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.

Employment in agriculture (% of total employment) (modeled ILO estimate) in India was reported at 43.51 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

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.

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.

Abstract

The millions of farmers of India have made significant contributions in providing food and nutrition to the entire nation and provided livelihood to millions of people of the country. During the five decades of planned economic development, India has moved from food-shortage and imports to self-sufficiency and exports. Food security and well being of the farmer appears to be major areas of concern of the planners of Indian agriculture. In order to have a snapshot picture of the farming community at the commencement of the third millennium and to analyze the impact of the transformation induced by public policy, investments and technological change on the farmers' access to resources and income as well as well-being of the farmer households at the end of five decades of planned economic development, Ministry of Agriculture have decided to collect information on Indian farmers through “Situation Assessment Survey” (SAS) on Indian farmers and entrusted the job of conducting the survey to National Sample Survey Organisation (NSSO).

The Situation Assessment Survey of Farmers is the first of its kind to be conducted by NSSO. Though information on a majority of items to be collected through SAS have been collected in some round or other of NSS, an integrated schedule, viz., Schedule 33, covering some basic characteristics of farmer households and their access to basic and modern farming resources will be canvassed for the first time in SAS. Moreover, information on consumption of various goods and services in an abridged form are also to be collected to have an idea about the pattern of consumption expenditure of the farmer households.

Schedule 33 is designed for collection of information on aspects relating to farming and other socio-economic characteristics of farmer households. The information will be collected in two visits to the same set of sample households. The first visit will be made during January to August 2003 and the second, during September to December 2003. The survey will be conducted in rural areas only. It will be canvassed in the Central Sample except for the States of Maharashtra and Meghalaya where it will be canvassed in both State and Central samples.

Geographic coverage

The survey covered rural sector of Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Household (farmer)

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

Outline of sample design: A stratified multi-stage design has been adopted for the 59th round survey. The first stage unit (FSU) is the census village in the rural sector and UFS block in the urban sector. The ultimate stage units (USUs) will be households in both the sectors. Hamlet-group / sub-block will constitute the intermediate stage if these are formed in the selected area.

Sampling Frame for First Stage Units: For rural areas, the list of villages (panchayat wards for Kerala) as per Population Census 1991 and for urban areas the latest UFS frame, will be used as sampling frame. For stratification of towns by size class, provisional population of towns as per Census 2001 will be used.

Stratification

Rural sector: Two special strata will be formed at the State/ UT level, viz.

• Stratum 1: all FSUs with population between 0 to 50 and
• Stratum 2: FSUs with population more than 15,000.

Special stratum 1 will be formed if at least 50 such FSU's are found in a State/UT. Similarly, special stratum 2 will be formed if at least 4 such FSUs are found in a State/UT. Otherwise, such FSUs will be merged with the general strata.

From FSUs other than those covered under special strata 1 & 2, general strata will be formed and its numbering will start from 3. Each district of a State/UT will be normally treated as a separate stratum. However, if the census rural population of the district is greater than or equal to 2 million as per population census 1991 or 2.5 million as per population census 2001, the district will be split into two or more strata, by grouping contiguous tehsils to form strata. However, in Gujarat, some districts are not wholly included in an NSS region. In such cases, the part of the district falling in an NSS region will constitute a separate stratum.

Urban sector: In the urban sector, strata will be formed within each NSS region on the basis of size class of towns as per Population Census 2001. The stratum numbers and their composition (within each region) are given below. - stratum 1: all towns with population less than 50,000 - stratum 2: all towns with population 50,000 or more but less than 2 lakhs - stratum 3: all towns with population 2 lakhs or more but less than 10 lakhs - stratum 4, 5, 6, ...: each city with population 10 lakhs or more The stratum numbers will remain as above even if, in some regions, some of the strata are not formed.

