Package {aridagri}

Type:	Package
Title:	Comprehensive Statistical Tools for Agricultural Research
Version:	2.1.0
Description:	Analysis of variance for the experimental designs used in agricultural research, with the correct error term for every stratum, standard error of mean (SEm), standard error of difference (SEd), critical difference (CD), coefficient of variation (CV) and letter groupings: completely randomized design (CRD), randomized complete block design (RCBD) with missing plots, Latin square, two- and three-factor factorials, split-plot designs (including factorial main or sub-plots), split-split-plot and strip-plot designs, and combined analysis over environments. Also covers the augmented randomized complete block design with adjusted means and genetic parameters (Federer, 1961 <doi:10.2307/2527837>), alpha lattice designs with intra-block or residual maximum likelihood (REML) analysis (Patterson and Williams, 1976 <doi:10.1093/biomet/63.1.83>) and repeated measures with Greenhouse-Geisser and Huynh-Feldt corrections and covariance structure models. Mean comparisons (least significant difference, Duncan, Tukey, Student-Newman-Keuls, Scheffe, Bonferroni and Dunnett) follow Gomez and Gomez (1984, ISBN:0471870927), with letters assigned by the algorithm of Piepho (2004) <doi:10.1198/1061860043515>. Stability analysis includes Eberhart and Russell (1966) <doi:10.2135/cropsci1966.0011183X000600010011x> regression, additive main effects and multiplicative interaction (AMMI), Finlay-Wilkinson, Shukla, Wricke, coefficient of variation and Lin-Binns methods, and genotype plus genotype-by-environment (GGE) biplots (Yan et al., 2000 <doi:10.2135/cropsci2000.403597x>). Further tools compute growing degree days, helio-thermal and photo-thermal units, crop growth rate, relative growth rate, net assimilation rate, leaf area index and leaf area duration, harvest index, yield gaps with the technology index, nutrient use efficiency, quadratic, plateau and Mitscherlich response curves with the economic optimum dose, benefit-cost ratio, correlation, principal component, path and structural equation analysis, publication tables for many traits and figures.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.0.0)
Imports:	grDevices, graphics, nlme, stats, utils
Suggests:	agricolae, corrplot, emmeans, factoextra, ggplot2 (≥ 3.4.0), knitr, lavaan, multcomp, rmarkdown, semPlot, testthat (≥ 3.0.0), tibble, writexl
VignetteBuilder:	knitr
URL:	https://github.com/lalitrolaniya/aridagri, https://lalitrolaniya.github.io/aridagri/
BugReports:	https://github.com/lalitrolaniya/aridagri/issues
Config/testthat/edition:	3
NeedsCompilation:	no
Config/roxygen2/version:	8.1.1
Packaged:	2026-11-09 12:28:43 UTC; root
Author:	Lalit Kumar Rolaniya [aut, cre], Ram Lal Jat [aut], Monika Punia [aut] (deceased), Raja Ram Choudhary [aut], Hemant Poonia [aut] (deceased)
Maintainer:	Lalit Kumar Rolaniya <lalitrolaniya@gmail.com>
Repository:	CRAN
Date/Publication:	2026-11-10 23:00:02 UTC

aridagri: Statistical Tools for Agricultural Research

Description

Analysis of variance for the common field designs with correct error terms, standard errors, critical differences and letter groupings, plus stability analysis, repeated measures, thermal indices, crop growth analysis, nutrient use efficiency, response curves and economics.

Experimental designs

• anova_crd, anova_rbd, anova_latin: single-factor designs (missing plots and unequal replication handled exactly)

• anova_factorial, anova_factorial_3way: factorial experiments

• anova_spd, anova_spd_ab_main, anova_spd_c_main_ab_sub, anova_spd_ab_cd, anova_sspd, anova_strip: split-plot family with SE and CD for every comparison

• anova_rbd_pooled, anova_spd_pooled, anova_sspd_pooled: combined analysis over environments

• anova_augmented, anova_alpha_lattice: designs for germplasm and breeding trials

• anova_repeated: repeated measures with sphericity corrections and covariance-structure models

• anova_traits: publication table for many traits

Mean comparisons and diagnostics

perform_posthoc, compare_means, check_assumptions.

Genotype x environment analysis

stability_analysis (Eberhart-Russell, AMMI, Finlay-Wilkinson, Shukla, Wricke, CV, Lin-Binns) and gge_biplot.

Agronomic indices

thermal_indices, day_length, crop_growth_analysis, harvest_index, yield_gap_analysis, economic_indices, economic_analysis, nue_calculate, nutrient_response.

Multivariate methods

correlation_analysis, pca_analysis, path_analysis, sem_analysis.

Figures and export

plot_means, plot_interaction, plot_stability, plot_response, plot() methods, theme_aridagri (ggplot2); arid_plot (base graphics); export_results.

Author(s)

Lalit Kumar Rolaniya (maintainer), Ram Lal Jat, Monika Punia (deceased), Raja Ram Choudhary and Hemant Poonia (deceased)

See Also

Useful links:

• https://github.com/lalitrolaniya/aridagri

• https://lalitrolaniya.github.io/aridagri/

• Report bugs at https://github.com/lalitrolaniya/aridagri/issues

Alpha Lattice Design ANOVA

Description

Analysis of an alpha lattice (resolvable incomplete block) design. Gives the intra-block ANOVA, genotype means adjusted for incomplete blocks with exact standard errors and letter groupings, and the efficiency of the design relative to a randomized complete block design. By default replications and blocks are treated as random (REML), so that inter-block information is recovered (Patterson and Williams, 1976); analysis = "intra" gives the classical intra-block estimates.

Usage

anova_alpha_lattice(
  data,
  response,
  genotype,
  replication,
  block,
  verbose = TRUE,
  analysis = c("reml", "intra"),
  posthoc = "lsd",
  alpha = 0.05
)

Arguments

Details

The intra-block ANOVA is the sequential analysis of the model replication + block(replication) + genotype: genotypes are tested after blocks. Blocks within replications are also shown adjusted for genotypes. Adjusted means are least-squares means. The efficiency relative to an RCBD is the average variance of a difference under the RCBD analysis (2 MSE_{RCBD} / r) divided by the average variance of a difference of adjusted means. With REML, letters and CD use the intra-block error degrees of freedom, an approximation.

Value

An object of class aridagri_alpha with anova_table, genotype_means (adjusted means with SE and letters, highest first), avg_sed, cd (from the average SEd), efficiency (percent, relative to RCBD), cv, grand_mean, posthoc, variance_components (REML) and model.

Author(s)

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

References

Patterson, H.D. and Williams, E.R. (1976). A new class of resolvable incomplete block designs. Biometrika, 63(1), 83-92. doi:10.1093/biomet/63.1.83

Examples

data(cowpea_alpha)
res <- anova_alpha_lattice(cowpea_alpha, response = "grain_yield",
                           genotype = "genotype", replication = "rep",
                           block = "block")
head(res$genotype_means)

Augmented Randomized Complete Block Design

Description

Analysis of an augmented randomized complete block design (Federer, 1956), where a few check varieties are replicated in every block and the new (test) entries appear once. Gives the ANOVA with the treatment sum of squares split into checks, tests and checks vs tests, adjusted means of all entries, the four standard errors of difference, a comparison of every test entry with the checks, and genetic parameters of the test entries.

Usage

anova_augmented(
  data,
  response,
  genotype,
  block,
  check_names,
  verbose = TRUE,
  alpha = 0.05,
  k = 2.063
)

Arguments

Details

Block effects are estimated from the checks and removed from the test entries: adjusted value = observed value - (mean of checks in its block - overall check mean). This equals the least-squares mean from the model response ~ block + genotype, which is used here, so the standard errors are exact. With b blocks and c checks the four standard errors of difference are: two check means \sqrt{2 MSE / b}; two test entries in the same block \sqrt{2 MSE}; two test entries in different blocks \sqrt{2 MSE (1 + 1/c)}; a test entry and a check mean \sqrt{MSE (1 + 1/b + 1/c - 1/(bc))}.

