Type: Package
Title: Comprehensive Statistical Tools for Agricultural Research
Version: 2.0.3
Description: A comprehensive suite of statistical and analytical tools for agricultural research. Includes complete analysis of variance (ANOVA) functions for all experimental designs: Completely Randomized Design (CRD), Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha Lattice, with proper error terms and comprehensive Standard Error (SE) and Critical Difference (CD) calculations. Features multiple post-hoc tests: Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), Scheffe, Bonferroni, and Dunnett, along with assumption checking and publication-ready output. Advanced methods include stability analysis using Eberhart-Russell regression, Additive Main Effects and Multiplicative Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority Index as described in Eberhart and Russell (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. Thermal indices include Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency calculations, correlation matrix, Principal Component Analysis (PCA), path analysis, and Structural Equation Modeling (SEM). Statistical methods follow Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, ISBN:8170271169).
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.0.0)
Suggests: ggplot2, dplyr, tidyr, emmeans, multcomp, corrplot, factoextra, FactoMineR, lavaan, semPlot, agricolae, car, writexl, rmarkdown, testthat (≥ 3.0.0)
URL: https://github.com/lalitrolaniya/aridagri
BugReports: https://github.com/lalitrolaniya/aridagri/issues
NeedsCompilation: no
Packaged: 2026-02-19 16:14:41 UTC; lalitrolaniya
Author: Lalit Kumar Rolaniya ORCID iD [aut, cre], Ram Lal Jat ORCID iD [aut], Monika Punia ORCID iD [aut], Raja Ram Choudhary [aut]
Maintainer: Lalit Kumar Rolaniya <lalitrolaniya@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-24 19:20:08 UTC

aridagri: Comprehensive Statistical Tools for Agricultural Research

Description

The aridagri package provides comprehensive statistical and analytical tools for agricultural research. It includes complete ANOVA functions for all experimental designs, multiple post-hoc tests, stability analysis methods, thermal indices, crop growth analysis, and advanced statistical methods.

Experimental Design ANOVA Functions

Post-Hoc Tests

Agronomic Analysis Functions

Statistical Functions

Nutrient Analysis Functions

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

See Also

Useful links:

Alpha Lattice Design ANOVA

Description

Performs ANOVA for Alpha Lattice (Resolvable Incomplete Block) Design.

Usage

anova_alpha_lattice(
  data,
  response,
  genotype,
  replication,
  block,
  verbose = TRUE
)

Arguments

data

Data frame containing the data

response

Name of response variable

genotype

Name of genotype column

replication

Name of replication column

block

Name of incomplete block column (nested within replication)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA results

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

data <- expand.grid(rep = 1:2, block = 1:5, entry = 1:4)
data$genotype <- paste0("G", 1:nrow(data))
data$yield <- rnorm(nrow(data), 1200, 150)

Augmented Block Design ANOVA

Description

Performs ANOVA for Augmented Randomized Block Design where checks are replicated and test entries appear once.

Usage

anova_augmented(data, response, genotype, block, check_names, verbose = TRUE)

Arguments

data

Data frame containing the data

response

Name of response variable

genotype

Name of genotype/entry column

block

Name of block column

check_names

Vector of check variety names

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA results and adjusted means

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

data <- data.frame(
  block = rep(1:5, each = 7),
  genotype = c(rep(c("C1","C2","C3"), 5), paste0("T", 1:20)),
  yield = rnorm(35, 1200, 150)
)
# Note: This is simplified example

============================================================================ COMPREHENSIVE EXPERIMENTAL DESIGN ANALYSIS FUNCTIONS Package: aridagri Author: Lalit Kumar Rolaniya ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner ============================================================================ Completely Randomized Design (CRD) ANOVA

Description

Performs complete ANOVA for Completely Randomized Design with post-hoc tests, assumptions checking, and publication-ready output.

