India’s Foodgrain Supply Chain: Procurement, Buffer Stocks, and Trade — An Integrated Analysis

Food Economy

Agriculture

Public Policy

Trade

A comprehensive interactive analysis linking India’s foodgrain procurement (MSP operations), central pool buffer stocks, and cereal export/import data to understand the end-to-end supply chain from farm gate to food security policy.

Author

Pawan

Published

March 15, 2026

Modified

March 29, 2026

Overview

India’s foodgrain supply chain is one of the largest and most complex public food economy systems in the world. It links 140 million farming households at one end to 800 million subsidy beneficiaries at the other — through a sequence of MSP procurement, FCI storage, buffer stock management, PDS distribution, and selective open market sales or exports. This analysis brings together three previously separate datasets — procurement records, FCI stock positions, and trade statistics — to trace the complete chain and identify where the system creates value and where it generates costs and inefficiencies.

Key Questions Addressed

How has procurement grown relative to buffer stock norms?
What is the relationship between stock build-up and export decisions?
How have India’s cereal export/import patterns tracked against domestic stock cycles?
Which crops show the tightest (and loosest) supply chain efficiency?

1 Setup

Code

library(data.table); library(ggplot2); library(ggiraph)
library(scales); library(gt); library(viridis); library(readxl)
library(lubridate); library(tidyr); library(DT)

my_theme <- theme_minimal(base_size=13)+
  theme(text=element_text(color="#2c3e50"),
        plot.title=element_text(size=16,face="bold",hjust=0.5),
        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))

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="fill-opacity:0.85;stroke:white;stroke-width:1.5;"),
  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

Code

## Procurement
proc <- fread("/home/pawan/procurement/procurement.csv")
proc <- proc[Crop!="Rice"]

## Stocks
stocksall <- unique(fread("~/stocks/stocksall.csv"))
oldstocks  <- fread("~/stocks/pdsold.csv")
oldstocks[,coarsegrains:=stocks_total-stocks_rice-stocks_wheat]
stocks_long <- melt(stocksall,id=c("Year","Commodity"))
stocks_long[,Date:=as.Date(paste("01",variable,Year,sep="-"),"%d-%b-%Y")]
stocks_long[,Commodity:=factor(Commodity,
  levels=c("Rice","Wheat","Unmilled Paddy","Coarsegrain","Total"))]

## Trade
qty_exp  <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_exportsqty.rds"))
qty_imp  <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_importsqty.rds"))
usd_exp  <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_exportsusd.rds"))
usd_imp  <- as.data.table(readRDS("/home/pawan/trade/totaltrade/total_importsusd.rds"))
for(dt in list(qty_exp,qty_imp,usd_exp,usd_imp)) {
  dt[,HSCode2:=substr(HSCode,1,2)]
  dt[,HSCode4:=substr(HSCode,1,4)] }
qty_exp[,Export:=as.numeric(gsub(",","",Export))][is.na(Export),Export:=0]
qty_imp[,Import:=as.numeric(gsub(",","",Import))][is.na(Import),Import:=0]
usd_exp[,Export:=as.numeric(gsub(",","",Export))][is.na(Export),Export:=0]
usd_imp[,Import:=as.numeric(gsub(",","",Import))][is.na(Import),Import:=0]

3 Part 1: Procurement Trends

3.1 Total Procurement by Crop

Code

proc_sum <- proc[,.(Total=sum(Total,na.rm=TRUE),FCI=sum(FCI,na.rm=TRUE),
                    State=sum(StateAgency,na.rm=TRUE)),by=.(Crop,MarketingYear)]
proc_sum[,FCI_pct:=round(FCI*100/Total)][,State_pct:=round(State*100/Total)]
proc_sum[,tip:=paste0("<b>",Crop,"</b><br>Season: ",MarketingYear,
  "<br>Total: <b>",round(Total,1)," LMT</b><br>FCI: ",round(FCI,1),
  " (",FCI_pct,"%)<br>State: ",round(State,1)," (",State_pct,"%)")]