Two ANOVA tables are given. In the first, treatments are fitted after blocks and their sum of squares is split into tests vs checks, checks and tests, each adjusted for blocks. In the second, blocks are fitted after treatments and the treatment sum of squares (ignoring blocks) is split into checks, tests and checks vs tests (Federer, 1961).

Genetic parameters of the test entries use the block-adjusted mean square among tests from the first table: phenotypic variance \sigma^2_p = MS(tests), error variance \sigma^2_e = MSE, genotypic variance \sigma^2_g = \sigma^2_p - \sigma^2_e, broad-sense heritability h^2 = \sigma^2_g / \sigma^2_p, GCV and PCV in percent of the mean of the adjusted test values, and genetic advance GA = k h^2 \sigma_p.

Value

An object of class aridagri_augmented with anova_adjusted (treatments after blocks), anova_partitioned (blocks after treatments, treatments split), adjusted_means (all entries with type, raw and adjusted mean, SE and block), se_diff and cd (the four comparisons), test_vs_checks (difference of each test entry from the check mean and the best check, with significance), block_effects, genetic_parameters, mse, df_error, anova_checks and model.

Author(s)

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

References

Federer, W.T. (1956). Augmented (or hoonuiaku) designs. Hawaiian Planters' Record, 55, 191-208.

Federer, W.T. (1961). Augmented designs with one-way elimination of heterogeneity. Biometrics, 17(3), 447-473. doi:10.2307/2527837

Examples

data(chickpea_augmented)
res <- anova_augmented(chickpea_augmented, response = "seed_yield",
                       genotype = "entry", block = "block",
                       check_names = c("C1", "C2", "C3", "C4"))
head(res$test_vs_checks)
res$genetic_parameters

Completely Randomized Design (CRD) ANOVA

Description

Analysis of variance for a completely randomized design with standard errors, critical difference, coefficient of variation, post-hoc letter groupings and assumption checks. Unequal replication is handled exactly.

Usage

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

Arguments

Details

SEm = sqrt(MSE / r), SEd = sqrt(2 MSE / r) and CD = t(alpha/2, error df) x SEd, where r is the number of replications (the harmonic mean when replication is unequal; the letters then use the exact SE of each pair). CV = 100 sqrt(MSE) / grand mean. Rows with a missing response are dropped with a message.

Value

An object of class aridagri_anova with anova_table, model, grand_mean, cv, ms_error, df_error, se_mean (SEm), se_diff (SEd), cd (at alpha), cd_5, cd_1, treatment_means (mean, SD, N, SE and letters), posthoc, diagnostics, significance and balanced.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. John Wiley and Sons, New York.

Examples

data <- data.frame(
  treatment = rep(c("T1", "T2", "T3", "T4"), each = 5),
  yield = c(1210, 1185, 1242, 1198, 1225, 1340, 1362, 1318, 1375, 1350,
            1105, 1090, 1122, 1131, 1098, 1445, 1462, 1430, 1471, 1452)
)
res <- anova_crd(data, response = "yield", treatment = "treatment",
                 posthoc = "duncan")
res$treatment_means

Two-Factor Factorial ANOVA

Description

Analysis of variance for a two-factor factorial experiment in a randomized complete block design (when replication is given) or a completely randomized design, with SEm, SEd and CD for both main effects and the interaction, and letter groupings for factor means and treatment combinations.

Usage

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

Arguments

Value

An object of class aridagri_factorial with anova_table, model, grand_mean, cv, ms_error, df_error, comparisons (SEm, SEd and CD for A, B and A x B), se_a, cd_a, se_b, cd_b, se_ab, cd_ab, mean_a, mean_b (with letters), interaction_means (all combinations with letters), mean_ab (two-way table) and posthoc.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- expand.grid(rep = 1:4, nitrogen = c("N0", "N40", "N80"),
                    phosphorus = c("P0", "P30"))
set.seed(1)
data$yield <- 900 + 3 * as.numeric(sub("N", "", data$nitrogen)) +
  2 * as.numeric(sub("P", "", data$phosphorus)) + rnorm(nrow(data), 0, 40)
res <- anova_factorial(data, response = "yield", factor1 = "nitrogen",
                       factor2 = "phosphorus", replication = "rep")
res$interaction_means

Three-Factor Factorial ANOVA

Description

Analysis of variance for a three-factor factorial experiment in a randomized complete block design (when replication is given) or a completely randomized design, with SEm, SEd and CD for every main effect and interaction and letter groupings for main-effect means and treatment combinations.

Usage

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

Arguments

Value

An object of class aridagri_factorial3 with anova_table, model, grand_mean, cv, ms_error, df_error, comparisons (SEm, SEd and CD for all seven effects), means (a list of tables for A, B, C, A x B, A x C, B x C and A x B x C, with letters) and posthoc.

Author(s)

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

Examples

data <- expand.grid(rep = 1:3, A = c("A1", "A2"), B = c("B1", "B2"),
                    C = c("C1", "C2", "C3"))
set.seed(2)
data$yield <- rnorm(nrow(data), 1200, 60)
res <- anova_factorial_3way(data, "yield", "A", "B", "C", "rep")
res$comparisons

Latin Square Design ANOVA

Description

Analysis of variance for a Latin square design with row and column blocking, SEm, SEd, CD, CV, the efficiency of the Latin square relative to a CRD and to an RCBD, and post-hoc letter groupings.

Usage

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

Arguments

Details

Relative efficiencies (Cochran and Cox, 1957; Gomez and Gomez, 1984) use the row (E_r), column (E_c) and error (E_e) mean squares: versus a CRD, [E_r + E_c + (t-1)E_e] / [(t+1)E_e]; versus an RCBD with rows as blocks, [E_c + (t-1)E_e] / (t E_e); versus an RCBD with columns as blocks, [E_r + (t-1)E_e] / (t E_e), each multiplied by the precision adjustment factor when the error degrees of freedom are below 20.

Value

An object of class aridagri_anova with the components of anova_crd and relative_efficiency (a named vector, percent).

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

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 = c(1180, 1265, 1342, 1410, 1238, 1270, 1335, 1402, 1251, 1176,
            1340, 1415, 1245, 1190, 1258, 1405, 1232, 1178, 1262, 1339,
            1236, 1183, 1271, 1348, 1398)
)
anova_latin(data, response = "yield", treatment = "treatment",
            row = "row", column = "col")

Randomized Complete Block Design (RCBD) ANOVA

Description

Analysis of variance for a randomized complete block design with standard errors, critical difference, CV, relative efficiency over a CRD, post-hoc letter groupings and assumption checks. Missing plots are handled by least squares: treatments are tested after blocks, means are least-squares (adjusted) means and every pair is compared with its own standard error.

Usage

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

Arguments

Details

Relative efficiency of the RCBD over a CRD follows Gomez and Gomez (1984):

RE = \frac{(r - 1) E_b + r (t - 1) E_e}{(r t - 1) E_e}

where E_b and E_e are the block and error mean squares, multiplied by the precision adjustment factor k = \frac{[(r-1)(t-1)+1][t(r-1)+3]}{[(r-1)(t-1)+3][t(r-1)+1]} when the error degrees of freedom are below 20. Values above 100% mean that blocking reduced the error.

Value

An object of class aridagri_anova. In addition to the components described in anova_crd it contains relative_efficiency (percent, adjusted), relative_efficiency_unadjusted and missing_plots.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. John Wiley and Sons, New York.

Examples

data(mothbean_rbd)
res <- anova_rbd(mothbean_rbd, response = "seed_yield",
                 treatment = "treatment", block = "block",
                 posthoc = "duncan")
res$treatment_means
res$relative_efficiency

Combined (Pooled) Analysis of RCBD Experiments over Environments

Description

Combined analysis of variance for the same set of treatments tested in a randomized complete block design in several environments (years, seasons or locations). Gives the analysis of each environment, Bartlett's test of homogeneity of error variances, the combined ANOVA with the correct error term for every source, SE and CD for treatment, environment and interaction means, and letter groupings for the treatment means.