Usage

anova_crd(
  data,
  response,
  treatment,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable (as string)

treatment

Name of treatment factor

posthoc

Post-hoc test: "lsd", "duncan", "tukey", "snk", "scheffe", or "all"

alpha

Significance level (default 0.05)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA table, means, post-hoc results, and diagnostics

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  treatment = rep(c("T1", "T2", "T3", "T4"), each = 5),
  yield = c(rnorm(5, 1200, 50), rnorm(5, 1350, 60), 
            rnorm(5, 1100, 55), rnorm(5, 1450, 65))
)
anova_crd(data, response = "yield", treatment = "treatment", posthoc = "all")

Factorial ANOVA (Two-Factor)

Description

Performs two-factor factorial ANOVA with interaction analysis.

Usage

anova_factorial(
  data,
  response,
  factor1,
  factor2,
  replication = NULL,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the data

response

Name of the response variable

factor1

Name of first factor (A)

factor2

Name of second factor (B)

replication

Name of replication factor (optional)

posthoc

Post-hoc test method

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA results

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- expand.grid(
  rep = 1:4,
  nitrogen = c("N0", "N40", "N80", "N120"),
  phosphorus = c("P0", "P30", "P60")
)
data$yield <- rnorm(nrow(data), 1200, 150)

anova_factorial(data, response = "yield", 
                factor1 = "nitrogen", factor2 = "phosphorus",
                replication = "rep")

Three-Factor Factorial ANOVA

Description

Performs three-factor factorial ANOVA with all interactions.

Usage

anova_factorial_3way(
  data,
  response,
  factor1,
  factor2,
  factor3,
  replication = NULL,
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the data

response

Name of the response variable

factor1

Name of first factor (A)

factor2

Name of second factor (B)

factor3

Name of third factor (C)

replication

Name of replication factor (optional)

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA results

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

data <- expand.grid(rep = 1:3, A = c("A1", "A2"), B = c("B1", "B2"), C = c("C1", "C2"))
data$yield <- rnorm(nrow(data), 1200, 150)
anova_factorial_3way(data, "yield", "A", "B", "C", "rep")

Latin Square Design ANOVA

Description

Performs ANOVA for Latin Square Design with row and column blocking.

Usage

anova_latin(
  data,
  response,
  treatment,
  row,
  column,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the data

response

Name of the response variable

treatment

Name of treatment factor

row

Name of row factor

column

Name of column factor

posthoc

Post-hoc test method

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA results

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

# 5x5 Latin Square
data <- data.frame(
  row = rep(1:5, each = 5),
  col = rep(1:5, 5),
  treatment = c("A","B","C","D","E", "B","C","D","E","A",
                "C","D","E","A","B", "D","E","A","B","C",
                "E","A","B","C","D"),
  yield = rnorm(25, 1200, 100)
)
anova_latin(data, response = "yield", treatment = "treatment",
            row = "row", column = "col")

Randomized Block Design (RBD) ANOVA

Description

Performs complete ANOVA for Randomized Block Design (RCBD) with post-hoc tests, assumptions checking, and publication-ready output.

Usage

anova_rbd(
  data,
  response,
  treatment,
  block,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable

treatment

Name of treatment factor

block

Name of block/replication factor

posthoc

Post-hoc test: "lsd", "duncan", "tukey", "snk", "scheffe", or "all"

alpha

Significance level (default 0.05)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA table, means, post-hoc results

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  rep = rep(1:4, each = 5),
  treatment = rep(c("T1", "T2", "T3", "T4", "T5"), 4),
  yield = c(rnorm(5, 1200, 50), rnorm(5, 1250, 55),
            rnorm(5, 1180, 45), rnorm(5, 1270, 60))
)
anova_rbd(data, response = "yield", treatment = "treatment", 
          block = "rep", posthoc = "duncan")

Pooled Analysis of RBD Experiments (Multi-Environment/Year)

Description

Performs pooled ANOVA for RBD experiments conducted across multiple environments, years, or locations. Tests homogeneity of error variances using Bartlett's test before pooling.

