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Updated: Nov 2, 2025

High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

Published on: July 6, 2022

A practical guide to estimating the light extinction coefficient with nonlinear models-a case study on maize.

Josefina Lacasa^1,2, Trevor J Hefley³, María E Otegui^4,5

¹Department of Agronomy, Kansas State University, 1712 Claflin Rd, Manhattan, KS, 66506, USA. lacasa@ksu.edu.

|June 13, 2021

Summary

This summary is machine-generated.

Accurate estimation of the light extinction coefficient (k) is crucial for understanding plant canopies. This study recommends using beta-distribution models over traditional log-transformed linear models for more reliable results in maize.

Keywords:

Bayesian models Light attenuation Non-linear models Radiation interception Zea Mays L.

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Area of Science:

Agricultural Science
Plant Physiology
Statistical Modeling

Background:

The light extinction coefficient (k) is vital for estimating fraction of intercepted photosynthetically active radiation (fPARi) in plant canopies.
Current methods for estimating k often use outdated statistical techniques with unrealistic assumptions.
This limits the adoption of advanced statistical approaches for crop modeling and phenotyping.

Purpose of the Study:

To systematically evaluate various statistical techniques for estimating the light extinction coefficient (k) in maize.
To compare the performance of traditional methods against modern approaches like Bayesian estimation.
To identify the most appropriate statistical models for accurate fPARi estimation and reproducible research.

Main Methods:

Estimated k for seven maize genotypes using five methods: least squares estimation (LSE), log-transformed linear models (LogTLM), maximum likelihood estimation (MLE) with normal distribution, MLE with beta distribution, and Bayesian estimation (BE) with beta distribution.
Compared methods based on statistical inference, point estimate properties, and predictive performance.
Assessed the impact of statistical assumptions on k estimates and genotype rankings.

Main Results:

LogTLM provided the poorest predictions for fPARi.
LSE and MLE with normal distribution produced unrealistic fPARi predictions and the highest k coefficients.
Models using a beta distribution for fPARi (MLE or BE) are recommended for accurate point estimates.

Conclusions:

Estimation techniques and their assumptions significantly influence k values and statistical inference.
LogTLMs should be avoided for estimating k due to their limitations.
Modeling fPARi with a beta distribution, using MLE or BE, is a recommended, yet underutilized, practice.
The findings and workflow are applicable to other plant canopy models and encourage careful method selection based on study objectives.