Usage

anova_rbd_pooled(
  data,
  response,
  treatment,
  environment,
  block,
  posthoc = "lsd",
  alpha = 0.05,
  verbose = TRUE,
  env_effect = c("fixed", "random"),
  control = NULL
)

Arguments

Details

Sources and their F-tests (McIntosh, 1983; Gomez and Gomez, 1984): environment is tested against blocks within environments; the treatment x environment interaction against the pooled error; the treatment effect against the pooled error when environments are fixed and against the treatment x environment mean square when they are random. With random environments the usual restricted mixed model is assumed, so environment is still tested against blocks within environments. The SE and CD of treatment means use the same denominator as the treatment F-test. Bartlett's chi-square tests whether the error variances of the environments can be pooled; when it is significant, interpret the combined analysis with care.

Value

An object of class aridagri_pooled with pooled_anova (combined ANOVA with correct F-tests), individual_anovas, environment_summary (mean, MSE, CV, F, p and CD of each environment), bartlett_test, treatment_means (with letters), interaction_means, se_treatment, cd_treatment, se_environment, cd_environment, se_interaction, cd_interaction, posthoc, cv, grand_mean, mse_values and model.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

McIntosh, M.S. (1983). Analysis of combined experiments. Agronomy Journal, 75(1), 153-155. doi:10.2134/agronj1983.00021962007500010041x

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. John Wiley and Sons, New York.

Examples

data(pearlmillet_met)
res <- anova_rbd_pooled(pearlmillet_met, response = "grain_yield",
                        treatment = "genotype", environment = "environment",
                        block = "rep", verbose = FALSE)
res$pooled_anova
res$bartlett_test

Repeated Measures ANOVA (Split-Plot in Time)

Description

Analysis of experiments in which the same experimental units (plots, animals, trees) are measured on several occasions: plant height or LAI at successive dates, soil moisture through the season, or body weight, heart rate or blood metabolites over time. Gives the univariate split-plot-in-time ANOVA, Mauchly's test of sphericity with Greenhouse-Geisser and Huynh-Feldt corrected p-values, a comparison of covariance structures by mixed models (compound symmetry, first-order autoregressive and unstructured), SE and CD for every comparison, letter groupings, and a separate analysis at each time.

Usage

anova_repeated(
  data,
  response,
  treatment,
  time,
  subject = NULL,
  block = NULL,
  posthoc = "lsd",
  alpha = 0.05,
  covariance = TRUE,
  verbose = TRUE
)

Arguments

Details

Univariate analysis. Treatment (and block) are tested against the between-unit error, Error(a). Time and the treatment x time interaction are tested against the within-unit error, Error(b). With blocks, the block x time interaction is fitted as a separate within-unit term, as in the univariate tests of SAS PROC GLM with a REPEATED statement, so that the sphericity corrections apply to the same error term. This is valid when the covariance of the repeated measurements is spherical. Mauchly's test checks this, and the Greenhouse-Geisser (1959) and Huynh-Feldt (1976) epsilons, the latter with the correction of Lecoutre (1991) as in anova.mlm(), scale the degrees of freedom of the within-unit tests when it fails. A common rule is to use the Greenhouse-Geisser p-value when epsilon is below 0.75 and the Huynh-Feldt p-value otherwise.

Mixed models. Generalized least squares models with the same fixed effects are fitted by REML with compound symmetry (CS), first-order autoregressive (AR1, on the time order) and unstructured (UN, with separate variances per time) covariance, and compared by AIC and BIC. The marginal F-tests of the best structure are reported (with the residual degrees of freedom used by nlme, so tests of between-unit effects are approximate; the univariate test of treatment is exact for balanced data). Structures that fail to converge are skipped.

Comparisons. With r units per treatment, a treatments and t times, the SEd are as in a split-plot design with treatments in main plots and times in sub-plots: treatment means \sqrt{2E_a/(rt)}, time means \sqrt{2E_b/(ra)}, times within a treatment \sqrt{2E_b/r}, and treatments at the same time \sqrt{2[(t-1)E_b + E_a]/(rt)} with the weighted t.

Value

An object of class aridagri_repeated with anova_table (univariate), sphericity (Mauchly's W and p, GG and HF epsilons), corrected (within-unit tests with GG and HF p-values), covariance_models (AIC/BIC table), best_structure, mixed_anova, se_cd, means (treatment x time table), treatment_means, time_means, letters, by_time (ANOVA at each time) and models.

References

Greenhouse, S.W. and Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24(2), 95-112. doi:10.1007/BF02289823

Lecoutre, B. (1991). A correction for the epsilon approximate test in repeated measures designs with two or more independent groups. Journal of Educational Statistics, 16(4), 371-372. doi:10.3102/10769986016004371

Huynh, H. and Feldt, L.S. (1976). Estimation of the Box correction for degrees of freedom from sample data in randomized block and split-plot designs. Journal of Educational Statistics, 1(1), 69-82. doi:10.3102/10769986001001069

Littell, R.C., Henry, P.R. and Ammerman, C.B. (1998). Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science, 76(4), 1216-1231. doi:10.2527/1998.7641216x

Examples

data(mungbean_repeated)
rm <- anova_repeated(mungbean_repeated, response = "spad",
                     treatment = "treatment", time = "das", block = "block")
rm$sphericity
rm$covariance_models

Split-Plot Design ANOVA

Description

Analysis of variance for a split-plot design in randomized complete blocks, with the main-plot factor tested against Error(a) and the sub-plot factor and interaction against Error(b). Gives CV(a), CV(b), SEm, SEd and CD for all four standard comparisons and letter groupings for main-plot means, sub-plot means and each factor within the levels of the other.

Usage

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

Arguments

Details

Standard errors of difference (Gomez and Gomez, 1984, table 3.10): main-plot means \sqrt{2E_a/(rb)}, sub-plot means \sqrt{2E_b/(ra)}, sub-plot means at the same main-plot level \sqrt{2E_b/r}, and main-plot means at the same or different sub-plot level \sqrt{2[(b-1)E_b + E_a]/(rb)}. For the last comparison the CD uses the weighted t value t' = [(b-1)E_b t_b + E_a t_a] / [(b-1)E_b + E_a]. The data must be complete (every combination once in each replication).

Value

An object of class aridagri_spd with the corrected anova_table, cv_a, cv_b, error mean squares and df, se_main, cd_main, se_sub, cd_sub, se_sub_same, cd_sub_same, se_main_same_sub, cd_main_same_sub, se_cd (all comparisons), main_means and sub_means (with letters), interaction_means, letters (each factor within the levels of the other) and model.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. John Wiley and Sons, New York.

Examples

data(clusterbean_spd)
res <- anova_spd(clusterbean_spd, response = "seed_yield",
                 main_plot = "irrigation", sub_plot = "nitrogen",
                 replication = "rep")
res$se_cd

Split-Plot Design with Factorial Main and Sub-Plots

Description

Split-plot design where the main plots carry A x B and the sub-plots carry C x D. Main-plot sources are tested against Error(a), all other sources against Error(b).

Usage

anova_spd_ab_cd(
  data,
  response,
  main_factor1,
  main_factor2,
  sub_factor1,
  sub_factor2,
  replication,
  verbose = TRUE,
  posthoc = "lsd",
  alpha = 0.05
)

Arguments

Value

An object of class aridagri_spd_abcd; see anova_spd_ab_main for the components.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- expand.grid(rep = 1:3, irrigation = c("I1", "I2"),
                    tillage = c("CT", "ZT"), variety = c("V1", "V2"),
                    nitrogen = c("N1", "N2", "N3"))
set.seed(5)
data$yield <- rnorm(nrow(data), 1200, 80)
res <- anova_spd_ab_cd(data, response = "yield",
                       main_factor1 = "irrigation", main_factor2 = "tillage",
                       sub_factor1 = "variety", sub_factor2 = "nitrogen",
                       replication = "rep", verbose = FALSE)
res$anova_table

Split-Plot Design with Two Factors in the Main Plot

Description

Split-plot design where the main plots carry the factorial combination of two factors (A x B) and the sub-plots carry factor C. A, B and A x B are tested against Error(a); C and every interaction with C against Error(b).