Usage

anova_rbd_pooled(
  data,
  response,
  treatment,
  environment,
  block,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing combined data from all environments

response

Name of the response variable

treatment

Name of treatment factor

environment

Name of environment/year/location factor

block

Name of block factor (nested within environment)

posthoc

Post-hoc test method

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing pooled ANOVA, individual ANOVAs, and interaction analysis

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

# Data from 3 years
data <- data.frame(
  year = rep(c("Y1", "Y2", "Y3"), each = 20),
  rep = rep(rep(1:4, each = 5), 3),
  treatment = rep(c("T1", "T2", "T3", "T4", "T5"), 12),
  yield = rnorm(60, 1200, 150)
)
anova_rbd_pooled(data, response = "yield", treatment = "treatment",
                 environment = "year", block = "rep")

============================================================================ SPLIT PLOT DESIGN ANALYSIS FUNCTIONS (All Variations) Package: aridagri Author: Lalit Kumar Rolaniya ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner ============================================================================ Split Plot Design ANOVA (Standard)

Description

Performs complete ANOVA for Split Plot Design with proper error terms for main plot and sub-plot factors. Includes all standard post-hoc comparisons.

Usage

anova_spd(
  data,
  response,
  main_plot,
  sub_plot,
  replication,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable

main_plot

Name of main plot factor

sub_plot

Name of sub-plot factor

replication

Name of replication/block factor

posthoc

Post-hoc test method

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA table, means, and post-hoc results

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  rep = rep(1:3, each = 12),
  irrigation = rep(rep(c("I1", "I2", "I3"), each = 4), 3),
  variety = rep(c("V1", "V2", "V3", "V4"), 9),
  yield = rnorm(36, 1200, 150)
)
anova_spd(data, response = "yield", main_plot = "irrigation", 
          sub_plot = "variety", replication = "rep")

Split Plot Design with (AB) Main and (CD) Sub

Description

Performs ANOVA for Split Plot Design where main plot contains factorial combination of AB and sub-plot contains factorial combination of CD. Complex design for multi-factor experiments.

Usage

anova_spd_ab_cd(
  data,
  response,
  main_factor1,
  main_factor2,
  sub_factor1,
  sub_factor2,
  replication,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable

main_factor1

First factor in main plot (A)

main_factor2

Second factor in main plot (B)

sub_factor1

First factor in sub-plot (C)

sub_factor2

Second factor in sub-plot (D)

replication

Name of replication factor

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA table and means

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

# Example: Irrigation  Tillage (main), Variety  Nitrogen (sub)
data <- expand.grid(
  rep = 1:3,
  irrigation = c("I1", "I2"),
  tillage = c("CT", "ZT"),
  variety = c("V1", "V2"),
  nitrogen = c("N1", "N2", "N3")
)
data$yield <- rnorm(nrow(data), 1200, 150)

anova_spd_ab_cd(data, response = "yield",
                main_factor1 = "irrigation", main_factor2 = "tillage",
                sub_factor1 = "variety", sub_factor2 = "nitrogen",
                replication = "rep")

Split Plot Design with AB in Main Plot

Description

Performs ANOVA for Split Plot Design where main plot contains factorial combination of two factors (AB) and sub-plot contains factor C. Common in irrigation variety as main plot and nitrogen as sub-plot.

