A Bayesian approach to analyzing long-term agricultural experiments

A - Papers appearing in refereed journals

Addy, J., Maclaren, C. and Lang, R. 2024. A Bayesian approach to analyzing long-term agricultural experiments . European Journal of Agronomy. 159 (Sept), p. 127227. https://doi.org/10.1016/j.eja.2024.127227

AuthorsAddy, J., Maclaren, C. and Lang, R.
Abstract

Effective and flexible statistical analyses are key to getting the most out of long-term experiments (LTEs). Here, we aim to introduce Bayesian analysis to the wider LTE community and show how the modelling process differs from traditional statistical analyses. Bayesian methods have become increasingly popular due to more flexibility in model development with better access to statistical software and sampling algorithms. Using Bayes' Theorem, model coefficients are estimated by incorporating any prior knowledge we may have on model terms. Including prior knowledge in this way requires a different estimating procedure for a fitted model. Bayesian model coefficients are usually sampled from thousands of samples from one or more runs of a Markov Chain. We present the use of Bayesian analyses through three examples. Example 1 illustrates a single regression with and without factors using the Broadbalk Long-Term Experiment, showing how the estimated model changes with more uncertainty in our prior knowledge of model coefficients. Example 2 demonstrates the use of multiple regression, predicting grain yield from factor variables and seasonal weather variables. Example 3 shows an estimation of soil carbon changes under crop rotation and fertilization treatments with a hierarchical time series model using a Swedish soil fertility experiment.

KeywordsRandom Effects; Long-Term Experiments; Bayesian Regression; Bayesian Multiple Regression; Hierarchical Models; Linear Models
Year of Publication2024
JournalEuropean Journal of Agronomy
Journal citation159 (Sept), p. 127227
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eja.2024.127227
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe Rothamsted Long-Term Experiments including Sample Archive and e-RA database [2012-2017]
The Rothamsted Long Term Experiments [2017-2022]
BB/CCG2280/1
Output statusPublished
Publication dates
Online21 Jun 2024
PublisherElsevier
ISSN1161-0301

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