Usage

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

Arguments

Value

An object of class aridagri_spd_ab with the corrected anova_table, cv, error mean squares and df, se_cd (SEm, SEd and CD for every main effect and interaction comparison), factor_means (with letters), letters, interaction_means and model.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- expand.grid(rep = 1:3, irrigation = c("I1", "I2"),
                    variety = c("V1", "V2", "V3"),
                    nitrogen = c("N0", "N1", "N2", "N3"))
set.seed(3)
data$yield <- rnorm(nrow(data), 1200, 80)
res <- anova_spd_ab_main(data, response = "yield",
                         main_factor1 = "irrigation",
                         main_factor2 = "variety",
                         sub_plot = "nitrogen", replication = "rep",
                         verbose = FALSE)
res$anova_table

Split-Plot Design with Two Factors in the Sub-Plot

Description

Split-plot design where the main plots carry factor C and the sub-plots carry the factorial combination of two factors (A x B). C is tested against Error(a); A, B, A x B and every interaction with C against Error(b).

Usage

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

Arguments

Value

An object of class aridagri_spd_cab; see anova_spd_ab_main for the components.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

data <- expand.grid(rep = 1:3, irrigation = c("I1", "I2", "I3"),
                    variety = c("V1", "V2"),
                    nitrogen = c("N1", "N2", "N3", "N4"))
set.seed(4)
data$yield <- rnorm(nrow(data), 1200, 80)
res <- anova_spd_c_main_ab_sub(data, response = "yield",
                               main_plot = "irrigation",
                               sub_factor1 = "variety",
                               sub_factor2 = "nitrogen",
                               replication = "rep", verbose = FALSE)
res$anova_table

Combined Split-Plot Analysis over Environments

Description

Combined (pooled) analysis of a split-plot experiment repeated in several environments (years, seasons or locations). Environments are fixed and replications random within environments: the environment effect is tested against replications within environments, main-plot sources against the pooled Error(a) and sub-plot sources against the pooled Error(b). SEm, SEd and CD are given for every comparison.

Usage

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

Arguments

Value

An object of class aridagri_spd_pooled with anova_table, cv (by stratum), se_cd, factor_means (with letters), main_means, sub_means, env_means and model.

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"))
set.seed(7)
data$yield <- rnorm(nrow(data), 1200, 90)
res <- anova_spd_pooled(data, response = "yield", main_plot = "irrigation",
                        sub_plot = "variety", environment = "year",
                        replication = "rep", verbose = FALSE)
res$anova_table

Split-Split-Plot Design ANOVA

Description

Analysis of variance for a split-split-plot design with main-plot sources tested against Error(a), sub-plot sources against Error(b) and sub-sub-plot sources against Error(c). Gives CVs for the three strata, SEm, SEd and CD for every type of comparison (including those that mix error terms) and letter groupings.

Usage

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

Arguments

Value

An object of class aridagri_sspd with the corrected anova_table, cv, ms_error_a/b/c, df_error_a/b/c, se_cd, factor_means, letters, interaction_means and model.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. John Wiley and Sons, New York.

Examples

data <- expand.grid(rep = 1:3, A = c("A1", "A2"), B = c("B1", "B2"),
                    C = c("C1", "C2", "C3"))
set.seed(6)
data$yield <- rnorm(nrow(data), 1200, 80)
res <- anova_sspd(data, response = "yield", main_plot = "A",
                  sub_plot = "B", sub_sub_plot = "C", replication = "rep")

Combined Split-Split-Plot Analysis over Environments

Description

Combined (pooled) analysis of a split-split-plot experiment repeated in several environments, with environments fixed and replications random within environments. See anova_spd_pooled.

Usage

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

Arguments

Value

An object of class aridagri_sspd_pooled; see anova_spd_pooled. Also contains subsub_means.

Author(s)

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

Examples

data <- expand.grid(env = c("E1", "E2"), rep = 1:3, A = c("A1", "A2"),
                    B = c("B1", "B2"), C = c("C1", "C2"))
set.seed(8)
data$yield <- rnorm(nrow(data), 1200, 90)
res <- anova_sspd_pooled(data, "yield", "A", "B", "C", "env", "rep",
                         verbose = FALSE)
res$anova_table

Strip-Plot (Strip-Block) Design ANOVA

Description

Analysis of variance for a strip-plot design, where factor A is applied to horizontal strips and factor B to vertical strips in each replication. A is tested against Error(a) = Rep x A, B against Error(b) = Rep x B, and A x B against Error(c). Gives CV(a), CV(b), CV(c), SEm, SEd and CD for the four standard comparisons, and letter groupings.

Usage

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

Arguments

Details

Standard errors of difference (Gomez and Gomez, 1984, table 3.15), with a and b levels of A and B and r replications: A means \sqrt{2E_a/(rb)}; B means \sqrt{2E_b/(ra)}; A means at the same level of B \sqrt{2[(b-1)E_c + E_a]/(rb)}; B means at the same level of A \sqrt{2[(a-1)E_c + E_b]/(ra)}. The last two use the weighted t of Cochran and Cox.

Value

An object of class aridagri_strip with the corrected anova_table, cv (a, b, c), error mean squares and df, se_cd, horizontal_means and vertical_means (with letters), interaction_means, letters and model.

Author(s)

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

References

Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. John Wiley and Sons, New York.

Examples

data <- expand.grid(rep = 1:4, irrigation = c("I1", "I2", "I3"),
                    tillage = c("CT", "MT", "ZT"))
set.seed(9)
data$yield <- rnorm(nrow(data), 1200, 70)
anova_strip(data, response = "yield", horizontal_factor = "irrigation",
            vertical_factor = "tillage", replication = "rep")

Publication Table of Treatment Means for Many Traits

Description

Runs the analysis of variance for several traits at once and assembles the table that agronomy journals and theses expect: treatment means followed by letters, then SEm, CD and CV for each trait (with "NS" when the F-test is not significant). Works for completely randomized, randomized block, two-factor factorial and split-plot designs.

Usage

anova_traits(
  data,
  traits,
  design = c("rbd", "crd", "factorial", "spd"),
  treatment = NULL,
  block = NULL,
  factor1 = NULL,
  factor2 = NULL,
  main_plot = NULL,
  sub_plot = NULL,
  posthoc = "lsd",
  alpha = 0.05,
  digits = 2,
  letters_if_ns = FALSE,
  verbose = TRUE
)

Arguments

Value

An object of class aridagri_traits with table (character data frame ready to export), stats (F, p, SEm, SEd, CD and CV for each trait and factor) and results (the full analysis for each trait). Export with export_results.

Examples

data(mothbean_rbd)
tab <- anova_traits(mothbean_rbd,
                    traits = c("plant_height", "pods_per_plant",
                               "seed_yield", "harvest_index"),
                    design = "rbd", treatment = "treatment",
                    block = "block", posthoc = "duncan")
tab$table

data(clusterbean_spd)
anova_traits(clusterbean_spd, traits = c("seed_yield", "wue"),
             design = "spd", main_plot = "irrigation",
             sub_plot = "nitrogen", block = "rep")

Base-Graphics Plots for aridagri Results

Description

Quick plots with base graphics (no extra packages): bar charts of treatment or factor means with their letters for ANOVA results, a heatmap for correlation results and the integrated ranking for stability results. For publication figures with ggplot2 see plot_means and the plot() methods.

Usage

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

Arguments

Value

No return value; called for its side effect of drawing a plot.

Author(s)

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

Examples

data(mothbean_rbd)
res <- anova_rbd(mothbean_rbd, "seed_yield", "treatment", "block",
                 verbose = FALSE)
arid_plot(res)

Check ANOVA Assumptions

Description

Checks the assumptions behind an analysis of variance: normality of residuals (Shapiro-Wilk), homogeneity of variances across treatments (Bartlett and Levene/Brown-Forsythe), additivity of block and treatment effects (Tukey's one-degree-of-freedom test, for two-way models), serial independence (Durbin-Watson, in data order) and outliers (studentized residuals).

Usage

check_assumptions(model, verbose = TRUE, factor = NULL, plot = FALSE)

Arguments

Details

The variance tests use the model residuals, so block effects do not inflate the group variances. Levene's test is the Brown-Forsythe form (absolute deviations from the group median). Tukey's test for non-additivity adds the squared fitted values as a one-degree-of-freedom covariate to a model with two additive factors; a significant result suggests a transformation (Gomez and Gomez, 1984, ch. 7). The Durbin-Watson statistic is only meaningful when the rows are in field or time order.

