Almost *** million were employed in India's agriculture sector in financial year 2024. However, the number of employees was the highest in fiscal year 2022 at over *** million. The industry saw an overall decline in the number of employees.

Actual value and historical data chart for India Employment In Agriculture Percent Of Total Employment

In 2023, 43.51 percent of the workforce in India were employed in agriculture, while the other half was almost evenly distributed among the two other sectors, industry and services. While the share of Indians working in agriculture is declining, it is still the main sector of employment. A BRIC powerhouseTogether with Brazil, Russia, and China, India makes up the four so-called BRIC countries. They are the four fastest-growing emerging countries dubbed BRIC, an acronym, by Jim O’Neill at Goldman Sachs. Being major economies themselves already, these four countries are said to be at a similar economic developmental stage -- on the verge of becoming industrialized countries -- and maybe even dominating the global economy. Together, they are already larger than the rest of the world when it comes to GDP and simple population figures. Among these four, India is ranked second across almost all key indicators, right behind China. Services on the riseWhile most of the Indian workforce is still employed in the agricultural sector, it is the services sector that generates most of the country’s GDP. In fact, when looking at GDP distribution across economic sectors, agriculture lags behind with a mere 15 percent contribution. Some of the leading services industries are telecommunications, software, textiles, and chemicals, and production only seems to increase – currently, the GDP in India is growing, as is employment.

India Employment: Public: Agriculture, Hunting, etc. data was reported at 473.000 Person th in 2012. This records a decrease from the previous number of 477.000 Person th for 2011. India Employment: Public: Agriculture, Hunting, etc. data is updated yearly, averaging 506.000 Person th from Mar 1982 (Median) to 2012, with 31 observations. The data reached an all-time high of 573.000 Person th in 1992 and a record low of 463.000 Person th in 1982. India Employment: Public: Agriculture, Hunting, etc. data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under Global Database’s India – Table IN.GBA001: Employment.

Actual value and historical data chart for India Employees Agriculture Female Percent Of Female Employment

In financial year 2022, over ** percent of the rural workforce in India was employed in the agriculture sector. The country noticed a decline in the share of agriculture in rural employment since the financial year 1991.

India Employment: Private: Agriculture, Hunting, etc. data was reported at 924.000 Person th in 2012. This records an increase from the previous number of 918.000 Person th for 2011. India Employment: Private: Agriculture, Hunting, etc. data is updated yearly, averaging 896.000 Person th from Mar 1982 (Median) to 2012, with 31 observations. The data reached an all-time high of 1,028.000 Person th in 2006 and a record low of 807.000 Person th in 1985. India Employment: Private: Agriculture, Hunting, etc. data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under Global Database’s India – Table IN.GBA001: Employment.

Actual value and historical data chart for India Employees Agriculture Male Percent Of Male Employment

India's agriculture sector was the leading industry in terms of employment in the financial year 2023 with the number of employees tallying over *** million. Meanwhile, the mining industry recorded almost *** million employees. The services sector is the next big sector in India after agriculture. Challenges facing the agriculture sector Agriculture is the mainstay of India’s workforce. It employs over 42 percent of India’s population. However, it is the lowest contributor to the country’s GDP when compared to other major sectors. Despite being one of the largest producers of crops in the world, agricultural productivity remains low. Key issues impacting productivity include the decreasing size of landholdings, dependence on monsoons, inadequate access to irrigation, lack of access to credit and finance for marginal farmers, inadequate agricultural infrastructure, vulnerability to market volatility, and climate change, among others. Service sector: Key GDP contributor The service sector contributes a lion’s share to India’s GDP. Driven by investments and a skilled workforce, India has now positioned itself on the global stage for services. Information technology, financial services, and communications are the key performing subsectors within the service industry. However, the rising labor productivity in the sector has reduced the demand for labor. This gap in output and employment parallels the disproportionately larger share of the service sector in GDP than employment.

The dataset contains All India Employment Situation in India - Per 1000 Distribution of Usually Employed by Broad Groups of Industry for Various Rounds from Handbook of Statistics on Indian Economy.

