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aridagri

Comprehensive Statistical Tools for Agricultural Research

aridagri is an R package providing 33 functions for statistical analysis in agricultural research, with a focus on experimental design analysis and agronomic calculations. It is written in base R with no hard dependencies.

Key Features

ANOVA suite covering all common field designs
Post-hoc tests: LSD, Duncan (DMRT), Tukey, SNK, Scheffe, Bonferroni, Dunnett
Stability analysis: 7 methods (Eberhart-Russell, AMMI, Finlay-Wilkinson, Shukla, Wricke, CV, Lin-Binns) with an integrated ranking
Thermal indices: GDD, HTU, PTU, Heat Use Efficiency
Crop growth analysis: CGR, RGR, NAR
Publication-ready output: formatted tables with SE, CD, CV

Installation

# From CRAN
install.packages("aridagri")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("lalitrolaniya/aridagri")

Function Overview

Experimental Design ANOVA (16 functions)

Function	Design
`anova_crd()`	Completely Randomized Design
`anova_rbd()`	Randomized Block Design
`anova_rbd_pooled()`	Pooled RBD (multi-environment)
`anova_latin()`	Latin Square Design
`anova_factorial()`	Two-Factor Factorial
`anova_factorial_3way()`	Three-Factor Factorial
`anova_spd()`	Split-Plot Design
`anova_spd_ab_main()`	SPD with (A x B) in main plot
`anova_spd_c_main_ab_sub()`	SPD with C main, (A x B) sub
`anova_spd_ab_cd()`	SPD with (A x B) main, (C x D) sub
`anova_spd_pooled()`	Pooled Split-Plot Design
`anova_sspd()`	Split-Split-Plot Design
`anova_sspd_pooled()`	Pooled SSPD
`anova_strip()`	Strip-Plot Design
`anova_augmented()`	Augmented Block Design
`anova_alpha_lattice()`	Alpha Lattice Design

Stability, agronomic, statistical and nutrient functions

Function	Analysis
`stability_analysis()`	7 stability methods with integrated ranking
`thermal_indices()`	GDD, HTU, PTU, HUE
`crop_growth_analysis()`	CGR, RGR, NAR
`harvest_index()`	Harvest index and partitioning
`yield_gap_analysis()`	Yield gap calculations
`economic_indices()`	B:C ratio, net returns
`correlation_analysis()`	Correlation matrix with significance
`pca_analysis()`	Principal Component Analysis
`path_analysis()`	Path coefficient analysis
`sem_analysis()`	Structural Equation Modeling
`nue_calculate()`	Nutrient Use Efficiency indices
`nutrient_response()`	Response curve and economic optimum
`economic_analysis()`	Gross/net return, B:C ratio

Post-hoc, diagnostics and utilities

Function	Purpose
`perform_posthoc()`	Post-hoc comparisons (7 methods)
`check_assumptions()`	Normality, homogeneity, independence, outliers
`arid_plot()`	Base-graphics plots for ANOVA, correlation and stability objects
`export_results()`	Export results to Excel or CSV

Usage

Every function is called with the data frame first, then the column names as character strings. Set verbose = FALSE to suppress the printed report and just capture the returned object.

Analysis of variance

library(aridagri)

## Completely Randomized Design
crd <- data.frame(
  treatment = factor(rep(c("T1", "T2", "T3", "T4"), each = 4)),
  yield     = c(20, 22, 19, 21,  25, 27, 24, 26,
                30, 32, 29, 31,  28, 30, 27, 29)
)

res <- anova_crd(crd, response = "yield", treatment = "treatment")

## the returned object holds the table, means and statistics
res$anova_table
res$treatment_means
res$cv

## Randomized Block Design
anova_rbd(data, response = "yield", treatment = "variety", block = "block")

## Two-factor factorial
anova_factorial(data, response = "yield", factor1 = "nitrogen", factor2 = "variety")

## Split-plot design
anova_spd(data, response = "yield",
          main_plot = "irrigation", sub_plot = "variety", replication = "rep")

Post-hoc comparisons

There are two equivalent ways to run a post-hoc test.

## Option 1: inline, as part of the ANOVA call
anova_crd(crd, "yield", "treatment", posthoc = "tukey")

## Option 2: standalone, on a fitted aov() model
model <- aov(yield ~ treatment, data = crd)
perform_posthoc(model, crd, response = "yield", treatment = "treatment",
                posthoc = "tukey")

Valid method names are "lsd", "duncan", "tukey", "snk", "scheffe", "dunnett", "bonferroni", or "all". Dunnett compares every level against the first factor level as the control; there is no separate control argument, so order the treatment factor with the control first if needed.

Assumption checks

model <- aov(yield ~ treatment, data = crd)
check_assumptions(model)   # Shapiro-Wilk, Bartlett, Durbin-Watson, outliers

Split-Split-Plot Design

data <- expand.grid(
  rep        = 1:3,
  irrigation = c("I1", "I2", "I3"),
  variety    = c("V1", "V2"),
  nitrogen   = c("N0", "N40", "N80")
)
data$yield <- rnorm(nrow(data), 1200, 150)

anova_sspd(data,
           response     = "yield",
           main_plot    = "irrigation",
           sub_plot     = "variety",
           sub_sub_plot = "nitrogen",
           replication  = "rep")

Stability analysis (7 methods)

data <- expand.grid(
  variety  = paste0("V", 1:10),
  location = paste0("L", 1:5),
  rep      = 1:3
)
data$yield <- rnorm(nrow(data), 1200, 200)

stability_analysis(data,
                   genotype    = "variety",
                   environment = "location",
                   replication = "rep",
                   trait       = "yield",
                   method      = "all")

Correlation, PCA and visualization

df <- data.frame(
  yield   = rnorm(30, 1200, 150),
  pods    = rnorm(30, 50, 6),
  biomass = rnorm(30, 3500, 400)
)

cor_res <- correlation_analysis(df, method = "pearson")
pca_res <- pca_analysis(df, scale = TRUE)

## arid_plot() draws factor/treatment means for any ANOVA design,
## a heatmap for a correlation object, and a ranking for a stability object
arid_plot(res)
arid_plot(cor_res)

Exporting results

export_results(res, "results.xlsx")                  # Excel (default)
export_results(res, "results.csv", format = "csv")   # CSV

Unique Features

All split-plot design variations in one package
SE and CD reported for each comparison type
Seven stability analysis methods in a single function
Thermal indices (GDD, HTU, PTU, HUE) and crop growth analysis (CGR, RGR, NAR)
Base-R implementation with no hard dependencies

Citation

Rolaniya, L.K., Jat, R.L., Punia, M., and Choudhary, R.R. (2026). aridagri:
Comprehensive Statistical Tools for Agricultural Research.
R package version 2.0.4. https://github.com/lalitrolaniya/aridagri

Authors

Lalit Kumar Rolaniya (Maintainer) Scientist (Agronomy) ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner, Rajasthan-334006, India ORCID: 0000-0001-8908-1211

Ram Lal Jat Senior Scientist (Agronomy) ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner, Rajasthan-334006, India ORCID: 0009-0003-4339-0555

Monika Punia Scientist (Genetics & Plant Breeding) ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner, Rajasthan-334006, India ORCID: 0009-0002-0294-6767

Raja Ram Choudhary Scientist (Agronomy) ICAR-Indian Institute of Groundnut Research, Regional Research Station, Bikaner, Rajasthan-334006, India

License

GPL-3

Contributing

Contributions are welcome. Please submit issues or pull requests on GitHub.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.