India’s Trade Statistics: Exports, Imports & Sectoral Composition

Trade

Economy

Interactive analysis of India’s total merchandise trade — exports, imports, trade balance, and sector-level composition including cereals, fertilizers, and petroleum — using HS code classification.

Author

Pawan

Published

October 29, 2025

Modified

March 15, 2026

Overview

India’s merchandise trade has grown from under USD 200 billion (2005) to over USD 1.1 trillion (2023), making it the world’s 10th largest merchandise trading nation. This analysis examines total export and import trends, the trade balance trajectory, and the composition of key sectors — cereals, fertilizers, petroleum, and edible oils — using Harmonised System (HS) code classification.

Key Findings at a Glance

📈 India’s exports crossed USD 450 billion in 2022–23, up from ~USD 170 billion a decade earlier
📉 Trade deficit is structural and widening: typically USD 200–250 billion annually in recent years
🌾 Rice dominates cereal exports: India is the world’s largest rice exporter, accounting for ~40% of global trade
⛽ Petroleum products are India’s top export category — a testament to refinery capacity expansions
🌱 Fertilizer imports are large and import-intensive: India imports ~$10 billion worth of fertilizers annually
🔄 Export coverage ratio has declined from ~70% (early 2000s) to ~55% (recent years), indicating growing import dependence

1 Setup

Code

library(data.table)
library(ggplot2)
library(ggiraph)
library(readxl)
library(viridis)
library(scales)

my_theme <- theme_minimal(base_size = 13) +
  theme(
    text             = element_text(color = "#2c3e50"),
    plot.title       = element_text(size = 16, face = "bold", hjust = 0.5,
                                    margin = margin(8, 0, 8, 0)),
    plot.subtitle    = element_text(size = 11, hjust = 0.5, color = "#7f8c8d"),
    plot.caption     = element_text(size = 9, color = "#95a5a6", hjust = 0),
    axis.title       = element_text(size = 12, face = "bold"),
    axis.text        = element_text(size = 10),
    axis.text.x      = element_text(angle = 45, vjust = 1, hjust = 1),
    legend.position  = "bottom",
    legend.title     = element_blank(),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.5),
    panel.grid.minor = element_blank(),
    strip.background = element_rect(fill = "#f8f9fa", color = NA),
    strip.text       = element_text(face = "bold")
  )

std_opts <- list(
  opts_tooltip(
    css = paste0("background-color:#2c3e50;color:white;padding:8px 12px;",
                 "border-radius:6px;font-size:12px;line-height:1.5;")
  ),
  opts_hover(css = "stroke-width:2.5;opacity:1;"),
  opts_hover_inv(css = "opacity:0.2;"),
  opts_zoom(min = 0.5, max = 3),
  opts_toolbar(position = "topright"),
  opts_sizing(rescale = TRUE, width = 1)
)

2 Data Loading & Cleaning

Code

usd <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_exportsusd.rds"))
qty <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_exportsqty.rds"))
musd <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_importsusd.rds"))
mqty <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_importsqty.rds"))
twodigit <- as.data.table(read_excel("/home/pawan/trade/totaltrade/two-digit.xlsx"))

## Standardised cleaning function
clean_trade <- function(dt, val_col) {
  dt[, `:=`(HSCode2 = substr(HSCode, 1, 2),
            HSCode4 = substr(HSCode, 1, 4),
            HSCode6 = substr(HSCode, 1, 6))]
  dt[, (val_col) := as.numeric(gsub(",", "", get(val_col)))]
  dt[is.na(get(val_col)), (val_col) := 0]
  dt
}

usd  <- clean_trade(usd,  "Export")
qty  <- clean_trade(qty,  "Export")
musd <- clean_trade(musd, "Import")
mqty <- clean_trade(mqty, "Import")

## Merge HS classification
twodigit[, HSCode2 := as.numeric(HSCode2)]
qty[,  HSCode2 := as.numeric(HSCode2)]
mqty[, HSCode2 := as.numeric(HSCode2)]
qty  <- merge(qty,  twodigit, by = "HSCode2", all.x = TRUE)
mqty <- merge(mqty, twodigit, by = "HSCode2", all.x = TRUE)

