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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.
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)
#> == Benefit-Cost Ratio (swcEcon) ==
#> BCR : 1.7253
#> PV Benefits : 44.8167
#> PV Costs : 25.9756
#> Discount rate : 12.0%
#> Project life : 20 yr
#> Verdict : Highly viable (BCR >= 1.5). Recommended for NABARD/PMKSY-WDC funding.The BCR of 1.73 exceeds 1.5, meeting the NABARD threshold for robust watershed investment (NABARD 2019).
n <- calc_npv(
investment = 20,
annual_benefit = 6,
annual_omc = 0.8,
life = 20,
discount_rate = 0.12
)
print(n)
#> == Net Present Value (swcEcon) ==
#> NPV : 18.8411
#> Discount rate : 12.0%
#> Decision : Accept (NPV > 0)
head(n$cashflows)
#> year benefit omc net pv_net cum_pv
#> 1 1 6 0.8 5.2 4.6429 -15.3571
#> 2 2 6 0.8 5.2 4.1454 -11.2117
#> 3 3 6 0.8 5.2 3.7013 -7.5105
#> 4 4 6 0.8 5.2 3.3047 -4.2058
#> 5 5 6 0.8 5.2 2.9506 -1.2552
#> 6 6 6 0.8 5.2 2.6345 1.3793i <- calc_irr(
investment = 20,
annual_benefit = 6,
annual_omc = 0.8,
life = 20
)
print(i)
#> == Internal Rate of Return (swcEcon) ==
#> IRR : 25.73%
#> vs GoI 12% : Above GoI 12% benchmark
#> vs NABARD 15% : Above NABARD 15% thresholdThe IRR of 25.7 per cent exceeds the Planning Commission benchmark of 12 per cent (GoI 2008) and the NABARD threshold of 15 per cent (NABARD 2019).
p <- calc_pbp(
investment = 20,
annual_benefit = 6,
annual_omc = 0.8
)
print(p)
#> == Payback Period (swcEcon) ==
#> Simple PBP : 3.85 yr
#> Discounted PBP : 6 yr
#> Adoption : Likely adoption (PBP 3-5 yr)A simple payback period of 3.9 years falls within the 3–5 year range that strongly predicts voluntary SWC adoption among smallholder farmers in rainfed India (Joshi et al. 2005).
m <- calc_mrr(
nb_with = 18000, # net benefit per ha with contour bund
nb_without = 11000, # net benefit per ha current practice
cost_with = 16000, # variable cost per ha with SWC
cost_without = 11500 # variable cost per ha current practice
)
print(m)
#> == Marginal Rate of Return -- CIMMYT (swcEcon) ==
#> MRR : 155.56%
#> Marginal benefit : 7000.00
#> Marginal cost : 4500.00
#> Recommendation : Recommend adoption: MRR (156%) >= minimum (100%)An MRR of 156 per cent far exceeds the CIMMYT (1988) minimum acceptable threshold of 100 per cent, recommending adoption.
calc_mbcr(total_benefit = 80, operating_cost = 12, capital_cost = 20)
#> MBCR : 3.4000 -- MBCR > 1: capital investment recoverabledata(usle_india_soils)
K_vert <- usle_india_soils[
usle_india_soils$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)
#> == USLE Soil Loss Economics (swcEcon) ==
#> Soil loss pre : 264.600 t/ha/yr
#> Soil loss post : 68.040 t/ha/yr
#> Soil saved : 196.560 t/ha/yr (74.3% reduction)
#> Annual benefit : INR 98280000The contour bund reduces soil loss by 74 per cent, saving 98,280,000 INR per year in nutrient replacement costs alone.
