diff --git a/DESCRIPTION b/DESCRIPTION index 57c8b76..dc1dcb7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -35,7 +35,8 @@ Imports: utils, rlang, stringr, - ggplot2 + ggplot2, + openxlsx Suggests: knitr, rmarkdown, diff --git a/R/compare_scenario.R b/R/compare_scenario.R index 254537b..4926e8c 100644 --- a/R/compare_scenario.R +++ b/R/compare_scenario.R @@ -33,365 +33,447 @@ #' #' @export #' -compare_scenario <- function(baseRun, outFile, oDir2){ - +compare_scenario <- function (baseRun, outFile, oDir2) +{ base_run <- baseRun - outFile <- jsonlite::fromJSON(outFile, flatten = TRUE) - oDir2 <- oDir2 - - base_run_df <- outFile[outFile$scenario == base_run,] - scenarios_df <- outFile[outFile$scenario != base_run,] - + base_run_df <- outFile[outFile$scenario == base_run, ] + scenarios_df <- outFile[outFile$scenario != base_run, ] scenarioList <- list() + output_list <- list() + for (i in 1:nrow(scenarios_df)) { - # Scenario - scenario_df <- scenarios_df[i,] + scenario_df <- scenarios_df[i, ] scenario <- scenario_df$scenario - - total_milk_produced_kg_fpcm_per_year <- ifelse(!is.finite(((scenario_df$total_milk_produced_kg_fpcm_per_year - base_run_df$total_milk_produced_kg_fpcm_per_year)/base_run_df$total_milk_produced_kg_fpcm_per_year)*100),0, - ((scenario_df$total_milk_produced_kg_fpcm_per_year - base_run_df$total_milk_produced_kg_fpcm_per_year)/base_run_df$total_milk_produced_kg_fpcm_per_year)*100) - - total_meat_produced_kg_per_year <- ifelse(!is.finite(((scenario_df$total_meat_produced_kg_per_year - base_run_df$total_meat_produced_kg_per_year)/base_run_df$total_meat_produced_kg_per_year)*100),0, - ((scenario_df$total_meat_produced_kg_per_year - base_run_df$total_meat_produced_kg_per_year)/base_run_df$total_meat_produced_kg_per_year)*100) - - total_protein_produced_kg_per_year <- ifelse(!is.finite(((scenario_df$total_protein_produced_kg_per_year - base_run_df$total_protein_produced_kg_per_year)/base_run_df$total_protein_produced_kg_per_year)*100),0, - ((scenario_df$total_protein_produced_kg_per_year - base_run_df$total_protein_produced_kg_per_year)/base_run_df$total_protein_produced_kg_per_year)*100) - - total_tlu <- ifelse(!is.finite(((scenario_df$total_tlu - base_run_df$total_tlu)/base_run_df$total_tlu)*100),0, - ((scenario_df$total_tlu - base_run_df$total_tlu)/base_run_df$total_tlu)*100) - - total_land_requirement_ha <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha - base_run_df$total_land_requirement_ha)/base_run_df$total_land_requirement_ha)*100),0, - ((scenario_df$total_land_requirement_ha - base_run_df$total_land_requirement_ha)/base_run_df$total_land_requirement_ha)*100) - - total_land_requirement_ha_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_kg_fpcm - base_run_df$total_land_requirement_ha_per_kg_fpcm)/base_run_df$total_land_requirement_ha_per_kg_fpcm)*100),0, - ((scenario_df$total_land_requirement_ha_per_kg_fpcm - base_run_df$total_land_requirement_ha_per_kg_fpcm)/base_run_df$total_land_requirement_ha_per_kg_fpcm)*100) - - total_land_requirement_ha_per_kg_meat <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_kg_meat - base_run_df$total_land_requirement_ha_per_kg_meat)/base_run_df$total_land_requirement_ha_per_kg_meat)*100),0, - ((scenario_df$total_land_requirement_ha_per_kg_meat - base_run_df$total_land_requirement_ha_per_kg_meat)/base_run_df$total_land_requirement_ha_per_kg_meat)*100) - - total_land_requirement_ha_per_kg_protein <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_kg_protein - base_run_df$total_land_requirement_ha_per_kg_protein)/base_run_df$total_land_requirement_ha_per_kg_protein)*100),0, - ((scenario_df$total_land_requirement_ha_per_kg_protein - base_run_df$total_land_requirement_ha_per_kg_protein)/base_run_df$total_land_requirement_ha_per_kg_protein)*100) - - total_land_requirement_ha_per_tlu <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_tlu - base_run_df$total_land_requirement_ha_per_tlu)/base_run_df$total_land_requirement_ha_per_tlu)*100),0, - ((scenario_df$total_land_requirement_ha_per_tlu - base_run_df$total_land_requirement_ha_per_tlu)/base_run_df$total_land_requirement_ha_per_tlu)*100) - - total_n_balance_kg_n_per_year <- ifelse(!is.finite(((scenario_df$total_n_balance_kg_n_per_year - base_run_df$total_n_balance_kg_n_per_year)/base_run_df$total_n_balance_kg_n_per_year)*100),0, - ((scenario_df$total_n_balance_kg_n_per_year - base_run_df$total_n_balance_kg_n_per_year)/base_run_df$total_n_balance_kg_n_per_year)*100) - - percent_area_mining <- ifelse(!is.finite(((scenario_df$percent_area_mining - base_run_df$percent_area_mining)/base_run_df$percent_area_mining)*100),0, - ((scenario_df$percent_area_mining - base_run_df$percent_area_mining)/base_run_df$percent_area_mining)*100) - - percent_area_leaching <- ifelse(!is.finite(((scenario_df$percent_area_leaching - base_run_df$percent_area_leaching)/base_run_df$percent_area_leaching)*100),0, - ((scenario_df$percent_area_leaching - base_run_df$percent_area_leaching)/base_run_df$percent_area_leaching)*100) - - n_balance_kg_n_per_ha_per_year <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_ha_per_year - base_run_df$n_balance_kg_n_per_ha_per_year)/base_run_df$n_balance_kg_n_per_ha_per_year)*100),0, - ((scenario_df$n_balance_kg_n_per_ha_per_year - base_run_df$n_balance_kg_n_per_ha_per_year)/base_run_df$n_balance_kg_n_per_ha_per_year)*100) - - n_balance_kg_n_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_kg_fpcm - base_run_df$n_balance_kg_n_per_kg_fpcm)/base_run_df$n_balance_kg_n_per_kg_fpcm)*100),0, - ((scenario_df$n_balance_kg_n_per_kg_fpcm - base_run_df$n_balance_kg_n_per_kg_fpcm)/base_run_df$n_balance_kg_n_per_kg_fpcm)*100) - - n_balance_kg_n_per_kg_meat <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_kg_meat - base_run_df$n_balance_kg_n_per_kg_meat)/base_run_df$n_balance_kg_n_per_kg_meat)*100),0, - ((scenario_df$n_balance_kg_n_per_kg_meat - base_run_df$n_balance_kg_n_per_kg_meat)/base_run_df$n_balance_kg_n_per_kg_meat)*100) - - n_balance_kg_n_per_kg_protein <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_kg_protein - base_run_df$n_balance_kg_n_per_kg_protein)/base_run_df$n_balance_kg_n_per_kg_protein)*100),0, - ((scenario_df$n_balance_kg_n_per_kg_protein - base_run_df$n_balance_kg_n_per_kg_protein)/base_run_df$n_balance_kg_n_per_kg_protein)*100) - - erosion_t_soil_year <- ifelse(!is.finite(((scenario_df$erosion_t_soil_year - base_run_df$erosion_t_soil_year)/base_run_df$erosion_t_soil_year)*100),0, - ((scenario_df$erosion_t_soil_year - base_run_df$erosion_t_soil_year)/base_run_df$erosion_t_soil_year)*100) - - erosion_t_soil_per_ha_per_year <- ifelse(!is.finite(((scenario_df$erosion_t_soil_per_ha_per_year - base_run_df$erosion_t_soil_per_ha_per_year)/base_run_df$erosion_t_soil_per_ha_per_year)*100),0, - ((scenario_df$erosion_t_soil_per_ha_per_year - base_run_df$erosion_t_soil_per_ha_per_year)/base_run_df$erosion_t_soil_per_ha_per_year)*100) - - erosion_kgsoil_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$erosion_kgsoil_per_kg_fpcm - base_run_df$erosion_kgsoil_per_kg_fpcm)/base_run_df$erosion_kgsoil_per_kg_fpcm)*100),0, - ((scenario_df$erosion_kgsoil_per_kg_fpcm - base_run_df$erosion_kgsoil_per_kg_fpcm)/base_run_df$erosion_kgsoil_per_kg_fpcm)*100) - - erosion_kgsoil_per_kg_meat <- ifelse(!is.finite(((scenario_df$erosion_kgsoil_per_kg_meat - base_run_df$erosion_kgsoil_per_kg_meat)/base_run_df$erosion_kgsoil_per_kg_meat)*100),0, - ((scenario_df$erosion_kgsoil_per_kg_meat - base_run_df$erosion_kgsoil_per_kg_meat)/base_run_df$erosion_kgsoil_per_kg_meat)*100) - - erosion_kgsoil_per_kg_protein <- ifelse(!is.finite(((scenario_df$erosion_kgsoil_per_kg_protein - base_run_df$erosion_kgsoil_per_kg_protein)/base_run_df$erosion_kgsoil_per_kg_protein)*100),0, - ((scenario_df$erosion_kgsoil_per_kg_protein - base_run_df$erosion_kgsoil_per_kg_protein)/base_run_df$erosion_kgsoil_per_kg_protein)*100) - - ghg_emission_t_co2_eq_per_year <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_year - base_run_df$ghg_emission_t_co2_eq_per_year)/base_run_df$ghg_emission_t_co2_eq_per_year)*100),0, - ((scenario_df$ghg_emission_t_co2_eq_per_year - base_run_df$ghg_emission_t_co2_eq_per_year)/base_run_df$ghg_emission_t_co2_eq_per_year)*100) - - ghg_emission_t_co2_eq_per_ha_per_year <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_ha_per_year - base_run_df$ghg_emission_t_co2_eq_per_ha_per_year)/base_run_df$ghg_emission_t_co2_eq_per_ha_per_year)*100),0, - ((scenario_df$ghg_emission_t_co2_eq_per_ha_per_year - base_run_df$ghg_emission_t_co2_eq_per_ha_per_year)/base_run_df$ghg_emission_t_co2_eq_per_ha_per_year)*100) - - ghg_emission_t_co2_eq_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_kg_fpcm - base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm)/base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm)*100),0, - ((scenario_df$ghg_emission_t_co2_eq_per_kg_fpcm - base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm)/base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm)*100) - - ghg_emission_t_co2_eq_per_kg_meat <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_kg_meat - base_run_df$ghg_emission_t_co2_eq_per_kg_meat)/base_run_df$ghg_emission_t_co2_eq_per_kg_meat)*100),0, - ((scenario_df$ghg_emission_t_co2_eq_per_kg_meat - base_run_df$ghg_emission_t_co2_eq_per_kg_meat)/base_run_df$ghg_emission_t_co2_eq_per_kg_meat)*100) - - ghg_emission_t_co2_eq_per_kg_protein <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_kg_protein - base_run_df$ghg_emission_t_co2_eq_per_kg_protein)/base_run_df$ghg_emission_t_co2_eq_per_kg_protein)*100),0, - ((scenario_df$ghg_emission_t_co2_eq_per_kg_protein - base_run_df$ghg_emission_t_co2_eq_per_kg_protein)/base_run_df$ghg_emission_t_co2_eq_per_kg_protein)*100) - - percent_precipitation_used_for_feed_production <- ifelse(!is.finite(((scenario_df$percent_precipitation_used_for_feed_production - base_run_df$percent_precipitation_used_for_feed_production)/base_run_df$percent_precipitation_used_for_feed_production)*100),0, - ((scenario_df$percent_precipitation_used_for_feed_production - base_run_df$percent_precipitation_used_for_feed_production)/base_run_df$percent_precipitation_used_for_feed_production)*100) - - total_water_use_m3 <- ifelse(!is.finite(((scenario_df$total_water_use_m3 - base_run_df$total_water_use_m3)/base_run_df$total_water_use_m3)*100),0, - ((scenario_df$total_water_use_m3 - base_run_df$total_water_use_m3)/base_run_df$total_water_use_m3)*100) - - total_water_use_m3_per_ha <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_ha - base_run_df$total_water_use_m3_per_ha)/base_run_df$total_water_use_m3_per_ha)*100),0, - ((scenario_df$total_water_use_m3_per_ha - base_run_df$total_water_use_m3_per_ha)/base_run_df$total_water_use_m3_per_ha)*100) - - total_water_use_m3_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_kg_fpcm - base_run_df$total_water_use_m3_per_kg_fpcm)/base_run_df$total_water_use_m3_per_kg_fpcm)*100),0, - ((scenario_df$total_water_use_m3_per_kg_fpcm - base_run_df$total_water_use_m3_per_kg_fpcm)/base_run_df$total_water_use_m3_per_kg_fpcm)*100) - - total_water_use_m3_per_kg_meat <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_kg_meat - base_run_df$total_water_use_m3_per_kg_meat)/base_run_df$total_water_use_m3_per_kg_meat)*100),0, - ((scenario_df$total_water_use_m3_per_kg_meat - base_run_df$total_water_use_m3_per_kg_meat)/base_run_df$total_water_use_m3_per_kg_meat)*100) - - total_water_use_m3_per_kg_protein <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_kg_protein - base_run_df$total_water_use_m3_per_kg_protein)/base_run_df$total_water_use_m3_per_kg_protein)*100),0, - ((scenario_df$total_water_use_m3_per_kg_protein - base_run_df$total_water_use_m3_per_kg_protein)/base_run_df$total_water_use_m3_per_kg_protein)*100) - - carbon_stock_change_t_co2eq_per_year <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_year - base_run_df$carbon_stock_change_t_co2eq_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_year)*100),0, - ((scenario_df$carbon_stock_change_t_co2eq_per_year - base_run_df$carbon_stock_change_t_co2eq_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_year)*100) - - carbon_stock_change_t_co2eq_per_ha_per_year <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_ha_per_year - base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year)*100),0, - ((scenario_df$carbon_stock_change_t_co2eq_per_ha_per_year - base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year)*100) - - carbon_stock_change_t_co2eq_per_fpcm <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_fpcm - base_run_df$carbon_stock_change_t_co2eq_per_fpcm)/base_run_df$carbon_stock_change_t_co2eq_per_fpcm)*100),0, - ((scenario_df$carbon_stock_change_t_co2eq_per_fpcm - base_run_df$carbon_stock_change_t_co2eq_per_fpcm)/base_run_df$carbon_stock_change_t_co2eq_per_fpcm)*100) - - carbon_stock_change_t_co2eq_per_meat <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_meat - base_run_df$carbon_stock_change_t_co2eq_per_meat)/base_run_df$carbon_stock_change_t_co2eq_per_meat)*100),0, - ((scenario_df$carbon_stock_change_t_co2eq_per_meat - base_run_df$carbon_stock_change_t_co2eq_per_meat)/base_run_df$carbon_stock_change_t_co2eq_per_meat)*100) - - carbon_stock_change_t_co2eq_per_protein <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_protein - base_run_df$carbon_stock_change_t_co2eq_per_protein)/base_run_df$carbon_stock_change_t_co2eq_per_protein)*100),0, - ((scenario_df$carbon_stock_change_t_co2eq_per_protein - base_run_df$carbon_stock_change_t_co2eq_per_protein)/base_run_df$carbon_stock_change_t_co2eq_per_protein)*100) - - total_milk_produced_energy_kcal_per_year <- ifelse(!is.finite(((scenario_df$total_milk_produced_energy_kcal_per_year - base_run_df$total_milk_produced_energy_kcal_per_year)/base_run_df$total_milk_produced_energy_kcal_per_year)*100),0, - ((scenario_df$total_milk_produced_energy_kcal_per_year - base_run_df$total_milk_produced_energy_kcal_per_year)/base_run_df$total_milk_produced_energy_kcal_per_year)*100) - - total_meat_produced_energy_kcal_per_year <- ifelse(!is.finite(((scenario_df$total_meat_produced_energy_kcal_per_year - base_run_df$total_meat_produced_energy_kcal_per_year)/base_run_df$total_meat_produced_energy_kcal_per_year)*100),0, - ((scenario_df$total_meat_produced_energy_kcal_per_year - base_run_df$total_meat_produced_energy_kcal_per_year)/base_run_df$total_meat_produced_energy_kcal_per_year)*100) - - total_milk_produced_ame_days_per_year <- ifelse(!is.finite(((scenario_df$total_milk_produced_ame_days_per_year - base_run_df$total_milk_produced_ame_days_per_year)/base_run_df$total_milk_produced_ame_days_per_year)*100),0, - ((scenario_df$total_milk_produced_ame_days_per_year - base_run_df$total_milk_produced_ame_days_per_year)/base_run_df$total_milk_produced_ame_days_per_year)*100) - - total_meat_produced_ame_days_per_year <- ifelse(!is.finite(((scenario_df$total_meat_produced_ame_days_per_year - base_run_df$total_meat_produced_ame_days_per_year)/base_run_df$total_meat_produced_ame_days_per_year)*100),0, - ((scenario_df$total_meat_produced_ame_days_per_year - base_run_df$total_meat_produced_ame_days_per_year)/base_run_df$total_meat_produced_ame_days_per_year)*100) - - total_carbon_balance_per_fpcm <- ifelse(!is.finite(((scenario_df$total_carbon_balance_per_fpcm - base_run_df$total_carbon_balance_per_fpcm)/base_run_df$total_carbon_balance_per_fpcm)*100),0, - ((scenario_df$total_carbon_balance_per_fpcm - base_run_df$total_carbon_balance_per_fpcm)/base_run_df$total_carbon_balance_per_fpcm)*100) - - total_carbon_balance_per_meat <- ifelse(!is.finite(((scenario_df$total_carbon_balance_per_meat - base_run_df$total_carbon_balance_per_meat)/base_run_df$total_carbon_balance_per_meat)*100),0, - ((scenario_df$total_carbon_balance_per_meat - base_run_df$total_carbon_balance_per_meat)/base_run_df$total_carbon_balance_per_meat)*100) - - total_carbon_balance_per_protein <- ifelse(!is.finite(((scenario_df$total_carbon_balance_per_protein - base_run_df$total_carbon_balance_per_protein)/base_run_df$total_carbon_balance_per_protein)*100),0, - ((scenario_df$total_carbon_balance_per_protein - base_run_df$total_carbon_balance_per_protein)/base_run_df$total_carbon_balance_per_protein)*100) - - scenarioList[[i]] <- data.frame(scenario, - total_milk_produced_kg_fpcm_per_year, - total_meat_produced_kg_per_year, - total_protein_produced_kg_per_year, - total_tlu, - total_land_requirement_ha, - total_land_requirement_ha_per_kg_fpcm, - total_land_requirement_ha_per_kg_meat, - total_land_requirement_ha_per_kg_protein, - total_land_requirement_ha_per_tlu, - total_n_balance_kg_n_per_year, - percent_area_mining, - percent_area_leaching, - n_balance_kg_n_per_ha_per_year, - n_balance_kg_n_per_kg_fpcm, - n_balance_kg_n_per_kg_meat, - n_balance_kg_n_per_kg_protein, - erosion_t_soil_year, - erosion_t_soil_per_ha_per_year, - erosion_kgsoil_per_kg_fpcm, - erosion_kgsoil_per_kg_meat, - erosion_kgsoil_per_kg_protein, - ghg_emission_t_co2_eq_per_year, - ghg_emission_t_co2_eq_per_ha_per_year, - ghg_emission_t_co2_eq_per_kg_fpcm, - ghg_emission_t_co2_eq_per_kg_meat, - ghg_emission_t_co2_eq_per_kg_protein, - percent_precipitation_used_for_feed_production, - total_water_use_m3, - total_water_use_m3_per_ha, - total_water_use_m3_per_kg_fpcm, - total_water_use_m3_per_kg_meat, - total_water_use_m3_per_kg_protein, - carbon_stock_change_t_co2eq_per_year, - carbon_stock_change_t_co2eq_per_ha_per_year, - carbon_stock_change_t_co2eq_per_fpcm, - carbon_stock_change_t_co2eq_per_meat, - carbon_stock_change_t_co2eq_per_protein, - total_milk_produced_energy_kcal_per_year, - total_meat_produced_energy_kcal_per_year, - total_milk_produced_ame_days_per_year, - total_meat_produced_ame_days_per_year, - total_carbon_balance_per_fpcm, - total_carbon_balance_per_meat, - total_carbon_balance_per_protein) + total_milk_produced_kg_fpcm_per_year <- ifelse(!is.finite(((scenario_df$total_milk_produced_kg_fpcm_per_year - + base_run_df$total_milk_produced_kg_fpcm_per_year)/base_run_df$total_milk_produced_kg_fpcm_per_year) * + 100), 0, ((scenario_df$total_milk_produced_kg_fpcm_per_year - + base_run_df$total_milk_produced_kg_fpcm_per_year)/base_run_df$total_milk_produced_kg_fpcm_per_year) * + 100) + total_meat_produced_kg_per_year <- ifelse(!is.finite(((scenario_df$total_meat_produced_kg_per_year - + base_run_df$total_meat_produced_kg_per_year)/base_run_df$total_meat_produced_kg_per_year) * + 100), 0, ((scenario_df$total_meat_produced_kg_per_year - + base_run_df$total_meat_produced_kg_per_year)/base_run_df$total_meat_produced_kg_per_year) * + 100) + total_protein_produced_kg_per_year <- ifelse(!is.finite(((scenario_df$total_protein_produced_kg_per_year - + base_run_df$total_protein_produced_kg_per_year)/base_run_df$total_protein_produced_kg_per_year) * + 100), 0, ((scenario_df$total_protein_produced_kg_per_year - + base_run_df$total_protein_produced_kg_per_year)/base_run_df$total_protein_produced_kg_per_year) * + 100) + total_tlu <- ifelse(!is.finite(((scenario_df$total_tlu - + base_run_df$total_tlu)/base_run_df$total_tlu) * + 100), 0, ((scenario_df$total_tlu - base_run_df$total_tlu)/base_run_df$total_tlu) * + 100) + total_land_requirement_ha <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha - + base_run_df$total_land_requirement_ha)/base_run_df$total_land_requirement_ha) * + 100), 0, ((scenario_df$total_land_requirement_ha - + base_run_df$total_land_requirement_ha)/base_run_df$total_land_requirement_ha) * + 100) + total_land_requirement_ha_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_kg_fpcm - + base_run_df$total_land_requirement_ha_per_kg_fpcm)/base_run_df$total_land_requirement_ha_per_kg_fpcm) * + 100), 0, ((scenario_df$total_land_requirement_ha_per_kg_fpcm - + base_run_df$total_land_requirement_ha_per_kg_fpcm)/base_run_df$total_land_requirement_ha_per_kg_fpcm) * + 100) + total_land_requirement_ha_per_kg_meat <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_kg_meat - + base_run_df$total_land_requirement_ha_per_kg_meat)/base_run_df$total_land_requirement_ha_per_kg_meat) * + 100), 0, ((scenario_df$total_land_requirement_ha_per_kg_meat - + base_run_df$total_land_requirement_ha_per_kg_meat)/base_run_df$total_land_requirement_ha_per_kg_meat) * + 100) + total_land_requirement_ha_per_kg_protein <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_kg_protein - + base_run_df$total_land_requirement_ha_per_kg_protein)/base_run_df$total_land_requirement_ha_per_kg_protein) * + 100), 0, ((scenario_df$total_land_requirement_ha_per_kg_protein - + base_run_df$total_land_requirement_ha_per_kg_protein)/base_run_df$total_land_requirement_ha_per_kg_protein) * + 100) + total_land_requirement_ha_per_tlu <- ifelse(!is.finite(((scenario_df$total_land_requirement_ha_per_tlu - + base_run_df$total_land_requirement_ha_per_tlu)/base_run_df$total_land_requirement_ha_per_tlu) * + 100), 0, ((scenario_df$total_land_requirement_ha_per_tlu - + base_run_df$total_land_requirement_ha_per_tlu)/base_run_df$total_land_requirement_ha_per_tlu) * + 100) + total_n_balance_kg_n_per_year <- ifelse(!is.finite(((scenario_df$total_n_balance_kg_n_per_year - + base_run_df$total_n_balance_kg_n_per_year)/base_run_df$total_n_balance_kg_n_per_year) * + 100), 0, ((scenario_df$total_n_balance_kg_n_per_year - + base_run_df$total_n_balance_kg_n_per_year)/base_run_df$total_n_balance_kg_n_per_year) * + 100) + percent_area_mining <- ifelse(!is.finite(((scenario_df$percent_area_mining - + base_run_df$percent_area_mining)/base_run_df$percent_area_mining) * + 100), 0, ((scenario_df$percent_area_mining - base_run_df$percent_area_mining)/base_run_df$percent_area_mining) * + 100) + percent_area_leaching <- ifelse(!is.finite(((scenario_df$percent_area_leaching - + base_run_df$percent_area_leaching)/base_run_df$percent_area_leaching) * + 100), 0, ((scenario_df$percent_area_leaching - base_run_df$percent_area_leaching)/base_run_df$percent_area_leaching) * + 100) + n_balance_kg_n_per_ha_per_year <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_ha_per_year - + base_run_df$n_balance_kg_n_per_ha_per_year)/base_run_df$n_balance_kg_n_per_ha_per_year) * + 100), 0, ((scenario_df$n_balance_kg_n_per_ha_per_year - + base_run_df$n_balance_kg_n_per_ha_per_year)/base_run_df$n_balance_kg_n_per_ha_per_year) * + 100) + n_balance_kg_n_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_kg_fpcm - + base_run_df$n_balance_kg_n_per_kg_fpcm)/base_run_df$n_balance_kg_n_per_kg_fpcm) * + 100), 0, ((scenario_df$n_balance_kg_n_per_kg_fpcm - + base_run_df$n_balance_kg_n_per_kg_fpcm)/base_run_df$n_balance_kg_n_per_kg_fpcm) * + 100) + n_balance_kg_n_per_kg_meat <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_kg_meat - + base_run_df$n_balance_kg_n_per_kg_meat)/base_run_df$n_balance_kg_n_per_kg_meat) * + 100), 0, ((scenario_df$n_balance_kg_n_per_kg_meat - + base_run_df$n_balance_kg_n_per_kg_meat)/base_run_df$n_balance_kg_n_per_kg_meat) * + 100) + n_balance_kg_n_per_kg_protein <- ifelse(!is.finite(((scenario_df$n_balance_kg_n_per_kg_protein - + base_run_df$n_balance_kg_n_per_kg_protein)/base_run_df$n_balance_kg_n_per_kg_protein) * + 100), 0, ((scenario_df$n_balance_kg_n_per_kg_protein - + base_run_df$n_balance_kg_n_per_kg_protein)/base_run_df$n_balance_kg_n_per_kg_protein) * + 100) + erosion_t_soil_year <- ifelse(!is.finite(((scenario_df$erosion_t_soil_year - + base_run_df$erosion_t_soil_year)/base_run_df$erosion_t_soil_year) * + 100), 0, ((scenario_df$erosion_t_soil_year - base_run_df$erosion_t_soil_year)/base_run_df$erosion_t_soil_year) * + 100) + erosion_t_soil_per_ha_per_year <- ifelse(!is.finite(((scenario_df$erosion_t_soil_per_ha_per_year - + base_run_df$erosion_t_soil_per_ha_per_year)/base_run_df$erosion_t_soil_per_ha_per_year) * + 100), 0, ((scenario_df$erosion_t_soil_per_ha_per_year - + base_run_df$erosion_t_soil_per_ha_per_year)/base_run_df$erosion_t_soil_per_ha_per_year) * + 100) + erosion_kgsoil_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$erosion_kgsoil_per_kg_fpcm - + base_run_df$erosion_kgsoil_per_kg_fpcm)/base_run_df$erosion_kgsoil_per_kg_fpcm) * + 100), 0, ((scenario_df$erosion_kgsoil_per_kg_fpcm - + base_run_df$erosion_kgsoil_per_kg_fpcm)/base_run_df$erosion_kgsoil_per_kg_fpcm) * + 100) + erosion_kgsoil_per_kg_meat <- ifelse(!is.finite(((scenario_df$erosion_kgsoil_per_kg_meat - + base_run_df$erosion_kgsoil_per_kg_meat)/base_run_df$erosion_kgsoil_per_kg_meat) * + 100), 0, ((scenario_df$erosion_kgsoil_per_kg_meat - + base_run_df$erosion_kgsoil_per_kg_meat)/base_run_df$erosion_kgsoil_per_kg_meat) * + 100) + erosion_kgsoil_per_kg_protein <- ifelse(!is.finite(((scenario_df$erosion_kgsoil_per_kg_protein - + base_run_df$erosion_kgsoil_per_kg_protein)/base_run_df$erosion_kgsoil_per_kg_protein) * + 100), 0, ((scenario_df$erosion_kgsoil_per_kg_protein - + base_run_df$erosion_kgsoil_per_kg_protein)/base_run_df$erosion_kgsoil_per_kg_protein) * + 100) + ghg_emission_t_co2_eq_per_year <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_year - + base_run_df$ghg_emission_t_co2_eq_per_year)/base_run_df$ghg_emission_t_co2_eq_per_year) * + 100), 0, ((scenario_df$ghg_emission_t_co2_eq_per_year - + base_run_df$ghg_emission_t_co2_eq_per_year)/base_run_df$ghg_emission_t_co2_eq_per_year) * + 100) + ghg_emission_t_co2_eq_per_ha_per_year <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_ha_per_year - + base_run_df$ghg_emission_t_co2_eq_per_ha_per_year)/base_run_df$ghg_emission_t_co2_eq_per_ha_per_year) * + 100), 0, ((scenario_df$ghg_emission_t_co2_eq_per_ha_per_year - + base_run_df$ghg_emission_t_co2_eq_per_ha_per_year)/base_run_df$ghg_emission_t_co2_eq_per_ha_per_year) * + 100) + ghg_emission_t_co2_eq_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_kg_fpcm - + base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm)/base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm) * + 100), 0, ((scenario_df$ghg_emission_t_co2_eq_per_kg_fpcm - + base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm)/base_run_df$ghg_emission_t_co2_eq_per_kg_fpcm) * + 100) + ghg_emission_t_co2_eq_per_kg_meat <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_kg_meat - + base_run_df$ghg_emission_t_co2_eq_per_kg_meat)/base_run_df$ghg_emission_t_co2_eq_per_kg_meat) * + 100), 0, ((scenario_df$ghg_emission_t_co2_eq_per_kg_meat - + base_run_df$ghg_emission_t_co2_eq_per_kg_meat)/base_run_df$ghg_emission_t_co2_eq_per_kg_meat) * + 100) + ghg_emission_t_co2_eq_per_kg_protein <- ifelse(!is.finite(((scenario_df$ghg_emission_t_co2_eq_per_kg_protein - + base_run_df$ghg_emission_t_co2_eq_per_kg_protein)/base_run_df$ghg_emission_t_co2_eq_per_kg_protein) * + 100), 0, ((scenario_df$ghg_emission_t_co2_eq_per_kg_protein - + base_run_df$ghg_emission_t_co2_eq_per_kg_protein)/base_run_df$ghg_emission_t_co2_eq_per_kg_protein) * + 100) + percent_precipitation_used_for_feed_production <- ifelse(!is.finite(((scenario_df$percent_precipitation_used_for_feed_production - + base_run_df$percent_precipitation_used_for_feed_production)/base_run_df$percent_precipitation_used_for_feed_production) * + 100), 0, ((scenario_df$percent_precipitation_used_for_feed_production - + base_run_df$percent_precipitation_used_for_feed_production)/base_run_df$percent_precipitation_used_for_feed_production) * + 100) + total_water_use_m3 <- ifelse(!is.finite(((scenario_df$total_water_use_m3 - + base_run_df$total_water_use_m3)/base_run_df$total_water_use_m3) * + 100), 0, ((scenario_df$total_water_use_m3 - base_run_df$total_water_use_m3)/base_run_df$total_water_use_m3) * + 100) + total_water_use_m3_per_ha <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_ha - + base_run_df$total_water_use_m3_per_ha)/base_run_df$total_water_use_m3_per_ha) * + 100), 0, ((scenario_df$total_water_use_m3_per_ha - + base_run_df$total_water_use_m3_per_ha)/base_run_df$total_water_use_m3_per_ha) * + 100) + total_water_use_m3_per_kg_fpcm <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_kg_fpcm - + base_run_df$total_water_use_m3_per_kg_fpcm)/base_run_df$total_water_use_m3_per_kg_fpcm) * + 100), 0, ((scenario_df$total_water_use_m3_per_kg_fpcm - + base_run_df$total_water_use_m3_per_kg_fpcm)/base_run_df$total_water_use_m3_per_kg_fpcm) * + 100) + total_water_use_m3_per_kg_meat <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_kg_meat - + base_run_df$total_water_use_m3_per_kg_meat)/base_run_df$total_water_use_m3_per_kg_meat) * + 100), 0, ((scenario_df$total_water_use_m3_per_kg_meat - + base_run_df$total_water_use_m3_per_kg_meat)/base_run_df$total_water_use_m3_per_kg_meat) * + 100) + total_water_use_m3_per_kg_protein <- ifelse(!is.finite(((scenario_df$total_water_use_m3_per_kg_protein - + base_run_df$total_water_use_m3_per_kg_protein)/base_run_df$total_water_use_m3_per_kg_protein) * + 100), 0, ((scenario_df$total_water_use_m3_per_kg_protein - + base_run_df$total_water_use_m3_per_kg_protein)/base_run_df$total_water_use_m3_per_kg_protein) * + 100) + carbon_stock_change_t_co2eq_per_year <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_year - + base_run_df$carbon_stock_change_t_co2eq_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_year) * + 100), 0, ((scenario_df$carbon_stock_change_t_co2eq_per_year - + base_run_df$carbon_stock_change_t_co2eq_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_year) * + 100) + carbon_stock_change_t_co2eq_per_ha_per_year <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_ha_per_year - + base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year) * + 100), 0, ((scenario_df$carbon_stock_change_t_co2eq_per_ha_per_year - + base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year)/base_run_df$carbon_stock_change_t_co2eq_per_ha_per_year) * + 100) + carbon_stock_change_t_co2eq_per_fpcm <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_fpcm - + base_run_df$carbon_stock_change_t_co2eq_per_fpcm)/base_run_df$carbon_stock_change_t_co2eq_per_fpcm) * + 100), 0, ((scenario_df$carbon_stock_change_t_co2eq_per_fpcm - + base_run_df$carbon_stock_change_t_co2eq_per_fpcm)/base_run_df$carbon_stock_change_t_co2eq_per_fpcm) * + 100) + carbon_stock_change_t_co2eq_per_meat <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_meat - + base_run_df$carbon_stock_change_t_co2eq_per_meat)/base_run_df$carbon_stock_change_t_co2eq_per_meat) * + 100), 0, ((scenario_df$carbon_stock_change_t_co2eq_per_meat - + base_run_df$carbon_stock_change_t_co2eq_per_meat)/base_run_df$carbon_stock_change_t_co2eq_per_meat) * + 100) + carbon_stock_change_t_co2eq_per_protein <- ifelse(!is.finite(((scenario_df$carbon_stock_change_t_co2eq_per_protein - + base_run_df$carbon_stock_change_t_co2eq_per_protein)/base_run_df$carbon_stock_change_t_co2eq_per_protein) * + 100), 0, ((scenario_df$carbon_stock_change_t_co2eq_per_protein - + base_run_df$carbon_stock_change_t_co2eq_per_protein)/base_run_df$carbon_stock_change_t_co2eq_per_protein) * + 100) + total_milk_produced_energy_kcal_per_year <- ifelse(!is.finite(((scenario_df$total_milk_produced_energy_kcal_per_year - + base_run_df$total_milk_produced_energy_kcal_per_year)/base_run_df$total_milk_produced_energy_kcal_per_year) * + 100), 0, ((scenario_df$total_milk_produced_energy_kcal_per_year - + base_run_df$total_milk_produced_energy_kcal_per_year)/base_run_df$total_milk_produced_energy_kcal_per_year) * + 100) + total_meat_produced_energy_kcal_per_year <- ifelse(!is.finite(((scenario_df$total_meat_produced_energy_kcal_per_year - + base_run_df$total_meat_produced_energy_kcal_per_year)/base_run_df$total_meat_produced_energy_kcal_per_year) * + 100), 0, ((scenario_df$total_meat_produced_energy_kcal_per_year - + base_run_df$total_meat_produced_energy_kcal_per_year)/base_run_df$total_meat_produced_energy_kcal_per_year) * + 100) + total_milk_produced_ame_days_per_year <- ifelse(!is.finite(((scenario_df$total_milk_produced_ame_days_per_year - + base_run_df$total_milk_produced_ame_days_per_year)/base_run_df$total_milk_produced_ame_days_per_year) * + 100), 0, ((scenario_df$total_milk_produced_ame_days_per_year - + base_run_df$total_milk_produced_ame_days_per_year)/base_run_df$total_milk_produced_ame_days_per_year) * + 100) + total_meat_produced_ame_days_per_year <- ifelse(!is.finite(((scenario_df$total_meat_produced_ame_days_per_year - + base_run_df$total_meat_produced_ame_days_per_year)/base_run_df$total_meat_produced_ame_days_per_year) * + 100), 0, ((scenario_df$total_meat_produced_ame_days_per_year - + base_run_df$total_meat_produced_ame_days_per_year)/base_run_df$total_meat_produced_ame_days_per_year) * + 100) + total_carbon_balance_per_fpcm <- ifelse(!is.finite(((scenario_df$total_carbon_balance_per_fpcm - + base_run_df$total_carbon_balance_per_fpcm)/base_run_df$total_carbon_balance_per_fpcm) * + 100), 0, ((scenario_df$total_carbon_balance_per_fpcm - + base_run_df$total_carbon_balance_per_fpcm)/base_run_df$total_carbon_balance_per_fpcm) * + 100) + total_carbon_balance_per_meat <- ifelse(!is.finite(((scenario_df$total_carbon_balance_per_meat - + base_run_df$total_carbon_balance_per_meat)/base_run_df$total_carbon_balance_per_meat) * + 100), 0, ((scenario_df$total_carbon_balance_per_meat - + base_run_df$total_carbon_balance_per_meat)/base_run_df$total_carbon_balance_per_meat) * + 100) + total_carbon_balance_per_protein <- ifelse(!is.finite(((scenario_df$total_carbon_balance_per_protein - + base_run_df$total_carbon_balance_per_protein)/base_run_df$total_carbon_balance_per_protein) * + 100), 0, ((scenario_df$total_carbon_balance_per_protein - + base_run_df$total_carbon_balance_per_protein)/base_run_df$total_carbon_balance_per_protein) * + 100) + scenarioList[[i]] <- data.frame(scenario, total_milk_produced_kg_fpcm_per_year, + total_meat_produced_kg_per_year, total_protein_produced_kg_per_year, + total_tlu, total_land_requirement_ha, total_land_requirement_ha_per_kg_fpcm, + total_land_requirement_ha_per_kg_meat, total_land_requirement_ha_per_kg_protein, + total_land_requirement_ha_per_tlu, total_n_balance_kg_n_per_year, + percent_area_mining, percent_area_leaching, n_balance_kg_n_per_ha_per_year, + n_balance_kg_n_per_kg_fpcm, n_balance_kg_n_per_kg_meat, + n_balance_kg_n_per_kg_protein, erosion_t_soil_year, + erosion_t_soil_per_ha_per_year, erosion_kgsoil_per_kg_fpcm, + erosion_kgsoil_per_kg_meat, erosion_kgsoil_per_kg_protein, + ghg_emission_t_co2_eq_per_year, ghg_emission_t_co2_eq_per_ha_per_year, + ghg_emission_t_co2_eq_per_kg_fpcm, ghg_emission_t_co2_eq_per_kg_meat, + ghg_emission_t_co2_eq_per_kg_protein, percent_precipitation_used_for_feed_production, + total_water_use_m3, total_water_use_m3_per_ha, total_water_use_m3_per_kg_fpcm, + total_water_use_m3_per_kg_meat, total_water_use_m3_per_kg_protein, + carbon_stock_change_t_co2eq_per_year, carbon_stock_change_t_co2eq_per_ha_per_year, + carbon_stock_change_t_co2eq_per_fpcm, carbon_stock_change_t_co2eq_per_meat, + carbon_stock_change_t_co2eq_per_protein, total_milk_produced_energy_kcal_per_year, + total_meat_produced_energy_kcal_per_year, total_milk_produced_ame_days_per_year, + total_meat_produced_ame_days_per_year, total_carbon_balance_per_fpcm, + total_carbon_balance_per_meat, total_carbon_balance_per_protein) } - results <- scenarioList %>% dplyr::bind_rows() - - # Save results in excel - excel_output_path <- paste0(oDir2, "/scenario_comparison.xlsx") - write.xlsx(results, excel_output_path, overwrite = TRUE) - - # Generate plots - - for(i in 2:ncol(results)){ - + for (i in 2:ncol(results)) { datos <- results %>% select(1, all_of(i)) - - # titles tt <- colnames(datos[2]) - - - if(tt == "total_milk_produced_kg_fpcm_per_year"){ + if (tt == "total_milk_produced_kg_fpcm_per_year") { title = "Total milk FPCM (kg/yr)" y_title = "% change in milk FPCM (kg/yr)" - }else if(tt == "total_meat_produced_kg_per_year"){ + } + else if (tt == "total_meat_produced_kg_per_year") { title = "Total meat (kg/yr)" y_title = "% change in meat (kg/yr)" - }else if(tt == "total_protein_produced_kg_per_year"){ + } + else if (tt == "total_protein_produced_kg_per_year") { title = "Total protein (kg/yr)" y_title = "% change in protein (kg/yr)" - }else if (tt == "total_tlu"){ + } + else if (tt == "total_tlu") { title = "Tropical Livestock Unit" y_title = "% change in Tropical Livestock Unit" - }else if (tt == "total_land_requirement_ha"){ + } + else if (tt == "total_land_requirement_ha") { title = "Land required (ha/yr)" y_title = "% change in land required (ha/yr)" - }else if (tt == "total_land_requirement_ha_per_kg_fpcm"){ + } + else if (tt == "total_land_requirement_ha_per_kg_fpcm") { title = "Land required (ha/kg FPCM)" y_title = "% change in land required (ha/kg FPCM)" - }else if (tt == "total_land_requirement_ha_per_kg_meat"){ + } + else if (tt == "total_land_requirement_ha_per_kg_meat") { title = "Land required (ha/kg meat)" y_title = "% change in land required (ha/kg meat)" - }else if (tt == "total_land_requirement_ha_per_kg_protein"){ + } + else if (tt == "total_land_requirement_ha_per_kg_protein") { title = "Land required (ha/kg protein)" y_title = "% change in land required (ha/kg protein)" - }else if (tt == "total_land_requirement_ha_per_tlu"){ + } + else if (tt == "total_land_requirement_ha_per_tlu") { title = "Land required (ha/TLU)" y_title = "% change in land required (ha/TLU)" - }else if (tt == "total_n_balance_kg_n_per_year"){ + } + else if (tt == "total_n_balance_kg_n_per_year") { title = "N balance (kg N/yr)" y_title = "% change in N balance (kg N/yr)" - }else if (tt == "percent_area_mining"){ + } + else if (tt == "percent_area_mining") { title = "Soil mining (%)" y_title = "% change in soil mining" - }else if (tt == "percent_area_leaching"){ + } + else if (tt == "percent_area_leaching") { title = "Soil leaching (%)" y_title = "% change in soil leaching" - }else if (tt == "n_balance_kg_n_per_ha_per_year"){ + } + else if (tt == "n_balance_kg_n_per_ha_per_year") { title = "N balance (kg N/ha/yr)" y_title = "% change in N balance (kg N/ha/yr)" - }else if (tt == "n_balance_kg_n_per_kg_fpcm"){ + } + else if (tt == "n_balance_kg_n_per_kg_fpcm") { title = "N balance (kg N/kg FPCM)" y_title = "% change in N balance (kg N/kg FPCM)" - }else if (tt == "n_balance_kg_n_per_kg_meat"){ + } + else if (tt == "n_balance_kg_n_per_kg_meat") { title = "N balance (kg N/kg meat)" y_title = "% change in N balance (kg N/kg meat)" - }else if (tt == "n_balance_kg_n_per_kg_protein"){ + } + else if (tt == "n_balance_kg_n_per_kg_protein") { title = "N balance (kg N/kg protein)" y_title = "% change in N balance (kg N/kg protein)" - }else if (tt == "erosion_t_soil_year"){ + } + else if (tt == "erosion_t_soil_year") { title = "Erosion (t soil/yr)" y_title = "% change in erosion (t soil/yr)" - }else if (tt == "erosion_t_soil_per_ha_per_year"){ + } + else if (tt == "erosion_t_soil_per_ha_per_year") { title = "Erosion (t soil/ha/yr)" y_title = "% change in erosion (t soil/ha/yr)" - }else if (tt == "erosion_kgsoil_per_kg_fpcm"){ + } + else if (tt == "erosion_kgsoil_per_kg_fpcm") { title = "Erosion (t soil/kg FPCM)" y_title = "% change in erosion (t soil/kg FPCM)" - }else if (tt == "erosion_kgsoil_per_kg_meat"){ + } + else if (tt == "erosion_kgsoil_per_kg_meat") { title = "Erosion (t soil/kg meat)" y_title = "% change in erosion (t soil/kg meat)" - }else if (tt == "erosion_kgsoil_per_kg_protein"){ + } + else if (tt == "erosion_kgsoil_per_kg_protein") { title = "Erosion (t soil/kg protein)" y_title = "% change in erosion (t soil/kg protein)" - }else if (tt == "ghg_emission_t_co2_eq_per_year"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_year") { title = "GHG (t CO2eq/yr)" y_title = "% change in GHG (t CO2eq/yr)" - }else if (tt == "ghg_emission_t_co2_eq_per_ha_per_year"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_ha_per_year") { title = "GHG (t CO2eq/ha/yr)" y_title = "% change in GHG (t CO2eq/ha/yr)" - }else if (tt == "ghg_emission_t_co2_eq_per_kg_fpcm"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_kg_fpcm") { title = "GHG (CO2eq/kg FPCM)" y_title = "% change in GHG (CO2eq/kg FPCM)" - }else if (tt == "ghg_emission_t_co2_eq_per_kg_meat"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_kg_meat") { title = "GHG (CO2eq/kg meat)" y_title = "% change in GHG (CO2eq/kg meat)" - }else if (tt == "ghg_emission_t_co2_eq_per_kg_protein"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_kg_protein") { title = "GHG (CO2eq/kg protein)" y_title = "% change in GHG (CO2eq/kg protein)" - }else if (tt == "percent_precipitation_used_for_feed_production"){ + } + else if (tt == "percent_precipitation_used_for_feed_production") { title = "% Precipitation use for feed production" y_title = "% change in % precipitation use for feed production" - }else if (tt == "total_water_use_m3"){ + } + else if (tt == "total_water_use_m3") { title = "Water use (m3/yr)" y_title = "% change water use (m3/yr)" - }else if (tt == "total_water_use_m3_per_ha"){ + } + else if (tt == "total_water_use_m3_per_ha") { title = "Water use (m3/ha)" y_title = "% change in water use (m3/ha)" - }else if (tt == "total_water_use_m3_per_kg_fpcm"){ + } + else if (tt == "total_water_use_m3_per_kg_fpcm") { title = "Water use (m3/kg FPCM)" y_title = "% change in water use (m3/kg FPCM)" - }else if (tt == "total_water_use_m3_per_kg_meat"){ + } + else if (tt == "total_water_use_m3_per_kg_meat") { title = "Water use (m3/kg meat)" y_title = "% change in water use (m3/kg meat)" - }else if (tt == "total_water_use_m3_per_kg_protein"){ + } + else if (tt == "total_water_use_m3_per_kg_protein") { title = "Water (m3/kg protein)" y_title = "% change in water (m3/kg protein)" - }else if (tt == "carbon_stock_change_t_co2eq_per_year"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_year") { title = "Carbon stock changes (t CO2eq/yr)" y_title = "% change in carbon stock changes (t CO2eq/yr)" - }else if (tt == "carbon_stock_change_t_co2eq_per_ha_per_year"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_ha_per_year") { title = "Carbon stock changes (t CO2eq/ha/yr)" y_title = "% change in carbon stock changes (t CO2eq/ha/yr)" - }else if (tt == "carbon_stock_change_t_co2eq_per_fpcm"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_fpcm") { title = "Carbon stock changes (t CO2eq/kg FPCM)" y_title = "% change in carbon stock changes (t CO2eq/kg FPCM)" - }else if (tt == "carbon_stock_change_t_co2eq_per_meat"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_meat") { title = "Carbon stock changes (t CO2eq/kg meat)" y_title = "% change in carbon stock changes (t CO2eq/kg meat)" - }else if (tt == "carbon_stock_change_t_co2eq_per_protein"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_protein") { title = "Carbon stock changes (t CO2eq/kg protein)" y_title = "% change in carbon stock changes (t CO2eq/kg protein)" - }else if (tt == "total_milk_produced_energy_kcal_per_year"){ + } + else if (tt == "total_milk_produced_energy_kcal_per_year") { title = "Energy (kcal/kg FPCM)" y_title = "% change in energy (kcal/kg FPCM)" - }else if (tt == "total_meat_produced_energy_kcal_per_year"){ + } + else if (tt == "total_meat_produced_energy_kcal_per_year") { title = "Energy (kcal/kg meat)" y_title = "% change in energy (kcal/kg meat)" - }else if (tt == "total_milk_produced_ame_days_per_year"){ + } + else if (tt == "total_milk_produced_ame_days_per_year") { title = "AME (days/kg FPCM)" y_title = "% change in AME (days/kg FPCM)" - }else if (tt == "total_meat_produced_ame_days_per_year"){ + } + else if (tt == "total_meat_produced_ame_days_per_year") { title = "AME (days/kg meat)" y_title = "% change in AME (days/kg meat)" - }else if (tt == "total_carbon_balance_per_fpcm"){ + } + else if (tt == "total_carbon_balance_per_fpcm") { title = "Carbon balance (t CO2eq/kg FPCM)" y_title = "% change in carbon balance (t CO2eq/kg FPCM)" - }else if (tt == "total_carbon_balance_per_meat"){ + } + else if (tt == "total_carbon_balance_per_meat") { title = "Carbon balance (t CO2eq/kg meat)" y_title = "% change in carbon balance (t CO2eq/kg meat)" - }else if (tt == "total_carbon_balance_per_protein"){ + } + else if (tt == "total_carbon_balance_per_protein") { title = "Carbon balance (t CO2eq/kg protein)" y_title = "% change in carbon balance (t CO2eq/kg protein)" - }else{ + } + else { NA } - ggplot(datos, aes_string("scenario", y = tt, fill = "scenario")) + - geom_bar(stat = "identity", - position = "dodge", - width = 0.2) + - geom_text(aes(label = round(datos[,2],2)), position = position_dodge(width = 0.2), vjust = -0.5, size = 2) + - labs(x = "", y = y_title, title = title, fill = "Scenario") + - theme_bw() - - ggsave(paste0(oDir2, tt, ".png"), width = 150, height = 100, units = "mm") - + output_list[[tt]] <- list( + datos = datos, + tt = tt, + title = title, + y_title = y_title + ) } + return(output_list) } - diff --git a/R/feed_quality.R b/R/feed_quality.R index 67541e2..6ad7b9f 100644 --- a/R/feed_quality.R +++ b/R/feed_quality.R @@ -39,7 +39,6 @@ feed_quality <- function(para) { livestock_type_selected <- livestock_df[livestock_df$livetype_code == livestock, ] - for (season in 1:nrow(seasons)) { feed_production <- unnest(para[["feed_items"]], cols = c(feed_type_name)) @@ -49,9 +48,7 @@ feed_quality <- function(para) { feed_allocation <- list() for (i in 1:length(feed_types)) { - - feed_selected <- feed_production %>% dplyr::filter(feed_type_name %in% - feed_production$feed_type_name[i]) + feed_selected <- feed_production %>% dplyr::filter(feed_type_name %in% feed_types[i]) #feed_item <- as.data.frame(feed_selected[["feed_items"]]) @@ -77,7 +74,7 @@ feed_quality <- function(para) { seasons$season_name[season]) feed_item_selected <- feeding_seasons %>% - dplyr::filter(feed_item_code == feed_selected[["feed_item_code"]]) + dplyr::filter(feed_item_code %in% feed_selected[["feed_item_code"]]) livestock_selected <- feed_item_selected[["livestock"]] %>% as.data.frame() %>% @@ -135,7 +132,7 @@ feed_quality <- function(para) { feed_allocation_all[is.na(feed_allocation_all)] <- 0 # Bind and add into the season list - season_allocation[[season]] <- cbind(season_name = rep(feed_item_selected$season_name, times = nrow(feed_allocation_all)), + season_allocation[[season]] <- cbind(season_name = rep(feed_item_selected$season_name[1],times = nrow(feed_allocation_all)), livestock_category_code = rep(livestock_selected$livetype_code, times = nrow(feed_allocation_all)), livestock_category_name = rep(livestock_type_selected$livetype_desc, times = nrow(feed_allocation_all)), feed_allocation_all) @@ -155,4 +152,6 @@ feed_quality <- function(para) { # Bind by rows livestock_feed_allocation <- livestock_allocation %>% bind_rows() + return(livestock_feed_allocation) + } diff --git a/R/ghg_emission.R b/R/ghg_emission.R index 55bccea..1d829ef 100644 --- a/R/ghg_emission.R +++ b/R/ghg_emission.R @@ -97,7 +97,7 @@ ghg_emission <- function(para, energy_required, ghg_ipcc_data, land_required, ni #Computing methane enteric emission factor ef <- ym1%>% - mutate(enteric_methane_emissions = (ge_intake*(ym/100)*no_days)/55.65) #equation 10.21 + mutate(enteric_methane_emissions = ((ge_intake/no_days)*(ym/100)*no_days)/55.65) #equation 10.21 ############################################################################################################################ #Computing methane emission from manure management T2 diff --git a/R/merge_outputs.R b/R/merge_outputs.R index 974bbfe..2f1e648 100644 --- a/R/merge_outputs.R +++ b/R/merge_outputs.R @@ -47,14 +47,15 @@ #' soil_carbon <- soil_organic_carbon(para, land_required, biomass) #' ghg_emission <- ghg_emission(para,energy_required,ghg_para,land_required,nitrogen_balance) #' combineOutputs(para,feed_basket_quality,energy_required,land_required,soil_erosion,water_required, -#' nitrogen_balance,livestock_productivity,biomass,soil_carbon,ghg_emission,filePath,readMe,benchMark) +#' nitrogen_balance,livestock_productivity,biomass,soil_carbon,ghg_emission,filePath) #' } #' #' @export #' + combineOutputs <- function(para, feed_basket_quality, energy_required, land_required, soil_erosion, water_required, nitrogen_balance, livestock_productivity, - biomass,soil_carbon, ghg_emission, filePath, readMe, benchMark){ + biomass,soil_carbon, ghg_emission, filePath, primary_excel){ if (exists("para")) { para = para }else {para = "ERROR: Data is not provided"} @@ -106,18 +107,11 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ # Extract the file name fileName <- sub("\\.