Water-Energy-Food-Nutrient-Carbon Nexus Analysis for Agronomic Systems

Provides functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. Includes functions for computing water use efficiency (WUE), water productivity (WP), and water footprint (WF) including green, blue, and grey components following the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy budgeting tools for energy use efficiency (EUE), energy return on investment (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), and partial factor productivity (PFP) as defined by Dobermann (2007) < https://digitalcommons.unl.edu/agronomyfacpub/316/> and Congreves et al. (2021) . Estimates carbon footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC) stocks, and global warming potential (GWP) using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) default values (CH4 = 27, N2O = 273) as reported in Forster et al. (2021) . Computes composite Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices, trade-off correlation matrices, and generates radar and heatmap visualizations for comparing agricultural treatments. Supports conservation agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid production environments. Methods follow Lal (2004) for carbon emissions from farm operations, and Hoover et al. (2023) for water use efficiency indicators. Version 1.1.0 adds a one-call workflow for replicated field trials, analysis of variance with standard error of mean (SEm), critical difference (CD) and coefficient of variation (CV), objective indicator weights by the entropy, CRITIC (Diakoulaki et al. (1995) ) and principal component methods, TOPSIS ranking of treatments (Hwang and Yoon, 1981), and energy budgeting from physical input quantities with an editable table of energy equivalents.

wefnexus

One package. Five dimensions. Ten interlinkages. A comprehensive R package for Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus analysis in agricultural production systems.

Why wefnexus?

Modern agriculture doesn't work in silos. When we save water, it affects energy use. When we add fertilizers, it impacts carbon emissions. When we change tillage, it affects food production, soil health, and everything in between.

wefnexus connects all 5 dimensions in one package — giving researchers, policymakers, and agronomists the tools to see the full picture and make decisions that are truly sustainable.

Installation

Install the stable version from CRAN:

install.packages("wefnexus")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("lalitrolaniya/wefnexus")

What's new in 1.1.0

• One call for a whole trial. nexus_analysis() takes plot-level data (treatment x replication) and returns indicators, an ANOVA table, the composite nexus index and a TOPSIS ranking.
• Journal-ready ANOVA. nexus_anova() gives means with letter groups, SEm, CD (5%) and CV for CRD and RCBD trials.
• Objective weights. nexus_weights() derives weights by entropy, CRITIC or PCA instead of assuming equal weights.
• TOPSIS ranking. nexus_topsis() ranks treatments on raw indicators.
• Energy budget from field records. energy_budget() turns litres, kilograms and labour hours into MJ/ha, with direct/indirect and renewable/non-renewable shares, using an editable energy_coefficients table.
• Robust rankings. nexus_rank_compare() ranks treatments eight ways and reports Kendall's W, so you can show a recommendation does not hinge on one choice of weights.
• Publication-ready figures. plot() works on every result: means with error bars and letters, radar small multiples, TOPSIS rankings, energy budgets, and a trade-off heatmap with significance stars, all in a colour-blind-checked palette.
• Legume-friendly. nexus_summary(nutrient_metric = "iue") works when no fertilizer N is applied; ef_n2o = 0.005 applies the IPCC dry-climate N2O factor.
• Important fixes. soil_carbon_stock() was 10 times too low, and nexus_heatmap_gg() swapped row labels; also fixed NHI in nutrient_use_efficiency(), water_footprint(area) and nexus_summary(). See NEWS.

Modules

Quick start

Replicated field trial in one call

library(wefnexus)
options(wefnexus.verbose = FALSE)   # silence messages
data(arid_pulse_trial)              # 6 treatments x 3 replications (RCBD)

res <- nexus_analysis(
  arid_pulse_trial,
  treatment = "treatment", block = "replication",
  yield = "grain_yield", water = "total_water",
  energy_in = "energy_input", energy_out = "energy_output",
  emission = "ghg_emission", n_uptake = "n_uptake",
  weights = "entropy"
)
res          # nexus index and TOPSIS ranks
res$anova    # means + letters, SEm, CD (5%), CV
plot(res$nexus)

Figures

plot(res$anova, response = c("grain_yield", "WUE", "EUE", "CI"), ncol = 2)

plot(res)   # one radar per treatment, dashed line = mean of all treatments

nx <- res$nexus
indicators <- data.frame(WUE = nx$WUE_kg_mm, EUE = nx$EUE_ratio,
                         Yield = nx$yield_kg_ha, IUE = nx$IUE_kg_kgN,
                         CI = nx$carbon_intensity)
rc <- nexus_rank_compare(indicators, benefit = c(TRUE, TRUE, TRUE, TRUE, FALSE),
                         treatment_names = nx$treatment)
plot(rc)    # do rankings hold across eight weighting/aggregation methods?

Energy budget from field records

inputs <- data.frame(
  treatment = c("CT", "ZT"),
  human_labour_man = c(320, 240), diesel = c(65, 38),
  electricity = c(180, 75), nitrogen = c(20, 20), phosphorus = c(40, 40),
  seed = c(20, 20), herbicide = c(0, 0.75),
  grain = c(980, 1280), straw = c(1450, 1850)
)
energy_budget(inputs)

Individual indicators

data(arid_pulse_nexus)
d <- arid_pulse_nexus

water_use_efficiency(d$grain_yield, d$total_water)
eroi(d$energy_output_grain + d$energy_output_straw, d$energy_input)
carbon_footprint(diesel_use = 65, electricity_use = 180,
                 n_fertilizer = 20, yield = 980, ef_n2o = 0.005)

Key features

• 59 functions across 7 modules
• One-call workflow for replicated trials (CRD and RCBD)
• Objective weighting (entropy, CRITIC, PCA) and TOPSIS ranking
• IPCC AR6 GWP defaults (CH4 = 27, N2O = 273) with adjustable emission factors
• Energy budgeting from physical quantities with an editable coefficient table
• S3 classes with print(), summary() and plot() methods
• Publication-ready figures (ggplot2) for every result, plus base-R radar and heatmap
• Vectorized: every indicator works on whole columns
• Arid and semi-arid, pulse-based and conservation agriculture focus
• 590+ test checks, verified against hand calculations and reference implementations
• Free and open source under GPL-3

Documentation

# Complete vignette with all 6 modules
browseVignettes("wefnexus")

# View the built-in sample dataset
?arid_pulse_nexus

# Help for any function
?carbon_footprint
?nexus_summary
?water_use_efficiency

📖 Full documentation website: https://lalitrolaniya.github.io/wefnexus/

Data format

Use replication-level data (one row per plot) where you can. It keeps the variability needed for ANOVA, SEm and CD, and nexus_analysis() handles the aggregation to treatment means. Treatment means also work; ANOVA is then skipped.

Citation

If you use wefnexus in your research, please cite:

citation("wefnexus")

Rolaniya, L.K., Poonia, H., Jat, R.L., Punia, M. & Choudhary, R.R. (2026).
wefnexus: Water-Energy-Food-Nutrient-Carbon Nexus Analysis for Agronomic
Systems. R package version 1.1.0. https://CRAN.R-project.org/package=wefnexus
DOI: 10.32614/CRAN.package.wefnexus

Authors

Version 1.1.0 is dedicated to the memory of our co-authors Dr. Monika Punia and Dr. Hemant Poonia.

Links

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Found a bug? Have a feature request? Open an issue

Support

If you find this package useful, please ⭐ star this repository — it helps others discover it!

License

GPL (>= 3)