## Aggregate totals
export_usd <- usd[,  .(Export_USD = sum(Export, na.rm = TRUE)), by = Year]
import_usd <- musd[, .(Import_USD = sum(Import, na.rm = TRUE)), by = Year]
export_qty <- qty[,  .(Export_Qty = sum(Export, na.rm = TRUE)), by = Year]
import_qty <- mqty[, .(Import_Qty = sum(Import, na.rm = TRUE)), by = Year]

trade <- Reduce(function(a, b) merge(a, b, by = "Year"),
                list(export_usd, import_usd, export_qty, import_qty))
trade[, `:=`(
  Trade_Balance   = Export_USD - Import_USD,
  Trade_Volume    = Export_USD + Import_USD,
  Export_Coverage = round(Export_USD * 100 / Import_USD, 1),
  Export_Growth   = round((Export_USD / shift(Export_USD) - 1) * 100, 1),
  Import_Growth   = round((Import_USD / shift(Import_USD) - 1) * 100, 1)
)]

3 Trade Balance Trend

Code

trade_long <- melt(trade[, .(Year, Export_USD, Import_USD)],
                   id = "Year", variable.name = "Flow", value.name = "USD")
trade_long[, tip := paste0("<b>", ifelse(Flow == "Export_USD", "Exports", "Imports"), "</b><br>",
                           "Year: ", Year, "<br>",
                           "Value: USD <b>", format(round(USD / 1000), big.mark = ","), "</b> Bn")]

pt <- ggplot() +
  ## Trade balance area
  geom_area(data = trade, aes(x = Year, y = Trade_Balance / 1000, group = 1),
            fill = "#e74c3c", alpha = 0.15) +
  ## Export & import lines
  geom_line_interactive(
    data = trade_long,
    aes(x = Year, y = USD / 1000, color = Flow, tooltip = tip,
        data_id = paste(Flow, Year), group = Flow),
    linewidth = 1.3
  ) +
  geom_point_interactive(
    data = trade_long,
    aes(x = Year, y = USD / 1000, color = Flow, tooltip = tip,
        data_id = paste(Flow, Year)),
    size = 2.5
  ) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "#7f8c8d") +
  scale_color_manual(values = c("Export_USD" = "#27ae60", "Import_USD" = "#e74c3c"),
                     labels = c("Export_USD" = "Exports", "Import_USD" = "Imports")) +
  scale_y_continuous(labels = label_dollar(suffix = "B", scale = 1)) +
  labs(title   = "India's Total Merchandise Trade",
       subtitle = "Exports, imports, and trade deficit (USD Billions)",
       x = "Year", y = "USD Billions",
       caption = "Source: Ministry of Commerce, India | Analysis: Pawan") +
  my_theme

girafe(ggobj = pt, width_svg = 11, height_svg = 6, options = std_opts)

3.1 Export Coverage Ratio

Code

trade[, tip_cov := paste0("Year: ", Year, "<br>",
                          "Export coverage: <b>", Export_Coverage, "%</b><br>",
                          "(Exports ÷ Imports × 100)")]
pc <- ggplot(trade[!is.na(Export_Coverage)],
             aes(x = Year, y = Export_Coverage, tooltip = tip_cov, data_id = Year, group = 1)) +
  geom_col_interactive(
    aes(fill = Export_Coverage < 60),
    width = 0.75, alpha = 0.85
  ) +
  geom_hline(yintercept = 100, linetype = "dashed", color = "#27ae60", linewidth = 0.8) +
  geom_hline(yintercept = 60,  linetype = "dotted", color = "#e74c3c",  linewidth = 0.8) +
  scale_fill_manual(values = c("FALSE" = "#3498db", "TRUE" = "#e67e22"), guide = "none") +
  scale_y_continuous(labels = label_percent(scale = 1)) +
  labs(title   = "Export Coverage Ratio",
       subtitle = "How much of India's imports are covered by export earnings?",
       x = "Year", y = "Exports as % of Imports",
       caption = "Green dashed = 100% (balanced trade); Red dotted = 60% threshold") +
  my_theme

girafe(ggobj = pc, width_svg = 10, height_svg = 5, options = std_opts)

4 Cereal Exports: Composition

India is the world’s largest rice exporter and a significant wheat exporter in surplus years. The 2022–23 wheat export ban reflects India’s policy of managing domestic food price inflation.

