Package 'wefnexus'

Description

Provides functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural systems. The package provides five core modules and a nexus integration layer:

Water

Water use efficiency (WUE), water productivity (WP), water footprint (WF) with green/blue/grey components, irrigation efficiency, crop water stress index (CWSI), and crop water productivity (CWP).

Energy

Energy budgeting including input-output analysis, energy use efficiency (EUE), energy return on investment (EROI), energy productivity (EP), net energy (NE), specific energy, and energy profitability.

Food

Food productivity indices, harvest index (HI), land equivalent ratio (LER), system productivity index (SPI), caloric and protein yield, and production efficiency index (PEI).

Nutrient

Nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), partial factor productivity (PFP), internal utilization efficiency (IUE), and nutrient harvest index (NHI).

Carbon

Carbon footprint (CF) with source-wise breakdown, greenhouse gas (GHG) emission estimation, soil organic carbon (SOC) stocks, carbon sequestration rate (CSR), and global warming potential (GWP) using IPCC AR6 values.

Author(s)

Maintainer: Lalit Kumar Rolaniya [email protected] (ORCID)

Authors:

• Hemant Poonia [email protected] (ORCID)

• Ram Lal Jat [email protected] (ORCID)

• Monika Punia [email protected] (ORCID)

• Raja Ram Choudhary [email protected]

See Also

Useful links:

• https://github.com/lalitrolaniya/wefnexus

• Report bugs at https://github.com/lalitrolaniya/wefnexus/issues

Description

Increase in economic yield per unit of nutrient applied.

Usage

agronomic_efficiency(
  yield_treated,
  yield_control,
  nutrient_applied,
  verbose = TRUE
)

Arguments

Details

$AE = \frac{Y_f - Y_0}{F}$

Value

Numeric vector. Agronomic efficiency (kg grain per kg nutrient).

References

Dobermann, A. (2007). Nutrient use efficiency: measurement and management. In Fertilizer Best Management Practices, IFA, Paris, pp. 1-28. https://digitalcommons.unl.edu/agronomyfacpub/316/

Fixen, P. et al. (2015). Nutrient/fertilizer use efficiency: Measurement, current situation and trends. In Managing Water and Fertilizer for Sustainable Agricultural Intensification, IFA/IWMI/IPNI/IPI, pp. 8-38.

Congreves, K.A. et al. (2021). Nitrogen use efficiency definitions of today and tomorrow. Frontiers in Plant Science, 12, 637108. doi:10.3389/fpls.2021.637108

Examples

agronomic_efficiency(yield_treated = 4500, yield_control = 3000,
                     nutrient_applied = 120)

Description

A sample dataset with six conservation agriculture treatments in an arid pulse-based cropping system from western Rajasthan, India. Includes water, energy, food, nutrient, and carbon data for WEFNC nexus analysis.

Usage

arid_pulse_nexus

Format

A data frame with 6 rows and 26 variables:

treatment

Character. Treatment combination name

tillage

Character. Tillage method: CT (conventional), ZT (zero), PB (permanent beds)

irrigation

Character. Irrigation method: Flood, Sprinkler, Drip, or SSDI (sub-surface drip)

residue

Logical. Whether crop residue was retained

grain_yield

Numeric. Grain yield (kg/ha)

straw_yield

Numeric. Straw yield (kg/ha)

irrigation_applied

Numeric. Irrigation water applied (mm)

effective_rainfall

Numeric. Effective rainfall (mm)

total_water

Numeric. Total water consumed (mm)

crop_et

Numeric. Crop evapotranspiration (mm)

energy_input

Numeric. Total energy input (MJ/ha)

energy_output_grain

Numeric. Energy output from grain (MJ/ha)

energy_output_straw

Numeric. Energy output from straw (MJ/ha)

n_applied

Numeric. Nitrogen applied (kg/ha)

p_applied

Numeric. Phosphorus applied (kg P2O5/ha)

k_applied

Numeric. Potassium applied (kg K2O/ha)

n_uptake

Numeric. Total plant nitrogen uptake (kg/ha)

p_uptake

Numeric. Total plant phosphorus uptake (kg/ha)

grain_n_uptake

Numeric. Grain nitrogen uptake (kg/ha)

diesel_use

Numeric. Diesel consumption (L/ha)

electricity_kwh

Numeric. Electricity consumption (kWh/ha)

soc_pct

Numeric. Soil organic carbon (percent)

bulk_density

Numeric. Soil bulk density (Mg/m3)

ghg_emission

Numeric. Total GHG emission (kg CO2-eq/ha)

cost_cultivation

Numeric. Cost of cultivation (INR/ha)

gross_return

Numeric. Gross return (INR/ha)

Source

Simulated dataset based on typical experimental data from ICAR-Indian Institute of Pulses Research, Regional Centre, Bikaner, Rajasthan, India.

Examples

data(arid_pulse_nexus)
str(arid_pulse_nexus)

Description

Total caloric output per hectare from crop production.

Usage

caloric_yield(yield, caloric_value, verbose = TRUE)

Arguments

Details

Formula:

$CY = Yield \times Caloric Value$

Value

Numeric vector. Caloric yield (kcal/ha).

Examples

caloric_yield(yield = 4500, caloric_value = 3400)

Description

Crop yield per unit of greenhouse gas emitted.

Usage

carbon_efficiency(yield, carbon_emission, verbose = TRUE)

Arguments

Details

Formula:

$CE = \frac{Yield}{Carbon Footprint}$

Value

Numeric vector. Carbon efficiency (kg yield per kg CO2-eq). Higher values indicate more yield per unit emission.