\\w+$", "", basename(filePath)) }else {filePath = "ERROR: File path is not provided"} - - if (exists("readMe")) { - readMe = readMe - # Load readme workbook - readMe_wb <- loadWorkbook(readMe) - }else {filePath = "ERROR: ReadMe file is not provided"} - - if (exists("benchMark")) { - benchMark = benchMark - # Load Benchmark workbook - benchMark_wb <- loadWorkbook(benchMark) - }else {filePath = "ERROR: Benchmark file is not provided"} + if (exists("primary_excel")) { + primary_excel = primary_excel + # Load primary_excel workbook + primary_excel_wb <- loadWorkbook(primary_excel) + }else {filePath = "ERROR: primary excel file is not provided"} feed_basket_quality <- lapply(feed_basket_quality, function(x) {x <- x[,-1]}) @@ -135,24 +129,12 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ # geom_text(aes(label = round(area_feed_total, 2)), vjust = -0.5, size = 1, angle = 45) + # theme_bw()+ # theme(axis.text.x = element_text(angle = 45, hjust = 1)) - land_required %>% + land_required_output <- land_required %>% group_by(feed, season_name) %>% summarise(area_feed_total = sum(area_feed, na.rm = TRUE)) %>% group_by(feed) %>% mutate(cumulative_area = cumsum(area_feed_total), - label_position = cumulative_area - 0.7 * area_feed_total) %>% - ggplot(aes(x = feed, y = area_feed_total, fill = season_name)) + - geom_bar(stat = "identity", width = 0.6) + - geom_text(aes(label = round(area_feed_total, 2), y = label_position), - size = 1, angle = 45, vjust = -0.) + - labs(x = "Feed Item", y = "Area (Ha)", fill = "Seasons", title = "Land Requirement and Feed Basket") + - theme_bw() + - theme(axis.text.x = element_text(size = 5, angle = 45, hjust = 1), - axis.text.y = element_text(size = 5)) + - scale_fill_discrete(guide = guide_legend(title = "Seasons")) - - ggsave(paste0(directoryPath, "/", fileName, "_land_required.png"), width = 150, height = 100, units = "mm") - + label_position = cumulative_area - 0.7 * area_feed_total) # Expanded table land requirement ex_land_required <- land_required %>% @@ -293,7 +275,7 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ production_kg_per_year = c("",cattle_milk_kg,cattle_meat_kg,"",other_milk_kg,other_meat_kg,"",total_milk_kg,total_meat_kg), production_energy_kcal_per_year = c("",cattle_milk_energy,cattle_meat_energy,"",other_milk_energy,other_meat_energy,"",total_milk_energy,total_meat_energy), protein_kg_per_year = c("",cattle_milk_protein,cattle_meat_protein,"",other_milk_protein,other_meat_protein,"",total_milk_protein,total_meat_protein) - ) %>% mutate(ame_days = as.numeric(production_energy_kcal_per_year)/2500) + ) %>% mutate(ame_days = as.numeric(production_energy_kcal_per_year)/2500) # Produced manure cattle_number <- sum(livestock_productivity$number_cattle, na.rm = T) @@ -328,18 +310,9 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ ## Soil impact ############################################################################################### # Plotting N balance - nitrogen_balance %>% + nitrogen_balance_output <- nitrogen_balance %>% group_by(feed) %>% - summarise(nbalance_kg_n_total = sum(nbalance_kg_n_total, na.rm = T)) %>% - ggplot2::ggplot(aes(x=feed, y=nbalance_kg_n_total))+ - geom_bar(stat = "identity", width = 0.6)+ - labs(x = "Feed Item", y = "Kg N", title = "Total Nitrogen Balance by Feed Item") + - geom_text(aes(label = round(nbalance_kg_n_total, 2)), vjust = -0.5, size = 1, angle = 0) + - theme_bw()+ - theme(axis.text.x = element_text(size = 5, angle = 45, hjust = 1), - axis.text.y = element_text(size = 5)) - - ggsave(paste0(directoryPath, "/", fileName, "_nbalance.png"), width = 150, height = 100, units = "mm") + summarise(nbalance_kg_n_total = sum(nbalance_kg_n_total, na.rm = T)) ## OVERALL SOIL IMPACTS nitrogen_balance <- nitrogen_balance @@ -350,7 +323,7 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ conc_ip_soil_loss = ifelse(stringr::str_detect(feed_item, "IP"), as.numeric(soil_loss_plot), 0), farm_soil_loss = (as.numeric(soil_loss_plot) - rough_of_soil_loss - conc_of_soil_loss - conc_ip_soil_loss)) - overal_soil_impact <- data.frame( + overal_soil_impact <- data.frame( sources = c("total", "on-farm", "rough of", "conc of", "conc ip"), balance_N_kg_N_year = c(sum(nitrogen_balance$nbalance_feed_only_kg_n, na.rm = T), sum(nitrogen_balance$farm_kg_n, na.rm = T), sum(nitrogen_balance$rough_of_kg_n, na.rm = T), sum(nitrogen_balance$conc_of_kg_n, na.rm = T), sum(nitrogen_balance$conc_ip_kg_n, na.rm = T)), @@ -363,16 +336,16 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ erosion_t_soil_year = c(sum(as.numeric(soil_erosion$soil_loss_plot, na.rm = T)), sum(soil_erosion$rough_of_soil_loss, na.rm = T), sum(soil_erosion$conc_of_soil_loss, na.rm = T), sum(soil_erosion$conc_ip_soil_loss, na.rm = T), sum(soil_erosion$farm_soil_loss, na.rm = T)) )%>% - mutate(percent_area_mining = ifelse(!is.finite(percent_area_mining),0,percent_area_mining), - percent_area_leaching = ifelse(!is.finite(percent_area_leaching),0,percent_area_leaching), - erosion_t_soil_ha = erosion_t_soil_year/c(sum(nitrogen_balance$area_total, na.rm = T), sum(nitrogen_balance$farm_area, na.rm = T), sum(nitrogen_balance$rough_of_area, na.rm = T), - sum(nitrogen_balance$conc_of_area, na.rm = T), sum(nitrogen_balance$conc_ip_area, na.rm = T))) + mutate(percent_area_mining = ifelse(!is.finite(percent_area_mining),0,percent_area_mining), + percent_area_leaching = ifelse(!is.finite(percent_area_leaching),0,percent_area_leaching), + erosion_t_soil_ha = erosion_t_soil_year/c(sum(nitrogen_balance$area_total, na.rm = T), sum(nitrogen_balance$farm_area, na.rm = T), sum(nitrogen_balance$rough_of_area, na.rm = T), + sum(nitrogen_balance$conc_of_area, na.rm = T), sum(nitrogen_balance$conc_ip_area, na.rm = T))) # Feed items specific N balance nitrogen_balance <- nitrogen_balance %>% - select(c(feed,nin,nout,nbalance_kg_n_total,nbalance_kg_n_ha_total,nbalance_feed_only_kg_n,nbalance_feed_only_kg_n_ha)) %>% - mutate(nbalance_food_only_kg_n = nbalance_kg_n_total-nbalance_feed_only_kg_n, - nbalance_food_only_kg_n_ha = nbalance_kg_n_ha_total-nbalance_feed_only_kg_n_ha) + select(c(feed,nin,nout,nbalance_kg_n_total,nbalance_kg_n_ha_total,nbalance_feed_only_kg_n,nbalance_feed_only_kg_n_ha)) %>% + mutate(nbalance_food_only_kg_n = nbalance_kg_n_total-nbalance_feed_only_kg_n, + nbalance_food_only_kg_n_ha = nbalance_kg_n_ha_total-nbalance_feed_only_kg_n_ha) soil_impacts <- list(overal_soil_impact = overal_soil_impact, nitrogen_balance = nitrogen_balance) @@ -382,16 +355,7 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ ############################################################################################### water_use_per_feed_item <- water_required[["water_use_per_feed_item"]] - ggplot(water_use_per_feed_item, aes(x = "", y = feed_water_use, fill = feed)) + - geom_bar(stat = "identity", width = 1) + - coord_polar("y", start = 0) + - labs(fill = "Feed Item", title = "Water Use per Feed Crop") + - theme_bw()+ - geom_text(aes(label = scales::percent(feed_water_use / sum(feed_water_use))), - position = position_stack(vjust = 0.5), - size = 1) - - ggsave(paste0(directoryPath, "/", fileName, "_water_use_per_feed.png"), width = 150, height = 100, units = "mm") + water_use_per_feed_item_output <- water_use_per_feed_item ############################################################################################### ## GHG Impacts @@ -447,8 +411,12 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ rice_production_methane_tot_kg_co2_e <- rice_production_methane*methane rice_production_methane_per_ha_kg_co2_e <- ifelse(area_required_on_farm_ha<0.001,0,rice_production_methane_tot_kg_co2_e/area_required_on_farm_ha) rice_production_methane_kg_co2_e_per_kg_fpcm <- rice_production_methane_tot_kg_co2_e/total_milk_produced_kg_fpcm_per_year - - on_farm_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$farm_fertiliser_emission) + # quick fix for the case with no fertilizer + if (!is.list(ghg_emissions[["fetilizer_ghg"]])) { + on_farm_fertilizer_emission <- 0 + } else { + on_farm_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$farm_fertiliser_emission) + } on_farm_fertilizer_emission_per_ha_kg_co2_e <- ifelse(area_required_on_farm_ha<0.001,0,on_farm_fertilizer_emission/area_required_on_farm_ha) on_farm_fertilizer_emission_kg_co2_e_per_kg_fpcm <- on_farm_fertilizer_emission/total_milk_produced_kg_fpcm_per_year @@ -464,13 +432,18 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ rough_of_soil_indirect_N2O_per_ha_kg_co2_e <- ifelse(area_required_roughages_off_farm_ha<0.001,0,rough_of_soil_indirect_N2O_tot_kg_co2_e/area_required_roughages_off_farm_ha) rough_of_soil_indirect_N2O_kg_co2_e_per_kg_fpcm <- rough_of_soil_indirect_N2O_tot_kg_co2_e/total_milk_produced_kg_fpcm_per_year - rough_of_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$rough_of_fertiliser_emission) + # fix for the case with no fertilizer + if (!is.list(ghg_emissions[["fetilizer_ghg"]])) { + rough_of_fertilizer_emission <- 0 + } else { + rough_of_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$rough_of_fertiliser_emission) + } rough_of_fertilizer_emission_per_ha_kg_co2_e <- ifelse(area_required_roughages_off_farm_ha<0.001,0,rough_of_fertilizer_emission/area_required_roughages_off_farm_ha) rough_of_fertilizer_emission_kg_co2_e_per_kg_fpcm <- rough_of_fertilizer_emission/total_milk_produced_kg_fpcm_per_year # Concentrates off-farm conc_of_Soil_direct_N2O <- sum(ghg_emissions[["ghg_soil"]][["annual_N20N_soil_direct_emission"]][ghg_emissions[["ghg_soil"]][["annual_N20N_soil_direct_emission"]]$anthropogenic_N_input == "conc_of_n_synthetic_fertilizer_managed_soil","annual_N20N_direct_emission_from_managed_soil"], - ghg_emissions[["ghg_soil"]][["annual_N20N_soil_direct_emission"]][ghg_emissions[["ghg_soil"]][["annual_N20N_soil_direct_emission"]]$anthropogenic_N_input == "conc_of_n_from_crop_residue_managed_soil","annual_N20N_direct_emission_from_managed_soil"],na.rm = T) + ghg_emissions[["ghg_soil"]][["annual_N20N_soil_direct_emission"]][ghg_emissions[["ghg_soil"]][["annual_N20N_soil_direct_emission"]]$anthropogenic_N_input == "conc_of_n_from_crop_residue_managed_soil","annual_N20N_direct_emission_from_managed_soil"],na.rm = T) conc_of_Soil_direct_N2O_tot_kg_co2_e <- conc_of_Soil_direct_N2O*N2O conc_of_Soil_direct_N2O_per_ha_kg_co2_e <- ifelse(area_required_concentrates_off_farm_ha<0.001,0,conc_of_Soil_direct_N2O_tot_kg_co2_e/area_required_concentrates_off_farm_ha) conc_of_Soil_direct_N2O_kg_co2_e_per_kg_fpcm <- conc_of_Soil_direct_N2O_tot_kg_co2_e/total_milk_produced_kg_fpcm_per_year @@ -480,7 +453,12 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ conc_of_soil_indirect_N2O_per_ha_kg_co2_e <- ifelse(area_required_concentrates_off_farm_ha<0.001,0,conc_of_soil_indirect_N2O_tot_kg_co2_e/area_required_concentrates_off_farm_ha) conc_of_soil_indirect_N2O_kg_co2_e_per_kg_fpcm <- conc_of_soil_indirect_N2O_tot_kg_co2_e/total_milk_produced_kg_fpcm_per_year - conc_of_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$conc_of_fertiliser_emission) + if (!is.list(ghg_emissions[["fetilizer_ghg"]])) { + conc_of_fertilizer_emission <- 0 + } else { + conc_of_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$conc_of_fertiliser_emission) + } + conc_of_fertilizer_emission_per_ha_kg_co2_e <- ifelse(area_required_concentrates_off_farm_ha<0.001,0,conc_of_fertilizer_emission/area_required_concentrates_off_farm_ha) conc_of_fertilizer_emission_kg_co2_e_per_kg_fpcm <- conc_of_fertilizer_emission/total_milk_produced_kg_fpcm_per_year @@ -496,7 +474,12 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ conc_ip_soil_indirect_N2O_per_ha_kg_co2_e <- ifelse(area_required_imported_concentrates_ha<0.001,0,conc_ip_soil_indirect_N2O_tot_kg_co2_e/area_required_imported_concentrates_ha) conc_ip_soil_indirect_N2O_kg_co2_e_per_kg_fpcm <- conc_ip_soil_indirect_N2O_tot_kg_co2_e/total_milk_produced_kg_fpcm_per_year - conc_ip_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$conc_ip_fertiliser_emission) + if (!is.list(ghg_emissions[["fetilizer_ghg"]])) { + conc_ip_fertilizer_emission <- 0 + } else { + conc_ip_fertilizer_emission <- sum(ghg_emissions[["fetilizer_ghg"]][["fertlizer_emission_by_crop"]]$conc_ip_fertiliser_emission) + } + conc_ip_fertilizer_emission_per_ha_kg_co2_e <- ifelse(area_required_imported_concentrates_ha<0.001,0,conc_ip_fertilizer_emission/area_required_imported_concentrates_ha) conc_ip_fertilizer_emission_kg_co2_e_per_kg_fpcm <- conc_ip_fertilizer_emission/total_milk_produced_kg_fpcm_per_year @@ -609,18 +592,18 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ "", conc_ip_Soil_direct_N2O_kg_co2_e_per_kg_fpcm, conc_ip_soil_indirect_N2O_kg_co2_e_per_kg_fpcm, conc_ip_fertilizer_emission_kg_co2_e_per_kg_fpcm) - ) + ) # Global warming potential (CO2eq) soil_on_farm <- (as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Direct N2O", "kg_co2_e_per_ha"][1]) + - as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][1]))/1000 + as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][1]))/1000 soil_off_farm <- (sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Direct N2O", "kg_co2_e_per_ha"][-1])) + - sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][-1])))/1000 + sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][-1])))/1000 livestock_manure <- (as.numeric(ghg_balance[ghg_balance$GHG_balance == "Manure-Methane", "kg_co2_e_per_ha"]) + - as.numeric(ghg_balance[ghg_balance$GHG_balance == "Manure-Direct N2O", "kg_co2_e_per_ha"]) + - as.numeric(ghg_balance[ghg_balance$GHG_balance == "Manure-Indirect N2O", "kg_co2_e_per_ha"]))/1000 + as.numeric(ghg_balance[ghg_balance$GHG_balance == "Manure-Direct N2O", "kg_co2_e_per_ha"]) + + as.numeric(ghg_balance[ghg_balance$GHG_balance == "Manure-Indirect N2O", "kg_co2_e_per_ha"]))/1000 livestock_enteric_fermentation <- as.numeric(ghg_balance[ghg_balance$GHG_balance == "Enteric fermentation-Methane", "kg_co2_e_per_ha"])/1000 @@ -636,12 +619,12 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ fertilizer_off_farm_rough <- as.numeric(ghg_balance[ghg_balance$GHG_balance == "Production fertilizer", "kg_co2_e_per_ha"][2])/1000 soil_off_farm_conc <- (sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Direct N2O", "kg_co2_e_per_ha"][3])) + - sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][3])))/1000 + sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][3])))/1000 fertilizer_off_farm_conc <- as.numeric(ghg_balance[ghg_balance$GHG_balance == "Production fertilizer", "kg_co2_e_per_ha"][3])/1000 soil_ip_farm_conc <- (sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Direct N2O", "kg_co2_e_per_ha"][4])) + - sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][4])))/1000 + sum(as.numeric(ghg_balance[ghg_balance$GHG_balance == "Soil-Indirect N2O", "kg_co2_e_per_ha"][4])))/1000 fertilizer_ip_farm_conc <- as.numeric(ghg_balance[ghg_balance$GHG_balance == "Production fertilizer", "kg_co2_e_per_ha"][4])/1000 @@ -659,19 +642,10 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ on_farm_table, off_farm_table) ghg_emission <- list(ghg_balance = ghg_balance, - global_warming_potential = global_warming_potential) + global_warming_potential = global_warming_potential) # Plotting GHG emission - on_farm_table %>% - ggplot2::ggplot(aes(x=sources_and_sinks, y=t_CO2e_per_ha))+ - geom_bar(stat = "identity", width = 0.6)+ - labs(x = "", y = "t CO2e/ha", title = "GHG emissions") + - geom_text(aes(label = round(t_CO2e_per_ha, 2)), vjust = -0.5, size = 1, angle = 0) + - theme_bw()+ - theme(axis.text.x = element_text(size = 5, angle = 45, hjust = 1), - axis.text.y = element_text(size = 5)) - - ggsave(paste0(directoryPath, "/", fileName, "_ghg_emission.png"), width = 150, height = 100, units = "mm") + on_farm_table_output <- on_farm_table ############################################################################################### ## Waste @@ -691,45 +665,58 @@ combineOutputs <- function(para, feed_basket_quality, energy_required, land_requ soil_carbon = soil_carbon, product_waste = product_waste) - # Add worksheet to the readme workbook - addWorksheet(readMe_wb, "Land Required") - writeData(readMe_wb, sheet = "Land Required", x = output_list$land_required$land_required) - addWorksheet(readMe_wb, "DM Required") - writeData(readMe_wb, sheet = "DM Required", x = output_list$land_required$dm_required) - addWorksheet(readMe_wb, "Land and DM Required") - writeData(readMe_wb, sheet = "Land and DM Required", x = output_list$land_required$land_and_dm_required) - addWorksheet(readMe_wb, "Overall Soil Impact") - writeData(readMe_wb, sheet = "Overall Soil Impact", x = output_list$soil_impacts$overal_soil_impact) - addWorksheet(readMe_wb, "Nitrogen Balance") - writeData(readMe_wb, sheet = "Nitrogen Balance", x = output_list$soil_impacts$nitrogen_balance) - addWorksheet(readMe_wb, "Water Use Per Feed Item") - writeData(readMe_wb, sheet = "Water Use Per Feed Item", x = output_list$water_required$water_use_per_feed_item) - addWorksheet(readMe_wb, "Water Use For Production") - writeData(readMe_wb, sheet = "Water Use For Production", x = output_list$water_required$water_use_for_production) - addWorksheet(readMe_wb, "Consumable Livestock Product") - writeData(readMe_wb, sheet = "Consumable Livestock Product", x = output_list$livestock_productivity$consumable_livestock_product) - addWorksheet(readMe_wb, "Manure Produced") - writeData(readMe_wb, sheet = "Manure Produced", x = output_list$livestock_productivity$manure_produced) - addWorksheet(readMe_wb, "GHG Balance") - writeData(readMe_wb, sheet = "GHG Balance", x = output_list$ghg_emission$ghg_balance) - addWorksheet(readMe_wb, "Global Warming Potential") - writeData(readMe_wb, sheet = "Global Warming Potential", x = output_list$ghg_emission$global_warming_potential) - addWorksheet(readMe_wb, "Biomass") - writeData(readMe_wb, sheet = "Biomass", x = output_list$biomass) - addWorksheet(readMe_wb, "Soil Carbon") - writeData(readMe_wb, sheet = "Soil Carbon", x = output_list$soil_carbon) - addWorksheet(readMe_wb, "Product Waste") - writeData(readMe_wb, sheet = "Product Waste", x = output_list$product_waste) + # Add worksheet to the primary_excel workbook + addWorksheet(primary_excel_wb, "Land Required") + writeData(primary_excel_wb, sheet = "Land Required", x = output_list$land_required$land_required) + addWorksheet(primary_excel_wb, "DM Required") + writeData(primary_excel_wb, sheet = "DM Required", x = output_list$land_required$dm_required) + addWorksheet(primary_excel_wb, "Land and DM Required") + writeData(primary_excel_wb, sheet = "Land and DM Required", x = output_list$land_required$land_and_dm_required) + addWorksheet(primary_excel_wb, "Overall Soil Impact") + writeData(primary_excel_wb, sheet = "Overall Soil Impact", x = output_list$soil_impacts$overal_soil_impact) + addWorksheet(primary_excel_wb, "Nitrogen Balance") + writeData(primary_excel_wb, sheet = "Nitrogen Balance", x = output_list$soil_impacts$nitrogen_balance) + addWorksheet(primary_excel_wb, "Water Use Per Feed Item") + writeData(primary_excel_wb, sheet = "Water Use Per Feed Item", x = output_list$water_required$water_use_per_feed_item) + addWorksheet(primary_excel_wb, "Water Use For Production") + writeData(primary_excel_wb, sheet = "Water Use For Production", x = output_list$water_required$water_use_for_production) + addWorksheet(primary_excel_wb, "Consumable Livestock Product") + writeData(primary_excel_wb, sheet = "Consumable Livestock Product", x = output_list$livestock_productivity$consumable_livestock_product) + addWorksheet(primary_excel_wb, "Manure Produced") + writeData(primary_excel_wb, sheet = "Manure Produced", x = output_list$livestock_productivity$manure_produced) + addWorksheet(primary_excel_wb, "GHG Balance") + writeData(primary_excel_wb, sheet = "GHG Balance", x = output_list$ghg_emission$ghg_balance) + addWorksheet(primary_excel_wb, "Global Warming Potential") + writeData(primary_excel_wb, sheet = "Global Warming Potential", x = output_list$ghg_emission$global_warming_potential) + addWorksheet(primary_excel_wb, "Biomass") + writeData(primary_excel_wb, sheet = "Biomass", x = output_list$biomass) + addWorksheet(primary_excel_wb, "Soil Carbon") + writeData(primary_excel_wb, sheet = "Soil Carbon", x = output_list$soil_carbon) + addWorksheet(primary_excel_wb, "Product Waste") + writeData(primary_excel_wb, sheet = "Product Waste", x = output_list$product_waste) + + # Change the order of the worksheets + worksheetOrder(primary_excel_wb) <- c("1", "8", "9", "10", "11", "12", "13", + "14", "15", "16", "17", "18", "19", "20", + "21", "2", "3", "4", "5", "6", "7") # Save to Excel with multiple sheets excel_output_path <- paste0(directoryPath, "/", fileName, ".xlsx") # Create path for excel - saveWorkbook(readMe_wb, excel_output_path, overwrite = TRUE) - - # Save the benchmark - benchmark_path <- paste0(directoryPath, "/benchMark.xlsx") - saveWorkbook(benchMark_wb, benchmark_path, overwrite = TRUE) + saveWorkbook(primary_excel_wb, excel_output_path, overwrite = TRUE) # Save json + + + return( + list( + json_output = jsonlite::toJSON(output_list, pretty = TRUE), + on_farm_table = on_farm_table_output, + nitrogen_balance = nitrogen_balance_output, + land_required = land_required_output, + water_use_per_feed_item = water_use_per_feed_item_output + ) + ) + jsonlite::toJSON(output_list, pretty = TRUE) } #end of output function diff --git a/R/misc/v37 loop.R b/R/misc/v37 loop.R new file mode 100644 index 0000000..938de46 --- /dev/null +++ b/R/misc/v37 loop.R @@ -0,0 +1,431 @@ + +# Load packages and function +pacman::p_load(readxl,cleaned,jsonlite,tidyr,dplyr,miceadds,data.table,openxlsx) + +source("R/ghg_emission_v2.R") +source("R/nitrogen_balance.R") +source("R/energy_requirement.R") +source("R/feed_quality.R") + +# 1) Load Data #### + ## 1.1) V37 data ##### + file<-"data/v37/Kenya NPA data.xlsx" + save_file<-file.path(dirname(file),"Kenya NPA emissions.xlsx") + # data <- readxl::read_excel(file,sheet=1) + + herd <- data.table(readxl::read_excel(file,sheet="milk-bodyweight")) + herd[,livetype_code:=as.character(livetype_code)] + setnames(herd,"livetype_desc","livetype_desc_v37") + + v37_feed_items<-data.table(readxl::read_excel(file,sheet="Feed_items")) + v37_feed_items$intercrop<-as.numeric(0) + v37_feed_type<-unique(data.table(readxl::read_excel(file,sheet="feed_type")))[order(feed_type_code)] + + # Deal with duplicate entry for item 5 Brachiaria + x<-v37_feed_type[feed_type_code==5][, feed_type_name := "Brachiaria"] + by_cols<-colnames(x)[1:5] + num_cols<-colnames(x)[!colnames(x) %in% by_cols] + + x <- x[, lapply(.SD, mean), by = by_cols, .SDcols = num_cols] + + v37_feed_type<-rbind(v37_feed_type[feed_type_code!=5],x) + + # V37 - Merge feed_items & feed type + v37_feed_items_merge<-merge(v37_feed_items,v37_feed_type,by="feed_type_code",all.x=T) + + # Feed basket + v37_feed_basket <- data.table(readxl::read_excel(file,sheet="Feedproportions")) + v37_feed_basket[,livetype_code:=tolower(livetype_code)] + + # Remove duplicates + v37_feed_basket<-unique(v37_feed_basket) + + v37_feed_basket<-melt(v37_feed_basket, + id.vars = c("Ids","v37_livestock_type","livetype_code","livetype_desc"), + variable.name = "feed_item_code", + value.name = "allocation") + + # Check proportions sum to 1 + v37_feed_basket<-v37_feed_basket[allocation!=0][order(Ids,livetype_code) + ][,total:=sum(allocation),by=.(Ids,livetype_code,livetype_desc)] + + unique(v37_feed_basket[total==2,.(Ids,livetype_code,total)]) + + v37_feed_basket<-v37_feed_basket[,.(Ids,livetype_code,livetype_desc,feed_item_code,allocation) + ][,allocation:=allocation*100 + ][,feed_item_code:=as.numeric(as.character(feed_item_code))] + + # Add feed type code + v37_feed_basket<-merge(v37_feed_basket,v37_feed_items[,.