Code

cereals <- qty[HSCode2 == 10]
cereals[, Commodity := fcase(
  HSCode4 == "1006", "Rice",
  HSCode4 == "1001", "Wheat",
  HSCode4 == "1005", "Maize",
  HSCode4 == "1007", "Grain Sorghum",
  HSCode4 == "1008", "Millet & Buckwheat",
  default = "Other Cereals"
)]
cereals_yr <- cereals[, .(Qty = sum(Export, na.rm = TRUE)), by = .(Year, Commodity)]
cereals_yr[, Total := sum(Qty), by = Year]
cereals_yr[, Share := round(Qty * 100 / Total)]
cereals_yr[, Commodity := factor(Commodity,
  levels = c("Other Cereals", "Millet & Buckwheat", "Grain Sorghum",
             "Maize", "Wheat", "Rice"))]
cereals_yr[, tip := paste0("<b>", Commodity, "</b><br>",
                           "Year: ", Year, "<br>",
                           "Qty: ", format(round(Qty), big.mark = ","), " '000T<br>",
                           "Share: ", Share, "%")]

pce <- ggplot(cereals_yr, aes(x = Year, y = Qty, fill = Commodity,
                               tooltip = tip, data_id = paste(Commodity, Year))) +
  geom_bar_interactive(stat = "identity", position = "stack", width = 0.75, alpha = 0.9) +
  geom_text(aes(label = ifelse(Share >= 8, paste0(Share, "%"), "")),
            position = position_stack(vjust = 0.5), size = 2.8,
            color = "white", fontface = "bold") +
  scale_fill_viridis(discrete = TRUE, option = "D") +
  scale_y_continuous(labels = comma_format()) +
  labs(title   = "Cereal Exports from India by Commodity",
       subtitle = "Rice dominates; wheat exports vary by policy and domestic supply conditions",
       x = "Year", y = "Quantity ('000 Tonnes)",
       caption = "Source: Ministry of Commerce | Analysis: Pawan") +
  my_theme

girafe(ggobj = pce, width_svg = 11, height_svg = 6, options = std_opts)

5 Fertilizer Imports: Composition

Code

fert <- mqty[HSCode2 == 31]
fert[, Commodity := fcase(
  HSCode4 == "3102", "Nitrogenous (Urea & Others)",
  HSCode4 == "3104", "Potassic (MOP)",
  HSCode4 == "3105", "DAP & NPK Compounds",
  default = "Other Fertilizers"
)]
fert_yr <- fert[, .(Qty = sum(Import, na.rm = TRUE)), by = .(Year, Commodity)]
fert_yr[, Total := sum(Qty), by = Year]
fert_yr[, Share := round(Qty * 100 / Total)]
fert_yr[, tip := paste0("<b>", Commodity, "</b><br>",
                        "Year: ", Year, "<br>",
                        "Qty: ", format(round(Qty), big.mark = ","), " '000T<br>",
                        "Share: ", Share, "%")]

pfe <- ggplot(fert_yr, aes(x = Year, y = Qty, fill = Commodity,
                            tooltip = tip, data_id = paste(Commodity, Year))) +
  geom_bar_interactive(stat = "identity", position = "stack", width = 0.75, alpha = 0.9) +
  geom_text(aes(label = ifelse(Share >= 8, paste0(Share, "%"), "")),
            position = position_stack(vjust = 0.5), size = 2.8,
            color = "white", fontface = "bold") +
  scale_fill_viridis(discrete = TRUE, option = "C") +
  scale_y_continuous(labels = comma_format()) +
  labs(title   = "Fertilizer Imports by Type",
       subtitle = "Nitrogenous (urea-based) fertilizers dominate imports by volume",
       x = "Year", y = "Quantity ('000 Tonnes)",
       caption = "Source: Ministry of Commerce | Analysis: Pawan") +
  my_theme

girafe(ggobj = pfe, width_svg = 11, height_svg = 6, options = std_opts)