Examples

carbon_efficiency(yield = 4500, carbon_emission = 2500)

Description

Estimates the carbon footprint from major emission sources in crop production, with source-wise breakdown and yield-scaled intensity. Uses IPCC AR6 default GWP values.

Usage

carbon_footprint(
  diesel_use = 0,
  electricity_use = 0,
  n_fertilizer = 0,
  p_fertilizer = 0,
  k_fertilizer = 0,
  pesticide_use = 0,
  seed_rate = 0,
  n2o_direct = NULL,
  ch4_emission = 0,
  yield = NULL,
  ef_diesel = 2.68,
  ef_electricity = 0.82,
  ef_n_manufacture = 4.96,
  ef_p_manufacture = 1.61,
  ef_k_manufacture = 0.57,
  ef_pesticide = 10.97,
  ef_seed = 0.58,
  gwp_n2o = 273,
  gwp_ch4 = 27,
  verbose = TRUE
)

Arguments

Details

Formula:

$CF = \frac{\sum Activity_i \times EF_i}{Yield}$

Value

A list with components:

total_cf

Numeric. Total carbon footprint (kg CO2-eq/ha)

cf_intensity

Numeric. Carbon footprint intensity (kg CO2-eq/kg yield), returned only when yield is provided and positive

breakdown

Data frame with columns: source, emission_kg_CO2eq, and share_pct showing the contribution of each emission source

References

Lal, R. (2004). Carbon emission from farm operations. Environment International, 30(7), 981-990. doi:10.1016/j.envint.2004.01.005

Forster, P. et al. (2021). The Earth's energy budget, climate feedbacks, and climate sensitivity. In Climate Change 2021: The Physical Science Basis (IPCC AR6 WGI Chapter 7). doi:10.1017/9781009157896.009

IPCC (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use. ISBN:978-4-88788-232-4.

Examples

cf <- carbon_footprint(
  diesel_use = 60, electricity_use = 200,
  n_fertilizer = 120, p_fertilizer = 60, k_fertilizer = 40,
  pesticide_use = 1.5, seed_rate = 100, yield = 4500
)
cf$total_cf
cf$cf_intensity
cf$breakdown

Description

Annual rate of soil carbon change between two measurement time points.

Usage

carbon_sequestration_rate(soc_initial, soc_final, years, verbose = TRUE)

Arguments

Details

Formula:

$CSR = \frac{SOC_{final} - SOC_{initial}}{Years}$

Value

Numeric vector. Carbon sequestration rate (Mg C/ha/year). Positive values indicate net sequestration; negative values indicate net emission from soil.

References

Minasny, B. et al. (2017). Soil carbon 4 per mille. Geoderma, 292, 59-86. doi:10.1016/j.geoderma.2017.01.002

Examples

carbon_sequestration_rate(soc_initial = 28.5, soc_final = 31.2,
                          years = 5)

Description

Ratio of yield to net carbon emission, accounting for carbon sequestration by the soil.

Usage

carbon_sustainability_index(
  yield,
  carbon_emission,
  carbon_sequestered = 0,
  verbose = TRUE
)

Arguments

Details

Formula:

$CSI = \frac{Yield}{Carbon Footprint}$

Value

Numeric vector. Carbon sustainability index. Higher values indicate better carbon sustainability. Returns NA where net emission is zero or negative (net sequestration exceeds emission).

References

Brentrup, F. et al. (2004). Environmental impact assessment of agricultural production systems using the life cycle assessment methodology. European Journal of Agronomy, 20(3), 247-264. doi:10.1016/S1161-0301(03)00024-8

Examples

carbon_sustainability_index(yield = 4500, carbon_emission = 2500,
                             carbon_sequestered = 500)

Description

Computes crop water productivity as the ratio of yield to total evapotranspiration, analogous to the "more crop per drop" concept.

Usage

crop_water_productivity(yield, et, verbose = TRUE)

Arguments

Details

$CWP = \frac{Y}{ET \times 10}$

The factor 10 converts mm to m3/ha.

Value

Numeric vector. Crop water productivity (kg/m3).

References

Zwart, S.J. & Bastiaanssen, W.G.M. (2004). Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agricultural Water Management, 69(2), 115-133. doi:10.1016/j.agwat.2004.04.007

Examples

crop_water_productivity(yield = 4500, et = 400)

Description

Computes the Crop Water Stress Index based on the ratio of actual to potential evapotranspiration.

Usage

crop_water_stress_index(actual_et, potential_et, verbose = TRUE)

Arguments

Details

$CWSI = 1 - \frac{ET_a}{ET_p}$

Value

Numeric vector. CWSI values between 0 (no stress) and 1 (maximum stress). Values close to 0 indicate well-watered conditions; values close to 1 indicate severe water deficit.

References

Idso, S.B. et al. (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24, 45-55. doi:10.1016/0002-1571(81)90032-7

Examples

crop_water_stress_index(c(4.5, 3.8, 3.0, 2.2), c(5.0, 5.0, 5.0, 5.0))

Description

Relative yield performance compared to a check or control treatment.

Usage

crop_yield_index(yield, check_yield, verbose = TRUE)

Arguments

Details

Formula:

$CYI = \frac{Yield}{Maximum Yield} \times 100$

Value

Numeric vector. Yield index where 1.0 equals the check yield. Values above 1 indicate superiority over check.

Examples

crop_yield_index(yield = c(4500, 4200, 3800), check_yield = 4000)

Description

Ratio of water beneficially consumed (crop ET) to total water inflow, used to assess basin-level water allocation.

