Package 'GreenManureR' reference manual

Title:	Green Manure Decomposition, Nutrient Release and Crop Response
Description:	Tools for analysing green manure and cover crop experiments, for example with Mucuna, sunhemp and dhaincha. Fits single-pool, double-pool and asymptotic litterbag decomposition models and ranks them by the corrected Akaike information criterion, estimates the time to 50 percent mass loss with Monte-Carlo confidence intervals, and quantifies nitrogen, phosphorus, potassium and other nutrient release over time. Further functions compute nutrient inputs, crop uptake, apparent nitrogen recovery and fertilizer nitrogen replacement value, estimate residual effects of green manures on succeeding kharif and rabi crops, link decomposition, nutrient release and crop response in a joint analysis, and generate standardized tables, diagnostic plots and a Word report.
Authors:	Anbukkani Perumal [aut, cre, cph], Ramachandran S [aut, cph]
Maintainer:	Anbukkani Perumal <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2026-10-13 23:35:22 UTC
Source:	https://github.com/cran/GreenManureR

Residual (carry-over) effects of green manure on succeeding crops

Description

Quantifies the effect of each green manure treatment relative to a control on the yield (or any response) of successive crops, for example a kharif crop followed by a rabi crop.

Usage

carryover_effect(
  data,
  response = "grain_yield",
  treatment = "treatment",
  control = "Control",
  season = "season",
  block = "block",
  strata = NULL,
  season_levels = NULL,
  adjust = "holm",
  level = 0.95
)
carryover_effect(
  data,
  response = "grain_yield",
  treatment = "treatment",
  control = "Control",
  season = "season",
  block = "block",
  strata = NULL,
  season_levels = NULL,
  adjust = "holm",
  level = 0.95
)

Arguments

data

Plot-level data frame.

response

Column with the response (e.g. grain yield).

treatment

Column with treatment labels.

control

Label of the control treatment.

season

Column identifying the crop or season.

block

Column identifying blocks, or NULL.

strata

Optional columns to analyse separately (e.g. "fert_N", site).

season_levels

Order of seasons in the crop sequence (defaults to order of appearance).

adjust

Method passed to stats::p.adjust().

level

Confidence level for intervals.

Details

For every stratum and season the model response ~ block + treatment is fitted (randomized complete block design; without a block term if block = NULL). Treatment-versus-control contrasts use the model estimates and standard errors, with p-values adjusted across treatments by adjust. The percentage change is the contrast divided by the control mean; the log response ratio uses the pooled residual variance in a delta-method standard error. retention_vs_first expresses each seasonal effect as a percentage of the effect in the first season.

The pooled analysis fits response ~ season:block + treatment + treatment:season across seasons; a significant treatment x season term indicates that the effect changes between crops. Seasonal variances are assumed homogeneous, which should be checked. The cumulative effect is the sum of seasonal contrasts, with a standard error that assumes independence between seasons and is therefore approximate.

Value

An object of class "gm_carryover": a list with effects (contrasts per stratum, season and treatment), anova (treatment F-tests within season), pooled (pooled analysis, or NULL with one season), cumulative, response and control.

Examples

co <- carryover_effect(gm_agronomy, response = "grain_yield",
                       strata = "fert_N",
                       season_levels = c("Kharif", "Rabi"))
co
co <- carryover_effect(gm_agronomy, response = "grain_yield",
                       strata = "fert_N",
                       season_levels = c("Kharif", "Rabi"))
co

Fit litterbag decomposition models

Description

Fits single-pool, double-pool and asymptotic decay models to litterbag mass-loss data, separately for each group (for example each green manure species), and ranks the models by AICc.

Usage

decomp_fit(
  data,
  time = "time",
  mass = "mass",
  group = NULL,
  models = c("single", "double", "asymptotic"),
  mass_scale = c("percent", "fraction")
)
decomp_fit(
  data,
  time = "time",
  mass = "mass",
  group = NULL,
  models = c("single", "double", "asymptotic"),
  mass_scale = c("percent", "fraction")
)

Arguments

data

A data frame in long format with one row per litterbag and sampling date.

time

Name of the column holding time since incorporation (e.g. days).

mass

Name of the column holding the mass remaining.

group

Character vector of grouping columns (species, site, ...), or NULL to fit a single group.

models

Any of "single", "double", "asymptotic".

mass_scale

Either "percent" (default) or "fraction", the scale of mass.