Value

A list with shapiro, bartlett, levene, nonadditivity (where applicable), durbin_watson, outliers (row indices with |studentized residual| > 3) and groups_factor.

References

Tukey, J.W. (1949). One degree of freedom for non-additivity. Biometrics, 5(3), 232-242. doi:10.2307/3001938

Brown, M.B. and Forsythe, A.B. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69(346), 364-367. doi:10.1080/01621459.1974.10482955

Examples

data(mothbean_rbd)
fit <- aov(seed_yield ~ block + treatment, data = mothbean_rbd)
check_assumptions(fit)

Chickpea Germplasm Evaluation (Augmented RCBD, Simulated)

Description

Simulated augmented randomized complete block design with 48 chickpea accessions (tested once) and four checks replicated in each of six blocks.

Usage

chickpea_augmented

Format

A data frame with 72 rows and 4 variables:

block

Factor, block (B1-B6).

entry

Factor, checks C1-C4 and accessions IC-001 to IC-048.

seed_yield

Seed yield (kg/ha).

plant_height

Plant height (cm).

Source

Simulated with data-raw/make_datasets.R (seed 2026). Not real experimental data.

Examples

data(chickpea_augmented)
anova_augmented(chickpea_augmented, "seed_yield", "entry", "block",
                check_names = c("C1", "C2", "C3", "C4"))

Cluster Bean Irrigation x Nitrogen Trial (Split Plot, Simulated)

Description

Simulated split-plot experiment on cluster bean (Cyamopsis tetragonoloba) with three irrigation levels in main plots and four nitrogen levels in sub-plots, in three replications.

Usage

clusterbean_spd

Format

A data frame with 36 rows and 7 variables:

rep

Factor, replication (R1-R3).

irrigation

Factor, main-plot irrigation level.

nitrogen

Factor, sub-plot nitrogen level (kg N/ha; foliar = additional 2% urea spray).

seed_yield

Seed yield (kg/ha).

water_use

Seasonal water use (mm).

wue

Water use efficiency (kg/ha per mm).

stover_yield

Stover yield (kg/ha).

Source

Simulated with data-raw/make_datasets.R (seed 2026). Not real experimental data.

Examples

data(clusterbean_spd)
anova_spd(clusterbean_spd, "seed_yield", "irrigation", "nitrogen", "rep")

Mean Separation from Summary Statistics

Description

Assigns significance letters to treatment means when only the means, the error mean square and its degrees of freedom are available (for example from a published table or from another program). Uses the same tests as perform_posthoc.

Usage

compare_means(
  means,
  mse,
  df_error,
  reps,
  method = "lsd",
  alpha = 0.05,
  verbose = TRUE
)

Arguments

Value

The comparison object described in perform_posthoc: groups, table, comparisons, critical_range and statistics.

Examples

m <- c(T1 = 1180, T2 = 1265, T3 = 1342, T4 = 1410, T5 = 1238)
compare_means(m, mse = 3600, df_error = 12, reps = 4, method = "duncan")

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

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)

Cowpea Genotype Trial (Alpha Lattice, Simulated)

Description

Simulated alpha lattice trial of 24 cowpea genotypes in three replications, each with six incomplete blocks of four plots. The layout was generated with agricolae::design.alpha().

Usage

cowpea_alpha

Format

A data frame with 72 rows and 5 variables:

rep

Factor, replication (1-3).

block

Factor, incomplete block within replication (1-6).

plot

Plot number.

genotype

Factor, genotype code (CP-01 to CP-24).

grain_yield

Grain yield (kg/ha).

Source

Simulated with data-raw/make_datasets.R (seed 2026). Not real experimental data.

Examples

data(cowpea_alpha)
anova_alpha_lattice(cowpea_alpha, "grain_yield", "genotype", "rep",
                    "block")

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

Description

Classical growth analysis from sequential harvests: crop growth rate, relative growth rate, net assimilation rate, leaf area index and leaf area duration for each interval between samplings.

Usage

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

Arguments

Details

For two samplings at t_1 and t_2 (Watson, 1952; Radford, 1967):

• CGR = (W_2 - W_1) / [P (t_2 - t_1)], P = ground area

• RGR = (\ln W_2 - \ln W_1) / (t_2 - t_1) (g g-1 day-1)

• NAR = \frac{W_2 - W_1}{t_2 - t_1} \times \frac{\ln L_2 - \ln L_1}{L_2 - L_1}, reported in g m-2 (leaf) day-1

• LAD = (LAI_1 + LAI_2)(t_2 - t_1) / 2 (days)

Value

A data frame of class aridagri_growth with one row per interval: Period, CGR, RGR and, with leaf area, NAR, LAI_start, LAI_end and LAD.

Author(s)

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

References

Radford, P.J. (1967). Growth analysis formulae: their use and abuse. Crop Science, 7(3), 171-175. doi:10.2135/cropsci1967.0011183X000700030001x

Watson, D.J. (1952). The physiological basis of variation in yield. Advances in Agronomy, 4, 101-145. doi:10.1016/S0065-2113(08)60307-7

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, ground_area = 0.09)

Day Length from Latitude and Date

Description

Maximum possible daylight hours (astronomical day length) from latitude and day of year, following FAO Irrigation and Drainage Paper 56 (equations 24, 25 and 34). Useful for photo-thermal units when measured day length is not available.

Usage

day_length(latitude, date)

Arguments

Value

Numeric vector of day length in hours.

References

Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome.

Examples

# Bikaner (28.0 N) on 15 March and 15 June
day_length(28.0, as.Date(c("2026-03-15", "2026-06-15")))

Economic Analysis of a Crop

Description

Cost of cultivation, gross and net returns, benefit-cost ratio and cost of production. Arguments may be vectors (one value per treatment).

Usage

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

Arguments

Value

A data frame of class aridagri_economics.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

economic_analysis(yield = 1200, price = 65, cost_fixed = 15000,
                  cost_variable = 12000)
economic_analysis(yield = c(1050, 1200, 1340), price = 65,
                  cost_fixed = 15000, cost_variable = c(11000, 12000, 13500),
                  byproduct_yield = c(1600, 1800, 1950), byproduct_price = 5)

Economic Efficiency Indices (B:C Ratio)

Description

Net return, benefit-cost ratio and return per rupee invested from gross return and total cost.

Usage

economic_indices(gross_return, total_cost, verbose = TRUE)

Arguments

Value

A data frame of class aridagri_economics with Net_Return, BC_Ratio (gross return / cost) and Return_per_Rupee (net return / cost).

Author(s)

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

Examples

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

Export Results to Excel or CSV

Description

Writes the main tables of an aridagri result to an Excel workbook (one sheet per table) or a CSV file. Multi-trait tables from anova_traits are written as they appear in the report.

Usage

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

Arguments

Value

Invisibly returns the file path.

Author(s)

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

Examples

data(mothbean_rbd)
res <- anova_rbd(mothbean_rbd, "seed_yield", "treatment", "block",
                 verbose = FALSE)
export_results(res, tempfile(fileext = ".csv"), format = "csv")

GGE Biplot Analysis (Which-Won-Where and Mean vs Stability)

Description

Genotype plus genotype-by-environment (GGE) biplot analysis of a multi-environment trial (Yan et al., 2000). Gives the genotype and environment scores on the first two principal components, the winning genotype in each environment and the resulting mega-environments (which-won-where), and the average-environment coordination (AEC) ranking of genotypes for mean performance and stability.

Usage

gge_biplot(
  data,
  genotype,
  environment,
  trait,
  scaling = c("none", "sd"),
  svp = c("symmetric", "genotype", "environment"),
  verbose = TRUE
)

Arguments

Details

The genotype x environment table of means is centred on the environment means (and optionally scaled) and decomposed by singular value decomposition. With singular values d_k, genotype scores are u_{ik} d_k^f and environment scores v_{jk} d_k^{1-f}, with f = 0.5, 1, 0 for symmetric, genotype- and environment-focused partitioning. The winner in an environment is the genotype with the highest value predicted by the two-component biplot; environments with the same winner form a mega-environment. On the AEC view the abscissa (projection on the average-environment axis) ranks mean performance and the ordinate (distance from that axis) measures instability.