Total sample size (FSUs): 10736 FSUs have been allocated at all-India level on the basis of investigator strength in different States/UTs for central sample and 11624 for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to provisional population as per Census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

Allocation of State/UT level sample to rural and urban sectors: State/UT level sample is allocated between two sectors in proportion to provisional population as per Census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. Earlier practice of giving double weightage to urban sector has been modified considering the fact that two main topics (sch. 18.1 and sch 33) are rural based and there has been considerable growth in urban population. More samples have been allocated to rural sector of Meghalaya state sample at the request of the DES, Meghalaya. The sample sizes by sector and State/UT are given in Table 1 at the end of this Chapter.

Allocation to strata: Within each sector of a State/UT, the respective sample size will be allocated to the different strata in proportion to the stratum population as per census 2001. Allocations at stratum level will be adjusted to a multiple of 2 with a minimum sample size of 2. However, attempt will be made to allocate a multiple of 4 FSUs to a stratum as far as possible. Selection of FSUs: FSUs will be selected with Probability Proportional to Size with replacement (PPSWR), size being the population as per population census 1991 in all the strata for rural sector except for stratum 1. In stratum 1 of rural sector and in all the strata of urban sector, selection will be done using Simple Random Sampling without replacement (SRSWOR). Samples will be drawn in the form of two independent sub-samples.

Note: Detail sampling procedure is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule 33 (Situation Assessment Survey) has been split into several blocks to obtain detailed information on various aspects of farmer households.

Block 0- Descriptive identification of sample household: This block is meant for recording descriptive identification particulars of a sample household.

Block 1- Identification of sample household: items 1 to 12: The identification particulars for items 1, 6 - 11 will be copied from the corresponding items of block 1 of listing schedule (Sch.0.0). The particulars to be recorded in items 2, 3, 4 and 5 have already been printed in the schedule.

Block 2- Particulars of field operation: The identity of the Investigator, Assistant Superintendent and Superintendent associated, date of survey/inspection/scrutiny of schedules, despatch, etc., will be recorded in this block against the appropriate items in the relevant columns.

Block 3- Household characteristics: Characteristics which are mainly intended to be used to classify the households for tabulation will be recorded in this block.

Block 4- Demographic and other particulars of household members: All members of the sample household will be listed in this block. Demographic particulars (viz., relation to head, sex, age, marital status and general education), nature of work, current weekly status, wage and salary earnings etc. will be recorded for each member using one line for one member.

Block 5- Perception of household regarding sufficiency of food: This block will record information about perception of households regarding sufficiency of food.

Block 6- Perceptions regarding some general aspects of farming: In this block some information regarding perception of the farmer household about some general aspects of farming are to be recorded.

Block 7- Particulars of land possessed during Kharif/Rabi: This block is designed to record information regarding the land on which farming activities are carried out by the farmer household during Kharif/Rabi.

Block 8- Area under irrigation during Kharif/Rabi: In this block information regarding the area under irrigation during last 365 days for different crops will be recorded according to the source of irrigation.

Block 9- Some particulars of farming resources used for cultivation during Kharif / Rabi: Information regarding farming resources used for cultivation during the last 365 days will be ascertained from the farmer households and will be recorded in this block.

Block 10- Use of energy during last 365 days: This block will be

A survey conducted from 2021 to 2022 reported that around ** percent of all rural households surveyed across India were engaged in agriculture. The share increased from 2017 to 2022, with ***************** reporting the highest share of agricultural to rural households among other states.

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.

Abstract

In order to have a comprehensive picture of the farming community and to analyze the impact of the transformation induced by public policy, investments and technological change on the farmers' access to resources and income as well as well-being of the farmer households it was decided to collect information on Indian farmers through “Situation Assessment Survey” (SAS). The areas of interest for conducting SAS would include economic well-being of farmer households as measured by consumer expenditure, income and productive assets, and indebtedness; their farming practices and preferences, resource availability, and their awareness of technological developments and access to modern technology in the field of agriculture. In this survey, detailed information would be collected on receipts and expenses of households' farm and non-farm businesses, to arrive at their income from these sources. Income from other sources would also be ascertained, and so would be the consumption expenditure of the households.

