Same author

Entomopathogens (Beauveria bassiana and Steinernema carpocapsae) for biological control of bark-feeding moth Indarbela dea on field-infested litchi trees.

Pest management science·2008

Related Experiment Video

Updated: Apr 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Rubber yield prediction by meteorological conditions using mixed models and multi-model inference techniques.

Reza Golbon1, Joseph Ochieng Ogutu2, Marc Cotter3

  • 1Institute of Plant Production and Agroecology in the Tropics and Subtropics, University of Hohenheim, Garbenstrasse 13, 70599, Stuttgart, Germany. golbon@uni-hohenheim.de.

International Journal of Biometeorology
|April 1, 2015
PubMed
Summary

Accurate rubber yield predictions are possible using meteorological data. Linear mixed models identified precipitation and temperature averages over 30 days as key predictors, achieving over 99% accuracy.

Keywords:
Hevea brasiliensisMeteorological conditionsMixed modelsMulti-model inferencePredictionYield

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

14.0K

Related Experiment Videos

Last Updated: Apr 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

14.0K

Area of Science:

  • Agricultural Science
  • Meteorology
  • Statistical Modeling

Background:

  • Rubber (Hevea brasiliensis) yield is crucial for global supply chains.
  • Predicting rubber yield is complex due to biological and environmental factors.

Purpose of the Study:

  • To develop accurate predictive models for rubber yield.
  • To identify key meteorological factors influencing rubber production.

Main Methods:

  • Linear mixed models were employed to analyze yield data.
  • Meteorological variables (precipitation, temperature, humidity) were used as predictors.
  • Model selection utilized information theory (AIC, AICc, Akaike weights).
  • Serial autocorrelation was addressed using random effects and spatial covariance structures.

Main Results:

  • Moving averages of precipitation, min/max temperature, and max relative humidity over a 30-day period were optimal predictors.
  • The best model demonstrated over 99% prediction accuracy.
  • Accuracy was validated using leave-one-out cross-validation and an independent test set.

Conclusions:

  • Meteorological conditions significantly influence Hevea brasiliensis yield.
  • The developed models provide a highly accurate method for predicting rubber production.
  • These findings can aid in optimizing rubber cultivation and supply chain management.