Value

An object of class aridagri_gge with genotype_scores, environment_scores, variance_percent, which_won_where (environment and winner), mega_environments, aec (genotype AEC abscissa, ordinate and ranks) and the settings used. Plot it with plot().

References

Yan, W., Hunt, L.A., Sheng, Q. and Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40(3), 597-605. doi:10.2135/cropsci2000.403597x

Yan, W. and Kang, M.S. (2003). GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton.

Examples

data(pearlmillet_met)
gg <- gge_biplot(pearlmillet_met, "genotype", "environment", "grain_yield")
gg$which_won_where
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot(gg, type = "which_won_where")
}

Harvest Index

Description

Harvest index and grain-to-straw ratio.

Usage

harvest_index(grain_yield, straw_yield, verbose = TRUE)

Arguments

Value

A data frame of class aridagri_hi with grain, straw and total biomass, HI_percent and Grain_Straw_Ratio.

Author(s)

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

Examples

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

Package imports

Description

Package imports

Moth Bean Nutrient Management Trial (RCBD, Simulated)

Description

Simulated randomized complete block experiment on moth bean (Vigna aconitifolia) with eight nutrient management treatments in four blocks and eight traits.

Usage

mothbean_rbd

Format

A data frame with 32 rows and 10 variables:

block

Factor, block (R1-R4).

treatment

Factor, eight treatments (control, recommended dose of fertilizer (RDF), and RDF with biofertilizers, FYM, sulphur or zinc).

seed_yield

Seed yield (kg/ha).

plant_height

Plant height at harvest (cm).

branches_per_plant

Branches per plant.

pods_per_plant

Pods per plant.

seeds_per_pod

Seeds per pod.

test_weight

1000-seed weight (g).

stover_yield

Stover yield (kg/ha).

harvest_index

Harvest index (%).

Source

Simulated with data-raw/make_datasets.R (seed 2026). Not real experimental data.

Examples

data(mothbean_rbd)
anova_rbd(mothbean_rbd, "seed_yield", "treatment", "block")

Mung Bean Chlorophyll over Time (Repeated Measures, Simulated)

Description

Simulated repeated measurements of leaf chlorophyll (SPAD) on the same plots at 25, 35, 45 and 55 days after sowing, for four foliar spray treatments in four randomized complete blocks. Errors within a plot follow a first-order autoregressive process.

Usage

mungbean_repeated

Format

A data frame with 64 rows and 5 variables:

block

Factor, block (R1-R4).

treatment

Factor, foliar spray treatment.

das

Days after sowing (25, 35, 45, 55).

plot

Factor, plot identifier (block-treatment).

spad

SPAD chlorophyll meter reading.

Source

Simulated with data-raw/make_datasets.R (seed 2026). Not real experimental data.

Examples

data(mungbean_repeated)
anova_repeated(mungbean_repeated, "spad", "treatment", "das",
               block = "block")

Nutrient Use Efficiency Indices

Description

Agronomic efficiency (AE), partial factor productivity (PFP), apparent recovery efficiency (ARE), physiological efficiency (PE), internal utilization efficiency (IE) and, when biomass is given, the biomass-based physiological efficiency and utilization efficiency of Fageria and Baligar (2005). All arguments may be vectors (one value per treatment).

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

Details

• AE = (Yf - Yc) / N (kg grain per kg nutrient)

• PFP = Yf / N

• ARE = 100 (Uf - Uc) / N (percent)

• PE = (Yf - Yc) / (Uf - Uc) (kg grain per kg nutrient taken up; called agro-physiological efficiency by Fageria and Baligar)

• IE = Yf / Uf

• PE_biomass = (Bf - Bc) / (Uf - Uc) and UE = PE_biomass x ARE / 100 (Fageria and Baligar, 2005)

AE = PE x ARE / 100 holds by definition. PE is not computed when the uptake difference is zero or negative.

Value

A data frame of class aridagri_nue.

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

Dobermann, A. (2005). Nitrogen use efficiency - state of the art. IFA International Workshop on Enhanced-Efficiency Fertilizers, Frankfurt.

Fageria, N.K. and Baligar, V.C. (2005). Enhancing nitrogen use efficiency in crop plants. Advances in Agronomy, 88, 97-185. doi:10.1016/S0065-2113(05)88004-6

Examples

nue_calculate(yield_fertilized = 1850, yield_control = 1200,
              nutrient_applied = 40)
# Several N doses with uptake and biomass
nue_calculate(yield_fertilized = c(1450, 1720, 1850),
              yield_control = 1200, nutrient_applied = c(20, 40, 60),
              nutrient_uptake_fert = c(48, 61, 68),
              nutrient_uptake_ctrl = 35,
              biomass_fert = c(4300, 4950, 5300), biomass_ctrl = 3600)

Nutrient Response Curves and Economic Optimum Dose

Description

Fits nutrient response models and finds the dose for maximum yield and the economic optimum dose, where the marginal return equals the price ratio. Quadratic, linear-plateau, quadratic-plateau and Mitscherlich models are available; model = "all" fits all four and compares them.

Usage

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

Arguments

Details

• Quadratic: Y = a + bX + cX^2; maximum at -b/(2c); economic optimum (P_n/P_y - b)/(2c).

• Linear-plateau: Y = a + b \min(X, X_0); economic optimum at the join point X_0 when b > P_n/P_y.

• Quadratic-plateau: quadratic up to its vertex X_0, then flat.

• Mitscherlich: Y = A - B e^{-cX}; economic optimum \ln(B c P_y / P_n)/c; also the dose giving 95% of the asymptote.

Plateau models are fitted by profile least squares over the join point; the Mitscherlich model by nls with a self-starting model. All four models have three parameters (the join point counts as one), so they are compared by R-squared, adjusted R-squared, RMSE and AIC on an equal footing (AIC differences equal those of stats::AIC()). An optimum beyond the highest dose tested is flagged as an extrapolation.

Value

For a single model, an object of class aridagri_response with model, coefficients, r_squared, adj_r_squared, rmse, aic, dose_max_yield, max_yield, dose_economic, yield_economic and fit. For model = "all", a list with comparison (one row per model), models and best (lowest AIC).

Author(s)

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

References

Cerrato, M.E. and Blackmer, A.M. (1990). Comparison of models for describing corn yield response to nitrogen fertilizer. Agronomy Journal, 82(1), 138-143. doi:10.2134/agronj1990.00021962008200010030x

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)
nutrient_response(dose, yield, model = "all", 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

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

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)

Pearl Millet Multi-Environment Trial (Simulated)

Description

Simulated trial of ten pearl millet genotypes in six environments (three locations x two years) with three replications, for stability and GGE biplot analysis.

Usage

pearlmillet_met

Format

A data frame with 180 rows and 5 variables:

environment

Factor, location-year (BKN = Bikaner, JDH = Jodhpur, JSM = Jaisalmer).

rep

Factor, replication within environment.

genotype

Factor, genotype code (PM-01 to PM-10).

grain_yield

Grain yield (kg/ha).

days_to_maturity

Days to maturity.

Source

Simulated with data-raw/make_datasets.R (seed 2026). Not real experimental data.

Examples

data(pearlmillet_met)
stability_analysis(pearlmillet_met, "genotype", "environment", "rep",
                   "grain_yield", method = "eberhart")

Post-Hoc Mean Comparisons After ANOVA

Description

Compares treatment means after an analysis of variance and returns compact letter displays (means sharing a letter do not differ). Fisher's LSD, Duncan's multiple range test (DMRT), Tukey's HSD, Student-Newman-Keuls, Scheffe and Bonferroni are computed inside the package, so no extra package is needed. Dunnett's test against a control uses multcomp when it is installed.

Usage

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

Arguments

Details

Means are least-squares means of the treatment factor averaged over the other factors in the model. With complete, balanced data they equal the arithmetic means. With missing plots or unequal replication every pair is tested with its own standard error of difference, so the letters stay exact (Tukey-Kramer form for the range tests).