Usage

depleted_fraction(crop_et, total_inflow, verbose = TRUE)

Arguments

Details

Formula:

$DF = \frac{ET_{actual}}{Water_{supplied}}$

Value

Numeric vector. Depleted fraction (proportion, 0 to 1).

References

Perry, C., Steduto, P., Allen, R.G. & Burt, C.M. (2009). Increasing productivity in irrigated agriculture: Agronomic constraints and hydrological realities. Agricultural Water Management, 96(11), 1517-1524. doi:10.1016/j.agwat.2009.05.005

Examples

depleted_fraction(crop_et = 320, total_inflow = 500)

Description

Computes total energy input for a cropping system from individual input components with energy equivalents.

Usage

energy_input(
  seed = 0,
  fertilizer_n = 0,
  fertilizer_p = 0,
  fertilizer_k = 0,
  fym = 0,
  pesticide = 0,
  diesel = 0,
  electricity = 0,
  human_labour = 0,
  machinery = 0,
  irrigation = 0,
  micronutrient = 0,
  biofertilizer = 0,
  solar_energy = 0,
  other = 0,
  verbose = TRUE
)

Arguments

Details

Formula:

$EI = \sum (Input_i \times EE_i)$

Value

Numeric. Total energy input (MJ/ha), computed as the sum of all individual energy input components.

References

Mittal, V.K. & Dhawan, K.C. (1988). Research manual on energy requirements in agricultural sector. Punjab Agricultural University, Ludhiana, India.

Chaudhary, V.P. et al. (2009). Energy auditing of diversified rice-wheat cropping systems in the Indo-Gangetic Plains. Energy, 34(9), 1091-1096. doi:10.1016/j.energy.2009.04.017

Examples

energy_input(seed = 250, fertilizer_n = 3600, fertilizer_p = 500,
             diesel = 2800, human_labour = 180, machinery = 1200,
             irrigation = 1500)

Description

Energy consumed per unit of crop yield.

Usage

energy_intensity(energy_in, yield, verbose = TRUE)

Arguments

Details

Formula:

$EI = \frac{Energy Input}{Yield}$

Value

Numeric vector. Energy intensity (MJ/kg). Lower values indicate more energy-efficient production.

Examples

energy_intensity(energy_in = 12000, yield = 4500)

Description

Computes total energy output from grain and straw/stover yields using crop-specific energy coefficients.

Usage

energy_output(
  grain_yield,
  straw_yield = 0,
  grain_energy_coeff = 14.7,
  straw_energy_coeff = 12.5,
  verbose = TRUE
)

Arguments

Details

$E_{out} = (Y_g \times C_g) + (Y_s \times C_s)$

where Y_g and Y_s are grain and straw yields, and C_g and C_s are their respective energy coefficients.

Value

Numeric vector. Total energy output (MJ/ha).

References

Devasenapathy, P., Senthilkumar, G. & Shanmugam, P.M. (2009). Energy management in crop production. Indian Journal of Agronomy, 54(1), 80-90.

Examples

energy_output(grain_yield = 4500, straw_yield = 5500)
energy_output(grain_yield = 1500, straw_yield = 2000,
              grain_energy_coeff = 14.3, straw_energy_coeff = 12.5)

Description

Crop yield produced per unit of energy input.

Usage

energy_productivity(yield, energy_in, verbose = TRUE)

Arguments

Details

Formula:

$EP = \frac{Yield}{Energy Input}$

Value

Numeric vector. Energy productivity (kg/MJ). Higher values indicate better yield per unit energy invested.

Examples

energy_productivity(yield = 4500, energy_in = 12000)

Description

Economic return per unit of energy invested.

Usage

energy_profitability(gross_return, energy_in, verbose = TRUE)

Arguments

Details

Formula:

$EPr = \frac{Energy Output - Energy Input}{Energy Input}$

Value

Numeric vector. Energy profitability (currency/MJ).

Examples

energy_profitability(gross_return = 112500, energy_in = 12000)

Description

Ratio of total energy output to total energy input.

Usage

energy_use_efficiency(energy_out, energy_in, verbose = TRUE)

Arguments

Details

$EUE = \frac{E_{out}}{E_{in}}$

Value

Numeric vector. Energy use efficiency (dimensionless ratio). Values greater than 1 indicate a positive energy balance.

Examples

energy_use_efficiency(energy_out = 135000, energy_in = 12000)

Description

Computes the Energy Return on Investment, a key metric for assessing whether an agricultural system produces more energy than it consumes. EROI is mathematically identical to energy use efficiency (EUE) but is the preferred term in energy and sustainability science literature.

Usage

eroi(energy_out, energy_in, include_solar = FALSE, verbose = TRUE)

Arguments

Details

$EROI = \frac{E_{out}}{E_{in}}$

EROI is the fundamental metric of net energy analysis. In agricultural contexts, EROI typically ranges from 2 to 15 for conventional cropping systems, with higher values for low-input or conservation agriculture systems.

Value

Numeric vector. EROI values (dimensionless ratio). Interpreted as follows:

EROI > 5

Highly energy-profitable system

1 < EROI < 5

Energy-positive but moderate return

EROI = 1

Break-even: energy output equals input

EROI < 1

Energy sink: system consumes more than it produces

References

Hall, C.A.S., Lambert, J.G. & Balogh, S.B. (2014). EROI of different fuels and the implications for society. Energy Policy, 64, 141-152. doi:10.1016/j.enpol.2013.05.049

Murphy, D.J. & Hall, C.A.S. (2010). Year in review - EROI or energy return on (energy) invested. Annals of the New York Academy of Sciences, 1185(1), 102-118. doi:10.1111/j.1749-6632.2009.05282.x

Murphy, D.J. et al. (2022). Energy return on investment of major energy carriers: Review and harmonization. Sustainability, 14(12), 7098. doi:10.3390/su14127098

Examples

# Conservation agriculture with low input
eroi(energy_out = 59800, energy_in = 8500)

# Conventional tillage with high input
eroi(energy_out = 40800, energy_in = 12500)

# Multiple treatments
eroi(energy_out = c(40800, 50500, 59800),
     energy_in = c(12500, 9800, 8500))

Description

Computes a composite food productivity index considering yield relative to potential, and optionally nutritional quality.