(feed_item_code,feed_type_code)],by="feed_item_code",all.x=T) + v37_feed_basket[is.na(feed_type_code)] + v37_feed_basket[,feed_item_code:=as.character(feed_item_code)] + v37_feed_basket[,feed_type_code:=as.character(feed_type_code)] + + # Simple field parameters + v37_simple_fields<-data.table(readxl::read_excel(file,sheet="simple_fields")) + + ## 1.2) Read in parameter tables ##### + lkp_livetype<-fread("https://raw.githubusercontent.com/CIAT/icleaned/refs/heads/staging/data/primary_database/lkp_livetype.csv") + lkp_manureman<-fread("https://raw.githubusercontent.com/CIAT/icleaned/refs/heads/staging/data/primary_database/lkp_manureman.csv") + + energy_parameters <- fromJSON( + system.file("extdata", "energy_parameters.json", package = "cleaned"), + flatten = TRUE + ) + + # Read static parameters directory and files + ghg_ipcc_data <- fromJSON( + system.file("extdata", "ghg_parameters.json", package = "cleaned"), + flatten = TRUE + ) + + ## 1.3) Input template ##### + file<-"https://raw.githubusercontent.com/CIAT/icleaned/refs/heads/staging/data/shared_folder/study_objects/Study_2.json" + template <- fromJSON(file) + colnames(template$livestock) + + feed_items<-data.table(template$feed_items) + fertilizer<-data.table(template$fertilizer)[0,] + fertilizer<-NULL + +# 2) Set "fixed" parameters #### + + ## 2.0) "Simple" fields ##### + template_names<-data.table(field_name=names(template), + class=sapply(template,base::class), + length=sapply(template,length)) + + simple_field_names<-template_names[length==1,field_name] + + simple_fields<-template[simple_field_names] + + for(i in 1:length(simple_fields)){ + field<-names(simple_fields)[i] + simple_fields[field]<-v37_simple_fields[field_name==field,value] + } + + + ## 2.1) Livestock ##### + mm_code<-"storage" + mm_des<-lkp_manureman[manureman_code==mm_code,manureman_desc] + + mm_code2<-"pasture" + mm_des2<-lkp_manureman[manureman_code==mm_code,manureman_desc] + + livestock_fixed<-data.frame( + manureman_stable=mm_des, + manureman_onfarm_grazing=mm_des2, + manureman_non_roofed_enclosure=0, + manureman_offfarm_grazing=mm_des2, + annual_growth=0, + annual_wool=0, + manure_in_stable=1, + manure_in_non_roofed_enclosure=0, + manure_in_field=0, + manure_onfarm_fraction=0, + manure_sales_fraction=0, + body_weight_weaning=0, + body_weight_year_one=0, + adult_weight=600, + work_hour=0, + piglets_relying_on_milk=0 + ) + + ## 2.2) Feed_Items ##### + feed_fixed<-c( + slope=1, + slope_desc="Flat (0-5%)", + slope_p_factor=0.11, + slope_length=15, + water_regime=0, + n_content=0, + cultivation_period=0, + ecosystem_type=0, + organic_amendment=0, + cut_carry_fraction=0, + fraction_as_fertilizer=0, # Needs to be distributed by crop + fraction_as_manure=0, # Needs to be distributed by crop + n_fertilizer=0, + urea=0, + npk=0, + dap=0, + ammonium_nitrate=0, + ammonium_sulfate=0, + n_solutions=0, + ammonia=0, + time_horizon=0 + ) + + fi_cols<-colnames(feed_items) + zero_cols<-fi_cols[grep("trees|dbh|diameter_",fi_cols)] + names(zero_cols)<-zero_cols + zero_cols[1:length(zero_cols)]<-0 + feed_fixed<-c(feed_fixed,zero_cols) + + ### 2.2.1) Add fixed items to input template ##### + for(k in 1:length(feed_fixed)){ + variable<-names(feed_fixed)[k] + v37_feed_items_merge[,(variable):=feed_fixed[k]] + } + + # Check all cols in template are present + colnames(feed_items)[!colnames(feed_items) %in% colnames(v37_feed_items_merge)] + + # Enforce numeric class + num_cols<-c("slope_p_factor","slope_length") + v37_feed_items_merge[, (num_cols) := lapply(.SD, as.numeric), .SDcols = num_cols] + + ## 2.3) Seasons ##### + season_dat<-data.frame(season_length=180,season_name="season_x") + +# 3) Loop through farms #### +# unique farm ids +farms<-na.omit(unique(herd$Ids)) +farms<-farms[2] + +farm_paras<-lapply(1:length(farms),FUN=function(i){ + farm<-farms[i] + + ## 3.1) Herds ##### + # Make a blank herd template to populate + livestock<-data.table(template$livestock[1,]) + livestock[1,1:ncol(livestock)]<-NA + + # Subset v37 data to selected farm + livestock_37<-herd[Ids==farm & !grepl("calve",livetype_desc_v37,ignore.case = T)] + + # Loop through herds on the farm + paras<-lapply(1:nrow(livestock_37),FUN=function(j){ + cat("\r", farm, "i =", i, "/", length(farms), "herd (j) =", j, "/", nrow(livestock_37)) + + # Subset herds + herd_1<-livestock_37[j] + + # Merge lkp_livetype + l_code<-herd_1$livetype_code + livestock$livetype_code<-l_code + livestock$livetype_code<-as.integer(livestock$livetype_code) + + mergedat<-lkp_livetype[livetype_code==l_code] + + cols_m<-colnames(mergedat) + cols_m<-cols_m[cols_m != "livetype_code"] + cols_l<-c(colnames(livestock)[!colnames(livestock) %in% cols_m]) + livestock<-livestock[,cols_l,with=F] + + livestock<-merge(livestock,mergedat,by="livetype_code",all.x = T) + + # Insert farm specific V37 herd data + cols_m<-colnames(herd_1) + livestock$livetype_desc_v37<-herd_1$livetype_desc_v37 + livestock$herd_composition<-herd_1$number + livestock$body_weight<-herd_1$body_weight + livestock$annual_milk<-herd_1$annual_milk + livestock$time_in_stable<-herd_1$time_in_stable + livestock$time_in_onfarm_grazing<-herd_1$time_in_onfarm_grazing + livestock$time_in_offfarm_grazing<-herd_1$time_in_offfarm_grazing + livestock$time_in_non_roofed_enclosure<-herd_1$time_in_non_roofed_enclosure + livestock$distance_to_pasture<-herd_1$distance_to_pasture + + # Add fixed parameters + for(k in 1:ncol(livestock_fixed)){ + variable<-colnames(livestock_fixed)[k] + livestock[,(variable):=livestock_fixed[1,k]] + } + + # Rename ipcc parameter columns + ipcc_new<-c( + "ipcc_ef_category_t1", + "ipcc_ef_category_t2", + "ipcc_meth_man_category", + "ipcc_n_exc_category" + ) + + ipcc_old<-c( + "ipcc_meth_ef_t1", + "ipcc_meth_ef_t2", + "ipcc_meth_man", + "ipcc_meth_exc" + ) + + livestock<-livestock[,!ipcc_new,with = F] + setnames(livestock,ipcc_old,ipcc_new) + + # Enforce codes to be character + livestock[,livetype_code:=as.character(livetype_code)] + + ## 3.2) Fertilizer ##### + # Blank table created in section 1.3 + # 3.3) Feed basket #### + ### 3.3.1) Seasons ##### + # Created in section 2.3 + ### 3.3.2) Feeds ##### + feeds<-v37_feed_basket[Ids==farm & tolower(livetype_desc)==tolower(livestock$livetype_desc_v37)] + if(nrow(feeds)==0){ + stop("Feed table is blank, livetype_desc in feed basket not matching herd.") + } + + # Average across duplicate rows + #feeds<-feeds[,.(allocation=mean(allocation)),by=.(Ids,feed_type_code,feed_item_code,livetype_code)] + + feeds<-feeds[,.(livestock=list(data.table(livetype_code=livetype_code,allocation=allocation))),by=.(feed_item_code,feed_type_code)] + feeds<-data.frame(feeds) + ### 3.3.3) Combine Season and Feeds ##### + feed_basket <- data.table( + feeds = list(feeds=feeds), + season_name = season_dat$season_name[1] + ) + feed_basket<-data.frame(feed_basket) + + ## 3.4) Feed Items ##### + # v37_feed_items_merge is creted in section 2.2, subset to items in the diet + feed_items<-v37_feed_items_merge[feed_item_code %in% feeds$feed_item_code] + ## 3.5) Join sections into list structure #### + input_object<-c(simple_fields,list(livestock=livestock, + feed_items=feed_items, + fertilizer=fertilizer, + seasons=season_dat, + feed_basket=feed_basket)) + input_object$farm_name<-farm + return(input_object) + }) + names(paras)<-paste0("herd-",1:nrow(livestock_37)) + + return(paras) +}) + +names(farm_paras)<-paste0("f",farms) + + +# 4) Pass to cleaned functions #### +messages<-F + +ghg_emissions<-lapply(1:length(farm_paras),FUN=function(i){ + + farm_para<-farm_paras[[i]] + + result<-lapply(1:length(farm_para),FUN=function(j){ + + cat("farm",names(farm_paras)[i],i,"/",length(farm_paras),"herd",j," \r") + + para<-farm_para[[j]] + + ## 4.1) feed basket quality ##### + feed_basket_quality <- feed_quality(para) + + ## 4.2) energy ##### + # Table 10.4 is in list form this throws an error, convert to data.frame + energy_required <- energy_requirement(para,feed_basket_quality,energy_parameters = energy_parameters) + + ## 4.3) land ##### + para$feed_items$intercrop<-as.numeric(para$feed_items$intercrop) + land_required <- land_requirement(feed_basket_quality, energy_required, para) + + ## 4.4) soil erosion ##### + para$feed_items$slope_p_factor<-as.numeric(para$feed_items$slope_p_factor) + soil_erosion <- soil_health(para, land_required) + + ## 4.5) n balance ##### + para$feed_items$ammonia<-as.numeric(para$feed_items$ammonia) + para$feed_items$ammonium_nitrate<-as.numeric(para$feed_items$ammonium_nitrate) + para$feed_items$ammonium_sulfate<-as.numeric(para$feed_items$ammonium_sulfate) + para$feed_items$dap<-as.numeric(para$feed_items$dap) + para$feed_items$n_solutions<-as.numeric(para$feed_items$n_solutions) + para$feed_items$npk<-as.numeric(para$feed_items$npk) + para$feed_items$urea<-as.numeric(para$feed_items$urea) + + nitrogen_balance <- n_balance(para, land_required, soil_erosion,energy_required) + + ## 4.6) livestock productivity ##### + livestock_productivity <- land_productivity(para,energy_required) + + ## 4.7) biomass ##### + num_cols<-c("trees_ha_dbh25","trees_ha_dbh2550","trees_ha_dbh50","increase_dbh25","increase_dbh2550","increase_dbh50", + "time_horizon","average_dbh2550","average_dbh25","average_dbh50") + para$feed_items<-para$feed_items[, (num_cols) := lapply(.SD, as.numeric), .SDcols = num_cols] + + biomass <- biomass_calculation(para, land_required) + + ## 4.8) ghg_emissions ##### + char_cols<-c("manureman_non_roofed_enclosure") + para$livestock<-para$livestock[, (char_cols) := lapply(.SD, as.character), .SDcols = char_cols] + + if(messages==F){ + results<-suppressMessages(ghg_emission(para,energy_required,ghg_ipcc_data,land_required,nitrogen_balance,feed_basket_quality,ym_prod=F)) + }else{ + results<-ghg_emission(para,energy_required,ghg_ipcc_data,land_required,nitrogen_balance,feed_basket_quality,ym_prod=F) + } + + # Unpack soil into 3 tables not a list + results$soil_annual_N20N_soil_direct_emission<-results$ghg_soil$annual_N20N_soil_direct_emission + results$soil_annual_N20N_soil_indirect_emission<-results$ghg_soil$annual_N20N_soil_indirect_emission + results$soil_N20N_off_farm<-results$ghg_soil$N20N_off_farm + results$ghg_soil<-NULL + results + }) + names(result)<-names(farm_para) + return(result) +}) + +names(ghg_emissions)<-names(farm_paras) + +## 4.9) Add farm and herd name to tables +ghg_emissions_merge<-lapply(1:length(ghg_emissions),FUN=function(i){ + farm_name<-names(ghg_emissions)[i] + ghg_farm<-ghg_emissions[[i]] + + ghg_farm_updated<-lapply(1:length(ghg_farm),FUN=function(j){ + cat("\r","i",i,"j",j," ") + herd_name<-names(ghg_farm)[j] + ghg_herd<-ghg_farm[[j]] + ghg_herd_updated<-lapply(1:length(ghg_herd),FUN=function(k){ + data<-ghg_herd[[k]] + data$farm<-farm_name + data$herd<-herd_name + return(data) + }) + names(ghg_herd_updated)<-names(ghg_herd) + return(ghg_herd_updated) + }) + + tab_names<-names(ghg_farm_updated[[1]]) + ghg_farm_merged<-lapply(tab_names,FUN=function(tab_name){ + x<-lapply(ghg_farm_updated,"[[",tab_name) + x<-rbindlist(x) + x + }) + + names(ghg_farm_merged)<-tab_names + return(ghg_farm_merged) +}) +names(ghg_emissions_merge)<-names(ghg_emissions) + +## 4.10) Combine tables ##### +tab_names<-names(ghg_emissions_merge[[1]]) + +ghg_emissions_merge_all<-lapply(tab_names,FUN=function(tab_name){ + x<-lapply(ghg_emissions_merge,"[[",tab_name) + x<-rbindlist(x) + x +}) +names(ghg_emissions_merge_all)<-tab_names + +n<-nchar(names(ghg_emissions_merge_all)) +names(ghg_emissions_merge_all)[n>31] +names(ghg_emissions_merge_all)[n>31]<-c("annual_N20N_soil_direct","annual_N20N_soil_indirect") + +ghg_emissions_merge_all$ef[farm=="f596171010",.(farm,livetype_desc_v37,ipcc_ef_category_t1,ipcc_ef_category_t2,de,de_intake,ge_intake,dmi_tot,ym,enteric_methane_emissions)] + +# Create a new Excel workbook +wb <- createWorkbook() + +# Loop through the list and add each data.frame as a sheet +for (sheet_name in names(ghg_emissions_merge_all)) { + addWorksheet(wb, sheet_name) + writeData(wb, sheet = sheet_name, x = ghg_emissions_merge_all[[sheet_name]]) +} + +# Save the workbook to a file +saveWorkbook(wb, file = save_file, overwrite = TRUE) + +print("Excel file saved as 'output.xlsx'") diff --git a/R/nitrogen_balance.R b/R/nitrogen_balance.R index d72f0eb..9d56dae 100644 --- a/R/nitrogen_balance.R +++ b/R/nitrogen_balance.R @@ -74,34 +74,46 @@ n_balance <- function(para, land_required, soil_erosion){ manure_fraction <- as.numeric(feed_selected$fraction_as_fertilizer) # Computing fertilizer rate - ammonia_n_frac <- ifelse(feed_selected$ammonia==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Ammonia"),]$fraction)) - ammonia_n_frac <- ifelse(!is.finite(ammonia_n_frac),0,ammonia_n_frac) - - ammonium_nitrate_n_frac <- ifelse(feed_selected$ammonium_nitrate==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Ammonium nitrate"),]$fraction)) - ammonium_nitrate_n_frac <- ifelse(!is.finite(ammonium_nitrate_n_frac),0,ammonium_nitrate_n_frac) - - ammonium_sulfate_n_frac <- ifelse(feed_selected$ammonium_sulfate==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Ammonium sulfate"),]$fraction)) - ammonium_sulfate_n_frac <- ifelse(!is.finite(ammonium_sulfate_n_frac),0,ammonium_sulfate_n_frac) - - dap_n_frac <- ifelse(feed_selected$dap==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "DAP"),]$fraction)) - dap_n_frac <- ifelse(!is.finite(dap_n_frac),0,dap_n_frac) - - n_solutions_n_frac <- ifelse(feed_selected$n_solutions==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "N solutions"),]$fraction)) - n_solutions_n_frac <- ifelse(!is.finite(n_solutions_n_frac),0,n_solutions_n_frac) - - npk_n_frac <- ifelse(feed_selected$npk==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "NPK"),]$fraction)) - npk_n_frac <- ifelse(!is.finite(npk_n_frac),0,npk_n_frac) - - urea_n_frac <- ifelse(feed_selected$urea==0,0, - as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Urea"),]$fraction)) - urea_n_frac <- ifelse(!is.finite(urea_n_frac),0,urea_n_frac) - + if( + length(para[["fertilizer"]]) == 0 + ) { + ammonia_n_frac <- 0 + ammonium_nitrate_n_frac <- 0 + ammonium_sulfate_n_frac <- 0 + dap_n_frac <- 0 + n_solutions_n_frac <- 0 + npk_n_frac <- 0 + urea_n_frac <- 0 + fertilizer_rate <- 0 + } else { + ammonia_n_frac <- ifelse(feed_selected$ammonia==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Ammonia"),]$fraction)) + ammonia_n_frac <- ifelse(!is.finite(ammonia_n_frac),0,ammonia_n_frac) + + ammonium_nitrate_n_frac <- ifelse(feed_selected$ammonium_nitrate==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Ammonium nitrate"),]$fraction)) + ammonium_nitrate_n_frac <- ifelse(!is.finite(ammonium_nitrate_n_frac),0,ammonium_nitrate_n_frac) + + ammonium_sulfate_n_frac <- ifelse(feed_selected$ammonium_sulfate==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Ammonium sulfate"),]$fraction)) + ammonium_sulfate_n_frac <- ifelse(!is.finite(ammonium_sulfate_n_frac),0,ammonium_sulfate_n_frac) + + dap_n_frac <- ifelse(feed_selected$dap==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "DAP"),]$fraction)) + dap_n_frac <- ifelse(!is.finite(dap_n_frac),0,dap_n_frac) + + n_solutions_n_frac <- ifelse(feed_selected$n_solutions==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "N solutions"),]$fraction)) + n_solutions_n_frac <- ifelse(!is.finite(n_solutions_n_frac),0,n_solutions_n_frac) + + npk_n_frac <- ifelse(feed_selected$npk==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "NPK"),]$fraction)) + npk_n_frac <- ifelse(!is.finite(npk_n_frac),0,npk_n_frac) + + urea_n_frac <- ifelse(feed_selected$urea==0,0, + as.numeric(para[["fertilizer"]][which(para[["fertilizer"]]$fertilizer_desc == "Urea"),]$fraction)) + urea_n_frac <- ifelse(!is.finite(urea_n_frac),0,urea_n_frac) + } fertilizer_rate <- (feed_selected$ammonia*ammonia_n_frac)+(feed_selected$ammonium_nitrate*ammonium_nitrate_n_frac)+(feed_selected$ammonium_sulfate*ammonium_sulfate_n_frac)+ (feed_selected$dap*dap_n_frac)+(feed_selected$n_solutions*n_solutions_n_frac)+(feed_selected$npk*npk_n_frac)+(feed_selected$urea*urea_n_frac) diff --git a/R/plotting.R b/R/plotting.R index 5f7270b..8d8026f 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -31,169 +31,207 @@ #' @export #' -clean_plotting <- function(outFile,oDir){ - +clean_plotting <- function (outFile, oDir) +{ outFile <- jsonlite::fromJSON(outFile, flatten = TRUE) - outFile$scenario <- as.factor(outFile$scenario) - oDir <- oDir - if (!file.exists(oDir)) {dir.create(oDir, recursive=T)} - for(i in 2:ncol(outFile)){ + if (!file.exists(oDir)) { + dir.create(oDir, recursive = T) + } - datos <- outFile %>% select(1, all_of(i)) + output_list <- list() - # titles + for (i in 2:ncol(outFile)) { + datos <- outFile %>% select(1, all_of(i)) tt <- colnames(datos[2]) - - - if(tt == "total_milk_produced_kg_fpcm_per_year"){ + if (tt == "total_milk_produced_kg_fpcm_per_year") { title = "Total milk FPCM (kg/yr)" y_title = "Milk FPCM (kg/yr)" - }else if(tt == "total_meat_produced_kg_per_year"){ + } + else if (tt == "total_meat_produced_kg_per_year") { title = "Total meat (kg/yr)" y_title = "Meat (kg/yr)" - }else if(tt == "total_protein_produced_kg_per_year"){ + } + else if (tt == "total_protein_produced_kg_per_year") { title = "Total protein (kg/yr)" y_title = "Protein (kg/yr)" - }else if (tt == "total_tlu"){ + } + else if (tt == "total_tlu") { title = "Tropical Livestock Unit" y_title = "Tropical Livestock Unit" - }else if (tt == "total_land_requirement_ha"){ + } + else if (tt == "total_land_requirement_ha") { title = "Land required (ha/yr)" y_title = "Land required (ha/yr)" - }else if (tt == "total_land_requirement_ha_per_kg_fpcm"){ + } + else if (tt == "total_land_requirement_ha_per_kg_fpcm") { title = "Land required (ha/kg FPCM)" y_title = "Land required (ha/kg FPCM)" - }else if (tt == "total_land_requirement_ha_per_kg_meat"){ + } + else if (tt == "total_land_requirement_ha_per_kg_meat") { title = "Land required (ha/kg meat)" y_title = "Land required (ha/kg meat)" - }else if (tt == "total_land_requirement_ha_per_kg_protein"){ + } + else if (tt == "total_land_requirement_ha_per_kg_protein") { title = "Land required (ha/kg protein)" y_title = "Land required (ha/kg protein)" - }else if (tt == "total_land_requirement_ha_per_tlu"){ + } + else if (tt == "total_land_requirement_ha_per_tlu") { title = "Land required (ha/TLU)" y_title = "Land required (ha/TLU)" - }else if (tt == "total_n_balance_kg_n_per_year"){ + } + else if (tt == "total_n_balance_kg_n_per_year") { title = "N balance (kg N/yr)" y_title = "N balance (kg N/yr)" - }else if (tt == "percent_area_mining"){ + } + else if (tt == "percent_area_mining") { title = "Soil mining (%)" y_title = "Soil mining (%)" - }else if (tt == "percent_area_leaching"){ + } + else if (tt == "percent_area_leaching") { title = "Soil leaching (%)" y_title = "Soil leaching (%)" - }else if (tt == "n_balance_kg_n_per_ha_per_year"){ + } + else if (tt == "n_balance_kg_n_per_ha_per_year") { title = "N balance (kg N/ha/yr)" y_title = "N balance (kg N/ha/yr)" - }else if (tt == "n_balance_kg_n_per_kg_fpcm"){ + } + else if (tt == "n_balance_kg_n_per_kg_fpcm") { title = "N balance (kg N/kg FPCM)" y_title = "N balance (kg N/kg FPCM)" - }else if (tt == "n_balance_kg_n_per_kg_meat"){ + } + else if (tt == "n_balance_kg_n_per_kg_meat") { title = "N balance (kg N/kg meat)" y_title = "N balance (kg N/kg meat)" - }else if (tt == "n_balance_kg_n_per_kg_protein"){ + } + else if (tt == "n_balance_kg_n_per_kg_protein") { title = "N balance (kg N/kg protein)" y_title = "N balance (kg N/kg protein)" - }else if (tt == "erosion_t_soil_year"){ + } + else if (tt == "erosion_t_soil_year") { title = "Erosion (t soil/yr)" y_title = "Erosion (t soil/yr)" - }else if (tt == "erosion_t_soil_per_ha_per_year"){ + } + else if (tt == "erosion_t_soil_per_ha_per_year") { title = "Erosion (t soil/ha/yr)" y_title = "Erosion (t soil/ha/yr)" - }else if (tt == "erosion_kgsoil_per_kg_fpcm"){ + } + else if (tt == "erosion_kgsoil_per_kg_fpcm") { title = "Erosion (t soil/kg FPCM)" y_title = "Erosion (t soil/kg FPCM)" - }else if (tt == "erosion_kgsoil_per_kg_meat"){ + } + else if (tt == "erosion_kgsoil_per_kg_meat") { title = "Erosion (t soil/kg meat)" y_title = "Erosion (t soil/kg meat)" - }else if (tt == "erosion_kgsoil_per_kg_protein"){ + } + else if (tt == "erosion_kgsoil_per_kg_protein") { title = "Erosion (t soil/kg protein)" y_title = "Erosion (t soil/kg protein)" - }else if (tt == "ghg_emission_t_co2_eq_per_year"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_year") { title = "GHG (t CO2eq/yr)" y_title = "GHG (t CO2eq/yr)" - }else if (tt == "ghg_emission_t_co2_eq_per_ha_per_year"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_ha_per_year") { title = "GHG (t CO2eq/ha/yr)" y_title = "GHG (t CO2eq/ha/yr)" - }else if (tt == "ghg_emission_t_co2_eq_per_kg_fpcm"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_kg_fpcm") { title = "GHG (CO2eq/kg FPCM)" y_title = "GHG (CO2eq/kg FPCM)" - }else if (tt == "ghg_emission_t_co2_eq_per_kg_meat"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_kg_meat") { title = "GHG (CO2eq/kg meat)" y_title = "GHG (CO2eq/kg meat)" - }else if (tt == "ghg_emission_t_co2_eq_per_kg_protein"){ + } + else if (tt == "ghg_emission_t_co2_eq_per_kg_protein") { title = "GHG (CO2eq/kg protein)" y_title = "GHG (CO2eq/kg protein)" - }else if (tt == "percent_precipitation_used_for_feed_production"){ + } + else if (tt == "percent_precipitation_used_for_feed_production") { title = "% Precipitation use for feed production" y_title = "% Precipitation use for feed production" - }else if (tt == "total_water_use_m3"){ + } + else if (tt == "total_water_use_m3") { title = "Water use (m3/yr)" y_title = "Water use (m3/yr)" - }else if (tt == "total_water_use_m3_per_ha"){ + } + else if (tt == "total_water_use_m3_per_ha") { title = "Water use (m3/ha)" y_title = "Water use (m3/ha)" - }else if (tt == "total_water_use_m3_per_kg_fpcm"){ + } + else if (tt == "total_water_use_m3_per_kg_fpcm") { title = "Water use (m3/kg FPCM)" y_title = "Water use (m3/kg FPCM)" - }else if (tt == "total_water_use_m3_per_kg_meat"){ + } + else if (tt == "total_water_use_m3_per_kg_meat") { title = "Water use (m3/kg meat)" y_title = "Water use (m3/kg meat)" - }else if (tt == "total_water_use_m3_per_kg_protein"){ + } + else if (tt == "total_water_use_m3_per_kg_protein") { title = "Water (m3/kg protein)" y_title = "Water (m3/kg protein)" - }else if (tt == "carbon_stock_change_t_co2eq_per_year"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_year") { title = "Carbon stock changes (t CO2eq/yr)" y_title = "Carbon stock changes (t CO2eq/yr)" - }else if (tt == "carbon_stock_change_t_co2eq_per_ha_per_year"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_ha_per_year") { title = "Carbon stock changes (t CO2eq/ha/yr)" y_title = "Carbon stock changes (t CO2eq/ha/yr)" - }else if (tt == "carbon_stock_change_t_co2eq_per_fpcm"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_fpcm") { title = "Carbon stock changes (t CO2eq/kg FPCM)" y_title = "Carbon stock changes (t CO2eq/kg FPCM)" - }else if (tt == "carbon_stock_change_t_co2eq_per_meat"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_meat") { title = "Carbon stock changes (t CO2eq/kg meat)" y_title = "Carbon stock changes (t CO2eq/kg meat)" - }else if (tt == "carbon_stock_change_t_co2eq_per_protein"){ + } + else if (tt == "carbon_stock_change_t_co2eq_per_protein") { title = "Carbon stock changes (t CO2eq/kg protein)" y_title = "Carbon stock changes (t CO2eq/kg protein)" - }else if (tt == "total_milk_produced_energy_kcal_per_year"){ + } + else if (tt == "total_milk_produced_energy_kcal_per_year") { title = "Energy (kcal/kg FPCM)" y_title = "Energy (kcal/kg FPCM)" - }else if (tt == "total_meat_produced_energy_kcal_per_year"){ + } + else if (tt == "total_meat_produced_energy_kcal_per_year") { title = "Energy (kcal/kg meat)" y_title = "Energy (kcal/kg meat)" - }else if (tt == "total_milk_produced_ame_days_per_year"){ + } + else if (tt == "total_milk_produced_ame_days_per_year") { title = "AME (days/kg FPCM)" y_title = "AME (days/kg FPCM)" - }else if (tt == "total_meat_produced_ame_days_per_year"){ + } + else if (tt == "total_meat_produced_ame_days_per_year") { title = "AME (days/kg meat)" y_title = "AME (days/kg meat)" - }else if (tt == "total_carbon_balance_per_fpcm"){ + } + else if (tt == "total_carbon_balance_per_fpcm") { title = "Carbon balance (t CO2eq/kg FPCM)" y_title = "Carbon balance (t CO2eq/kg FPCM)" - }else if (tt == "total_carbon_balance_per_meat"){ + } + else if (tt == "total_carbon_balance_per_meat") { title = "Carbon balance (t CO2eq/kg meat)" y_title = "Carbon balance (t CO2eq/kg meat)" - }else if (tt == "total_carbon_balance_per_protein"){ + } + else if (tt == "total_carbon_balance_per_protein") { title = "Carbon balance (t CO2eq/kg protein)" y_title = "Carbon balance (t CO2eq/kg protein)" - }else{ + } + else { NA } - ggplot(datos, aes_string("scenario", y = tt, fill = "scenario")) + - geom_bar(stat = "identity", - position = "dodge", - width = 0.2) + - geom_text(aes(label = round(datos[,2],2)), position = position_dodge(width = 0.2), vjust = -0.5, size = 2) + - labs(x = "", y = y_title, title = title, fill = "Scenario") + - theme_bw() - - ggsave(paste0(oDir, tt, ".png"), width = 150, height = 100, units = "mm") - + output_list[[tt]] <- list( + datos = datos, + tt = tt, + title = title, + y_title = y_title + ) } - + return(output_list) } diff --git a/README.md b/README.md index ac85037..162b2b2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# Comprehensive Livestock Environmental Assessment for Improved Nutrition, a Secured Environment, and Sustainable Development along Livestock and Fish Value Chains (CLEANED) +# Inclusive and Comprehensive Livestock Environmental Assessment for Improved Nutrition, a Secured Environment, and Sustainable Development along Livestock Value Chains (I-CLEANED) -The goal of cleaned is to assess changes from transforming livestock value chains. +The goal of I-CLEANED is to assess changes from transforming livestock value chains by prioritizing profitable, environmentally sustainable and inclusive livestock interventions. ## Installation diff --git a/data/cleaned.sqlite b/data/cleaned.sqlite new file mode 100644 index 0000000..e22d755 Binary files /dev/null and b/data/cleaned.sqlite differ diff --git a/data/energy_parameters.json b/data/energy_parameters.json new file mode 100644 index 0000000..7a08b3f --- /dev/null +++ b/data/energy_parameters.json @@ -0,0 +1,39 @@ +{ + "Table 10.4": [ + { + "animal_category": "Cattle_Buffalo", + "maintenance_cfi": 0.322, + "comments": "All non-lactating cows, steers, heifers and calves" + }, + { + "animal_category": "Cattle_Buffalo_lactating_cows", + "maintenance_cfi": 0.386, + "comments": "Maintenance energy requirements are 20% higher during lactation" + }, + { + "animal_category": "Cattle_Buffalo_bulls", + "maintenance_cfi": 0.37, + "comments": "Maintenance energy requirements are 15% higher for intact males than non lactating females" + }, + { + "animal_category": "Sheep_lamb_to _1_year", + "maintenance_cfi": 0.236, + "comments": "This value can be increased by 15% for intact males" + }, + { + "animal_category": "Sheep_older_than_1_year", + "maintenance_cfi": 0.217, + "comments": "This value can be increased by 15% for intact males." + }, + { + "animal_category": "Goats", + "maintenance_cfi": 0.315, + "comments": "NA" + }, + { + "animal_category": "Pigs", + "maintenance_cfi": 0.44, + "comments": "NA" + } + ] +} diff --git a/data/explore_qt_json.html b/data/explore_qt_json.html new file mode 100644 index 0000000..bd10de6 --- /dev/null +++ b/data/explore_qt_json.html @@ -0,0 +1,6882 @@ + + + + + + + + + + + + + + + +Exploration of CLEANED input files + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + +
## Loading required package: pacman
+
+