Details

With M the percentage of the initial mass remaining and t the time since incorporation, the models are:

single: M = 100 * exp(-k * t)
double: M = 100 * (A * exp(-kf * t) + (1 - A) * exp(-ks * t)), fitted with ks = r * kf and ⁠0 < r <= 1⁠, so the fast pool can never decay more slowly than the slow pool.
asymptotic: M = C + (100 - C) * exp(-k * t), where C is the recalcitrant residue (percent of initial mass).

The initial mass is fixed at 100%, so time-zero observations should be included (or the data expressed relative to the initial mass). Each model is fitted by nonlinear least squares (port algorithm with bounds) from a small grid of starting values, and the best-fitting converged solution is kept.

Value

An object of class "gm_decomp", a list with components table (one row per group and model with parameters k, k_slow, pool_A, C_resistant, fit statistics RSS, RMSE, R2, AIC, AICc, dAICc, Akaike weight, and selected marking the lowest AICc), fits (the nls objects), data, gcols and groups. For the double-pool model k is the fast-pool rate and k_slow the slow-pool rate.

Examples

fit <- decomp_fit(gm_litterbag, time = "time", mass = "mass",
                  group = "treatment")
fit
fit$table[fit$table$selected, c("treatment", "model", "k", "R2")]
fit <- decomp_fit(gm_litterbag, time = "time", mass = "mass",
                  group = "treatment")
fit
fit$table[fit$table$selected, c("treatment", "model", "k", "R2")]

Time to a given percentage of mass (or nutrient) loss

Description

Solves the fitted decay model for the time at which a proportion p of the initial mass has been lost (p = 0.5 gives t50), with an optional Monte-Carlo confidence interval.

Usage

decomp_t50(
  fit,
  p = 0.5,
  model = "best",
  ci = TRUE,
  nsim = 2000,
  level = 0.95,
  seed = 123
)
decomp_t50(
  fit,
  p = 0.5,
  model = "best",
  ci = TRUE,
  nsim = 2000,
  level = 0.95,
  seed = 123
)

Arguments

fit

An object returned by decomp_fit().

p

Proportion lost, strictly between 0 and 1 (0.5 gives t50, 0.95 gives t95).

model

"best" (lowest AICc, default) or the name of a model.

ci

Logical; compute the Monte-Carlo interval?

nsim

Number of Monte-Carlo draws.

level

Confidence level.

seed

Integer seed used for the draws. The global random number state is left unchanged. Use NULL to draw without fixing a seed.

Details

The interval is obtained by drawing nsim parameter vectors from a multivariate normal distribution centred on the estimates with the covariance matrix of the nls fit, discarding draws outside the admissible parameter space, and taking quantiles of the resulting times. If the model never reaches the target (for example an asymptotic model with a residue above the target), NA is returned.

Value

A data frame with the grouping columns, the model used, p, the time (column named e.g. t50) and the interval limits lwr and upr.

Examples

fit <- decomp_fit(gm_litterbag, group = "treatment")
decomp_t50(fit)
decomp_t50(fit, p = 0.95, ci = FALSE)
fit <- decomp_fit(gm_litterbag, group = "treatment")
decomp_t50(fit)
decomp_t50(fit, p = 0.95, ci = FALSE)

Simulated two-season agronomic data

Description

Simulated field data for a control and three green manures at three fertilizer N levels, in four blocks, for a kharif crop followed by a rabi crop. Generated with simulate_greenmanure() (seed 42); the values are synthetic.

Usage

gm_agronomy
gm_agronomy

Format

A data frame with 96 rows and 10 variables:

treatment: Control or green manure species.
fert_N: Fertilizer N applied (kg/ha).
block: Block (1-4).
season: Crop season (Kharif, Rabi).
gm_biomass: Green manure dry matter (t/ha).
gm_N: Green manure N concentration (%).
grain_yield, straw_yield: Yields (t/ha).
grain_N, straw_N: N concentrations (%).

Source

Simulated; see data-raw/simulate-data.R.

Joint analysis of decomposition, nutrient release and crop response

Description

Builds one row per treatment that joins the decomposition rate and t50, the cumulative release of one nutrient by a chosen day, apparent N recovery and the percentage yield response in each season, and reports Spearman rank correlations among these variables.

