Quantifying critical N dilution curves across G × E × M effects for potato using a partially-pooled Bayesian hierarchical method
This repoistory contains the data, analysis, and manuscript version history of Bohman et al. (2023) "Quantifying critical N dilution curves across G × E × M effects for potato using a partially-pooled Bayesian hierarchical method".
- Brian J. Bohman (@bohm0072)
- Michael J. Culshaw-Maurer (@MCMaurer)
- Feriel Ben Abdallah
- Claudia Giletto
- Gilles Bélanger
- Fabián G. Fernández
- Yuxin Miao
- David J. Mulla
- Carl J. Rosen
Multiple critical N dilution curves [CNDCs] have been previously developed for potato; however, attempts to directly compare differences in CNDCs across genotype [G], environment [E], and management [M] interactions have been confounded by nonuniform statistical methods, biased experimental data, and lack of proper quantification of uncertainty in the critical N concentration [%Nc]. This study implements a partiallypooled Bayesian hierarchical method to develop CNDCs for previously published and newly reported experimental data, systematically evaluates the difference in %Nc [∆%Nc] across G × E × M effects, and directly compare CNDCs from the Bayesian framework to CNDCs from conventional statistical methods. The partially-pooled Bayesian hierarchical method implemented in this study has the advantage of being less susceptible to inferential bias at the level of individual G × E × M interactions compared to alternative statistical methods that result from insufficient quantity and quality of experimental datasets (e.g., unbalanced distribution of N limiting and non-N limiting observations). This method also allows for a direct statistical comparison of differences in %Nc across levels of the G × E × M interactions. Where found to be significant, ∆%Nc was attributed to variation in the timing of tuber initiation (e.g., maturity class) and the relative rate of tuber bulking (e.g., planting density) across G x E × M interactions. In addition to using the median value for %Nc (i.e., CNDC), the lower and upper boundary values for the credible region (i.e., CNDClo and CNDCup) derived using the Bayesian framework should be used in calculation of N nutrition index (and other calculations) to account for uncertainty in %Nc. Overall, this study provides additional evidence that %Nc is dependent upon G × E × M interactions; therefore, evaluation of crop N status or N use efficiency must account for variation in %Nc across G × E × M interactions.
This directory contains all materials used to develop the compiled manuscript, including analysis scripts, draft manuscript versions, figure image files, stored model objects, and csv formatted tables
This directory contains all source data, scripts to format source data, and analysis-ready data used for subsequent analysis in the manuscript directory
This directory contains a small sub-set of previously published work on this topic, including published origin of source data and reference methods.
This directory contains various notes.
This directory contains a set of minimal examples demonstrating various analysis methods, including data visualization and a comparison of model fit using brms vs. JAGS
This directory contains the renv files used to reproduce this analysis. Note: this is currently incompatible with the manuscript/analysis/model-fit.R script due to conflicts with rstan from file naming issues... In order to run manuscript/analysis/model-fit.R, need to ensure that system packages match renv.lock file and disable renv with renv::deactivate().