Cleaned Data

+
+

Input data structure

+

The aim of this markdown document is to summarize the structure of +the .json object that is required as an input for the R cleaned +packages. It also explores any additional datasets that are used as +inputs to create this input object, these include +cleaned/data/ghg_para.R, +cleaned/data/stock_change_para.rda and +qlands/cleaned-desktop/cleaned.sqlite.

+
+

Load input json file into R

+

Load the qt_example.json example input file stored in +cleaned/data.

+
# Read in example json file #####
+json_data <- fromJSON("qt_example.json")
+
+# Summarize level 1 of the list
+json_names<-data.table(field_name=names(json_data),
+                          class=sapply(json_data,base::class),
+                          length=sapply(json_data,length))
+
+kable(json_names, caption = "Summary of Level 1 of the JSON Object")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Summary of Level 1 of the JSON Object
field_nameclasslength
annual_precinteger1
arable_tograsslandinteger1
climate_zonecharacter1
climate_zone_2character1
cropland_orgmattercharacter1
cropland_orgmatter_ipccnumeric1
cropland_systemcharacter1
cropland_system_ipccnumeric1
cropland_tillagecharacter1
cropland_tillage_ipccinteger1
database_codecharacter1
etinteger1
farm_codecharacter1
farm_namecharacter1
feed_basketdata.frame2
feed_itemsdata.frame65
fertilizerdata.frame4
grassland_implevelcharacter1
grassland_implevel_ipccinteger1
grassland_managementcharacter1
grassland_management_ipccinteger1
grassland_toarableinteger1
livestockdata.frame59
purchased_beddinginteger1
purchased_compostinteger1
purchased_manureinteger1
purchased_organic_ninteger1
rain_lengthinteger1
regioncharacter1
seasonsdata.frame2
soil_bulkinteger1
soil_cinteger1
soil_clayinteger1
soil_depthinteger1
soil_descriptioncharacter1
soil_k_valuenumeric1
soil_nnumeric1
waste_consume_meatinteger1
waste_consume_milkinteger1
waste_distribution_meatinteger1
waste_distribution_milkinteger1
waste_processing_meatinteger1
waste_processing_milkinteger1
waste_production_meatinteger1
waste_production_milkinteger1
+
+
+

Simple fields

+
simple_fields<-json_names[class!="data.frame" & length==1,field_name]
+simple_fields<-json_data[json_names[class!="data.frame" & length==1,field_name]]
+simple_fields<-data.table(field=names(simple_fields),
+                          value=unlist(simple_fields),
+                          class=json_names[class!="data.frame" & length==1,class])
+ + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Summary of simple fields containing a single value
fieldvalueclass
annual_prec1500integer
arable_tograssland0integer
climate_zoneTemperatecharacter
climate_zone_2Warm Temperate Drycharacter
cropland_orgmatterLow, temperate/boreal, drycharacter
cropland_orgmatter_ipcc0.95numeric
cropland_systemLong term cultivated, temperate/boreal, drycharacter
cropland_system_ipcc0.8numeric
cropland_tillageFullcharacter
cropland_tillage_ipcc1integer
database_codebasecharacter
et1460integer
farm_code001character
farm_nametest 3character
grassland_implevelMediumcharacter
grassland_implevel_ipcc1integer
grassland_managementNominally managedcharacter
grassland_management_ipcc1integer
grassland_toarable0integer
purchased_bedding0integer
purchased_compost0integer
purchased_manure0integer
purchased_organic_n0integer
rain_length5integer
regionAFRICAcharacter
soil_bulk6integer
soil_c12integer
soil_clay45integer
soil_depth2integer
soil_descriptionLixisolcharacter
soil_k_value0.25numeric
soil_n3.5numeric
waste_consume_meat2integer
waste_consume_milk2integer
waste_distribution_meat3integer
waste_distribution_milk3integer
waste_processing_meat5integer
waste_processing_milk5integer
waste_production_meat3integer
waste_production_milk3integer
+
+
+

Livestock

+

The json_data$livestock of the json list is a data.table +that contains information about livestock”]] herd structure and +management, manure management and productivity.

+ + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Transposed livestock herd data t(json_data$livestock)
livetype_code_2livetype_code_1livetype_code_5
adult_weight8004000
annual_growth0090
annual_milk300015000
annual_wool000
birth_interval1.1666671.5000000.000000
body_weight600350200
body_weight_weaning000
body_weight_year_one000
carcass_fraction0.480.000.48
cp_grazing030
cp_growth0.00.00.4
cp_lactation520
cp_lactmilk0.090.090.00
cp_maintenance0.600.350.20
cp_pregnancy12.219.640.00
distance_to_pasture0.00.50.5
energy_eggcontent000
energy_meatcontent220022002200
energy_milkcontent9709700
fat_content4.35.80.0
grazing_displacement020
herd_composition250
ipcc_ef_category_t1Dairy cattleOther mature femaleOther mature female-grazing
ipcc_ef_category_t2Dairy cowsDairy cowsNon-dairy
ipcc_meth_man_categoryDairy cowsDairy cowsOther cattle
ipcc_n_exc_categoryDairy cattleDairy cattleOther cattle
lactation_length000
litter_size000
livetype_code215
livetype_descCattle - Cows (improved)Cattle - Cows (local)Cattle - Steers/heifers
lw_gain000
manure_in_field000
manure_in_non_roofed_enclosure000
manure_in_stable100
manure_onfarm_fraction100
manure_sales_fraction000
manureman_non_roofed_enclosureSolid storageSolid storageSolid storage
manureman_offfarm_grazingSolid storageSolid storageSolid storage
manureman_onfarm_grazingSolid storagePasture / range / paddockSolid storage
manureman_stableSolid storageSolid storageSolid storage
me_grazing2.02.01.5
me_growth0050
me_lactation520
me_lactmilk5.55.50.0
me_maintenance60.6154740.4595526.59148
me_pregnancy150012600
meat_productbeefbeefbeef
milk_productcow milkcow milkcow milk
n_content0.0290.0290.029
piglets_relying_on_milk000
proportion_growth000
protein_meatcontent262626
protein_milkcontent3.73.20.0
time_in_non_roofed_enclosure000
time_in_offfarm_grazing000
time_in_onfarm_grazing011
time_in_stable100
water_requirement14012080
work_hour000
+

The livestock are linked to the +feed_basket$feeds tables by the livetype_code +keyfield.

+
json_data$livestock[,c("livetype_code","livetype_desc")] # Codes in the feed_items table
+
+ +
+
lapply(json_data$feed_basket$feeds,"[[","livestock") # Codes in the feed_basket tables
+
## [[1]]
+## [[1]][[1]]
+##   allocation livetype_code
+## 1         40             2
+## 2         17             1
+## 3         40             5
+## 
+## [[1]][[2]]
+##   allocation livetype_code
+## 1         25             2
+## 2         43             1
+## 3         20             5
+## 
+## [[1]][[3]]
+##   allocation livetype_code
+## 1         35             2
+## 2         40             1
+## 3         40             5
+## 
+## 
+## [[2]]
+## [[2]][[1]]
+##   allocation livetype_code
+## 1         40             2
+## 2         17             1
+## 3         40             5
+## 
+## [[2]][[2]]
+##   allocation livetype_code
+## 1         25             2
+## 2         43             1
+## 3         20             5
+## 
+## [[2]][[3]]
+##   allocation livetype_code
+## 1         35             2
+## 2         40             1
+## 3         40             5
+
+
+

Feed items

+

The json_data$feed_items level of the json list is a +data.table that contains information about the production management and +context of feed production.

+ + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Transposed feed item data t(json_data$feed_items)
feed_item_code_65feed_item_code_99feed_item_code_18
ammonia000
ammonium_nitrate000
ammonium_sulfate000
average_dbh25000
average_dbh2550000
average_dbh50000
categorycerealcereallegume
cp_content13.603.8518.40
cultivation_period000
cut_carry_fraction000
dap000
diameter_breast000
dm_content89.0091.8890.00
dry_yield30158
ecosystem_type
energy0360336
feed_item_code659918
feed_item_nameOats (Avena sativa) - grain IPRice (Oryza sativa) - strawCowpea (Vigna unguiculata) - crop residue
feed_type_code2459
feed_type_nameAvena sativaRiceCowpea
fraction_as_fertilizer110
fraction_as_manureNANANA
grassman111
grassman_change_factor111
grassman_descNominally managedNominally managedNominally managed
increase_dbh25000
increase_dbh2550000
increase_dbh50000
intercrop000
intercrop_fraction000
kc_initial0.101.050.15
kc_late0.550.750.60
kc_midseason1.101.201.05
land_cover771
land_cover_descCerealsCerealsDense forest
landcover_c_factor0.1500.1500.001
main_n0.01760.00900.0380
main_product_removal110
me_content12.2673245.6400009.880848
n_content0.20.20.0
n_fertilizerNANANA
n_solutions50300100
npk000
organic_amendment
residue_burnt000
residue_dry_yield006
residue_n0.00.20.0
residue_removal0.00.80.0
slope111
slope_descFlat (0-5%)Flat (0-5%)Flat (0-5%)
slope_length000
slope_p_factor0.110.110.11
source_typeMainResidueMain
time_horizon000
trees_dhb000
trees_growth000
trees_ha000
trees_ha_dbh25000
trees_ha_dbh2550000
trees_ha_dbh50000
trees_removal000
urea400700200
usda_value02045016062
water_content0.0012.8911.95
water_regime
+

The feed_items are linked to the +feed_basket$feeds tables by the feed_item_code +field.

+
json_data$feed_items[,c("feed_item_code","feed_item_name")] # Codes in the feed_items table
+
+ +
+
lapply(json_data$feed_basket$feeds,"[[","feed_item_code") # Codes in the feed_basket tables
+
## [[1]]
+## [1] "65" "99" "18"
+## 
+## [[2]]
+## [1] "65" "99" "18"
+

Other keyfields include feed_type_code and +land_cover:

+
json_data$feed_items[,c("feed_type_code","feed_type_name","land_cover","land_cover_desc")]
+
+ +
+
+
+

Fertilizer

+

The json_data$fertilizer level of the json list is a +data.table that contains information about the production management and +context of feed production.

+ + + + + + + + + + + + + + + + + + + + + + + + +
Fertilizer data input table (json_data$fertilizer)
fertilizer_codefertilizer_descfractionpercentage_n
4Ammonium nitrate012
6N solutions010
+

The values in fertilizer_code field do not appear +directly correspond to any fields in the feed basket or feed item +tables. However columns with similar names do appear in the field item +tables

+
fertilizers<-json_data$fertilizer$fertilizer_desc
+fi_cols<-colnames(json_data$feed_items)
+
+# Reformat fertilizer names to match column names in the feed_items table
+(fertilizers<-gsub(" ","_",tolower(json_data$fertilizer$fertilizer_desc)))
+
## [1] "ammonium_nitrate" "n_solutions"
+
# Find matching columns
+fi_cols[fi_cols %in% fertilizers]
+
## [1] "ammonium_nitrate" "n_solutions"
+
+
+

Seasons

+

The json_data$season level of the input data is 2-column +table that records the length of each season (adding up to 365 days +max).

+ + + + + + + + + + + + + + + + + + +
Feed items data input table (json_data$season)
season_lengthseason_name
200Wet season
165Dry season
+

The season_name field is the key field that links to the +feed basket.

+
json_data$feed_basket$season_name
+
## [1] "Wet season" "Dry season"
+
+
+

Feed basket

+

The json_data$feed_basket level of the input data +contains a futher 2 list levels called feeds and season_name. These +sub-levels appear to be the same length: feeds = 2, season_name = 2.

+
+
Seasons
+

The json_data$feed_basket$season_name object is simple, +being a vector containing the names of the seasons:

+ + + + + + + + + + + + + + + +
Feed basket/season data input table (json_data\(feed_basket\)season)
x
Wet season
Dry season
+
+
+
Feed Basket
+

The json_data$feed_basket$feed object is the most +structurally complex element of the input json data containing several +levels of nesting.

+
str(json_data$feed_basket$feeds)
+
## List of 2
+##  $ :'data.frame':    3 obs. of  3 variables:
+##   ..$ feed_item_code: chr [1:3] "65" "99" "18"
+##   ..$ feed_type_code: chr [1:3] "2" "45" "9"
+##   ..$ livestock     :List of 3
+##   .. ..$ :'data.frame':  3 obs. of  2 variables:
+##   .. .. ..$ allocation   : int [1:3] 40 17 40
+##   .. .. ..$ livetype_code: chr [1:3] "2" "1" "5"
+##   .. ..$ :'data.frame':  3 obs. of  2 variables:
+##   .. .. ..$ allocation   : int [1:3] 25 43 20
+##   .. .. ..$ livetype_code: chr [1:3] "2" "1" "5"
+##   .. ..$ :'data.frame':  3 obs. of  2 variables:
+##   .. .. ..$ allocation   : int [1:3] 35 40 40
+##   .. .. ..$ livetype_code: chr [1:3] "2" "1" "5"
+##  $ :'data.frame':    3 obs. of  3 variables:
+##   ..$ feed_item_code: chr [1:3] "65" "99" "18"
+##   ..$ feed_type_code: chr [1:3] "2" "45" "9"
+##   ..$ livestock     :List of 3
+##   .. ..$ :'data.frame':  3 obs. of  2 variables:
+##   .. .. ..$ allocation   : int [1:3] 40 17 40
+##   .. .. ..$ livetype_code: chr [1:3] "2" "1" "5"
+##   .. ..$ :'data.frame':  3 obs. of  2 variables:
+##   .. .. ..$ allocation   : int [1:3] 25 43 20
+##   .. .. ..$ livetype_code: chr [1:3] "2" "1" "5"
+##   .. ..$ :'data.frame':  3 obs. of  2 variables:
+##   .. .. ..$ allocation   : int [1:3] 35 40 40
+##   .. .. ..$ livetype_code: chr [1:3] "2" "1" "5"
+
+
Feeds
+

Each feed basket table (e.g., +json_data$feed_basket$feeds[[1]]) is constructed using key +fields that describe feed items found in the +json_data$feed_items table, specifically +feed_item_code and feed_type_code. The feeds +are then allocated to the herd elements described in the +json_data$livestock table.

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Feed basket/feed data input table tablejson_data\(feed_basket\)feeds[[1]]
feed_item_codefeed_type_codelivestock
65240, 17, 40, 2 , 1 , 5
994525, 43, 20, 2 , 1 , 5
18935, 40, 40, 2 , 1 , 5
+
+
+
Livestock
+

The livestock field in the feed basket table contains a list with 3 +elements, indicating a one-to-many relationship between the diet item +and elements of the livestock herd. Each feed item represented by a row +in the feed basket table is allocated to different herd elements, with +the livetype_code field serving as the key field linking +the two tables.

+ + + + + + + + + +
+Feed basket/feed/livestock data input table tablejson_data\(feed_basket\)feeds[[1]]$livestock +
+ + + + + + + + + + + + + + + + + + + + + +
allocationlivetype_code
402
171
405
+		 + + + + + + + + + + + + + + + + + + + + + +
allocationlivetype_code
252
431
205
+		 + + + + + + + + + + + + + + + + + + + + + +
allocationlivetype_code
352
401
405
+
+

The first element of the livestock list contains no further +nesting:

+
str(json_data$feed_basket$feeds[[1]]$livestock[[1]])
+
## 'data.frame':    3 obs. of  2 variables:
+##  $ allocation   : int  40 17 40
+##  $ livetype_code: chr  "2" "1" "5"
+
+
+
+
+
+

cleaned cleaned/data .rda files

+
rda_files<-list.files(".rda")
+
+

GHG parameters

+

The ghg para object (cleaned/data/ghg_para.rda) is a +list of tables that appear to refer to IPCC equations, livestock +parameters and fertilizers.