6 Petroleum Imports: Composition

Code

petro <- mqty[HSCode2 == 27]
petro[, Commodity := fcase(
  HSCode4 == "2701", "Coal",
  HSCode4 == "2709", "Crude Petroleum Oils",
  HSCode4 == "2711", "Petroleum Gases (LNG/LPG)",
  default = "Other Mineral Fuels"
)]
petro_yr <- petro[, .(Qty = sum(Import, na.rm = TRUE)), by = .(Year, Commodity)]
petro_yr[, Total := sum(Qty), by = Year]
petro_yr[, Share := round(Qty * 100 / Total)]
petro_yr[, tip := paste0("<b>", Commodity, "</b><br>",
                         "Year: ", Year, "<br>",
                         "Qty: ", format(round(Qty), big.mark = ","), " '000T<br>",
                         "Share: ", Share, "%")]

ppe <- ggplot(petro_yr, aes(x = Year, y = Qty, fill = Commodity,
                             tooltip = tip, data_id = paste(Commodity, Year))) +
  geom_bar_interactive(stat = "identity", position = "stack", width = 0.75, alpha = 0.9) +
  geom_text(aes(label = ifelse(Share >= 8, paste0(Share, "%"), "")),
            position = position_stack(vjust = 0.5), size = 2.8,
            color = "white", fontface = "bold") +
  scale_fill_manual(values = c("Crude Petroleum Oils"    = "#e67e22",
                               "Coal"                     = "#2c3e50",
                               "Petroleum Gases (LNG/LPG)" = "#8e44ad",
                               "Other Mineral Fuels"       = "#bdc3c7")) +
  scale_y_continuous(labels = comma_format()) +
  labs(title   = "Petroleum & Mineral Fuel Imports by Type",
       subtitle = "Crude petroleum dominates; LNG imports rising as gas infrastructure expands",
       x = "Year", y = "Quantity ('000 Tonnes)",
       caption = "Source: Ministry of Commerce | Analysis: Pawan") +
  my_theme

girafe(ggobj = ppe, width_svg = 11, height_svg = 6, options = std_opts)

7 Edible Oils & Fats Import Volume

Code

oils <- mqty[HSCode2 == 15]
oils_yr <- oils[, .(Qty = sum(Import, na.rm = TRUE)), by = Year]
oils_yr[, tip := paste0("Year: <b>", Year, "</b><br>",
                        "Edible oils import: ",
                        format(round(Qty / 1000), big.mark = ","), " '000 Tonnes")]

po <- ggplot(oils_yr, aes(x = Year, y = Qty / 1000, tooltip = tip, data_id = Year, group = 1)) +
  geom_area_interactive(fill = "#f39c12", alpha = 0.3) +
  geom_line_interactive(linewidth = 1.2, color = "#d68910") +
  geom_point_interactive(size = 2.5, color = "#b7770d") +
  scale_y_continuous(labels = comma_format(suffix = " '000T")) +
  labs(title   = "Vegetable Fats & Edible Oils Import Volume",
       subtitle = "Annual import trend — HS Chapter 15",
       x = "Year", y = "Quantity ('000 Tonnes)",
       caption = "Source: Ministry of Commerce | Analysis: Pawan") +
  my_theme

girafe(ggobj = po, width_svg = 10, height_svg = 5, options = std_opts)

8 Key Takeaways

Summary of Findings

India’s trade deficit is deep and structural: Petroleum, fertilizers, and edible oils together account for the bulk of the import bill, and all three are heavily import-dependent by structural necessity.
Cereal exports are policy-sensitive: The government alternates between encouraging exports (when there is surplus) and banning them (when domestic prices rise) — illustrating the tension between farmer welfare and consumer welfare.
India is the world’s largest rice exporter: Rice contributes the bulk of cereal export earnings, though the recent quality and residue concerns in some markets require attention.
Petroleum imports are the largest single category: India’s energy import dependence (~85% crude oil from overseas) is both an economic vulnerability and a geopolitical consideration.
The export coverage ratio declining toward 55% suggests imports are growing faster than exports — a medium-term current account concern.
Fertilizer import dependence reinforces the need to expand domestic production capacity and reduce over-subsidisation, which incentivises excessive fertilizer use and soil degradation.