Usage

anova_spd_ab_main(
  data,
  response,
  main_factor1,
  main_factor2,
  sub_plot,
  replication,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable

main_factor1

First factor in main plot (A)

main_factor2

Second factor in main plot (B)

sub_plot

Sub-plot factor (C)

replication

Name of replication factor

posthoc

Post-hoc test method

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Details

Design structure:

Value

List containing ANOVA table and means

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  rep = rep(1:3, each = 24),
  irrigation = rep(rep(c("I1", "I2"), each = 12), 3),
  variety = rep(rep(c("V1", "V2", "V3"), each = 4), 6),
  nitrogen = rep(c("N0", "N1", "N2", "N3"), 18),
  yield = rnorm(72, 1200, 150)
)
anova_spd_ab_main(data, response = "yield", 
                  main_factor1 = "irrigation", main_factor2 = "variety",
                  sub_plot = "nitrogen", replication = "rep")

Split Plot Design with C in Main Plot, AB in Sub-Plot

Description

Performs ANOVA for Split Plot Design where main plot contains single factor C and sub-plot contains factorial combination of AB.

Usage

anova_spd_c_main_ab_sub(
  data,
  response,
  main_plot,
  sub_factor1,
  sub_factor2,
  replication,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable

main_plot

Main plot factor (C)

sub_factor1

First factor in sub-plot (A)

sub_factor2

Second factor in sub-plot (B)

replication

Name of replication factor

posthoc

Post-hoc test method

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA table and means

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  rep = rep(1:3, each = 24),
  irrigation = rep(rep(c("I1", "I2", "I3"), each = 8), 3),
  variety = rep(rep(c("V1", "V2"), each = 4), 9),
  nitrogen = rep(c("N1", "N2", "N3", "N4"), 18),
  yield = rnorm(72, 1200, 150)
)
anova_spd_c_main_ab_sub(data, response = "yield",
                         main_plot = "irrigation",
                         sub_factor1 = "variety", sub_factor2 = "nitrogen",
                         replication = "rep")

Pooled Split Plot Design Analysis

Description

Performs pooled analysis of Split Plot Design experiments conducted across multiple environments/years/locations.

Usage

anova_spd_pooled(
  data,
  response,
  main_plot,
  sub_plot,
  environment,
  replication,
  verbose = TRUE
)

Arguments

data

Data frame containing combined data

response

Name of the response variable

main_plot

Name of main plot factor

sub_plot

Name of sub-plot factor

environment

Name of environment factor

replication

Name of replication factor (nested within environment)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing pooled ANOVA and component analyses

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- expand.grid(
  year = c("Y1", "Y2", "Y3"),
  rep = 1:3,
  irrigation = c("I1", "I2", "I3"),
  variety = c("V1", "V2", "V3", "V4")
)
data$yield <- rnorm(nrow(data), 1200, 180)

anova_spd_pooled(data, response = "yield", main_plot = "irrigation",
                 sub_plot = "variety", environment = "year", replication = "rep")

Split-Split Plot Design ANOVA

Description

Performs complete ANOVA for Split-Split Plot Design with proper error terms for main plot, sub-plot, and sub-sub-plot factors. Generates publication-ready ANOVA table with significance levels.

Usage

anova_sspd(
  data,
  response,
  main_plot,
  sub_plot,
  sub_sub_plot,
  replication,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable (as string)

main_plot

Name of main plot factor

sub_plot

Name of sub-plot factor

sub_sub_plot

Name of sub-sub-plot factor

replication

Name of replication/block factor

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA table, means, and significance tests

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

# Example with sample data
data <- expand.grid(rep=1:3, A=c('A1','A2'), B=c('B1','B2'), C=c('C1','C2'))
data$yield <- rnorm(24, 1200, 150)
anova_sspd(data, response='yield', main_plot='A', sub_plot='B',
           sub_sub_plot='C', replication='rep')

Pooled Split-Split Plot Design ANOVA

Description

Performs pooled ANOVA for SSPD experiments across multiple environments.

