India’s Public Foodgrain Procurement: Trends, Patterns & Policy Implications

Food Economy

Procurement

Agriculture Policy

A detailed analysis of foodgrain procurement by FCI and state agencies across marketing seasons, with regional breakdowns and MSP evolution.

Author

Pawan

Published

October 30, 2025

Modified

March 29, 2026

Overview

India operates one of the world’s largest public food procurement systems. The government procures wheat (rabi), paddy/rice (kharif), and coarse grains at the Minimum Support Price (MSP) through the Food Corporation of India (FCI) and state government agencies. Procured stocks underpin the Public Distribution System (PDS), midday-meal schemes, and the National Food Security Act (NFSA) welfare programmes.

Key policy features: - Procurement is price-floor insurance for farmers — FCI and state agencies buy all offered quantities at MSP. - Stocks feed welfare: NFSA covers 80 crore beneficiaries with 5 kg/person/month of subsidised grain. - Buffer norms guide minimum stock requirements for food security.

Setup

Code

library(data.table)
library(ggplot2)
library(ggiraph)
library(scales)
library(gt)
library(plotly)
library(viridis)
library(patchwork)

my_theme <- theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(size = 16, face = "bold", hjust = 0.5, margin = margin(b = 8)),
    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 = 0.4),
    panel.grid.minor= element_blank(),
    strip.background= element_rect(fill = "#F8F9FA", color = NA),
    strip.text      = element_text(face = "bold", size = 11)
  )

Section 1 · Total Procurement by Crop

Code

t1 <- fread("/home/pawan/procurement/procurement.csv")
t1[Crop!="Rice"]->t1
## Summarise by crop and marketing season
proc_sum <- t1[, .(Total     = sum(Total, na.rm=TRUE),
                   FCI       = sum(FCI, na.rm=TRUE),
                   StateAgency = sum(StateAgency, na.rm=TRUE)),
               by = .(Crop, MarketingYear, MarketingSeason)]

proc_sum[, FCI_Share   := round(FCI/Total*100, 1)]
proc_sum[, State_Share := round(StateAgency/Total*100, 1)]
proc_sum[, tt := paste0("<b>",Crop,"</b> | Season: ",MarketingSeason,
                         "<br>Total: ",round(Total,1)," LMT",
                         "<br>FCI: ",round(FCI,1)," (",FCI_Share,"%)",
                         "<br>States: ",round(StateAgency,1)," (",State_Share,"%)")]

## By-crop faceted bar
p1 <- ggplot(proc_sum, aes(x=MarketingYear, y=Total, fill=Crop)) +
  geom_bar_interactive(aes(tooltip=tt, data_id=paste(Crop,MarketingYear)),
                        stat="identity", width=0.75, alpha=0.9) +
  facet_wrap(~Crop, scales="free_y") +
  scale_fill_brewer(palette="Set2") +
  scale_y_continuous(labels=comma_format()) +
  my_theme + theme(legend.position="none") +
  labs(title="Foodgrain Procurement by Crop & Marketing Season",
       subtitle="Hover over bars for FCI vs state agency breakdown",
       y="Quantity (Lakh Metric Tonnes)", x="Marketing Year",
       caption="Source: DFPD Procurement Portal | Analysis: Pawan")

girafe(ggobj=p1, width_svg=13, height_svg=7,
       options=list(
         opts_tooltip(css="background:#fff;color:#333;padding:10px;border-radius:6px;border:1px solid #ddd;font-size:12px;"),
         opts_hover(css="opacity:0.9;stroke:#333;stroke-width:1.5;"),
         opts_hover_inv(css="opacity:0.3;"),
         opts_zoom(min=0.8, max=3),
         opts_sizing(rescale=TRUE, width=1)))

Section 2 · FCI vs State Agency Split

The procurement machinery involves two tiers: FCI (central agency) and state government agencies. Their relative roles have shifted over time — states have taken on more responsibility as part of decentralisation.

Code

proc_long <- melt(proc_sum[, .(Crop,MarketingYear,FCI,StateAgency)],
                  id.vars=c("Crop","MarketingYear"),
                  variable.name="Agency", value.name="Qty")
proc_long[, tt := paste0(Agency,"\nCrop: ",Crop,"\nSeason: ",MarketingYear,
                          "\n",round(Qty,1)," LMT")]

p2 <- ggplot(proc_long, aes(x=MarketingYear, y=Qty, fill=Agency)) +
  geom_bar_interactive(aes(tooltip=tt, data_id=Agency),
                        stat="identity", position="stack", width=0.75, alpha=0.9) +
  facet_wrap(~Crop, scales="free_y") +
  scale_fill_manual(values=c("FCI"="#2C3E50","StateAgency"="#E67E22"),
                    labels=c("FCI"="FCI","StateAgency"="State Agencies")) +
  scale_y_continuous(labels=comma_format()) +
  my_theme +
  labs(title="FCI vs State Agency Procurement Split",
       subtitle="States increasingly driving procurement in newer seasons",
       y="Quantity (LMT)", x="Marketing Year",
       caption="Source: DFPD | Analysis: Pawan")

