--- title: "WEFNC Nexus Analysis for Agricultural Systems" author: "Lalit Kumar Rolaniya" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{WEFNC Nexus Analysis for Agricultural Systems} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>") library(wefnexus) # Silence informational messages from all wefnexus functions options(wefnexus.verbose = FALSE) has_gg <- requireNamespace("ggplot2", quietly = TRUE) ``` ## Introduction The **wefnexus** package provides a comprehensive toolkit for analyzing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. This vignette demonstrates a complete workflow using a conservation agriculture dataset from western Rajasthan, India. If you have replicated field data and want the whole analysis in one step, jump to [Section 7](#workflow). Sections 1 to 6 show the individual modules. ## Load Sample Data ```{r data} data(arid_pulse_nexus) d <- arid_pulse_nexus str(d) ``` ## 1. Water Module ```{r water} # Water Use Efficiency wue <- water_use_efficiency(d$grain_yield, d$total_water) # Crop Water Productivity cwp <- crop_water_productivity(d$grain_yield, d$crop_et) # Water Footprint (green + blue) wf <- water_footprint( green_water = d$effective_rainfall, blue_water = d$irrigation_applied, yield = d$grain_yield ) wf ``` ## 2. Energy Module ```{r energy} # Total energy output e_out <- d$energy_output_grain + d$energy_output_straw # Energy Use Efficiency eue <- energy_use_efficiency(e_out, d$energy_input) # Energy Return on Investment (EROI) eroi_values <- eroi(e_out, d$energy_input) # Net Energy Balance ne <- net_energy(e_out, d$energy_input) # Compare treatments data.frame(Treatment = d$treatment, EUE = eue, EROI = eroi_values, Net_Energy = ne) ``` ### Energy budget from physical quantities Most field records hold litres of diesel, kilograms of fertilizer and labour hours rather than megajoules. `energy_budget()` converts them with the `energy_coefficients` table and splits input energy into direct, indirect, renewable and non-renewable shares. ```{r energy-budget} head(energy_coefficients[, c("input", "unit", "mj_per_unit", "form", "source")]) inputs <- data.frame( treatment = c("CT+Flood", "ZT+Drip"), 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) ) eb <- energy_budget(inputs) eb$summary[, c("treatment", "total_input_MJ", "direct_pct", "renewable_pct", "EUE", "energy_intensity_MJ_kg")] ``` Energy equivalents vary between sources. To use your own, edit a copy of the table and pass it as `coefficients`. ```{r energy-plot, fig.width = 7.5, fig.height = 3.4, eval = has_gg} plot(eb) plot(eb, type = "source", percent = TRUE) ``` ## 3. Food Module ```{r food} # Harvest Index bio_yield <- d$grain_yield + d$straw_yield hi <- harvest_index(d$grain_yield, bio_yield) # Protein Yield (assuming 22% protein in pulses) py <- protein_yield(d$grain_yield, protein_content = 22) data.frame(Treatment = d$treatment, HI = hi, Protein_kg_ha = py) ``` ## 4. Nutrient Module ```{r nutrient} # Partial Factor Productivity of N pfp <- partial_factor_productivity(d$grain_yield, d$n_applied) # Nutrient Harvest Index for N nhi <- nutrient_harvest_index(d$grain_n_uptake, d$n_uptake) data.frame(Treatment = d$treatment, PFP_N = pfp, NHI_N = nhi) ``` ## 5. Carbon Module The default direct N2O emission factor is the IPCC Tier 1 value of 0.01. For dry climates the 2019 IPCC Refinement gives 0.005, which can be set with `ef_n2o`. ```{r carbon} # Carbon Footprint (IPCC AR6 GWP: CH4=27, N2O=273) cf <- carbon_footprint( diesel_use = d$diesel_use[1], electricity_use = d$electricity_kwh[1], n_fertilizer = d$n_applied[1], p_fertilizer = d$p_applied[1], yield = d$grain_yield[1], ef_n2o = 0.005 ) cf$breakdown # Soil Carbon Stock soc <- soil_carbon_stock(d$soc_pct, d$bulk_density, depth = 30) # Global Warming Potential (example) gwp100 <- global_warming_potential(co2 = 500, ch4 = 10, n2o = 2) gwp20 <- global_warming_potential(co2 = 500, ch4 = 10, n2o = 2, time_horizon = "20yr") ``` ## 6. Nexus Integration Every treatment in this trial received the same starter dose of 20 kg N/ha, so partial factor productivity would only mirror yield. The nutrient dimension therefore uses internal N utilization efficiency (`nutrient_metric = "iue"`), and