--- title: "Economic Analysis of SWC Measures using swcEcon" author: "swcEcon package" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 number_sections: true vignette: > %\VignetteIndexEntry{Economic Analysis of SWC Measures using swcEcon} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE ) library(swcEcon) ``` # Introduction Sound investment decisions in watershed development require rigorous economic appraisal. The **swcEcon** package provides a complete toolkit for evaluating soil and water conservation (SWC) measures from project inception through to report generation. This vignette demonstrates all major functions using the bundled benchmark datasets. Methods follow Gittinger (1982), the CIMMYT (1988) on-farm economics manual, Wischmeier and Smith (1978), and NABARD (2019) appraisal guidelines. # Financial appraisal ## Benefit-cost ratio ```{r bcr} r <- calc_bcr( investment = 20, # INR 20 lakh capital cost annual_benefit = 6, # INR 6 lakh per year annual_omc = 0.8, # INR 0.8 lakh O&M per year life = 20, # 20-year design life discount_rate = 0.12 # 12% discount rate (GoI 2008) ) print(r) ``` The BCR of `r round(r$bcr, 2)` exceeds 1.5, meeting the NABARD threshold for robust watershed investment (NABARD 2019). ## Net present value ```{r npv} n <- calc_npv( investment = 20, annual_benefit = 6, annual_omc = 0.8, life = 20, discount_rate = 0.12 ) print(n) head(n$cashflows) ``` ## Internal rate of return ```{r irr} i <- calc_irr( investment = 20, annual_benefit = 6, annual_omc = 0.8, life = 20 ) print(i) ``` The IRR of `r round(i$irr_pct, 1)` per cent exceeds the Planning Commission benchmark of 12 per cent (GoI 2008) and the NABARD threshold of 15 per cent (NABARD 2019). ## Payback period ```{r pbp} p <- calc_pbp( investment = 20, annual_benefit = 6, annual_omc = 0.8 ) print(p) ``` A simple payback period of `r round(p$simple_pbp, 1)` years falls within the 3--5 year range that strongly predicts voluntary SWC adoption among smallholder farmers in rainfed India (Joshi et al. 2005). ## Marginal rate of return (CIMMYT method) ```{r mrr} m <- calc_mrr( nb_with = 18000, # net benefit per ha with contour bund nb_without = 11000, # net benefit per ha current practice cost_with = 16000, # variable cost per ha with SWC cost_without = 11500 # variable cost per ha current practice ) print(m) ``` An MRR of `r round(m$mrr, 0)` per cent far exceeds the CIMMYT (1988) minimum acceptable threshold of 100 per cent, recommending adoption. ## Modified BCR ```{r mbcr} calc_mbcr(total_benefit = 80, operating_cost = 12, capital_cost = 20) ``` # Soil loss economic valuation ## USLE-based soil loss cost ```{r soil} data(usle_india_soils) K_vert <- usle_india_soils[ usle_india_soils$soil_series == "Vertisols", "k_mean"] s <- calc_soil_loss_cost( R = 720, # R-factor from rainfall_erosivity_india (Pune) K = K_vert, # K-factor from usle_india_soils LS = 4.2, # slope length-gradient factor C_pre = 0.35, # cover factor before contour bund C_post = 0.18, # cover factor after contour bund P_pre = 1.0, # no support practice before P_post = 0.5, # support practice P after bunding area_ha = 500 # watershed area ) print(s) ``` The contour bund reduces soil loss by `r round(s$pct_reduction, 0)` per cent, saving `r format(s$annual_benefit_inr, big.mark=",")` INR per year in nutrient replacement costs alone. ## Nutrient replacement cost ```{r nutrient} data(usle_india_soils) soil <- usle_india_soils[usle_india_soils$soil_series == "Vertisols", ] calc_nutrient_cost( soil_loss_t_ha = s$soil_loss_pre, area_ha = 500, n_kg_per_t = soil$n_kg_per_t, p_kg_per_t = soil$p_kg_per_t, k_kg_per_t = soil$k_kg_per_t ) ``` # Water resource valuation ```{r water} data(rainfall_erosivity_india) rf_pune <- rainfall_erosivity_india[ rainfall_erosivity_india$district == "Pune", "annual_rf_mm"] w <- calc_water_value( area_ha = 500, rainfall_mm = rf_pune, rc_pre = 0.35, # runoff coefficient before SWC rc_post = 0.20, # runoff coefficient after SWC harvest_pct = 45, # percentage of reduced runoff harvested gw_recharge_pct = 20, # percentage percolating to groundwater water_value_m3 = 3.5 # INR per cubic metre (Joshi et al. 2005) ) print(w) ``` ```{r irrigation} calc_irrigation_benefit( irrig_area_ha = 80, yield_increase_t_ha = 1.6, crop_price_inr_t = 18000, input_cost_inr_ha = 8000 ) ``` # Social indicators ```{r employment} calc_employment( employment_days = 45000, # total person-days investment_lakh = 50, # INR 50 lakh project cost wages_per_day = 250 # daily wage rate (INR) ) ``` # Risk analysis ## Sensitivity analysis ```{r sensitivity} sa <- sensitivity_analysis( investment = 20, annual_benefit = 6, annual_omc = 0.8, life = 20, discount_rate = 0.12, cost_range_pct = 20, benefit_range_pct = 20, rate_range_pct = 3 ) print(sa) ``` ## Switching value ```{r switching} sv <- calc_switching_value( investment = 20, annual_benefit = 6, annual_omc = 0.8, life = 20, discount_rate = 0.12 ) print(sv) ``` ## Monte Carlo simulation ```{r mc, eval = FALSE} mc <- monte_carlo_swc( inv_mean = 20, inv_cv = 0.10, ben_mean = 6, ben_cv = 0.15, omc_mean = 0.8, omc_cv = 0.20, life_min = 15, life_max = 25, r_min = 0.10, r_max = 0.14, n_sim = 5000, seed = 42 ) print(mc) ``` # Benchmark datasets ## State-wise BCR benchmarks ```{r benchmarks} data(swc_benchmarks) swc_benchmarks[, c("state", "agro_zone", "bcr_typical", "irr_pct", "pbp_years")] ``` ## USLE parameters for Indian soils ```{r soils_table} data(usle_india_soils) usle_india_soils[, c("soil_series", "soil_order", "k_mean", "t_value", "n_kg_per_t")] ``` ## Rainfall erosivity ```{r erosivity} data(rainfall_erosivity_india) rainfall_erosivity_india[, c("district", "state", "annual_rf_mm", "r_factor")] ``` ## SWC unit cost norms (PMKSY-WDC 2015) ```{r costs} data(swc_cost_norms) swc_cost_norms[, c("measure", "norm_2024_inr", "design_life_yr", "labour_pct")] ``` # Full pipeline and report ```{r pipeline, eval = FALSE} pl <- run_swc_pipeline( investment = 20, annual_benefit = 6, annual_omc = 0.8, life = 20, discount_rate = 0.12, project_name = "Hypothetical Check Dam, Semi-arid India", include_sensitivity = TRUE, include_monte_carlo = FALSE ) print(pl) # AFTER generate_swc_report( pl, output_file = "swcEcon_appraisal.html", title = "Economic Appraisal: Hypothetical Watershed", author = "Your Name", organisation = "Your Organisation" ) ``` # References Brent, R.P. (1973). *Algorithms for Minimization Without Derivatives*. Prentice-Hall, Englewood Cliffs, NJ. ISBN: 9780130223715. CIMMYT (1988). *From Agronomic Data to Farmer Recommendations: An Economics Training Manual*. Completely revised edition. CIMMYT, Mexico DF. ISBN: 9686127127. Gittinger, J.P. (1982). *Economic Analysis of Agricultural Projects*, 2nd ed. Johns Hopkins University Press, Baltimore. ISBN: 9780801825439. GoI (2008). *Guidelines for Economic Analysis of Projects*. Planning Commission of India, New Delhi. GoI (2015). *Common Guidelines for Watershed Development Projects under PMKSY-WDC*. Ministry of Rural Development, New Delhi. Joshi, P.K., Jha, A.K., Wani, S.P., Joshi, L. and Shiyani, R.L. (2005). *Meta-Analysis to Assess Impact of Watershed Program and People's Participation*. IWMI Research Report 8. ISBN: 9290906677. NABARD (2019). *Operational Guidelines: Watershed Development Fund*. National Bank for Agriculture and Rural Development, Mumbai. Pouliquen, L.Y. (1970). *Risk Analysis in Project Appraisal*. World Bank Staff Occasional Papers No. 11. Johns Hopkins University Press. Squire, L. and van der Tak, H.G. (1975). *Economic Analysis of Projects*. Johns Hopkins University Press. ISBN: 9780801816697. Wischmeier, W.H. and Smith, D.D. (1978). *Predicting Rainfall Erosion Losses: A Guide to Conservation Planning*. USDA Agriculture Handbook No. 537. ISBN: 0160016258.