Geographic coverage

National, State, Rural, Urban

Analysis unit

Houdeholds

Universe

All Households of the type : 1-self-employed in agriculture 2-self-employed in non-agriculture 3-regular wage/salary earning 4-casual labour in agriculture 5-casual labour in non-agriculture 6-others

Kind of data

Sample survey data [ssd]

Sampling procedure

Total sample size (FSUs): 8042 FSUs have been allocated for the central sample at all-India level. For the state sample, there are 8998 FSUs allocated for all-India. sample design: A stratified multi-stage design has been adopted for the 70th round survey. The first stage units (FSU) are the census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) are households in both the sectors. In case of large FSUs, one intermediate stage of sampling is the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages updated by excluding the villages urbanised and including the towns de-urbanised after 2001 census (henceforth the term 'village' would mean Panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the latest updated list of UFS blocks (2007-12) is considered as the sampling frame.

Stratification:

(a) Stratum has been formed at district level. Within each district of a State/ UT, generally speaking, two basic strata have been formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising all the urban areas of the district. However, within the urban areas of a district, if there were one or more towns with population 10 lakhs or more as per population census 2011 in a district, each of them formed a separate basic stratum and the remaining urban areas of the district was considered as another basic stratum.

(b) However, a special stratum in the rural sector only was formed at State/UT level before district- strata were formed in case of each of the following 20 States/UTs: Andaman & Nicobar Islands, Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Jharkhand, Karnataka, Lakshadweep, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. This stratum will comprise all the villages of the State with population less than 50 as per census 2001.

(c) In case of rural sectors of Nagaland one special stratum has been formed within the State consisting of all the interior and inaccessible villages. Similarly, for Andaman & Nicobar Islands, one more special stratum has been formed within the UT consisting of all inaccessible villages. Thus for Andaman & Nicobar Islands, two special strata have been formed at the UT level:

(i) special stratum 1 comprising all the interior and inaccessible villages (ii) special stratum 2 containing all the villages, other than those in special stratum 1, having population less than 50 as per census 2001.

Sub-stratification:

Rural sector: Different sub-stratifications are done for 'hilly' States and other States. Ten (10) States are considered as hilly States. They are: Jammu & Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, Meghalaya, Tripura, Mizoram, Manipur, Nagaland and Arunachal Pradesh.

(a) sub-stratification for hilly States: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed was 'r/2'. The villages within a district as per frame have been first arranged in ascending order of population. Then sub-strata 1 to 'r/2' have been demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal population.

(b) sub-stratification for other States (non-hilly States except Kerala): The villages within a district as per frame were first arranged in ascending order of proportion of irrigated area in the cultivated area of the village. Then sub-strata 1 to 'r/2' have been demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal cultivated area. The information on irrigated area and cultivated area was obtained from the village directory of census 2001.

(c) sub-stratification for Kerala: Although Kerala is a non-hilly State but because of non-availability of information on irrigation at FSU (Panchayat Ward) level, sub-stratification by proportion of irrigated area was not possible. Hence the procedure for sub-stratification was same as that of hilly States in case of Kerala.

Urban sector: There was no sub-stratification for the strata of million plus cities. For other strata, each district was divided into 2 sub-strata as follows:

 sub-stratum 1: all towns of the district with population less than 50000 as per census 2011 
 sub-stratum 2: remaining non-million plus towns of the district

Allocation of total sample to States and UTs: The total number of sample FSUs have been allocated to the States and UTs in proportion to population as per census 2011 subject to a minimum sample allocation to each State/ UT.
Allocation to strata: Within each sector of a State/ UT, the respective sample size has been allocated to the different strata in proportion to the population as per census 2011. Allocations at stratum level are adjusted to multiples of 2 with a minimum sample size of 2. Allocation to sub-strata:

1 Rural: Allocation is 2 for each sub-stratum in rural.

2 Urban: Stratum allocations have been distributed among the two sub-strata in proportion to the number of FSUs in the sub-strata. Minimum allocation for each sub-stratum is 2. Selection of FSUs: For the rural sector, from each stratum x sub-stratum, required number of sample villages has been selected by Simple Random Sampling Without Replacement (SRSWOR). For the urban sector, FSUs have been selected by using Simple Random Sampling Without Replacement (SRSWOR) from each stratum x sub-stratum. Both rural and urban samples were drawn in the form of two independent sub-samples and equal number of samples has been allocated among the two sub rounds.