carbon is scored per kg of grain. ```{r nexus} # Full Nexus Summary ns <- nexus_summary( yield = d$grain_yield, water_consumed = d$total_water, energy_input = d$energy_input, energy_output = e_out, n_applied = d$n_applied, n_uptake = d$n_uptake, carbon_emission = d$ghg_emission, treatment_names = d$treatment, nutrient_metric = "iue", carbon_basis = "intensity" ) ns[, c("treatment", "EROI", "nexus_index")] ``` ### Radar Plot ```{r radar, fig.width=7, fig.height=6} scores <- as.matrix(ns[, c("W_score", "E_score", "F_score", "N_score", "C_score")]) nexus_radar(scores, treatment_names = d$treatment) ``` ### Sustainability Scoring ```{r sustainability} nss <- nexus_sustainability_score( water_score = ns$W_score, energy_score = ns$E_score, food_score = ns$F_score, nutrient_score = ns$N_score, carbon_score = ns$C_score ) nss[, c("nexus_score", "category")] ``` ### Objective weights and TOPSIS ranking Equal weights are a value judgement. `nexus_weights()` derives weights from the data (entropy, CRITIC or PCA), and `nexus_topsis()` ranks treatments on raw indicators in their own units. ```{r weights} ind <- data.frame( WUE = ns$WUE_kg_mm, EUE = ns$EUE_ratio, Yield = ns$yield_kg_ha, IUE = ns$IUE_kg_kgN, CI = ns$carbon_intensity ) benefit <- c(TRUE, TRUE, TRUE, TRUE, FALSE) # lower carbon intensity is better rbind( entropy = nexus_weights(ind, "entropy", benefit), critic = nexus_weights(ind, "critic", benefit), pca = nexus_weights(ind, "pca", benefit) ) nexus_topsis(ind, weights = "entropy", benefit = benefit, treatment_names = d$treatment) ``` ### Is the ranking robust? A recommendation is more convincing when it does not depend on one choice of weights. `nexus_rank_compare()` ranks the treatments eight ways (composite index and TOPSIS, each with equal, entropy, CRITIC and PCA weights) and measures agreement with Kendall's W (0 = none, 1 = identical rankings). ```{r robustness} rc <- nexus_rank_compare(ind, benefit = benefit, treatment_names = d$treatment) rc ``` ```{r robustness-plot, fig.width = 8, fig.height = 4.2, eval = has_gg} plot(rc) ``` ### Synergies and trade-offs `nexus_tradeoff()` returns the correlation matrix with p-values attached; `plot_tradeoff()` draws it. Carbon intensity is lower-is-better, so pass `benefit` to orient the indicators: blue then always means the two improve together (synergy) and red means one improves as the other gets worse (trade-off). ```{r tradeoff, fig.width = 6, fig.height = 4.8, eval = has_gg} plot_tradeoff(nexus_tradeoff(ind), benefit = benefit) ``` ## 7. One-call workflow for replicated trials {#workflow} `arid_pulse_trial` holds plot-level data from a randomized complete block design with three replications. `nexus_analysis()` computes plot-level indicators, runs ANOVA, builds the nexus index from treatment means and ranks treatments by TOPSIS. ```{r workflow} data(arid_pulse_trial) res <- nexus_analysis( arid_pulse_trial, treatment = "treatment", block = "replication", yield = "grain_yield", water = "total_water", energy_in = "energy_input", energy_out = "energy_output", emission = "ghg_emission", n_uptake = "n_uptake", weights = "entropy", carbon_basis = "intensity" ) res ``` The ANOVA table is laid out as in most agronomy journals: means followed by letter groups (Fisher's LSD), then SEm, CD at 5 percent and CV. ```{r anova} res$anova ``` `nexus_anova()` can also be used on its own for any set of response variables, in a CRD (no `block`) or RCBD. ### Figures for the manuscript Every result has a `plot()` method that returns a ggplot2 object, so you can add your own theme or labels and save it with `ggplot2::ggsave()`. ```{r fig-anova, fig.width = 8, fig.height = 5.6, eval = has_gg} plot(res$anova, response = c("grain_yield", "WUE", "EUE", "CI"), labels = c(grain_yield = "Grain yield (kg/ha)", WUE = "WUE (kg/ha/mm)", EUE = "Energy use efficiency", CI = "Carbon intensity (kg CO2-eq/kg)"), ncol = 2) ``` ```{r fig-radar, fig.width = 8, fig.height = 5.4, eval = has_gg} plot(res) # one radar per treatment ``` ```{r fig-topsis, fig.width = 7, fig.height = 3.4, eval = has_gg} plot(res, which = "topsis") ``` ## References - Hoekstra, A.Y. et al. 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