Usage

gm_joint_analysis(
  decomp,
  nutrient,
  nbal,
  carry,
  key = "treatment",
  nutrient_name = "N",
  at_day = 60,
  anr_season = NULL,
  fert_N_level = NULL
)
gm_joint_analysis(
  decomp,
  nutrient,
  nbal,
  carry,
  key = "treatment",
  nutrient_name = "N",
  at_day = 60,
  anr_season = NULL,
  fert_N_level = NULL
)

Arguments

decomp

A gm_decomp object from decomp_fit() (one grouping column named as key).

nutrient

A gm_nutrient object from nutrient_release().

nbal

A gm_nbalance object from greenmanure_n_balance().

carry

A gm_carryover object from carryover_effect().

key

Name of the treatment column shared by all inputs.

nutrient_name

Nutrient (as named in nutrient_release()) whose release is linked to crop response.

at_day

Day at which model-based cumulative release is evaluated.

anr_season, fert_N_level

Optional filters restricting the plots used to average apparent N recovery: the first by stratum of nbal and the fertilizer N level.

Details

With only a few treatments (for example three green manure species) the correlations are descriptive only, and a warning is issued when fewer than five rows are available. To draw inference, pool several sites or years.

Value

An object of class "gm_joint": a list with data (the linkage table) and cor (Spearman correlation matrix, or NULL).

Examples

fit <- decomp_fit(gm_litterbag, group = "treatment")
nr  <- nutrient_release(gm_litterbag, group = "treatment",
                        biomass = "biomass_tha")
nb  <- greenmanure_n_balance(gm_agronomy, by = "season")
co  <- carryover_effect(gm_agronomy, strata = "fert_N")
ja  <- suppressWarnings(
  gm_joint_analysis(fit, nr, nb, co, at_day = 60, fert_N_level = 0))
ja$data
fit <- decomp_fit(gm_litterbag, group = "treatment")
nr  <- nutrient_release(gm_litterbag, group = "treatment",
                        biomass = "biomass_tha")
nb  <- greenmanure_n_balance(gm_agronomy, by = "season")
co  <- carryover_effect(gm_agronomy, strata = "fert_N")
ja  <- suppressWarnings(
  gm_joint_analysis(fit, nr, nb, co, at_day = 60, fert_N_level = 0))
ja$data

Simulated litterbag decomposition data

Description

Simulated litterbag data for three green manure species, with mass and nutrient concentrations over 112 days. Generated with simulate_greenmanure() (seed 42); the values are synthetic.

Usage

gm_litterbag
gm_litterbag

Format

A data frame with 108 rows and 8 variables:

treatment: Green manure species (Mucuna, Sunhemp, Dhaincha).
rep: Litterbag replicate (1-4).
time: Days after incorporation (0-112).
mass: Dry mass remaining (% of initial).
N, P, K: Nutrient concentrations (% dry weight).
biomass_tha: Green manure dry matter incorporated (t/ha).

Source

Simulated; see data-raw/simulate-data.R.

Methods for GreenManureR objects

Description

Print, prediction and plotting methods for the objects returned by decomp_fit(), nutrient_release(), greenmanure_n_balance(), carryover_effect() and gm_joint_analysis(). Plot methods return ggplot objects and require the 'ggplot2' package.

Usage

## S3 method for class 'gm_decomp'
print(x, digits = 4, ...)

## S3 method for class 'gm_decomp'
predict(object, times, model = "best", ...)

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

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

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

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

## S3 method for class 'gm_decomp'
plot(x, y, model = "best", ...)

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

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

## S3 method for class 'gm_carryover'
plot(x, y, ...)
## S3 method for class 'gm_decomp'
print(x, digits = 4, ...)

## S3 method for class 'gm_decomp'
predict(object, times, model = "best", ...)

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

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

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

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

## S3 method for class 'gm_decomp'
plot(x, y, model = "best", ...)

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

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

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

Arguments

x, object

An object returned by one of the package functions.

digits

Number of significant digits to print.

...

Further arguments (ignored).

times

Times (e.g. days) at which to predict mass remaining.

model

"best" (lowest AICc) or the name of a model.

y

Unused; present for consistency with the plot() generic.

Value

print methods return x invisibly. predict.gm_decomp() returns a data frame with the grouping columns, model, time and pred (predicted mass remaining, %). plot methods return a ggplot object.

Examples

fit <- decomp_fit(gm_litterbag, group = "treatment")
predict(fit, times = c(30, 60, 90))
if (requireNamespace("ggplot2", quietly = TRUE)) plot(fit)
fit <- decomp_fit(gm_litterbag, group = "treatment")
predict(fit, times = c(30, 60, 90))
if (requireNamespace("ggplot2", quietly = TRUE)) plot(fit)

Nitrogen inputs, crop uptake and apparent balance

Description

Plot-level nitrogen accounting for green manure experiments, with treatment summaries.