--- title: "India's Trade Statistics: Exports, Imports & Sectoral Composition" description: "Interactive analysis of India's total merchandise trade — exports, imports, trade balance, and sector-level composition including cereals, fertilizers, and petroleum — using HS code classification." author: "Pawan" date: "2025/10/29" date-modified: last-modified categories: - "Trade" - "Economy" format: html: code-fold: true toc: true toc-depth: 3 number-sections: true theme: flatly editor_options: chunk_output_type: console execute: error: false message: false warning: false eval: true --- ## Overview {.unnumbered} India's merchandise trade has grown from under USD 200 billion (2005) to over USD 1.1 trillion (2023), making it the world's 10th largest merchandise trading nation. This analysis examines **total export and import trends**, the **trade balance trajectory**, and the composition of key sectors — cereals, fertilizers, petroleum, and edible oils — using Harmonised System (HS) code classification. --- ::: {.callout-tip title="Key Findings at a Glance"} - 📈 **India's exports crossed USD 450 billion** in 2022–23, up from ~USD 170 billion a decade earlier - 📉 **Trade deficit is structural and widening**: typically USD 200–250 billion annually in recent years - 🌾 **Rice dominates cereal exports**: India is the world's largest rice exporter, accounting for ~40% of global trade - ⛽ **Petroleum products are India's top export category** — a testament to refinery capacity expansions - 🌱 **Fertilizer imports are large and import-intensive**: India imports ~$10 billion worth of fertilizers annually - 🔄 **Export coverage ratio has declined** from ~70% (early 2000s) to ~55% (recent years), indicating growing import dependence ::: --- ## Setup ```{r setup} library(data.table) library(ggplot2) library(ggiraph) library(readxl) library(viridis) library(scales) my_theme <- theme_minimal(base_size = 13) + theme( text = element_text(color = "#2c3e50"), plot.title = element_text(size = 16, face = "bold", hjust = 0.5, margin = margin(8, 0, 8, 0)), plot.subtitle = element_text(size = 11, hjust = 0.5, color = "#7f8c8d"), plot.caption = element_text(size = 9, color = "#95a5a6", hjust = 0), axis.title = element_text(size = 12, face = "bold"), axis.text = element_text(size = 10), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1), legend.position = "bottom", legend.title = element_blank(), panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.5), panel.grid.minor = element_blank(), strip.background = element_rect(fill = "#f8f9fa", color = NA), strip.text = element_text(face = "bold") ) std_opts <- list( opts_tooltip( css = paste0("background-color:#2c3e50;color:white;padding:8px 12px;", "border-radius:6px;font-size:12px;line-height:1.5;") ), opts_hover(css = "stroke-width:2.5;opacity:1;"), opts_hover_inv(css = "opacity:0.2;"), opts_zoom(min = 0.5, max = 3), opts_toolbar(position = "topright"), opts_sizing(rescale = TRUE, width = 1) ) ``` ## Data Loading & Cleaning ```{r data-load} usd <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_exportsusd.rds")) qty <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_exportsqty.rds")) musd <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_importsusd.rds")) mqty <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_importsqty.rds")) twodigit <- as.data.table(read_excel("/home/pawan/trade/totaltrade/two-digit.xlsx")) ## Standardised cleaning function clean_trade <- function(dt, val_col) { dt[, `:=`(HSCode2 = substr(HSCode, 1, 2), HSCode4 = substr(HSCode, 1, 4), HSCode6 = substr(HSCode, 1, 6))] dt[, (val_col) := as.numeric(gsub(",", "", get(val_col)))] dt[is.na(get(val_col)), (val_col) := 0] dt } usd <- clean_trade(usd, "Export") qty <- clean_trade(qty, "Export") musd <- clean_trade(musd, "Import") mqty <- clean_trade(mqty, "Import") ## Merge HS classification twodigit[, HSCode2 := as.numeric(HSCode2)] qty[, HSCode2 := as.numeric(HSCode2)] mqty[, HSCode2 := as.numeric(HSCode2)] qty <- merge(qty, twodigit, by = "HSCode2", all.x = TRUE) mqty <- merge(mqty, twodigit, by = "HSCode2", all.x = TRUE) ## Aggregate totals export_usd <- usd[, .(Export_USD = sum(Export, na.rm = TRUE)), by = Year] import_usd <- musd[, .(Import_USD = sum(Import, na.rm = TRUE)), by = Year] export_qty <- qty[, .(Export_Qty = sum(Export, na.rm = TRUE)), by = Year] import_qty <- mqty[, .(Import_Qty = sum(Import, na.rm = TRUE)), by = Year] trade <- Reduce(function(a, b) merge(a, b, by = "Year"), list(export_usd, import_usd, export_qty, import_qty)) trade[, `:=`( Trade_Balance = Export_USD - Import_USD, Trade_Volume = Export_USD + Import_USD, Export_Coverage = round(Export_USD * 100 / Import_USD, 1), Export_Growth = round((Export_USD / shift(Export_USD) - 1) * 100, 1), Import_Growth = round((Import_USD / shift(Import_USD) - 1) * 100, 1) )] ``` --- ## Trade Balance Trend ```{r trade-balance} trade_long <- melt(trade[, .(Year, Export_USD, Import_USD)], id = "Year", variable.name = "Flow", value.name = "USD") trade_long[, tip := paste0("", ifelse(Flow == "Export_USD", "Exports", "Imports"), " ", "Year: ", Year, " ", "Value: USD ", format(round(USD / 1000), big.mark = ","), " Bn")] pt <- ggplot() + ## Trade balance area geom_area(data = trade, aes(x = Year, y = Trade_Balance / 1000, group = 1), fill = "#e74c3c", alpha = 0.15) + ## Export & import lines geom_line_interactive( data = trade_long, aes(x = Year, y = USD / 1000, color = Flow, tooltip = tip, data_id = paste(Flow, Year), group = Flow), linewidth = 1.3 ) + geom_point_interactive( data = trade_long, aes(x = Year, y = USD / 1000, color = Flow, tooltip = tip, data_id = paste(Flow, Year)), size = 2.5 ) + geom_hline(yintercept = 0, linetype = "dashed", color = "#7f8c8d") + scale_color_manual(values = c("Export_USD" = "#27ae60", "Import_USD" = "#e74c3c"), labels = c("Export_USD" = "Exports", "Import_USD" = "Imports")) + scale_y_continuous(labels = label_dollar(suffix = "B", scale = 1)) + labs(title = "India's Total Merchandise Trade", subtitle = "Exports, imports, and trade deficit (USD Billions)", x = "Year", y = "USD Billions", caption = "Source: Ministry of Commerce, India | Analysis: Pawan") + my_theme girafe(ggobj = pt, width_svg = 11, height_svg = 6, options = std_opts) ``` ### Export Coverage Ratio ```{r coverage-ratio} trade[, tip_cov := paste0("Year: ", Year, " ", "Export coverage: ", Export_Coverage, "% ", "(Exports ÷ Imports × 100)")] pc <- ggplot(trade[!is.na(Export_Coverage)], aes(x = Year, y = Export_Coverage, tooltip = tip_cov, data_id = Year, group = 1)) + geom_col_interactive( aes(fill = Export_Coverage < 60), width = 0.75, alpha = 0.85 ) + geom_hline(yintercept = 100, linetype = "dashed", color = "#27ae60", linewidth = 0.8) + geom_hline(yintercept = 60, linetype = "dotted", color = "#e74c3c", linewidth = 0.8) + scale_fill_manual(values = c("FALSE" = "#3498db", "TRUE" = "#e67e22"), guide = "none") + scale_y_continuous(labels = label_percent(scale = 1)) + labs(title = "Export Coverage Ratio", subtitle = "How