For details reexternal refer to external resouce "Note on Sample Design and Estimation Procedure of NSS 70th Round" Page no.2

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are 17 blocks in visit 1. In Visits 1 & 2, Each sample FSU will be visited twice during this round. Since the workload of the first visit (i.e. visit 1) will be more, the first visit will continue till the end of July 2013. Thus, period of the first visit will be January - July 2013 and that of the second visit (i.e. visit 2) will be August - December 2013.

This scatter chart displays agricultural land (km²) against urban population (people) in India. The data is filtered where the date is 2021. The data is about countries per year.

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23% of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2%. In most countries, the decade-long declines appear to be primarily due to unsustainable crop management practices rather than climate alone. One quarter of the statistically significant declines are observed in India, which with the world’s largest population of food-insecure people and largest WLT croplands, is a leading example of the observed declines. Here we show geographically matching patterns of enhanced crop production and irrigation expansion with groundwater that have leveled off in the past decade. We estimate that, in the absence of irrigation, the enhancement in dry-season food grain production in India, during 1982–2002, would have required an increase in annual rainfall of at least 30% over almost half of the cropland area. This suggests that the past expansion of use of irrigation has not been sustainable. We expect that improved surface and groundwater management practices will be required to reverse the recent food grain production declines. MDPI and ACS Style Milesi, C.; Samanta, A.; Hashimoto, H.; Kumar, K.K.; Ganguly, S.; Thenkabail, P.S.; Srivastava, A.N.; Nemani, R.R.; Myneni, R.B. Decadal Variations in NDVI and Food Production in India. Remote Sens. 2010, 2, 758-776. AMA Style Milesi C., Samanta A., Hashimoto H., Kumar K.K., Ganguly S., Thenkabail P.S., Srivastava A.N., Nemani R.R., Myneni R.B. Decadal Variations in NDVI and Food Production in India. Remote Sensing. 2010; 2(3):758-776. Chicago/Turabian Style Milesi, Cristina; Samanta, Arindam; Hashimoto, Hirofumi; Kumar, K. Krishna; Ganguly, Sangram; Thenkabail, Prasad S.; Srivastava, Ashok N.; Nemani, Ramakrishna R.; Myneni, Ranga B. 2010. "Decadal Variations in NDVI and Food Production in India." Remote Sens. 2, no. 3: 758-776.

Agriculture is considered the backbone of the Indian economy. The existence of Indian agriculture is traced back to the Indus Valley culture. The importance of agriculture as a means of livelihood and trade in the pre-independence period is still alive today. Farming, once practiced in a traditional way, is now turning to modernity. If we look at this modernity from the Indian point of view, it is clear that the green revolution in the agricultural sector in the country after 1960 is a milestone. Going further, it can be seen that in 2007, the share of agriculture and allied sectors in the country's GDP was 16.6 percent. During the same period, 52% of the population in the country was engaged in agriculture. The share of agriculture in the country's 7 2.7 trillion economies in 2018-19 is about 15.9 percent and employment 49 percent. The dependence of the Indian people on agriculture and the contribution of the agricultural sector to the economic development of the country is declining day by day. The main objective of this research paper is to study the impact of the agricultural policies of this country so far and the plight of the farmers.

Agricultural Inputs Market Outlook

The global agricultural inputs market size was valued at approximately USD 345.6 billion in 2023 and is projected to reach around USD 537.8 billion by 2032, growing at a CAGR of 5.0% during the forecast period. Key growth factors include increasing demand for food due to a growing global population, technological advancements in agriculture, and the rising adoption of sustainable farming practices.

