4.2 Labour productivity

Purpose of indicator

Labour inputs required for agricultural production is one of the primary factors determining which practices are used, how profitable they are, and the social sustainability of the production system. Distinguishing which productive systems promote sustainable employment contributes to SDG 2 and SDG8.

Key Metadata

Metadata Item Description
Indicator Name Labour inputs per unit agricultural land area
Theme Labour productivity
SDGs Targeted SDG2, SDG8
Data Source Farmer recall during household survey
Measurement Person hours per year, disaggregated by gender, season, and productive activity
Measurement Units Person hours per year per hectare of cultivated land

Guidance on Measurement

This indicator can be computed by collecting data on number of labour hours per year per hectare of land actively used for agricultural purposes. Labour hours should be disaggregated by:

  • gender
  • age
  • type of worker: hired or not hired, permanent or seasonal
  • type of activity: routine (e.g. manure collection, milking) or seasonal work (e.g. seeding, harvesting, pruning).

Guidance on Data Entry and Reporting

no information is available

Calculation Method


  #NEEDS TO ACCOUNT FOR HIRED WOKERS EITHER BY COALESCING TABLE IN PROCESSING OR CALCUALTING HERE

  ## calculate hours_per_year for permanent and seasonal workers
  tmp_permanent <- permanent_workers %>%
    mutate(
      hours_per_year = perm_labour_group_n_workers * perm_labour_hours * 365
    ) %>%
    group_by(farm_id) %>%
    summarise(total_perm_hours_per_year = sum(hours_per_year, na.rm = TRUE))

  tmp_seasonal <- seasonal_workers %>%
    mutate(
      hours_per_season = seasonal_labour_n_working *
        seasonal_labour_hours *
        seasonal_labour_months_count *
        30
    ) %>%
    group_by(farm_id) %>%
    summarise(
      total_seasonal_hours_per_year = sum(hours_per_season, na.rm = TRUE)
    )

  ## calculate total agricultural land of all types
  tmp_land <- main_surveys %>%
    select(
      farm_id,
      total_crop_area_ha,
      livestock_land_own_ha,
      livestock_land_share_ha,
      fish_area_ha
    ) %>%
    rowwise() %>%
    mutate(
      total_agricultural_land_ha = sum(
        c_across(total_crop_area_ha:fish_area_ha),
        na.rm = TRUE
      )
    )

  ## calculate total agricultural income from all types
  tmp_income <- main_surveys %>%
    select(farm_id, income_crops, income_livestock, income_fish) %>%
    rowwise() %>%
    mutate(total_agricultural_income = sum(c_across(income_crops:income_fish)))

  ## bring calculated sets together
  tmp <- tmp_permanent %>%
    left_join(tmp_seasonal) %>%
    left_join(tmp_land) %>%
    left_join(tmp_income) %>%
    rowwise() %>%

    ## total labour hours per year = permanent + seasonal totals
    mutate(
      total_labour_hours_per_year = sum(
        c_across(total_perm_hours_per_year:total_seasonal_hours_per_year),
        na.rm = TRUE
      )
    ) %>%

    ## labour input = hours / land area
    mutate(
      kpi13a_labour_input = total_labour_hours_per_year /
        total_agricultural_land_ha
    ) %>%

    ## labour productivity = income / labour hours
    mutate(
      kpi13b_labour_productivity = total_agricultural_income /
        total_labour_hours_per_year
    )

  ### include the new variables in the performance_indicators data frame
  performance_indicators <- performance_indicators %>%
    left_join(
      tmp %>% select(farm_id, kpi13a_labour_input, kpi13b_labour_productivity)
    )

Indicator Interpretation and Threshold Setting

no information is available

Limitations

no information is available

References