Usage

anova_sspd_pooled(
  data,
  response,
  main_plot,
  sub_plot,
  sub_sub_plot,
  environment,
  replication,
  verbose = TRUE
)

Arguments

data

Data frame containing combined data

response

Name of the response variable

main_plot

Name of main plot factor

sub_plot

Name of sub-plot factor

sub_sub_plot

Name of sub-sub-plot factor

environment

Name of environment factor

replication

Name of replication factor

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing pooled ANOVA results

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

data <- expand.grid(env = c("E1","E2"), rep = 1:3, A = c("A1","A2"), 
                    B = c("B1","B2"), C = c("C1","C2"))
data$yield <- rnorm(nrow(data), 1200, 150)
anova_sspd_pooled(data, "yield", "A", "B", "C", "env", "rep")

============================================================================ ADDITIONAL EXPERIMENTAL DESIGN FUNCTIONS Package: aridagri Authors: Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary ============================================================================ Strip Plot Design ANOVA

Description

Performs ANOVA for Strip Plot (Strip-Split) Design where two factors are applied in horizontal and vertical strips.

Usage

anova_strip(
  data,
  response,
  horizontal_factor,
  vertical_factor,
  replication,
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

Data frame containing the experimental data

response

Name of the response variable

horizontal_factor

Factor applied in horizontal strips (A)

vertical_factor

Factor applied in vertical strips (B)

replication

Name of replication factor

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing ANOVA results

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

data <- expand.grid(
  rep = 1:4,
  irrigation = c("I1", "I2", "I3"),
  tillage = c("CT", "MT", "ZT")
)
data$yield <- rnorm(nrow(data), 1200, 150)
anova_strip(data, response = "yield", horizontal_factor = "irrigation",
            vertical_factor = "tillage", replication = "rep")

Visualization Functions for aridagri

Description

Generate publication-quality plots for aridagri analyses.

Usage

arid_plot(x, type = "bar", ...)

Arguments

x

An object from aridagri analysis functions

type

Plot type: "bar", "line", "interaction", "boxplot"

...

Additional arguments passed to plotting functions

Value

A ggplot2 object

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

df <- data.frame(
  yield = c(1200, 1350, 1100, 1450, 1280),
  wue = c(4.2, 4.8, 3.9, 5.1, 4.5),
  protein = c(22.1, 23.5, 21.8, 24.2, 22.9)
)
result <- correlation_analysis(df, plot = FALSE)
arid_plot(result)

Check ANOVA Assumptions

Description

Tests assumptions of ANOVA: normality and homogeneity of variances.

Usage

check_assumptions(model, verbose = TRUE)

Arguments

model

ANOVA model object

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing diagnostic test results

Correlation Analysis with Significance

Description

Computes correlation matrix with significance levels and generates publication-ready correlation table and plot.

Usage

correlation_analysis(
  data,
  method = "pearson",
  plot = TRUE,
  digits = 3,
  verbose = TRUE
)

Arguments

data

Data frame with numeric variables

method

Correlation method: "pearson", "spearman", or "kendall"

plot

Logical, whether to generate correlation plot

digits

Number of decimal places

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List with correlation matrix and significance matrix

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  yield = c(1200, 1350, 1100, 1450, 1280),
  wue = c(4.2, 4.8, 3.9, 5.1, 4.5),
  protein = c(22.1, 23.5, 21.8, 24.2, 22.9)
)
correlation_analysis(data)

Crop Growth Analysis (CGR, RGR, NAR, LAI)

Description

Calculates crop growth parameters from sequential harvest data.

Usage

crop_growth_analysis(dry_weight, leaf_area, days, verbose = TRUE)

Arguments

dry_weight

Vector of dry matter at different stages

leaf_area

Vector of leaf area at different stages

days

Vector of days after sowing

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

Data frame with growth parameters

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

dry_weight <- c(0.5, 2.1, 8.5, 25, 45, 62, 75)
leaf_area <- c(15, 85, 350, 800, 950, 850, 600)
days <- c(15, 30, 45, 60, 75, 90, 105)
crop_growth_analysis(dry_weight, leaf_area, days)

Economic Analysis for Arid Agriculture

Description

Calculates economic indicators including Cost of Cultivation, Gross Returns, Net Returns, B:C Ratio, and profitability indices for arid farming systems.

