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farmPartial

farmPartial provides a focused toolkit for farm partial-budget analysis in R. It is designed for farm-management economics, agricultural extension, on-farm experiments, and technology-adoption appraisal.

Economic framework

Only items that change between a baseline farm plan and an alternative plan are included. The central calculation is

[ NR = (AR + RC) - (AC + RR), ]

where AR = added returns, RC = reduced costs, AC = added costs, and RR = reduced returns. A positive value is an economic signal in favor of the alternative, conditional on the assumptions used. It is not, by itself, a full farm-planning or risk-preference decision rule.

Main functions

Function	Purpose
`partial_budget()`	Build the standard four-quadrant partial budget
`farm_budget()`	Create baseline or alternative farm-budget tables
`compare_budgets()`	Convert two plans into incremental changes automatically
`budget_summary()`	Return a tidy one-row economic summary
`break_even_component()`	Find the component value that makes net change zero
`sensitivity_analysis()`	One-way sensitivity analysis
`two_way_sensitivity()`	Two-way sensitivity surface
`scenario_analysis()`	Named multi-item scenarios
`simulate_partial_budget()`	Monte Carlo uncertainty and probability of gain
`annualize_investment()`	Equivalent annual cost of a capital change
`trial_budget()`	Calculate adjusted yield, gross benefit, and net benefit
`dominance_analysis()`	Identify economically dominated treatments
`marginal_analysis()`	Marginal rate-of-return analysis for treatments
`wheat_example()`	Illustrative wheat-management example

The core package has no non-base runtime dependency beyond standard R packages. testthat, knitr, and rmarkdown are suggested for tests and the vignette.

Quick start

library(farmPartial)

changes <- wheat_example("changes")
pb <- partial_budget(changes, currency = "INR", unit = "per ha")

pb
budget_summary(pb)
plot(pb)

For the included example, the calculation is:

added returns = INR 6,000/ha;
reduced costs = INR 4,800/ha;
added costs = INR 3,800/ha;
reduced returns = INR 500/ha; and
expected net change = INR 6,500/ha in favor of the alternative.

The values are illustrative, not survey estimates or official recommendations.

Compare two farm plans

base <- wheat_example("baseline")
alternative <- wheat_example("alternative")

pb2 <- compare_budgets(base, alternative)
pb2
pb2$comparison

You can build your own plans from values:

base <- farm_budget(
  item = c("Grain", "Seed", "Irrigation"),
  category = c("return", "cost", "cost"),
  value = c(120000, 6500, 9000),
  currency = "INR",
  unit = "per ha"
)

or from quantities and unit prices:

farm_budget(
  item = c("Grain", "Seed"),
  category = c("return", "cost"),
  quantity = c(50, 100),
  unit_price = c(2500, 65)
)

Sensitivity and break-even analysis

s <- sensitivity_analysis(
  pb,
  item = "Higher grain return",
  multipliers = seq(0.7, 1.3, by = 0.1)
)
plot(s)

break_even_component(pb, "Additional herbicide")

Two-way sensitivity is also available:

s2 <- two_way_sensitivity(
  pb,
  item_x = "Higher grain return",
  item_y = "Additional herbicide"
)
plot(s2)

Scenario analysis

scenarios <- data.frame(
  scenario = c(
    "Output price stress", "Input price stress",
    "Combined stress", "Combined stress"
  ),
  item = c(
    "Higher grain return", "Additional herbicide",
    "Higher grain return", "Additional herbicide"
  ),
  multiplier = c(0.75, 1.30, 0.75, 1.30)
)

sc <- scenario_analysis(pb, scenarios)
sc
plot(sc)

Monte Carlo uncertainty

uncertainty <- data.frame(
  item = c("Higher grain return", "Additional herbicide"),
  distribution = c("normal", "triangular"),
  mean = c(6000, NA),
  sd = c(900, NA),
  min = c(NA, 900),
  mode = c(NA, 1200),
  max = c(NA, 1700)
)

sim <- simulate_partial_budget(pb, uncertainty, n = 5000, seed = 2026)
summary(sim)
plot(sim)

The plotted interval is a Monte Carlo uncertainty interval under the specified component distributions, not a sampling-theory confidence interval.

On-farm trial economics

trials <- trial_budget(
  treatment = c("Farmer practice", "Treatment A", "Treatment B", "Treatment C"),
  yield = c(3.0, 3.4, 3.8, 4.1),
  price = 22000,
  variable_cost = c(18000, 22000, 28000, 39000),
  yield_adjustment = 0.90
)

dominance_analysis(trials)
marginal_analysis(trials, minimum_mrr = 50)

Methodological caution

Partial budgeting is deliberately partial. It is most appropriate when a change affects a limited set of returns and costs while the rest of the farm plan is unchanged. Opportunity costs of family labor and non-market inputs should be valued when they change. Whole-farm resource constraints, liquidity, farmer risk preferences, tax consequences, and major interactions across enterprises may require a broader whole-farm or investment analysis.

Reference

CIMMYT. (1988). From agronomic data to farmer recommendations: An economics training manual (completely revised edition). CIMMYT. ISBN 968-6127-19-4.

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.