Usage

greenmanure_n_balance(
  data,
  treatment = "treatment",
  control = "Control",
  rep = "block",
  by = NULL,
  gm_biomass = "gm_biomass",
  gm_N = "gm_N",
  fert_N = "fert_N",
  grain_yield = "grain_yield",
  grain_N = "grain_N",
  straw_yield = "straw_yield",
  straw_N = "straw_N",
  nfrv = TRUE
)
greenmanure_n_balance(
  data,
  treatment = "treatment",
  control = "Control",
  rep = "block",
  by = NULL,
  gm_biomass = "gm_biomass",
  gm_N = "gm_N",
  fert_N = "fert_N",
  grain_yield = "grain_yield",
  grain_N = "grain_N",
  straw_yield = "straw_yield",
  straw_N = "straw_N",
  nfrv = TRUE
)

Arguments

data

Plot-level data frame.

treatment

Column with the treatment label.

control

Label of the treatment without green manure.

rep

Column identifying replicates or blocks (not used in the calculations; kept for documentation and future use).

by

Optional columns defining strata (e.g. season, site, year).

gm_biomass, gm_N

Columns with green manure dry matter (t/ha) and N concentration (%).

fert_N

Column with fertilizer N applied (kg/ha). If absent it is taken as zero.

grain_yield, grain_N, straw_yield, straw_N

Columns with yields (t/ha) and N concentrations (%). Straw columns may be absent.

nfrv

Logical; compute the fertilizer N replacement value?

Details

All quantities are in kg N/ha unless stated otherwise:

N_gm_input: green manure dry matter (t/ha) x 1000 x N percent / 100.
N_uptake: grain plus straw N removal.
N_balance: (green manure N + fertilizer N) minus crop N uptake, an apparent balance (losses and soil N are not measured).
ANR: apparent N recovery from green manure (percent): (uptake - mean control uptake at the same stratum and fertilizer N) / green manure N input x 100.
AE_gmN: additional grain (kg) per kg of green manure N.
NFRV: fertilizer N replacement value, the fertilizer N needed in the control plots (quadratic response fitted per by stratum, requiring at least three fertilizer levels) to match the green manure plot yield, minus the fertilizer N applied. NA when the yield lies outside the fitted response range.

Value

An object of class "gm_nbalance": a list with plot (plot-level results), summary (means, standard deviations and standard errors per stratum, treatment and fertilizer level), by and treatment.

Examples

nb <- greenmanure_n_balance(gm_agronomy, by = "season")
nb
head(nb$summary[, c("season", "treatment", "fert_N", "ANR_mean")])
nb <- greenmanure_n_balance(gm_agronomy, by = "season")
nb
head(nb$summary[, c("season", "treatment", "fert_N", "ANR_mean")])

Standardized tables, diagnostic plots and Word report

Description

Writes tables (CSV), figures (PNG) and, if the 'officer' package is installed, a Word report containing all tables and figures for any combination of analysis objects.

Usage

greenmanure_report(
  decomp = NULL,
  nutrient = NULL,
  nbal = NULL,
  carry = NULL,
  joint = NULL,
  outdir,
  docx = TRUE,
  dpi = 300,
  digits = 3
)
greenmanure_report(
  decomp = NULL,
  nutrient = NULL,
  nbal = NULL,
  carry = NULL,
  joint = NULL,
  outdir,
  docx = TRUE,
  dpi = 300,
  digits = 3
)

Arguments

decomp, nutrient, nbal, carry, joint

Objects returned by decomp_fit(), nutrient_release(), greenmanure_n_balance(), carryover_effect() and gm_joint_analysis(). Any may be NULL.

outdir

Directory in which to write the files (required).

docx

Logical; also write GreenManureR_report.docx?

dpi

Resolution of the PNG figures.

digits

Significant digits used in the exported tables.

Details

Nothing is written unless outdir is supplied; the directory is created if needed. Figures require 'ggplot2' and the Word report requires 'officer'; when either is missing the corresponding output is skipped with a message and the remaining outputs are still produced.

Value

Invisibly, a list with tables (data frames) and figures (file paths).