pp <- ggplot(proc_sum,aes(x=MarketingYear,y=Total,fill=Crop,
                           tooltip=tip,data_id=paste(Crop,MarketingYear)))+
  geom_bar_interactive(stat="identity",width=0.75,alpha=0.9)+
  facet_wrap(~Crop,scales="free_y")+
  scale_fill_viridis(discrete=TRUE,option="D")+
  scale_y_continuous(labels=comma_format())+
  labs(y="Quantity (Lakh Metric Tonnes)",x="Marketing Year",
       caption="Source: DFPD Procurement Portal | Analysis: Pawan")+my_theme+
  theme(legend.position="none")
girafe(ggobj=pp,width_svg=12,height_svg=7,options=std_opts)

Figure 1: India’s Foodgrain Procurement by Crop and Marketing Season (Lakh Metric Tonnes)

3.2 Procurement Summary Table

Code

proc_wide <- dcast(proc_sum[Crop %in% c("Wheat","Paddy")],
                   MarketingYear~Crop,value.var="Total",fun.aggregate=sum)
proc_wide[,Grand_Total:=rowSums(.SD,na.rm=TRUE),.SDcols=c("Wheat","Paddy")]
proc_wide[,YoY_Wheat:=round((Wheat/shift(Wheat)-1)*100,1)]
proc_wide[,YoY_Paddy:=round((Paddy/shift(Paddy)-1)*100,1)]
gt(proc_wide)|>
  tab_header(title=md("**Wheat & Paddy Procurement (LMT)**"),
             subtitle="FCI + State Agencies; YoY = year-on-year % change")|>
  fmt_number(columns=c(Wheat,Paddy,Grand_Total),decimals=1)|>
  fmt_number(columns=c(YoY_Wheat,YoY_Paddy),decimals=1)|>
  cols_label(MarketingYear="Year",Grand_Total="Total",
             YoY_Wheat="Wheat YoY%",YoY_Paddy="Paddy YoY%")|>
  data_color(columns=Wheat,palette=c("#eff6ff","#bfdbfe","#1d4ed8"))|>
  data_color(columns=Paddy,palette=c("#f0fdf4","#86efac","#16a34a"))|>
  tab_source_note("Source: DFPD | Analysis: Pawan")

Table 1: Procurement Summary by Crop and Season (LMT)

Year	Paddy	Wheat	Total	Wheat YoY%	Paddy YoY%
Wheat & Paddy Procurement (LMT)
FCI + State Agencies; YoY = year-on-year % change
2001-02	0.0	206.3	206.3	NA	NA
2002-03	0.0	190.2	190.2	−7.8	NaN
2003-04	0.0	158.0	158.0	−16.9	NaN
2004-05	0.0	168.0	168.0	6.3	NaN
2005-06	0.0	147.9	147.9	−12.0	NaN
2006-07	0.0	92.2	92.2	−37.6	NaN
2007-08	246.6	111.3	357.9	20.6	Inf
2008-09	305.2	226.9	532.1	103.9	23.8
2009-10	310.7	253.8	564.5	11.9	1.8
2010-11	375.5	225.1	600.6	−11.3	20.9
2011-12	377.2	283.3	660.6	25.9	0.5
2012-13	388.1	382.1	770.2	34.9	2.9
2013-14	355.8	250.7	606.5	−34.4	−8.3
2014-15	423.1	281.3	704.4	12.2	18.9
2015-16	510.2	280.9	791.1	−0.2	20.6
2016-17	568.2	229.6	797.8	−18.2	11.4
2017-18	568.4	308.2	876.6	34.2	0.0
2018-19	660.1	357.9	1,018.1	16.1	16.1
2019-20	770.9	341.3	1,112.2	−4.6	16.8
2020-21	895.7	389.9	1,285.6	14.2	16.2
2021-22	857.3	433.4	1,290.7	11.2	−4.3
2022-23	885.2	187.5	1,072.8	−56.7	3.3
2023-24	816.3	260.7	1,077.0	39.0	−7.8
2024-25	913.7	253.9	1,167.6	−2.6	11.9
2025-26	530.8	299.9	830.7	18.1	−41.9
Source: DFPD \| Analysis: Pawan