Usage

economic_analysis(
  yield,
  price,
  cost_fixed,
  cost_variable,
  byproduct_yield = 0,
  byproduct_price = 0,
  verbose = TRUE
)

Arguments

yield

Crop yield (kg/ha)

price

Market price (Rs/kg)

cost_fixed

Fixed costs (Rs/ha)

cost_variable

Variable costs (Rs/ha)

byproduct_yield

Byproduct yield (kg/ha), optional

byproduct_price

Byproduct price (Rs/kg), optional

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

Data frame with economic analysis

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

economic_analysis(yield = 1200, price = 65, cost_fixed = 15000, cost_variable = 12000)

# With byproduct
economic_analysis(yield = 1200, price = 65, cost_fixed = 15000, 
                  cost_variable = 12000, byproduct_yield = 1800, byproduct_price = 5)

Economic Efficiency Indices (B:C Ratio)

Description

Calculates B:C ratio and economic efficiency metrics.

Usage

economic_indices(gross_return, total_cost, verbose = TRUE)

Arguments

gross_return

Gross returns (Rs/ha)

total_cost

Total cost (Rs/ha)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

Data frame with economic indices

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

economic_indices(gross_return = c(75000, 82000), total_cost = c(35000, 38000))

Export Results to Publication Format

Description

Exports aridagri analysis results to Excel format for publication.

Usage

export_results(x, file, format = "xlsx", digits = 3, verbose = TRUE)

Arguments

x

An object from aridagri analysis functions

file

Output file path

format

Output format: "xlsx"

digits

Number of decimal places

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

Invisibly returns the file path

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples


df <- data.frame(
  yield = c(1200, 1350, 1100, 1450, 1280),
  wue = c(4.2, 4.8, 3.9, 5.1, 4.5)
)
result <- correlation_analysis(df, plot = FALSE)
export_results(result, tempfile(fileext = ".xlsx"))

============================================================================ POST-HOC TESTS AND HELPER FUNCTIONS Package: aridagri Author: Lalit Kumar Rolaniya ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner ============================================================================ Get Significance Symbol

Description

Returns significance symbol based on p-value.

Usage

get_sig_symbol(p_value)

Arguments

p_value

P-value from statistical test

Value

Character string with significance symbol

Harvest Index Calculation

Description

Calculates Harvest Index and related partitioning indices.

Usage

harvest_index(grain_yield, straw_yield, verbose = TRUE)

Arguments

grain_yield

Grain/economic yield

straw_yield

Straw/stover yield

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

Data frame with harvest indices

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

harvest_index(grain_yield = c(1200, 1350, 1100), straw_yield = c(2400, 2500, 2300))

Package imports

Description

Package imports

Nutrient Use Efficiency Calculations

Description

Comprehensive nutrient use efficiency calculations including Agronomic Efficiency (AE), Physiological Efficiency (PE), Apparent Recovery Efficiency (ARE), and Partial Factor Productivity (PFP). Essential for INM research in arid regions.

Usage

nue_calculate(
  yield_fertilized,
  yield_control,
  nutrient_applied,
  nutrient_uptake_fert = NULL,
  nutrient_uptake_ctrl = NULL,
  biomass_fert = NULL,
  biomass_ctrl = NULL,
  verbose = TRUE
)

Arguments

yield_fertilized

Yield with fertilizer application (kg/ha)

yield_control

Yield in control/unfertilized plot (kg/ha)

nutrient_applied

Amount of nutrient applied (kg/ha)

nutrient_uptake_fert

Nutrient uptake in fertilized plot (kg/ha), optional

nutrient_uptake_ctrl

Nutrient uptake in control plot (kg/ha), optional

biomass_fert

Total biomass in fertilized plot (kg/ha), optional

biomass_ctrl

Total biomass in control plot (kg/ha), optional

verbose

Logical. If TRUE (default), prints formatted output to console.