One of the significant growth drivers in the agricultural inputs market is the escalating demand for food products spurred by the rising global population. With the world population projected to reach 9.7 billion by 2050, there is an urgent need to enhance agricultural productivity. This drives the demand for various agricultural inputs such as fertilizers, pesticides, and advanced seeds, aimed at improving crop yield and quality. Additionally, urbanization trends are reducing arable land, further compelling the need for efficient agricultural practices and inputs to maximize productivity on available land.

Technological advancements in the agricultural sector have also significantly contributed to market growth. The integration of precision agriculture, agricultural drones, and IoT-based solutions has revolutionized farming practices, leading to better resource management and increased productivity. These technologies enable farmers to apply inputs more efficiently and monitor crop health more effectively, thereby reducing waste and enhancing yield quality. Moreover, the development of genetically modified seeds and bio-based fertilizers and pesticides has opened new avenues for sustainable agriculture, supporting market growth.

The increasing awareness and adoption of sustainable farming practices are further bolstering the agricultural inputs market. There is a growing trend towards organic farming and the use of bio-fertilizers and bio-pesticides, driven by consumer preferences for organic products and stringent environmental regulations. Governments and agricultural organizations worldwide are promoting sustainable agricultural practices through subsidies and incentives, encouraging farmers to adopt eco-friendly inputs. This shift towards sustainability is expected to drive demand for organic seeds, natural soil conditioners, and other sustainable agricultural inputs.

In this context, the role of an Agricultural Synergist becomes increasingly important. These synergists are substances that enhance the effectiveness of agricultural inputs, such as fertilizers and pesticides, by improving their absorption and utilization by plants. By acting as catalysts, agricultural synergists can significantly boost crop productivity and quality, making them a valuable addition to modern farming practices. As the agricultural sector continues to face challenges such as limited arable land and the need for sustainable solutions, the integration of synergists in farming practices offers a promising avenue for maximizing resource efficiency and achieving higher yields.

Regionally, the Asia Pacific region holds a substantial market share and is expected to witness significant growth during the forecast period. This can be attributed to the large agricultural base, increasing population, and supportive government policies promoting advanced agricultural practices. Countries like China and India are investing heavily in agricultural infrastructure and providing subsidies for the purchase of agricultural inputs. Additionally, the region's favorable climatic conditions and diverse crop cultivation further contribute to the robust demand for agricultural inputs.

Product Type Analysis

The agricultural inputs market is segmented by product type into fertilizers, pesticides, seeds, soil conditioners, plant growth regulators, and others. Fertilizers dominate the market due to their crucial role in enhancing soil fertility and improving crop yield. The adoption of chemical and organic fertilizers has seen a significant rise, driven by the need to meet the growing food demand. Technological advancements in fertilizer production, such as controlled-release and liquid fertilizers, have further augmented their application in modern farming practices.

Pesticides are another critical segment within the agricultural inputs market, addressing the challenges posed by pests and diseases that threaten crop production. The development of advanced pesticides, including herbicides, insecticides, and f