much of India's imports are covered by export earnings?", x = "Year", y = "Exports as % of Imports", caption = "Green dashed = 100% (balanced trade); Red dotted = 60% threshold") + my_theme girafe(ggobj = pc, width_svg = 10, height_svg = 5, options = std_opts) ``` --- ## Cereal Exports: Composition India is the world's largest rice exporter and a significant wheat exporter in surplus years. The 2022–23 wheat export ban reflects India's policy of managing domestic food price inflation. ```{r cereals} cereals <- qty[HSCode2 == 10] cereals[, Commodity := fcase( HSCode4 == "1006", "Rice", HSCode4 == "1001", "Wheat", HSCode4 == "1005", "Maize", HSCode4 == "1007", "Grain Sorghum", HSCode4 == "1008", "Millet & Buckwheat", default = "Other Cereals" )] cereals_yr <- cereals[, .(Qty = sum(Export, na.rm = TRUE)), by = .(Year, Commodity)] cereals_yr[, Total := sum(Qty), by = Year] cereals_yr[, Share := round(Qty * 100 / Total)] cereals_yr[, Commodity := factor(Commodity, levels = c("Other Cereals", "Millet & Buckwheat", "Grain Sorghum", "Maize", "Wheat", "Rice"))] cereals_yr[, tip := paste0("", Commodity, " ", "Year: ", Year, " ", "Qty: ", format(round(Qty), big.mark = ","), " '000T ", "Share: ", Share, "%")] pce <- ggplot(cereals_yr, aes(x = Year, y = Qty, fill = Commodity, tooltip = tip, data_id = paste(Commodity, Year))) + geom_bar_interactive(stat = "identity", position = "stack", width = 0.75, alpha = 0.9) + geom_text(aes(label = ifelse(Share >= 8, paste0(Share, "%"), "")), position = position_stack(vjust = 0.5), size = 2.8, color = "white", fontface = "bold") + scale_fill_viridis(discrete = TRUE, option = "D") + scale_y_continuous(labels = comma_format()) + labs(title = "Cereal Exports from India by Commodity", subtitle = "Rice dominates; wheat exports vary by policy and domestic supply conditions", x = "Year", y = "Quantity ('000 Tonnes)", caption = "Source: Ministry of Commerce | Analysis: Pawan") + my_theme girafe(ggobj = pce, width_svg = 11, height_svg = 6, options = std_opts) ``` --- ## Fertilizer Imports: Composition ```{r fert-imports} fert <- mqty[HSCode2 == 31] fert[, Commodity := fcase( HSCode4 == "3102", "Nitrogenous (Urea & Others)", HSCode4 == "3104", "Potassic (MOP)", HSCode4 == "3105", "DAP & NPK Compounds", default = "Other Fertilizers" )] fert_yr <- fert[, .(Qty = sum(Import, na.rm = TRUE)), by = .(Year, Commodity)] fert_yr[, Total := sum(Qty), by = Year] fert_yr[, Share := round(Qty * 100 / Total)] fert_yr[, tip := paste0("", Commodity, " ", "Year: ", Year, " ", "Qty: ", format(round(Qty), big.mark = ","), " '000T ", "Share: ", Share, "%")] pfe <- ggplot(fert_yr, aes(x = Year, y = Qty, fill = Commodity, tooltip = tip, data_id = paste(Commodity, Year))) + geom_bar_interactive(stat = "identity", position = "stack", width = 0.75, alpha = 0.9) + geom_text(aes(label = ifelse(Share >= 8, paste0(Share, "%"), "")), position = position_stack(vjust = 0.5), size = 2.8, color = "white", fontface = "bold") + scale_fill_viridis(discrete = TRUE, option = "C") + scale_y_continuous(labels = comma_format()) + labs(title = "Fertilizer Imports by Type", subtitle = "Nitrogenous (urea-based) fertilizers dominate imports by volume", x = "Year", y = "Quantity ('000 Tonnes)", caption = "Source: Ministry of Commerce | Analysis: Pawan") + my_theme girafe(ggobj = pfe, width_svg = 11, height_svg = 6, options = std_opts) ``` --- ## Petroleum Imports: Composition ```{r petroleum} petro <- mqty[HSCode2 == 27] petro[, Commodity := fcase( HSCode4 == "2701", "Coal", HSCode4 == "2709", "Crude Petroleum Oils", HSCode4 == "2711", "Petroleum Gases (LNG/LPG)", default = "Other Mineral Fuels" )] petro_yr <- petro[, .