girafe(ggobj=p2, width_svg=13, height_svg=7,
       options=list(
         opts_tooltip(css="background:#fff;color:#333;padding:8px;border-radius:5px;border:1px solid #ddd;"),
         opts_hover(css="opacity:0.85;stroke-width:1.5;"),
         opts_sizing(rescale=TRUE, width=1)))

Section 3 · Procurement Trend Analysis

Code

## Total annual procurement across all crops
annual_total <- proc_sum[, .(Total_All = sum(Total)), by = MarketingYear]
setorder(annual_total, MarketingYear)
annual_total[, YoY := round((Total_All/shift(Total_All)-1)*100, 1)]

## Line chart with growth annotations
p3 <- ggplot(annual_total, aes(x=MarketingYear, y=Total_All, group=1,
                                text=paste0(MarketingYear,"\n",round(Total_All,1)," LMT",
                                            "\nYoY: ",YoY,"%"))) +
  geom_col(fill="#3498DB", alpha=0.4, width=0.7) +
  geom_line(color="#2C3E50", linewidth=1.3) +
  geom_point(color="#2C3E50", size=3.5) +
  geom_text(aes(label=ifelse(!is.na(YoY), paste0(YoY,"%"), "")),
            vjust=-1.2, size=3.2, color="#E74C3C") +
  my_theme +
  labs(title="Total Foodgrain Procurement — All Crops",
       subtitle="YoY growth rates annotated in red",
       y="Quantity (LMT)", x="Marketing Year",
       caption="Source: DFPD | Analysis: Pawan")

ggplotly(p3, tooltip="text") |> layout(hovermode="x unified")

Section 4 · Summary Statistics Table

Code

proc_stats <- proc_sum[, .(
  Avg_Procurement = round(mean(Total),1),
  Max_Procurement = round(max(Total),1),
  Min_Procurement = round(min(Total),1),
  Avg_FCI_Share   = round(mean(FCI_Share),1),
  Seasons         = .N
), by=Crop]

proc_stats |>
  gt() |>
  tab_header(title="Procurement Summary Statistics by Crop") |>
  cols_label(Crop="Crop", Avg_Procurement="Avg (LMT)",
             Max_Procurement="Max (LMT)", Min_Procurement="Min (LMT)",
             Avg_FCI_Share="Avg FCI Share %", Seasons="No. of Seasons") |>
  fmt_number(columns=2:4, decimals=1) |>
  data_color(Avg_Procurement,
             colors=scales::col_numeric(c("#D5F5E3","#1A5276"), domain=NULL)) |>
  opt_stylize(style=3) |>
  tab_source_note("Source: DFPD Central Food Grains Procurement Portal")

Crop	Avg (LMT)	Max (LMT)	Min (LMT)	Avg FCI Share %	No. of Seasons
Procurement Summary Statistics by Crop
Paddy	566.3	913.7	246.6	2.3	19
Wheat	252.8	433.4	92.2	5.3	25
Source: DFPD Central Food Grains Procurement Portal

Section 5 · Policy Context

Code

## Paddy procurement regional breakdown (if available)
if ("State" %in% names(t1)) {
  paddy_state <- t1[Crop=="Paddy",
    .(Total=sum(Total,na.rm=TRUE)), .(State, MarketingYear)]
  top_states <- paddy_state[, .(Total=sum(Total)), by=State][order(-Total)][1:10, State]
  paddy_top  <- paddy_state[State %in% top_states]

  p_state <- ggplot(paddy_top, aes(x=MarketingYear, y=Total, fill=State)) +
    geom_bar(stat="identity", position="stack", width=0.75, alpha=0.9) +
    scale_fill_viridis_d() +
    my_theme +
    labs(title="Paddy Procurement by Top States",
         y="Quantity (LMT)", x="Marketing Year",
         caption="Source: DFPD | Analysis: Pawan")
  ggplotly(p_state) |> layout(hovermode="x unified")
}

Key insights:

Marketing Season for procurement is different for rabi and kharif crops, For Rabi crops (Wheat) is begin from 1st April and end 31st March, For Kharif crops (Paddy) is begin from 1st October end at 30th September

Paddy dominates procurement in volume terms, with Chhattisgarh, MP, Punjab and Haryana as the largest procuring states.

Wheat procurement fluctuates more sharply due to production variability in the Indo-Gangetic plain.