Details

Efficiency calculations:

Value

Data frame with efficiency indices

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

# Basic NUE calculation
nue_calculate(yield_fertilized = 1850, yield_control = 1200, nutrient_applied = 40)

# Complete NUE with uptake data
nue_calculate(
  yield_fertilized = 1850, 
  yield_control = 1200,
  nutrient_applied = 40,
  nutrient_uptake_fert = 65,
  nutrient_uptake_ctrl = 35
)

Nutrient Response Curve Analysis

Description

Fits nutrient response curves using quadratic, linear-plateau, or Mitscherlich models to determine economic optimum dose.

Usage

nutrient_response(
  dose,
  yield,
  model = "quadratic",
  price_output = 50,
  price_nutrient = 15,
  verbose = TRUE
)

Arguments

dose

Numeric vector of nutrient doses (kg/ha)

yield

Numeric vector of corresponding yields (kg/ha)

model

Model type: "quadratic", "linear_plateau", or "mitscherlich"

price_output

Price of output (Rs/kg)

price_nutrient

Price of nutrient (Rs/kg)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List with model parameters and economic optimum

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

dose <- c(0, 20, 40, 60, 80, 100)
yield <- c(1100, 1350, 1520, 1610, 1650, 1660)
nutrient_response(dose, yield, model = "quadratic", 
                  price_output = 60, price_nutrient = 15)

Path Coefficient Analysis

Description

Performs path analysis to determine direct and indirect effects of independent variables on a dependent variable. Essential for understanding yield contributing factors.

Usage

path_analysis(data, dependent, independent, digits = 4, verbose = TRUE)

Arguments

data

Data frame with numeric variables

dependent

Name of dependent variable (e.g., "yield")

independent

Character vector of independent variable names

digits

Number of decimal places

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List with direct effects, indirect effects, and correlation breakdown

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  yield = c(1200, 1350, 1100, 1450, 1280, 1380, 1220, 1400),
  pods = c(45, 52, 42, 58, 48, 54, 46, 56),
  seeds = c(8.2, 9.1, 7.8, 9.5, 8.5, 9.0, 8.3, 9.3),
  weight = c(32, 35, 30, 38, 33, 36, 31, 37)
)
path_analysis(data, dependent = "yield", 
              independent = c("pods", "seeds", "weight"))

Principal Component Analysis

Description

Performs PCA with visualization suitable for agricultural research data. Includes scree plot, biplot, and variable contributions.

Usage

pca_analysis(data, scale = TRUE, ncp = 5, plot = TRUE, verbose = TRUE)

Arguments

data

Data frame with numeric variables

scale

Logical, whether to scale variables (default TRUE)

ncp

Number of components to retain (default 5)

plot

Logical, whether to generate plots

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

PCA results with eigenvalues, loadings, and scores

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- data.frame(
  yield = rnorm(30, 1200, 200),
  wue = rnorm(30, 4.5, 0.5),
  protein = rnorm(30, 22, 2),
  biomass = rnorm(30, 3500, 500)
)
pca_analysis(data)

Perform Post-Hoc Tests

Description

Performs multiple comparison tests after ANOVA.

Usage

perform_posthoc(
  model,
  data,
  response,
  treatment,
  mse,
  df_error,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

model

ANOVA model object

data

Data frame

response

Response variable name

treatment

Treatment factor name

mse

Mean square error

df_error

Error degrees of freedom

posthoc

Test type: "lsd", "duncan", "tukey", "snk", "scheffe", or "all"

alpha

Significance level

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing post-hoc test results

Print Method for PCA Results

Description

Prints a formatted summary of Principal Component Analysis (PCA) results.

Usage

## S3 method for class 'aridagri_pca'
print(x, ...)

Arguments

x

An object of class 'aridagri_pca' from pca_analysis

...

