IndFarmCost

IndFarmCost is an R package for calculating and analysing the Indian farm cost concepts A1, A2, B1, B2, C1, C2, and C3 used in agricultural cost-of-cultivation and farm management studies.

Cost concepts implemented

Concept	Formula implemented
A1	Sum of specified A1 cost components
A2	A1 + rent paid for leased-in land
B1	A1 + interest on owned fixed capital excluding land
B2	B1 + rental value of owned land + rent paid for leased-in land
C1	B1 + imputed value of family labour
C2	B2 + imputed value of family labour
C3	C2 + managerial charge; default is 10% of C2

The managerial rate is configurable through managerial_rate.

Main features

farm_costs() - calculate A1 to C3 for each farm/observation.
farm_costs_aggregate() - aggregate cost concepts by crop, state, farm size, or other grouping variables.
summarize_costs() - descriptive statistics and coefficient of variation.
farm_returns() - gross return, net returns, and benefit-cost ratios.
cost_of_production() - unit cost under a selected cost concept.
break_even_price() and break_even_yield() - break-even analysis.
cost_shares() - additive cost decomposition and percentage shares.
cost_sensitivity() - one-way sensitivity analysis for an input cost.
plot.farmcost() - base-R concept-wise cost plot.
farm_cost_example() - ready-to-use example farm dataset.

Installation from a local source package

install.packages("IndFarmCost_0.1.0.tar.gz", repos = NULL, type = "source")

Quick example

library(IndFarmCost)

dat <- farm_cost_example()
costs <- farm_costs(dat)
costs[, c("farm_id", "crop", "A1", "A2", "B1", "B2", "C1", "C2", "C3")]

summary(costs)
plot(costs, row = 1)

farm_returns(
  costs,
  main_output = "main_output_q",
  main_price = "main_price_rs_q",
  byproduct_output = "byproduct_output_q",
  byproduct_price = "byproduct_price_rs_q"
)

Methodological note

The package follows the standard structure of Indian cost concepts used in cost-of-cultivation studies. Users should verify the valuation rules, wage imputation, rental valuation, interest rates, and any study-specific treatment of inputs against the methodology applicable to their dataset and reference period.