State agencies have grown as a share of total procurement, reflecting greater state-level operational capacity.

COVID-19 impact (2020–21): procurement held firm, underpinning the free-grain scheme (PMGKAY) for 80 crore beneficiaries.

--- title: "India's Public Foodgrain Procurement: Trends, Patterns & Policy Implications" description: "A detailed analysis of foodgrain procurement by FCI and state agencies across marketing seasons, with regional breakdowns and MSP evolution." author: "Pawan" date: "2025/10/30" date-modified: last-modified categories: - "Food Economy" - "Procurement" - "Agriculture Policy" format: html: code-fold: true toc: true toc-depth: 3 theme: cosmo editor_options: chunk_output_type: console execute: error: false message: false warning: false eval: true --- ## Overview India operates one of the world's largest public food procurement systems. The government procures wheat (rabi), paddy/rice (kharif), and coarse grains at the **Minimum Support Price (MSP)** through the Food Corporation of India (FCI) and state government agencies. Procured stocks underpin the **Public Distribution System (PDS)**, midday-meal schemes, and the National Food Security Act (NFSA) welfare programmes. **Key policy features:** - Procurement is price-floor insurance for farmers — FCI and state agencies buy all offered quantities at MSP. - Stocks feed welfare: NFSA covers 80 crore beneficiaries with 5 kg/person/month of subsidised grain. - Buffer norms guide minimum stock requirements for food security. --- ## Setup ```{r setup} library(data.table) library(ggplot2) library(ggiraph) library(scales) library(gt) library(plotly) library(viridis) library(patchwork) my_theme <- theme_minimal(base_size = 13) + theme( plot.title = element_text(size = 16, face = "bold", hjust = 0.5, margin = margin(b = 8)), 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 = 0.4), panel.grid.minor= element_blank(), strip.background= element_rect(fill = "#F8F9FA", color = NA), strip.text = element_text(face = "bold", size = 11) ) ``` --- ## Section 1 · Total Procurement by Crop ```{r procurement-total} t1 <- fread("/home/pawan/procurement/procurement.csv") t1[Crop!="Rice"]->t1 ## Summarise by crop and marketing season proc_sum <- t1[, .(Total = sum(Total, na.rm=TRUE), FCI = sum(FCI, na.rm=TRUE), StateAgency = sum(StateAgency, na.rm=TRUE)), by = .(Crop, MarketingYear, MarketingSeason)] proc_sum[, FCI_Share := round(FCI/Total*100, 1)] proc_sum[, State_Share := round(StateAgency/Total*100, 1)] proc_sum[, tt := paste0("",Crop," | Season: ",MarketingSeason, " Total: ",round(Total,1)," LMT", " FCI: ",round(FCI,1)," (",FCI_Share,"%)", " States: ",round(StateAgency,1)," (",State_Share,"%)")] ## By-crop faceted bar p1 <- ggplot(proc_sum, aes(x=MarketingYear, y=Total, fill=Crop)) + geom_bar_interactive(aes(tooltip=tt, data_id=paste(Crop,MarketingYear)), stat="identity", width=0.75, alpha=0.9) + facet_wrap(~Crop, scales="free_y") + scale_fill_brewer(palette="Set2") + scale_y_continuous(labels=comma_format()) + my_theme + theme(legend.position="none") + labs(title="Foodgrain Procurement by Crop & Marketing Season", subtitle="Hover over bars for FCI vs state agency breakdown", y="Quantity (Lakh Metric Tonnes)", x="Marketing Year", caption="Source: DFPD Procurement Portal | Analysis: Pawan") girafe(ggobj=p1, width_svg=13, height_svg=7, options=list( opts_tooltip(css="background:#fff;color:#333;padding:10px;border-radius:6px;border:1px solid #ddd;font-size:12px;"), opts_hover(css="opacity:0.9;stroke:#333;stroke-width:1.5;"), opts_hover_inv(css="opacity:0.3;"), opts_zoom(min=0.8, max=3), opts_sizing(rescale=TRUE, width=1))) ``` --- ## Section 2 · FCI vs State Agency Split The procurement machinery involves two tiers: FCI (central agency) and state government agencies. Their relative roles have shifted over time — states have taken on more responsibility as part of decentralisation. ```{r fci-state-split} proc_long <- melt(proc_sum[, .