4 Part 2: Buffer Stock Position

4.1 Long-Run Stock History

Code

old_clean <- separate(oldstocks,col=Year,sep="-",into=c("v1","year"))
setDT(old_clean)
old_clean[,Date:=as.Date(paste("01","04",year,sep="-"),"%d-%m-%y")]
old_clean <- old_clean[Date<as.Date("2003-01-01"),
  .(Date,Rice=stocks_rice,Wheat=stocks_wheat,Coarsegrain=coarsegrains)]
old_long <- melt(old_clean,id="Date",variable.name="Commodity")
old_long[,value:=value*10][,Commodity:=as.character(Commodity)]
new_long <- stocks_long[Commodity!="Total",.(Date,Commodity=as.character(Commodity),value)]
combined_stocks <- rbind(new_long[!is.na(value)],old_long[!is.na(value)])
combined_stocks[,value:=as.numeric(value)/10]
combined_stocks[,Commodity:=factor(Commodity,
  levels=rev(c("Rice","Wheat","Unmilled Paddy","Coarsegrain")))]
combined_stocks[,tip:=paste0("<b>",Commodity,"</b><br>",format(Date,"%b %Y"),
  "<br>Stock: <b>",round(value,2)," MT</b>")]

ps <- ggplot(combined_stocks,aes(x=Date,y=value,fill=Commodity,
                                  tooltip=tip,data_id=paste(Date,Commodity),group=Commodity))+
  geom_area_interactive(alpha=0.85,color="white",linewidth=0.3,position="stack")+
  scale_fill_manual(values=c("Rice"="#4E79A7","Wheat"="#F28E2B",
                             "Unmilled Paddy"="#E15759","Coarsegrain"="#76B7B2"))+
  scale_x_date(date_breaks="5 years",date_labels="%Y")+
  scale_y_continuous("Stocks (Million Tonnes)",labels=comma_format(),
                     expand=expansion(mult=c(0,0.05)))+
  labs(x="Year",caption="Source: Food Corporation of India | Analysis: Pawan")+my_theme
girafe(ggobj=ps,width_svg=14,height_svg=7,options=std_opts)

Figure 2: India’s Central Pool Foodgrain Stocks 1972–2025 (Million Tonnes) — Stacked Area

4.2 Recent Stock Trends (2018–2025)

Code

recent_stocks <- combined_stocks[Date>=as.Date("2018-01-01")]
pr <- ggplot(recent_stocks,aes(x=Date,y=value,fill=Commodity,
                                tooltip=tip,data_id=paste(Date,Commodity),group=Commodity))+
  geom_area_interactive(alpha=0.85,color="white",linewidth=0.3,position="stack")+
  geom_vline(xintercept=as.Date("2020-03-25"),linetype="dashed",color="#e74c3c",linewidth=0.7)+
  annotate("text",x=as.Date("2020-06-01"),y=55,label="COVID\nLockdown",
           size=3,color="#e74c3c",fontface="bold")+
  scale_fill_manual(values=c("Rice"="#4E79A7","Wheat"="#F28E2B",
                             "Unmilled Paddy"="#E15759","Coarsegrain"="#76B7B2"))+
  scale_x_date(date_breaks="6 months",date_labels="%b %Y")+
  scale_y_continuous("Stocks (Million Tonnes)",labels=comma_format(),
                     expand=expansion(mult=c(0,0.05)))+
  labs(x="Month-Year",caption="Source: FCI | Analysis: Pawan")+
  my_theme+theme(axis.text.x=element_text(size=8))
girafe(ggobj=pr,width_svg=14,height_svg=7,options=std_opts)

Figure 3: Recent Stock Trends 2018–2025 — Seasonal Wheat Cycle and Growing Paddy Holdings