(Qty = sum(Import, na.rm = TRUE)), by = .(Year, Commodity)] petro_yr[, Total := sum(Qty), by = Year] petro_yr[, Share := round(Qty * 100 / Total)] petro_yr[, tip := paste0("", Commodity, " ", "Year: ", Year, " ", "Qty: ", format(round(Qty), big.mark = ","), " '000T ", "Share: ", Share, "%")] ppe <- ggplot(petro_yr, aes(x = Year, y = Qty, fill = Commodity, tooltip = tip, data_id = paste(Commodity, Year))) + geom_bar_interactive(stat = "identity", position = "stack", width = 0.75, alpha = 0.9) + geom_text(aes(label = ifelse(Share >= 8, paste0(Share, "%"), "")), position = position_stack(vjust = 0.5), size = 2.8, color = "white", fontface = "bold") + scale_fill_manual(values = c("Crude Petroleum Oils" = "#e67e22", "Coal" = "#2c3e50", "Petroleum Gases (LNG/LPG)" = "#8e44ad", "Other Mineral Fuels" = "#bdc3c7")) + scale_y_continuous(labels = comma_format()) + labs(title = "Petroleum & Mineral Fuel Imports by Type", subtitle = "Crude petroleum dominates; LNG imports rising as gas infrastructure expands", x = "Year", y = "Quantity ('000 Tonnes)", caption = "Source: Ministry of Commerce | Analysis: Pawan") + my_theme girafe(ggobj = ppe, width_svg = 11, height_svg = 6, options = std_opts) ``` --- ## Edible Oils & Fats Import Volume ```{r oils-fats} oils <- mqty[HSCode2 == 15] oils_yr <- oils[, .(Qty = sum(Import, na.rm = TRUE)), by = Year] oils_yr[, tip := paste0("Year: ", Year, " ", "Edible oils import: ", format(round(Qty / 1000), big.mark = ","), " '000 Tonnes")] po <- ggplot(oils_yr, aes(x = Year, y = Qty / 1000, tooltip = tip, data_id = Year, group = 1)) + geom_area_interactive(fill = "#f39c12", alpha = 0.3) + geom_line_interactive(linewidth = 1.2, color = "#d68910") + geom_point_interactive(size = 2.5, color = "#b7770d") + scale_y_continuous(labels = comma_format(suffix = " '000T")) + labs(title = "Vegetable Fats & Edible Oils Import Volume", subtitle = "Annual import trend — HS Chapter 15", x = "Year", y = "Quantity ('000 Tonnes)", caption = "Source: Ministry of Commerce | Analysis: Pawan") + my_theme girafe(ggobj = po, width_svg = 10, height_svg = 5, options = std_opts) ``` --- ## Key Takeaways ::: {.callout-important title="Summary of Findings"} 1. **India's trade deficit is deep and structural**: Petroleum, fertilizers, and edible oils together account for the bulk of the import bill, and all three are heavily import-dependent by structural necessity. 2. **Cereal exports are policy-sensitive**: The government alternates between encouraging exports (when there is surplus) and banning them (when domestic prices rise) — illustrating the tension between farmer welfare and consumer welfare. 3. **India is the world's largest rice exporter**: Rice contributes the bulk of cereal export earnings, though the recent quality and residue concerns in some markets require attention. 4. **Petroleum imports are the largest single category**: India's energy import dependence (~85% crude oil from overseas) is both an economic vulnerability and a geopolitical consideration. 5. **The export coverage ratio declining** toward 55% suggests imports are growing faster than exports — a medium-term current account concern. 6. **Fertilizer import dependence** reinforces the need to expand domestic production capacity and reduce over-subsidisation, which incentivises excessive fertilizer use and soil degradation. :::