+
load("ghg_para.rda")
+
+# Summarize level 1 of the list
+ghg_names<-data.table(field_name=names(ghg_para),
+                          dim=sapply(ghg_para,dim))
+
+kable(ghg_names,caption="Tables within ghg_names list")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Tables within ghg_names list
field_namedim
livestock_parameters18, 7
Table_10.126, 2
table_10.173, 2
table_10.1912, 3
table_10.213, 2
table_10.227, 4
table_10A_918, 3
table_11.1_&_table_11.310, 5
table_2.55, 2
fertilizer_table5, 5
table_5.11NULL
table_5.128, 4
table_5.135, 3
table_5.146, 2
+

The exception is ghg_para$table_5.11 which is a +list:

+
ghg_para$table_5.11
+
## $baseline_emission_factor
+## [1] 1.3
+## 
+## $soil_type_scaling_factor
+## [1] 1
+
+
Livestock_parameters
+

Note there is an invalid character in this table:

+
ghg_para$livestock_parameters$`IPCC Category - methane emissions enteric fermentation - Tier 2`[11]<-"Other Cattle or Buffalo - grazing"
+
kable(ghg_para$livestock_parameters,caption="ghg_para$livestock_parameters")
+ + +++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ghg_para$livestock_parameters
livestock_category_nameUrinary_energy_fracash_contentIPCC Category - methane emissions enteric fermentation +- Tier 1IPCC Category - methane emissions enteric fermentation +- Tier 2IPCC Category - methane emissions manure - Tier 1IPCC-Category - Default N-excretion rates Tier 1
Cows (local)0.040.08Other mature femaleDairy cattleDairy cowsDairy cattle
Cows (improved)0.040.08Dairy cattleDairy cattleDairy cowsDairy cattle
Cows (high productive)0.040.08Dairy cattleDairy cattleDairy cowsDairy cattle
Adult cattle - male0.040.08Other draft bullOther Cattle and Buffaloes that are primarily fed low +quality crop residues and byproductsOther cattleOther cattle
Steers/heifers0.040.08Other Mature female-grazingOther Cattle and Buffaloes that are primarily fed low +quality crop residues and byproductsOther cattleOther cattle
Steers/heifers (improved)0.040.08Other Mature female-grazingOther Cattle and Buffaloes that are primarily fed low +quality crop residues and byproductsOther cattleOther cattle
Calves0.040.08Other youngOther Cattle and Buffaloes that are primarily fed low +quality crop residues and byproductsOther cattleOther cattle
Calves (improved)0.040.08Other youngOther Cattle and Buffaloes that are primarily fed low +quality crop residues and byproductsOther cattleOther cattle
Buffalo (dairy)0.040.08Other draft bullOther Cattle or Buffalo - grazingBuffaloOther cattle
Buffalo steers/heifers0.040.08Other youngOther Cattle or Buffalo - grazingBuffaloOther cattle
Buffalo calves0.040.08Other youngOther Cattle or Buffalo - grazingBuffaloOther cattle
Sheep/Goats - Ewes/Does0.040.08GoatsSheepSheepSheep
Sheep/Goats - Breeding Rams/Bucks0.040.08GoatsSheepSheepSheep
Sheep/Goats - Fattening Rams/Bucks0.040.08GoatsSheepSheepsheep
Sheep/Goats - Lambs/Kids0.040.08Goatslambs (less 1 yr old)Sheepsheep
Pigs - lactating/pregnant sows0.020.08PigsN/ASwinepigs
Pigs - dry sows/boars0.020.08PigsN/ASwinepigs
Pigs - growers0.020.08PigsN/ASwinepigs
+
+
+
Fertilizer table
+
kable(ghg_para$fertilizer_table,caption="ghg_para$fertilizer_table")
+ + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ghg_para$fertilizer_table
fertilizer_typepercent_Nemission_factor_kg_CO2eq_per_kg Nkg_N_per_1_kg_of_fertilzeremissions_factor_kg_CO2_eq_per_kg_fertilizer
DAP182.800.180.5040
CAN278.660.272.3382
UreaNANANA0.7850
NPKNANANA1.2100
Lime-applicationNANANANA
+
+
+
IPCC Tables
+
+
Table_10.12
+
kable(ghg_para$Table_10.12)
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
animal_category_ipccmethane_conversion_factor
Dairy cattle6.5
Other Cattle and Buffaloes that are primarily fed low +quality crop residues and byproducts6.5
Other Cattle or Buffalo - grazing6.5
sheep6.5
lambs (less 1 yr old)4.5
N/A0.0
+
+
+
table_10.17
+
kable(ghg_para$table_10.17)
+ + + + + + + + + + + + + + + + + + + + + +
systemmcf_by_average_annual_temperature
Pasture / range / paddock0.015
solid storage0.040
dry lot0.015
+
+
+
table_10.19
+
kable(ghg_para$table_10.19)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
anaimal_categoryContinentn_rate
Dairy cattleLATIN AMERICA0.48
Dairy cattleAFRICA0.60
Dairy cattleASIA0.47
Other cattleLATIN AMERICA0.37
Other cattleAFRICA0.63
Other cattleASIA0.34
SheepLATIN AMERICA1.17
SheepAFRICA1.17
SheepASIA1.17
PigsLATIN AMERICA1.64
PigsAFRICA1.64
PigsASIA0.50
+
+
+
table_10.21
+
kable(ghg_para$table_10.21)
+ + + + + + + + + + + + + + + + + + + + + +
systemdirect_nitrous_oxide_factor
Pasture / range / paddock0.010
solid storage0.005
dry lot0.020
+
+
+
table_10.22
+
kable(ghg_para$table_10.22)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
anaimal_categorysystemfraction_n_loss_mmsrange
Dairy cowspit storage0.28(10-40)
Dairy cowsdry lot0.20(10-35)
Dairy cowssolid storage0.30(10-40)
Dairy cowsdaily spread0.07(5-60)
Other cattledry lot0.30(20-50)
Other cattlesolid storage0.45(10-65)
Other cattledeep bedding0.30(20-40)
+
+
+
table_10A_9
+
kable(ghg_para$Table_10.12)
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
animal_category_ipccmethane_conversion_factor
Dairy cattle6.5
Other Cattle and Buffaloes that are primarily fed low +quality crop residues and byproducts6.5
Other Cattle or Buffalo - grazing6.5
sheep6.5
lambs (less 1 yr old)4.5
N/A0.0
+
+
+
table_11.1_&_table_11.3
+
kable(ghg_para$`table_11.1_&_table_11.3`)
+ +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
emission_factorsunitdescriptionn2o_emissions_from_managed_soilsuncertainty_range
EF1kg N2O-N (kg N input)-1emission factor for N2O emissions from N inputs0.01000.003-0.03
EF2kg N2O-N ha-1 yr-1emission factor for N2O emissions from drained/managed +organic soils16.00005 TO 48
EF3PRP-CPPkg N2O-N (kg N input)-1emission factor for N2O emissions from urine and dung N +deposited on pasture, range and paddock by grazing animals0.02000.007-0.06
EF3PRP-SOkg N2O-N (kg N input)-1emission factor for N2O emissions from urine and dung N +deposited on pasture, range and paddock by grazing animals0.01000.003-0.03
EF4[kg N-N2O (kg NH3-N + NOx-N volatilised)-1]emission factor for N2O emissions from atmospheric +deposition of N on soils and water surfaces0.01000.002-0.05
EF5kg N2O-N (kg N leached and runoff)-1emission factor for N2O emissions from N leaching and +runoff0.00750.005-0.025
EF1Rkg N2O-N (kg N input)-1emission factor for N2O emission from N inputs for +flooded rice0.00300.000 - 0.006
FracGASFkg N volatilised (kg of N applied)-1fraction of synthetic fertilzer N that volatilises as +NH3 and NOx0.10000.03-0.3
FracGASMkg N volatilised (kg of N applied or deposited)-1fraction of applied organic N fertiliser materials +(FON) and of urine and dung N deposited by grazing animals (FPRP) that +volatilises as NH3 and NOx0.20000.05-0.5
FracLEACH-(H)kg N (kg of N additions)-1fraction of all N added to/mineralised in managed soils +in regions where leaching/runoff occurs that is lost through leaching +and runoff0.3000if sum of rain - sum of PE > soil water holding +capacity during rainy season
+
+
+
table_2.5
+
kable(ghg_para$table_2.5)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ghg_gasburnt_emission_factor
CO21515.00
CO92.00
CH42.70
N2O0.07
Nox2.50
+
+
+
table_5.12
+
kable(ghg_para$table_5.12)
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ecosystemecosystem_typeaggregated_scaling_factor_wdisaggregated_scaling_factor_w
irrigatedIrrigated-Continuously flooded0.781.00
irrigatedintermittently flooded-single aeration0.780.60
irrigatedIntermittently flooded-multiple aeration0.780.52
Rain fed and deep waterRainfed-regular rainfed0.270.28
Rain fed and deep waterRainfed-drought prone0.270.25
Rain fed and deep waterRainfed-deep water0.270.31
UplandUpland0.000.00
NoneNone0.000.00
+
+
+
table_5.13
+
kable(ghg_para$table_5.13)
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
water_regimeaggregated_scaling_factor_pdisaggregated_scaling_factor_p
non-flooded pre-season <180 days( often in double +cropping of rice)1.221.00
non-flooded pre-season >180 days (single rice crop +following a dry fallow period)1.220.68
flooded pre-season (>30 days)1.221.90
flooded pre-season (<30 days)1.220.00
None0.000.00
+
+
+
table_5.14
+
kable(ghg_para$table_5.14)
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
organic_amendmentconversion_factor
straw incorporated in soil shortly (<30 days) before +cultivation1.00
straw incorporated in soil long (>30 days) before +cultivation0.29
Compost0.05
Farm yard manure0.14
green manure0.50
None0.00
+
+
+
+
+

Stock change

+

The stock change object +(cleaned/data/stock_change_para.rda) is a series of nested +lists that describe: 1) landuse, management, and input factor_variables +for grassland; and 2) landuse, tillage, and input input factor_variables +for cropland.

+
load("stock_change_para.rda")
+str(stock_change_para)
+
## List of 2
+##  $ cropland :'data.frame':   1 obs. of  3 variables:
+##   ..$ landuse:List of 1
+##   .. ..$ :'data.frame':  1 obs. of  1 variable:
+##   .. .. ..$ factor_variables:List of 1
+##   .. .. .. ..$ :'data.frame':    1 obs. of  10 variables:
+##   .. .. .. .. ..$ Long term cultivated, temperate/boreal, dry                     : num 0.8
+##   .. .. .. .. ..$ Long term cultivated, temperate/boreal, moist                   : num 0.69
+##   .. .. .. .. ..$ Long term cultivated, tropical, dry                             : num 0.58
+##   .. .. .. .. ..$ Long term cultivated, tropical, moist/wet                       : num 0.48
+##   .. .. .. .. ..$ Long term cultivated, tropical montane, all                     : num 0.64
+##   .. .. .. .. ..$ Paddy rice                                                      : num 1.1
+##   .. .. .. .. ..$ Perennial/tree crop                                             : int 1
+##   .. .. .. .. ..$ Set aside (< 20 years), temperate/boreal and tropical, dry      : num 0.93
+##   .. .. .. .. ..$ Set aside (< 20 years), temperate/boreal and tropical, moist/wet: num 0.82
+##   .. .. .. .. ..$ Set aside (< 20 years),tropical montane, all                    : num 0.88
+##   ..$ tillage:List of 1
+##   .. ..$ :'data.frame':  1 obs. of  1 variable:
+##   .. .. ..$ factor_variables:List of 1
+##   .. .. .. ..$ :'data.frame':    1 obs. of  11 variables:
+##   .. .. .. .. ..$ Full                            : int 1
+##   .. .. .. .. ..$ Reduced, temperate/boreal, dry  : num 1.02
+##   .. .. .. .. ..$ Reduced, temperate/boreal, moist: num 1.08
+##   .. .. .. .. ..$ Reduced, tropical, dry          : num 1.09
+##   .. .. .. .. ..$ Reduced, tropical, moist        : num 1.15
+##   .. .. .. .. ..$ Reduced, tropical montane, all  : num 1.09
+##   .. .. .. .. ..$ No-till, temperate/boreal, dry  : num 1.1
+##   .. .. .. .. ..$ No-till, temperate/boreal, moist: num 1.15
+##   .. .. .. .. ..$ No-till, tropical, dry          : num 1.17
+##   .. .. .. .. ..$ No-till, tropical, moist/wet    : num 1.22
+##   .. .. .. .. ..$ No-till, tropical montane, all  : num 1.16
+##   ..$ input  :List of 1
+##   .. ..$ :'data.frame':  1 obs. of  1 variable:
+##   .. .. ..$ factor_variables:List of 1
+##   .. .. .. ..$ :'data.frame':    1 obs. of  12 variables:
+##   .. .. .. .. ..$ Low, temperate/boreal, dry                                : num 0.95
+##   .. .. .. .. ..$ Low, temperate/boreal, moist                              : num 0.92
+##   .. .. .. .. ..$ Low, tropical, dry                                        : num 0.95
+##   .. .. .. .. ..$ Low, tropical, moist                                      : num 0.92
+##   .. .. .. .. ..$ Low, tropical montane, all                                : num 0.94
+##   .. .. .. .. ..$ Medium, all                                               : int 1
+##   .. .. .. .. ..$ High w/OUT manure, temperate/boral and tropical, dry      : num 1.04
+##   .. .. .. .. ..$ High w/OUT manure, temperate/boral and tropical, moist/wet: num 1.11
+##   .. .. .. .. ..$ High w/OUT manure, tropical montane                       : num 1.08
+##   .. .. .. .. ..$ High with manure, temperate/boral and tropical, dry       : num 1.37
+##   .. .. .. .. ..$ High with manure, temperate/boral and tropical, moist/wet : num 1.44
+##   .. .. .. .. ..$ High with manure, tropical montane                        : num 1.41
+##  $ grassland:'data.frame':   1 obs. of  3 variables:
+##   ..$ landuse   :List of 1
+##   .. ..$ :'data.frame':  1 obs. of  1 variable:
+##   .. .. ..$ factor_variables:List of 1
+##   .. .. .. ..$ :'data.frame':    1 obs. of  1 variable:
+##   .. .. .. .. ..$ All: int 1
+##   ..$ management:List of 1
+##   .. ..$ :'data.frame':  1 obs. of  1 variable:
+##   .. .. ..$ factor_variables:List of 1
+##   .. .. .. ..$ :'data.frame':    1 obs. of  8 variables:
+##   .. .. .. .. ..$ Nominally managed                              : num 1
+##   .. .. .. .. ..$ Moderately degraded grassland, temperate/boreal: num 0.95
+##   .. .. .. .. ..$ Moderately degraded grassland, tropical        : num 0.97
+##   .. .. .. .. ..$ Moderately degraded grassland, tropical montane: num 0.96
+##   .. .. .. .. ..$ Severely degraded                              : num 0.7
+##   .. .. .. .. ..$ Improved grassland, temperate/boreal           : num 1.14
+##   .. .. .. .. ..$ Improved grassland, tropical                   : num 1.17
+##   .. .. .. .. ..$ Improved grassland, tropical montane           : num 1.16
+##   ..$ input     :List of 1
+##   .. ..$ :'data.frame':  1 obs. of  1 variable:
+##   .. .. ..$ factor_variables:List of 1
+##   .. .. .. ..$ :'data.frame':    1 obs. of  3 variables:
+##   .. .. .. .. ..$ Medium: int 1
+##   .. .. .. .. ..$ High  : num 1.11
+##   .. .. .. .. ..$ none  : int 1
+
+
Grass factor variables
+

Grassland: landuse

+
x<-unlist(stock_change_para$grassland$landuse[[1]]$factor_variables)
+kable(data.frame(variable=names(x),value=as.numeric(x)))
+ + + + + + + + + + + + + +
variablevalue
All1
+

Grassland: management

+
x<-unlist(stock_change_para$grassland$management[[1]]$factor_variables)
+kable(data.frame(variable=names(x),value=as.numeric(x)))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
variablevalue
Nominally managed1.00
Moderately degraded grassland, temperate/boreal0.95
Moderately degraded grassland, tropical0.97
Moderately degraded grassland, tropical montane0.96
Severely degraded0.70
Improved grassland, temperate/boreal1.14
Improved grassland, tropical1.17
Improved grassland, tropical montane1.16
+

Grassland: input

+
x<-unlist(stock_change_para$grassland$input[[1]]$factor_variables)
+kable(data.frame(variable=names(x),value=as.numeric(x)))
+ + + + + + + + + + + + + + + + + + + + + +
variablevalue
Medium1.00
High1.11
none1.00
+
+
+
Cropland factor variables
+

Cropland: landuse

+
x<-unlist(stock_change_para$cropland$landuse[[1]]$factor_variables)
+kable(data.frame(variable=names(x),value=as.numeric(x)))
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
variablevalue
Long term cultivated, temperate/boreal, dry0.80
Long term cultivated, temperate/boreal, moist0.69
Long term cultivated, tropical, dry0.58
Long term cultivated, tropical, moist/wet0.48
Long term cultivated, tropical montane, all0.64
Paddy rice1.10
Perennial/tree crop1.00
Set aside (< 20 years), temperate/boreal and +tropical, dry0.93
Set aside (< 20 years), temperate/boreal and +tropical, moist/wet0.82
Set aside (< 20 years),tropical montane, all0.88
+

Cropland: tillage

+
x<-unlist(stock_change_para$cropland$tillage[[1]]$factor_variables)
+kable(data.frame(variable=names(x),value=as.numeric(x)))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
variablevalue
Full1.00
Reduced, temperate/boreal, dry1.02
Reduced, temperate/boreal, moist1.08
Reduced, tropical, dry1.09
Reduced, tropical, moist1.15
Reduced, tropical montane, all1.09
No-till, temperate/boreal, dry1.10
No-till, temperate/boreal, moist1.15
No-till, tropical, dry1.17
No-till, tropical, moist/wet1.22
No-till, tropical montane, all1.16
+

Cropland: input

+
x<-unlist(stock_change_para$cropland$input[[1]]$factor_variables)
+kable(data.frame(variable=names(x),value=as.numeric(x)))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
variablevalue
Low, temperate/boreal, dry0.95
Low, temperate/boreal, moist0.92
Low, tropical, dry0.95
Low, tropical, moist0.92
Low, tropical montane, all0.94
Medium, all1.00
High w/OUT manure, temperate/boral and tropical, +dry1.04
High w/OUT manure, temperate/boral and tropical, +moist/wet1.11
High w/OUT manure, tropical montane1.08
High with manure, temperate/boral and tropical, +dry1.37
High with manure, temperate/boral and tropical, +moist/wet1.44
High with manure, tropical montane1.41
+
+
+
+

mufindi

+

The mufindi object (cleaned/data/mufindi.rda) appears to +be an input dataset similar to the qt_example.json +file.

+
load("mufindi.rda")
+str(mufindi)
+
## List of 48
+##  $ cba_discount_rate            : int 0
+##  $ cba_years                    : int 0
+##  $ cropland_orgmatter           : chr "High w/OUT manure, temperate/boral and tropical, moist/wet"
+##  $ cropland_system              : chr "Long term cultivated, temperate/boreal, moist"
+##  $ cropland_tillage             : chr "Reduced, tropical, moist"
+##  $ farm_code                    : chr "ddd"
+##  $ farm_name                    : chr "ooo"
+##  $ feed_basket                  :'data.frame':   2 obs. of  2 variables:
+##   ..$ feeds      :List of 2
+##   .. ..$ :'data.frame':  4 obs. of  3 variables:
+##   .. .. ..$ feed_item_code: chr [1:4] "16" "31" "51" "82"
+##   .. .. ..$ feed_type_code: chr [1:4] "8" "17" "29" "40"
+##   .. .. ..$ livestock     :List of 4
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 2 10
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 20 10
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 30 10
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 48 70
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. ..$ :'data.frame':  4 obs. of  3 variables:
+##   .. .. ..$ feed_item_code: chr [1:4] "16" "31" "51" "82"
+##   .. .. ..$ feed_type_code: chr [1:4] "8" "17" "29" "40"
+##   .. .. ..$ livestock     :List of 4
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 70 50
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 20 5
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 5 5
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   .. .. .. ..$ :'data.frame':    2 obs. of  2 variables:
+##   .. .. .. .. ..$ allocation   : int [1:2] 5 40
+##   .. .. .. .. ..$ livetype_code: chr [1:2] "3" "6"
+##   ..$ season_name: chr [1:2] "Dry season" "Wet season"
+##  $ feed_items                   :'data.frame':   4 obs. of  64 variables:
+##   ..$ ammonia               : int [1:4] 14 0 0 8
+##   ..$ ammonium_nitrate      : int [1:4] 11 0 5 0
+##   ..$ ammonium_sulfate      : int [1:4] 12 6 0 0
+##   ..$ c_factor              : num [1:4] 0.05 0.117 0.05 0.027
+##   ..$ category              : chr [1:4] "" "legume" "cereal" "grass"
+##   ..$ cp_content            : num [1:4] 1.81 16.29 8.9 11
+##   ..$ cp_fresh              : num [1:4] 1.61 15.17 7.3 1.65
+##   ..$ crop_coefficient      : num [1:4] 0 0.633 0.533 0.917
+##   ..$ cut_carry_fraction    : int [1:4] 2 0 0 0
+##   ..$ dap                   : int [1:4] 10 0 0 4
+##   ..$ de                    : num [1:4] 0.71 0.433 0.674 0.652
+##   ..$ dm_content            : num [1:4] 88.8 93.1 82 15
+##   ..$ dry_yield             : num [1:4] 5.04 1.16 8.6 6
+##   ..$ emission_factor       : int [1:4] 0 0 0 0
+##   ..$ energy                : int [1:4] 160 567 365 0
+##   ..$ energy_dm             : num [1:4] 397 606 407 0
+##   ..$ establishment_cost    : int [1:4] 0 0 0 20
+##   ..$ establishment_labour  : int [1:4] 0 0 0 6
+##   ..$ feed_item_code        : chr [1:4] "16" "31" "51" "82"
+##   ..$ feed_item_name        : chr [1:4] "Cassava (Manihot esculenta) - tubers" "Groundnut (Arachis hypogaea) - seed hulls" "Maize (Zea mays) - forage" "Pennisetum purpureum - forage"
+##   ..$ feed_type_code        : chr [1:4] "8" "17" "29" "40"
+##   ..$ feed_type_name        : chr [1:4] "Cassava" "Groundnut" "Maize" "Pennisetum purpureum"
+##   ..$ fraction_as_fertilizer: int [1:4] 7 1 0 0
+##   ..$ fresh_yield           : num [1:4] 12.5 1.22 10 20
+##   ..$ grassman              : chr [1:4] "1" "1" "1" "1"
+##   ..$ grassman_change_factor: int [1:4] 1 1 1 1
+##   ..$ harvest_index         : num [1:4] 0.5 0.29 0.47 0.9
+##   ..$ intercrop             : int [1:4] 1 0 0 1
+##   ..$ intercrop_fraction    : int [1:4] 1 0 0 2
+##   ..$ kc_initial            : num [1:4] 0 0.15 0.15 0.6
+##   ..$ kc_late               : num [1:4] 0 0.6 0.3 1.05
+##   ..$ kc_midseason          : num [1:4] 0 1.15 1.15 1.1
+##   ..$ land_cover            : chr [1:4] "1" "1" "1" "1"
+##   ..$ landcover_c_factor    : num [1:4] 0.001 0.001 0.001 0.001
+##   ..$ main_n                : num [1:4] 0.004 0.037 0.017 0.023
+##   ..$ main_product_removal  : int [1:4] 4 0 0 0
+##   ..$ me_content            : num [1:4] 10.76 6.56 10.22 9.88
+##   ..$ me_fresh              : num [1:4] 9.56 6.11 8.38 1.48
+##   ..$ n_fertilizer          : int [1:4] 15 0 9 0
+##   ..$ n_fixation            : num [1:4] 0 38.2 0 0
+##   ..$ n_solutions           : int [1:4] 13 0 7 0
+##   ..$ npk                   : int [1:4] 9 0 3 0
+##   ..$ operational_cost      : int [1:4] 0 0 0 18
+##   ..$ operational_labour    : num [1:4] 0 0 0.203 0.5
+##   ..$ residue_burnt         : int [1:4] 6 0 0 0
+##   ..$ residue_dm_content    : num [1:4] 0.403 0.935 0.896 0.85
+##   ..$ residue_dry_yield     : num [1:4] 5.04 2.79 3 0
+##   ..$ residue_fresh_yield   : num [1:4] 12.5 2.99 3.5 2.22
+##   ..$ residue_n             : int [1:4] 0 0 0 0
+##   ..$ residue_n_dm          : num [1:4] 0.003 0.012 0.007 0.023
+##   ..$ residue_removal       : int [1:4] 5 0 0 0
+##   ..$ slope                 : chr [1:4] "1" "1" "1" "1"
+##   ..$ slope_length          : int [1:4] 3 0 0 0
+##   ..$ slope_p_factor        : num [1:4] 0.11 0.11 0.11 0.11
+##   ..$ trees_dhb             : int [1:4] 0 0 0 0
+##   ..$ trees_growth          : int [1:4] 0 0 0 0
+##   ..$ trees_ha              : int [1:4] 0 0 0 0
+##   ..$ trees_removal         : int [1:4] 0 0 0 0
+##   ..$ urea                  : int [1:4] 8 2 0 0
+##   ..$ usda_value            : int [1:4] 11134 16067 20314 0
+##   ..$ water_content         : num [1:4] 59.7 6.5 10.4 0
+##   ..$ wfp_blue              : num [1:4] 0 0 0.002 0
+##   ..$ wfp_green             : num [1:4] 0 0 0.199 0
+##   ..$ wfp_grey              : num [1:4] 0 0 0.002 0
+##  $ ferlitizer                   :'data.frame':   1 obs. of  5 variables:
+##   ..$ cost           : int 2
+##   ..$ fertilizer_code: chr "4"
+##   ..$ fertilizer_desc: chr "Ammonium nitrate"
+##   ..$ fraction       : int 3
+##   ..$ quantity       : int 1
+##  $ grassland_implevel           : chr "High"
+##  $ grassland_management         : chr "Moderately degraded grassland, tropical montane"
+##  $ land_oppcost                 : int 0
+##  $ livestock                    :'data.frame':   2 obs. of  53 variables:
+##   ..$ annual_growth                 : int [1:2] 3 9
+##   ..$ annual_milk                   : int [1:2] 2 10
+##   ..$ birth_interval                : num [1:2] 1.17 0
+##   ..$ body_weight                   : int [1:2] 600 300
+##   ..$ carcass_fraction              : num [1:2] 0.45 0.49
+##   ..$ cp_grazing                    : int [1:2] 0 0
+##   ..$ cp_growth                     : num [1:2] 0 0.4
+##   ..$ cp_lactation                  : int [1:2] 0 0
+##   ..$ cp_lactmilk                   : num [1:2] 0.09 0
+##   ..$ cp_maintenance                : num [1:2] 0.6 0.3
+##   ..$ cp_pregnancy                  : int [1:2] 15 0
+##   ..$ distance_to_pasture           : int [1:2] 8 4
+##   ..$ energy_eggcontent             : int [1:2] 0 0
+##   ..$ energy_meatcontent            : int [1:2] 2200 2200
+##   ..$ energy_milkcontent            : int [1:2] 970 0
+##   ..$ er_grazing                    : num [1:2] 2 1.5
+##   ..$ er_growth                     : int [1:2] 0 50
+##   ..$ er_lactation                  : int [1:2] 0 0
+##   ..$ er_lactmilk                   : num [1:2] 5.5 0
+##   ..$ er_maintenance                : num [1:2] 60.6 36
+##   ..$ er_pregnancy                  : int [1:2] 2000 0
+##   ..$ fat_content                   : int [1:2] 4 0
+##   ..$ grazing_displacement          : int [1:2] 2 2
+##   ..$ herd_composition              : int [1:2] 1 11
+##   ..$ ipcc_meth_ef_t1               : int [1:2] 68 46
+##   ..$ ipcc_meth_ef_t2               : num [1:2] 6.5 6.5
+##   ..$ ipcc_meth_exc                 : num [1:2] 0.47 0.34
+##   ..$ ipcc_meth_man                 : int [1:2] 19 1
+##   ..$ lactation_length              : int [1:2] 0 0
+##   ..$ litter_size                   : int [1:2] 0 0
+##   ..$ livetype_code                 : chr [1:2] "3" "6"
+##   ..$ livetype_desc                 : chr [1:2] "Cows (high productive)" "Steers/heifers (improved)"
+##   ..$ lw_gain                       : int [1:2] 0 0
+##   ..$ manure_in_field               : int [1:2] 11 1
+##   ..$ manure_in_non_roofed_enclosure: int [1:2] 10 2
+##   ..$ manure_in_stable              : int [1:2] 9 3
+##   ..$ meat_price                    : int [1:2] 23 23
+##   ..$ meat_product                  : chr [1:2] "beef" "beef"
+##   ..$ milk_price                    : num [1:2] 1.04 1.04
+##   ..$ milk_product                  : chr [1:2] "cow milk" "cow milk"
+##   ..$ n_content                     : num [1:2] 0.029 0.029
+##   ..$ oneoff_cost                   : int [1:2] 6400 3450
+##   ..$ oneoff_labour                 : int [1:2] 0 0
+##   ..$ operational_cost              : int [1:2] 4000 1650
+##   ..$ operational_labour            : num [1:2] 50.7 7.6
+##   ..$ proportion_growth             : int [1:2] 0 0
+##   ..$ protein_meatcontent           : int [1:2] 26 26
+##   ..$ protein_milkcontent           : num [1:2] 3.7 0
+##   ..$ time_in_non_roofed_enclosure  : int [1:2] 5 7
+##   ..$ time_in_offfarm_grazing       : int [1:2] 7 5
+##   ..$ time_in_onfarm_grazing        : int [1:2] 6 6
+##   ..$ time_in_stable                : int [1:2] 4 8
+##   ..$ water_requirement             : int [1:2] 160 100
+##  $ manure_onfarm_fraction       : int 1
+##  $ manure_sales_fraction        : int 2
+##  $ manureman_pasture            : chr "Pasture / range / paddock"
+##  $ manureman_stable             : chr "Solid storage"
+##  $ manureman_yard               : chr "Dry slot"
+##  $ purchased_bedding            : int 6
+##  $ purchased_compost            : int 4
+##  $ purchased_manure             : int 3
+##  $ purchased_organic_n          : int 5
+##  $ region                       : chr "ASIA"
+##  $ seasons                      :'data.frame':   2 obs. of  2 variables:
+##   ..$ season_length: int [1:2] 200 165
+##   ..$ season_name  : chr [1:2] "Dry season" "Wet season"
+##  $ txt_annual_prec              : int 1
+##  $ txt_arable_tograssland       : int 16
+##  $ txt_cropland_orgmatter_ipcc  : num 1.11
+##  $ txt_cropland_system_ipcc     : num 0.69
+##  $ txt_cropland_tillage_ipcc    : num 1.15
+##  $ txt_et                       : int 9
+##  $ txt_grassland_implevel_ipcc  : num 1.11
+##  $ txt_grassland_management_ipcc: num 0.96
+##  $ txt_grassland_toarable       : int 15
+##  $ txt_rain_length              : int 2
+##  $ txt_soil_bulk                : int 7
+##  $ txt_soil_c                   : int 5
+##  $ txt_soil_clay                : int 6
+##  $ txt_soil_depth               : int 8
+##  $ txt_soil_k_value             : num 0.25
+##  $ txt_soil_n                   : int 4
+##  $ waste_consume_milk           : int 0
+##  $ waste_distribution_meat      : int 0
+##  $ waste_distribution_milk      : int 0
+##  $ waste_processing_meat        : int 0
+##  $ waste_processing_milk        : int 0
+##  $ waste_production_meat        : int 0
+##  $ waste_production_milk        : int 0
+
+
+
+