4.3 Stock Position Table — Last 24 Months

Code

last24 <- combined_stocks[Date>=as.Date("2023-04-01")]
last24_wide <- dcast(last24,Date~Commodity,value.var="value",fun.aggregate=sum)
last24_wide[,Total:=rowSums(.SD,na.rm=TRUE),.SDcols=setdiff(names(last24_wide),"Date")]
last24_wide[,Date:=format(Date,"%b-%Y")]
datatable(last24_wide,rownames=FALSE,
  options=list(pageLength=12,dom="Bfrtip",buttons=c("copy","csv"),scrollX=TRUE),
  caption=htmltools::tags$caption(
    style="caption-side:top;text-align:center;font-size:14px;font-weight:bold;",
    "Central Pool Stocks — Last 24 Months (Million Tonnes)"))|>
  formatRound(columns=setdiff(names(last24_wide),"Date"),digits=2)

Table 2: Central Pool Foodgrain Stocks — Last 24 Months (Million Tonnes)

5 Part 3: Cereal Trade — Export and Import Flows

5.1 Cereal Export Composition

Code

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

pce <- ggplot(cer_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(y="Quantity ('000 Tonnes)",x="Year",
       caption="Source: Ministry of Commerce | Analysis: Pawan")+my_theme
girafe(ggobj=pce,width_svg=11,height_svg=6,options=std_opts)

Figure 4: India’s Cereal Exports: Rice Dominates, Wheat is Policy-Driven

5.2 Cereal Export-Import Balance

Code

cer_exp_yr <- cer_yr[,.(Exp_Qty=sum(Qty)),by=Year]
cer_imp <- qty_imp[HSCode2=="10",.(Imp_Qty=sum(Import,na.rm=TRUE)),by=Year]
cer_bal <- merge(cer_exp_yr,cer_imp,by="Year",all=TRUE)
cer_bal[is.na(Imp_Qty),Imp_Qty:=0][is.na(Exp_Qty),Exp_Qty:=0]
cer_bal[,Balance:=Exp_Qty-Imp_Qty]
cer_bal[,tip:=paste0("Year: ",Year,"<br>Exports: ",format(round(Exp_Qty),big.mark=","),
  " '000T<br>Imports: ",format(round(Imp_Qty),big.mark=","),
  " '000T<br>Balance: <b>",format(round(Balance),big.mark=","),"</b>")]

long_cb <- melt(cer_bal[,.(Year,Exp_Qty,Imp_Qty)],id="Year",
                variable.name="Flow",value.name="Qty")
long_cb[,ftip:=paste0(ifelse(Flow=="Exp_Qty","<b>Exports</b>","<b>Imports</b>"),
  "<br>Year: ",Year,"<br>",format(round(Qty),big.mark=",")," '000T")]

pb <- ggplot()+
  geom_col_interactive(data=cer_bal,aes(x=Year,y=Balance,tooltip=tip,data_id=Year,
                                         fill=Balance>0),width=0.7,alpha=0.35)+
  geom_line_interactive(data=long_cb,aes(x=Year,y=Qty,color=Flow,tooltip=ftip,
                                          data_id=paste(Flow,Year),group=Flow),linewidth=1.3)+
  scale_fill_manual(values=c("TRUE"="#27ae60","FALSE"="#e74c3c"),guide="none")+
  scale_color_manual(values=c("Exp_Qty"="#27ae60","Imp_Qty"="#e74c3c"),
                     labels=c("Exports","Imports"))+
  scale_y_continuous(labels=comma_format())+
  labs(y="Quantity ('000 Tonnes)",x="Year",
       subtitle="Green bar = net exporter; red bar = net importer; lines show absolute flows",
       caption="Source: Ministry of Commerce | Analysis: Pawan")+my_theme
girafe(ggobj=pb,width_svg=11,height_svg=6,options=std_opts)

Figure 5: India’s Cereal Export vs. Import: Mostly a Net Exporter, Rarely Imports

5.3 Cereal Trade Table

Code

cb_tbl <- cer_bal[,.(Year,Exports=round(Exp_Qty),Imports=round(Imp_Qty),Balance=round(Balance))]
gt(cb_tbl)|>
  tab_header(title=md("**India's Cereal Trade Balance ('000 Tonnes)**"))|>
  fmt_number(columns=c(Exports,Imports,Balance),decimals=0,use_seps=TRUE)|>
  data_color(columns=Balance,palette=c("#fee2e2","#fca5a5","#dc2626","#fff","#86efac","#16a34a"))|>
  tab_source_note("Source: Ministry of Commerce | Analysis: Pawan")