IntroductionThe combined populations of China and India were 2.78 billion in 2020, representing 36% of the world population (7.75 billion). Wheat is the second most important staple grain in both China and India. In 2019, the aggregate wheat consumption in China was 96.4 million ton and in India it was 82.5 million ton, together it was more than 35% of the world's wheat that year. In China, in 2050, the projected population will be 1294–1515 million, and in India, it is projected to be 14.89–1793 million, under the low and high-fertility rate assumptions. A question arises as to, what will be aggregate demand for wheat in China and India in 2030 and 2050?MethodsApplying the Vector Error Correction model estimation process in the time series econometric estimation setting, this study projected the per capita and annual aggregate wheat consumptions of China and India during 2019-2050. In the process, this study relies on agricultural data sourced from the Food and Agriculture Organization of the United States (FAO) database (FAOSTAT), as well as the World Bank's World Development Indicators (WDI) data catalog. The presence of unit root in the data series are tested by applying the augmented Dickey-Fuller test; Philips-Perron unit root test; Kwiatkowski-Phillips-Schmidt-Shin test, and Zivot-Andrews Unit Root test allowing for a single break in intercept and/or trend. The test statistics suggest that a natural log transformation and with the first difference of the variables provides stationarity of the data series for both China and India. The Zivot-Andrews Unit Root test, however, suggested that there is a structural break in urban population share and GDP per capita. To tackle the issue, we have included a year dummy and two multiplicative dummies in our model. Furthermore, the Johansen cointegration test suggests that at least one variable in both data series were cointegrated. These tests enable us to apply Vector Error Correction (VEC) model estimation procedure. In estimation the model, the appropriate number of lags of the variables is confirmed by applying the “varsoc” command in Stata 17 software interface. The estimated yearly per capita wheat consumption in 2030 and 2050 from the VEC model, are multiplied by the projected population in 2030 and 2050 to calculate the projected aggregate wheat demand in China and India in 2030 and 2050. After projecting the yearly per capita wheat consumption (KG), we multiply with the projected population to get the expected consumption demand.ResultsThis study found that the yearly per capita wheat consumption of China will increase from 65.8 kg in 2019 to 76 kg in 2030, and 95 kg in 2050. In India, the yearly per capita wheat consumption will increase to 74 kg in 2030 and 94 kg in 2050 from 60.4 kg in 2019. Considering the projected population growth rates under low-fertility assumptions, aggregate wheat consumption of China will increase by more than 13% in 2030 and by 28% in 2050. Under the high-fertility rate assumption, however the aggregate wheat consumption of China will increase by 18% in 2030 and nearly 50% in 2050. In the case of India, under both low and high-fertility rate assumptions, aggregate wheat demand in India will increase by 32-38% in 2030 and by 70-104% in 2050 compared to 2019 level of consumption.DiscussionsOur results underline the importance of wheat in both countries, which are the world's top wheat producers and consumers, and suggest the importance of research and development investments to maintain sufficient national wheat grain production levels to meet China and India's domestic demand. This is critical both to ensure the food security of this large segment of the world populace, which also includes 23% of the total population of the world who live on less than US $1.90/day, as well as to avoid potential grain market destabilization and price hikes that arise in the event of large import demands.

India is one of the major players in the agriculture sector worldwide and it is the primary source of livelihood for ~55% of India’s population. India has the world's largest cattle herd (buffaloes), largest area planted to wheat, rice, and cotton, and is the largest producer of milk, pulses, and spices in the world. It is the second-largest producer of fruit, vegetables, tea, farmed fish, cotton, sugarcane, wheat, rice, cotton, and sugar. Agriculture sector in India holds the record for second-largest agricultural land in the world generating employment for about half of the country’s population. Thus, farmers become an integral part of the sector to provide us with means of sustenance.

Consumer spending in India will return to growth in 2021 post the pandemic-led contraction, expanding by as much as 6.6%. The Indian food industry is poised for huge growth, increasing its contribution to world food trade every year due to its immense potential for value addition, particularly within the food processing industry. The Indian food processing industry accounts for 32% of the country’s total food market, one of the largest industries in India and is ranked fifth in terms of production, consumption, export and expected growth.

This data contains the production and area grown for each crop at ditrict level from 1997 to 2015.

Agriculture is considered the backbone of the Indian economy. The existence of Indian agriculture is traced back to the Indus Valley culture. The importance of agriculture as a means of livelihood and trade in the pre-independence period is still alive today. Farming, once practiced in a traditional way, is now turning to modernity. If we look at this modernity from the Indian point of view, it is clear that the green revolution in the agricultural sector in the country after 1960 is a milestone. Going further, it can be seen that in 2007, the share of agriculture and allied sectors in the country's GDP was 16.6 percent. During the same period, 52% of the population in the country was engaged in agriculture. The share of agriculture in the country's 7 2.7 trillion economies in 2018-19 is about 15.9 percent and employment 49 percent. The dependence of the Indian people on agriculture and the contribution of the agricultural sector to the economic development of the country is declining day by day. The main objective of this research paper is to study the impact of the agricultural policies of this country so far and the plight of the farmers.