cleaned-desktop cleaned.sqlite file

+

This sqlite database object is found in the qlands/cleaned-desktop +github it contains several tables of look-up values.

+
+

Load sqlite file into R

+
conn <- dbConnect(RSQLite::SQLite(), "cleaned.sqlite")
+tables <- dbListTables(conn)
+print(tables)
+
##  [1] "lkp_climate"         "lkp_climate2"        "lkp_croplandsystem" 
+##  [4] "lkp_feeditem"        "lkp_feedtype"        "lkp_grassinputlevel"
+##  [7] "lkp_grasslandman"    "lkp_landcover"       "lkp_livetype"       
+## [10] "lkp_manureman"       "lkp_organicmatter"   "lkp_orgfertilizer"  
+## [13] "lkp_region"          "lkp_slope"           "lkp_soil"           
+## [16] "lkp_tillageregime"
+
sq_tables<-lapply(tables,FUN=function(TAB){
+  query<-paste0("SELECT * FROM ",TAB)
+  data <- dbGetQuery(conn,query)
+  data
+})
+names(sq_tables)<-tables
+
+
Climate
+ + + + + + + + + + + + + + + + + +
climate_codeclimate_desc
TemperateTemperate
WarmWarm
+
+
+
Climate2
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
climate_codeclimate2_codeclimate2_desc
TemperateWarm Temperate MoistWarm Temperate Moist
TemperateWarm Temperate DryWarm Temperate Dry
WarmTropical MontaneTropical Montane
WarmTropical WetTropical Wet
WarmTropical MoistTropical Moist
WarmTropical DryTropical Dry
+
+
+
Region
+ + + + + + + + + + + + + + + + + + + + + +
region_coderegion_desc
AFRICAAFRICA
LATINLATIN AMERICA
ASIAASIA
+
+
+
Cropland system
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
sys_codesys_descchange_factor
1Long term cultivated, temperate/boreal, dry0.80
2Long term cultivated, temperate/boreal, moist0.69
3Long term cultivated, tropical, dry0.58
4Long term cultivated, tropical, moist/wet0.48
5Long term cultivated, tropical montane, all0.64
6Paddy rice1.10
7Perennial/tree crop1.00
8Set aside (< 20 years), temperate/boreal and +tropical, dry0.93
9Set aside (< 20 years), temperate/boreal and +tropical, moist/wet0.82
10Set aside (< 20 years),tropical montane, all0.88
+
+
+
Feeds
+
+
Item
+
+ +
+
+
+
Type
+
+ +
+
+
+
+
Grass input level
+ + + + + + + + + + + + + + + + + + + + + + + + + +
grassinputlevel_codegrassinputlevel_descchange_factor
1Medium1.00
2High1.11
3None1.00
+
+
+
Grassland management
+
kable(sq_tables$lkp_grasslandman)
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
management_codemanagement_descchange_factor
1Nominally managed1.00
2Moderately degraded grassland, temperate/boreal0.95
3Moderately degraded grassland, tropical0.97
4Moderately degraded grassland, tropical montane0.96
5Severely degraded0.70
6Improved grassland, temperate/boreal1.14
7Improved grassland, tropical1.17
8Improved grassland, tropical montane1.16
+
+
+
Landcover
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
landcover_codelandcover_descc_factor
1Dense forest0.001
2Other forest0.050
3Badlands hard0.050
4Badlands soft0.400
5Sorghum0.100
6Maize0.100
7Cereals0.150
8Pulses0.150
9Dense grass0.010
10Degraded grass0.050
11Fallow hard0.050
12Fallow plouged0.600
13Ethiopian teff0.250
14Continuous fallow1.000
+
+
+
Livestock type
+
+ +
+
+
+
Manure management
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
manureman_codemanureman_descemission_factor
pasturePasture / range / paddock0.010
storageSolid storage0.005
drylotDry lot0.020
Uncovered anaerobic lagoonUncovered anaerobic lagoon0.000
Liquid/Slurry Pit below animals 1 MonthLiquid/Slurry Pit below animals 1 Month0.000
Liquid/Slurry Pit below animals 3 MonthLiquid/Slurry Pit below animals 3 Month0.000
Liquid/Slurry Pit below animals 4 MonthLiquid/Slurry Pit below animals 4 Month0.000
Liquid/Slurry Pit below animals 6 MonthLiquid/Slurry Pit below animals 6 Month0.000
Liquid/Slurry Pit below animals 12 MonthLiquid/Slurry Pit below animals 12 Month0.000
Cattle and swine deep bedding < 1 monthCattle and swine deep bedding < 1 month0.000
Cattle and swine deep bedding > 1 monthCattle and swine deep bedding > 1 month0.000
Solid storageSolid storage0.000
Solid storage - Covered/compactedSolid storage - Covered/compacted0.000
Solid storage - Bulking agent additionSolid storage - Bulking agent addition0.000
Solid storage - AdditivesSolid storage - Additives0.000
Daily spreadDaily spread0.000
Composting - In-vesselComposting - In-vessel0.000
Composting - Static pile (forced aeration)Composting - Static pile (forced aeration)0.000
Composting - Intensive windrowComposting - Intensive windrow0.000
Composting - Unfrequent turningComposting - Unfrequent turning0.000
Pasture/Range/PaddockPasture/Range/Paddock0.000
Poultry manure with and without litterPoultry manure with and without litter0.000
Aerobic treatmentAerobic treatment0.000
Burned for fuelBurned for fuel0.000
Anaer digester, Low leak, HQ stor, HQ tec.Anaer digester, Low leak, HQ stor, HQ tec.0.000
Anaer digester, Low leak, HQ stor, LQ tecAnaer digester, Low leak, HQ stor, LQ tec0.000
Anaer digester, Low leak, open stor, HQ tecAnaer digester, Low leak, open stor, HQ tec0.000
Anaer digester, High leak, open stor, HQ tecAnaer digester, High leak, open stor, HQ tec0.000
Anaer digester, High leak, LQ stor, LQ tecAnaer digester, High leak, LQ stor, LQ tec0.000
Anaer digester, High leak, open stor, LQ tecAnaer digester, High leak, open stor, LQ tec0.000
+
+
+
Organic matter
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
orgmatter_codeorgmatter_descchange_factor
1Low, temperate/boreal, dry0.95
2Low, temperate/boreal, moist0.92
3Low, tropical, dry0.95
4Low, tropical, moist0.92
5Low, tropical montane, all0.94
6Medium, all1.00
7High w/OUT manure, temperate/boral and tropical, +dry1.04
8High w/OUT manure, temperate/boral and tropical, +moist/wet1.11
9High w/OUT manure, tropical montane1.08
10High with manure, temperate/boral and tropical, +dry1.37
11High with manure, temperate/boral and tropical, +moist/wet1.44
12High with manure, tropical montane1.41
+
+
+
Organic fertilizer
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fertilizer_codefertilizer_desc
1Urea
3DAP
2NPK
4Ammonium nitrate
5Ammonium sulfate
6N solutions
7Ammonia
8Manure
9Compost
10Other organic N additions
11Bedding material
+
+
+
Slope
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
slope_codeslope_descp_factor
1Flat (0-5%)0.11
2Hilly (5-20%)0.13
3Steep (20-30%)0.22
4Extremely steep (30%+)0.37
5Non-agricultural (all slope categories)1.00
+
+
+
Soil
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
soil_codesoil_desck_value
andosolAndosol0.15
cambisolCambisol0.20
lixisolLixisol0.25
fluvisolFluvisol0.30
vertisolVertisol0.15
phaeozemPhaeozem0.20
nitosolNitosol0.25
xerosolXerosol0.30
+
+
+
Tillage regime
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tillage_codetillage_descchange_factor
1Full1.00
2Reduced, temperate/boreal, dry1.02
3Reduced, temperate/boreal, moist1.08
4Reduced, tropical, dry1.09
5Reduced, tropical, moist1.15
6Reduced, tropical montane, all1.09
7No-till, temperate/boreal, dry1.10
8No-till, temperate/boreal, moist1.15
9No-till, tropical, dry1.17
10No-till, tropical, moist/wet1.22
11No-till, tropical montane, all1.16
+
+
+
+
+

cleaned-desktop energy_parameters.json file

+

The energy_parameters.json object is found in the qlands/cleaned-desktop +github it contains a single table Table 10.4 that +does not seem to appear in the cleaned/data/ghg_para.rda +object.