Usage

food_productivity_index(
  yield,
  reference_yield,
  protein_content = NULL,
  caloric_value = NULL,
  verbose = TRUE
)

Arguments

Details

Formula:

$FPI = \frac{Yield}{Reference Yield} \times 100$

Value

Numeric vector. Food productivity index on a 0 to 1 scale. When protein and caloric values are provided, weights are 0.50 for yield ratio, 0.25 for protein, and 0.25 for caloric content.

Examples

food_productivity_index(yield = 4500, reference_yield = 6000,
                        protein_content = 12.5, caloric_value = 340)

Description

Quick estimation of greenhouse gas emissions (N2O, CH4, CO2) from agricultural field operations using IPCC Tier 1 default factors.

Usage

ghg_emission(
  n_applied,
  residue_burned = FALSE,
  residue_amount = 0,
  paddy_days = 0,
  tillage = "conventional",
  gwp_n2o = 273,
  gwp_ch4 = 27,
  verbose = TRUE
)

Arguments

Details

Formula:

$GHG = \sum (Activity_i \times EF_i)$

where EF is the emission factor for each activity.

Value

A data frame with four columns:

source

Character. Emission source description

gas

Character. Greenhouse gas type

emission_kg

Numeric. Raw emission (kg/ha)

CO2_eq_kg

Numeric. Emission in CO2 equivalents (kg CO2-eq/ha)

References

IPCC (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4, Chapter 11: N2O emissions from managed soils, and CO2 emissions from lime and urea application. ISBN:978-4-88788-232-4.

Examples

ghg_emission(n_applied = 120, tillage = "zero")
ghg_emission(n_applied = 150, paddy_days = 90)

Description

Converts individual greenhouse gas emissions to CO2 equivalents using IPCC AR6 Global Warming Potential values.

Usage

global_warming_potential(
  co2 = 0,
  ch4 = 0,
  n2o = 0,
  gwp_ch4 = 27,
  gwp_n2o = 273,
  time_horizon = "100yr",
  verbose = TRUE
)

Arguments

Details

Formula:

$GWP = CO_2 + CH_4 \times 27 + N_2O \times 273$

IPCC AR6 100-year GWP values (with climate-carbon feedbacks): CH4 = 27, N2O = 273.

IPCC AR6 20-year GWP values: CH4 = 81, N2O = 273.

Note: Previous IPCC AR5 values were CH4 = 34, N2O = 298 (without climate-carbon feedbacks) or CH4 = 28, N2O = 265 (without).

Value

Numeric. Total global warming potential (kg CO2-eq/ha).

References

Forster, P. et al. (2021). The Earth's energy budget, climate feedbacks, and climate sensitivity. In Climate Change 2021: The Physical Science Basis (IPCC AR6 WGI Chapter 7), Table 7.15. doi:10.1017/9781009157896.009

Examples

global_warming_potential(co2 = 500, ch4 = 50, n2o = 2)
global_warming_potential(co2 = 500, ch4 = 50, n2o = 2,
                         time_horizon = "20yr")

Description

Proportion of economic yield to total above-ground biological yield.

Usage

harvest_index(economic_yield, biological_yield, verbose = TRUE)

Arguments

Details

$HI = \frac{Y_{econ}}{Y_{biol}}$

Value

Numeric vector. Harvest index as a proportion (0 to 1). A warning is issued if any value exceeds 1.

References

Hay, R.K.M. (1995). Harvest index: A review of its use in plant breeding and crop physiology. Annals of Applied Biology, 126(1), 197-216. doi:10.1111/j.1744-7348.1995.tb05015.x

Examples

harvest_index(economic_yield = 4500, biological_yield = 10000)

Description

Ratio of energy output to human labour energy input, measuring the degree of human labour amplification by the farming system.

Usage

human_energy_profitability(energy_out, human_energy, verbose = TRUE)

Arguments

Details

Formula:

$HEP = \frac{Energy Output}{Human Energy Input}$

Value

Numeric vector. Human energy profitability (dimensionless ratio).

Examples

human_energy_profitability(energy_out = 135000, human_energy = 180)

Description

Yield produced per unit of total nutrient in the plant.

Usage

internal_utilization_efficiency(yield, total_uptake, verbose = TRUE)

Arguments

Details

Formula:

$IE = \frac{Yield}{Total Nutrient Uptake}$

Value

Numeric vector. Internal utilization efficiency (kg yield per kg nutrient uptake).

Examples

internal_utilization_efficiency(yield = 4500, total_uptake = 100)

Description

Computes multiple irrigation efficiency metrics: conveyance, application, overall, and consumptive use efficiency.

Usage

irrigation_efficiency(
  water_delivered,
  water_diverted,
  water_stored = NULL,
  crop_et = NULL,
  verbose = TRUE
)

Arguments

Details

Formula:

$IE = \frac{Water_{crop}}{Water_{applied}} \times 100$

Value

A data frame with efficiency metrics expressed as percentages. Columns depend on inputs provided:

conveyance_eff

Conveyance efficiency (percent), always computed

application_eff

Application efficiency (percent), if water_stored is provided

overall_eff

Overall efficiency (percent), if water_stored is provided

consumptive_eff

Consumptive use efficiency (percent), if crop_et is provided

References

Grafton, R.Q. et al. (2018). The paradox of irrigation efficiency. Science, 361(6404), 748-750. doi:10.1126/science.aat9314

Examples

irrigation_efficiency(water_delivered = 400, water_diverted = 500,
                      water_stored = 350, crop_et = 320)

Description

Evaluates the productivity advantage of intercropping systems over sole cropping.