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
)
#> $n_lost_kg
#> [1] 66150
#>
#> $p_lost_kg
#> [1] 10584
#>
#> $k_lost_kg
#> [1] 158760
#>
#> $cost_n_inr
#> [1] 1323000
#>
#> $cost_p_inr
#> [1] 529200
#>
#> $cost_k_inr
#> [1] 3969000
#>
#> $total_cost_inr
#> [1] 5821200
#>
#> $cost_per_ha_inr
#> [1] 11642data(rainfall_erosivity_india)
rf_pune <- rainfall_erosivity_india[
rainfall_erosivity_india$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)
#> == Water Valuation (swcEcon) ==
#> Runoff reduced : 510,000 m3/yr
#> Water harvested : 229,500 m3/yr
#> GW recharge : 102,000 m3/yr
#> Annual benefit : INR 1,160,250calc_irrigation_benefit(
irrig_area_ha = 80,
yield_increase_t_ha = 1.6,
crop_price_inr_t = 18000,
input_cost_inr_ha = 8000
)
#> $gross_benefit_inr
#> [1] 2304000
#>
#> $additional_cost_inr
#> [1] 640000
#>
#> $net_benefit_inr
#> [1] 1664000
#>
#> $net_benefit_per_ha
#> [1] 20800sa <- 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)
#> == Sensitivity Analysis (swcEcon) ==
#> Base BCR: 1.725 | Base NPV: 18.841
#>
#> scenario bcr npv status
#> Base case 1.725 18.841 OK
#> Cost +20% 1.438 13.646 OK
#> Cost -20% 2.157 24.036 OK
#> Benefit -20% 1.380 9.878 OK
#> Benefit +20% 2.070 27.804 OK
#> Rate +3pp 1.502 12.549 OK
#> Rate -3pp 2.006 27.468 OK
#> All adverse 1.001 0.036 OK
#>
#> Robust: BCR >= 1.0 in all scenarios.sv <- calc_switching_value(
investment = 20,
annual_benefit = 6,
annual_omc = 0.8,
life = 20,
discount_rate = 0.12
)
print(sv)
#> == Switching Value Analysis (swcEcon) ==
#> Base BCR : 1.7253
#> Cost switch val : 72.5% -- robust (cost can rise this much)
#> Benefit switch v : 42.0% -- robust (benefit can fall this much)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)data(swc_benchmarks)
swc_benchmarks[, c("state", "agro_zone", "bcr_typical",
"irr_pct", "pbp_years")]
#> state agro_zone bcr_typical irr_pct pbp_years
#> 1 Rajasthan Arid 1.6 14 4.2
#> 2 Maharashtra Semi-arid 2.1 18 3.1
#> 3 Karnataka Semi-arid 1.9 17 3.4
#> 4 Andhra Pradesh Semi-arid 2.2 20 3.0
#> 5 Madhya Pradesh Semi-arid 1.9 17 3.5
#> 6 Gujarat Arid 1.7 15 4.0
#> 7 Tamil Nadu Dry sub-humid 1.8 16 3.6
#> 8 Telangana Semi-arid 2.0 19 3.2
#> 9 Uttar Pradesh Sub-humid 2.1 18 3.3
#> 10 Haryana Sub-humid 1.8 16 3.8data(usle_india_soils)
usle_india_soils[, c("soil_series", "soil_order",
"k_mean", "t_value", "n_kg_per_t")]
#> soil_series soil_order k_mean t_value n_kg_per_t
#> 1 Vertisols Black cotton 0.25 10 0.50
#> 2 Alfisols Red laterite 0.32 8 0.35
#> 3 Inceptisols Alluvial 0.28 10 0.60
#> 4 Entisols Sandy desert 0.19 5 0.20
#> 5 Aridisols Desert soils 0.15 5 0.15
#> 6 Ultisols Red yellow 0.35 8 0.40
#> 7 Mollisols Meadow soils 0.21 12 0.80
#> 8 Oxisols Laterite ferruginous 0.29 8 0.45data(rainfall_erosivity_india)
rainfall_erosivity_india[, c("district", "state",
"annual_rf_mm", "r_factor")]
#> district state annual_rf_mm r_factor
#> 1 Udaipur Rajasthan 620 580
#> 2 Pune Maharashtra 680 720
#> 3 Bellary Karnataka 500 510
#> 4 Kurnool Andhra Pradesh 540 540
#> 5 Sagar Madhya Pradesh 1080 1120
#> 6 Bhavnagar Gujarat 520 495
#> 7 Coimbatore Tamil Nadu 700 730
#> 8 Warangal Telangana 800 860
#> 9 Jhansi Uttar Pradesh 760 810
#> 10 Hisar Haryana 480 450
#> 11 Jodhpur Rajasthan 360 320
#> 12 Nashik Maharashtra 750 790
#> 13 Dharwad Karnataka 860 920
#> 14 Anantapur Andhra Pradesh 540 520
#> 15 Rewa MP 1150 1180
#> 16 Surendranagar Gujarat 500 470
#> 17 Salem Tamil Nadu 820 770
#> 18 Nalgonda Telangana 780 820
#> 19 Banda UP 900 940
#> 20 Rohtak Haryana 520 480data(swc_cost_norms)
swc_cost_norms[, c("measure", "norm_2024_inr",
"design_life_yr", "labour_pct")]
#> measure norm_2024_inr design_life_yr labour_pct
#> 1 Check dam (masonry) 577500 25 45
#> 2 Check dam (earthen) 140250 15 60
#> 3 Percolation pond 462000 20 55
#> 4 Farm pond (lined) 156750 20 65
#> 5 Farm pond (unlined) 74250 15 70
#> 6 Contour bund 19800 15 75
#> 7 Graded bund 14850 15 75
#> 8 Gully plug (stone) 41250 10 65
#> 9 Gully plug (brush wood) 13200 5 80
#> 10 Nala bund 297000 20 50
#> 11 Field bunding 14025 12 80
#> 12 Terracing (bench) 74250 20 70
#> 13 Vegetative barrier 9900 10 85
#> 14 Drainage line treatment 24750 15 65
#> 15 Land levelling 19800 15 80
#> 16 Pasture development 29700 20 70
#> 17 Afforestation 36300 25 65
#> 18 Agri-horti plantation 46200 20 60pl <- 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"
)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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.
5 Social indicators