+
+

Table 10.4

+
json_data2 <- fromJSON("energy_parameters.json")
+names(json_data2)
+
## [1] "Table 10.4"
+
kable(json_data2$`Table 10.4`)
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
animal_categorymaintenance_cficomments
Cattle_Buffalo0.322All non-lactating cows, steers, heifers and calves
Cattle_Buffalo_lactating_cows0.386Maintenance energy requirements are 20% higher during +lactation
Cattle_Buffalo_bulls0.370Maintenance energy requirements are 15% higher for +intact males than non lactating females
Sheep_lamb_to _1_year0.236This value can be increased by 15% for intact +males
Sheep_older_than_1_year0.217This value can be increased by 15% for intact +males.
Goats0.315NA
Pigs0.440NA
+
+
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/data/explore_qt_json.pdf b/data/explore_qt_json.pdf new file mode 100644 index 0000000..d181acc Binary files /dev/null and b/data/explore_qt_json.pdf differ diff --git a/data/explore_qt_json.rmd b/data/explore_qt_json.rmd new file mode 100644 index 0000000..8bd9b5a --- /dev/null +++ b/data/explore_qt_json.rmd @@ -0,0 +1,404 @@ +--- +title: "Exploration of CLEANED input files" +author: "Peter Steward" +date: "2024-07-01" +output: + html_document: + toc: true + toc_float: true + toc_depth: 6 + df_print: paged + pdf_document: + toc: true + toc_depth: 6 +--- + +```{r setup,echo=F} +knitr::opts_chunk$set(echo = TRUE) + +# Load packages ##### +if (!require("pacman")) { + install.packages("pacman") + require(pacman) +} + +if (!tinytex::is_tinytex()) { + p_load(tinytex) + tinytex::install_tinytex() +} + +# Use p_load to install if not present and load the packages +pacman::p_load(jsonlite,data.table,rmarkdown,knitr,RSQLite) +``` + +## Cleaned Data +### Input data structure + +The aim of this markdown document is to summarize the structure of the .json object that is required as an input for the R cleaned packages. +It also explores any additional datasets that are used as inputs to create this input object, these include `cleaned/data/ghg_para.R`, `cleaned/data/stock_change_para.rda` and `qlands/cleaned-desktop/cleaned.sqlite`. + +#### Load input json file into R +Load the `qt_example.json` example input file stored in `cleaned/data`. +```{r load data,echo=T} +# Read in example json file ##### +json_data <- fromJSON("qt_example.json") + +# Summarize level 1 of the list +json_names<-data.table(field_name=names(json_data), + class=sapply(json_data,base::class), + length=sapply(json_data,length)) + +kable(json_names, caption = "Summary of Level 1 of the JSON Object") +``` + +#### Simple fields +```{r simple fields,echo=T} +simple_fields<-json_names[class!="data.frame" & length==1,field_name] +simple_fields<-json_data[json_names[class!="data.frame" & length==1,field_name]] +simple_fields<-data.table(field=names(simple_fields), + value=unlist(simple_fields), + class=json_names[class!="data.frame" & length==1,class]) +``` + +```{r simple feilds 2,echo=F} +kable(simple_fields, caption = "Summary of simple fields containing a single value") +``` + +#### Livestock +The `json_data$livestock` of the json list is a data.table that contains information about livestock"]] herd structure and management, manure management and productivity. + +```{r livestock 0 ,echo=F} +livestock<-t(json_data$livestock) # Transpose +colnames(livestock)<-paste0("livetype_code_",json_data$livestock$livetype_code) +kable(livestock,caption="Transposed livestock herd data t(json_data$livestock)") +``` + +The `livestock` are linked to the `feed_basket$feeds` tables by the `livetype_code` keyfield. +```{r livestock 1 ,echo=T} +json_data$livestock[,c("livetype_code","livetype_desc")] # Codes in the feed_items table +lapply(json_data$feed_basket$feeds,"[[","livestock") # Codes in the feed_basket tables +``` + +#### Feed items +The `json_data$feed_items` level of the json list is a data.table that contains information about the production management and context of feed production. + +```{r feed items 0 ,echo=F} +feed_items<-t(json_data$feed_items) # Transpose +colnames(feed_items)<-paste0("feed_item_code_",json_data$feed_items$feed_item_code) +kable(feed_items,caption="Transposed feed item data t(json_data$feed_items)") +``` + +The `feed_items` are linked to the `feed_basket$feeds` tables by the `feed_item_code` field. +```{r feed items 1 ,echo=T} +json_data$feed_items[,c("feed_item_code","feed_item_name")] # Codes in the feed_items table +lapply(json_data$feed_basket$feeds,"[[","feed_item_code") # Codes in the feed_basket tables +``` + +Other keyfields include `feed_type_code` and `land_cover`: +```{r feed items 2 ,echo=T} +json_data$feed_items[,c("feed_type_code","feed_type_name","land_cover","land_cover_desc")] +``` + +#### Fertilizer +The `json_data$fertilizer` level of the json list is a data.table that contains information about the production management and context of feed production. + +```{r fertilizer 1,echo=F} +kable(json_data$fertilizer,caption="Fertilizer data input table (json_data$fertilizer)") +``` + +The values in `fertilizer_code` field do not appear directly correspond to any fields in the feed basket or feed item tables. However columns with similar names do appear in the field item tables + +```{r fertilizer 2,echo=T} +fertilizers<-json_data$fertilizer$fertilizer_desc +fi_cols<-colnames(json_data$feed_items) + +# Reformat fertilizer names to match column names in the feed_items table +(fertilizers<-gsub(" ","_",tolower(json_data$fertilizer$fertilizer_desc))) + +# Find matching columns +fi_cols[fi_cols %in% fertilizers] +``` + +#### Seasons +The `json_data$season` level of the input data is 2-column table that records the length of each season (adding up to 365 days max). +```{r seasons 1,echo=F} +kable(json_data$seasons,caption="Feed items data input table (json_data$season)") +``` + +The `season_name` field is the key field that links to the feed basket. +```{r seasons 2 ,echo=T} +json_data$feed_basket$season_name +``` + +#### Feed basket +The `json_data$feed_basket` level of the input data contains a futher `r length(json_data$feed_basket)` list levels called +`r paste(names(json_data$feed_basket),collapse=" and ")`. These sub-levels appear to be the same length: `r paste0(names(json_data$feed_basket)," = ", sapply(json_data$feed_basket,length))`. + +##### Seasons +The `json_data$feed_basket$season_name` object is simple, being a vector containing the names of the seasons: +```{r feed basket seasons,echo=F} +kable(json_data$feed_basket$season_name,caption="Feed basket/season data input table (json_data$feed_basket$season)") +``` + +##### Feed Basket +The `json_data$feed_basket$feed` object is the most structurally complex element of the input json data containing several levels of nesting. +```{r feed basket feeds 1 ,echo=T} +str(json_data$feed_basket$feeds) +``` + +###### Feeds +Each feed basket table (e.g., `json_data$feed_basket$feeds[[1]]`) is constructed using key fields that describe feed items found in the `json_data$feed_items table`, specifically `feed_item_code` and `feed_type_code.` The feeds are then allocated to the herd elements described in the `json_data$livestock table`. + +```{r feed basket feeds 0 ,echo=F} +kable(json_data$feed_basket$feeds[[1]],caption="Feed basket/feed data input table tablejson_data$feed_basket$feeds[[1]]") +``` + +###### Livestock +The livestock field in the feed basket table contains a list with `r length(json_data$feed_basket$feeds[[1]]$livestock)` elements, indicating a one-to-many relationship between the diet item and elements of the livestock herd. Each feed item represented by a row in the feed basket table is allocated to different herd elements, with the `livetype_code` field serving as the key field linking the two tables. + +```{r feed basket feeds 2 ,echo=F} +kable(json_data$feed_basket$feeds[[1]]$livestock,caption="Feed basket/feed/livestock data input table tablejson_data$feed_basket$feeds[[1]]$livestock") +``` + +The first element of the livestock list contains no further nesting: +```{r feed basket feeds 3 ,echo=T} +str(json_data$feed_basket$feeds[[1]]$livestock[[1]]) +``` + +### cleaned `cleaned/data .rda` files +```{r list rda files} +rda_files<-list.files(".rda") +``` + +#### GHG parameters +The ghg para object (`cleaned/data/ghg_para.rda`) is a list of tables that appear to refer to IPCC equations, livestock parameters and fertilizers. +```{r ghg_para 1} +load("ghg_para.rda") + +# Summarize level 1 of the list +ghg_names<-data.table(field_name=names(ghg_para), + dim=sapply(ghg_para,dim)) + +kable(ghg_names,caption="Tables within ghg_names list") +``` +The exception is `ghg_para$table_5.11` which is a list: +```{r ghg_para 2} +ghg_para$table_5.11 +``` + +##### Livestock_parameters +Note there is an invalid character in this table: +```{r ghg_para 3a} +ghg_para$livestock_parameters$`IPCC Category - methane emissions enteric fermentation - Tier 2`[11]<-"Other Cattle or Buffalo - grazing" +``` + + +```{r ghg_para 3} +kable(ghg_para$livestock_parameters,caption="ghg_para$livestock_parameters") +``` + +##### Fertilizer table +```{r ghg_para 4} +kable(ghg_para$fertilizer_table,caption="ghg_para$fertilizer_table") +``` + +##### IPCC Tables + +###### Table_10.12 +```{r ghg_para Table_10.12} +kable(ghg_para$Table_10.12) +``` + +###### table_10.17 +```{r ghg_para table_10.17} +kable(ghg_para$table_10.17) +``` + +###### table_10.19 +```{r ghg_para table_10.19} +kable(ghg_para$table_10.19) +``` + +###### table_10.21 +```{r ghg_para table_10.21} +kable(ghg_para$table_10.21) +``` + +###### table_10.22 +```{r ghg_para table_10.22} +kable(ghg_para$table_10.22) +``` + +###### table_10A_9 +```{r ghg_para table_10A_9} +kable(ghg_para$Table_10.12) +``` + +###### table_11.1_&_table_11.3 +```{r ghg_para table_11.1_&_table_11.3} +kable(ghg_para$`table_11.1_&_table_11.3`) +``` + +###### table_2.5 +```{r ghg_para table_2.5} +kable(ghg_para$table_2.5) +``` + +###### table_5.12 +```{r ghg_para table_5.12} +kable(ghg_para$table_5.12) +``` + +###### table_5.13 +```{r ghg_para table_5.13} +kable(ghg_para$table_5.13) +``` + +###### table_5.14 +```{r ghg_para table_5.14} +kable(ghg_para$table_5.14) +``` + +#### Stock change +The stock change object (`cleaned/data/stock_change_para.rda`) is a series of nested lists that describe: 1) landuse, management, and input factor_variables for grassland; and 2) landuse, tillage, and input input factor_variables for cropland. + +```{r stock change 1} +load("stock_change_para.rda") +str(stock_change_para) +``` + +##### Grass factor variables +**Grassland: landuse** +```{r stock change grass 1} +x<-unlist(stock_change_para$grassland$landuse[[1]]$factor_variables) +kable(data.frame(variable=names(x),value=as.numeric(x))) +``` +**Grassland: management** +```{r stock change grass 2} +x<-unlist(stock_change_para$grassland$management[[1]]$factor_variables) +kable(data.frame(variable=names(x),value=as.numeric(x))) + +``` +**Grassland: input** +```{r stock change grass 3} +x<-unlist(stock_change_para$grassland$input[[1]]$factor_variables) +kable(data.frame(variable=names(x),value=as.numeric(x))) +``` + +##### Cropland factor variables +**Cropland: landuse** +```{r stock change cropland 1} +x<-unlist(stock_change_para$cropland$landuse[[1]]$factor_variables) +kable(data.frame(variable=names(x),value=as.numeric(x))) +``` +**Cropland: tillage** +```{r stock change cropland 2} +x<-unlist(stock_change_para$cropland$tillage[[1]]$factor_variables) +kable(data.frame(variable=names(x),value=as.numeric(x))) +``` +**Cropland: input** +```{r stock change cropland 3} +x<-unlist(stock_change_para$cropland$input[[1]]$factor_variables) +kable(data.frame(variable=names(x),value=as.numeric(x))) +``` + +#### mufindi +The mufindi object (`cleaned/data/mufindi.rda`) appears to be an input dataset similar to the `qt_example.json` file. +```{r mufindi 1} +load("mufindi.rda") +str(mufindi) +``` + +### cleaned-desktop `cleaned.sqlite` file +This sqlite database object is found in the [qlands/cleaned-desktop github](https://github.com/qlands/cleaned-desktop/blob/feature/add-comparison/cleaned.sqlite) it contains several tables of look-up values. + +#### Load sqlite file into R +```{r connect sqlite} +conn <- dbConnect(RSQLite::SQLite(), "cleaned.sqlite") +tables <- dbListTables(conn) +print(tables) +``` +```{r load sqlite,warning=FALSE} +sq_tables<-lapply(tables,FUN=function(TAB){ + query<-paste0("SELECT * FROM ",TAB) + data <- dbGetQuery(conn,query) + data +}) +names(sq_tables)<-tables +``` + +##### Climate +```{r sqlite climate,echo=F} +kable(sq_tables$lkp_climate) +``` +##### Climate2 +```{r sqlite climate 2,echo=F} +kable(sq_tables$lkp_climate2) +``` +##### Region +```{r sqlite region,echo=F} +kable(sq_tables$lkp_region) +``` +##### Cropland system +```{r sqlite cropland,echo=F} +kable(sq_tables$lkp_croplandsystem) +``` +##### Feeds +###### Item +```{r sqlite feed item,echo=F} +sq_tables$lkp_feeditem +``` +###### Type +```{r sqlite feed type,echo=F} +sq_tables$lkp_feedtype +``` +##### Grass input level +```{r sqlite grass input,echo=F} +kable(sq_tables$lkp_grassinputlevel) +``` +##### Grassland management +```{r sqlite grass management} +kable(sq_tables$lkp_grasslandman) +``` +##### Landcover +```{r sqlite land cover,echo=F} +kable(sq_tables$lkp_landcover) +``` +##### Livestock type +```{r sqlite livestock type,echo=F} +sq_tables$lkp_livetype +``` +##### Manure management +```{r sqlite manure management,echo=F} +kable(sq_tables$lkp_manureman) +``` +##### Organic matter +```{r sqlite organic matter,echo=F} +kable(sq_tables$lkp_organicmatter) +``` +##### Organic fertilizer +```{r sqlite organic fertilizer,echo=F} +kable(sq_tables$lkp_orgfertilizer) +``` +##### Slope +```{r sqlite slope,echo=F} +kable(sq_tables$lkp_slope) +``` +##### Soil +```{r sqlite soil,echo=F} +kable(sq_tables$lkp_soil) +``` +##### Tillage regime +```{r sqlite tillage,echo=F} +kable(sq_tables$lkp_tillageregime) +``` + +### cleaned-desktop `energy_parameters.json` file +The energy_parameters.json object is found in the [qlands/cleaned-desktop github](https://github.com/qlands/cleaned-desktop/blob/feature/add-comparison/R/energy_parameters.jsone) it contains a single table **Table 10.4** that does not seem to appear in the `cleaned/data/ghg_para.rda` object. + +#### Table 10.4 +```{r load energy parameters} +json_data2 <- fromJSON("energy_parameters.json") +names(json_data2) +kable(json_data2$`Table 10.4`) +``` diff --git a/data/mappings/input_mappings.csv b/data/mappings/input_mappings.csv new file mode 100644 index 0000000..9c3560e --- /dev/null +++ b/data/mappings/input_mappings.csv @@ -0,0 +1,176 @@ +focal_file,focal_path,focal_class,focal_example_value,focal_type,focal_userselected,focal_free,connected_file,connected_path,focal_keyfield,connected_keyfield +input_json,annual_prec,integer,1500,,,,,,, +input_json,arable_tograssland,integer,0,,,,,,, +input_json,climate_zone,character,Temperate,,,,,,, +input_json,climate_zone_2,character,Warm Temperate Dry,,,,,,, +input_json,cropland_orgmatter,character,"Low, temperate/boreal, dry",,,,,,, +input_json,cropland_orgmatter_ipcc,numeric,0.95,,,,,,, +input_json,cropland_system,character,"Long term cultivated, temperate/boreal, dry",,,,,,, +input_json,cropland_system_ipcc,numeric,0.8,,,,,,, +input_json,cropland_tillage,character,Full,,,,,,, +input_json,cropland_tillage_ipcc,integer,1,,,,,,, +input_json,database_code,character,base,,,,,,, +input_json,et,integer,1460,,,,,,, +input_json,farm_code,character,001,,,,,,, +input_json,farm_name,character,test 3,,,,,,, +input_json,grassland_implevel,character,Medium,,,,,,, +input_json,grassland_implevel_ipcc,integer,1,,,,,,, +input_json,grassland_management,character,Nominally managed,,,,,,, +input_json,grassland_management_ipcc,integer,1,,,,,,, +input_json,grassland_toarable,integer,0,,,,,,, +input_json,purchased_bedding,integer,0,,,,,,, +input_json,purchased_compost,integer,0,,,,,,, +input_json,purchased_manure,integer,0,,,,,,, +input_json,purchased_organic_n,integer,0,,,,,,, +input_json,rain_length,integer,5,,,,,,, +input_json,region,character,AFRICA,,,,,,, +input_json,soil_bulk,integer,6,,,,,,, +input_json,soil_c,integer,12,,,,,,, +input_json,soil_clay,integer,45,,,,,,, +input_json,soil_depth,integer,2,,,,,,, +input_json,soil_description,character,Lixisol,,,,,,, +input_json,soil_k_value,numeric,0.25,,,,,,, +input_json,soil_n,numeric,3.5,,,,,,, +input_json,waste_consume_meat,integer,2,,,,,,, +input_json,waste_consume_milk,integer,2,,,,,,, +input_json,waste_distribution_meat,integer,3,,,,,,, +input_json,waste_distribution_milk,integer,3,,,,,,, +input_json,waste_processing_meat,integer,5,,,,,,, +input_json,waste_processing_milk,integer,5,,,,,,, +input_json,waste_production_meat,integer,3,,,,,,, +input_json,waste_production_milk,integer,3,,,,,,, +input_json,feed_items/ammonia,character,0,,,,,,, +input_json,feed_items/ammonium_nitrate,character,0,,,,,,, +input_json,feed_items/ammonium_sulfate,character,0,,,,,,, +input_json,feed_items/average_dbh25,character,0,,,,,,, +input_json,feed_items/average_dbh2550,character,0,,,,,,, +input_json,feed_items/average_dbh50,character,0,,,,,,, +input_json,feed_items/category,character,cereal|legume,,,,,,, +input_json,feed_items/cp_content,character,13.60| 3.85|18.40,,,,,,, +input_json,feed_items/cultivation_period,character,0,,,,,,, +input_json,feed_items/cut_carry_fraction,character,0,,,,,,, +input_json,feed_items/dap,character,0,,,,,,, +input_json,feed_items/diameter_breast,character,0,,,,,,, +input_json,feed_items/dm_content,character,89.00|91.88|90.00,,,,,,, +input_json,feed_items/dry_yield,character,30|15| 8,,,,,,, +input_json,feed_items/ecosystem_type,character,"",,,,,,, +input_json,feed_items/energy,character, 0|360|336,,,,,,, +input_json,feed_items/feed_item_code,character,65|99|18,,,,,,, +input_json,feed_items/feed_item_name,character,Oats (Avena sativa) - grain IP|Rice (Oryza sativa) - straw|Cowpea (Vigna unguiculata) - crop residue,,,,,,, +input_json,feed_items/feed_type_code,character,2|45|9,,,,,,, +input_json,feed_items/feed_type_name,character,Avena sativa|Rice|Cowpea,,,,,,, +input_json,feed_items/fraction_as_fertilizer,character,1|0,,,,,,, +input_json,feed_items/fraction_as_manure,character,NA,,,,,,, +input_json,feed_items/grassman,character,1,,,,,,, +input_json,feed_items/grassman_change_factor,character,1,,,,,,, +input_json,feed_items/grassman_desc,character,Nominally managed,,,,,,, +input_json,feed_items/increase_dbh25,character,0,,,,,,, +input_json,feed_items/increase_dbh2550,character,0,,,,,,, +input_json,feed_items/increase_dbh50,character,0,,,,,,, +input_json,feed_items/intercrop,character,0,,,,,,, +input_json,feed_items/intercrop_fraction,character,0,,,,,,, +input_json,feed_items/kc_initial,character,0.10|1.05|0.15,,,,,,, +input_json,feed_items/kc_late,character,0.55|0.75|0.60,,,,,,, +input_json,feed_items/kc_midseason,character,1.10|1.20|1.05,,,,,,, +input_json,feed_items/land_cover,character,7|1,,,,,,, +input_json,feed_items/land_cover_desc,character,Cereals|Dense forest,,,,,,, +input_json,feed_items/landcover_c_factor,character,0.150|0.001,,,,,,, +input_json,feed_items/main_n,character,0.0176|0.0090|0.0380,,,,,,, +input_json,feed_items/main_product_removal,character,1|0,,,,,,, +input_json,feed_items/me_content,character,12.267324| 5.640000| 9.880848,,,,,,, +input_json,feed_items/n_content,character,0.2|0.0,,,,,,, +input_json,feed_items/n_fertilizer,character,NA,,,,,,, +input_json,feed_items/n_solutions,character, 50|300|100,,,,,,, +input_json,feed_items/npk,character,0,,,,,,, +input_json,feed_items/organic_amendment,character,"",,,,,,, +input_json,feed_items/residue_burnt,character,0,,,,,,, +input_json,feed_items/residue_dry_yield,character,0|6,,,,,,, +input_json,feed_items/residue_n,character,0.0|0.2,,,,,,, +input_json,feed_items/residue_removal,character,0.0|0.8,,,,,,, +input_json,feed_items/slope,character,1,,,,,,, +input_json,feed_items/slope_desc,character,Flat (0-5%),,,,,,, +input_json,feed_items/slope_length,character,0,,,,,,, +input_json,feed_items/slope_p_factor,character,0.11,,,,,,, +input_json,feed_items/source_type,character,Main|Residue,,,,,,, +input_json,feed_items/time_horizon,character,0,,,,,,, +input_json,feed_items/trees_dhb,character,0,,,,,,, +input_json,feed_items/trees_growth,character,0,,,,,,, +input_json,feed_items/trees_ha,character,0,,,,,,, +input_json,feed_items/trees_ha_dbh25,character,0,,,,,,, +input_json,feed_items/trees_ha_dbh2550,character,0,,,,,,, +input_json,feed_items/trees_ha_dbh50,character,0,,,,,,, +input_json,feed_items/trees_removal,character,0,,,,,,, +input_json,feed_items/urea,character,400|700|200,,,,,,, +input_json,feed_items/usda_value,character, 0|20450|16062,,,,,,, +input_json,feed_items/water_content,character, 0.00|12.89|11.95,,,,,,, +input_json,feed_items/water_regime,character,"",,,,,,, +input_json,livestock/adult_weight,character,800|400| 0,,,,,,, +input_json,livestock/annual_growth,character, 0|90,,,,,,, +input_json,livestock/annual_milk,character,3000|1500| 0,,,,,,, +input_json,livestock/annual_wool,character,0,,,,,,, +input_json,livestock/birth_interval,character,1.166667|1.500000|0.000000,,,,,,, +input_json,livestock/body_weight,character,600|350|200,,,,,,, +input_json,livestock/body_weight_weaning,character,0,,,,,,, +input_json,livestock/body_weight_year_one,character,0,,,,,,, +input_json,livestock/carcass_fraction,character,0.48|0.00,,,,,,, +input_json,livestock/cp_grazing,character,0|3,,,,,,, +input_json,livestock/cp_growth,character,0.0|0.4,,,,,,, +input_json,livestock/cp_lactation,character,5|2|0,,,,,,, +input_json,livestock/cp_lactmilk,character,0.09|0.00,,,,,,, +input_json,livestock/cp_maintenance,character,0.60|0.35|0.20,,,,,,, +input_json,livestock/cp_pregnancy,character,12.21| 9.64| 0.00,,,,,,, +input_json,livestock/distance_to_pasture,character,0.0|0.5,,,,,,, +input_json,livestock/energy_eggcontent,character,0,,,,,,, +input_json,livestock/energy_meatcontent,character,2200,,,,,,, +input_json,livestock/energy_milkcontent,character,970| 0,,,,,,, +input_json,livestock/fat_content,character,4.3|5.8|0.0,,,,,,, +input_json,livestock/grazing_displacement,character,0|2,,,,,,, +input_json,livestock/herd_composition,character,2|5|0,,,,,,, +input_json,livestock/ipcc_ef_category_t1,character,Dairy cattle|Other mature female|Other mature female-grazing,,,,,,, +input_json,livestock/ipcc_ef_category_t2,character,Dairy cows|Non-dairy,,,,,,, +input_json,livestock/ipcc_meth_man_category,character,Dairy cows|Other cattle,,,,,,, +input_json,livestock/ipcc_n_exc_category,character,Dairy cattle|Other cattle,,,,,,, +input_json,livestock/lactation_length,character,0,,,,,,, +input_json,livestock/litter_size,character,0,,,,,,, +input_json,livestock/livetype_code,character,2|1|5,,,,,,, +input_json,livestock/livetype_desc,character,Cattle - Cows (improved)|Cattle - Cows (local)|Cattle - Steers/heifers,,,,,,, +input_json,livestock/lw_gain,character,0,,,,,,, +input_json,livestock/manure_in_field,character,0,,,,,,, +input_json,livestock/manure_in_non_roofed_enclosure,character,0,,,,,,, +input_json,livestock/manure_in_stable,character,1|0,,,,,,, +input_json,livestock/manure_onfarm_fraction,character,1|0,,,,,,, +input_json,livestock/manure_sales_fraction,character,0,,,,,,, +input_json,livestock/manureman_non_roofed_enclosure,character,Solid storage,,,,,,, +input_json,livestock/manureman_offfarm_grazing,character,Solid storage,,,,,,, +input_json,livestock/manureman_onfarm_grazing,character,Solid storage|Pasture / range / paddock,,,,,,, +input_json,livestock/manureman_stable,character,Solid storage,,,,,,, +input_json,livestock/me_grazing,character,2.0|1.5,,,,,,, +input_json,livestock/me_growth,character, 0|50,,,,,,, +input_json,livestock/me_lactation,character,5|2|0,,,,,,, +input_json,livestock/me_lactmilk,character,5.5|0.0,,,,,,, +input_json,livestock/me_maintenance,character,60.61547|40.45955|26.59148,,,,,,, +input_json,livestock/me_pregnancy,character,1500|1260| 0,,,,,,, +input_json,livestock/meat_product,character,beef,,,,,,, +input_json,livestock/milk_product,character,cow milk,,,,,,, +input_json,livestock/n_content,character,0.029,,,,,,, +input_json,livestock/piglets_relying_on_milk,character,0,,,,,,, +input_json,livestock/proportion_growth,character,0,,,,,,, +input_json,livestock/protein_meatcontent,character,26,,,,,,, +input_json,livestock/protein_milkcontent,character,3.7|3.2|0.0,,,,,,, +input_json,livestock/time_in_non_roofed_enclosure,character,0,,,,,,, +input_json,livestock/time_in_offfarm_grazing,character,0,,,,,,, +input_json,livestock/time_in_onfarm_grazing,character,0|1,,,,,,, +input_json,livestock/time_in_stable,character,1|0,,,,,,, +input_json,livestock/water_requirement,character,140|120| 80,,,,,,, +input_json,livestock/work_hour,character,0,,,,,,, +input_json,seasons/season_length,character,200|165,,,,,,, +input_json,seasons/season_name,character,Wet season|Dry season,,,,,,, +input_json,feed_basket/season_name,character,Wet season|Dry season,,,,,,, +input_json,feed_basket/feeds/feed_item_code,character,65|99|18,,,,,,, +input_json,feed_basket/feeds/feed_type_code,character,2|45|9,,,,,,, +input_json,feed_basket/feeds/livestock/allocation,character,40|17|25|43|20|35,,,,,,, +input_json,feed_basket/feeds/livestock/livetype_code,character,2|1|5,,,,,,, +input_json,fertilizer/fertilizer_code,character,4|6,,,,,,, +input_json,fertilizer/fertilizer_desc,character,Ammonium nitrate|N solutions,,,,,,, +input_json,fertilizer/fraction,character,0,,,,,,, +input_json,fertilizer/percentage_n,character,12|10,,,,,,, diff --git a/data/mappings/input_mappings_metadata.csv b/data/mappings/input_mappings_metadata.csv new file mode 100644 index 0000000..34333a4 --- /dev/null +++ b/data/mappings/input_mappings_metadata.csv @@ -0,0 +1,21 @@ +field_name,field_type,description +focal_file,character,The filename of the object containing the focal field +focal_path,character,The path to the focal field within the focal object +focal_example_value,character,Example value(s) from the focal field +focal_class,character,The class of data in the focal field +focal_type,character,"Enter one these values: + 1) standard_parameter - values that cannot be changed by users, for example parameters in IPCC tables. Sometimes referred to has hard_coded values by the team. + 2) user_parameter - descriptors of a study area - these filed might be prefilled, but can be changed by users (normally only needs to be done once for a new study area). + 3) user_input - user input, to be changed for each livestock enterprise type and scenario." +focal_userselected,logical,Is the field user selected or entered in the UI (TRUE/FALSE)? FALSE means that the field is auto-populated. +focal_free,logical,Is the field free-text (TRUE/FALSE)? (i.e. it is entered or selected by the user and there is no lookup field) +connected_file,character,The filename of the connected object (use when focal_free field is FALSE) +connected_path,character,The path to the field in the connected object (use when focal_free field is FALSE) +focal_keyfield,character,"For a field that is autopopulated, what is the path of the key field in the **focal** object that is used to subset the connected table? + Use when focal_free field is FALSE AND focal_userselected is FALSE. + Example = the soil_k_value (focal_path) in the input_json comes from the sqlite Soil/k_value field (connected_path), the user selected input_json soil_description field (e.g. Lixisol) is used to connect the sqlite Soil/k_value. + This means we enter the value soil_description in the focal_keyfield column and the value Soil/soil_desc in the connected_keyfield column." +connected_keyfield,character,"For a field that is autopopulated, what is the path of the key field in the **connected** table that links to the focal object? + Use when focal_free field is FALSE AND focal_userselected is FALSE. + Example = the soil_k_value (focal_path) in the input_json comes from the sqlite Soil/k_value field (connected_path), the user selected input_json soil_description field (e.g. Lixisol) is used to connect the sqlite Soil/k_value. + This means we enter the value soil_description in the focal_keyfield column and the value Soil/soil_desc in the connected_keyfield column" diff --git a/data/qt_example.json b/data/qt_example.json new file mode 100644 index 0000000..49a6ec8 --- /dev/null +++ b/data/qt_example.json @@ -0,0 +1,574 @@ +{ + "annual_prec": 1500, + "arable_tograssland": 0, + "climate_zone": "Temperate", + "climate_zone_2": "Warm Temperate Dry", + "cropland_orgmatter": "Low, temperate/boreal, dry", + "cropland_orgmatter_ipcc": 0.95, + "cropland_system": "Long term cultivated, temperate/boreal, dry", + "cropland_system_ipcc": 0.8, + "cropland_tillage": "Full", + "cropland_tillage_ipcc": 1, + "database_code": "base", + "et": 1460, + "farm_code": "001", + "farm_name": "test 3", + "feed_basket": [ + { + "feeds": [ + { + "feed_item_code": "65", + "feed_type_code": "2", + "livestock": [ + { + "allocation": 40, + "livetype_code": "2" + }, + { + "allocation": 17, + "livetype_code": "1" + }, + { + "allocation": 40, + "livetype_code": "5" + } + ] + }, + { + "feed_item_code": "99", + "feed_type_code": "45", + "livestock": [ + { + "allocation": 25, + "livetype_code": "2" + }, + { + "allocation": 43, + "livetype_code": "1" + }, + { + "allocation": 20, + "livetype_code": "5" + } + ] + }, + { + "feed_item_code": "18", + "feed_type_code": "9", + "livestock": [ + { + "allocation": 35, + "livetype_code": "2" + }, + { + "allocation": 40, + "livetype_code": "1" + }, + { + "allocation": 40, + "livetype_code": "5" + } + ] + } + ], + "season_name": "Wet season" + }, + { + "feeds": [ + { + "feed_item_code": "65", + "feed_type_code": "2", + "livestock": [ + { + "allocation": 40, + "livetype_code": "2" + }, + { + "allocation": 17, + "livetype_code": "1" + }, + { + "allocation": 40, + "livetype_code": "5" + } + ] + }, + { + "feed_item_code": "99", + "feed_type_code": "45", + "livestock": [ + { + "allocation": 25, + "livetype_code": "2" + }, + { + "allocation": 43, + "livetype_code": "1" + }, + { + "allocation": 20, + "livetype_code": "5" + } + ] + }, + { + "feed_item_code": "18", + "feed_type_code": "9", + "livestock": [ + { + "allocation": 35, + "livetype_code": "2" + }, + { + "allocation": 40, + "livetype_code": "1" + }, + { + "allocation": 40, + "livetype_code": "5" + } + ] + } + ], + "season_name": "Dry season" + } + ], + "feed_items": [ + { + "ammonia": 0, + "ammonium_nitrate": 0, + "ammonium_sulfate": 0, + "average_dbh25": 0, + "average_dbh2550": 0, + "average_dbh50": 0, + "category": "cereal", + "cp_content": 13.6, + "cultivation_period": 0, + "cut_carry_fraction": 0, + "dap": 0, + "diameter_breast": 0, + "dm_content": 89, + "dry_yield": 30, + "ecosystem_type": "", + "energy": 0, + "feed_item_code": "65", + "feed_item_name": "Oats (Avena sativa) - grain IP", + "feed_type_code": "2", + "feed_type_name": "Avena sativa", + "fraction_as_fertilizer": 1, + "fraction_as_manure": null, + "grassman": "1", + "grassman_change_factor": 1, + "grassman_desc": "Nominally managed", + "increase_dbh25": 0, + "increase_dbh2550": 0, + "increase_dbh50": 0, + "intercrop": 0, + "intercrop_fraction": 0, + "kc_initial": 0.1, + "kc_late": 0.55, + "kc_midseason": 1.1, + "land_cover": "7", + "land_cover_desc": "Cereals", + "landcover_c_factor": 0.15, + "main_n": 0.0176, + "main_product_removal": 1, + "me_content": 12.267324, + "n_content": 0.2, + "n_fertilizer": null, + "n_solutions": 50, + "npk": 0, + "organic_amendment": "", + "residue_burnt": 0, + "residue_dry_yield": 0, + "residue_n": 0, + "residue_removal": 0, + "slope": "1", + "slope_desc": "Flat (0-5%)", + "slope_length": 0, + "slope_p_factor": 0.11, + "source_type": "Main", + "time_horizon": 0, + "trees_dhb": 0, + "trees_growth": 0, + "trees_ha": 0, + "trees_ha_dbh25": 0, + "trees_ha_dbh2550": 0, + "trees_ha_dbh50": 0, + "trees_removal": 0, + "urea": 400, + "usda_value": 0, + "water_content": 0, + "water_regime": "" + }, + { + "ammonia": 0, + "ammonium_nitrate": 0, + "ammonium_sulfate": 0, + "average_dbh25": 0, + "average_dbh2550": 0, + "average_dbh50": 0, + "category": "cereal", + "cp_content": 3.85, + "cultivation_period": 0, + "cut_carry_fraction": 0, + "dap": 0, + "diameter_breast": 0, + "dm_content": 91.88, + "dry_yield": 15, + "ecosystem_type": "", + "energy": 360, + "feed_item_code": "99", + "feed_item_name": "Rice (Oryza sativa) - straw", + "feed_type_code": "45", + "feed_type_name": "Rice", + "fraction_as_fertilizer": 1, + "fraction_as_manure": null, + "grassman": "1", + "grassman_change_factor": 1, + "grassman_desc": "Nominally managed", + "increase_dbh25": 0, + "increase_dbh2550": 0, + "increase_dbh50": 0, + "intercrop": 0, + "intercrop_fraction": 0, + "kc_initial": 1.05, + "kc_late": 0.75, + "kc_midseason": 1.2, + "land_cover": "7", + "land_cover_desc": "Cereals", + "landcover_c_factor": 0.15, + "main_n": 0.009, + "main_product_removal": 1, + "me_content": 5.64, + "n_content": 0.2, + "n_fertilizer": null, + "n_solutions": 300, + "npk": 0, + "organic_amendment": "", + "residue_burnt": 0, + "residue_dry_yield": 0, + "residue_n": 0.2, + "residue_removal": 0.8, + "slope": "1", + "slope_desc": "Flat (0-5%)", + "slope_length": 0, + "slope_p_factor": 0.11, + "source_type": "Residue", + "time_horizon": 0, + "trees_dhb": 0, + "trees_growth": 0, + "trees_ha": 0, + "trees_ha_dbh25": 0, + "trees_ha_dbh2550": 0, + "trees_ha_dbh50": 0, + "trees_removal": 0, + "urea": 700, + "usda_value": 20450, + "water_content": 12.89, + "water_regime": "" + }, + { + "ammonia": 0, + "ammonium_nitrate": 0, + "ammonium_sulfate": 0, + "average_dbh25": 0, + "average_dbh2550": 0, + "average_dbh50": 0, + "category": "legume", + "cp_content": 18.4, + "cultivation_period": 0, + "cut_carry_fraction": 0, + "dap": 0, + "diameter_breast": 0, + "dm_content": 90, + "dry_yield": 8, + "ecosystem_type": "", + "energy": 336, + "feed_item_code": "18", + "feed_item_name": "Cowpea (Vigna unguiculata) - crop residue", + "feed_type_code": "9", + "feed_type_name": "Cowpea", + "fraction_as_fertilizer": 0, + "fraction_as_manure": null, + "grassman": "1", + "grassman_change_factor": 1, + "grassman_desc": "Nominally managed", + "increase_dbh25": 0, + "increase_dbh2550": 0, + "increase_dbh50": 0, + "intercrop": 0, + "intercrop_fraction": 0, + "kc_initial": 0.15, + "kc_late": 0.6, + "kc_midseason": 1.05, + "land_cover": "1", + "land_cover_desc": "Dense forest", + "landcover_c_factor": 0.001, + "main_n": 0.038, + "main_product_removal": 0, + "me_content": 9.880848, + "n_content": 0, + "n_fertilizer": null, + "n_solutions": 100, + "npk": 0, + "organic_amendment": "", + "residue_burnt": 0, + "residue_dry_yield": 6, + "residue_n": 0, + "residue_removal": 0, + "slope": "1", + "slope_desc": "Flat (0-5%)", + "slope_length": 0, + "slope_p_factor": 0.11, + "source_type": "Main", + "time_horizon": 0, + "trees_dhb": 0, + "trees_growth": 0, + "trees_ha": 0, + "trees_ha_dbh25": 0, + "trees_ha_dbh2550": 0, + "trees_ha_dbh50": 0, + "trees_removal": 0, + "urea": 200, + "usda_value": 16062, + "water_content": 11.95, + "water_regime": "" + } + ], + "fertilizer": [ + { + "fertilizer_code": "4", + "fertilizer_desc": "Ammonium nitrate", + "fraction": 0, + "percentage_n": 12 + }, + { + "fertilizer_code": "6", + "fertilizer_desc": "N solutions", + "fraction": 0, + "percentage_n": 10 + } + ], + "grassland_implevel": "Medium", + "grassland_implevel_ipcc": 1, + "grassland_management": "Nominally managed", + "grassland_management_ipcc": 1, + "grassland_toarable": 0, + "livestock": [ + { + "adult_weight": 800, + "annual_growth": 0, + "annual_milk": 3000, + "annual_wool": 0, + "birth_interval": 1.16666666666667, + "body_weight": 600, + "body_weight_weaning": 0, + "body_weight_year_one": 0, + "carcass_fraction": 0.48, + "cp_grazing": 0, + "cp_growth": 0, + "cp_lactation": 5, + "cp_lactmilk": 0.09, + "cp_maintenance": 0.6, + "cp_pregnancy": 12.21, + "distance_to_pasture": 0, + "energy_eggcontent": 0, + "energy_meatcontent": 2200, + "energy_milkcontent": 970, + "fat_content": 4.3, + "grazing_displacement": 0, + "herd_composition": 2, + "ipcc_ef_category_t1": "Dairy cattle", + "ipcc_ef_category_t2": "Dairy cows", + "ipcc_meth_man_category": "Dairy cows", + "ipcc_n_exc_category": "Dairy cattle", + "lactation_length": 0, + "litter_size": 0, + "livetype_code": "2", + "livetype_desc": "Cattle - 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