Additional arguments (currently unused)

Value

No return value, called for side effects. Prints the number of components retained by Kaiser criterion and cumulative variance explained to the console. The input object is returned invisibly.

Structural Equation Modeling for Field Experiments

Description

Performs SEM analysis for agricultural field experiments. Allows testing of hypothesized causal relationships among variables.

Usage

sem_analysis(data, model, plot = TRUE, verbose = TRUE)

Arguments

data

Data frame with variables

model

Model specification in lavaan syntax

plot

Logical, whether to generate path diagram

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

SEM results including fit indices and parameter estimates

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples


if (requireNamespace("lavaan", quietly = TRUE)) {
  set.seed(42)
  n <- 100
  nitrogen <- rnorm(n, 60, 10)
  phosphorus <- rnorm(n, 30, 5)
  yield <- 0.5 * nitrogen + 0.3 * phosphorus + rnorm(n, 0, 5)
  df <- data.frame(yield = yield, nitrogen = nitrogen, phosphorus = phosphorus)
  model <- 'yield ~ nitrogen + phosphorus'
  result <- sem_analysis(df, model, plot = FALSE)
}

============================================================================ ADVANCED AGRONOMIC STATISTICAL METHODS Package: aridagri Authors: Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner ICAR-Indian Institute of Groundnut Research, Regional Research Station, Bikaner ============================================================================ Stability Analysis for Agronomic Traits (Multiple Methods)

Description

Performs comprehensive stability analysis using multiple established methods for evaluating genotype/treatment performance across environments.

Methods included:

Usage

stability_analysis(
  data,
  genotype,
  environment,
  replication,
  trait,
  method = "all",
  verbose = TRUE
)

Arguments

data

Data frame with genotype/treatment, environment, replication, and trait

genotype

Name of genotype/treatment column

environment

Name of environment/location/year column

replication

Name of replication column

trait

Name of trait/response variable

method

Method: "eberhart", "ammi", "finlay", "shukla", "wricke", "cv", "superiority", or "all"

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List containing stability parameters and rankings

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

References

Eberhart, S.A. and Russell, W.A. (1966). Crop Science, 6: 36-40.

Examples

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")

Growing Degree Days (GDD) and Thermal Indices

Description

Calculates Growing Degree Days, Helio-thermal Units, Photo-thermal Units, and Heat Use Efficiency from temperature data.

Usage

thermal_indices(
  tmax,
  tmin,
  base_temp = 10,
  sunshine_hours = NULL,
  day_length = NULL,
  crop_yield = NULL,
  biomass = NULL,
  verbose = TRUE
)

Arguments

tmax

Vector of daily maximum temperatures (C)

tmin

Vector of daily minimum temperatures (C)

base_temp

Base temperature (C)

sunshine_hours

Vector of daily sunshine hours (optional)

day_length

Vector of day length in hours (optional)

crop_yield

Crop yield for HUE calculation (optional)

biomass

Biomass for HUE calculation (optional)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

List with thermal indices

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

tmax <- runif(90, 30, 42)
tmin <- runif(90, 18, 28)
thermal_indices(tmax, tmin, base_temp = 10)

Yield Gap Analysis

Description

Calculates yield gaps comparing actual with potential yields.

Usage

yield_gap_analysis(
  actual_yield,
  potential_yield,
  attainable_yield = NULL,
  verbose = TRUE
)

Arguments

actual_yield

Actual yield (kg/ha)

potential_yield

Potential yield (kg/ha)

attainable_yield

Attainable yield (kg/ha, optional)

verbose

Logical. If TRUE (default), prints formatted output to console.

Value

Data frame with yield gap analysis

Author(s)

Lalit Kumar Rolaniya, Ram Lal Jat, Monika Punia, Raja Ram Choudhary

Examples

yield_gap_analysis(actual_yield = c(800, 950, 720), potential_yield = 1500)