Usage

land_equivalent_ratio(
  yield_inter_a,
  yield_sole_a,
  yield_inter_b,
  yield_sole_b,
  verbose = TRUE
)

Arguments

Details

$LER = \frac{Y_{iA}}{Y_{sA}} + \frac{Y_{iB}}{Y_{sB}}$

Value

Numeric vector. LER values. Values greater than 1 indicate an intercropping advantage; less than 1 indicates a disadvantage.

References

Mead, R. & Willey, R.W. (1980). The concept of a land equivalent ratio and advantages in yields from intercropping. Experimental Agriculture, 16(3), 217-228. doi:10.1017/S0014479700010978

Examples

land_equivalent_ratio(yield_inter_a = 3500, yield_sole_a = 4500,
                      yield_inter_b = 800, yield_sole_b = 1200)

Description

Difference between total energy output and total energy input.

Usage

net_energy(energy_out, energy_in, verbose = TRUE)

Arguments

Details

Formula:

$NE = Energy Output - Energy Input$

Value

Numeric vector. Net energy (MJ/ha). Positive values indicate net energy gain; negative values indicate net energy loss.

Examples

net_energy(energy_out = 135000, energy_in = 12000)

Description

Creates a heatmap showing nexus dimension scores across treatments.

Usage

nexus_heatmap(
  scores,
  row_labels = NULL,
  col_labels = NULL,
  title = "WEFNC Nexus Heatmap",
  color_palette = NULL
)

Arguments

Value

Invisibly returns the input scores matrix. Called primarily for the side effect of generating a heatmap plot.

Examples

scores <- matrix(c(0.85, 0.70, 0.90, 0.65, 0.80,
                    0.72, 0.80, 0.85, 0.70, 0.60),
                 nrow = 2, byrow = TRUE)
nexus_heatmap(scores,
  row_labels = c("Conservation", "Conventional"),
  col_labels = c("Water", "Energy", "Food", "Nutrient", "Carbon"))

Description

Publication-quality heatmap using ggplot2 for WEFNC nexus scores.

Usage

nexus_heatmap_gg(
  scores,
  treatment_names = NULL,
  dim_labels = c("Water", "Energy", "Food", "Nutrient", "Carbon"),
  title = "WEFNC Nexus Heatmap"
)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  scores <- matrix(c(0.85, 0.70, 0.90, 0.65, 0.80,
                      0.72, 0.80, 0.85, 0.70, 0.60),
                   nrow = 2, byrow = TRUE)
  nexus_heatmap_gg(scores, treatment_names = c("CA", "CT"))
}

Description

Computes a weighted composite Water-Energy-Food-Nutrient-Carbon nexus sustainability index from normalized dimension scores.

Usage

nexus_index(
  water_score,
  energy_score,
  food_score,
  nutrient_score,
  carbon_score,
  weights = rep(0.2, 5),
  verbose = TRUE
)

Arguments

Details

$NI = w_W S_W + w_E S_E + w_F S_F + w_N S_N + w_C S_C$

Value

Numeric vector. Composite nexus index on a 0 to 1 scale, where higher values indicate better overall sustainability.

Examples

nexus_index(water_score = c(0.85, 0.72), energy_score = c(0.70, 0.80),
            food_score = c(0.90, 0.85), nutrient_score = c(0.65, 0.70),
            carbon_score = c(0.80, 0.60))

Description

Creates a radar (spider/web) chart showing the WEFNC nexus profile of one or more treatments.

Usage

nexus_radar(
  scores,
  labels = c("Water", "Energy", "Food", "Nutrient", "Carbon"),
  treatment_names = NULL,
  colors = NULL,
  title = "WEFNC Nexus Profile",
  fill = TRUE,
  alpha = 0.2
)

Arguments

Value

Invisibly returns the input scores matrix. Called primarily for the side effect of generating a radar plot.

Examples

scores <- matrix(c(0.85, 0.70, 0.90, 0.65, 0.80,
                    0.72, 0.80, 0.85, 0.70, 0.60),
                 nrow = 2, byrow = TRUE)
nexus_radar(scores,
  treatment_names = c("CA+SSDI", "CT+Flood"))

Description

Publication-quality radar chart using ggplot2 for WEFNC nexus profiles.

Usage

nexus_radar_gg(
  scores,
  treatment_names = NULL,
  dim_labels = c("Water", "Energy", "Food", "Nutrient", "Carbon"),
  title = "WEFNC Nexus Profile"
)

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  scores <- matrix(c(0.85, 0.70, 0.90, 0.65, 0.80,
                      0.72, 0.80, 0.85, 0.70, 0.60),
                   nrow = 2, byrow = TRUE)
  nexus_radar_gg(scores, treatment_names = c("CA", "CT"))
}

Description

Evaluates how the composite nexus index changes as the weight of one dimension varies from 0 to its maximum feasible value, with remaining weight distributed equally among the other four dimensions.

Usage

nexus_sensitivity(
  water_score,
  energy_score,
  food_score,
  nutrient_score,
  carbon_score,
  treatment_names = NULL,
  steps = 20,
  verbose = TRUE
)

Arguments

Value

A data frame with columns: dimension, weight, treatment, and nexus_index showing how the index changes as each dimension's weight varies.