Duncan's and the Student-Newman-Keuls tests use the studentized range for the number of means spanned by each pair, with the protection level 1 - (1 - \alpha)^{p - 1} for Duncan's test, and apply the closure rule: two means are declared different only if every range of means containing them is also significant (Duncan, 1955). The p-value reported for a pair is the largest p-value of the ranges containing it, so it agrees with the letters.

Least-squares means that cannot be estimated (for example a level whose cells are all empty in a model with interactions) are left out with a warning. Dunnett's test averages differences over interacting factors and uses mse and df_error when they are supplied.

Letters are assigned with the insert-and-absorb algorithm of Piepho (2004), which gives a valid display for any pattern of significant pairs. The group containing the highest mean gets the letter "a".

Value

A list with one element per method. Each element contains groups (means and letters, ordered from highest to lowest, with treatments as row names), table (level, mean, SE, letter, replications), comparisons (all pairwise differences with their SE, p-value and significance), critical_range and statistics. The Dunnett element is a data frame of comparisons with the control.

References

Duncan, D.B. (1955). Multiple range and multiple F tests. Biometrics, 11(1), 1-42. doi:10.2307/3001478

Piepho, H.-P. (2004). An algorithm for a letter-based representation of all-pairwise comparisons. Journal of Computational and Graphical Statistics, 13(2), 456-466. doi:10.1198/1061860043515

Kramer, C.Y. (1956). Extension of multiple range tests to group means with unequal numbers of replications. Biometrics, 12(3), 307-310. doi:10.2307/3001469

See Also

compare_means for summary statistics, anova_rbd.

Examples

data(mothbean_rbd)
fit <- aov(seed_yield ~ block + treatment, data = mothbean_rbd)
ph <- perform_posthoc(fit, mothbean_rbd, response = "seed_yield",
                      treatment = "treatment", posthoc = c("lsd", "duncan"))
ph$duncan$groups

Plot Methods for aridagri Results

Description

plot() methods that draw the standard publication figure for each kind of result with ggplot2: a bar chart of means with letters (CRD, RCBD, Latin square, pooled RCBD, alpha lattice, augmented), an interaction plot or bar chart (factorial and split-plot designs), a time course (repeated measures) or a stability plot. For base graphics without ggplot2, use arid_plot.

Usage

## S3 method for class 'aridagri_anova'
plot(x, ...)

## S3 method for class 'aridagri_pooled'
plot(x, ...)

## S3 method for class 'aridagri_alpha'
plot(x, ...)

## S3 method for class 'aridagri_augmented'
plot(x, ...)

## S3 method for class 'aridagri_factorial'
plot(x, type = c("interaction", "means"), ...)

## S3 method for class 'aridagri_split'
plot(x, type = c("interaction", "means"), ...)

## S3 method for class 'aridagri_strip'
plot(x, ...)

## S3 method for class 'aridagri_repeated'
plot(x, ...)

## S3 method for class 'aridagri_stability'
plot(x, type = c("eberhart", "ammi1", "ammi2"), ...)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(mothbean_rbd)
  res <- anova_rbd(mothbean_rbd, "seed_yield", "treatment", "block",
                   verbose = FALSE)
  plot(res)
}

Plot Method for GGE Biplot Results

Description

Plot Method for GGE Biplot Results

Usage

## S3 method for class 'aridagri_gge'
plot(
  x,
  type = c("which_won_where", "mean_stability", "basic"),
  title = NULL,
  ...
)

Arguments

Value

A ggplot2 object.

Interaction Plot of Two Factors

Description

Line chart of the means of every combination of two factors, with the first factor on the x-axis and one line per level of the second factor. Works with anova_factorial, anova_spd and the other split-plot functions (first two factors, or the two named in factors), anova_strip and anova_repeated (time course).

Usage

plot_interaction(x, factors = NULL, title = NULL, ylab = NULL)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(clusterbean_spd)
  res <- anova_spd(clusterbean_spd, "seed_yield", "irrigation",
                   "nitrogen", "rep", verbose = FALSE)
  plot_interaction(res, ylab = "Seed yield (kg/ha)")
}

Bar Chart of Treatment Means with Letters

Description

Publication-ready bar chart of treatment means with standard-error bars and the letter groupings from an aridagri analysis. Works with the results of anova_crd, anova_rbd, anova_latin, anova_rbd_pooled, anova_factorial (one factor or the combinations), anova_spd and the other split-plot functions (one factor), anova_alpha_lattice and anova_augmented (adjusted means).

Usage

plot_means(
  x,
  which = NULL,
  sort = TRUE,
  top = 20,
  title = NULL,
  ylab = NULL,
  horizontal = NULL
)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(mothbean_rbd)
  res <- anova_rbd(mothbean_rbd, "seed_yield", "treatment", "block",
                   posthoc = "duncan", verbose = FALSE)
  plot_means(res, ylab = "Seed yield (kg/ha)")
}

Plot a Nutrient Response Curve

Description

Observed yields and the fitted response curve(s) from nutrient_response, with the economic optimum dose marked.

Usage

plot_response(x, dose, yield, title = NULL)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  dose <- c(0, 20, 40, 60, 80, 100)
  yield <- c(1100, 1350, 1520, 1610, 1650, 1660)
  nr <- nutrient_response(dose, yield, model = "all", verbose = FALSE)
  plot_response(nr, dose, yield)
}

Stability Plots for Multi-Environment Trials

Description

Figures for stability_analysis results:

• "eberhart": genotype mean against the regression coefficient b_i, with reference lines at the grand mean and b_i = 1.

• "ammi1": AMMI1 biplot of mean against IPCA1 scores for genotypes and environments.

• "ammi2": AMMI2 biplot of IPCA1 against IPCA2, with environments as vectors.

Usage

plot_stability(x, type = c("eberhart", "ammi1", "ammi2"), title = NULL)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(pearlmillet_met)
  st <- stability_analysis(pearlmillet_met, "genotype", "environment",
                           "rep", "grain_yield", verbose = FALSE)
  plot_stability(st, "eberhart")
  plot_stability(st, "ammi2")
}

Print Method for Alpha Lattice Results

Description

Print Method for Alpha Lattice Results

Usage

## S3 method for class 'aridagri_alpha'
print(x, digits = 2, n = 15, ...)

Arguments

Value

Invisibly returns x.

Print Method for CRD and RCBD Results

Description

Print Method for CRD and RCBD Results

Usage

## S3 method for class 'aridagri_anova'
print(x, digits = 2, ...)

## S3 method for class 'aridagri_latin'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x. Called for its side effect of printing the ANOVA report.

Print Method for Augmented Design Results

Description

Print Method for Augmented Design Results

Usage

## S3 method for class 'aridagri_augmented'
print(x, digits = 2, n = 10, ...)

Arguments

Value

Invisibly returns x.

Print Methods for Factorial Results

Description

Print Methods for Factorial Results

Usage

## S3 method for class 'aridagri_factorial'
print(x, digits = 2, ...)

## S3 method for class 'aridagri_factorial3'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x.

Print Method for GGE Biplot Results

Description

Print Method for GGE Biplot Results

Usage

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

Arguments

Value

Invisibly returns x.

Print Method for PCA Results

Description

Prints the number of components retained and the variance they explain.

Usage

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

Arguments

Value

The input object, invisibly.

Print Method for Combined RCBD Analysis

Description

Print Method for Combined RCBD Analysis

Usage

## S3 method for class 'aridagri_pooled'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x.

Print Method for Repeated Measures Results

Description

Print Method for Repeated Measures Results

Usage

## S3 method for class 'aridagri_repeated'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x.

Print Methods for Nutrient Response Results

Description

Print Methods for Nutrient Response Results

Usage

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

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

Arguments

Value

Invisibly returns x.

Print Method for Stability Analysis

Description

Print Method for Stability Analysis

Usage

## S3 method for class 'aridagri_stability'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns x.

Print Method for Split-Plot Family Results

Description

Print Method for Split-Plot Family Results

Usage

## S3 method for class 'aridagri_strip'
print(x, digits = 2, ...)

## S3 method for class 'aridagri_split'
print(x, digits = 2, ...)

## S3 method for class 'aridagri_split_pooled'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x.

Print Method for Multi-Trait Tables

Description

Print Method for Multi-Trait Tables

Usage

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

Arguments

Value

Invisibly returns x.