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Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

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Agriculture has been a major occupation of the Indian people since ancient times. Today 65% of the population is dependent on agriculture in various ways. The backbone of agriculture, once considered the backbone of the Indian economy, is crumbling today. The main reason for this is the neglect of agriculture by the government system and the newly started process of globalization. The rural economy that has survived is now on the brink of collapse due to the globalization process in agriculture, which is the only component of agriculture.By signing the WTO Agreement on January 1, 1995, India incorporated the agricultural sector into the process of globalization. In this regard, a secret agreement was reached between India and the United States on December 16, 1999, and India had to lift the numerical restrictions on its protected 715 agricultural commodities. The concept of globalization has been realized through the GATT Agreement, the Dunkel Proposal, the World Trade Organization, the International Monetary Fund, and the World Bank.

This report published in 1935 is one of the most comprehensive sources of data on the allotment era with data and statistics on land loss, forced fee patenting, allotment, leasing, agriculture, and population trends and makes a recommendation for loans and the restoration of millions of acres of land to Native people. The Native Lands Advocacy Project has transcribed a number of the tables and has included them in CSV form as part of this dataset. SECTION I. Complexities of Indian Land Tenure Arising From the Allotment System. Past policy of land liquidation and its results. SECTION II. Social and Economic Survey of Selected Indian Reservations. SECTION III. Agricultural Credit Needs of the Indians.

Abstract

Millions of farmers in India have made significant contributions in providing food and nutrition to the entire nation, while also providing livelihoods to millions of people in the country. During the past five decades of planned economic development, India has moved from food-shortage and imports to self-sufficiency and exports. Food security and well being of the farmer appears to be major areas of concern of the planners and policy makers of Indian agriculture. In order to have a comprehensive picture of the farming community at the commencement of the third millennium, and to analyze the impact of the transformation induced by public policy, investments and technological change on the farmers' access to resources and income, as well as well-being; the Ministry of Agriculture decided to collect information on Indian farmers through a Situation Assessment Survey (SAS) and entrusted the job of conducting the survey to the National Sample Survey Organisation (NSSO).

The SAS 2003 is the first of its kind to be conducted by NSSO. Though information on a majority of items to be collected through SAS have been collected in some round or other of NSS, an integrated schedule - Schedule 33, covering some basic characteristics of farming households and their access to basic and modern farming resources was canvassed for the first time in SAS. Moreover, information on consumption of various goods and services in an abridged form were also collected to have an idea about the pattern of consumption expenditure of the farming households.

Schedule 33 was designed for collecting information on aspects relating to farming and other socio-economic characteristics of farming households. The information was collected in two visits to the same set of sample households. The first visit was made during January to August 2003 and the second, during September to December 2003. The survey was conducted in rural areas only. It was canvassed in the Central Sample except for the States of Maharashtra and Meghalaya where it was canvassed in both State and Central samples.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified multi-stage sampling design was adopted for the SAS 2003, 59th round. The First Stage Unit (FSU), also known as the primary sampling unit, was the census village in the rural sector and UFS block in the urban sector. The Ultimate Stage Units (USUs) were households in both sectors. Hamlet-group / sub-block constitute the intermediate stage, if these are formed in the selected area.

The list of villages (panchayat wards for Kerala) based on the Population Census of 1991 constituted the sampling frame for FSUs in rural areas, while the latest UFS frame was the sampling frame used for urban areas. For stratification of towns by size class, provisional population of towns as per Census 2001 was used. A detailed description of the sampling strrategy can be found in the estimation procedure document attached in the documentation/external resource.

Mode of data collection

Face-to-face paper [f2f]