Primary sector : Agriculture and allied activities Secondary sector : Mining, manufacture, electricity, gas,water, etc. and construction Tertiary sector : Trade, hotel and restuarant, Transport,storage and communication and Other services Note: 1. Data on NSS rounds 38, 43, 50, 55, 61, 66 and 68 relates to quinquennial rounds. 2: Data for July 2017-June 2018, pertain to the Periodic Labour Force Survey (PLFS), that accounts labour force estimates for both status (ps and ss).

Context

The dataset contains comprehensive data on crop production statistics for India, categorized by state and district. The dataset covers four major crop seasons, namely kharif, rabbi, summer, and autumn, from the year 1997 to 2023. The data provides information on the annual production and yield of crops grown in different parts of the country.

The dataset will be useful for researchers, policymakers, and farmers who are interested in understanding crop production patterns in different regions of India. By analyzing the data, researchers can identify the factors that influence crop yields and production and can make informed decisions on how to improve agricultural productivity in the country. Policymakers can use the data to design and implement agricultural policies that promote sustainable farming practices and improve food security.

Farmers can also benefit from the dataset by gaining insights into the best crops to grow in their region and making informed decisions on crop management practices. Additionally, the dataset can be used to train machine learning models to predict crop yields and production in different parts of the country, which can be valuable for agricultural businesses and organizations. Overall, the dataset provides a comprehensive overview of crop production statistics in India, which is essential for understanding the country's agricultural landscape and developing effective strategies for sustainable agriculture.

Sources

This dataset contains comprehensive information on agricultural production statistics in India, sourced from the Indian government's Area Production Statistics (APS) database. The APS is maintained by the Ministry of Agriculture and Farmers Welfare and provides detailed data on crop production, yield, and area under cultivation across different states and districts in India. Overall, this dataset is an important resource for anyone interested in agriculture and its impact on the Indian economy and society.

Link :- https://aps.dac.gov.in/APY/Public_Report1.aspx

Inspiration

Agriculture is the backbone of the Indian economy, providing employment to millions of people and contributing significantly to the country's GDP. However, the sector is faced with several challenges, including climate change, low productivity, and food security issues. To address these challenges, there is a need for data-driven solutions that can inform policy and decision-making.

Creating a dataset that captures agricultural production statistics in India can help in this regard. The dataset can provide valuable insights into crop yields, area under cultivation, and other metrics that can inform agricultural policies and practices. It can also be used to identify trends and patterns in agricultural production, helping farmers and policymakers make informed decisions about crop selection, irrigation, and other important factors that affect agricultural productivity.

Additionally, the dataset can be used for machine learning and predictive modeling to generate insights and make accurate predictions about crop production in different parts of the country. Overall, the dataset has the potential to contribute significantly to the development of the agriculture sector in India and help address some of the challenges faced by the sector.

Agriculture was the major source of employment in rural India accounting for almost ** percent of the workforce in 2024. Meanwhile, the construction sector was the major employer within non-agricultural activities during the same period.

India Number of Job Postings: Removed: Agriculture, Forestry, Fishing and Hunting data was reported at 54.000 Unit in 05 May 2025. This records a decrease from the previous number of 61.000 Unit for 28 Apr 2025. India Number of Job Postings: Removed: Agriculture, Forestry, Fishing and Hunting data is updated weekly, averaging 0.000 Unit from Jan 2008 (Median) to 05 May 2025, with 905 observations. The data reached an all-time high of 201.000 Unit in 02 Dec 2024 and a record low of 0.000 Unit in 23 Aug 2021. India Number of Job Postings: Removed: Agriculture, Forestry, Fishing and Hunting data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s India – Table IN.RL.JP: Number of Job Postings: Removed: by Industry.

Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)

Overview :

The dataset contains comprehensive information about the implementation and impact of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in various districts and states of India. MGNREGA is a pivotal social welfare program designed to enhance the livelihood security of households in rural areas by providing a guarantee of at least one hundred days of wage employment per financial year to adult members who volunteer for unskilled manual work. This dataset offers valuable insights into the program's operation and performance across different regions of the country.