Examples


fit <- decomp_fit(gm_litterbag, group = "treatment")
out <- file.path(tempdir(), "gm_report")
greenmanure_report(decomp = fit, outdir = out, docx = FALSE)
list.files(out)

fit <- decomp_fit(gm_litterbag, group = "treatment")
out <- file.path(tempdir(), "gm_report")
greenmanure_report(decomp = fit, outdir = out, docx = FALSE)
list.files(out)

Nutrient release from decomposing green manure residues

Description

Calculates the percentage of the initial nutrient content remaining in litterbags, fits decay models to each nutrient and reports the time to 50% release and model-based cumulative release at chosen times.

Usage

nutrient_release(
  data,
  time = "time",
  mass = "mass",
  nutrients = c("N", "P", "K"),
  group = NULL,
  biomass = NULL,
  at = c(7, 14, 30, 60, 90),
  models = c("single", "double", "asymptotic")
)
nutrient_release(
  data,
  time = "time",
  mass = "mass",
  nutrients = c("N", "P", "K"),
  group = NULL,
  biomass = NULL,
  at = c(7, 14, 30, 60, 90),
  models = c("single", "double", "asymptotic")
)

Arguments

data

Long data frame of litterbag observations including the initial (time zero) sampling.

time, mass

Names of the time and mass-remaining (%) columns.

nutrients

Names of the columns holding nutrient concentrations (% dry weight), e.g. c("N", "P", "K").

group

Character vector of grouping columns, or NULL.

biomass

Optional. Name of a column, or a single number, giving the dry-matter input in t/ha. If supplied, nutrient input and release are also reported in kg/ha.

at

Times at which model-based cumulative release is reported.

models

Models passed to decomp_fit().

Details

Nutrient remaining (% of initial content) is ⁠mass * concentration / initial concentration⁠, where mass is the percentage of the initial dry mass remaining and the initial concentration is the group mean at the earliest sampling time (which must therefore be present in data). Values above 100% indicate net immobilization. Decay models are fitted with decomp_fit() to the nutrient-remaining percentages.

Value

An object of class "gm_nutrient": a list with data (long table with remaining, released and optionally input_kg_ha, released_kg_ha), fits (a gm_decomp object per nutrient), t50, release_at, gcols and at.

Examples

nr <- nutrient_release(gm_litterbag, nutrients = c("N", "P", "K"),
                       group = "treatment", biomass = "biomass_tha",
                       at = c(14, 30, 60))
nr
subset(nr$release_at, nutrient == "N")
nr <- nutrient_release(gm_litterbag, nutrients = c("N", "P", "K"),
                       group = "treatment", biomass = "biomass_tha",
                       at = c(14, 30, 60))
nr
subset(nr$release_at, nutrient == "N")

Simulate litterbag and two-season agronomic data

Description

Generates synthetic data with the structure expected by the package functions: a litterbag decomposition experiment with three green manure species (Mucuna, sunhemp, dhaincha) and a replicated field experiment with a control, three fertilizer N levels and two successive crops (kharif and rabi). The values are simulated for testing and teaching and do not represent real measurements.

Usage

simulate_greenmanure(seed = 42)
simulate_greenmanure(seed = 42)

Arguments

seed

Integer seed. The global random number state is left unchanged. Use NULL to draw without fixing a seed.

Value

A list with data frames litterbag (see gm_litterbag) and agronomy (see gm_agronomy).

Examples

sim <- simulate_greenmanure(seed = 1)
str(sim$litterbag)
sim <- simulate_greenmanure(seed = 1)
str(sim$litterbag)

Package 'GreenManureR'

Help Index

Residual (carry-over) effects of green manure on succeeding crops

Description

Usage

Arguments

Details

Value

See Also

Examples

Fit litterbag decomposition models

Description

Usage

Arguments

Details

Value

See Also

Examples

Time to a given percentage of mass (or nutrient) loss

Description

Usage

Arguments

Details

Value

See Also

Examples

Simulated two-season agronomic data

Description

Usage

Format

Source

See Also

Joint analysis of decomposition, nutrient release and crop response

Description

Usage

Arguments

Details

Value

See Also

Examples

Simulated litterbag decomposition data

Description

Usage

Format

Source

See Also

Methods for GreenManureR objects

Description

Usage

Arguments

Value

Examples

Nitrogen inputs, crop uptake and apparent balance

Description

Usage

Arguments

Details

Value

See Also

Examples

Standardized tables, diagnostic plots and Word report

Description

Usage

Arguments

Details

Value

Examples

Nutrient release from decomposing green manure residues

Description

Usage

Arguments

Details

Value

See Also

Examples

Simulate litterbag and two-season agronomic data

Description

Usage

Arguments

Value