(Crop,MarketingYear,FCI,StateAgency)], id.vars=c("Crop","MarketingYear"), variable.name="Agency", value.name="Qty") proc_long[, tt := paste0(Agency,"\nCrop: ",Crop,"\nSeason: ",MarketingYear, "\n",round(Qty,1)," LMT")] p2 <- ggplot(proc_long, aes(x=MarketingYear, y=Qty, fill=Agency)) + geom_bar_interactive(aes(tooltip=tt, data_id=Agency), stat="identity", position="stack", width=0.75, alpha=0.9) + facet_wrap(~Crop, scales="free_y") + scale_fill_manual(values=c("FCI"="#2C3E50","StateAgency"="#E67E22"), labels=c("FCI"="FCI","StateAgency"="State Agencies")) + scale_y_continuous(labels=comma_format()) + my_theme + labs(title="FCI vs State Agency Procurement Split", subtitle="States increasingly driving procurement in newer seasons", y="Quantity (LMT)", x="Marketing Year", caption="Source: DFPD | Analysis: Pawan") girafe(ggobj=p2, width_svg=13, height_svg=7, options=list( opts_tooltip(css="background:#fff;color:#333;padding:8px;border-radius:5px;border:1px solid #ddd;"), opts_hover(css="opacity:0.85;stroke-width:1.5;"), opts_sizing(rescale=TRUE, width=1))) ``` --- ## Section 3 · Procurement Trend Analysis ```{r trend-analysis} ## Total annual procurement across all crops annual_total <- proc_sum[, .(Total_All = sum(Total)), by = MarketingYear] setorder(annual_total, MarketingYear) annual_total[, YoY := round((Total_All/shift(Total_All)-1)*100, 1)] ## Line chart with growth annotations p3 <- ggplot(annual_total, aes(x=MarketingYear, y=Total_All, group=1, text=paste0(MarketingYear,"\n",round(Total_All,1)," LMT", "\nYoY: ",YoY,"%"))) + geom_col(fill="#3498DB", alpha=0.4, width=0.7) + geom_line(color="#2C3E50", linewidth=1.3) + geom_point(color="#2C3E50", size=3.5) + geom_text(aes(label=ifelse(!is.na(YoY), paste0(YoY,"%"), "")), vjust=-1.2, size=3.2, color="#E74C3C") + my_theme + labs(title="Total Foodgrain Procurement — All Crops", subtitle="YoY growth rates annotated in red", y="Quantity (LMT)", x="Marketing Year", caption="Source: DFPD | Analysis: Pawan") ggplotly(p3, tooltip="text") |> layout(hovermode="x unified") ``` --- ## Section 4 · Summary Statistics Table ```{r summary-table} proc_stats <- proc_sum[, .( Avg_Procurement = round(mean(Total),1), Max_Procurement = round(max(Total),1), Min_Procurement = round(min(Total),1), Avg_FCI_Share = round(mean(FCI_Share),1), Seasons = .N ), by=Crop] proc_stats |> gt() |> tab_header(title="Procurement Summary Statistics by Crop") |> cols_label(Crop="Crop", Avg_Procurement="Avg (LMT)", Max_Procurement="Max (LMT)", Min_Procurement="Min (LMT)", Avg_FCI_Share="Avg FCI Share %", Seasons="No. of Seasons") |> fmt_number(columns=2:4, decimals=1) |> data_color(Avg_Procurement, colors=scales::col_numeric(c("#D5F5E3","#1A5276"), domain=NULL)) |> opt_stylize(style=3) |> tab_source_note("Source: DFPD Central Food Grains Procurement Portal") ``` --- ## Section 5 · Policy Context ```{r policy-notes} ## Paddy procurement regional breakdown (if available) if ("State" %in% names(t1)) { paddy_state <- t1[Crop=="Paddy", .(Total=sum(Total,na.rm=TRUE)), .(State, MarketingYear)] top_states <- paddy_state[, .(Total=sum(Total)), by=State][order(-Total)][1:10, State] paddy_top <- paddy_state[State %in% top_states] p_state <- ggplot(paddy_top, aes(x=MarketingYear, y=Total, fill=State)) + geom_bar(stat="identity", position="stack", width=0.75, alpha=0.9) + scale_fill_viridis_d() + my_theme + labs(title="Paddy Procurement by Top States", y="Quantity (LMT)", x="Marketing Year", caption="Source: DFPD | Analysis: Pawan") ggplotly(p_state) |> layout(hovermode="x unified") } ``` > **Key insights:** >1. **Marketing Season** for procurement is different for rabi and kharif crops, For Rabi crops RMS (Wheat) is begin from 1st April and end 31st March, For Kharif crops KMS (Paddy) is begin from 1st October end at 30th September > 2. **Paddy dominates** procurement in volume terms, with Chhattisgarh, MP, Punjab and Haryana as the largest procuring states. > 3. **Wheat procurement** fluctuates more sharply due to production variability in the Indo-Gangetic plain. > 4. **State agencies** have grown as a share of total procurement, reflecting greater state-level operational capacity. > 5. **COVID-19 impact** (2020–21): procurement held firm, underpinning the free-grain scheme (PMGKAY) for 80 crore beneficiaries.