Examples

sa <- nexus_sensitivity(
  water_score = c(0.9, 0.5), energy_score = c(0.6, 0.8),
  food_score = c(0.8, 0.7), nutrient_score = c(0.7, 0.6),
  carbon_score = c(0.5, 0.9),
  treatment_names = c("CA", "CT"), steps = 10
)
head(sa)

Description

Generates a comprehensive one-call nexus analysis from raw agronomic field data, computing all dimension metrics, normalizing, and producing the composite index.

Usage

nexus_summary(
  yield,
  water_consumed,
  energy_input,
  energy_output,
  n_applied,
  n_uptake,
  carbon_emission,
  treatment_names = NULL,
  weights = rep(0.2, 5),
  verbose = TRUE
)

Arguments

Value

A data frame with one row per treatment and columns:

treatment

Character. Treatment name

yield_kg_ha

Numeric. Crop yield

WUE_kg_mm

Numeric. Water use efficiency

EUE_ratio

Numeric. Energy use efficiency ratio

EROI

Numeric. Energy return on investment

net_energy_MJ

Numeric. Net energy balance

PFP_kg_kgN

Numeric. Partial factor productivity of N

carbon_eff

Numeric. Carbon efficiency

carbon_intensity

Numeric. Carbon intensity

W_score

Numeric. Normalized water score

E_score

Numeric. Normalized energy score

F_score

Numeric. Normalized food score

N_score

Numeric. Normalized nutrient score

C_score

Numeric. Normalized carbon score

nexus_index

Numeric. Composite nexus index

Examples

nexus_summary(
  yield = c(4500, 4200, 3800),
  water_consumed = c(450, 400, 350),
  energy_input = c(12000, 11000, 9500),
  energy_output = c(135000, 125000, 112000),
  n_applied = c(120, 120, 120),
  n_uptake = c(100, 90, 80),
  carbon_emission = c(2500, 2200, 1800),
  treatment_names = c("CA+Drip", "CT+Sprinkler", "ZT+Rainfed")
)

Description

Comprehensive sustainability score with categorization based on user-defined thresholds.

Usage

nexus_sustainability_score(
  water_score,
  energy_score,
  food_score,
  nutrient_score,
  carbon_score,
  weights = rep(0.2, 5),
  thresholds = c(0.4, 0.6, 0.8),
  verbose = TRUE
)

Arguments

Details

Formula:

$NSS = \frac{1}{n} \sum_{i=1}^{n} S_i$

where S_i are normalized sub-dimension scores.

Value

A data frame with columns:

nexus_score

Numeric. Composite sustainability score (0 to 1)

category

Character. Sustainability category: Unsustainable, Low Sustainability, Moderately Sustainable, or Highly Sustainable

water

Numeric. Water dimension input score

energy

Numeric. Energy dimension input score

food

Numeric. Food dimension input score

nutrient

Numeric. Nutrient dimension input score

carbon

Numeric. Carbon dimension input score

Examples

nexus_sustainability_score(
  water_score = c(0.85, 0.60, 0.45),
  energy_score = c(0.70, 0.55, 0.35),
  food_score = c(0.90, 0.80, 0.50),
  nutrient_score = c(0.65, 0.70, 0.40),
  carbon_score = c(0.80, 0.50, 0.30)
)

Description

Computes pairwise correlation matrix among nexus dimensions to identify synergies (positive) and trade-offs (negative).

Usage

nexus_tradeoff(data, method = "pearson", verbose = TRUE)

Arguments

Value

A numeric correlation matrix with pairwise correlations. Positive values suggest synergies; negative values suggest trade-offs between dimensions.

Examples

df <- data.frame(WUE = c(8.5, 7.2, 6.5, 9.0, 7.8),
                 EUE = c(11.2, 12.5, 9.8, 10.5, 11.0),
                 Yield = c(1500, 1380, 1250, 1650, 1580),
                 NUE = c(25, 23, 20.8, 27.5, 26.3),
                 CF = c(2500, 3000, 1800, 2800, 2200))
nexus_tradeoff(df)

Description

Min-max normalization to scale values between 0 and 1.

Usage

normalize_minmax(x, inverse = FALSE)

Arguments

Details

Formula:

$X_{norm} = \frac{X - X_{min}}{X_{max} - X_{min}}$

Value

Numeric vector normalized to the 0 to 1 range. Returns 0.5 for all elements if the input has zero range.

Examples

normalize_minmax(c(10, 20, 30, 40, 50))
normalize_minmax(c(10, 20, 30, 40, 50), inverse = TRUE)

Description

Standardization to z-scores (mean = 0, standard deviation = 1).

Usage

normalize_zscore(x)

Arguments

Details

Formula:

$Z = \frac{X - \mu}{\sigma}$

Value

Numeric vector of z-scores. Returns all zeros if standard deviation is zero.

Examples

normalize_zscore(c(10, 20, 30, 40, 50))

Description

Input-output nutrient balance for major nutrients (N, P, K).

Usage

nutrient_balance(
  n_input,
  p_input,
  k_input,
  n_output,
  p_output,
  k_output,
  verbose = TRUE
)

Arguments

Details

Formula:

$NB = Nutrient Applied - Nutrient Removed$

Value

A data frame with five columns:

nutrient

Character. Nutrient name: N, P, or K

input_kg_ha

Numeric. Total nutrient input (kg/ha)

output_kg_ha

Numeric. Total nutrient output (kg/ha)

balance_kg_ha

Numeric. Balance: input minus output (kg/ha). Positive = surplus; negative = depletion

output_input_ratio

Numeric. Output to input ratio

Examples

nutrient_balance(n_input = 120, p_input = 60, k_input = 40,
                 n_output = 95, p_output = 25, k_output = 80)

Description

Proportion of total plant nutrient partitioned to grain.