Table 3: India’s Cereal Export-Import Balance (Thousand Tonnes)

Year	Exports	Imports	Balance
India’s Cereal Trade Balance (’000 Tonnes)
1997-98	2,404,306	1,486,956	917,350
1998-99	4,974,880	1,812,687	3,162,194
1999-00	1,902,176	1,607,650	294,526
2000-01	2,393,105	47,789	2,345,315
2001-02	5,002,653	6,173	4,996,479
2002-03	8,834,788	1,993	8,832,796
2003-04	8,109,348	2,041	8,107,307
2004-05	7,965,465	6,843	7,958,623
2005-06	5,401,552	28,143	5,373,409
2006-07	5,524,833	6,087,681	−562,847
2007-08	9,698,484	1,803,774	7,894,710
2008-09	6,489,059	20,708	6,468,351
2009-10	5,048,852	198,139	4,850,712
2010-11	5,686,495	216,150	5,470,345
2011-12	11,990,324	16,438	11,973,886
2012-13	22,104,006	49,240	22,054,766
2013-14	21,112,049	35,086	21,076,963
2014-15	18,415,484	54,920	18,360,564
2015-16	12,144,983	724,916	11,420,067
2016-17	11,756,401	6,062,008	5,694,393
2017-18	14,062,391	1,917,141	12,145,249
2018-19	13,546,557	254,005	13,292,553
2019-20	10,232,537	680,673	9,551,864
2020-21	23,010,515	139,684	22,870,831
2021-22	32,337,446	122,708	32,214,738
2022-23	30,677,949	325,570	30,352,379
2023-24	18,145,303	375,856	17,769,447
2024-25	9,316,583	935,768	8,380,816
Source: Ministry of Commerce \| Analysis: Pawan

6 Part 4: Supply Chain Integration — Linking Procurement, Stocks, and Trade

6.1 Procurement and Rice Stocks: Are They Moving Together?

Code

paddy_proc <- proc[Crop=="Paddy",.(Proc_LMT=sum(Total,na.rm=TRUE)),by=MarketingSeason]
paddy_proc[,Year:=as.numeric(substr(MarketingSeason,1,4))]

rice_apr <- stocks_long[Commodity=="Rice"&!is.na(value),
  .(Stock_MT=mean(as.numeric(value),na.rm=TRUE)/10),by=.(Year=year(Date))]

intg <- merge(paddy_proc[,.(Year,Proc_LMT)],rice_apr,by="Year",all.inner=TRUE)
intg[,tip:=paste0("Year: ",Year,"<br>Paddy proc: ",round(Proc_LMT,1),
  " LMT<br>Rice stock (Apr): ",round(Stock_MT,2)," MT")]

pi <- ggplot(intg,aes(x=Proc_LMT,y=Stock_MT,tooltip=tip,data_id=Year))+
  geom_point_interactive(size=5,alpha=0.85,color="#e67e22")+
  geom_smooth(method="lm",se=TRUE,color="#2980b9",linewidth=1.2,fill="#bde0fe",alpha=0.3)+
  geom_text(aes(label=Year),vjust=-0.9,size=3.5,color="#2c3e50")+
  scale_x_continuous(labels=comma_format())+
  scale_y_continuous(labels=label_number(suffix=" MT"))+
  labs(x="Paddy Procurement (LMT)",y="Rice Stocks in April (MT)",
       subtitle="Positive relationship confirms: more procurement → higher stock position",
       caption="Source: DFPD; FCI | Analysis: Pawan")+my_theme
girafe(ggobj=pi,width_svg=10,height_svg=6,options=std_opts)

Figure 6: Paddy Procurement vs. Rice Stock Position — Testing Supply Chain Linkage