Domain :

Open Government Data (OGD) Platform India

Data Columns

The dataset includes the following columns, each offering unique insights into MGNREGA activities:

1. state_name: The name of the Indian state where the data is recorded.
2. district_name: The name of the specific district within the state.
3. Total No. of JobCards issued: The total count of job cards issued to households in the district.
4. Total No. of Workers: The total number of registered workers associated with MGNREGA.
5. Total No. of Active Job Cards: The count of currently active job cards.
6. Total No. of Active Workers: The number of workers currently engaged in MGNREGA employment.
7. SC workers against active workers: The count of Scheduled Caste (SC) workers among the active workforce.
8. ST workers against active workers: The count of Scheduled Tribe (ST) workers among the active workforce.
9. Approved Labour Budget: The budget allocation approved for labor under MGNREGA.
10. Persondays of Central Liability so far: The cumulative person-days of work generated under the program.
11. SC persondays: The person-days of work generated specifically for Scheduled Caste (SC) workers.
12. ST persondays: The person-days of work generated specifically for Scheduled Tribe (ST) workers.
13. Women Persondays: The person-days of work generated for women workers.
14. Average days of employment provided per Household: The average number of days of employment provided per household in the district.
15. Average Wage rate per day per person(Rs.): The average daily wage rate per person in Indian Rupees (Rs.).
16. Total No of HHs completed 100 Days of Wage Employment: The total number of households that have completed 100 days of wage employment under MGNREGA.
17. Total Households Worked: The total number of households that have participated in MGNREGA employment.
18. Total Individuals Worked: The total number of individual workers who have participated in MGNREGA employment.
19. Differently abled persons worked: The count of differently-abled individuals who have worked under MGNREGA.
20. Number of GPs with NIL exp: The number of Gram Panchayats (local self-government units) with zero expenditure under MGNREGA.
21. Total No. of Works Takenup (New+Spill Over): The total number of works initiated, including new projects and spill-over works from previous years.
22. Number of Ongoing Works: The number of works that are currently in progress.
23. Number of Completed Works: The number of works that have been successfully completed.
24. % of NRM Expenditure(Public + Individual): The percentage of expenditure allocated to Natural Resource Management (NRM) activities, which may include land and water resource-related projects.
25. % of Category B Works: The percentage of expenditure allocated to Category B works under MGNREGA.
26. % of Expenditure on Agriculture & Agriculture Allied Works: The percentage of expenditure allocated to agriculture and agriculture-related projects.
27. Total Exp(Rs. in Lakhs.): The total expenditure incurred in Indian Rupees (in lakhs).
28. Wages(Rs. In Lakhs): The total expenditure on wages paid to workers in Indian Rupees (in lakhs).
29. Material and skilled Wages(Rs. In Lakhs): The total expenditure on materials and skilled wages paid in Indian Rupees (in lakhs).
30. Total Adm Expenditure (Rs. in Lakhs.): The total administrative expenditure incurred in Indian Rupees (in lakhs).

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

This statistic illustrates the employment size of micro, small and medium enterprises or MSMEs in agriculture, hunting and related service activities across India in financial year 2007, broken down by type. The largest employment size of MSMEs in agriculture industry were in the registered sector at ** thousand during the measured time period.

Abstract

Agriculture plays an important role in India's economy. It provides gainful employment to a large section of population of the country, particularly, the rural population. It contributes to the socio-cultural development of the farming community. The land holding provides them the confidence and strength to stay and survive in the society. In view of the importance of agriculture, Government of India has been conducting comprehensive Agriculture Censuses for collection of data on structure and characteristics of agricultural holdings, as part of World Census of Agriculture Programme since 1970-71. Operational holding, being the basic unit of decision-making in agriculture, detailed data on structure of agricultural holdings and its characteristics are necessary for formulation of any meaningful and effective strategy for agricultural development.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the operational holding, defined as an entity comprising all land that is used wholly or partly for agricultural production and is operated as one technical unit by one person alone or with others, without regard to the title, legal form, size or location. A technical unit was defined as the unit that is under the same management and has the same means of production, such as labour force, machinery, animals, credit, etc. The operated area includes both cultivated and uncultivated area, provided that a part of it is put to agricultural production during the reference period.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Sampling design For the collection of data in the Agriculture Census, an approach of Census-cum-sample survey has been adopted. Various States in the country have been grouped in to two categories i.e. land record States and non-land record States. Those States where comprehensive land records are maintained giving information on land and its utilization, cropping pattern etc are called land record States and those States where such information is not maintained in the form of land-records are called nonland record States. In land record States data on Agriculture Census is pooled for all the parcels of an operational holding irrespective of its location. However, for operational convenience the outer limit for pooling is restricted to taluka. This pooling is done for each operational holder in the village of his residence. In the non-land record States the data is collected through sample survey following household enquiry approach in 20% of villages in each block. In these selected villages, all the operational holdings are enumerated following household enquiry approach.Thus in land record States no sampling is resorted to for data collection for the number and area of operational holdings and in nonland record States sampling of villages in each block/taluka is resorted to