Usage

nutrient_harvest_index(
  grain_nutrient_uptake,
  total_nutrient_uptake,
  verbose = TRUE
)

Arguments

Details

Formula:

$HI = \frac{Economic Yield}{Biological Yield}$

Value

Numeric vector. Nutrient harvest index (proportion, 0 to 1).

Examples

nutrient_harvest_index(grain_nutrient_uptake = 75,
                       total_nutrient_uptake = 100)

Description

Computes all major nutrient use efficiency metrics for a given nutrient in a single call.

Usage

nutrient_use_efficiency(
  yield_treated,
  yield_control,
  nutrient_applied,
  uptake_treated,
  uptake_control,
  nutrient_name = "N",
  verbose = TRUE
)

Arguments

Details

Formula:

$NUE = \frac{Yield}{Nutrient Applied}$

Value

A data frame with one row and seven columns:

nutrient

Character. Nutrient name

agronomic_eff

Numeric. Agronomic efficiency (kg/kg)

physiological_eff

Numeric. Physiological efficiency (kg/kg)

recovery_eff

Numeric. Recovery efficiency (proportion)

partial_factor_prod

Numeric. Partial factor productivity (kg/kg)

internal_util_eff

Numeric. Internal utilization efficiency (kg/kg)

nutrient_harvest_idx

Numeric. Estimated nutrient harvest index (proportion, based on 0.75 grain-to-total ratio assumption)

References

Dobermann, A. (2007). Nutrient use efficiency: measurement and management. In Fertilizer Best Management Practices, IFA, Paris, pp. 1-28. https://digitalcommons.unl.edu/agronomyfacpub/316/

Congreves, K.A. et al. (2021). Nitrogen use efficiency definitions of today and tomorrow. Frontiers in Plant Science, 12, 637108. doi:10.3389/fpls.2021.637108

Examples

nutrient_use_efficiency(
  yield_treated = 4500, yield_control = 3000,
  nutrient_applied = 120, uptake_treated = 100,
  uptake_control = 60, nutrient_name = "N"
)

Description

Total yield per unit of nutrient applied, without deduction of control yield.

Usage

partial_factor_productivity(yield, nutrient_applied, verbose = TRUE)

Arguments

Details

$PFP = \frac{Y}{F}$

Value

Numeric vector. Partial factor productivity (kg yield per kg nutrient applied).

Examples

partial_factor_productivity(yield = 4500, nutrient_applied = 120)

Description

Yield increase per unit of nutrient uptake increase.

Usage

physiological_efficiency(
  yield_treated,
  yield_control,
  uptake_treated,
  uptake_control,
  verbose = TRUE
)

Arguments

Details

$PE = \frac{Y_f - Y_0}{U_f - U_0}$

Value

Numeric vector. Physiological efficiency (kg grain per kg nutrient uptake). Returns NA where uptake difference is zero.

Examples

physiological_efficiency(yield_treated = 4500, yield_control = 3000,
                         uptake_treated = 100, uptake_control = 60)

Description

Visualises the output of nexus_sensitivity as faceted line plots showing how the nexus index changes with dimension weights.

Usage

plot_sensitivity(sa, title = "Nexus Weight Sensitivity")

Arguments

Value

A ggplot2 object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  sa <- nexus_sensitivity(
    water_score = c(0.9, 0.5), energy_score = c(0.6, 0.8),
    food_score = c(0.8, 0.7), nutrient_score = c(0.7, 0.6),
    carbon_score = c(0.5, 0.9),
    treatment_names = c("CA", "CT"), steps = 10
  )
  plot_sensitivity(sa)
}

Description

Plot method for cf_result

Usage

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

Arguments

Value

No return value, called for the side effect of generating a horizontal bar plot showing carbon footprint by emission source.

Description

Plot method for nexus_result

Usage

## S3 method for class 'nexus_result'
plot(x, type = c("radar", "heatmap"), ...)

Arguments

Value

Invisibly returns the scores matrix. Called for the side effect of generating a radar or heatmap plot.

Description

Print method for cf_result

Usage

## S3 method for class 'cf_result'
print(x, ...)

Arguments

Value

Invisibly returns the input object x. Called for the side effect of printing a formatted carbon footprint summary to the console.

Description

Print method for nexus_result

Usage

## S3 method for class 'nexus_result'
print(x, ...)

Arguments

Value

Invisibly returns the input object x. Called for the side effect of printing a formatted nexus analysis summary to the console.

Description

Print method for sustainability_result

Usage

## S3 method for class 'sustainability_result'
print(x, ...)

Arguments

Value

Invisibly returns the input object x. Called for the side effect of printing sustainability scores and categories to the console.

Description

Yield produced per unit of production cost, measuring economic efficiency of crop production.

Usage

production_efficiency_index(yield, cost, verbose = TRUE)

Arguments

Details

Formula:

$PEI = \frac{Yield}{Total Input Cost} \times 100$

Value

Numeric vector. Production efficiency index (kg/currency unit).

Examples

production_efficiency_index(yield = 4500, cost = 28500)

Description

Total protein output per hectare from crop production.

Usage

protein_yield(yield, protein_content, verbose = TRUE)

Arguments

Details

Formula:

$PY = Yield \times Protein Content / 100$

Value

Numeric vector. Protein yield (kg/ha).

Examples

protein_yield(yield = 4500, protein_content = 12.5)

Description

Proportion of applied nutrient that is recovered in crop biomass.