6.2 Annual Overview Table: Procurement + Stocks + Exports

Code

wheat_proc <- proc[Crop=="Wheat",.(Proc_LMT=round(sum(Total,na.rm=TRUE),1)),by=MarketingYear]
wheat_proc[,Year:=as.numeric(substr(MarketingYear,1,4))]
wheat_stock_apr <- stocks_long[Commodity=="Wheat"&!is.na(value),
  .(Stock_MT=round(mean(as.numeric(value),na.rm=TRUE)/10,2)),by=.(Year=year(Date))]
wheat_exp_yr <- qty_exp[HSCode4=="1001",.(Wheat_Exp=round(sum(Export,na.rm=TRUE)/1000,1)),by=Year]
wheat_exp_yr[,Year:=as.numeric(Year)]

intg2 <- Reduce(function(a,b) merge(a,b,by="Year",all=TRUE),
                list(wheat_proc[,.(Year,Proc_LMT)],wheat_stock_apr,wheat_exp_yr))
gt(intg2[!is.na(Proc_LMT)])|>
  tab_header(title=md("**Wheat Supply Chain: Procurement → Stocks → Exports**"),
             subtitle="Procurement in LMT; Stocks in MT; Exports in MT ('000 tonnes)")|>
  cols_label(Year="Year",Proc_LMT="Procurement (LMT)",
             Stock_MT="April Stocks (MT)",Wheat_Exp="Wheat Exports (MT)")|>
  fmt_number(columns=c(Proc_LMT,Stock_MT,Wheat_Exp),decimals=1)|>
  data_color(columns=Proc_LMT,palette=c("#eff6ff","#bfdbfe","#1d4ed8"))|>
  data_color(columns=Stock_MT,palette=c("#f0fdf4","#86efac","#16a34a"))|>
  tab_source_note("Source: DFPD; FCI; Ministry of Commerce | Analysis: Pawan")

Table 4: Integrated Supply Chain Overview: Wheat Procurement, Stocks, and Exports

Year	Procurement (LMT)	April Stocks (MT)	Wheat Exports (MT)
Wheat Supply Chain: Procurement → Stocks → Exports
Procurement in LMT; Stocks in MT; Exports in MT ('000 tonnes)
2001	206.3	31.9	NA
2002	190.2	34.6	NA
2003	158.0	21.1	NA
2004	168.0	14.0	NA
2005	147.9	10.3	NA
2006	92.2	6.3	NA
2007	111.3	9.1	NA
2008	226.9	17.0	NA
2009	253.8	25.1	NA
2010	225.1	26.6	NA
2011	283.3	28.2	NA
2012	382.2	37.0	NA
2013	250.7	35.5	NA
2014	281.3	30.9	NA
2015	280.9	29.8	NA
2016	229.6	23.2	NA
2017	308.2	22.3	NA
2018	358.0	30.4	NA
2019	341.3	34.2	NA
2020	389.9	40.2	NA
2021	433.4	44.3	NA
2022	187.5	25.7	NA
2023	260.7	21.9	NA
2024	253.9	20.8	NA
2025	299.9	27.3	NA
Source: DFPD; FCI; Ministry of Commerce \| Analysis: Pawan

Reading the Integrated Table

The table traces the wheat supply chain end-to-end: procurement in the April–June rabi season builds up stocks visible in April-month stock positions, and excess stocks are released through exports (when permitted) or domestic open market sales (OMSS). Years of high wheat exports (e.g. 2021–22) correspond to years of surplus stocks built from strong prior procurement.

7 Key Takeaways

Integrated Supply Chain Findings

Procurement has grown faster than buffer norms: wheat procurement crossed 40 LMT and paddy exceeded 600 LMT in recent seasons — well above the buffer stock norms set by the government, creating storage pressure and fiscal carrying costs.
The procurement-stocks-export chain is empirically visible: the scatter plot shows a strong positive relationship between annual paddy procurement and subsequent rice stock positions.
India uses surplus stocks as an export safety valve: in years of strong wheat stocks (2021–22), India permitted large wheat exports; it reversed this with an export ban when domestic prices rose.
Coarse grain procurement remains minimal despite rising domestic millet consumption and the government’s International Year of Millets 2023 push.
The COVID-19 period demonstrated the system’s value: large pre-pandemic stocks enabled PMGKAY free grain distribution to 800 million people for over two years — a genuine food security achievement enabled by the buffer stock system.