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used, one for each of the three phases of the census:

· Phase I questionnaire, for collecting data on number and area of operational holdings, according to the prescribed size classes2 for different social groups,3 types of holdings' and gender.

· Phase II questionnaire, for collecting data on: (i) dispersal of holdings; (ii) tenancy and terms of leasing; (iii) land utilization; (iv) irrigation status and source-wise area irrigated; (v) cropping pattern

· Phase III questionnaire, for collecting additional data.

The AC 2011 questionnaires covered 12 items of the 16 core items recommended for the WCA 2010 round. The exceptions were: (i) "Presence of aquaculture on the holding" (ii) "Other economic production activities of the holding's enterprise" (iii) "Number of animals on the holding for each livestock type" (iv) "Presence of forests and other woodland on the holding"

See questionnaire in external materials.

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING In-house software was developed for data entry and processing of census data. Data entry, data validation and error correction, the generation of trial tables, and the generation of final tables and their examination by states or UTs took place according to the three phases of the census. All questionnaires were manually scrutinized by the statistical staff before they were submitted for data entry. Data are archived at tehsil level and are available in the public domain. The data entry and processing software included checks of census data for inconsistencies and mismatch.

Data appraisal

Census data are compiled at the national and tehsil level. The All India Report of Agriculture Census 2010-2011 is based on the data collected during Phase-II of the Census. The detailed data of AC 2010/2011 results are available on the website of the Department of Agriculture, Cooperation & Farmers' Welfare.

India Average Daily Wage Rate: Rural: Agricultural: Women: General Agricultural including Watering and Irrigation Workers data was reported at 216.630 INR in Aug 2018. This records an increase from the previous number of 215.400 INR for Jul 2018. India Average Daily Wage Rate: Rural: Agricultural: Women: General Agricultural including Watering and Irrigation Workers data is updated monthly, averaging 188.425 INR from Nov 2013 (Median) to Aug 2018, with 58 observations. The data reached an all-time high of 216.630 INR in Aug 2018 and a record low of 161.280 INR in Dec 2013. India Average Daily Wage Rate: Rural: Agricultural: Women: General Agricultural including Watering and Irrigation Workers data remains active status in CEIC and is reported by Labour Bureau Government of India. The data is categorized under India Premium Database’s Labour Market – Table IN.GBB002: Average Daily Wage Rate: Rural: Agricultural.

Average Daily Wage Rate: Rural: Agricultural: Packaging and Agriculture: Men: Gujarat data was reported at 261.430 INR in Feb 2025. This stayed constant from the previous number of 261.430 INR for Jan 2025. Average Daily Wage Rate: Rural: Agricultural: Packaging and Agriculture: Men: Gujarat data is updated monthly, averaging 195.000 INR from Dec 2013 (Median) to Feb 2025, with 133 observations. The data reached an all-time high of 261.430 INR in Feb 2025 and a record low of 152.860 INR in Mar 2014. Average Daily Wage Rate: Rural: Agricultural: Packaging and Agriculture: Men: Gujarat data remains active status in CEIC and is reported by Labour Bureau. The data is categorized under India Premium Database’s Labour Market – Table IN.GBD011: Average Daily Wage Rate: Rural: Agricultural: by State: Packaging and Agriculture.

In a survey conducted during 2022-23, it was found that over ** percent of employment seekers in the non-farm employment in the informal sector were women and ** percent were men. The share of such employment increased across male and female categories.