Usage

recovery_efficiency(
  uptake_treated,
  uptake_control,
  nutrient_applied,
  verbose = TRUE
)

Arguments

Details

$RE = \frac{U_f - U_0}{F}$

Value

Numeric vector. Recovery efficiency as a proportion (0 to 1, but can exceed 1 due to priming effects or added benefits).

Examples

recovery_efficiency(uptake_treated = 100, uptake_control = 60,
                    nutrient_applied = 120)

Description

Estimates soil organic carbon stock for a given soil depth.

Usage

soil_carbon_stock(
  soc_pct,
  bulk_density,
  depth = 30,
  coarse_fraction = 0,
  verbose = TRUE
)

Arguments

Details

$SOC = SOC_{pct} \times BD \times D \times (1 - CF) \times 0.1$

where BD is bulk density (Mg/m3), D is depth (cm), CF is coarse fraction, and 0.1 is the conversion factor from (percent * Mg/m3 * cm) to Mg/ha.

Value

Numeric vector. SOC stock (Mg C/ha, equivalent to t C/ha).

References

Batjes, N.H. (1996). Total carbon and nitrogen in the soils of the world. European Journal of Soil Science, 47(2), 151-163. doi:10.1111/j.1365-2389.1996.tb01386.x

Examples

soil_carbon_stock(soc_pct = 0.65, bulk_density = 1.45, depth = 30)

Description

Energy input per unit area per unit time, useful for comparing cropping systems of different durations.

Usage

specific_energy(energy_in, duration, verbose = TRUE)

Arguments

Details

Formula:

$SE = \frac{Energy Input}{Area}$

Value

Numeric vector. Specific energy (MJ/ha/day).

Examples

specific_energy(energy_in = 12000, duration = 150)

Description

Summary method for nexus_result

Usage

## S3 method for class 'nexus_result'
summary(object, ...)

Arguments

Value

Invisibly returns the input object object. Called for the side effect of printing a detailed dimension-wise summary to the console.

Description

Converts yields from a cropping system to a common crop-equivalent yield based on economic value.

Usage

system_productivity_index(yields, prices, base_price, verbose = TRUE)

Arguments

Details

$SPI = \frac{\sum(Y_i \times P_i)}{P_{base}}$

Value

Numeric. System productivity expressed as base crop equivalent yield (kg/ha).

Examples

system_productivity_index(yields = c(5000, 4500),
                          prices = c(22, 25), base_price = 25)

Description

Computes the water footprint of crop production decomposed into green, blue, and grey components following the Water Footprint Assessment framework.

Usage

water_footprint(
  green_water,
  blue_water,
  grey_water = 0,
  yield,
  area = 1,
  per_ton = FALSE,
  verbose = TRUE
)

Arguments

Details

The water footprint per unit mass of product is:

$WF = \frac{CWU \times 10}{Y}$

where CWU is crop water use (mm) and Y is yield (kg/ha). The factor 10 converts mm over 1 ha to m3.

Value

A data frame with four numeric columns:

green_wf

Green water footprint (m3/kg or m3/ton)

blue_wf

Blue water footprint (m3/kg or m3/ton)

grey_wf

Grey water footprint (m3/kg or m3/ton)

total_wf

Total water footprint (m3/kg or m3/ton)

References

Hoekstra, A.Y., Chapagain, A.K., Aldaya, M.M. & Mekonnen, M.M. (2011). The Water Footprint Assessment Manual: Setting the Global Standard. Earthscan, London. ISBN:9781849712798.

Mialyk, O. et al. (2024). Water footprints and crop water use of 175 individual crops for 1990-2019 simulated with a global crop model. Scientific Data, 11, 200. doi:10.1038/s41597-024-03051-3

Examples

water_footprint(green_water = 300, blue_water = 200,
                grey_water = 50, yield = 4000)

Description

Computes physical or economic water productivity per unit of water used.

Usage

water_productivity(
  yield,
  water_applied,
  price = NULL,
  unit = "mm",
  verbose = TRUE
)

Arguments

Details

Physical water productivity:

$WP = \frac{Y}{W_{m3}}$

Economic water productivity:

$WP_{econ} = \frac{Y \times P}{W_{m3}}$

When unit = "mm", water is converted to m3/ha using 1 mm = 10 m3/ha.

Value

Numeric vector. Physical water productivity in kg/m3 when price is NULL, or economic water productivity in currency/m3 when price is specified.

References

Molden, D. et al. (2010). Improving agricultural water productivity: Between optimism and caution. Agricultural Water Management, 97(4), 528-535. doi:10.1016/j.agwat.2009.03.023

Examples

water_productivity(4500, 500, unit = "mm")
water_productivity(4500, 500, price = 25, unit = "mm")

Description

Computes water use efficiency as the ratio of economic yield to total water consumed (evapotranspiration or irrigation plus rainfall).

Usage

water_use_efficiency(yield, water_consumed, verbose = TRUE)

Arguments

Details

Water use efficiency is defined as:

$WUE = \frac{Y}{W}$

where Y is economic yield (kg/ha) and W is total water consumed (mm).

Value

Numeric vector. Water use efficiency in kg/ha/mm. Higher values indicate more efficient use of water for crop production.

References

Hoover, D.L. et al. (2023). Indicators of water use efficiency across diverse agroecosystems and scales. Science of the Total Environment, 864, 160992. doi:10.1016/j.scitotenv.2022.160992

Examples

# Wheat yield with different irrigation levels
yield <- c(4500, 4200, 3800, 3500)
water <- c(450, 400, 350, 300)
water_use_efficiency(yield, water)