Data Affairs
Published in

Data Affairs

Wheat Procurement India 2021: Complete analysis

With the procurement season ending in a day or 2, we have the data available about the total wheat that came for procurement as of now. This was a rough year for the government as well as farmers due to 3 farm bills bulldozed through the parliament by the ruling government. Some of the emotional farmers even destroyed their standing crop and few others decided to completely boycott the harvest.

This is why it was obligatory for me to check the procurement trends this year. I have downloaded the data from the FCI official website and for this year from here.

Data Cleaning

Like I say in all of my articles if you are here for the graphs and figures skip this portion and move straight to the visualization, if you are here to learn data analysis then stay.

I downloaded the data in PDF and converted it into CSV using an online tool.

import pyforest
df=pd.read_csv("Wheat/wheat.csv")
df

Since I want to analyze the procurement trends for states over the years, I will have to reshape the data. Different states should be displayed as columns and year data in rows because to have to pass years data into the X-axis, which is not possible as long as it is displayed column-wise.

reshape=pd.melt(df, id_vars=["STATES"], value_vars=["2010_11", "2011_12", "2012_13", "2013_14", "2014_15", "2015_16", "2016_17", "2017_18", "2018_19","2019_20", "2020_21", "2021_22"])

This is how the reshaped data look like. It is displaying data year-wise, I can filter out the data for different states.

punjab=reshape[reshape["STATES"]=="Punjab"]

Visualization for Punjab

fig = px.bar( 
x=punjab['variable'],
y=punjab['value'],
orientation='v')
fig.update_layout(uniformtext_minsize=2, uniformtext_mode='hide', autosize=False,
xaxis_title="Fiscal Year",
yaxis_title="Procurement count",
width=900,
height=500,
title_text = 'wheat procurment in punjab in last 10 years ')
fig.show()

Observation: Despite the farmers protest and some farmers opting out the procurement was a record high and highest of all times which is a good sign.

Procurement by agency

the data can be downloaded from the FCI website.

agency=pd.read_csv("wheat/101_4.csv")
agency
fig = px.bar( 
x=agency['AGENCY'],
y=agency["2021_22"],
orientation='v')
fig.update_layout(uniformtext_minsize=2, uniformtext_mode='hide', autosize=False,
xaxis_title="Agency",
yaxis_title="Procurement count",
width=800,
height=500,
title_text = 'Total wheat procured in Punjab in 2021 by diff agencies')
fig.show()

Observation: PUNGRAIN agency was the leader in procurement in Punjab. FCI procured 12 LMT from Punjab which is 40 percent of their total procurement.

Haryana

haryana=reshape[reshape["STATES"]=="Haryana"]
haryana
fig = px.bar( 
x=haryana['variable'],
y=haryana['value'],
orientation='v')
fig.update_layout(uniformtext_minsize=2, uniformtext_mode='hide', autosize=False,
xaxis_title="Fiscal Year",
yaxis_title="Procurement count",
width=900,
height=500,
title_text = 'wheat procurement in haryana in last 10 years ')
fig.show()

Observation: 84 LMT was procured in Haryana which is significantly higher than last year. This also means some distressing reports of crops being destroyed did not impact the procurement, Which is again a good sign.

Uttar Pradesh

You can use the same code as used above for the 2 states.

Observation: UP is witnessing a downward trend in procurement over the years.

All States

fig = px.bar( 
x=df['STATES'],
y=df['2021_22'],
orientation='v')
fig.update_layout(uniformtext_minsize=2, uniformtext_mode='hide', autosize=False,
xaxis_title="Fiscal Year",
yaxis_title="Procurement count",
width=900,
height=500,
title_text = 'Total wheat procured in india 2021-22 years ')
fig.show()

Observation: Punjab and MP got the highest procurement. it is interesting to note that MP recorded the highest procurement last year. it fell from 129 LMT to 112 LMT.

MSP Wheat 2021

I have downloaded the data from google. It is easily available in public domain

msp=pd.read_csv("wheat/msp.csv")
msp
fig = px.bar(msp.sort_values(by="Years", ascending=True,inplace=True), 
x=msp["Years"],
y=msp["Wheat"],
orientation='v')
fig.update_layout(uniformtext_minsize=2, uniformtext_mode='hide', autosize=False,
xaxis_title="MSP",
yaxis_title="Year",
width=800,
height=800,
title_text = 'MSP trend over the years ')
fig.show()

Observation: The increase in MSP has been rather slow and gradual compared to other costs. It would be hard for farmers to catch up with the input cost if the trend continues.

Conclusion

It was a decent season of procurement with few days still to go. Not many changes were observed in Punjab and Haryana which are currently witnessing massive protests due to 3 farm laws.

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Anmol Hans

Anmol Hans

Geopolitics and Data Science enthusiast.