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

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)
}

Stability Analysis for Multi-Environment Trials (Seven Methods)

Description

Stability analysis of genotypes (or treatments) tested in several environments, with the combined ANOVA and seven established methods:

• Eberhart and Russell (1966) joint regression: b_i, S^2_{di}, tests of b_i = 1 and S^2_{di} = 0, and the Eberhart-Russell ANOVA

• AMMI: ANOVA with Gollob's degrees of freedom for each IPCA, IPCA scores, AMMI stability value (ASV) and yield stability index

• Finlay and Wilkinson (1963) regression on the environment mean

• Shukla (1972) stability variance with its F-test

• Wricke (1962) ecovalence

• Coefficient of variation (Francis and Kannenberg, 1978)

• Cultivar superiority index (Lin and Binns, 1988)

An integrated ranking averages the stability ranks of the seven methods.

Usage

stability_analysis(
  data,
  genotype,
  environment,
  replication,
  trait,
  method = "all",
  verbose = TRUE,
  alpha = 0.05,
  fw_log = FALSE
)

Arguments

Details

Calculations use the genotype x environment table of means (g genotypes, e environments, r replications). The environmental index is I_j = \bar{y}_{.j} - \bar{y}_{..}.

Eberhart-Russell. b_i = \sum_j y_{ij} I_j / \sum_j I_j^2; S^2_{di} = \sum_j \delta^2_{ij}/(e - 2) - MSE/r, where \delta_{ij} are deviations from the regression and MSE is the pooled error. b_i = 1 is tested with t = (b_i - 1)/SE(b_i) on e - 2 df, and S^2_{di} = 0 with F = [\sum_j \delta^2_{ij}/(e-2)] / (MSE/r).

Shukla. \sigma^2_i = [g(g-1)W_i - \sum W] / [(e-1)(g-1)(g-2)], where W_i is Wricke's ecovalence \sum_j (\bar{y}_{ij} - \bar{y}_{i.} - \bar{y}_{.j} + \bar{y}_{..})^2; tested with F = \sigma^2_i / (MSE/r) on e - 1 and the error df. Values are on the scale of the means.

AMMI. The interaction matrix is decomposed by singular value decomposition. IPCA k has sum of squares r \lambda_k^2 and g + e - 1 - 2k df (Gollob, 1968). Scores are u_{ik}\sqrt{\lambda_k}. ASV = \sqrt{[(SS_1/SS_2) IPCA1]^2 + IPCA2^2} (Purchase et al., 2000) and the yield stability index is the sum of the ranks of mean (high = 1) and ASV.

Lin-Binns. P_i = \sum_j (y_{ij} - M_j)^2 / (2e), with M_j the highest mean in environment j.

Value

An object of class aridagri_stability containing anova (combined ANOVA, when replications are given), genotype_means, environment_means, ge_matrix and, depending on method, eberhart (with eberhart_anova), ammi (anova, components, stability, genotype_scores, environment_scores), finlay, shukla, wricke, cv_stability, superiority, integrated and concordance (Kendall's W among the seven stability rankings).

Author(s)

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

References

Eberhart, S.A. and Russell, W.A. (1966). Stability parameters for comparing varieties. Crop Science, 6(1), 36-40. doi:10.2135/cropsci1966.0011183X000600010011x

Finlay, K.W. and Wilkinson, G.N. (1963). The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research, 14(6), 742-754. doi:10.1071/AR9630742

Shukla, G.K. (1972). Some statistical aspects of partitioning genotype-environmental components of variability. Heredity, 29(2), 237-245. doi:10.1038/hdy.1972.87

Wricke, G. (1962). Uber eine Methode zur Erfassung der okologischen Streubreite in Feldversuchen. Zeitschrift fur Pflanzenzuchtung, 47, 92-96.

Gollob, H.F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika, 33(1), 73-115. doi:10.1007/BF02289676

Purchase, J.L., Hatting, H. and van Deventer, C.S. (2000). Genotype x environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil, 17(3), 101-107. doi:10.1080/02571862.2000.10634878

Francis, T.R. and Kannenberg, L.W. (1978). Yield stability studies in short-season maize. I. A descriptive method for grouping genotypes. Canadian Journal of Plant Science, 58(4), 1029-1034. doi:10.4141/cjps78-157

Lin, C.S. and Binns, M.R. (1988). A superiority measure of cultivar performance for cultivar x location data. Canadian Journal of Plant Science, 68(1), 193-198. doi:10.4141/cjps88-018

Examples

data(pearlmillet_met)
st <- stability_analysis(pearlmillet_met, genotype = "genotype",
                         environment = "environment", replication = "rep",
                         trait = "grain_yield")
st$eberhart
st$ammi$anova

A Clean ggplot2 Theme for Agricultural Figures

Description

The theme used by the plotting functions of aridagri: white background, light grid, dark text and a bold left-aligned title. Add it to any ggplot2 figure.

Usage

theme_aridagri(base_size = 11)

Arguments

Value

A ggplot2 theme object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg)) +
    ggplot2::geom_point() + theme_aridagri()
}

Growing Degree Days and Thermal Indices

Description

Growing degree days (GDD), helio-thermal units (HTU), photo-thermal units (PTU), heat use efficiency (HUE) and helio-thermal use efficiency from daily temperatures.

Usage

thermal_indices(
  tmax,
  tmin,
  base_temp = 10,
  sunshine_hours = NULL,
  day_length = NULL,
  crop_yield = NULL,
  biomass = NULL,
  verbose = TRUE,
  upper_temp = NULL,
  method = c("average", "modified")
)

Arguments

Details

HTU = sum of GDD x bright sunshine hours; PTU = sum of GDD x day length (Rajput, 1980). HUE = yield / GDD (kg/ha per degree-day); HTUE = yield / HTU.

Value

An object of class aridagri_thermal: a list with summary (index, value, unit), gdd_total and daily (daily and cumulative GDD, HTU and PTU).

Author(s)

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

References

McMaster, G.S. and Wilhelm, W.W. (1997). Growing degree-days: one equation, two interpretations. Agricultural and Forest Meteorology, 87(4), 291-300. doi:10.1016/S0168-1923(97)00027-0

Rajput, R.P. (1980). Response of soybean crop to climatic and soil environments. PhD thesis, IARI, New Delhi.

Examples

tmax <- c(31, 33, 35, 36, 38, 40, 41, 39, 37, 36)
tmin <- c(16, 17, 19, 20, 22, 24, 25, 24, 22, 21)
thermal_indices(tmax, tmin, base_temp = 10, upper_temp = 40,
                sunshine_hours = rep(9.5, 10),
                day_length = day_length(28, 70:79), crop_yield = 1250)

Yield Gap and Front-Line Demonstration Indices

Description

Yield gap between actual and potential yields. When an attainable yield (for example the yield of an improved-practice or front-line demonstration plot) is supplied, the gap is split into a technology gap and an extension gap and the technology index is given.

Usage

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

Arguments

Details

Total gap = potential - actual; yield efficiency = 100 x actual / potential. With attainable yield (Samui et al., 2000): technology gap = potential - attainable; extension gap = attainable - actual; technology index = 100 x technology gap / potential; yield increase over farmers' practice = 100 x (attainable - actual) / actual.

Value

A data frame of class aridagri_yield_gap.

Author(s)

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

References

Samui, S.K., Maitra, S., Roy, D.K., Mondal, A.K. and Saha, D. (2000). Evaluation of front line demonstration on groundnut (Arachis hypogaea L.). Journal of the Indian Society of Coastal Agricultural Research, 18(2), 180-183.

Examples

yield_gap_analysis(actual_yield = c(800, 950, 720), potential_yield = 1500)
# Front-line demonstrations: farmers' practice vs demonstration plots
yield_gap_analysis(actual_yield = c(820, 760, 905),
                   potential_yield = 1500,
                   attainable_yield = c(1080, 1010, 1150))