Fertilizer consumption (% of fertilizer production)

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 248 -2.84% 18
Argentina Argentina 465 -14.4% 10
Australia Australia 337 -3.79% 13
Azerbaijan Azerbaijan 31.2 -2.02% 54
Bangladesh Bangladesh 666 -1.17% 8
Bahrain Bahrain 0.0617 -92.9% 71
Bosnia & Herzegovina Bosnia & Herzegovina 265 -40.6% 17
Belarus Belarus 12.5 +15% 64
Bolivia Bolivia 21.4 -61% 57
Brazil Brazil 663 -12.4% 9
Canada Canada 26.6 -4.69% 56
Chile Chile 27.5 -31.9% 55
China China 101 +9.63% 38
Colombia Colombia 412 -19% 11
Cuba Cuba 763 -48.5% 7
Czechia Czechia 296 +16.6% 16
Germany Germany 39.9 +1.55% 51
Algeria Algeria 14.5 0% 63
Egypt Egypt 42.9 0% 49
Spain Spain 84.8 -12.9% 40
Estonia Estonia 22,974 -16.3% 1
Finland Finland 112 +169% 33
France France 7,181 +406% 2
United Kingdom United Kingdom 216 -39.4% 20
Georgia Georgia 15.7 -16.4% 60
Greece Greece 73 -74.8% 41
Croatia Croatia 211 +231% 23
Hungary Hungary 108 +26.2% 35
Indonesia Indonesia 122 -14.8% 31
India India 144 -10.4% 27
Iran Iran 58.7 0% 47
Iraq Iraq 231 +0.68% 19
Italy Italy 126 +43.6% 30
Jordan Jordan 0.932 +12.7% 68
Japan Japan 133 +12.7% 29
Kazakhstan Kazakhstan 32.1 -12.9% 53
South Korea South Korea 56.3 +10.1% 48
Lebanon Lebanon 38.6 -17.1% 52
Libya Libya 187 +10.8% 25
Sri Lanka Sri Lanka 1,461 0% 4
Lithuania Lithuania 41.7 +32.2% 50
Morocco Morocco 5.67 -3.32% 65
Mexico Mexico 372 +24.5% 12
Malaysia Malaysia 149 -25% 26
Nigeria Nigeria 20.4 -80.9% 58
Netherlands Netherlands 15 -9.66% 61
Norway Norway 17.7 -3.93% 59
New Zealand New Zealand 305 -2.19% 15
Oman Oman 3.03 +63% 67
Pakistan Pakistan 107 -12.8% 36
Philippines Philippines 199 -39.7% 24
Poland Poland 72 +22.6% 42
Portugal Portugal 91.8 -37.2% 39
Qatar Qatar 0.195 0% 70
Romania Romania 909 +634% 5
Russia Russia 14.6 +15% 62
Saudi Arabia Saudi Arabia 5.05 -31.8% 66
Senegal Senegal 804 +510% 6
Serbia Serbia 213 +54.9% 21
Slovakia Slovakia 64.1 +21.8% 46
Thailand Thailand 1,568 -22.1% 3
Turkmenistan Turkmenistan 67.7 +19.1% 45
Trinidad & Tobago Trinidad & Tobago 0.58 +44.2% 69
Tunisia Tunisia 111 +0.382% 34
Turkey Turkey 134 +15.4% 28
United States United States 101 -8.37% 37
Uzbekistan Uzbekistan 68.6 -13% 43
Venezuela Venezuela 67.9 0% 44
Vietnam Vietnam 116 -35.2% 32
South Africa South Africa 328 -12.6% 14
Zimbabwe Zimbabwe 212 0% 22 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'AG.CON.FERT.PT.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'AG.CON.FERT.PT.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))